From 09a9509278dc8f6255a742a1392080042d4f6fd7 Mon Sep 17 00:00:00 2001 From: Maxim Zhiltsov Date: Fri, 11 Sep 2020 17:07:15 +0300 Subject: [PATCH] Install Datumaro as package (#2163) * Remove Datumaro sources * Install Datumaro as package * Update Datumaro links * fix * remove unnecessary dependencies * Update travis * update coverage config --- .coveragerc | 3 - .travis.yml | 4 +- .vscode/python.env | 1 - .vscode/settings.json | 6 - Dockerfile | 5 - Dockerfile.ci | 2 +- README.md | 5 +- .../formats/datumaro/__init__.py | 11 +- .../datumaro/export_templates/README.md | 4 +- cvat/requirements/base.txt | 3 +- cvat/settings/base.py | 3 - datumaro/.gitignore | 1 - datumaro/CONTRIBUTING.md | 75 - datumaro/LICENSE | 22 - datumaro/README.md | 205 --- datumaro/datum.py | 8 - datumaro/datumaro/__init__.py | 4 - datumaro/datumaro/__main__.py | 12 - datumaro/datumaro/cli/__init__.py | 4 - datumaro/datumaro/cli/__main__.py | 125 -- datumaro/datumaro/cli/commands/__init__.py | 6 - datumaro/datumaro/cli/commands/add.py | 8 - datumaro/datumaro/cli/commands/convert.py | 137 -- datumaro/datumaro/cli/commands/create.py | 8 - datumaro/datumaro/cli/commands/explain.py | 183 -- datumaro/datumaro/cli/commands/export.py | 8 - datumaro/datumaro/cli/commands/merge.py | 124 -- datumaro/datumaro/cli/commands/remove.py | 8 - datumaro/datumaro/cli/contexts/__init__.py | 6 - .../datumaro/cli/contexts/item/__init__.py | 36 - .../datumaro/cli/contexts/model/__init__.py | 183 -- .../datumaro/cli/contexts/project/__init__.py | 826 --------- .../datumaro/cli/contexts/project/diff.py | 290 ---- .../datumaro/cli/contexts/source/__init__.py | 273 --- datumaro/datumaro/cli/util/__init__.py | 74 - datumaro/datumaro/cli/util/project.py | 39 - datumaro/datumaro/components/__init__.py | 5 - .../components/algorithms/__init__.py | 5 - .../datumaro/components/algorithms/rise.py | 203 --- datumaro/datumaro/components/cli_plugin.py | 44 - datumaro/datumaro/components/config.py | 237 --- datumaro/datumaro/components/config_model.py | 63 - datumaro/datumaro/components/converter.py | 79 - .../datumaro/components/dataset_filter.py | 261 --- datumaro/datumaro/components/extractor.py | 621 ------- datumaro/datumaro/components/launcher.py | 67 - datumaro/datumaro/components/operations.py | 1504 ----------------- datumaro/datumaro/components/project.py | 850 ---------- datumaro/datumaro/plugins/__init__.py | 0 .../accuracy_checker_plugin/__init__.py | 4 - .../accuracy_checker_plugin/details/ac.py | 116 -- .../details/representation.py | 62 - .../accuracy_checker_plugin/launcher.py | 37 - .../datumaro/plugins/coco_format/__init__.py | 0 .../datumaro/plugins/coco_format/converter.py | 596 ------- .../datumaro/plugins/coco_format/extractor.py | 261 --- .../datumaro/plugins/coco_format/format.py | 23 - .../datumaro/plugins/coco_format/importer.py | 95 -- .../datumaro/plugins/cvat_format/__init__.py | 0 .../datumaro/plugins/cvat_format/converter.py | 331 ---- 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datumaro/docs/design.md | 185 -- datumaro/docs/developer_guide.md | 200 --- datumaro/docs/images/cli_design.png | Bin 35845 -> 0 bytes datumaro/docs/images/mvvm.png | Bin 30318 -> 0 bytes datumaro/docs/user_manual.md | 1003 ----------- datumaro/requirements.txt | 12 - datumaro/setup.py | 73 - datumaro/tests/__init__.py | 0 .../annotations/instances_val.json | 59 - .../coco_dataset/images/val/000000000001.jpg | Bin 631 -> 0 bytes .../cvat_dataset/for_images/images/img0.jpg | Bin 631 -> 0 bytes .../cvat_dataset/for_images/images/img1.jpg | Bin 631 -> 0 bytes .../assets/cvat_dataset/for_images/train.xml | 45 - .../cvat_dataset/for_video/annotations.xml | 92 - .../for_video/images/frame_000010.png | Bin 111 -> 0 bytes .../for_video/images/frame_000013.png | Bin 111 -> 0 bytes .../labelme_dataset/Masks/img1_mask_1.png | Bin 211 -> 0 bytes .../labelme_dataset/Masks/img1_mask_5.png | Bin 388 -> 0 bytes .../Scribbles/img1_scribble_1.png | Bin 206 -> 0 bytes .../Scribbles/img1_scribble_5.png | 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.../voc_dataset/ImageSets/Main/train.txt | 1 - .../ImageSets/Main/train_train.txt | 1 - .../ImageSets/Main/tvmonitor_train.txt | 1 - .../ImageSets/Segmentation/test.txt | 1 - .../ImageSets/Segmentation/train.txt | 1 - .../voc_dataset/JPEGImages/2007_000002.jpg | Bin 635 -> 0 bytes .../SegmentationClass/2007_000001.png | Bin 87 -> 0 bytes .../SegmentationObject/2007_000001.png | Bin 82 -> 0 bytes datumaro/tests/assets/yolo_dataset/obj.data | 4 - datumaro/tests/assets/yolo_dataset/obj.names | 10 - .../assets/yolo_dataset/obj_train_data/1.jpg | Bin 631 -> 0 bytes .../assets/yolo_dataset/obj_train_data/1.txt | 2 - datumaro/tests/assets/yolo_dataset/train.txt | 1 - datumaro/tests/test_RISE.py | 231 --- datumaro/tests/test_coco_format.py | 479 ------ datumaro/tests/test_command_targets.py | 128 -- datumaro/tests/test_cvat_format.py | 278 --- datumaro/tests/test_datumaro_format.py | 108 -- datumaro/tests/test_diff.py | 251 --- datumaro/tests/test_image.py | 64 - datumaro/tests/test_image_dir_format.py | 48 - datumaro/tests/test_images.py | 81 - datumaro/tests/test_labelme_format.py | 206 --- datumaro/tests/test_masks.py | 197 --- datumaro/tests/test_mot_format.py | 136 -- datumaro/tests/test_ops.py | 451 ----- datumaro/tests/test_project.py | 549 ------ datumaro/tests/test_tfrecord_format.py | 210 --- datumaro/tests/test_transforms.py | 415 ----- datumaro/tests/test_voc_format.py | 677 -------- datumaro/tests/test_yolo_format.py | 140 -- 191 files changed, 11 insertions(+), 20447 deletions(-) delete mode 100644 .vscode/python.env delete mode 100644 datumaro/.gitignore delete mode 100644 datumaro/CONTRIBUTING.md delete mode 100644 datumaro/LICENSE delete mode 100644 datumaro/README.md delete mode 100755 datumaro/datum.py delete mode 100644 datumaro/datumaro/__init__.py delete mode 100644 datumaro/datumaro/__main__.py delete mode 100644 datumaro/datumaro/cli/__init__.py delete mode 100644 datumaro/datumaro/cli/__main__.py delete mode 100644 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+3,10 @@ branch = true # relative_files = true # does not work? source = - datumaro/datumaro/ cvat/apps/ utils/cli/ omit = - datumaro/datumaro/__main__.py - datumaro/datumaro/version.py cvat/settings/* */tests/* */test_* diff --git a/.travis.yml b/.travis.yml index 9acef6b6..665ca9ee 100644 --- a/.travis.yml +++ b/.travis.yml @@ -1,5 +1,3 @@ -sudo: required - language: python python: @@ -36,7 +34,7 @@ before_script: script: # FIXME: Git package and application name conflict in PATH and try to leave only one python test execution - - docker-compose -f docker-compose.yml -f docker-compose.ci.yml run cvat_ci /bin/bash -c 'coverage run -a manage.py test cvat/apps && coverage run -a manage.py test --pattern="_test*.py" cvat/apps/dataset_manager/tests cvat/apps/engine/tests utils/cli && coverage run -a manage.py test datumaro/ && mv .coverage ${CONTAINER_COVERAGE_DATA_DIR}' + - docker-compose -f docker-compose.yml -f docker-compose.ci.yml run cvat_ci /bin/bash -c 'coverage run -a manage.py test cvat/apps && coverage run -a manage.py test --pattern="_test*.py" cvat/apps/dataset_manager/tests cvat/apps/engine/tests utils/cli && mv .coverage ${CONTAINER_COVERAGE_DATA_DIR}' - docker-compose -f docker-compose.yml -f docker-compose.ci.yml run cvat_ci /bin/bash -c 'cd cvat-data && npm install && cd ../cvat-core && npm install && npm run test && coveralls-lcov -v -n ./reports/coverage/lcov.info > ${CONTAINER_COVERAGE_DATA_DIR}/coverage.json' # Up all containers - docker-compose up -d diff --git a/.vscode/python.env b/.vscode/python.env deleted file mode 100644 index a624ab13..00000000 --- a/.vscode/python.env +++ /dev/null @@ -1 +0,0 @@ -PYTHONPATH="datumaro/:$PYTHONPATH" \ No newline at end of file diff --git a/.vscode/settings.json b/.vscode/settings.json index 3b796a0b..506e3105 100644 --- a/.vscode/settings.json +++ b/.vscode/settings.json @@ -25,13 +25,7 @@ } ], "python.linting.pylintEnabled": true, - "python.envFile": "${workspaceFolder}/.vscode/python.env", "python.testing.unittestEnabled": true, - "python.testing.unittestArgs": [ - "-v", - "-s", - "./datumaro", - ], "licenser.license": "Custom", "licenser.customHeader": "Copyright (C) @YEAR@ Intel Corporation\n\nSPDX-License-Identifier: MIT", "files.trimTrailingWhitespace": true diff --git a/Dockerfile b/Dockerfile index 18fb7f75..844a18c0 100644 --- a/Dockerfile +++ b/Dockerfile @@ -73,8 +73,6 @@ COPY components /tmp/components COPY cvat/requirements/ /tmp/requirements/ COPY supervisord.conf mod_wsgi.conf wait-for-it.sh manage.py ${HOME}/ RUN python3 -m pip install --no-cache-dir -r /tmp/requirements/${DJANGO_CONFIGURATION}.txt -# pycocotools package is impossible to install with its dependencies by one pip install command -RUN python3 -m pip install --no-cache-dir pycocotools==2.0.0 ARG CLAM_AV ENV CLAM_AV=${CLAM_AV} @@ -95,9 +93,6 @@ COPY cvat/ ${HOME}/cvat COPY cvat-core/ ${HOME}/cvat-core COPY cvat-data/ ${HOME}/cvat-data COPY tests ${HOME}/tests -COPY datumaro/ ${HOME}/datumaro - -RUN python3 -m pip install --no-cache-dir -r ${HOME}/datumaro/requirements.txt RUN chown -R ${USER}:${USER} . diff --git a/Dockerfile.ci b/Dockerfile.ci index f65cf36c..b51268dd 100644 --- a/Dockerfile.ci +++ b/Dockerfile.ci @@ -9,7 +9,7 @@ RUN apt-get update && \ apt-utils \ build-essential \ python3-dev \ - ruby \ + ruby \ && \ curl https://dl-ssl.google.com/linux/linux_signing_key.pub | apt-key add - && \ echo 'deb [arch=amd64] http://dl.google.com/linux/chrome/deb/ stable main' | tee /etc/apt/sources.list.d/google-chrome.list && \ diff --git a/README.md b/README.md index a82492ec..e6e55788 100644 --- a/README.md +++ b/README.md @@ -41,7 +41,8 @@ annotation team. Try it online [cvat.org](https://cvat.org). ## Supported annotation formats Format selection is possible after clicking on the Upload annotation -and Dump annotation buttons. [Datumaro](datumaro/README.md) dataset +and Dump annotation buttons. +[Datumaro](https://github.com/openvinotoolkit/datumaro/README.md) dataset framework allows additional dataset transformations via its command line tool and Python library. @@ -49,7 +50,7 @@ via its command line tool and Python library. | ------------------------------------------------------------------------------------------ | ------ | ------ | | [CVAT for images](cvat/apps/documentation/xml_format.md#annotation) | X | X | | [CVAT for a video](cvat/apps/documentation/xml_format.md#interpolation) | X | X | -| [Datumaro](datumaro/README.md) | | X | +| [Datumaro](https://github.com/openvinotoolkit/datumaro) | | X | | [PASCAL VOC](http://host.robots.ox.ac.uk/pascal/VOC/) | X | X | | Segmentation masks from [PASCAL VOC](http://host.robots.ox.ac.uk/pascal/VOC/) | X | X | | [YOLO](https://pjreddie.com/darknet/yolo/) | X | X | diff --git a/cvat/apps/dataset_manager/formats/datumaro/__init__.py b/cvat/apps/dataset_manager/formats/datumaro/__init__.py index 9dd3f9ab..3e5b1e6c 100644 --- a/cvat/apps/dataset_manager/formats/datumaro/__init__.py +++ b/cvat/apps/dataset_manager/formats/datumaro/__init__.py @@ -11,7 +11,7 @@ from tempfile import TemporaryDirectory from cvat.apps.dataset_manager.bindings import (CvatTaskDataExtractor, import_dm_annotations) from cvat.apps.dataset_manager.util import make_zip_archive -from cvat.settings.base import BASE_DIR, DATUMARO_PATH +from cvat.settings.base import BASE_DIR from datumaro.components.project import Project from ..registry import dm_env, exporter @@ -79,14 +79,7 @@ class DatumaroProjectExporter: osp.join(templates_dir, self._REMOTE_IMAGES_EXTRACTOR + '.py'), osp.join(target_dir, self._REMOTE_IMAGES_EXTRACTOR + '.py')) - # Make Datumaro and CVAT CLI modules available to the user - shutil.copytree(DATUMARO_PATH, osp.join(save_dir, 'datumaro'), - ignore=lambda src, names: ['__pycache__'] + [ - n for n in names - if sum([int(n.endswith(ext)) for ext in - ['.pyx', '.pyo', '.pyd', '.pyc']]) - ]) - + # Make CVAT CLI module available to the user cvat_utils_dst_dir = osp.join(save_dir, 'cvat', 'utils') os.makedirs(cvat_utils_dst_dir) shutil.copytree(osp.join(BASE_DIR, 'utils', 'cli'), diff --git a/cvat/apps/dataset_manager/formats/datumaro/export_templates/README.md b/cvat/apps/dataset_manager/formats/datumaro/export_templates/README.md index a375bbdc..9d1e0097 100644 --- a/cvat/apps/dataset_manager/formats/datumaro/export_templates/README.md +++ b/cvat/apps/dataset_manager/formats/datumaro/export_templates/README.md @@ -6,7 +6,7 @@ python -m virtualenv .venv . .venv/bin/activate # install dependencies -pip install -e datumaro/ +pip install 'git+https://github.com/openvinotoolkit/datumaro' pip install -r cvat/utils/cli/requirements.txt # set up environment @@ -17,4 +17,4 @@ export PYTHONPATH datum --help ``` -Check Datumaro [docs](datumaro/README.md) for more info. +Check [Datumaro docs](https://github.com/openvinotoolkit/datumaro/README.md) for more info. diff --git a/cvat/requirements/base.txt b/cvat/requirements/base.txt index d4457948..09fa5fe8 100644 --- a/cvat/requirements/base.txt +++ b/cvat/requirements/base.txt @@ -44,4 +44,5 @@ tensorflow==2.2.0 # Optional requirement of Datumaro # The package is used by pyunpack as a command line tool to support multiple # archives. Don't use as a python module because it has GPL license. patool==1.12 -diskcache==5.0.2 \ No newline at end of file +diskcache==5.0.2 +git+https://github.com/openvinotoolkit/datumaro@v0.1.0 \ No newline at end of file diff --git a/cvat/settings/base.py b/cvat/settings/base.py index c803b9f3..d05ca390 100644 --- a/cvat/settings/base.py +++ b/cvat/settings/base.py @@ -409,9 +409,6 @@ DATA_UPLOAD_MAX_NUMBER_FIELDS = None # this django check disabled LOCAL_LOAD_MAX_FILES_COUNT = 500 LOCAL_LOAD_MAX_FILES_SIZE = 512 * 1024 * 1024 # 512 MB -DATUMARO_PATH = os.path.join(BASE_DIR, 'datumaro') -sys.path.append(DATUMARO_PATH) - RESTRICTIONS = { 'user_agreements': [], diff --git a/datumaro/.gitignore b/datumaro/.gitignore deleted file mode 100644 index 17c3ea8b..00000000 --- a/datumaro/.gitignore +++ /dev/null @@ -1 +0,0 @@ -/datumaro.egg-info diff --git a/datumaro/CONTRIBUTING.md b/datumaro/CONTRIBUTING.md deleted file mode 100644 index f9a1afc1..00000000 --- a/datumaro/CONTRIBUTING.md +++ /dev/null @@ -1,75 +0,0 @@ -## Table of Contents - -- [Installation](#installation) -- [Usage](#usage) -- [Testing](#testing) -- [Design](#design-and-code-structure) - -## Installation - -### Prerequisites - -- Python (3.5+) -- OpenVINO (optional) - -``` bash -git clone https://github.com/opencv/cvat -``` - -Optionally, install a virtual environment: - -``` bash -python -m pip install virtualenv -python -m virtualenv venv -. venv/bin/activate -``` - -Then install all dependencies: - -``` bash -while read -r p; do pip install $p; done < requirements.txt -``` - -If you're working inside CVAT environment: -``` bash -. .env/bin/activate -while read -r p; do pip install $p; done < datumaro/requirements.txt -``` - -## Usage - -> The directory containing Datumaro should be in the `PYTHONPATH` -> environment variable or `cvat/datumaro/` should be the current directory. - -``` bash -datum --help -python -m datumaro --help -python datumaro/ --help -python datum.py --help -``` - -``` python -import datumaro -``` - -## Testing - -It is expected that all Datumaro functionality is covered and checked by -unit tests. Tests are placed in `tests/` directory. - -To run tests use: - -``` bash -python -m unittest discover -s tests -``` - -If you're working inside CVAT environment, you can also use: - -``` bash -python manage.py test datumaro/ -``` - -## Design and code structure - -- [Design document](docs/design.md) -- [Developer guide](docs/developer_guide.md) \ No newline at end of file diff --git a/datumaro/LICENSE b/datumaro/LICENSE deleted file mode 100644 index ae9cf710..00000000 --- a/datumaro/LICENSE +++ /dev/null @@ -1,22 +0,0 @@ -MIT License - -Copyright (C) 2019-2020 Intel Corporation -  -Permission is hereby granted, free of charge, to any person obtaining a copy -of this software and associated documentation files (the "Software"), -to deal in the Software without restriction, including without limitation -the rights to use, copy, modify, merge, publish, distribute, sublicense, -and/or sell copies of the Software, and to permit persons to whom -the Software is furnished to do so, subject to the following conditions: -  -The above copyright notice and this permission notice shall be included -in all copies or substantial portions of the Software. -  -THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS -OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, -FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.  IN NO EVENT SHALL -THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES -OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, -ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE -OR OTHER DEALINGS IN THE SOFTWARE. -  diff --git a/datumaro/README.md b/datumaro/README.md deleted file mode 100644 index 2d83cc4d..00000000 --- a/datumaro/README.md +++ /dev/null @@ -1,205 +0,0 @@ -# Dataset Management Framework (Datumaro) - -A framework to build, transform, and analyze datasets. - - -``` -CVAT annotations -- ---> Annotation tool - \ / -COCO-like dataset -----> Datumaro ---> dataset ------> Model training - / \ -VOC-like dataset -- ---> Publication etc. -``` - - -## Contents - -- [Documentation](#documentation) -- [Features](#features) -- [Installation](#installation) -- [Usage](#usage) -- [Examples](#examples) -- [Contributing](#contributing) - -## Documentation - -- [User manual](docs/user_manual.md) -- [Design document](docs/design.md) -- [Contributing](CONTRIBUTING.md) - -## Features - -- Dataset format conversions: - - COCO (`image_info`, `instances`, `person_keypoints`, `captions`, `labels`*) - - [Format specification](http://cocodataset.org/#format-data) - - [Dataset example](tests/assets/coco_dataset) - - `labels` are our extension - like `instances` with only `category_id` - - PASCAL VOC (`classification`, `detection`, `segmentation` (class, instances), `action_classification`, `person_layout`) - - [Format specification](http://host.robots.ox.ac.uk/pascal/VOC/voc2012/htmldoc/index.html) - - [Dataset example](tests/assets/voc_dataset) - - YOLO (`bboxes`) - - [Format specification](https://github.com/AlexeyAB/darknet#how-to-train-pascal-voc-data) - - [Dataset example](tests/assets/yolo_dataset) - - TF Detection API (`bboxes`, `masks`) - - Format specifications: [bboxes](https://github.com/tensorflow/models/blob/master/research/object_detection/g3doc/using_your_own_dataset.md), [masks](https://github.com/tensorflow/models/blob/master/research/object_detection/g3doc/instance_segmentation.md) - - [Dataset example](tests/assets/tf_detection_api_dataset) - - MOT sequences - - [Format specification](https://arxiv.org/pdf/1906.04567.pdf) - - [Dataset example](tests/assets/mot_dataset) - - CVAT - - [Format specification](https://github.com/opencv/cvat/blob/develop/cvat/apps/documentation/xml_format.md) - - [Dataset example](tests/assets/cvat_dataset) - - LabelMe - - [Format specification](http://labelme.csail.mit.edu/Release3.0) - - [Dataset example](tests/assets/labelme_dataset) -- Dataset building operations: - - Merging multiple datasets into one - - Dataset filtering with custom conditions, for instance: - - remove polygons of a certain class - - remove images without a specific class - - remove `occluded` annotations from images - - keep only vertically-oriented images - - remove small area bounding boxes from annotations - - Annotation conversions, for instance: - - polygons to instance masks and vise-versa - - apply a custom colormap for mask annotations - - rename or remove dataset labels -- Dataset comparison -- Model integration: - - Inference (OpenVINO and custom models) - - Explainable AI ([RISE algorithm](https://arxiv.org/abs/1806.07421)) - -> Check the [design document](docs/design.md) for a full list of features - -## Installation - -Optionally, create a virtual environment: - -``` bash -python -m pip install virtualenv -python -m virtualenv venv -. venv/bin/activate -``` - -Install Datumaro package: - -``` bash -pip install 'git+https://github.com/opencv/cvat#egg=datumaro&subdirectory=datumaro' -``` - -## Usage - -There are several options available: -- [A standalone command-line tool](#standalone-tool) -- [A python module](#python-module) - -### Standalone tool - - -``` - User - | - v -+------------------+ -| CVAT | -+--------v---------+ +------------------+ +--------------+ -| Datumaro module | ----> | Datumaro project | <---> | Datumaro CLI | <--- User -+------------------+ +------------------+ +--------------+ -``` - - -``` bash -datum --help -python -m datumaro --help -``` - -### Python module - -Datumaro can be used in custom scripts as a library in the following way: - -``` python -from datumaro.components.project import Project # project-related things -import datumaro.components.extractor # annotations and high-level interfaces -# etc. -project = Project.load('directory') -``` - -## Examples - - - - -- Convert [PASCAL VOC](http://host.robots.ox.ac.uk/pascal/VOC/voc2012/index.html#data) to COCO, keep only images with `cat` class presented: - ```bash - # Download VOC dataset: - # http://host.robots.ox.ac.uk/pascal/VOC/voc2012/VOCtrainval_11-May-2012.tar - datum convert --input-format voc --input-path \ - --output-format coco --filter '/item[annotation/label="cat"]' - ``` - -- Convert only non-occluded annotations from a CVAT-annotated project to TFrecord: - ```bash - # export Datumaro dataset in CVAT UI, extract somewhere, go to the project dir - datum project extract --filter '/item/annotation[occluded="False"]' \ - --mode items+anno --output-dir not_occluded - datum project export --project not_occluded \ - --format tf_detection_api -- --save-images - ``` - -- Annotate COCO, extract image subset, re-annotate it in CVAT, update old dataset: - ```bash - # Download COCO dataset http://cocodataset.org/#download - # Put images to coco/images/ and annotations to coco/annotations/ - datum project import --format coco --input-path - datum project export --filter '/image[images_I_dont_like]' --format cvat \ - --output-dir reannotation - # import dataset and images to CVAT, re-annotate - # export Datumaro project, extract to 'reannotation-upd' - datum project project merge reannotation-upd - datum project export --format coco - ``` - -- Annotate instance polygons in CVAT, export as masks in COCO: - ```bash - datum convert --input-format cvat --input-path \ - --output-format coco -- --segmentation-mode masks - ``` - -- Apply an OpenVINO detection model to some COCO-like dataset, - then compare annotations with ground truth and visualize in TensorBoard: - ```bash - datum project import --format coco --input-path - # create model results interpretation script - datum model add mymodel openvino \ - --weights model.bin --description model.xml \ - --interpretation-script parse_results.py - datum model run --model mymodel --output-dir mymodel_inference/ - datum project diff mymodel_inference/ --format tensorboard --output-dir diff - ``` - -- Change colors in PASCAL VOC-like `.png` masks: - ```bash - datum project import --format voc --input-path - - # Create a color map file with desired colors: - # - # label : color_rgb : parts : actions - # cat:0,0,255:: - # dog:255,0,0:: - # - # Save as mycolormap.txt - - datum project export --format voc_segmentation -- --label-map mycolormap.txt - # add "--apply-colormap=0" to save grayscale (indexed) masks - # check "--help" option for more info - # use "datum --loglevel debug" for extra conversion info - ``` - - - - -## Contributing - -Feel free to [open an Issue](https://github.com/opencv/cvat/issues/new) if you -think something needs to be changed. You are welcome to participate in development, -development instructions are available in our [developer manual](CONTRIBUTING.md). diff --git a/datumaro/datum.py b/datumaro/datum.py deleted file mode 100755 index 12c150bd..00000000 --- a/datumaro/datum.py +++ /dev/null @@ -1,8 +0,0 @@ -#!/usr/bin/env python -import sys - -from datumaro.cli.__main__ import main - - -if __name__ == '__main__': - sys.exit(main()) diff --git a/datumaro/datumaro/__init__.py b/datumaro/datumaro/__init__.py deleted file mode 100644 index eb864e52..00000000 --- a/datumaro/datumaro/__init__.py +++ /dev/null @@ -1,4 +0,0 @@ - -# Copyright (C) 2019-2020 Intel Corporation -# -# SPDX-License-Identifier: MIT diff --git a/datumaro/datumaro/__main__.py b/datumaro/datumaro/__main__.py deleted file mode 100644 index be1cb092..00000000 --- a/datumaro/datumaro/__main__.py +++ /dev/null @@ -1,12 +0,0 @@ - -# Copyright (C) 2019-2020 Intel Corporation -# -# SPDX-License-Identifier: MIT - -import sys - -from datumaro.cli.__main__ import main - - -if __name__ == '__main__': - sys.exit(main()) diff --git a/datumaro/datumaro/cli/__init__.py b/datumaro/datumaro/cli/__init__.py deleted file mode 100644 index eb864e52..00000000 --- a/datumaro/datumaro/cli/__init__.py +++ /dev/null @@ -1,4 +0,0 @@ - -# Copyright (C) 2019-2020 Intel Corporation -# -# SPDX-License-Identifier: MIT diff --git a/datumaro/datumaro/cli/__main__.py b/datumaro/datumaro/cli/__main__.py deleted file mode 100644 index 80a8805f..00000000 --- a/datumaro/datumaro/cli/__main__.py +++ /dev/null @@ -1,125 +0,0 @@ - -# Copyright (C) 2019-2020 Intel Corporation -# -# SPDX-License-Identifier: MIT - -import argparse -import logging as log -import sys - -from . import contexts, commands -from .util import CliException, add_subparser -from ..version import VERSION - - -_log_levels = { - 'debug': log.DEBUG, - 'info': log.INFO, - 'warning': log.WARNING, - 'error': log.ERROR, - 'critical': log.CRITICAL -} - -def loglevel(name): - return _log_levels[name] - -class _LogManager: - @classmethod - def init_logger(cls, args=None): - # Define minimalistic parser only to obtain loglevel - parser = argparse.ArgumentParser(add_help=False) - cls._define_loglevel_option(parser) - args, _ = parser.parse_known_args(args) - - log.basicConfig(format='%(asctime)s %(levelname)s: %(message)s', - level=args.loglevel) - - @staticmethod - def _define_loglevel_option(parser): - parser.add_argument('--loglevel', type=loglevel, default='info', - help="Logging level (options: %s; default: %s)" % \ - (', '.join(_log_levels.keys()), "%(default)s")) - return parser - - -def _make_subcommands_help(commands, help_line_start=0): - desc = "" - for command_name, _, command_help in commands: - desc += (" %-" + str(max(0, help_line_start - 2 - 1)) + "s%s\n") % \ - (command_name, command_help) - return desc - -def make_parser(): - parser = argparse.ArgumentParser(prog="datumaro", - description="Dataset Framework", - formatter_class=argparse.RawDescriptionHelpFormatter) - - parser.add_argument('--version', action='version', version=VERSION) - _LogManager._define_loglevel_option(parser) - - known_contexts = [ - ('project', contexts.project, "Actions on projects (datasets)"), - ('source', contexts.source, "Actions on data sources"), - ('model', contexts.model, "Actions on models"), - ] - known_commands = [ - ('create', commands.create, "Create project"), - ('add', commands.add, "Add source to project"), - ('remove', commands.remove, "Remove source from project"), - ('export', commands.export, "Export project"), - ('explain', commands.explain, "Run Explainable AI algorithm for model"), - ('merge', commands.merge, "Merge datasets"), - ('convert', commands.convert, "Convert dataset"), - ] - - # Argparse doesn't support subparser groups: - # https://stackoverflow.com/questions/32017020/grouping-argparse-subparser-arguments - help_line_start = max((len(e[0]) for e in known_contexts + known_commands), - default=0) - help_line_start = max((2 + help_line_start) // 4 + 1, 6) * 4 # align to tabs - subcommands_desc = "" - if known_contexts: - subcommands_desc += "Contexts:\n" - subcommands_desc += _make_subcommands_help(known_contexts, - help_line_start) - if known_commands: - if subcommands_desc: - subcommands_desc += "\n" - subcommands_desc += "Commands:\n" - subcommands_desc += _make_subcommands_help(known_commands, - help_line_start) - if subcommands_desc: - subcommands_desc += \ - "\nRun '%s COMMAND --help' for more information on a command." % \ - parser.prog - - subcommands = parser.add_subparsers(title=subcommands_desc, - description="", help=argparse.SUPPRESS) - for command_name, command, _ in known_contexts + known_commands: - add_subparser(subcommands, command_name, command.build_parser) - - return parser - - -def main(args=None): - _LogManager.init_logger(args) - - parser = make_parser() - args = parser.parse_args(args) - - if 'command' not in args: - parser.print_help() - return 1 - - try: - return args.command(args) - except CliException as e: - log.error(e) - return 1 - except Exception as e: - log.error(e) - raise - - -if __name__ == '__main__': - sys.exit(main()) \ No newline at end of file diff --git a/datumaro/datumaro/cli/commands/__init__.py b/datumaro/datumaro/cli/commands/__init__.py deleted file mode 100644 index fe74bc2b..00000000 --- a/datumaro/datumaro/cli/commands/__init__.py +++ /dev/null @@ -1,6 +0,0 @@ - -# Copyright (C) 2019-2020 Intel Corporation -# -# SPDX-License-Identifier: MIT - -from . import add, create, explain, export, remove, merge, convert diff --git a/datumaro/datumaro/cli/commands/add.py b/datumaro/datumaro/cli/commands/add.py deleted file mode 100644 index 288d7c04..00000000 --- a/datumaro/datumaro/cli/commands/add.py +++ /dev/null @@ -1,8 +0,0 @@ - -# Copyright (C) 2019-2020 Intel Corporation -# -# SPDX-License-Identifier: MIT - -# pylint: disable=unused-import - -from ..contexts.source import build_add_parser as build_parser diff --git a/datumaro/datumaro/cli/commands/convert.py b/datumaro/datumaro/cli/commands/convert.py deleted file mode 100644 index 6398bac7..00000000 --- a/datumaro/datumaro/cli/commands/convert.py +++ /dev/null @@ -1,137 +0,0 @@ - -# Copyright (C) 2019-2020 Intel Corporation -# -# SPDX-License-Identifier: MIT - -import argparse -import logging as log -import os -import os.path as osp - -from datumaro.components.project import Environment - -from ..contexts.project import FilterModes -from ..util import CliException, MultilineFormatter, make_file_name -from ..util.project import generate_next_file_name - - -def build_parser(parser_ctor=argparse.ArgumentParser): - builtin_importers = sorted(Environment().importers.items) - builtin_converters = sorted(Environment().converters.items) - - parser = parser_ctor(help="Convert an existing dataset to another format", - description=""" - Converts a dataset from one format to another. - You can add your own formats using a project.|n - |n - Supported input formats: %s|n - |n - Supported output formats: %s|n - |n - Examples:|n - - Export a dataset as a PASCAL VOC dataset, include images:|n - |s|sconvert -i src/path -f voc -- --save-images|n - |n - - Export a dataset as a COCO dataset to a specific directory:|n - |s|sconvert -i src/path -f coco -o path/I/like/ - """ % (', '.join(builtin_importers), ', '.join(builtin_converters)), - formatter_class=MultilineFormatter) - - parser.add_argument('-i', '--input-path', default='.', dest='source', - help="Path to look for a dataset") - parser.add_argument('-if', '--input-format', - help="Input dataset format. Will try to detect, if not specified.") - parser.add_argument('-f', '--output-format', required=True, - help="Output format") - parser.add_argument('-o', '--output-dir', dest='dst_dir', - help="Directory to save output (default: a subdir in the current one)") - parser.add_argument('--overwrite', action='store_true', - help="Overwrite existing files in the save directory") - parser.add_argument('-e', '--filter', - help="Filter expression for dataset items") - parser.add_argument('--filter-mode', default=FilterModes.i.name, - type=FilterModes.parse, - help="Filter mode (options: %s; default: %s)" % \ - (', '.join(FilterModes.list_options()) , '%(default)s')) - parser.add_argument('extra_args', nargs=argparse.REMAINDER, - help="Additional arguments for output format (pass '-- -h' for help)") - parser.set_defaults(command=convert_command) - - return parser - -def convert_command(args): - env = Environment() - - try: - converter = env.converters.get(args.output_format) - except KeyError: - raise CliException("Converter for format '%s' is not found" % \ - args.output_format) - extra_args = converter.from_cmdline(args.extra_args) - def converter_proxy(extractor, save_dir): - return converter.convert(extractor, save_dir, **extra_args) - - filter_args = FilterModes.make_filter_args(args.filter_mode) - - if not args.input_format: - matches = [] - for format_name in env.importers.items: - log.debug("Checking '%s' format...", format_name) - importer = env.make_importer(format_name) - try: - match = importer.detect(args.source) - if match: - log.debug("format matched") - matches.append((format_name, importer)) - except NotImplementedError: - log.debug("Format '%s' does not support auto detection.", - format_name) - - if len(matches) == 0: - log.error("Failed to detect dataset format. " - "Try to specify format with '-if/--input-format' parameter.") - return 1 - elif len(matches) != 1: - log.error("Multiple formats match the dataset: %s. " - "Try to specify format with '-if/--input-format' parameter.", - ', '.join(m[0] for m in matches)) - return 2 - - format_name, importer = matches[0] - args.input_format = format_name - log.info("Source dataset format detected as '%s'", args.input_format) - else: - try: - importer = env.make_importer(args.input_format) - if hasattr(importer, 'from_cmdline'): - extra_args = importer.from_cmdline() - except KeyError: - raise CliException("Importer for format '%s' is not found" % \ - args.input_format) - - source = osp.abspath(args.source) - - dst_dir = args.dst_dir - if dst_dir: - if not args.overwrite and osp.isdir(dst_dir) and os.listdir(dst_dir): - raise CliException("Directory '%s' already exists " - "(pass --overwrite to overwrite)" % dst_dir) - else: - dst_dir = generate_next_file_name('%s-%s' % \ - (osp.basename(source), make_file_name(args.output_format))) - dst_dir = osp.abspath(dst_dir) - - project = importer(source) - dataset = project.make_dataset() - - log.info("Exporting the dataset") - dataset.export_project( - save_dir=dst_dir, - converter=converter_proxy, - filter_expr=args.filter, - **filter_args) - - log.info("Dataset exported to '%s' as '%s'" % \ - (dst_dir, args.output_format)) - - return 0 diff --git a/datumaro/datumaro/cli/commands/create.py b/datumaro/datumaro/cli/commands/create.py deleted file mode 100644 index 97e3c9b4..00000000 --- a/datumaro/datumaro/cli/commands/create.py +++ /dev/null @@ -1,8 +0,0 @@ - -# Copyright (C) 2019-2020 Intel Corporation -# -# SPDX-License-Identifier: MIT - -# pylint: disable=unused-import - -from ..contexts.project import build_create_parser as build_parser \ No newline at end of file diff --git a/datumaro/datumaro/cli/commands/explain.py b/datumaro/datumaro/cli/commands/explain.py deleted file mode 100644 index 4d5d16b2..00000000 --- a/datumaro/datumaro/cli/commands/explain.py +++ /dev/null @@ -1,183 +0,0 @@ - -# Copyright (C) 2019-2020 Intel Corporation -# -# SPDX-License-Identifier: MIT - -import argparse -import logging as log -import os -import os.path as osp - -from datumaro.components.project import Project -from datumaro.util.command_targets import (TargetKinds, target_selector, - ProjectTarget, SourceTarget, ImageTarget, is_project_path) -from datumaro.util.image import load_image, save_image -from ..util import MultilineFormatter -from ..util.project import load_project - - -def build_parser(parser_ctor=argparse.ArgumentParser): - parser = parser_ctor(help="Run Explainable AI algorithm", - description="Runs an explainable AI algorithm for a model.") - - parser.add_argument('-m', '--model', required=True, - help="Model to use for inference") - parser.add_argument('-t', '--target', default=None, - help="Inference target - image, source, project " - "(default: current dir)") - parser.add_argument('-o', '--output-dir', dest='save_dir', default=None, - help="Directory to save output (default: display only)") - - method_sp = parser.add_subparsers(dest='algorithm') - - rise_parser = method_sp.add_parser('rise', - description=""" - RISE: Randomized Input Sampling for - Explanation of Black-box Models algorithm|n - |n - See explanations at: https://arxiv.org/pdf/1806.07421.pdf - """, - formatter_class=MultilineFormatter) - rise_parser.add_argument('-s', '--max-samples', default=None, type=int, - help="Number of algorithm iterations (default: mask size ^ 2)") - rise_parser.add_argument('--mw', '--mask-width', - dest='mask_width', default=7, type=int, - help="Mask width (default: %(default)s)") - rise_parser.add_argument('--mh', '--mask-height', - dest='mask_height', default=7, type=int, - help="Mask height (default: %(default)s)") - rise_parser.add_argument('--prob', default=0.5, type=float, - help="Mask pixel inclusion probability (default: %(default)s)") - rise_parser.add_argument('--iou', '--iou-thresh', - dest='iou_thresh', default=0.9, type=float, - help="IoU match threshold for detections (default: %(default)s)") - rise_parser.add_argument('--nms', '--nms-iou-thresh', - dest='nms_iou_thresh', default=0.0, type=float, - help="IoU match threshold in Non-maxima suppression (default: no NMS)") - rise_parser.add_argument('--conf', '--det-conf-thresh', - dest='det_conf_thresh', default=0.0, type=float, - help="Confidence threshold for detections (default: include all)") - rise_parser.add_argument('-b', '--batch-size', default=1, type=int, - help="Inference batch size (default: %(default)s)") - rise_parser.add_argument('--display', action='store_true', - help="Visualize results during computations") - - parser.add_argument('-p', '--project', dest='project_dir', default='.', - help="Directory of the project to operate on (default: current dir)") - parser.set_defaults(command=explain_command) - - return parser - -def explain_command(args): - project_path = args.project_dir - if is_project_path(project_path): - project = Project.load(project_path) - else: - project = None - args.target = target_selector( - ProjectTarget(is_default=True, project=project), - SourceTarget(project=project), - ImageTarget() - )(args.target) - if args.target[0] == TargetKinds.project: - if is_project_path(args.target[1]): - args.project_dir = osp.dirname(osp.abspath(args.target[1])) - - - import cv2 - from matplotlib import cm - - project = load_project(args.project_dir) - - model = project.make_executable_model(args.model) - - if str(args.algorithm).lower() != 'rise': - raise NotImplementedError() - - from datumaro.components.algorithms.rise import RISE - rise = RISE(model, - max_samples=args.max_samples, - mask_width=args.mask_width, - mask_height=args.mask_height, - prob=args.prob, - iou_thresh=args.iou_thresh, - nms_thresh=args.nms_iou_thresh, - det_conf_thresh=args.det_conf_thresh, - batch_size=args.batch_size) - - if args.target[0] == TargetKinds.image: - image_path = args.target[1] - image = load_image(image_path) - - log.info("Running inference explanation for '%s'" % image_path) - heatmap_iter = rise.apply(image, progressive=args.display) - - image = image / 255.0 - file_name = osp.splitext(osp.basename(image_path))[0] - if args.display: - for i, heatmaps in enumerate(heatmap_iter): - for j, heatmap in enumerate(heatmaps): - hm_painted = cm.jet(heatmap)[:, :, 2::-1] - disp = (image + hm_painted) / 2 - cv2.imshow('heatmap-%s' % j, hm_painted) - cv2.imshow(file_name + '-heatmap-%s' % j, disp) - cv2.waitKey(10) - print("Iter", i, "of", args.max_samples, end='\r') - else: - heatmaps = next(heatmap_iter) - - if args.save_dir is not None: - log.info("Saving inference heatmaps at '%s'" % args.save_dir) - os.makedirs(args.save_dir, exist_ok=True) - - for j, heatmap in enumerate(heatmaps): - save_path = osp.join(args.save_dir, - file_name + '-heatmap-%s.png' % j) - save_image(save_path, heatmap * 255.0) - else: - for j, heatmap in enumerate(heatmaps): - disp = (image + cm.jet(heatmap)[:, :, 2::-1]) / 2 - cv2.imshow(file_name + '-heatmap-%s' % j, disp) - cv2.waitKey(0) - elif args.target[0] == TargetKinds.source or \ - args.target[0] == TargetKinds.project: - if args.target[0] == TargetKinds.source: - source_name = args.target[1] - dataset = project.make_source_project(source_name).make_dataset() - log.info("Running inference explanation for '%s'" % source_name) - else: - project_name = project.config.project_name - dataset = project.make_dataset() - log.info("Running inference explanation for '%s'" % project_name) - - for item in dataset: - image = item.image.data - if image is None: - log.warn( - "Dataset item %s does not have image data. Skipping." % \ - (item.id)) - continue - - heatmap_iter = rise.apply(image) - - image = image / 255.0 - heatmaps = next(heatmap_iter) - - if args.save_dir is not None: - log.info("Saving inference heatmaps to '%s'" % args.save_dir) - os.makedirs(args.save_dir, exist_ok=True) - - for j, heatmap in enumerate(heatmaps): - save_image(osp.join(args.save_dir, - item.id + '-heatmap-%s.png' % j), - heatmap * 255.0, create_dir=True) - - if not args.save_dir or args.display: - for j, heatmap in enumerate(heatmaps): - disp = (image + cm.jet(heatmap)[:, :, 2::-1]) / 2 - cv2.imshow(item.id + '-heatmap-%s' % j, disp) - cv2.waitKey(0) - else: - raise NotImplementedError() - - return 0 diff --git a/datumaro/datumaro/cli/commands/export.py b/datumaro/datumaro/cli/commands/export.py deleted file mode 100644 index be47245d..00000000 --- a/datumaro/datumaro/cli/commands/export.py +++ /dev/null @@ -1,8 +0,0 @@ - -# Copyright (C) 2019-2020 Intel Corporation -# -# SPDX-License-Identifier: MIT - -# pylint: disable=unused-import - -from ..contexts.project import build_export_parser as build_parser \ No newline at end of file diff --git a/datumaro/datumaro/cli/commands/merge.py b/datumaro/datumaro/cli/commands/merge.py deleted file mode 100644 index 2583cd86..00000000 --- a/datumaro/datumaro/cli/commands/merge.py +++ /dev/null @@ -1,124 +0,0 @@ - -# Copyright (C) 2020 Intel Corporation -# -# SPDX-License-Identifier: MIT - -import argparse -import json -import logging as log -import os.path as osp -from collections import OrderedDict - -from datumaro.components.project import Project -from datumaro.components.operations import (IntersectMerge, - QualityError, MergeError) - -from ..util import at_least, MultilineFormatter, CliException -from ..util.project import generate_next_file_name, load_project - - -def build_parser(parser_ctor=argparse.ArgumentParser): - parser = parser_ctor(help="Merge few projects", - description=""" - Merges multiple datasets into one. This can be useful if you - have few annotations and wish to merge them, - taking into consideration potential overlaps and conflicts. - This command can try to find a common ground by voting or - return a list of conflicts.|n - |n - Examples:|n - - Merge annotations from 3 (or more) annotators:|n - |s|smerge project1/ project2/ project3/|n - - Check groups of the merged dataset for consistence:|n - |s|s|slook for groups consising of 'person', 'hand' 'head', 'foot'|n - |s|smerge project1/ project2/ -g 'person,hand?,head,foot?' - """, - formatter_class=MultilineFormatter) - - def _group(s): - return s.split(',') - - parser.add_argument('project', nargs='+', action=at_least(2), - help="Path to a project (repeatable)") - parser.add_argument('-iou', '--iou-thresh', default=0.25, type=float, - help="IoU match threshold for segments (default: %(default)s)") - parser.add_argument('-oconf', '--output-conf-thresh', - default=0.0, type=float, - help="Confidence threshold for output " - "annotations (default: %(default)s)") - parser.add_argument('--quorum', default=0, type=int, - help="Minimum count for a label and attribute voting " - "results to be counted (default: %(default)s)") - parser.add_argument('-g', '--groups', action='append', type=_group, - default=[], - help="A comma-separated list of labels in " - "annotation groups to check. '?' postfix can be added to a label to" - "make it optional in the group (repeatable)") - parser.add_argument('-o', '--output-dir', dest='dst_dir', default=None, - help="Output directory (default: current project's dir)") - parser.add_argument('--overwrite', action='store_true', - help="Overwrite existing files in the save directory") - parser.set_defaults(command=merge_command) - - return parser - -def merge_command(args): - source_projects = [load_project(p) for p in args.project] - - dst_dir = args.dst_dir - if dst_dir: - if not args.overwrite and osp.isdir(dst_dir) and os.listdir(dst_dir): - raise CliException("Directory '%s' already exists " - "(pass --overwrite to overwrite)" % dst_dir) - else: - dst_dir = generate_next_file_name('merged') - - source_datasets = [] - for p in source_projects: - log.debug("Loading project '%s' dataset", p.config.project_name) - source_datasets.append(p.make_dataset()) - - merger = IntersectMerge(conf=IntersectMerge.Conf( - pairwise_dist=args.iou_thresh, groups=args.groups, - output_conf_thresh=args.output_conf_thresh, quorum=args.quorum - )) - merged_dataset = merger(source_datasets) - - merged_project = Project() - output_dataset = merged_project.make_dataset() - output_dataset.define_categories(merged_dataset.categories()) - merged_dataset = output_dataset.update(merged_dataset) - merged_dataset.save(save_dir=dst_dir) - - report_path = osp.join(dst_dir, 'merge_report.json') - save_merge_report(merger, report_path) - - dst_dir = osp.abspath(dst_dir) - log.info("Merge results have been saved to '%s'" % dst_dir) - log.info("Report has been saved to '%s'" % report_path) - - return 0 - -def save_merge_report(merger, path): - item_errors = OrderedDict() - source_errors = OrderedDict() - all_errors = [] - - for e in merger.errors: - if isinstance(e, QualityError): - item_errors[str(e.item_id)] = item_errors.get(str(e.item_id), 0) + 1 - elif isinstance(e, MergeError): - for s in e.sources: - source_errors[s] = source_errors.get(s, 0) + 1 - item_errors[str(e.item_id)] = item_errors.get(str(e.item_id), 0) + 1 - - all_errors.append(str(e)) - - errors = OrderedDict([ - ('Item errors', item_errors), - ('Source errors', source_errors), - ('All errors', all_errors), - ]) - - with open(path, 'w') as f: - json.dump(errors, f, indent=4) \ No newline at end of file diff --git a/datumaro/datumaro/cli/commands/remove.py b/datumaro/datumaro/cli/commands/remove.py deleted file mode 100644 index 7b9c0d3a..00000000 --- a/datumaro/datumaro/cli/commands/remove.py +++ /dev/null @@ -1,8 +0,0 @@ - -# Copyright (C) 2019-2020 Intel Corporation -# -# SPDX-License-Identifier: MIT - -# pylint: disable=unused-import - -from ..contexts.source import build_remove_parser as build_parser \ No newline at end of file diff --git a/datumaro/datumaro/cli/contexts/__init__.py b/datumaro/datumaro/cli/contexts/__init__.py deleted file mode 100644 index 433efe9b..00000000 --- a/datumaro/datumaro/cli/contexts/__init__.py +++ /dev/null @@ -1,6 +0,0 @@ - -# Copyright (C) 2019-2020 Intel Corporation -# -# SPDX-License-Identifier: MIT - -from . import project, source, model, item \ No newline at end of file diff --git a/datumaro/datumaro/cli/contexts/item/__init__.py b/datumaro/datumaro/cli/contexts/item/__init__.py deleted file mode 100644 index 8f74826d..00000000 --- a/datumaro/datumaro/cli/contexts/item/__init__.py +++ /dev/null @@ -1,36 +0,0 @@ - -# Copyright (C) 2019-2020 Intel Corporation -# -# SPDX-License-Identifier: MIT - -import argparse - -from ...util import add_subparser - - -def build_export_parser(parser_ctor=argparse.ArgumentParser): - parser = parser_ctor() - return parser - -def build_stats_parser(parser_ctor=argparse.ArgumentParser): - parser = parser_ctor() - return parser - -def build_diff_parser(parser_ctor=argparse.ArgumentParser): - parser = parser_ctor() - return parser - -def build_edit_parser(parser_ctor=argparse.ArgumentParser): - parser = parser_ctor() - return parser - -def build_parser(parser_ctor=argparse.ArgumentParser): - parser = parser_ctor() - - subparsers = parser.add_subparsers() - add_subparser(subparsers, 'export', build_export_parser) - add_subparser(subparsers, 'stats', build_stats_parser) - add_subparser(subparsers, 'diff', build_diff_parser) - add_subparser(subparsers, 'edit', build_edit_parser) - - return parser diff --git a/datumaro/datumaro/cli/contexts/model/__init__.py b/datumaro/datumaro/cli/contexts/model/__init__.py deleted file mode 100644 index 69b7da1e..00000000 --- a/datumaro/datumaro/cli/contexts/model/__init__.py +++ /dev/null @@ -1,183 +0,0 @@ - -# Copyright (C) 2019-2020 Intel Corporation -# -# SPDX-License-Identifier: MIT - -import argparse -import logging as log -import os -import os.path as osp -import re - -from datumaro.components.config import DEFAULT_FORMAT -from datumaro.components.project import Environment - -from ...util import CliException, MultilineFormatter, add_subparser -from ...util.project import load_project, \ - generate_next_name, generate_next_file_name - - -def build_add_parser(parser_ctor=argparse.ArgumentParser): - builtins = sorted(Environment().launchers.items) - - parser = parser_ctor(help="Add model to project", - description=""" - Registers an executable model into a project. A model requires - a launcher to be executed. Each launcher has its own options, which - are passed after '--' separator, pass '-- -h' for more info. - |n - List of builtin launchers: %s - """ % ', '.join(builtins), - formatter_class=MultilineFormatter) - - parser.add_argument('-l', '--launcher', required=True, - help="Model launcher") - parser.add_argument('extra_args', nargs=argparse.REMAINDER, default=None, - help="Additional arguments for converter (pass '-- -h' for help)") - parser.add_argument('--copy', action='store_true', - help="Copy the model to the project") - parser.add_argument('-n', '--name', default=None, - help="Name of the model to be added (default: generate automatically)") - parser.add_argument('--overwrite', action='store_true', - help="Overwrite if exists") - parser.add_argument('-p', '--project', dest='project_dir', default='.', - help="Directory of the project to operate on (default: current dir)") - parser.set_defaults(command=add_command) - - return parser - -def add_command(args): - project = load_project(args.project_dir) - - if args.name: - if not args.overwrite and args.name in project.config.models: - raise CliException("Model '%s' already exists " - "(pass --overwrite to overwrite)" % args.name) - else: - args.name = generate_next_name( - project.config.models, 'model', '-', default=0) - assert args.name not in project.config.models, args.name - - try: - launcher = project.env.launchers.get(args.launcher) - except KeyError: - raise CliException("Launcher '%s' is not found" % args.launcher) - - cli_plugin = getattr(launcher, 'cli_plugin', launcher) - model_args = cli_plugin.from_cmdline(args.extra_args) - - if args.copy: - log.info("Copying model data") - - model_dir = project.local_model_dir(args.name) - os.makedirs(model_dir, exist_ok=False) - - try: - cli_plugin.copy_model(model_dir, model_args) - except (AttributeError, NotImplementedError): - log.error("Can't copy: copying is not available for '%s' models" % \ - args.launcher) - - log.info("Checking the model") - project.add_model(args.name, { - 'launcher': args.launcher, - 'options': model_args, - }) - project.make_executable_model(args.name) - - project.save() - - log.info("Model '%s' with launcher '%s' has been added to project '%s'" % \ - (args.name, args.launcher, project.config.project_name)) - - return 0 - -def build_remove_parser(parser_ctor=argparse.ArgumentParser): - parser = parser_ctor() - - parser.add_argument('name', - help="Name of the model to be removed") - parser.add_argument('-p', '--project', dest='project_dir', default='.', - help="Directory of the project to operate on (default: current dir)") - parser.set_defaults(command=remove_command) - - return parser - -def remove_command(args): - project = load_project(args.project_dir) - - project.remove_model(args.name) - project.save() - - return 0 - -def build_run_parser(parser_ctor=argparse.ArgumentParser): - parser = parser_ctor() - - parser.add_argument('-o', '--output-dir', dest='dst_dir', - help="Directory to save output") - parser.add_argument('-m', '--model', dest='model_name', required=True, - help="Model to apply to the project") - parser.add_argument('-p', '--project', dest='project_dir', default='.', - help="Directory of the project to operate on (default: current dir)") - parser.add_argument('--overwrite', action='store_true', - help="Overwrite if exists") - parser.set_defaults(command=run_command) - - return parser - -def run_command(args): - project = load_project(args.project_dir) - - dst_dir = args.dst_dir - if dst_dir: - if not args.overwrite and osp.isdir(dst_dir) and os.listdir(dst_dir): - raise CliException("Directory '%s' already exists " - "(pass --overwrite overwrite)" % dst_dir) - else: - dst_dir = generate_next_file_name('%s-inference' % \ - project.config.project_name) - - project.make_dataset().apply_model( - save_dir=osp.abspath(dst_dir), - model=args.model_name) - - log.info("Inference results have been saved to '%s'" % dst_dir) - - return 0 - -def build_info_parser(parser_ctor=argparse.ArgumentParser): - parser = parser_ctor() - - parser.add_argument('-n', '--name', - help="Model name") - parser.add_argument('-v', '--verbose', action='store_true', - help="Show details") - parser.add_argument('-p', '--project', dest='project_dir', default='.', - help="Directory of the project to operate on (default: current dir)") - parser.set_defaults(command=info_command) - - return parser - -def info_command(args): - project = load_project(args.project_dir) - - if args.name: - model = project.get_model(args.name) - print(model) - else: - for name, conf in project.config.models.items(): - print(name) - if args.verbose: - print(dict(conf)) - -def build_parser(parser_ctor=argparse.ArgumentParser): - parser = parser_ctor() - - subparsers = parser.add_subparsers() - add_subparser(subparsers, 'add', build_add_parser) - add_subparser(subparsers, 'remove', build_remove_parser) - add_subparser(subparsers, 'run', build_run_parser) - add_subparser(subparsers, 'info', build_info_parser) - - return parser diff --git a/datumaro/datumaro/cli/contexts/project/__init__.py b/datumaro/datumaro/cli/contexts/project/__init__.py deleted file mode 100644 index bab5da6f..00000000 --- a/datumaro/datumaro/cli/contexts/project/__init__.py +++ /dev/null @@ -1,826 +0,0 @@ - -# Copyright (C) 2019-2020 Intel Corporation -# -# SPDX-License-Identifier: MIT - -import argparse -import json -import logging as log -import os -import os.path as osp -import shutil -from enum import Enum - -from datumaro.components.cli_plugin import CliPlugin -from datumaro.components.dataset_filter import DatasetItemEncoder -from datumaro.components.extractor import AnnotationType -from datumaro.components.operations import (DistanceComparator, - ExactComparator, compute_ann_statistics, compute_image_statistics, mean_std) -from datumaro.components.project import \ - PROJECT_DEFAULT_CONFIG as DEFAULT_CONFIG -from datumaro.components.project import Environment, Project - -from ...util import (CliException, MultilineFormatter, add_subparser, - make_file_name) -from ...util.project import generate_next_file_name, load_project -from .diff import DiffVisualizer - - -def build_create_parser(parser_ctor=argparse.ArgumentParser): - parser = parser_ctor(help="Create empty project", - description=""" - Create a new empty project.|n - |n - Examples:|n - - Create a project in the current directory:|n - |s|screate -n myproject|n - |n - - Create a project in other directory:|n - |s|screate -o path/I/like/ - """, - formatter_class=MultilineFormatter) - - parser.add_argument('-o', '--output-dir', default='.', dest='dst_dir', - help="Save directory for the new project (default: current dir") - parser.add_argument('-n', '--name', default=None, - help="Name of the new project (default: same as project dir)") - parser.add_argument('--overwrite', action='store_true', - help="Overwrite existing files in the save directory") - parser.set_defaults(command=create_command) - - return parser - -def create_command(args): - project_dir = osp.abspath(args.dst_dir) - - project_env_dir = osp.join(project_dir, DEFAULT_CONFIG.env_dir) - if osp.isdir(project_env_dir) and os.listdir(project_env_dir): - if not args.overwrite: - raise CliException("Directory '%s' already exists " - "(pass --overwrite to overwrite)" % project_env_dir) - else: - shutil.rmtree(project_env_dir, ignore_errors=True) - - own_dataset_dir = osp.join(project_dir, DEFAULT_CONFIG.dataset_dir) - if osp.isdir(own_dataset_dir) and os.listdir(own_dataset_dir): - if not args.overwrite: - raise CliException("Directory '%s' already exists " - "(pass --overwrite to overwrite)" % own_dataset_dir) - else: - # NOTE: remove the dir to avoid using data from previous project - shutil.rmtree(own_dataset_dir) - - project_name = args.name - if project_name is None: - project_name = osp.basename(project_dir) - - log.info("Creating project at '%s'" % project_dir) - - Project.generate(project_dir, { - 'project_name': project_name, - }) - - log.info("Project has been created at '%s'" % project_dir) - - return 0 - -def build_import_parser(parser_ctor=argparse.ArgumentParser): - builtins = sorted(Environment().importers.items) - - parser = parser_ctor(help="Create project from existing dataset", - description=""" - Creates a project from an existing dataset. The source can be:|n - - a dataset in a supported format (check 'formats' section below)|n - - a Datumaro project|n - |n - Formats:|n - Datasets come in a wide variety of formats. Each dataset - format defines its own data structure and rules on how to - interpret the data. For example, the following data structure - is used in COCO format:|n - /dataset/|n - - /images/.jpg|n - - /annotations/|n - |n - In Datumaro dataset formats are supported by - Extractor-s and Importer-s. - An Extractor produces a list of dataset items corresponding - to the dataset. An Importer creates a project from the - data source location. - It is possible to add a custom Extractor and Importer. - To do this, you need to put an Extractor and - Importer implementation scripts to - /.datumaro/extractors - and /.datumaro/importers.|n - |n - List of builtin dataset formats: %s|n - |n - Examples:|n - - Create a project from VOC dataset in the current directory:|n - |s|simport -f voc -i path/to/voc|n - |n - - Create a project from COCO dataset in other directory:|n - |s|simport -f coco -i path/to/coco -o path/I/like/ - """ % ', '.join(builtins), - formatter_class=MultilineFormatter) - - parser.add_argument('-o', '--output-dir', default='.', dest='dst_dir', - help="Directory to save the new project to (default: current dir)") - parser.add_argument('-n', '--name', default=None, - help="Name of the new project (default: same as project dir)") - parser.add_argument('--copy', action='store_true', - help="Copy the dataset instead of saving source links") - parser.add_argument('--skip-check', action='store_true', - help="Skip source checking") - parser.add_argument('--overwrite', action='store_true', - help="Overwrite existing files in the save directory") - parser.add_argument('-i', '--input-path', required=True, dest='source', - help="Path to import project from") - parser.add_argument('-f', '--format', - help="Source project format. Will try to detect, if not specified.") - parser.add_argument('extra_args', nargs=argparse.REMAINDER, - help="Additional arguments for importer (pass '-- -h' for help)") - parser.set_defaults(command=import_command) - - return parser - -def import_command(args): - project_dir = osp.abspath(args.dst_dir) - - project_env_dir = osp.join(project_dir, DEFAULT_CONFIG.env_dir) - if osp.isdir(project_env_dir) and os.listdir(project_env_dir): - if not args.overwrite: - raise CliException("Directory '%s' already exists " - "(pass --overwrite to overwrite)" % project_env_dir) - else: - shutil.rmtree(project_env_dir, ignore_errors=True) - - own_dataset_dir = osp.join(project_dir, DEFAULT_CONFIG.dataset_dir) - if osp.isdir(own_dataset_dir) and os.listdir(own_dataset_dir): - if not args.overwrite: - raise CliException("Directory '%s' already exists " - "(pass --overwrite to overwrite)" % own_dataset_dir) - else: - # NOTE: remove the dir to avoid using data from previous project - shutil.rmtree(own_dataset_dir) - - project_name = args.name - if project_name is None: - project_name = osp.basename(project_dir) - - env = Environment() - log.info("Importing project from '%s'" % args.source) - - extra_args = {} - if not args.format: - if args.extra_args: - raise CliException("Extra args can not be used without format") - - log.info("Trying to detect dataset format...") - - matches = [] - for format_name in env.importers.items: - log.debug("Checking '%s' format...", format_name) - importer = env.make_importer(format_name) - try: - match = importer.detect(args.source) - if match: - log.debug("format matched") - matches.append((format_name, importer)) - except NotImplementedError: - log.debug("Format '%s' does not support auto detection.", - format_name) - - if len(matches) == 0: - log.error("Failed to detect dataset format automatically. " - "Try to specify format with '-f/--format' parameter.") - return 1 - elif len(matches) != 1: - log.error("Multiple formats match the dataset: %s. " - "Try to specify format with '-f/--format' parameter.", - ', '.join(m[0] for m in matches)) - return 2 - - format_name, importer = matches[0] - args.format = format_name - else: - try: - importer = env.make_importer(args.format) - if hasattr(importer, 'from_cmdline'): - extra_args = importer.from_cmdline(args.extra_args) - except KeyError: - raise CliException("Importer for format '%s' is not found" % \ - args.format) - - log.info("Importing project as '%s'" % args.format) - - source = osp.abspath(args.source) - project = importer(source, **extra_args) - project.config.project_name = project_name - project.config.project_dir = project_dir - - if not args.skip_check or args.copy: - log.info("Checking the dataset...") - dataset = project.make_dataset() - if args.copy: - log.info("Cloning data...") - dataset.save(merge=True, save_images=True) - else: - project.save() - - log.info("Project has been created at '%s'" % project_dir) - - return 0 - - -class FilterModes(Enum): - # primary - items = 1 - annotations = 2 - items_annotations = 3 - - # shortcuts - i = 1 - a = 2 - i_a = 3 - a_i = 3 - annotations_items = 3 - - @staticmethod - def parse(s): - s = s.lower() - s = s.replace('+', '_') - return FilterModes[s] - - @classmethod - def make_filter_args(cls, mode): - if mode == cls.items: - return {} - elif mode == cls.annotations: - return { - 'filter_annotations': True - } - elif mode == cls.items_annotations: - return { - 'filter_annotations': True, - 'remove_empty': True, - } - else: - raise NotImplementedError() - - @classmethod - def list_options(cls): - return [m.name.replace('_', '+') for m in cls] - -def build_export_parser(parser_ctor=argparse.ArgumentParser): - builtins = sorted(Environment().converters.items) - - parser = parser_ctor(help="Export project", - description=""" - Exports the project dataset in some format. Optionally, a filter - can be passed, check 'filter' command description for more info. - Each dataset format has its own options, which - are passed after '--' separator (see examples), pass '-- -h' - for more info. If not stated otherwise, by default - only annotations are exported, to include images pass - '--save-images' parameter.|n - |n - Formats:|n - In Datumaro dataset formats are supported by Converter-s. - A Converter produces a dataset of a specific format - from dataset items. It is possible to add a custom Converter. - To do this, you need to put a Converter - definition script to /.datumaro/converters.|n - |n - List of builtin dataset formats: %s|n - |n - Examples:|n - - Export project as a VOC-like dataset, include images:|n - |s|sexport -f voc -- --save-images|n - |n - - Export project as a COCO-like dataset in other directory:|n - |s|sexport -f coco -o path/I/like/ - """ % ', '.join(builtins), - formatter_class=MultilineFormatter) - - parser.add_argument('-e', '--filter', default=None, - help="Filter expression for dataset items") - parser.add_argument('--filter-mode', default=FilterModes.i.name, - type=FilterModes.parse, - help="Filter mode (options: %s; default: %s)" % \ - (', '.join(FilterModes.list_options()) , '%(default)s')) - parser.add_argument('-o', '--output-dir', dest='dst_dir', default=None, - help="Directory to save output (default: a subdir in the current one)") - parser.add_argument('--overwrite', action='store_true', - help="Overwrite existing files in the save directory") - parser.add_argument('-p', '--project', dest='project_dir', default='.', - help="Directory of the project to operate on (default: current dir)") - parser.add_argument('-f', '--format', required=True, - help="Output format") - parser.add_argument('extra_args', nargs=argparse.REMAINDER, default=None, - help="Additional arguments for converter (pass '-- -h' for help)") - parser.set_defaults(command=export_command) - - return parser - -def export_command(args): - project = load_project(args.project_dir) - - dst_dir = args.dst_dir - if dst_dir: - if not args.overwrite and osp.isdir(dst_dir) and os.listdir(dst_dir): - raise CliException("Directory '%s' already exists " - "(pass --overwrite to overwrite)" % dst_dir) - else: - dst_dir = generate_next_file_name('%s-%s' % \ - (project.config.project_name, make_file_name(args.format))) - dst_dir = osp.abspath(dst_dir) - - try: - converter = project.env.converters.get(args.format) - except KeyError: - raise CliException("Converter for format '%s' is not found" % \ - args.format) - - extra_args = converter.from_cmdline(args.extra_args) - def converter_proxy(extractor, save_dir): - return converter.convert(extractor, save_dir, **extra_args) - - filter_args = FilterModes.make_filter_args(args.filter_mode) - - log.info("Loading the project...") - dataset = project.make_dataset() - - log.info("Exporting the project...") - dataset.export_project( - save_dir=dst_dir, - converter=converter_proxy, - filter_expr=args.filter, - **filter_args) - log.info("Project exported to '%s' as '%s'" % \ - (dst_dir, args.format)) - - return 0 - -def build_filter_parser(parser_ctor=argparse.ArgumentParser): - parser = parser_ctor(help="Extract subproject", - description=""" - Extracts a subproject that contains only items matching filter. - A filter is an XPath expression, which is applied to XML - representation of a dataset item. Check '--dry-run' parameter - to see XML representations of the dataset items.|n - |n - To filter annotations use the mode ('-m') parameter.|n - Supported modes:|n - - 'i', 'items'|n - - 'a', 'annotations'|n - - 'i+a', 'a+i', 'items+annotations', 'annotations+items'|n - When filtering annotations, use the 'items+annotations' - mode to point that annotation-less dataset items should be - removed. To select an annotation, write an XPath that - returns 'annotation' elements (see examples).|n - |n - Examples:|n - - Filter images with width < height:|n - |s|sextract -e '/item[image/width < image/height]'|n - |n - - Filter images with large-area bboxes:|n - |s|sextract -e '/item[annotation/type="bbox" and - annotation/area>2000]'|n - |n - - Filter out all irrelevant annotations from items:|n - |s|sextract -m a -e '/item/annotation[label = "person"]'|n - |n - - Filter out all irrelevant annotations from items:|n - |s|sextract -m a -e '/item/annotation[label="cat" and - area > 99.5]'|n - |n - - Filter occluded annotations and items, if no annotations left:|n - |s|sextract -m i+a -e '/item/annotation[occluded="True"]' - """, - formatter_class=MultilineFormatter) - - parser.add_argument('-e', '--filter', default=None, - help="XML XPath filter expression for dataset items") - parser.add_argument('-m', '--mode', default=FilterModes.i.name, - type=FilterModes.parse, - help="Filter mode (options: %s; default: %s)" % \ - (', '.join(FilterModes.list_options()) , '%(default)s')) - parser.add_argument('--dry-run', action='store_true', - help="Print XML representations to be filtered and exit") - parser.add_argument('-o', '--output-dir', dest='dst_dir', default=None, - help="Output directory (default: update current project)") - parser.add_argument('--overwrite', action='store_true', - help="Overwrite existing files in the save directory") - parser.add_argument('-p', '--project', dest='project_dir', default='.', - help="Directory of the project to operate on (default: current dir)") - parser.set_defaults(command=filter_command) - - return parser - -def filter_command(args): - project = load_project(args.project_dir) - - if not args.dry_run: - dst_dir = args.dst_dir - if dst_dir: - if not args.overwrite and osp.isdir(dst_dir) and os.listdir(dst_dir): - raise CliException("Directory '%s' already exists " - "(pass --overwrite to overwrite)" % dst_dir) - else: - dst_dir = generate_next_file_name('%s-filter' % \ - project.config.project_name) - dst_dir = osp.abspath(dst_dir) - - dataset = project.make_dataset() - - filter_args = FilterModes.make_filter_args(args.mode) - - if args.dry_run: - dataset = dataset.filter(expr=args.filter, **filter_args) - for item in dataset: - encoded_item = DatasetItemEncoder.encode(item, dataset.categories()) - xml_item = DatasetItemEncoder.to_string(encoded_item) - print(xml_item) - return 0 - - if not args.filter: - raise CliException("Expected a filter expression ('-e' argument)") - - dataset.filter_project(save_dir=dst_dir, expr=args.filter, **filter_args) - - log.info("Subproject has been extracted to '%s'" % dst_dir) - - return 0 - -def build_merge_parser(parser_ctor=argparse.ArgumentParser): - parser = parser_ctor(help="Merge two projects", - description=""" - Updates items of the current project with items - from other project.|n - |n - Examples:|n - - Update a project with items from other project:|n - |s|smerge -p path/to/first/project path/to/other/project - """, - formatter_class=MultilineFormatter) - - parser.add_argument('other_project_dir', - help="Path to a project") - parser.add_argument('-o', '--output-dir', dest='dst_dir', default=None, - help="Output directory (default: current project's dir)") - parser.add_argument('--overwrite', action='store_true', - help="Overwrite existing files in the save directory") - parser.add_argument('-p', '--project', dest='project_dir', default='.', - help="Directory of the project to operate on (default: current dir)") - parser.set_defaults(command=merge_command) - - return parser - -def merge_command(args): - first_project = load_project(args.project_dir) - second_project = load_project(args.other_project_dir) - - dst_dir = args.dst_dir - if dst_dir: - if not args.overwrite and osp.isdir(dst_dir) and os.listdir(dst_dir): - raise CliException("Directory '%s' already exists " - "(pass --overwrite to overwrite)" % dst_dir) - - first_dataset = first_project.make_dataset() - second_dataset = second_project.make_dataset() - - first_dataset.update(second_dataset) - first_dataset.save(save_dir=dst_dir) - - if dst_dir is None: - dst_dir = first_project.config.project_dir - dst_dir = osp.abspath(dst_dir) - log.info("Merge results have been saved to '%s'" % dst_dir) - - return 0 - -def build_diff_parser(parser_ctor=argparse.ArgumentParser): - parser = parser_ctor(help="Compare projects", - description=""" - Compares two projects, match annotations by distance.|n - |n - Examples:|n - - Compare two projects, match boxes if IoU > 0.7,|n - |s|s|s|sprint results to Tensorboard: - |s|sdiff path/to/other/project -o diff/ -v tensorboard --iou-thresh 0.7 - """, - formatter_class=MultilineFormatter) - - parser.add_argument('other_project_dir', - help="Directory of the second project to be compared") - parser.add_argument('-o', '--output-dir', dest='dst_dir', default=None, - help="Directory to save comparison results (default: do not save)") - parser.add_argument('-v', '--visualizer', - default=DiffVisualizer.DEFAULT_FORMAT, - choices=[f.name for f in DiffVisualizer.Format], - help="Output format (default: %(default)s)") - parser.add_argument('--iou-thresh', default=0.5, type=float, - help="IoU match threshold for detections (default: %(default)s)") - parser.add_argument('--conf-thresh', default=0.5, type=float, - help="Confidence threshold for detections (default: %(default)s)") - parser.add_argument('--overwrite', action='store_true', - help="Overwrite existing files in the save directory") - parser.add_argument('-p', '--project', dest='project_dir', default='.', - help="Directory of the first project to be compared (default: current dir)") - parser.set_defaults(command=diff_command) - - return parser - -def diff_command(args): - first_project = load_project(args.project_dir) - second_project = load_project(args.other_project_dir) - - comparator = DistanceComparator(iou_threshold=args.iou_thresh) - - dst_dir = args.dst_dir - if dst_dir: - if not args.overwrite and osp.isdir(dst_dir) and os.listdir(dst_dir): - raise CliException("Directory '%s' already exists " - "(pass --overwrite to overwrite)" % dst_dir) - else: - dst_dir = generate_next_file_name('%s-%s-diff' % ( - first_project.config.project_name, - second_project.config.project_name) - ) - dst_dir = osp.abspath(dst_dir) - log.info("Saving diff to '%s'" % dst_dir) - - dst_dir_existed = osp.exists(dst_dir) - try: - visualizer = DiffVisualizer(save_dir=dst_dir, comparator=comparator, - output_format=args.visualizer) - visualizer.save_dataset_diff( - first_project.make_dataset(), - second_project.make_dataset()) - except BaseException: - if not dst_dir_existed and osp.isdir(dst_dir): - shutil.rmtree(dst_dir, ignore_errors=True) - raise - - return 0 - -def build_ediff_parser(parser_ctor=argparse.ArgumentParser): - parser = parser_ctor(help="Compare projects for equality", - description=""" - Compares two projects for equality.|n - |n - Examples:|n - - Compare two projects, exclude annotation group |n - |s|s|sand the 'is_crowd' attribute from comparison:|n - |s|sediff other/project/ -if group -ia is_crowd - """, - formatter_class=MultilineFormatter) - - parser.add_argument('other_project_dir', - help="Directory of the second project to be compared") - parser.add_argument('-iia', '--ignore-item-attr', action='append', - help="Ignore item attribute (repeatable)") - parser.add_argument('-ia', '--ignore-attr', action='append', - help="Ignore annotation attribute (repeatable)") - parser.add_argument('-if', '--ignore-field', - action='append', default=['id', 'group'], - help="Ignore annotation field (repeatable, default: %(default)s)") - parser.add_argument('--match-images', action='store_true', - help='Match dataset items by images instead of ids') - parser.add_argument('--all', action='store_true', - help="Include matches in the output") - parser.add_argument('-p', '--project', dest='project_dir', default='.', - help="Directory of the first project to be compared (default: current dir)") - parser.set_defaults(command=ediff_command) - - return parser - -def ediff_command(args): - first_project = load_project(args.project_dir) - second_project = load_project(args.other_project_dir) - - comparator = ExactComparator( - match_images=args.match_images, - ignored_fields=args.ignore_field, - ignored_attrs=args.ignore_attr, - ignored_item_attrs=args.ignore_item_attr) - matches, mismatches, a_extra, b_extra, errors = \ - comparator.compare_datasets( - first_project.make_dataset(), second_project.make_dataset()) - output = { - "mismatches": mismatches, - "a_extra_items": sorted(a_extra), - "b_extra_items": sorted(b_extra), - "errors": errors, - } - if args.all: - output["matches"] = matches - - output_file = generate_next_file_name('diff', ext='.json') - with open(output_file, 'w') as f: - json.dump(output, f, indent=4, sort_keys=True) - - print("Found:") - print("The first project has %s unmatched items" % len(a_extra)) - print("The second project has %s unmatched items" % len(b_extra)) - print("%s item conflicts" % len(errors)) - print("%s matching annotations" % len(matches)) - print("%s mismatching annotations" % len(mismatches)) - - log.info("Output has been saved to '%s'" % output_file) - - return 0 - -def build_transform_parser(parser_ctor=argparse.ArgumentParser): - builtins = sorted(Environment().transforms.items) - - parser = parser_ctor(help="Transform project", - description=""" - Applies some operation to dataset items in the project - and produces a new project.|n - |n - Builtin transforms: %s|n - |n - Examples:|n - - Convert instance polygons to masks:|n - |s|stransform -t polygons_to_masks - """ % ', '.join(builtins), - formatter_class=MultilineFormatter) - - parser.add_argument('-t', '--transform', required=True, - help="Transform to apply to the project") - parser.add_argument('-o', '--output-dir', dest='dst_dir', default=None, - help="Directory to save output (default: current dir)") - parser.add_argument('--overwrite', action='store_true', - help="Overwrite existing files in the save directory") - parser.add_argument('-p', '--project', dest='project_dir', default='.', - help="Directory of the project to operate on (default: current dir)") - parser.add_argument('extra_args', nargs=argparse.REMAINDER, default=None, - help="Additional arguments for transformation (pass '-- -h' for help)") - parser.set_defaults(command=transform_command) - - return parser - -def transform_command(args): - project = load_project(args.project_dir) - - dst_dir = args.dst_dir - if dst_dir: - if not args.overwrite and osp.isdir(dst_dir) and os.listdir(dst_dir): - raise CliException("Directory '%s' already exists " - "(pass --overwrite to overwrite)" % dst_dir) - else: - dst_dir = generate_next_file_name('%s-%s' % \ - (project.config.project_name, make_file_name(args.transform))) - dst_dir = osp.abspath(dst_dir) - - try: - transform = project.env.transforms.get(args.transform) - except KeyError: - raise CliException("Transform '%s' is not found" % args.transform) - - extra_args = {} - if hasattr(transform, 'from_cmdline'): - extra_args = transform.from_cmdline(args.extra_args) - - log.info("Loading the project...") - dataset = project.make_dataset() - - log.info("Transforming the project...") - dataset.transform_project( - method=transform, - save_dir=dst_dir, - **extra_args - ) - - log.info("Transform results have been saved to '%s'" % dst_dir) - - return 0 - -def build_stats_parser(parser_ctor=argparse.ArgumentParser): - parser = parser_ctor(help="Get project statistics", - description=""" - Outputs various project statistics like image mean and std, - annotations count etc. - """, - formatter_class=MultilineFormatter) - - parser.add_argument('-p', '--project', dest='project_dir', default='.', - help="Directory of the project to operate on (default: current dir)") - parser.set_defaults(command=stats_command) - - return parser - -def stats_command(args): - project = load_project(args.project_dir) - - dataset = project.make_dataset() - stats = {} - stats.update(compute_image_statistics(dataset)) - stats.update(compute_ann_statistics(dataset)) - - dst_file = generate_next_file_name('statistics', ext='.json') - log.info("Writing project statistics to '%s'" % dst_file) - with open(dst_file, 'w') as f: - json.dump(stats, f, indent=4, sort_keys=True) - -def build_info_parser(parser_ctor=argparse.ArgumentParser): - parser = parser_ctor(help="Get project info", - description=""" - Outputs project info. - """, - formatter_class=MultilineFormatter) - - parser.add_argument('--all', action='store_true', - help="Print all information") - parser.add_argument('-p', '--project', dest='project_dir', default='.', - help="Directory of the project to operate on (default: current dir)") - parser.set_defaults(command=info_command) - - return parser - -def info_command(args): - project = load_project(args.project_dir) - config = project.config - env = project.env - dataset = project.make_dataset() - - print("Project:") - print(" name:", config.project_name) - print(" location:", config.project_dir) - print("Plugins:") - print(" importers:", ', '.join(env.importers.items)) - print(" extractors:", ', '.join(env.extractors.items)) - print(" converters:", ', '.join(env.converters.items)) - print(" launchers:", ', '.join(env.launchers.items)) - - print("Sources:") - for source_name, source in config.sources.items(): - print(" source '%s':" % source_name) - print(" format:", source.format) - print(" url:", source.url) - print(" location:", project.local_source_dir(source_name)) - - def print_extractor_info(extractor, indent=''): - print("%slength:" % indent, len(extractor)) - - categories = extractor.categories() - print("%scategories:" % indent, ', '.join(c.name for c in categories)) - - for cat_type, cat in categories.items(): - print("%s %s:" % (indent, cat_type.name)) - if cat_type == AnnotationType.label: - print("%s count:" % indent, len(cat.items)) - - count_threshold = 10 - if args.all: - count_threshold = len(cat.items) - labels = ', '.join(c.name for c in cat.items[:count_threshold]) - if count_threshold < len(cat.items): - labels += " (and %s more)" % ( - len(cat.items) - count_threshold) - print("%s labels:" % indent, labels) - - print("Dataset:") - print_extractor_info(dataset, indent=" ") - - subsets = dataset.subsets() - print(" subsets:", ', '.join(subsets)) - for subset_name in subsets: - subset = dataset.get_subset(subset_name) - print(" subset '%s':" % subset_name) - print_extractor_info(subset, indent=" ") - - print("Models:") - for model_name, model in config.models.items(): - print(" model '%s':" % model_name) - print(" type:", model.launcher) - - return 0 - - -def build_parser(parser_ctor=argparse.ArgumentParser): - parser = parser_ctor( - description=""" - Manipulate projects.|n - |n - By default, the project to be operated on is searched for - in the current directory. An additional '-p' argument can be - passed to specify project location. - """, - formatter_class=MultilineFormatter) - - subparsers = parser.add_subparsers() - add_subparser(subparsers, 'create', build_create_parser) - add_subparser(subparsers, 'import', build_import_parser) - add_subparser(subparsers, 'export', build_export_parser) - add_subparser(subparsers, 'filter', build_filter_parser) - add_subparser(subparsers, 'merge', build_merge_parser) - add_subparser(subparsers, 'diff', build_diff_parser) - add_subparser(subparsers, 'ediff', build_ediff_parser) - add_subparser(subparsers, 'transform', build_transform_parser) - add_subparser(subparsers, 'info', build_info_parser) - add_subparser(subparsers, 'stats', build_stats_parser) - - return parser diff --git a/datumaro/datumaro/cli/contexts/project/diff.py b/datumaro/datumaro/cli/contexts/project/diff.py deleted file mode 100644 index 358f3860..00000000 --- a/datumaro/datumaro/cli/contexts/project/diff.py +++ /dev/null @@ -1,290 +0,0 @@ - -# Copyright (C) 2019-2020 Intel Corporation -# -# SPDX-License-Identifier: MIT - -from collections import Counter -from enum import Enum -import numpy as np -import os -import os.path as osp - -_formats = ['simple'] - -import warnings -with warnings.catch_warnings(): - warnings.simplefilter("ignore") - import tensorboardX as tb - _formats.append('tensorboard') - -from datumaro.components.extractor import AnnotationType -from datumaro.util.image import save_image - - -Format = Enum('Formats', _formats) - -class DiffVisualizer: - Format = Format - DEFAULT_FORMAT = Format.simple - - _UNMATCHED_LABEL = -1 - - - def __init__(self, comparator, save_dir, output_format=DEFAULT_FORMAT): - self.comparator = comparator - - if isinstance(output_format, str): - output_format = Format[output_format] - assert output_format in Format - self.output_format = output_format - - self.save_dir = save_dir - if output_format is Format.tensorboard: - logdir = osp.join(self.save_dir, 'logs', 'diff') - self.file_writer = tb.SummaryWriter(logdir) - if output_format is Format.simple: - self.label_diff_writer = None - - self.categories = {} - - self.label_confusion_matrix = Counter() - self.bbox_confusion_matrix = Counter() - - def save_dataset_diff(self, extractor_a, extractor_b): - if self.save_dir: - os.makedirs(self.save_dir, exist_ok=True) - - if len(extractor_a) != len(extractor_b): - print("Datasets have different lengths: %s vs %s" % \ - (len(extractor_a), len(extractor_b))) - - self.categories = {} - - label_mismatch = self.comparator. \ - compare_dataset_labels(extractor_a, extractor_b) - if label_mismatch is None: - print("Datasets have no label information") - elif len(label_mismatch) != 0: - print("Datasets have mismatching labels:") - for a_label, b_label in label_mismatch: - if a_label is None: - print(" > %s" % b_label.name) - elif b_label is None: - print(" < %s" % a_label.name) - else: - print(" %s != %s" % (a_label.name, b_label.name)) - else: - self.categories.update(extractor_a.categories()) - self.categories.update(extractor_b.categories()) - - self.label_confusion_matrix = Counter() - self.bbox_confusion_matrix = Counter() - - if self.output_format is Format.tensorboard: - self.file_writer.reopen() - - ids_a = set((item.id, item.subset) for item in extractor_a) - ids_b = set((item.id, item.subset) for item in extractor_b) - ids = ids_a & ids_b - - if len(ids) != len(ids_a): - print("Unmatched items in the first dataset: ") - print(ids_a - ids) - if len(ids) != len(ids_b): - print("Unmatched items in the second dataset: ") - print(ids_b - ids) - - for item_id, item_subset in ids: - item_a = extractor_a.get(item_id, item_subset) - item_b = extractor_a.get(item_id, item_subset) - - label_diff = self.comparator.compare_item_labels(item_a, item_b) - self.update_label_confusion(label_diff) - - bbox_diff = self.comparator.compare_item_bboxes(item_a, item_b) - self.update_bbox_confusion(bbox_diff) - - self.save_item_label_diff(item_a, item_b, label_diff) - self.save_item_bbox_diff(item_a, item_b, bbox_diff) - - if len(self.label_confusion_matrix) != 0: - self.save_conf_matrix(self.label_confusion_matrix, - 'labels_confusion.png') - if len(self.bbox_confusion_matrix) != 0: - self.save_conf_matrix(self.bbox_confusion_matrix, - 'bbox_confusion.png') - - if self.output_format is Format.tensorboard: - self.file_writer.flush() - self.file_writer.close() - elif self.output_format is Format.simple: - if self.label_diff_writer: - self.label_diff_writer.flush() - self.label_diff_writer.close() - - def update_label_confusion(self, label_diff): - matches, a_unmatched, b_unmatched = label_diff - for label in matches: - self.label_confusion_matrix[(label, label)] += 1 - for a_label in a_unmatched: - self.label_confusion_matrix[(a_label, self._UNMATCHED_LABEL)] += 1 - for b_label in b_unmatched: - self.label_confusion_matrix[(self._UNMATCHED_LABEL, b_label)] += 1 - - def update_bbox_confusion(self, bbox_diff): - matches, mispred, a_unmatched, b_unmatched = bbox_diff - for a_bbox, b_bbox in matches: - self.bbox_confusion_matrix[(a_bbox.label, b_bbox.label)] += 1 - for a_bbox, b_bbox in mispred: - self.bbox_confusion_matrix[(a_bbox.label, b_bbox.label)] += 1 - for a_bbox in a_unmatched: - self.bbox_confusion_matrix[(a_bbox.label, self._UNMATCHED_LABEL)] += 1 - for b_bbox in b_unmatched: - self.bbox_confusion_matrix[(self._UNMATCHED_LABEL, b_bbox.label)] += 1 - - @classmethod - def draw_text_with_background(cls, frame, text, origin, - font=None, scale=1.0, - color=(0, 0, 0), thickness=1, bgcolor=(1, 1, 1)): - import cv2 - - if not font: - font = cv2.FONT_HERSHEY_SIMPLEX - - text_size, baseline = cv2.getTextSize(text, font, scale, thickness) - cv2.rectangle(frame, - tuple((origin + (0, baseline)).astype(int)), - tuple((origin + (text_size[0], -text_size[1])).astype(int)), - bgcolor, cv2.FILLED) - cv2.putText(frame, text, - tuple(origin.astype(int)), - font, scale, color, thickness) - return text_size, baseline - - def draw_detection_roi(self, frame, x, y, w, h, label, conf, color): - import cv2 - - cv2.rectangle(frame, (x, y), (x + w, y + h), color, 2) - - text = '%s %.2f%%' % (label, 100.0 * conf) - text_scale = 0.5 - font = cv2.FONT_HERSHEY_SIMPLEX - text_size = cv2.getTextSize(text, font, text_scale, 1) - line_height = np.array([0, text_size[0][1]]) - self.draw_text_with_background(frame, text, - np.array([x, y]) - line_height * 0.5, - font, scale=text_scale, color=[255 - c for c in color]) - - def get_label(self, label_id): - cat = self.categories.get(AnnotationType.label) - if cat is None: - return str(label_id) - return cat.items[label_id].name - - def draw_bbox(self, img, shape, color): - x, y, w, h = shape.get_bbox() - self.draw_detection_roi(img, int(x), int(y), int(w), int(h), - self.get_label(shape.label), shape.attributes.get('score', 1), - color) - - def get_label_diff_file(self): - if self.label_diff_writer is None: - self.label_diff_writer = \ - open(osp.join(self.save_dir, 'label_diff.txt'), 'w') - return self.label_diff_writer - - def save_item_label_diff(self, item_a, item_b, diff): - _, a_unmatched, b_unmatched = diff - - if 0 < len(a_unmatched) + len(b_unmatched): - if self.output_format is Format.simple: - f = self.get_label_diff_file() - f.write(item_a.id + '\n') - for a_label in a_unmatched: - f.write(' >%s\n' % self.get_label(a_label)) - for b_label in b_unmatched: - f.write(' <%s\n' % self.get_label(b_label)) - elif self.output_format is Format.tensorboard: - tag = item_a.id - for a_label in a_unmatched: - self.file_writer.add_text(tag, - '>%s\n' % self.get_label(a_label)) - for b_label in b_unmatched: - self.file_writer.add_text(tag, - '<%s\n' % self.get_label(b_label)) - - def save_item_bbox_diff(self, item_a, item_b, diff): - _, mispred, a_unmatched, b_unmatched = diff - - if 0 < len(a_unmatched) + len(b_unmatched) + len(mispred): - img_a = item_a.image.data.copy() - img_b = img_a.copy() - for a_bbox, b_bbox in mispred: - self.draw_bbox(img_a, a_bbox, (0, 255, 0)) - self.draw_bbox(img_b, b_bbox, (0, 0, 255)) - for a_bbox in a_unmatched: - self.draw_bbox(img_a, a_bbox, (255, 255, 0)) - for b_bbox in b_unmatched: - self.draw_bbox(img_b, b_bbox, (255, 255, 0)) - - img = np.hstack([img_a, img_b]) - - path = osp.join(self.save_dir, item_a.id) - - if self.output_format is Format.simple: - save_image(path + '.png', img, create_dir=True) - elif self.output_format is Format.tensorboard: - self.save_as_tensorboard(img, path) - - def save_as_tensorboard(self, img, name): - img = img[:, :, ::-1] # to RGB - img = np.transpose(img, (2, 0, 1)) # to (C, H, W) - img = img.astype(dtype=np.uint8) - self.file_writer.add_image(name, img) - - def save_conf_matrix(self, conf_matrix, filename): - import matplotlib.pyplot as plt - - classes = None - label_categories = self.categories.get(AnnotationType.label) - if label_categories is not None: - classes = { id: c.name for id, c in enumerate(label_categories.items) } - if classes is None: - classes = { c: 'label_%s' % c for c, _ in conf_matrix } - classes[self._UNMATCHED_LABEL] = 'unmatched' - - class_idx = { id: i for i, id in enumerate(classes.keys()) } - matrix = np.zeros((len(classes), len(classes)), dtype=int) - for idx_pair in conf_matrix: - index = (class_idx[idx_pair[0]], class_idx[idx_pair[1]]) - matrix[index] = conf_matrix[idx_pair] - - labels = [label for id, label in classes.items()] - - fig = plt.figure() - fig.add_subplot(111) - table = plt.table( - cellText=matrix, - colLabels=labels, - rowLabels=labels, - loc ='center') - table.auto_set_font_size(False) - table.set_fontsize(8) - table.scale(3, 3) - # Removing ticks and spines enables you to get the figure only with table - plt.tick_params(axis='x', which='both', bottom=False, top=False, labelbottom=False) - plt.tick_params(axis='y', which='both', right=False, left=False, labelleft=False) - for pos in ['right','top','bottom','left']: - plt.gca().spines[pos].set_visible(False) - - for idx_pair in conf_matrix: - i = class_idx[idx_pair[0]] - j = class_idx[idx_pair[1]] - if conf_matrix[idx_pair] != 0: - if i != j: - table._cells[(i + 1, j)].set_facecolor('#FF0000') - else: - table._cells[(i + 1, j)].set_facecolor('#00FF00') - - plt.savefig(osp.join(self.save_dir, filename), - bbox_inches='tight', pad_inches=0.05) diff --git a/datumaro/datumaro/cli/contexts/source/__init__.py b/datumaro/datumaro/cli/contexts/source/__init__.py deleted file mode 100644 index 45dbdb1b..00000000 --- a/datumaro/datumaro/cli/contexts/source/__init__.py +++ /dev/null @@ -1,273 +0,0 @@ - -# Copyright (C) 2019-2020 Intel Corporation -# -# SPDX-License-Identifier: MIT - -import argparse -import logging as log -import os -import os.path as osp -import shutil - -from datumaro.components.project import Environment -from ...util import add_subparser, CliException, MultilineFormatter -from ...util.project import load_project - - -def build_add_parser(parser_ctor=argparse.ArgumentParser): - builtins = sorted(Environment().extractors.items) - - base_parser = argparse.ArgumentParser(add_help=False) - base_parser.add_argument('-n', '--name', default=None, - help="Name of the new source") - base_parser.add_argument('-f', '--format', required=True, - help="Source dataset format") - base_parser.add_argument('--skip-check', action='store_true', - help="Skip source checking") - base_parser.add_argument('-p', '--project', dest='project_dir', default='.', - help="Directory of the project to operate on (default: current dir)") - - parser = parser_ctor(help="Add data source to project", - description=""" - Adds a data source to a project. The source can be:|n - - a dataset in a supported format (check 'formats' section below)|n - - a Datumaro project|n - |n - The source can be either a local directory or a remote - git repository. Each source type has its own parameters, which can - be checked by:|n - '%s'.|n - |n - Formats:|n - Datasets come in a wide variety of formats. Each dataset - format defines its own data structure and rules on how to - interpret the data. For example, the following data structure - is used in COCO format:|n - /dataset/|n - - /images/.jpg|n - - /annotations/|n - |n - In Datumaro dataset formats are supported by Extractor-s. - An Extractor produces a list of dataset items corresponding - to the dataset. It is possible to add a custom Extractor. - To do this, you need to put an Extractor - definition script to /.datumaro/extractors.|n - |n - List of builtin source formats: %s|n - |n - Examples:|n - - Add a local directory with VOC-like dataset:|n - |s|sadd path path/to/voc -f voc_detection|n - - Add a local file with CVAT annotations, call it 'mysource'|n - |s|s|s|sto the project somewhere else:|n - |s|sadd path path/to/cvat.xml -f cvat -n mysource -p somewhere/else/ - """ % ('%(prog)s SOURCE_TYPE --help', ', '.join(builtins)), - formatter_class=MultilineFormatter, - add_help=False) - parser.set_defaults(command=add_command) - - sp = parser.add_subparsers(dest='source_type', metavar='SOURCE_TYPE', - help="The type of the data source " - "(call '%s SOURCE_TYPE --help' for more info)" % parser.prog) - - dir_parser = sp.add_parser('path', help="Add local path as source", - parents=[base_parser]) - dir_parser.add_argument('url', - help="Path to the source") - dir_parser.add_argument('--copy', action='store_true', - help="Copy the dataset instead of saving source links") - - repo_parser = sp.add_parser('git', help="Add git repository as source", - parents=[base_parser]) - repo_parser.add_argument('url', - help="URL of the source git repository") - repo_parser.add_argument('-b', '--branch', default='master', - help="Branch of the source repository (default: %(default)s)") - repo_parser.add_argument('--checkout', action='store_true', - help="Do branch checkout") - - # NOTE: add common parameters to the parent help output - # the other way could be to use parse_known_args() - display_parser = argparse.ArgumentParser( - parents=[base_parser, parser], - prog=parser.prog, usage="%(prog)s [-h] SOURCE_TYPE ...", - description=parser.description, formatter_class=MultilineFormatter) - class HelpAction(argparse._HelpAction): - def __call__(self, parser, namespace, values, option_string=None): - display_parser.print_help() - parser.exit() - - parser.add_argument('-h', '--help', action=HelpAction, - help='show this help message and exit') - - # TODO: needed distinction on how to add an extractor or a remote source - - return parser - -def add_command(args): - project = load_project(args.project_dir) - - if args.source_type == 'git': - name = args.name - if name is None: - name = osp.splitext(osp.basename(args.url))[0] - - if project.env.git.has_submodule(name): - raise CliException("Git submodule '%s' already exists" % name) - - try: - project.get_source(name) - raise CliException("Source '%s' already exists" % name) - except KeyError: - pass - - rel_local_dir = project.local_source_dir(name) - local_dir = osp.join(project.config.project_dir, rel_local_dir) - url = args.url - project.env.git.create_submodule(name, local_dir, - url=url, branch=args.branch, no_checkout=not args.checkout) - elif args.source_type == 'path': - url = osp.abspath(args.url) - if not osp.exists(url): - raise CliException("Source path '%s' does not exist" % url) - - name = args.name - if name is None: - name = osp.splitext(osp.basename(url))[0] - - if project.env.git.has_submodule(name): - raise CliException("Git submodule '%s' already exists" % name) - - try: - project.get_source(name) - raise CliException("Source '%s' already exists" % name) - except KeyError: - pass - - rel_local_dir = project.local_source_dir(name) - local_dir = osp.join(project.config.project_dir, rel_local_dir) - - if args.copy: - log.info("Copying from '%s' to '%s'" % (url, local_dir)) - if osp.isdir(url): - # copytree requires destination dir not to exist - shutil.copytree(url, local_dir) - url = rel_local_dir - elif osp.isfile(url): - os.makedirs(local_dir) - shutil.copy2(url, local_dir) - url = osp.join(rel_local_dir, osp.basename(url)) - else: - raise Exception("Expected file or directory") - else: - os.makedirs(local_dir) - - project.add_source(name, { 'url': url, 'format': args.format }) - - if not args.skip_check: - log.info("Checking the source...") - try: - project.make_source_project(name).make_dataset() - except Exception: - shutil.rmtree(local_dir, ignore_errors=True) - raise - - project.save() - - log.info("Source '%s' has been added to the project, location: '%s'" \ - % (name, rel_local_dir)) - - return 0 - -def build_remove_parser(parser_ctor=argparse.ArgumentParser): - parser = parser_ctor(help="Remove source from project", - description="Remove a source from a project.") - - parser.add_argument('-n', '--name', required=True, - help="Name of the source to be removed") - parser.add_argument('--force', action='store_true', - help="Ignore possible errors during removal") - parser.add_argument('--keep-data', action='store_true', - help="Do not remove source data") - parser.add_argument('-p', '--project', dest='project_dir', default='.', - help="Directory of the project to operate on (default: current dir)") - parser.set_defaults(command=remove_command) - - return parser - -def remove_command(args): - project = load_project(args.project_dir) - - name = args.name - if not name: - raise CliException("Expected source name") - try: - project.get_source(name) - except KeyError: - if not args.force: - raise CliException("Source '%s' does not exist" % name) - - if project.env.git.has_submodule(name): - if args.force: - log.warning("Forcefully removing the '%s' source..." % name) - - project.env.git.remove_submodule(name, force=args.force) - - source_dir = osp.join(project.config.project_dir, - project.local_source_dir(name)) - project.remove_source(name) - project.save() - - if not args.keep_data: - shutil.rmtree(source_dir, ignore_errors=True) - - log.info("Source '%s' has been removed from the project" % name) - - return 0 - -def build_info_parser(parser_ctor=argparse.ArgumentParser): - parser = parser_ctor() - - parser.add_argument('-n', '--name', - help="Source name") - parser.add_argument('-v', '--verbose', action='store_true', - help="Show details") - parser.add_argument('-p', '--project', dest='project_dir', default='.', - help="Directory of the project to operate on (default: current dir)") - parser.set_defaults(command=info_command) - - return parser - -def info_command(args): - project = load_project(args.project_dir) - - if args.name: - source = project.get_source(args.name) - print(source) - else: - for name, conf in project.config.sources.items(): - print(name) - if args.verbose: - print(dict(conf)) - -def build_parser(parser_ctor=argparse.ArgumentParser): - parser = parser_ctor(description=""" - Manipulate data sources inside of a project.|n - |n - A data source is a source of data for a project. - The project combines multiple data sources into one dataset. - The role of a data source is to provide dataset items - images - and/or annotations.|n - |n - By default, the project to be operated on is searched for - in the current directory. An additional '-p' argument can be - passed to specify project location. - """, - formatter_class=MultilineFormatter) - - subparsers = parser.add_subparsers() - add_subparser(subparsers, 'add', build_add_parser) - add_subparser(subparsers, 'remove', build_remove_parser) - add_subparser(subparsers, 'info', build_info_parser) - - return parser diff --git a/datumaro/datumaro/cli/util/__init__.py b/datumaro/datumaro/cli/util/__init__.py deleted file mode 100644 index 4ee0b72b..00000000 --- a/datumaro/datumaro/cli/util/__init__.py +++ /dev/null @@ -1,74 +0,0 @@ - -# Copyright (C) 2019-2020 Intel Corporation -# -# SPDX-License-Identifier: MIT - -import argparse -import textwrap - - -class CliException(Exception): pass - -def add_subparser(subparsers, name, builder): - return builder(lambda **kwargs: subparsers.add_parser(name, **kwargs)) - -class MultilineFormatter(argparse.HelpFormatter): - """ - Keeps line breaks introduced with '|n' separator - and spaces introduced with '|s'. - """ - - def __init__(self, keep_natural=False, **kwargs): - super().__init__(**kwargs) - self._keep_natural = keep_natural - - def _fill_text(self, text, width, indent): - text = self._whitespace_matcher.sub(' ', text).strip() - text = text.replace('|s', ' ') - - paragraphs = text.split('|n ') - if self._keep_natural: - paragraphs = sum((p.split('\n ') for p in paragraphs), []) - - multiline_text = '' - for paragraph in paragraphs: - formatted_paragraph = textwrap.fill(paragraph, width, - initial_indent=indent, subsequent_indent=indent) + '\n' - multiline_text += formatted_paragraph - return multiline_text - -def required_count(nmin=0, nmax=0): - assert 0 <= nmin and 0 <= nmax and nmin or nmax - - class RequiredCount(argparse.Action): - def __call__(self, parser, args, values, option_string=None): - k = len(values) - if not ((nmin and (nmin <= k) or not nmin) and \ - (nmax and (k <= nmax) or not nmax)): - msg = "Argument '%s' requires" % self.dest - if nmin and nmax: - msg += " from %s to %s arguments" % (nmin, nmax) - elif nmin: - msg += " at least %s arguments" % nmin - else: - msg += " no more %s arguments" % nmax - raise argparse.ArgumentTypeError(msg) - setattr(args, self.dest, values) - return RequiredCount - -def at_least(n): - return required_count(n, 0) - -def make_file_name(s): - # adapted from - # https://docs.djangoproject.com/en/2.1/_modules/django/utils/text/#slugify - """ - Normalizes string, converts to lowercase, removes non-alpha characters, - and converts spaces to hyphens. - """ - import unicodedata, re - s = unicodedata.normalize('NFKD', s).encode('ascii', 'ignore') - s = s.decode() - s = re.sub(r'[^\w\s-]', '', s).strip().lower() - s = re.sub(r'[-\s]+', '-', s) - return s \ No newline at end of file diff --git a/datumaro/datumaro/cli/util/project.py b/datumaro/datumaro/cli/util/project.py deleted file mode 100644 index 56590a4d..00000000 --- a/datumaro/datumaro/cli/util/project.py +++ /dev/null @@ -1,39 +0,0 @@ - -# Copyright (C) 2019-2020 Intel Corporation -# -# SPDX-License-Identifier: MIT - -import os -import re - -from datumaro.components.project import Project -from datumaro.util import cast - - -def load_project(project_dir): - return Project.load(project_dir) - -def generate_next_file_name(basename, basedir='.', sep='.', ext=''): - """ - If basedir does not contain basename, returns basename, - otherwise generates a name by appending sep to the basename - and the number, next to the last used number in the basedir for - files with basename prefix. Optionally, appends ext. - """ - - return generate_next_name(os.listdir(basedir), basename, sep, ext) - -def generate_next_name(names, basename, sep='.', suffix='', default=None): - pattern = re.compile(r'%s(?:%s(\d+))?%s' % \ - tuple(map(re.escape, [basename, sep, suffix]))) - matches = [match for match in (pattern.match(n) for n in names) if match] - - max_idx = max([cast(match[1], int, 0) for match in matches], default=None) - if max_idx is None: - if default is not None: - idx = sep + str(default) - else: - idx = '' - else: - idx = sep + str(max_idx + 1) - return basename + idx + suffix \ No newline at end of file diff --git a/datumaro/datumaro/components/__init__.py b/datumaro/datumaro/components/__init__.py deleted file mode 100644 index 5a1ec10f..00000000 --- a/datumaro/datumaro/components/__init__.py +++ /dev/null @@ -1,5 +0,0 @@ - -# Copyright (C) 2019-2020 Intel Corporation -# -# SPDX-License-Identifier: MIT - diff --git a/datumaro/datumaro/components/algorithms/__init__.py b/datumaro/datumaro/components/algorithms/__init__.py deleted file mode 100644 index 5a1ec10f..00000000 --- a/datumaro/datumaro/components/algorithms/__init__.py +++ /dev/null @@ -1,5 +0,0 @@ - -# Copyright (C) 2019-2020 Intel Corporation -# -# SPDX-License-Identifier: MIT - diff --git a/datumaro/datumaro/components/algorithms/rise.py b/datumaro/datumaro/components/algorithms/rise.py deleted file mode 100644 index 3fb9a895..00000000 --- a/datumaro/datumaro/components/algorithms/rise.py +++ /dev/null @@ -1,203 +0,0 @@ - -# Copyright (C) 2019-2020 Intel Corporation -# -# SPDX-License-Identifier: MIT - -# pylint: disable=unused-variable - -import numpy as np -from math import ceil - -from datumaro.components.extractor import AnnotationType -from datumaro.util.annotation_util import nms - - -def flatmatvec(mat): - return np.reshape(mat, (len(mat), -1)) - -def expand(array, axis=None): - if axis is None: - axis = len(array.shape) - return np.expand_dims(array, axis=axis) - -class RISE: - """ - Implements RISE: Randomized Input Sampling for - Explanation of Black-box Models algorithm - See explanations at: https://arxiv.org/pdf/1806.07421.pdf - """ - - def __init__(self, model, - max_samples=None, mask_width=7, mask_height=7, prob=0.5, - iou_thresh=0.9, nms_thresh=0.0, det_conf_thresh=0.0, - batch_size=1): - self.model = model - self.max_samples = max_samples - self.mask_height = mask_height - self.mask_width = mask_width - self.prob = prob - self.iou_thresh = iou_thresh - self.nms_thresh = nms_thresh - self.det_conf_thresh = det_conf_thresh - self.batch_size = batch_size - - @staticmethod - def split_outputs(annotations): - labels = [] - bboxes = [] - for r in annotations: - if r.type is AnnotationType.label: - labels.append(r) - elif r.type is AnnotationType.bbox: - bboxes.append(r) - return labels, bboxes - - def normalize_hmaps(self, heatmaps, counts): - eps = np.finfo(heatmaps.dtype).eps - mhmaps = flatmatvec(heatmaps) - mhmaps /= expand(counts * self.prob + eps) - mhmaps -= expand(np.min(mhmaps, axis=1)) - mhmaps /= expand(np.max(mhmaps, axis=1) + eps) - return np.reshape(mhmaps, heatmaps.shape) - - def apply(self, image, progressive=False): - import cv2 - - assert len(image.shape) in [2, 3], \ - "Expected an input image in (H, W, C) format" - if len(image.shape) == 3: - assert image.shape[2] in [3, 4], "Expected BGR or BGRA input" - image = image[:, :, :3].astype(np.float32) - - model = self.model - iou_thresh = self.iou_thresh - - image_size = np.array((image.shape[:2])) - mask_size = np.array((self.mask_height, self.mask_width)) - cell_size = np.ceil(image_size / mask_size) - upsampled_size = np.ceil((mask_size + 1) * cell_size) - - rng = lambda shape=None: np.random.rand(*shape) - samples = np.prod(image_size) - if self.max_samples is not None: - samples = min(self.max_samples, samples) - batch_size = self.batch_size - - result = next(iter(model.launch(expand(image, 0)))) - result_labels, result_bboxes = self.split_outputs(result) - if 0 < self.det_conf_thresh: - result_bboxes = [b for b in result_bboxes \ - if self.det_conf_thresh <= b.attributes['score']] - if 0 < self.nms_thresh: - result_bboxes = nms(result_bboxes, self.nms_thresh) - - predicted_labels = set() - if len(result_labels) != 0: - predicted_label = max(result_labels, - key=lambda r: r.attributes['score']).label - predicted_labels.add(predicted_label) - if len(result_bboxes) != 0: - for bbox in result_bboxes: - predicted_labels.add(bbox.label) - predicted_labels = { label: idx \ - for idx, label in enumerate(predicted_labels) } - - predicted_bboxes = result_bboxes - - heatmaps_count = len(predicted_labels) + len(predicted_bboxes) - heatmaps = np.zeros((heatmaps_count, *image_size), dtype=np.float32) - total_counts = np.zeros(heatmaps_count, dtype=np.int32) - confs = np.zeros(heatmaps_count, dtype=np.float32) - - heatmap_id = 0 - - label_heatmaps = None - label_total_counts = None - label_confs = None - if len(predicted_labels) != 0: - step = len(predicted_labels) - label_heatmaps = heatmaps[heatmap_id : heatmap_id + step] - label_total_counts = total_counts[heatmap_id : heatmap_id + step] - label_confs = confs[heatmap_id : heatmap_id + step] - heatmap_id += step - - bbox_heatmaps = None - bbox_total_counts = None - bbox_confs = None - if len(predicted_bboxes) != 0: - step = len(predicted_bboxes) - bbox_heatmaps = heatmaps[heatmap_id : heatmap_id + step] - bbox_total_counts = total_counts[heatmap_id : heatmap_id + step] - bbox_confs = confs[heatmap_id : heatmap_id + step] - heatmap_id += step - - ups_mask = np.empty(upsampled_size.astype(int), dtype=np.float32) - masks = np.empty((batch_size, *image_size), dtype=np.float32) - - full_batch_inputs = np.empty((batch_size, *image.shape), dtype=np.float32) - current_heatmaps = np.empty_like(heatmaps) - for b in range(ceil(samples / batch_size)): - batch_pos = b * batch_size - current_batch_size = min(samples - batch_pos, batch_size) - - batch_masks = masks[: current_batch_size] - for i in range(current_batch_size): - mask = (rng(mask_size) < self.prob).astype(np.float32) - cv2.resize(mask, (int(upsampled_size[1]), int(upsampled_size[0])), - ups_mask) - - offsets = np.round(rng((2,)) * cell_size) - mask = ups_mask[ - int(offsets[0]):int(image_size[0] + offsets[0]), - int(offsets[1]):int(image_size[1] + offsets[1]) ] - batch_masks[i] = mask - - batch_inputs = full_batch_inputs[:current_batch_size] - np.multiply(expand(batch_masks), expand(image, 0), out=batch_inputs) - - results = model.launch(batch_inputs) - for mask, result in zip(batch_masks, results): - result_labels, result_bboxes = self.split_outputs(result) - - confs.fill(0) - if len(predicted_labels) != 0: - for r in result_labels: - idx = predicted_labels.get(r.label, None) - if idx is not None: - label_total_counts[idx] += 1 - label_confs[idx] += r.attributes['score'] - for r in result_bboxes: - idx = predicted_labels.get(r.label, None) - if idx is not None: - label_total_counts[idx] += 1 - label_confs[idx] += r.attributes['score'] - - if len(predicted_bboxes) != 0 and len(result_bboxes) != 0: - if 0 < self.det_conf_thresh: - result_bboxes = [b for b in result_bboxes \ - if self.det_conf_thresh <= b.attributes['score']] - if 0 < self.nms_thresh: - result_bboxes = nms(result_bboxes, self.nms_thresh) - - for detection in result_bboxes: - for pred_idx, pred in enumerate(predicted_bboxes): - if pred.label != detection.label: - continue - - iou = pred.iou(detection) - assert iou == -1 or 0 <= iou and iou <= 1 - if iou < iou_thresh: - continue - - bbox_total_counts[pred_idx] += 1 - - conf = detection.attributes['score'] - bbox_confs[pred_idx] += conf - - np.multiply.outer(confs, mask, out=current_heatmaps) - heatmaps += current_heatmaps - - if progressive: - yield self.normalize_hmaps(heatmaps.copy(), total_counts) - - yield self.normalize_hmaps(heatmaps, total_counts) \ No newline at end of file diff --git a/datumaro/datumaro/components/cli_plugin.py b/datumaro/datumaro/components/cli_plugin.py deleted file mode 100644 index e85f5c4f..00000000 --- a/datumaro/datumaro/components/cli_plugin.py +++ /dev/null @@ -1,44 +0,0 @@ - -# Copyright (C) 2020 Intel Corporation -# -# SPDX-License-Identifier: MIT - -import argparse - -from datumaro.cli.util import MultilineFormatter -from datumaro.util import to_snake_case - - -class CliPlugin: - @staticmethod - def _get_name(cls): - return getattr(cls, 'NAME', - remove_plugin_type(to_snake_case(cls.__name__))) - - @staticmethod - def _get_doc(cls): - return getattr(cls, '__doc__', "") - - @classmethod - def build_cmdline_parser(cls, **kwargs): - args = { - 'prog': cls._get_name(cls), - 'description': cls._get_doc(cls), - 'formatter_class': MultilineFormatter, - } - args.update(kwargs) - - return argparse.ArgumentParser(**args) - - @classmethod - def from_cmdline(cls, args=None): - if args and args[0] == '--': - args = args[1:] - parser = cls.build_cmdline_parser() - args = parser.parse_args(args) - return vars(args) - -def remove_plugin_type(s): - for t in {'transform', 'extractor', 'converter', 'launcher', 'importer'}: - s = s.replace('_' + t, '') - return s diff --git a/datumaro/datumaro/components/config.py b/datumaro/datumaro/components/config.py deleted file mode 100644 index a79cda15..00000000 --- a/datumaro/datumaro/components/config.py +++ /dev/null @@ -1,237 +0,0 @@ - -# Copyright (C) 2019-2020 Intel Corporation -# -# SPDX-License-Identifier: MIT - -import yaml - - -class Schema: - class Item: - def __init__(self, ctor, internal=False): - self.ctor = ctor - self.internal = internal - - def __call__(self, *args, **kwargs): - return self.ctor(*args, **kwargs) - - def __init__(self, items=None, fallback=None): - self._items = {} - if items is not None: - self._items.update(items) - self._fallback = fallback - - def _get_items(self, allow_fallback=True): - all_items = {} - - if allow_fallback and self._fallback is not None: - all_items.update(self._fallback) - all_items.update(self._items) - - return all_items - - def items(self, allow_fallback=True): - return self._get_items(allow_fallback=allow_fallback).items() - - def keys(self, allow_fallback=True): - return self._get_items(allow_fallback=allow_fallback).keys() - - def values(self, allow_fallback=True): - return self._get_items(allow_fallback=allow_fallback).values() - - def __contains__(self, key): - return key in self.keys() - - def __len__(self): - return len(self._get_items()) - - def __iter__(self): - return iter(self._get_items()) - - def __getitem__(self, key): - default = object() - value = self.get(key, default=default) - if value is default: - raise KeyError('Key "%s" does not exist' % (key)) - return value - - def get(self, key, default=None): - found = self._items.get(key, default) - if found is not default: - return found - - if self._fallback is not None: - return self._fallback.get(key, default) - -class SchemaBuilder: - def __init__(self): - self._items = {} - - def add(self, name, ctor=str, internal=False): - if name in self._items: - raise KeyError('Key "%s" already exists' % (name)) - - self._items[name] = Schema.Item(ctor, internal=internal) - return self - - def build(self): - return Schema(self._items) - -class Config: - def __init__(self, config=None, fallback=None, schema=None, mutable=True): - # schema should be established first - self.__dict__['_schema'] = schema - self.__dict__['_mutable'] = True - - self.__dict__['_config'] = {} - if fallback is not None: - for k, v in fallback.items(allow_fallback=False): - self.set(k, v) - if config is not None: - self.update(config) - - self.__dict__['_mutable'] = mutable - - def _items(self, allow_fallback=True, allow_internal=True): - all_config = {} - if allow_fallback and self._schema is not None: - for key, item in self._schema.items(): - all_config[key] = item() - all_config.update(self._config) - - if not allow_internal and self._schema is not None: - for key, item in self._schema.items(): - if item.internal: - all_config.pop(key) - return all_config - - def items(self, allow_fallback=True, allow_internal=True): - return self._items( - allow_fallback=allow_fallback, - allow_internal=allow_internal - ).items() - - def keys(self, allow_fallback=True, allow_internal=True): - return self._items( - allow_fallback=allow_fallback, - allow_internal=allow_internal - ).keys() - - def values(self, allow_fallback=True, allow_internal=True): - return self._items( - allow_fallback=allow_fallback, - allow_internal=allow_internal - ).values() - - def __contains__(self, key): - return key in self.keys() - - def __len__(self): - return len(self.items()) - - def __iter__(self): - return iter(self.keys()) - - def __getitem__(self, key): - default = object() - value = self.get(key, default=default) - if value is default: - raise KeyError('Key "%s" does not exist' % (key)) - return value - - def __setitem__(self, key, value): - return self.set(key, value) - - def __getattr__(self, key): - return self.get(key) - - def __setattr__(self, key, value): - return self.set(key, value) - - def __eq__(self, other): - try: - for k, my_v in self.items(allow_internal=False): - other_v = other[k] - if my_v != other_v: - return False - return True - except Exception: - return False - - def update(self, other): - for k, v in other.items(): - self.set(k, v) - - def remove(self, key): - if not self._mutable: - raise Exception("Cannot set value of immutable object") - - self._config.pop(key, None) - - def get(self, key, default=None): - found = self._config.get(key, default) - if found is not default: - return found - - if self._schema is not None: - found = self._schema.get(key, default) - if found is not default: - # ignore mutability - found = found() - self._config[key] = found - return found - - return found - - def set(self, key, value): - if not self._mutable: - raise Exception("Cannot set value of immutable object") - - if self._schema is not None: - if key not in self._schema: - raise Exception("Can not set key '%s' - schema mismatch" % (key)) - - schema_entry = self._schema[key] - schema_entry_instance = schema_entry() - if not isinstance(value, type(schema_entry_instance)): - if isinstance(value, dict) and \ - isinstance(schema_entry_instance, Config): - schema_entry_instance.update(value) - value = schema_entry_instance - else: - raise Exception("Can not set key '%s' - schema mismatch" % (key)) - - self._config[key] = value - return value - - @staticmethod - def parse(path): - with open(path, 'r') as f: - return Config(yaml.safe_load(f)) - - @staticmethod - def yaml_representer(dumper, value): - return dumper.represent_data( - value._items(allow_internal=False, allow_fallback=False)) - - def dump(self, path): - with open(path, 'w+') as f: - yaml.dump(self, f) - -yaml.add_multi_representer(Config, Config.yaml_representer) - - -class DefaultConfig(Config): - def __init__(self, default=None): - super().__init__() - self.__dict__['_default'] = default - - def set(self, key, value): - if key not in self.keys(allow_fallback=False): - value = self._default(value) - return super().set(key, value) - else: - return super().set(key, value) - - -DEFAULT_FORMAT = 'datumaro' \ No newline at end of file diff --git a/datumaro/datumaro/components/config_model.py b/datumaro/datumaro/components/config_model.py deleted file mode 100644 index c6f65179..00000000 --- a/datumaro/datumaro/components/config_model.py +++ /dev/null @@ -1,63 +0,0 @@ - -# Copyright (C) 2019-2020 Intel Corporation -# -# SPDX-License-Identifier: MIT - -from datumaro.components.config import Config, \ - DefaultConfig as _DefaultConfig, \ - SchemaBuilder as _SchemaBuilder - - -SOURCE_SCHEMA = _SchemaBuilder() \ - .add('url', str) \ - .add('format', str) \ - .add('options', dict) \ - .build() - -class Source(Config): - def __init__(self, config=None): - super().__init__(config, schema=SOURCE_SCHEMA) - - -MODEL_SCHEMA = _SchemaBuilder() \ - .add('launcher', str) \ - .add('options', dict) \ - .build() - -class Model(Config): - def __init__(self, config=None): - super().__init__(config, schema=MODEL_SCHEMA) - - -PROJECT_SCHEMA = _SchemaBuilder() \ - .add('project_name', str) \ - .add('format_version', int) \ - \ - .add('subsets', list) \ - .add('sources', lambda: _DefaultConfig( - lambda v=None: Source(v))) \ - .add('models', lambda: _DefaultConfig( - lambda v=None: Model(v))) \ - \ - .add('models_dir', str, internal=True) \ - .add('plugins_dir', str, internal=True) \ - .add('sources_dir', str, internal=True) \ - .add('dataset_dir', str, internal=True) \ - .add('project_filename', str, internal=True) \ - .add('project_dir', str, internal=True) \ - .add('env_dir', str, internal=True) \ - .build() - -PROJECT_DEFAULT_CONFIG = Config({ - 'project_name': 'undefined', - 'format_version': 1, - - 'sources_dir': 'sources', - 'dataset_dir': 'dataset', - 'models_dir': 'models', - 'plugins_dir': 'plugins', - - 'project_filename': 'config.yaml', - 'project_dir': '', - 'env_dir': '.datumaro', -}, mutable=False, schema=PROJECT_SCHEMA) diff --git a/datumaro/datumaro/components/converter.py b/datumaro/datumaro/components/converter.py deleted file mode 100644 index 05dedb48..00000000 --- a/datumaro/datumaro/components/converter.py +++ /dev/null @@ -1,79 +0,0 @@ - -# Copyright (C) 2019-2020 Intel Corporation -# -# SPDX-License-Identifier: MIT - -import logging as log -import os -import os.path as osp -import shutil - -from datumaro.components.cli_plugin import CliPlugin -from datumaro.util.image import save_image - - -class IConverter: - @classmethod - def convert(cls, extractor, save_dir, **options): - raise NotImplementedError("Should be implemented in a subclass") - -class Converter(IConverter, CliPlugin): - DEFAULT_IMAGE_EXT = None - - @classmethod - def build_cmdline_parser(cls, **kwargs): - parser = super().build_cmdline_parser(**kwargs) - parser.add_argument('--save-images', action='store_true', - help="Save images (default: %(default)s)") - parser.add_argument('--image-ext', default=None, - help="Image extension (default: keep or use format default%s)" % \ - (' ' + cls.DEFAULT_IMAGE_EXT if cls.DEFAULT_IMAGE_EXT else '')) - - return parser - - @classmethod - def convert(cls, extractor, save_dir, **options): - converter = cls(extractor, save_dir, **options) - return converter.apply() - - def apply(self): - raise NotImplementedError("Should be implemented in a subclass") - - def __init__(self, extractor, save_dir, save_images=False, - image_ext=None, default_image_ext=None): - default_image_ext = default_image_ext or self.DEFAULT_IMAGE_EXT - assert default_image_ext - self._default_image_ext = default_image_ext - - self._save_images = save_images - self._image_ext = image_ext - - self._extractor = extractor - self._save_dir = save_dir - - def _find_image_ext(self, item): - src_ext = None - if item.has_image: - src_ext = osp.splitext(osp.basename(item.image.path))[1] - - return self._image_ext or src_ext or self._default_image_ext - - def _make_image_filename(self, item): - return item.id + self._find_image_ext(item) - - def _save_image(self, item, path=None): - image = item.image.data - if image is None: - log.warning("Item '%s' has no image", item.id) - return item.image.path - - path = path or self._make_image_filename(item) - - src_ext = osp.splitext(osp.basename(item.image.path))[1] - dst_ext = osp.splitext(osp.basename(path))[1] - - os.makedirs(osp.dirname(path), exist_ok=True) - if src_ext == dst_ext and osp.isfile(item.image.path): - shutil.copyfile(item.image.path, path) - else: - save_image(path, image) diff --git a/datumaro/datumaro/components/dataset_filter.py b/datumaro/datumaro/components/dataset_filter.py deleted file mode 100644 index 2fe1443d..00000000 --- a/datumaro/datumaro/components/dataset_filter.py +++ /dev/null @@ -1,261 +0,0 @@ - -# Copyright (C) 2019-2020 Intel Corporation -# -# SPDX-License-Identifier: MIT - -import logging as log -from lxml import etree as ET # lxml has proper XPath implementation -from datumaro.components.extractor import (Transform, - Annotation, AnnotationType, - Label, Mask, Points, Polygon, PolyLine, Bbox, Caption, -) - - -class DatasetItemEncoder: - @classmethod - def encode(cls, item, categories=None): - item_elem = ET.Element('item') - ET.SubElement(item_elem, 'id').text = str(item.id) - ET.SubElement(item_elem, 'subset').text = str(item.subset) - ET.SubElement(item_elem, 'path').text = str('/'.join(item.path)) - - image = item.image - if image is not None: - item_elem.append(cls.encode_image(image)) - - for ann in item.annotations: - item_elem.append(cls.encode_annotation(ann, categories)) - - return item_elem - - @classmethod - def encode_image(cls, image): - image_elem = ET.Element('image') - - size = image.size - if size is not None: - h, w = size - else: - h = 'unknown' - w = h - ET.SubElement(image_elem, 'width').text = str(w) - ET.SubElement(image_elem, 'height').text = str(h) - - ET.SubElement(image_elem, 'has_data').text = '%d' % int(image.has_data) - ET.SubElement(image_elem, 'path').text = image.path - - return image_elem - - @classmethod - def encode_annotation_base(cls, annotation): - assert isinstance(annotation, Annotation) - ann_elem = ET.Element('annotation') - ET.SubElement(ann_elem, 'id').text = str(annotation.id) - ET.SubElement(ann_elem, 'type').text = str(annotation.type.name) - - for k, v in annotation.attributes.items(): - ET.SubElement(ann_elem, k.replace(' ', '-')).text = str(v) - - ET.SubElement(ann_elem, 'group').text = str(annotation.group) - - return ann_elem - - @staticmethod - def _get_label(label_id, categories): - label = '' - if label_id is None: - return '' - if categories is not None: - label_cat = categories.get(AnnotationType.label) - if label_cat is not None: - label = label_cat.items[label_id].name - return label - - @classmethod - def encode_label_object(cls, obj, categories): - ann_elem = cls.encode_annotation_base(obj) - - ET.SubElement(ann_elem, 'label').text = \ - str(cls._get_label(obj.label, categories)) - ET.SubElement(ann_elem, 'label_id').text = str(obj.label) - - return ann_elem - - @classmethod - def encode_mask_object(cls, obj, categories): - ann_elem = cls.encode_annotation_base(obj) - - ET.SubElement(ann_elem, 'label').text = \ - str(cls._get_label(obj.label, categories)) - ET.SubElement(ann_elem, 'label_id').text = str(obj.label) - - return ann_elem - - @classmethod - def encode_bbox_object(cls, obj, categories): - ann_elem = cls.encode_annotation_base(obj) - - ET.SubElement(ann_elem, 'label').text = \ - str(cls._get_label(obj.label, categories)) - ET.SubElement(ann_elem, 'label_id').text = str(obj.label) - ET.SubElement(ann_elem, 'x').text = str(obj.x) - ET.SubElement(ann_elem, 'y').text = str(obj.y) - ET.SubElement(ann_elem, 'w').text = str(obj.w) - ET.SubElement(ann_elem, 'h').text = str(obj.h) - ET.SubElement(ann_elem, 'area').text = str(obj.get_area()) - - return ann_elem - - @classmethod - def encode_points_object(cls, obj, categories): - ann_elem = cls.encode_annotation_base(obj) - - ET.SubElement(ann_elem, 'label').text = \ - str(cls._get_label(obj.label, categories)) - ET.SubElement(ann_elem, 'label_id').text = str(obj.label) - - x, y, w, h = obj.get_bbox() - area = w * h - bbox_elem = ET.SubElement(ann_elem, 'bbox') - ET.SubElement(bbox_elem, 'x').text = str(x) - ET.SubElement(bbox_elem, 'y').text = str(y) - ET.SubElement(bbox_elem, 'w').text = str(w) - ET.SubElement(bbox_elem, 'h').text = str(h) - ET.SubElement(bbox_elem, 'area').text = str(area) - - points = obj.points - for i in range(0, len(points), 2): - point_elem = ET.SubElement(ann_elem, 'point') - ET.SubElement(point_elem, 'x').text = str(points[i]) - ET.SubElement(point_elem, 'y').text = str(points[i + 1]) - ET.SubElement(point_elem, 'visible').text = \ - str(obj.visibility[i // 2].name) - - return ann_elem - - @classmethod - def encode_polygon_object(cls, obj, categories): - ann_elem = cls.encode_annotation_base(obj) - - ET.SubElement(ann_elem, 'label').text = \ - str(cls._get_label(obj.label, categories)) - ET.SubElement(ann_elem, 'label_id').text = str(obj.label) - - x, y, w, h = obj.get_bbox() - area = w * h - bbox_elem = ET.SubElement(ann_elem, 'bbox') - ET.SubElement(bbox_elem, 'x').text = str(x) - ET.SubElement(bbox_elem, 'y').text = str(y) - ET.SubElement(bbox_elem, 'w').text = str(w) - ET.SubElement(bbox_elem, 'h').text = str(h) - ET.SubElement(bbox_elem, 'area').text = str(area) - - points = obj.points - for i in range(0, len(points), 2): - point_elem = ET.SubElement(ann_elem, 'point') - ET.SubElement(point_elem, 'x').text = str(points[i]) - ET.SubElement(point_elem, 'y').text = str(points[i + 1]) - - return ann_elem - - @classmethod - def encode_polyline_object(cls, obj, categories): - ann_elem = cls.encode_annotation_base(obj) - - ET.SubElement(ann_elem, 'label').text = \ - str(cls._get_label(obj.label, categories)) - ET.SubElement(ann_elem, 'label_id').text = str(obj.label) - - x, y, w, h = obj.get_bbox() - area = w * h - bbox_elem = ET.SubElement(ann_elem, 'bbox') - ET.SubElement(bbox_elem, 'x').text = str(x) - ET.SubElement(bbox_elem, 'y').text = str(y) - ET.SubElement(bbox_elem, 'w').text = str(w) - ET.SubElement(bbox_elem, 'h').text = str(h) - ET.SubElement(bbox_elem, 'area').text = str(area) - - points = obj.points - for i in range(0, len(points), 2): - point_elem = ET.SubElement(ann_elem, 'point') - ET.SubElement(point_elem, 'x').text = str(points[i]) - ET.SubElement(point_elem, 'y').text = str(points[i + 1]) - - return ann_elem - - @classmethod - def encode_caption_object(cls, obj): - ann_elem = cls.encode_annotation_base(obj) - - ET.SubElement(ann_elem, 'caption').text = str(obj.caption) - - return ann_elem - - @classmethod - def encode_annotation(cls, o, categories=None): - if isinstance(o, Label): - return cls.encode_label_object(o, categories) - if isinstance(o, Mask): - return cls.encode_mask_object(o, categories) - if isinstance(o, Bbox): - return cls.encode_bbox_object(o, categories) - if isinstance(o, Points): - return cls.encode_points_object(o, categories) - if isinstance(o, PolyLine): - return cls.encode_polyline_object(o, categories) - if isinstance(o, Polygon): - return cls.encode_polygon_object(o, categories) - if isinstance(o, Caption): - return cls.encode_caption_object(o) - raise NotImplementedError("Unexpected annotation object passed: %s" % o) - - @staticmethod - def to_string(encoded_item): - return ET.tostring(encoded_item, encoding='unicode', pretty_print=True) - -def XPathDatasetFilter(extractor, xpath=None): - if xpath is None: - return extractor - try: - xpath = ET.XPath(xpath) - except Exception: - log.error("Failed to create XPath from expression '%s'", xpath) - raise - f = lambda item: bool(xpath( - DatasetItemEncoder.encode(item, extractor.categories()))) - return extractor.select(f) - -class XPathAnnotationsFilter(Transform): - def __init__(self, extractor, xpath=None, remove_empty=False): - super().__init__(extractor) - - if xpath is not None: - try: - xpath = ET.XPath(xpath) - except Exception: - log.error("Failed to create XPath from expression '%s'", xpath) - raise - self._filter = xpath - - self._remove_empty = remove_empty - - def __iter__(self): - for item in self._extractor: - item = self.transform_item(item) - if item is not None: - yield item - - def transform_item(self, item): - if self._filter is None: - return item - - encoded = DatasetItemEncoder.encode(item, self._extractor.categories()) - filtered = self._filter(encoded) - filtered = [elem for elem in filtered if elem.tag == 'annotation'] - - encoded = encoded.findall('annotation') - annotations = [item.annotations[encoded.index(e)] for e in filtered] - - if self._remove_empty and len(annotations) == 0: - return None - return self.wrap_item(item, annotations=annotations) \ No newline at end of file diff --git a/datumaro/datumaro/components/extractor.py b/datumaro/datumaro/components/extractor.py deleted file mode 100644 index dcb7b036..00000000 --- a/datumaro/datumaro/components/extractor.py +++ /dev/null @@ -1,621 +0,0 @@ - -# Copyright (C) 2019-2020 Intel Corporation -# -# SPDX-License-Identifier: MIT - -from collections import namedtuple -from enum import Enum -import numpy as np - -import attr -from attr import attrs, attrib - -from datumaro.util.image import Image -from datumaro.util.attrs_util import not_empty, default_if_none - - -AnnotationType = Enum('AnnotationType', - [ - 'label', - 'mask', - 'points', - 'polygon', - 'polyline', - 'bbox', - 'caption', - ]) - -_COORDINATE_ROUNDING_DIGITS = 2 - -@attrs(kw_only=True) -class Annotation: - id = attrib(default=0, validator=default_if_none(int)) - attributes = attrib(factory=dict, validator=default_if_none(dict)) - group = attrib(default=0, validator=default_if_none(int)) - - def __attrs_post_init__(self): - assert isinstance(self.type, AnnotationType) - - @property - def type(self) -> AnnotationType: - return self._type # must be set in subclasses - - def wrap(self, **kwargs): - return attr.evolve(self, **kwargs) - -@attrs(kw_only=True) -class Categories: - attributes = attrib(factory=set, validator=default_if_none(set), eq=False) - -@attrs -class LabelCategories(Categories): - @attrs(repr_ns='LabelCategories') - class Category: - name = attrib(converter=str, validator=not_empty) - parent = attrib(default='', validator=default_if_none(str)) - attributes = attrib(factory=set, validator=default_if_none(set)) - - items = attrib(factory=list, validator=default_if_none(list)) - _indices = attrib(factory=dict, init=False, eq=False) - - @classmethod - def from_iterable(cls, iterable): - """Generation of LabelCategories from iterable object - - Args: - iterable ([type]): This iterable object can be: - 1)simple str - will generate one Category with str as name - 2)list of str - will interpreted as list of Category names - 3)list of positional argumetns - will generate Categories - with this arguments - - - Returns: - LabelCategories: LabelCategories object - """ - temp_categories = cls() - - if isinstance(iterable, str): - iterable = [[iterable]] - - for category in iterable: - if isinstance(category, str): - category = [category] - temp_categories.add(*category) - - return temp_categories - - def __attrs_post_init__(self): - self._reindex() - - def _reindex(self): - indices = {} - for index, item in enumerate(self.items): - assert item.name not in self._indices - indices[item.name] = index - self._indices = indices - - def add(self, name: str, parent: str = None, attributes: dict = None): - assert name not in self._indices, name - - index = len(self.items) - self.items.append(self.Category(name, parent, attributes)) - self._indices[name] = index - return index - - def find(self, name: str): - index = self._indices.get(name) - if index is not None: - return index, self.items[index] - return index, None - -@attrs -class Label(Annotation): - _type = AnnotationType.label - label = attrib(converter=int) - -@attrs(eq=False) -class MaskCategories(Categories): - colormap = attrib(factory=dict, validator=default_if_none(dict)) - _inverse_colormap = attrib(default=None, - validator=attr.validators.optional(dict)) - - @property - def inverse_colormap(self): - from datumaro.util.mask_tools import invert_colormap - if self._inverse_colormap is None: - if self.colormap is not None: - self._inverse_colormap = invert_colormap(self.colormap) - return self._inverse_colormap - - def __eq__(self, other): - if not super().__eq__(other): - return False - if not isinstance(other, __class__): - return False - for label_id, my_color in self.colormap.items(): - other_color = other.colormap.get(label_id) - if not np.array_equal(my_color, other_color): - return False - return True - -@attrs(eq=False) -class Mask(Annotation): - _type = AnnotationType.mask - _image = attrib() - label = attrib(converter=attr.converters.optional(int), - default=None, kw_only=True) - z_order = attrib(default=0, validator=default_if_none(int), kw_only=True) - - @property - def image(self): - if callable(self._image): - return self._image() - return self._image - - def as_class_mask(self, label_id=None): - if label_id is None: - label_id = self.label - return self.image * label_id - - def as_instance_mask(self, instance_id): - return self.image * instance_id - - def get_area(self): - return np.count_nonzero(self.image) - - def get_bbox(self): - from datumaro.util.mask_tools import find_mask_bbox - return find_mask_bbox(self.image) - - def paint(self, colormap): - from datumaro.util.mask_tools import paint_mask - return paint_mask(self.as_class_mask(), colormap) - - def __eq__(self, other): - if not super().__eq__(other): - return False - if not isinstance(other, __class__): - return False - return \ - (self.label == other.label) and \ - (self.z_order == other.z_order) and \ - (np.array_equal(self.image, other.image)) - -@attrs(eq=False) -class RleMask(Mask): - rle = attrib() - _image = attrib(default=attr.Factory( - lambda self: self._lazy_decode(self.rle), - takes_self=True), init=False) - - @staticmethod - def _lazy_decode(rle): - from pycocotools import mask as mask_utils - return lambda: mask_utils.decode(rle).astype(np.bool) - - def get_area(self): - from pycocotools import mask as mask_utils - return mask_utils.area(self.rle) - - def get_bbox(self): - from pycocotools import mask as mask_utils - return mask_utils.toBbox(self.rle) - - def __eq__(self, other): - if not isinstance(other, __class__): - return super().__eq__(other) - return self.rle == other.rle - -class CompiledMask: - @staticmethod - def from_instance_masks(instance_masks, - instance_ids=None, instance_labels=None): - from datumaro.util.mask_tools import merge_masks - - if instance_ids is not None: - assert len(instance_ids) == len(instance_masks) - else: - instance_ids = [None] * len(instance_masks) - - if instance_labels is not None: - assert len(instance_labels) == len(instance_masks) - else: - instance_labels = [None] * len(instance_masks) - - instance_masks = sorted( - zip(instance_masks, instance_ids, instance_labels), - key=lambda m: m[0].z_order) - - instance_mask = [m.as_instance_mask(id if id is not None else 1 + idx) - for idx, (m, id, _) in enumerate(instance_masks)] - instance_mask = merge_masks(instance_mask) - - cls_mask = [m.as_class_mask(c) for m, _, c in instance_masks] - cls_mask = merge_masks(cls_mask) - return __class__(class_mask=cls_mask, instance_mask=instance_mask) - - def __init__(self, class_mask=None, instance_mask=None): - self._class_mask = class_mask - self._instance_mask = instance_mask - - @staticmethod - def _get_image(image): - if callable(image): - return image() - return image - - @property - def class_mask(self): - return self._get_image(self._class_mask) - - @property - def instance_mask(self): - return self._get_image(self._instance_mask) - - @property - def instance_count(self): - return int(self.instance_mask.max()) - - def get_instance_labels(self): - class_shift = 16 - m = (self.class_mask.astype(np.uint32) << class_shift) \ - + self.instance_mask.astype(np.uint32) - keys = np.unique(m) - instance_labels = {k & ((1 << class_shift) - 1): k >> class_shift - for k in keys if k & ((1 << class_shift) - 1) != 0 - } - return instance_labels - - def extract(self, instance_id): - return self.instance_mask == instance_id - - def lazy_extract(self, instance_id): - return lambda: self.extract(instance_id) - -@attrs -class _Shape(Annotation): - points = attrib(converter=lambda x: - [round(p, _COORDINATE_ROUNDING_DIGITS) for p in x]) - label = attrib(converter=attr.converters.optional(int), - default=None, kw_only=True) - z_order = attrib(default=0, validator=default_if_none(int), kw_only=True) - - def get_area(self): - raise NotImplementedError() - - def get_bbox(self): - points = self.points - if not points: - return None - - xs = [p for p in points[0::2]] - ys = [p for p in points[1::2]] - x0 = min(xs) - x1 = max(xs) - y0 = min(ys) - y1 = max(ys) - return [x0, y0, x1 - x0, y1 - y0] - -@attrs -class PolyLine(_Shape): - _type = AnnotationType.polyline - - def as_polygon(self): - return self.points[:] - - def get_area(self): - return 0 - -@attrs -class Polygon(_Shape): - _type = AnnotationType.polygon - - def __attrs_post_init__(self): - super().__attrs_post_init__() - # keep the message on a single line to produce informative output - assert len(self.points) % 2 == 0 and 3 <= len(self.points) // 2, "Wrong polygon points: %s" % self.points - - def get_area(self): - import pycocotools.mask as mask_utils - - x, y, w, h = self.get_bbox() - rle = mask_utils.frPyObjects([self.points], y + h, x + w) - area = mask_utils.area(rle)[0] - return area - -@attrs -class Bbox(_Shape): - _type = AnnotationType.bbox - - # will be overridden by attrs, then will be overridden again by us - # attrs' method will be renamed to __attrs_init__ - def __init__(self, x, y, w, h, *args, **kwargs): - kwargs.pop('points', None) # comes from wrap() - self.__attrs_init__([x, y, x + w, y + h], *args, **kwargs) - __actual_init__ = __init__ # save pointer - - @property - def x(self): - return self.points[0] - - @property - def y(self): - return self.points[1] - - @property - def w(self): - return self.points[2] - self.points[0] - - @property - def h(self): - return self.points[3] - self.points[1] - - def get_area(self): - return self.w * self.h - - def get_bbox(self): - return [self.x, self.y, self.w, self.h] - - def as_polygon(self): - x, y, w, h = self.get_bbox() - return [ - x, y, - x + w, y, - x + w, y + h, - x, y + h - ] - - def iou(self, other): - from datumaro.util.annotation_util import bbox_iou - return bbox_iou(self.get_bbox(), other.get_bbox()) - - def wrap(item, **kwargs): - d = {'x': item.x, 'y': item.y, 'w': item.w, 'h': item.h} - d.update(kwargs) - return attr.evolve(item, **d) - -assert not hasattr(Bbox, '__attrs_init__') # hopefully, it will be supported -setattr(Bbox, '__attrs_init__', Bbox.__init__) -setattr(Bbox, '__init__', Bbox.__actual_init__) - -@attrs -class PointsCategories(Categories): - @attrs(repr_ns="PointsCategories") - class Category: - labels = attrib(factory=list, validator=default_if_none(list)) - joints = attrib(factory=set, validator=default_if_none(set)) - - items = attrib(factory=dict, validator=default_if_none(dict)) - - @classmethod - def from_iterable(cls, iterable): - """Generation of PointsCategories from iterable object - - Args: - iterable ([type]): This iterable object can be: - 1) list of positional argumetns - will generate Categories - with these arguments - - Returns: - PointsCategories: PointsCategories object - """ - temp_categories = cls() - - for category in iterable: - temp_categories.add(*category) - return temp_categories - - def add(self, label_id, labels=None, joints=None): - if joints is None: - joints = [] - joints = set(map(tuple, joints)) - self.items[label_id] = self.Category(labels, joints) - -@attrs -class Points(_Shape): - Visibility = Enum('Visibility', [ - ('absent', 0), - ('hidden', 1), - ('visible', 2), - ]) - _type = AnnotationType.points - - visibility = attrib(type=list, default=None) - @visibility.validator - def _visibility_validator(self, attribute, visibility): - if visibility is None: - visibility = [self.Visibility.visible] * (len(self.points) // 2) - else: - for i, v in enumerate(visibility): - if not isinstance(v, self.Visibility): - visibility[i] = self.Visibility(v) - assert len(visibility) == len(self.points) // 2 - self.visibility = visibility - - def __attrs_post_init__(self): - super().__attrs_post_init__() - assert len(self.points) % 2 == 0, self.points - - def get_area(self): - return 0 - - def get_bbox(self): - xs = [p for p, v in zip(self.points[0::2], self.visibility) - if v != __class__.Visibility.absent] - ys = [p for p, v in zip(self.points[1::2], self.visibility) - if v != __class__.Visibility.absent] - x0 = min(xs, default=0) - x1 = max(xs, default=0) - y0 = min(ys, default=0) - y1 = max(ys, default=0) - return [x0, y0, x1 - x0, y1 - y0] - -@attrs -class Caption(Annotation): - _type = AnnotationType.caption - caption = attrib(converter=str) - -@attrs -class DatasetItem: - id = attrib(converter=lambda x: str(x).replace('\\', '/'), - type=str, validator=not_empty) - annotations = attrib(factory=list, validator=default_if_none(list)) - subset = attrib(default='', validator=default_if_none(str)) - path = attrib(factory=list, validator=default_if_none(list)) - - image = attrib(type=Image, default=None) - @image.validator - def _image_validator(self, attribute, image): - if callable(image) or isinstance(image, np.ndarray): - image = Image(data=image) - elif isinstance(image, str): - image = Image(path=image) - assert image is None or isinstance(image, Image) - self.image = image - - attributes = attrib(factory=dict, validator=default_if_none(dict)) - - @property - def has_image(self): - return self.image is not None - - def wrap(item, **kwargs): - return attr.evolve(item, **kwargs) - -class IExtractor: - def __iter__(self): - raise NotImplementedError() - - def __len__(self): - raise NotImplementedError() - - def subsets(self): - raise NotImplementedError() - - def get_subset(self, name): - raise NotImplementedError() - - def categories(self): - raise NotImplementedError() - - def select(self, pred): - raise NotImplementedError() - -class _DatasetFilter: - def __init__(self, iterable, predicate): - self.iterable = iterable - self.predicate = predicate - - def __iter__(self): - return filter(self.predicate, self.iterable) - -class _ExtractorBase(IExtractor): - def __init__(self, length=None, subsets=None): - self._length = length - self._subsets = subsets - - def _init_cache(self): - subsets = set() - length = -1 - for length, item in enumerate(self): - subsets.add(item.subset) - length += 1 - - if self._length is None: - self._length = length - if self._subsets is None: - self._subsets = subsets - - def __len__(self): - if self._length is None: - self._init_cache() - return self._length - - def subsets(self): - if self._subsets is None: - self._init_cache() - return list(self._subsets) - - def get_subset(self, name): - if name in self.subsets(): - return self.select(lambda item: item.subset == name) - else: - raise Exception("Unknown subset '%s' requested" % name) - - def transform(self, method, *args, **kwargs): - return method(self, *args, **kwargs) - -class DatasetIteratorWrapper(_ExtractorBase): - def __init__(self, iterable, categories, subsets=None): - super().__init__(length=None, subsets=subsets) - self._iterable = iterable - self._categories = categories - - def __iter__(self): - return iter(self._iterable) - - def categories(self): - return self._categories - - def select(self, pred): - return DatasetIteratorWrapper( - _DatasetFilter(self, pred), self.categories(), self.subsets()) - -class Extractor(_ExtractorBase): - def __init__(self, length=None): - super().__init__(length=None) - - def categories(self): - return {} - - def select(self, pred): - return DatasetIteratorWrapper( - _DatasetFilter(self, pred), self.categories(), self.subsets()) - -DEFAULT_SUBSET_NAME = 'default' - - -class SourceExtractor(Extractor): - def __init__(self, length=None, subset=None): - super().__init__(length=length) - - if subset == DEFAULT_SUBSET_NAME: - subset = None - self._subset = subset - - def subsets(self): - return [self._subset] - - def get_subset(self, name): - if name != self._subset: - raise Exception("Unknown subset '%s' requested" % name) - return self - -class Importer: - @classmethod - def detect(cls, path): - raise NotImplementedError() - - def __call__(self, path, **extra_params): - raise NotImplementedError() - -class Transform(Extractor): - @staticmethod - def wrap_item(item, **kwargs): - return item.wrap(**kwargs) - - def __init__(self, extractor): - super().__init__() - - self._extractor = extractor - - def __iter__(self): - for item in self._extractor: - yield self.transform_item(item) - - def categories(self): - return self._extractor.categories() - - def transform_item(self, item: DatasetItem) -> DatasetItem: - raise NotImplementedError() diff --git a/datumaro/datumaro/components/launcher.py b/datumaro/datumaro/components/launcher.py deleted file mode 100644 index adc31fb5..00000000 --- a/datumaro/datumaro/components/launcher.py +++ /dev/null @@ -1,67 +0,0 @@ - -# Copyright (C) 2019-2020 Intel Corporation -# -# SPDX-License-Identifier: MIT - -import numpy as np - -from datumaro.components.extractor import (Transform, LabelCategories, - AnnotationType) -from datumaro.util import take_by - - -# pylint: disable=no-self-use -class Launcher: - def __init__(self, model_dir=None): - pass - - def launch(self, inputs): - raise NotImplementedError() - - def categories(self): - return None -# pylint: enable=no-self-use - -class ModelTransform(Transform): - def __init__(self, extractor, launcher, batch_size=1): - super().__init__(extractor) - self._launcher = launcher - self._batch_size = batch_size - - def __iter__(self): - for batch in take_by(self._extractor, self._batch_size): - inputs = np.array([item.image.data for item in batch]) - inference = self._launcher.launch(inputs) - - for item, annotations in zip(batch, inference): - self._check_annotations(annotations) - yield self.wrap_item(item, annotations=annotations) - - def get_subset(self, name): - subset = self._extractor.get_subset(name) - return __class__(subset, self._launcher, self._batch_size) - - def categories(self): - launcher_override = self._launcher.categories() - if launcher_override is not None: - return launcher_override - return self._extractor.categories() - - def transform_item(self, item): - inputs = np.expand_dims(item.image, axis=0) - annotations = self._launcher.launch(inputs)[0] - return self.wrap_item(item, annotations=annotations) - - def _check_annotations(self, annotations): - labels_count = len(self.categories().get( - AnnotationType.label, LabelCategories()).items) - - for ann in annotations: - label = getattr(ann, 'label') - if label is None: - continue - - if label not in range(labels_count): - raise Exception("Annotation has unexpected label id %s, " - "while there is only %s defined labels." % \ - (label, labels_count)) \ No newline at end of file diff --git a/datumaro/datumaro/components/operations.py b/datumaro/datumaro/components/operations.py deleted file mode 100644 index d887add9..00000000 --- a/datumaro/datumaro/components/operations.py +++ /dev/null @@ -1,1504 +0,0 @@ - -# Copyright (C) 2020 Intel Corporation -# -# SPDX-License-Identifier: MIT - -from collections import OrderedDict -from copy import deepcopy -import hashlib -import logging as log - -import attr -import cv2 -import numpy as np -from attr import attrib, attrs -from unittest import TestCase - -from datumaro.components.cli_plugin import CliPlugin -from datumaro.components.extractor import (AnnotationType, Bbox, Label, - LabelCategories, PointsCategories, MaskCategories) -from datumaro.components.project import Dataset -from datumaro.util import find, filter_dict -from datumaro.util.attrs_util import ensure_cls, default_if_none -from datumaro.util.annotation_util import (segment_iou, bbox_iou, - mean_bbox, OKS, find_instances, max_bbox, smooth_line) - -def get_ann_type(anns, t): - return [a for a in anns if a.type == t] - -def match_annotations_equal(a, b): - matches = [] - a_unmatched = a[:] - b_unmatched = b[:] - for a_ann in a: - for b_ann in b_unmatched: - if a_ann != b_ann: - continue - - matches.append((a_ann, b_ann)) - a_unmatched.remove(a_ann) - b_unmatched.remove(b_ann) - break - - return matches, a_unmatched, b_unmatched - -def merge_annotations_equal(a, b): - matches, a_unmatched, b_unmatched = match_annotations_equal(a, b) - return [ann_a for (ann_a, _) in matches] + a_unmatched + b_unmatched - -def merge_categories(sources): - categories = {} - for source in sources: - categories.update(source) - for source in sources: - for cat_type, source_cat in source.items(): - if not categories[cat_type] == source_cat: - raise NotImplementedError( - "Merging of datasets with different categories is " - "only allowed in 'merge' command.") - return categories - -class MergingStrategy(CliPlugin): - @classmethod - def merge(cls, sources, **options): - instance = cls(**options) - return instance(sources) - - def __init__(self, **options): - super().__init__(**options) - self.__dict__['_sources'] = None - - def __call__(self, sources): - raise NotImplementedError() - - -@attrs -class DatasetError: - item_id = attrib() - -@attrs -class QualityError(DatasetError): - pass - -@attrs -class TooCloseError(QualityError): - a = attrib() - b = attrib() - distance = attrib() - - def __str__(self): - return "Item %s: annotations are too close: %s, %s, distance = %s" % \ - (self.item_id, self.a, self.b, self.distance) - -@attrs -class WrongGroupError(QualityError): - found = attrib(converter=set) - expected = attrib(converter=set) - group = attrib(converter=list) - - def __str__(self): - return "Item %s: annotation group has wrong labels: " \ - "found %s, expected %s, group %s" % \ - (self.item_id, self.found, self.expected, self.group) - -@attrs -class MergeError(DatasetError): - sources = attrib(converter=set) - -@attrs -class NoMatchingAnnError(MergeError): - ann = attrib() - - def __str__(self): - return "Item %s: can't find matching annotation " \ - "in sources %s, annotation is %s" % \ - (self.item_id, self.sources, self.ann) - -@attrs -class NoMatchingItemError(MergeError): - def __str__(self): - return "Item %s: can't find matching item in sources %s" % \ - (self.item_id, self.sources) - -@attrs -class FailedLabelVotingError(MergeError): - votes = attrib() - ann = attrib(default=None) - - def __str__(self): - return "Item %s: label voting failed%s, votes %s, sources %s" % \ - (self.item_id, 'for ann %s' % self.ann if self.ann else '', - self.votes, self.sources) - -@attrs -class FailedAttrVotingError(MergeError): - attr = attrib() - votes = attrib() - ann = attrib() - - def __str__(self): - return "Item %s: attribute voting failed " \ - "for ann %s, votes %s, sources %s" % \ - (self.item_id, self.ann, self.votes, self.sources) - -@attrs -class IntersectMerge(MergingStrategy): - @attrs(repr_ns='IntersectMerge', kw_only=True) - class Conf: - pairwise_dist = attrib(converter=float, default=0.5) - sigma = attrib(converter=list, factory=list) - - output_conf_thresh = attrib(converter=float, default=0) - quorum = attrib(converter=int, default=0) - ignored_attributes = attrib(converter=set, factory=set) - - def _groups_conveter(value): - result = [] - for group in value: - rg = set() - for label in group: - optional = label.endswith('?') - name = label if not optional else label[:-1] - rg.add((name, optional)) - result.append(rg) - return result - groups = attrib(converter=_groups_conveter, factory=list) - close_distance = attrib(converter=float, default=0.75) - conf = attrib(converter=ensure_cls(Conf), factory=Conf) - - # Error trackers: - errors = attrib(factory=list, init=False) - def add_item_error(self, error, *args, **kwargs): - self.errors.append(error(self._item_id, *args, **kwargs)) - - # Indexes: - _dataset_map = attrib(init=False) # id(dataset) -> (dataset, index) - _item_map = attrib(init=False) # id(item) -> (item, id(dataset)) - _ann_map = attrib(init=False) # id(ann) -> (ann, id(item)) - _item_id = attrib(init=False) - _item = attrib(init=False) - - # Misc. - _categories = attrib(init=False) # merged categories - - def __call__(self, datasets): - self._categories = self._merge_categories( - [d.categories() for d in datasets]) - merged = Dataset(categories=self._categories) - - self._check_groups_definition() - - item_matches, item_map = self.match_items(datasets) - self._item_map = item_map - self._dataset_map = { id(d): (d, i) for i, d in enumerate(datasets) } - - for item_id, items in item_matches.items(): - self._item_id = item_id - - if len(items) < len(datasets): - missing_sources = set(id(s) for s in datasets) - set(items) - missing_sources = [self._dataset_map[s][1] - for s in missing_sources] - self.add_item_error(NoMatchingItemError, missing_sources) - merged.put(self.merge_items(items)) - - return merged - - def get_ann_source(self, ann_id): - return self._item_map[self._ann_map[ann_id][1]][1] - - def merge_items(self, items): - self._item = next(iter(items.values())) - - self._ann_map = {} - sources = [] - for item in items.values(): - self._ann_map.update({ id(a): (a, id(item)) - for a in item.annotations }) - sources.append(item.annotations) - log.debug("Merging item %s: source annotations %s" % \ - (self._item_id, list(map(len, sources)))) - - annotations = self.merge_annotations(sources) - - annotations = [a for a in annotations - if self.conf.output_conf_thresh <= a.attributes.get('score', 1)] - - return self._item.wrap(annotations=annotations) - - def merge_annotations(self, sources): - self._make_mergers(sources) - - clusters = self._match_annotations(sources) - - joined_clusters = sum(clusters.values(), []) - group_map = self._find_cluster_groups(joined_clusters) - - annotations = [] - for t, clusters in clusters.items(): - for cluster in clusters: - self._check_cluster_sources(cluster) - - merged_clusters = self._merge_clusters(t, clusters) - - for merged_ann, cluster in zip(merged_clusters, clusters): - attributes = self._find_cluster_attrs(cluster, merged_ann) - attributes = { k: v for k, v in attributes.items() - if k not in self.conf.ignored_attributes } - attributes.update(merged_ann.attributes) - merged_ann.attributes = attributes - - new_group_id = find(enumerate(group_map), - lambda e: id(cluster) in e[1][0]) - if new_group_id is None: - new_group_id = 0 - else: - new_group_id = new_group_id[0] + 1 - merged_ann.group = new_group_id - - if self.conf.close_distance: - self._check_annotation_distance(t, merged_clusters) - - annotations += merged_clusters - - if self.conf.groups: - self._check_groups(annotations) - - return annotations - - @staticmethod - def match_items(datasets): - item_ids = set((item.id, item.subset) for d in datasets for item in d) - - item_map = {} # id(item) -> (item, id(dataset)) - - matches = OrderedDict() - for (item_id, item_subset) in sorted(item_ids, key=lambda e: e[0]): - items = {} - for d in datasets: - try: - item = d.get(item_id, subset=item_subset) - items[id(d)] = item - item_map[id(item)] = (item, id(d)) - except KeyError: - pass - matches[(item_id, item_subset)] = items - - return matches, item_map - - def _merge_label_categories(self, sources): - same = True - common = None - for src_categories in sources: - src_cat = src_categories.get(AnnotationType.label) - if common is None: - common = src_cat - elif common != src_cat: - same = False - break - - if same: - return common - - dst_cat = LabelCategories() - for src_id, src_categories in enumerate(sources): - src_cat = src_categories.get(AnnotationType.label) - if src_cat is None: - continue - - for src_label in src_cat.items: - dst_label = dst_cat.find(src_label.name)[1] - if dst_label is not None: - if dst_label != src_label: - if src_label.parent and dst_label.parent and \ - src_label.parent != dst_label.parent: - raise ValueError("Can't merge label category " - "%s (from #%s): " - "parent label conflict: %s vs. %s" % \ - (src_label.name, src_id, - src_label.parent, dst_label.parent) - ) - dst_label.parent = dst_label.parent or src_label.parent - dst_label.attributes |= src_label.attributes - else: - pass - else: - dst_cat.add(src_label.name, - src_label.parent, src_label.attributes) - - return dst_cat - - def _merge_point_categories(self, sources, label_cat): - dst_point_cat = PointsCategories() - - for src_id, src_categories in enumerate(sources): - src_label_cat = src_categories.get(AnnotationType.label) - src_point_cat = src_categories.get(AnnotationType.points) - if src_label_cat is None or src_point_cat is None: - continue - - for src_label_id, src_cat in src_point_cat.items.items(): - src_label = src_label_cat.items[src_label_id].name - dst_label_id = label_cat.find(src_label)[0] - dst_cat = dst_point_cat.items.get(dst_label_id) - if dst_cat is not None: - if dst_cat != src_cat: - raise ValueError("Can't merge point category for label " - "%s (from #%s): %s vs. %s" % \ - (src_label, src_id, src_cat, dst_cat) - ) - else: - pass - else: - dst_point_cat.add(dst_label_id, - src_cat.labels, src_cat.joints) - - if len(dst_point_cat.items) == 0: - return None - - return dst_point_cat - - def _merge_mask_categories(self, sources, label_cat): - dst_mask_cat = MaskCategories() - - for src_id, src_categories in enumerate(sources): - src_label_cat = src_categories.get(AnnotationType.label) - src_mask_cat = src_categories.get(AnnotationType.mask) - if src_label_cat is None or src_mask_cat is None: - continue - - for src_label_id, src_cat in src_mask_cat.colormap.items(): - src_label = src_label_cat.items[src_label_id].name - dst_label_id = label_cat.find(src_label)[0] - dst_cat = dst_mask_cat.colormap.get(dst_label_id) - if dst_cat is not None: - if dst_cat != src_cat: - raise ValueError("Can't merge mask category for label " - "%s (from #%s): %s vs. %s" % \ - (src_label, src_id, src_cat, dst_cat) - ) - else: - pass - else: - dst_mask_cat.colormap[dst_label_id] = src_cat - - if len(dst_mask_cat.colormap) == 0: - return None - - return dst_mask_cat - - def _merge_categories(self, sources): - dst_categories = {} - - label_cat = self._merge_label_categories(sources) - if label_cat is None: - return dst_categories - - dst_categories[AnnotationType.label] = label_cat - - points_cat = self._merge_point_categories(sources, label_cat) - if points_cat is not None: - dst_categories[AnnotationType.points] = points_cat - - mask_cat = self._merge_mask_categories(sources, label_cat) - if mask_cat is not None: - dst_categories[AnnotationType.mask] = mask_cat - - return dst_categories - - def _match_annotations(self, sources): - all_by_type = {} - for s in sources: - src_by_type = {} - for a in s: - src_by_type.setdefault(a.type, []).append(a) - for k, v in src_by_type.items(): - all_by_type.setdefault(k, []).append(v) - - clusters = {} - for k, v in all_by_type.items(): - clusters.setdefault(k, []).extend(self._match_ann_type(k, v)) - - return clusters - - def _make_mergers(self, sources): - def _make(c, **kwargs): - kwargs.update(attr.asdict(self.conf)) - fields = attr.fields_dict(c) - return c(**{ k: v for k, v in kwargs.items() if k in fields }, - context=self) - - def _for_type(t, **kwargs): - if t is AnnotationType.label: - return _make(LabelMerger, **kwargs) - elif t is AnnotationType.bbox: - return _make(BboxMerger, **kwargs) - elif t is AnnotationType.mask: - return _make(MaskMerger, **kwargs) - elif t is AnnotationType.polygon: - return _make(PolygonMerger, **kwargs) - elif t is AnnotationType.polyline: - return _make(LineMerger, **kwargs) - elif t is AnnotationType.points: - return _make(PointsMerger, **kwargs) - elif t is AnnotationType.caption: - return _make(CaptionsMerger, **kwargs) - else: - raise NotImplementedError("Type %s is not supported" % t) - - instance_map = {} - for s in sources: - s_instances = find_instances(s) - for inst in s_instances: - inst_bbox = max_bbox([a for a in inst if a.type in - {AnnotationType.polygon, - AnnotationType.mask, AnnotationType.bbox} - ]) - for ann in inst: - instance_map[id(ann)] = [inst, inst_bbox] - - self._mergers = { t: _for_type(t, instance_map=instance_map) - for t in AnnotationType } - - def _match_ann_type(self, t, sources): - return self._mergers[t].match_annotations(sources) - - def _merge_clusters(self, t, clusters): - return self._mergers[t].merge_clusters(clusters) - - @staticmethod - def _find_cluster_groups(clusters): - cluster_groups = [] - visited = set() - for a_idx, cluster_a in enumerate(clusters): - if a_idx in visited: - continue - visited.add(a_idx) - - cluster_group = { id(cluster_a) } - - # find segment groups in the cluster group - a_groups = set(ann.group for ann in cluster_a) - for cluster_b in clusters[a_idx+1 :]: - b_groups = set(ann.group for ann in cluster_b) - if a_groups & b_groups: - a_groups |= b_groups - - # now we know all the segment groups in this cluster group - # so we can find adjacent clusters - for b_idx, cluster_b in enumerate(clusters[a_idx+1 :]): - b_idx = a_idx + 1 + b_idx - b_groups = set(ann.group for ann in cluster_b) - if a_groups & b_groups: - cluster_group.add( id(cluster_b) ) - visited.add(b_idx) - - if a_groups == {0}: - continue # skip annotations without a group - cluster_groups.append( (cluster_group, a_groups) ) - return cluster_groups - - def _find_cluster_attrs(self, cluster, ann): - quorum = self.conf.quorum or 0 - - # TODO: when attribute types are implemented, add linear - # interpolation for contiguous values - - attr_votes = {} # name -> { value: score , ... } - for s in cluster: - for name, value in s.attributes.items(): - votes = attr_votes.get(name, {}) - votes[value] = 1 + votes.get(value, 0) - attr_votes[name] = votes - - attributes = {} - for name, votes in attr_votes.items(): - winner, count = max(votes.items(), key=lambda e: e[1]) - if count < quorum: - if sum(votes.values()) < quorum: - # blame provokers - missing_sources = set( - self.get_ann_source(id(a)) for a in cluster - if s.attributes.get(name) == winner) - else: - # blame outliers - missing_sources = set( - self.get_ann_source(id(a)) for a in cluster - if s.attributes.get(name) != winner) - missing_sources = [self._dataset_map[s][1] - for s in missing_sources] - self.add_item_error(FailedAttrVotingError, - missing_sources, name, votes, ann) - continue - attributes[name] = winner - - return attributes - - def _check_cluster_sources(self, cluster): - if len(cluster) == len(self._dataset_map): - return - - def _has_item(s): - try: - item =self._dataset_map[s][0].get(*self._item_id) - if len(item.annotations) == 0: - return False - return True - except KeyError: - return False - - missing_sources = set(self._dataset_map) - \ - set(self.get_ann_source(id(a)) for a in cluster) - missing_sources = [self._dataset_map[s][1] for s in missing_sources - if _has_item(s)] - if missing_sources: - self.add_item_error(NoMatchingAnnError, missing_sources, cluster[0]) - - def _check_annotation_distance(self, t, annotations): - for a_idx, a_ann in enumerate(annotations): - for b_ann in annotations[a_idx+1:]: - d = self._mergers[t].distance(a_ann, b_ann) - if self.conf.close_distance < d: - self.add_item_error(TooCloseError, a_ann, b_ann, d) - - def _check_groups(self, annotations): - check_groups = [] - for check_group_raw in self.conf.groups: - check_group = set(l[0] for l in check_group_raw) - optional = set(l[0] for l in check_group_raw if l[1]) - check_groups.append((check_group, optional)) - - def _check_group(group_labels, group): - for check_group, optional in check_groups: - common = check_group & group_labels - real_miss = check_group - common - optional - extra = group_labels - check_group - if common and (extra or real_miss): - self.add_item_error(WrongGroupError, group_labels, - check_group, group) - break - - groups = find_instances(annotations) - for group in groups: - group_labels = set() - for ann in group: - if not hasattr(ann, 'label'): - continue - label = self._get_label_name(ann.label) - - if ann.group: - group_labels.add(label) - else: - _check_group({label}, [ann]) - - if not group_labels: - continue - _check_group(group_labels, group) - - def _get_label_name(self, label_id): - if label_id is None: - return None - return self._categories[AnnotationType.label].items[label_id].name - - def _get_label_id(self, label): - return self._categories[AnnotationType.label].find(label)[0] - - def _get_src_label_name(self, ann, label_id): - if label_id is None: - return None - item_id = self._ann_map[id(ann)][1] - dataset_id = self._item_map[item_id][1] - return self._dataset_map[dataset_id][0] \ - .categories()[AnnotationType.label].items[label_id].name - - def _get_any_label_name(self, ann, label_id): - if label_id is None: - return None - try: - return self._get_src_label_name(ann, label_id) - except KeyError: - return self._get_label_name(label_id) - - def _check_groups_definition(self): - for group in self.conf.groups: - for label, _ in group: - _, entry = self._categories[AnnotationType.label].find(label) - if entry is None: - raise ValueError("Datasets do not contain " - "label '%s', available labels %s" % \ - (label, [i.name for i in - self._categories[AnnotationType.label].items]) - ) - -@attrs(kw_only=True) -class AnnotationMatcher: - _context = attrib(type=IntersectMerge, default=None) - - def match_annotations(self, sources): - raise NotImplementedError() - -@attrs -class LabelMatcher(AnnotationMatcher): - def distance(self, a, b): - a_label = self._context._get_any_label_name(a, a.label) - b_label = self._context._get_any_label_name(b, b.label) - return a_label == b_label - - def match_annotations(self, sources): - return [sum(sources, [])] - -@attrs(kw_only=True) -class _ShapeMatcher(AnnotationMatcher): - pairwise_dist = attrib(converter=float, default=0.9) - cluster_dist = attrib(converter=float, default=-1.0) - - def match_annotations(self, sources): - distance = self.distance - label_matcher = self.label_matcher - pairwise_dist = self.pairwise_dist - cluster_dist = self.cluster_dist - - if cluster_dist < 0: cluster_dist = pairwise_dist - - id_segm = { id(a): (a, id(s)) for s in sources for a in s } - - def _is_close_enough(cluster, extra_id): - # check if whole cluster IoU will not be broken - # when this segment is added - b = id_segm[extra_id][0] - for a_id in cluster: - a = id_segm[a_id][0] - if distance(a, b) < cluster_dist: - return False - return True - - def _has_same_source(cluster, extra_id): - b = id_segm[extra_id][1] - for a_id in cluster: - a = id_segm[a_id][1] - if a == b: - return True - return False - - # match segments in sources, pairwise - adjacent = { i: [] for i in id_segm } # id(sgm) -> [id(adj_sgm1), ...] - for a_idx, src_a in enumerate(sources): - for src_b in sources[a_idx+1 :]: - matches, _, _, _ = match_segments(src_a, src_b, - dist_thresh=pairwise_dist, - distance=distance, label_matcher=label_matcher) - for a, b in matches: - adjacent[id(a)].append(id(b)) - - # join all segments into matching clusters - clusters = [] - visited = set() - for cluster_idx in adjacent: - if cluster_idx in visited: - continue - - cluster = set() - to_visit = { cluster_idx } - while to_visit: - c = to_visit.pop() - cluster.add(c) - visited.add(c) - - for i in adjacent[c]: - if i in visited: - continue - if 0 < cluster_dist and not _is_close_enough(cluster, i): - continue - if _has_same_source(cluster, i): - continue - - to_visit.add(i) - - clusters.append([id_segm[i][0] for i in cluster]) - - return clusters - - @staticmethod - def distance(a, b): - return segment_iou(a, b) - - def label_matcher(self, a, b): - a_label = self._context._get_any_label_name(a, a.label) - b_label = self._context._get_any_label_name(b, b.label) - return a_label == b_label - -@attrs -class BboxMatcher(_ShapeMatcher): - pass - -@attrs -class PolygonMatcher(_ShapeMatcher): - pass - -@attrs -class MaskMatcher(_ShapeMatcher): - pass - -@attrs(kw_only=True) -class PointsMatcher(_ShapeMatcher): - sigma = attrib(type=list, default=None) - instance_map = attrib(converter=dict) - - def distance(self, a, b): - a_bbox = self.instance_map[id(a)][1] - b_bbox = self.instance_map[id(b)][1] - if bbox_iou(a_bbox, b_bbox) <= 0: - return 0 - bbox = mean_bbox([a_bbox, b_bbox]) - return OKS(a, b, sigma=self.sigma, bbox=bbox) - -@attrs -class LineMatcher(_ShapeMatcher): - @staticmethod - def distance(a, b): - a_bbox = a.get_bbox() - b_bbox = b.get_bbox() - bbox = max_bbox([a_bbox, b_bbox]) - area = bbox[2] * bbox[3] - if not area: - return 1 - - # compute inter-line area, normalize by common bbox - point_count = max(max(len(a.points) // 2, len(b.points) // 2), 5) - a, sa = smooth_line(a.points, point_count) - b, sb = smooth_line(b.points, point_count) - dists = np.linalg.norm(a - b, axis=1) - dists = (dists[:-1] + dists[1:]) * 0.5 - s = np.sum(dists) * 0.5 * (sa + sb) / area - return abs(1 - s) - -@attrs -class CaptionsMatcher(AnnotationMatcher): - def match_annotations(self, sources): - raise NotImplementedError() - - -@attrs(kw_only=True) -class AnnotationMerger: - def merge_clusters(self, clusters): - raise NotImplementedError() - -@attrs(kw_only=True) -class LabelMerger(AnnotationMerger, LabelMatcher): - quorum = attrib(converter=int, default=0) - - def merge_clusters(self, clusters): - assert len(clusters) <= 1 - if len(clusters) == 0: - return [] - - votes = {} # label -> score - for ann in clusters[0]: - label = self._context._get_src_label_name(ann, ann.label) - votes[label] = 1 + votes.get(label, 0) - - merged = [] - for label, count in votes.items(): - if count < self.quorum: - sources = set(self.get_ann_source(id(a)) for a in clusters[0] - if label not in [self._context._get_src_label_name(l, l.label) - for l in a]) - sources = [self._context._dataset_map[s][1] for s in sources] - self._context.add_item_error(FailedLabelVotingError, - sources, votes) - continue - - merged.append(Label(self._context._get_label_id(label), attributes={ - 'score': count / len(self._context._dataset_map) - })) - - return merged - -@attrs(kw_only=True) -class _ShapeMerger(AnnotationMerger, _ShapeMatcher): - quorum = attrib(converter=int, default=0) - - def merge_clusters(self, clusters): - merged = [] - for cluster in clusters: - label, label_score = self.find_cluster_label(cluster) - shape, shape_score = self.merge_cluster_shape(cluster) - - shape.z_order = max(cluster, key=lambda a: a.z_order).z_order - shape.label = label - shape.attributes['score'] = label_score * shape_score \ - if label is not None else shape_score - - merged.append(shape) - - return merged - - def find_cluster_label(self, cluster): - votes = {} - for s in cluster: - label = self._context._get_src_label_name(s, s.label) - state = votes.setdefault(label, [0, 0]) - state[0] += s.attributes.get('score', 1.0) - state[1] += 1 - - label, (score, count) = max(votes.items(), key=lambda e: e[1][0]) - if count < self.quorum: - self._context.add_item_error(FailedLabelVotingError, votes) - label = None - score = score / len(self._context._dataset_map) - label = self._context._get_label_id(label) - return label, score - - @staticmethod - def _merge_cluster_shape_mean_box_nearest(cluster): - mbbox = Bbox(*mean_bbox(cluster)) - dist = (segment_iou(mbbox, s) for s in cluster) - nearest_pos, _ = max(enumerate(dist), key=lambda e: e[1]) - return cluster[nearest_pos] - - def merge_cluster_shape(self, cluster): - shape = self._merge_cluster_shape_mean_box_nearest(cluster) - shape_score = sum(max(0, self.distance(shape, s)) - for s in cluster) / len(cluster) - return shape, shape_score - -@attrs -class BboxMerger(_ShapeMerger, BboxMatcher): - pass - -@attrs -class PolygonMerger(_ShapeMerger, PolygonMatcher): - pass - -@attrs -class MaskMerger(_ShapeMerger, MaskMatcher): - pass - -@attrs -class PointsMerger(_ShapeMerger, PointsMatcher): - pass - -@attrs -class LineMerger(_ShapeMerger, LineMatcher): - pass - -@attrs -class CaptionsMerger(AnnotationMerger, CaptionsMatcher): - pass - -def match_segments(a_segms, b_segms, distance=segment_iou, dist_thresh=1.0, - label_matcher=lambda a, b: a.label == b.label): - assert callable(distance), distance - assert callable(label_matcher), label_matcher - - a_segms.sort(key=lambda ann: 1 - ann.attributes.get('score', 1)) - b_segms.sort(key=lambda ann: 1 - ann.attributes.get('score', 1)) - - # a_matches: indices of b_segms matched to a bboxes - # b_matches: indices of a_segms matched to b bboxes - a_matches = -np.ones(len(a_segms), dtype=int) - b_matches = -np.ones(len(b_segms), dtype=int) - - distances = np.array([[distance(a, b) for b in b_segms] for a in a_segms]) - - # matches: boxes we succeeded to match completely - # mispred: boxes we succeeded to match, having label mismatch - matches = [] - mispred = [] - - for a_idx, a_segm in enumerate(a_segms): - if len(b_segms) == 0: - break - matched_b = -1 - max_dist = -1 - b_indices = np.argsort([not label_matcher(a_segm, b_segm) - for b_segm in b_segms], - kind='stable') # prioritize those with same label, keep score order - for b_idx in b_indices: - if 0 <= b_matches[b_idx]: # assign a_segm with max conf - continue - d = distances[a_idx, b_idx] - if d < dist_thresh or d <= max_dist: - continue - max_dist = d - matched_b = b_idx - - if matched_b < 0: - continue - a_matches[a_idx] = matched_b - b_matches[matched_b] = a_idx - - b_segm = b_segms[matched_b] - - if label_matcher(a_segm, b_segm): - matches.append( (a_segm, b_segm) ) - else: - mispred.append( (a_segm, b_segm) ) - - # *_umatched: boxes of (*) we failed to match - a_unmatched = [a_segms[i] for i, m in enumerate(a_matches) if m < 0] - b_unmatched = [b_segms[i] for i, m in enumerate(b_matches) if m < 0] - - return matches, mispred, a_unmatched, b_unmatched - -def mean_std(dataset): - """ - Computes unbiased mean and std. dev. for dataset images, channel-wise. - """ - # Use an online algorithm to: - # - handle different image sizes - # - avoid cancellation problem - if len(dataset) == 0: - return [0, 0, 0], [0, 0, 0] - - stats = np.empty((len(dataset), 2, 3), dtype=np.double) - counts = np.empty(len(dataset), dtype=np.uint32) - - mean = lambda i, s: s[i][0] - var = lambda i, s: s[i][1] - - for i, item in enumerate(dataset): - counts[i] = np.prod(item.image.size) - - image = item.image.data - if len(image.shape) == 2: - image = image[:, :, np.newaxis] - else: - image = image[:, :, :3] - # opencv is much faster than numpy here - cv2.meanStdDev(image.astype(np.double) / 255, - mean=mean(i, stats), stddev=var(i, stats)) - - # make variance unbiased - np.multiply(np.square(stats[:, 1]), - (counts / (counts - 1))[:, np.newaxis], - out=stats[:, 1]) - - _, mean, var = StatsCounter().compute_stats(stats, counts, mean, var) - return mean * 255, np.sqrt(var) * 255 - -class StatsCounter: - # Implements online parallel computation of sample variance - # https://en.wikipedia.org/wiki/Algorithms_for_calculating_variance#Parallel_algorithm - - # Needed do avoid catastrophic cancellation in floating point computations - @staticmethod - def pairwise_stats(count_a, mean_a, var_a, count_b, mean_b, var_b): - delta = mean_b - mean_a - m_a = var_a * (count_a - 1) - m_b = var_b * (count_b - 1) - M2 = m_a + m_b + delta ** 2 * count_a * count_b / (count_a + count_b) - return ( - count_a + count_b, - mean_a * 0.5 + mean_b * 0.5, - M2 / (count_a + count_b - 1) - ) - - # stats = float array of shape N, 2 * d, d = dimensions of values - # count = integer array of shape N - # mean_accessor = function(idx, stats) to retrieve element mean - # variance_accessor = function(idx, stats) to retrieve element variance - # Recursively computes total count, mean and variance, does O(log(N)) calls - @staticmethod - def compute_stats(stats, counts, mean_accessor, variance_accessor): - m = mean_accessor - v = variance_accessor - n = len(stats) - if n == 1: - return counts[0], m(0, stats), v(0, stats) - if n == 2: - return __class__.pairwise_stats( - counts[0], m(0, stats), v(0, stats), - counts[1], m(1, stats), v(1, stats) - ) - h = n // 2 - return __class__.pairwise_stats( - *__class__.compute_stats(stats[:h], counts[:h], m, v), - *__class__.compute_stats(stats[h:], counts[h:], m, v) - ) - -def compute_image_statistics(dataset): - stats = { - 'dataset': {}, - 'subsets': {} - } - - def _extractor_stats(extractor): - available = True - for item in extractor: - if not (item.has_image and item.image.has_data): - available = False - log.warn("Item %s has no image. Image stats won't be computed", - item.id) - break - - stats = { - 'images count': len(extractor), - } - - if available: - mean, std = mean_std(extractor) - stats.update({ - 'image mean': [float(n) for n in mean[::-1]], - 'image std': [float(n) for n in std[::-1]], - }) - else: - stats.update({ - 'image mean': 'n/a', - 'image std': 'n/a', - }) - return stats - - stats['dataset'].update(_extractor_stats(dataset)) - - subsets = dataset.subsets() or [None] - if subsets and 0 < len([s for s in subsets if s]): - for subset_name in subsets: - stats['subsets'][subset_name] = _extractor_stats( - dataset.get_subset(subset_name)) - - return stats - -def compute_ann_statistics(dataset): - labels = dataset.categories().get(AnnotationType.label) - def get_label(ann): - return labels.items[ann.label].name if ann.label is not None else None - - stats = { - 'images count': len(dataset), - 'annotations count': 0, - 'unannotated images count': 0, - 'unannotated images': [], - 'annotations by type': { t.name: { - 'count': 0, - } for t in AnnotationType }, - 'annotations': {}, - } - by_type = stats['annotations by type'] - - attr_template = { - 'count': 0, - 'values count': 0, - 'values present': set(), - 'distribution': {}, # value -> (count, total%) - } - label_stat = { - 'count': 0, - 'distribution': { l.name: [0, 0] for l in labels.items - }, # label -> (count, total%) - - 'attributes': {}, - } - stats['annotations']['labels'] = label_stat - segm_stat = { - 'avg. area': 0, - 'area distribution': [], # a histogram with 10 bins - # (min, min+10%), ..., (min+90%, max) -> (count, total%) - - 'pixel distribution': { l.name: [0, 0] for l in labels.items - }, # label -> (count, total%) - } - stats['annotations']['segments'] = segm_stat - segm_areas = [] - pixel_dist = segm_stat['pixel distribution'] - total_pixels = 0 - - for item in dataset: - if len(item.annotations) == 0: - stats['unannotated images'].append(item.id) - continue - - for ann in item.annotations: - by_type[ann.type.name]['count'] += 1 - - if not hasattr(ann, 'label') or ann.label is None: - continue - - if ann.type in {AnnotationType.mask, - AnnotationType.polygon, AnnotationType.bbox}: - area = ann.get_area() - segm_areas.append(area) - pixel_dist[get_label(ann)][0] += int(area) - - label_stat['count'] += 1 - label_stat['distribution'][get_label(ann)][0] += 1 - - for name, value in ann.attributes.items(): - if name.lower() in { 'occluded', 'visibility', 'score', - 'id', 'track_id' }: - continue - attrs_stat = label_stat['attributes'].setdefault(name, - deepcopy(attr_template)) - attrs_stat['count'] += 1 - attrs_stat['values present'].add(str(value)) - attrs_stat['distribution'] \ - .setdefault(str(value), [0, 0])[0] += 1 - - stats['annotations count'] = sum(t['count'] for t in - stats['annotations by type'].values()) - stats['unannotated images count'] = len(stats['unannotated images']) - - for label_info in label_stat['distribution'].values(): - label_info[1] = label_info[0] / label_stat['count'] - - for label_attr in label_stat['attributes'].values(): - label_attr['values count'] = len(label_attr['values present']) - label_attr['values present'] = sorted(label_attr['values present']) - for attr_info in label_attr['distribution'].values(): - attr_info[1] = attr_info[0] / label_attr['count'] - - # numpy.sum might be faster, but could overflow with large datasets. - # Python's int can transparently mutate to be of indefinite precision (long) - total_pixels = sum(int(a) for a in segm_areas) - - segm_stat['avg. area'] = total_pixels / (len(segm_areas) or 1.0) - - for label_info in segm_stat['pixel distribution'].values(): - label_info[1] = label_info[0] / total_pixels - - if len(segm_areas) != 0: - hist, bins = np.histogram(segm_areas) - segm_stat['area distribution'] = [{ - 'min': float(bin_min), 'max': float(bin_max), - 'count': int(c), 'percent': int(c) / len(segm_areas) - } for c, (bin_min, bin_max) in zip(hist, zip(bins[:-1], bins[1:]))] - - return stats - -@attrs -class DistanceComparator: - iou_threshold = attrib(converter=float, default=0.5) - - @staticmethod - def match_datasets(a, b): - a_items = set((item.id, item.subset) for item in a) - b_items = set((item.id, item.subset) for item in b) - - matches = a_items & b_items - a_unmatched = a_items - b_items - b_unmatched = b_items - a_items - return matches, a_unmatched, b_unmatched - - @staticmethod - def match_classes(a, b): - a_label_cat = a.categories().get(AnnotationType.label, LabelCategories()) - b_label_cat = b.categories().get(AnnotationType.label, LabelCategories()) - - a_labels = set(c.name for c in a_label_cat) - b_labels = set(c.name for c in b_label_cat) - - matches = a_labels & b_labels - a_unmatched = a_labels - b_labels - b_unmatched = b_labels - a_labels - return matches, a_unmatched, b_unmatched - - def match_annotations(self, item_a, item_b): - return { t: self._match_ann_type(t, item_a, item_b) } - - def _match_ann_type(self, t, *args): - # pylint: disable=no-value-for-parameter - if t == AnnotationType.label: - return self.match_labels(*args) - elif t == AnnotationType.bbox: - return self.match_boxes(*args) - elif t == AnnotationType.polygon: - return self.match_polygons(*args) - elif t == AnnotationType.mask: - return self.match_masks(*args) - elif t == AnnotationType.points: - return self.match_points(*args) - elif t == AnnotationType.polyline: - return self.match_lines(*args) - # pylint: enable=no-value-for-parameter - else: - raise NotImplementedError("Unexpected annotation type %s" % t) - - @staticmethod - def _get_ann_type(t, item): - return get_ann_type(item.annotations, t) - - def match_labels(self, item_a, item_b): - a_labels = set(a.label for a in - self._get_ann_type(AnnotationType.label, item_a)) - b_labels = set(a.label for a in - self._get_ann_type(AnnotationType.label, item_b)) - - matches = a_labels & b_labels - a_unmatched = a_labels - b_labels - b_unmatched = b_labels - a_labels - return matches, a_unmatched, b_unmatched - - def _match_segments(self, t, item_a, item_b): - a_boxes = self._get_ann_type(t, item_a) - b_boxes = self._get_ann_type(t, item_b) - return match_segments(a_boxes, b_boxes, dist_thresh=self.iou_threshold) - - def match_polygons(self, item_a, item_b): - return self._match_segments(AnnotationType.polygon, item_a, item_b) - - def match_masks(self, item_a, item_b): - return self._match_segments(AnnotationType.mask, item_a, item_b) - - def match_boxes(self, item_a, item_b): - return self._match_segments(AnnotationType.bbox, item_a, item_b) - - def match_points(self, item_a, item_b): - a_points = self._get_ann_type(AnnotationType.points, item_a) - b_points = self._get_ann_type(AnnotationType.points, item_b) - - instance_map = {} - for s in [item_a.annotations, item_b.annotations]: - s_instances = find_instances(s) - for inst in s_instances: - inst_bbox = max_bbox(inst) - for ann in inst: - instance_map[id(ann)] = [inst, inst_bbox] - matcher = PointsMatcher(instance_map=instance_map) - - return match_segments(a_points, b_points, - dist_thresh=self.iou_threshold, distance=matcher.distance) - - def match_lines(self, item_a, item_b): - a_lines = self._get_ann_type(AnnotationType.polyline, item_a) - b_lines = self._get_ann_type(AnnotationType.polyline, item_b) - - matcher = LineMatcher() - - return match_segments(a_lines, b_lines, - dist_thresh=self.iou_threshold, distance=matcher.distance) - -def match_items_by_id(a, b): - a_items = set((item.id, item.subset) for item in a) - b_items = set((item.id, item.subset) for item in b) - - matches = a_items & b_items - matches = [([m], [m]) for m in matches] - a_unmatched = a_items - b_items - b_unmatched = b_items - a_items - return matches, a_unmatched, b_unmatched - -def match_items_by_image_hash(a, b): - def _hash(item): - if not item.image.has_data: - log.warning("Image (%s, %s) has no image " - "data, counted as unmatched", item.id, item.subset) - return None - return hashlib.md5(item.image.data.tobytes()).hexdigest() - - def _build_hashmap(source): - d = {} - for item in source: - h = _hash(item) - if h is None: - h = str(id(item)) # anything unique - d.setdefault(h, []).append((item.id, item.subset)) - return d - - a_hash = _build_hashmap(a) - b_hash = _build_hashmap(b) - - a_items = set(a_hash) - b_items = set(b_hash) - - matches = a_items & b_items - a_unmatched = a_items - b_items - b_unmatched = b_items - a_items - - matches = [(a_hash[h], b_hash[h]) for h in matches] - a_unmatched = set(i for h in a_unmatched for i in a_hash[h]) - b_unmatched = set(i for h in b_unmatched for i in b_hash[h]) - - return matches, a_unmatched, b_unmatched - -@attrs -class ExactComparator: - match_images = attrib(kw_only=True, type=bool, default=False) - ignored_fields = attrib(kw_only=True, - factory=set, validator=default_if_none(set)) - ignored_attrs = attrib(kw_only=True, - factory=set, validator=default_if_none(set)) - ignored_item_attrs = attrib(kw_only=True, - factory=set, validator=default_if_none(set)) - - _test = attrib(init=False, type=TestCase) - errors = attrib(init=False, type=list) - - def __attrs_post_init__(self): - self._test = TestCase() - self._test.maxDiff = None - - - def _match_items(self, a, b): - if self.match_images: - return match_items_by_image_hash(a, b) - else: - return match_items_by_id(a, b) - - def _compare_categories(self, a, b): - test = self._test - errors = self.errors - - try: - test.assertEqual( - sorted(a, key=lambda t: t.value), - sorted(b, key=lambda t: t.value) - ) - except AssertionError as e: - errors.append({'type': 'categories', 'message': str(e)}) - - if AnnotationType.label in a: - try: - test.assertEqual( - a[AnnotationType.label].items, - b[AnnotationType.label].items, - ) - except AssertionError as e: - errors.append({'type': 'labels', 'message': str(e)}) - if AnnotationType.mask in a: - try: - test.assertEqual( - a[AnnotationType.mask].colormap, - b[AnnotationType.mask].colormap, - ) - except AssertionError as e: - errors.append({'type': 'colormap', 'message': str(e)}) - if AnnotationType.points in a: - try: - test.assertEqual( - a[AnnotationType.points].items, - b[AnnotationType.points].items, - ) - except AssertionError as e: - errors.append({'type': 'points', 'message': str(e)}) - - def _compare_annotations(self, a, b): - ignored_fields = self.ignored_fields - ignored_attrs = self.ignored_attrs - - a_fields = { k: None for k in vars(a) if k in ignored_fields } - b_fields = { k: None for k in vars(b) if k in ignored_fields } - if 'attributes' not in ignored_fields: - a_fields['attributes'] = filter_dict(a.attributes, ignored_attrs) - b_fields['attributes'] = filter_dict(b.attributes, ignored_attrs) - - result = a.wrap(**a_fields) == b.wrap(**b_fields) - - return result - - def _compare_items(self, item_a, item_b): - test = self._test - - a_id = (item_a.id, item_a.subset) - b_id = (item_b.id, item_b.subset) - - matched = [] - unmatched = [] - errors = [] - - try: - test.assertEqual( - filter_dict(item_a.attributes, self.ignored_item_attrs), - filter_dict(item_b.attributes, self.ignored_item_attrs) - ) - except AssertionError as e: - errors.append({'type': 'item_attr', - 'a_item': a_id, 'b_item': b_id, 'message': str(e)}) - - b_annotations = item_b.annotations[:] - for ann_a in item_a.annotations: - ann_b_candidates = [x for x in item_b.annotations - if x.type == ann_a.type] - - ann_b = find(enumerate(self._compare_annotations(ann_a, x) - for x in ann_b_candidates), lambda x: x[1]) - if ann_b is None: - unmatched.append({ - 'item': a_id, 'source': 'a', 'ann': str(ann_a), - }) - continue - else: - ann_b = ann_b_candidates[ann_b[0]] - - b_annotations.remove(ann_b) # avoid repeats - matched.append({'a_item': a_id, 'b_item': b_id, - 'a': str(ann_a), 'b': str(ann_b)}) - - for ann_b in b_annotations: - unmatched.append({'item': b_id, 'source': 'b', 'ann': str(ann_b)}) - - return matched, unmatched, errors - - def compare_datasets(self, a, b): - self.errors = [] - errors = self.errors - - self._compare_categories(a.categories(), b.categories()) - - matched = [] - unmatched = [] - - matches, a_unmatched, b_unmatched = self._match_items(a, b) - - if a.categories().get(AnnotationType.label) != \ - b.categories().get(AnnotationType.label): - return matched, unmatched, a_unmatched, b_unmatched, errors - - _dist = lambda s: len(s[1]) + len(s[2]) - for a_ids, b_ids in matches: - # build distance matrix - match_status = {} # (a_id, b_id): [matched, unmatched, errors] - a_matches = { a_id: None for a_id in a_ids } - b_matches = { b_id: None for b_id in b_ids } - - for a_id in a_ids: - item_a = a.get(*a_id) - candidates = {} - - for b_id in b_ids: - item_b = b.get(*b_id) - - i_m, i_um, i_err = self._compare_items(item_a, item_b) - candidates[b_id] = [i_m, i_um, i_err] - - if len(i_um) == 0: - a_matches[a_id] = b_id - b_matches[b_id] = a_id - matched.extend(i_m) - errors.extend(i_err) - break - - match_status[a_id] = candidates - - # assign - for a_id in a_ids: - if len(b_ids) == 0: - break - - # find the closest, ignore already assigned - matched_b = a_matches[a_id] - if matched_b is not None: - continue - min_dist = -1 - for b_id in b_ids: - if b_matches[b_id] is not None: - continue - d = _dist(match_status[a_id][b_id]) - if d < min_dist and 0 <= min_dist: - continue - min_dist = d - matched_b = b_id - - if matched_b is None: - continue - a_matches[a_id] = matched_b - b_matches[matched_b] = a_id - - m = match_status[a_id][matched_b] - matched.extend(m[0]) - unmatched.extend(m[1]) - errors.extend(m[2]) - - a_unmatched |= set(a_id for a_id, m in a_matches.items() if not m) - b_unmatched |= set(b_id for b_id, m in b_matches.items() if not m) - - return matched, unmatched, a_unmatched, b_unmatched, errors \ No newline at end of file diff --git a/datumaro/datumaro/components/project.py b/datumaro/datumaro/components/project.py deleted file mode 100644 index 07f8f019..00000000 --- a/datumaro/datumaro/components/project.py +++ /dev/null @@ -1,850 +0,0 @@ -# Copyright (C) 2019-2020 Intel Corporation -# -# SPDX-License-Identifier: MIT - -from collections import OrderedDict, defaultdict -from functools import reduce -from glob import glob -from typing import Iterable, Union, Dict, List -import git -import importlib -import inspect -import logging as log -import os -import os.path as osp -import shutil -import sys - -from datumaro.components.config import Config, DEFAULT_FORMAT -from datumaro.components.config_model import (Model, Source, - PROJECT_DEFAULT_CONFIG, PROJECT_SCHEMA) -from datumaro.components.extractor import Extractor, LabelCategories,\ - AnnotationType, DatasetItem -from datumaro.components.launcher import ModelTransform -from datumaro.components.dataset_filter import \ - XPathDatasetFilter, XPathAnnotationsFilter - - -def import_foreign_module(name, path, package=None): - module = None - default_path = sys.path.copy() - try: - sys.path = [ osp.abspath(path), ] + default_path - sys.modules.pop(name, None) # remove from cache - module = importlib.import_module(name, package=package) - sys.modules.pop(name) # remove from cache - except Exception: - raise - finally: - sys.path = default_path - return module - - -class Registry: - def __init__(self, config=None, item_type=None): - self.item_type = item_type - - self.items = {} - - if config is not None: - self.load(config) - - def load(self, config): - pass - - def register(self, name, value): - if self.item_type: - value = self.item_type(value) - self.items[name] = value - return value - - def unregister(self, name): - return self.items.pop(name, None) - - def get(self, key): - return self.items[key] # returns a class / ctor - - -class ModelRegistry(Registry): - def __init__(self, config=None): - super().__init__(config, item_type=Model) - - def load(self, config): - # TODO: list default dir, insert values - if 'models' in config: - for name, model in config.models.items(): - self.register(name, model) - - -class SourceRegistry(Registry): - def __init__(self, config=None): - super().__init__(config, item_type=Source) - - def load(self, config): - # TODO: list default dir, insert values - if 'sources' in config: - for name, source in config.sources.items(): - self.register(name, source) - -class PluginRegistry(Registry): - def __init__(self, config=None, builtin=None, local=None): - super().__init__(config) - - from datumaro.components.cli_plugin import CliPlugin - - if builtin is not None: - for v in builtin: - k = CliPlugin._get_name(v) - self.register(k, v) - if local is not None: - for v in local: - k = CliPlugin._get_name(v) - self.register(k, v) - -class GitWrapper: - def __init__(self, config=None): - self.repo = None - - if config is not None and config.project_dir: - self.init(config.project_dir) - - @staticmethod - def _git_dir(base_path): - return osp.join(base_path, '.git') - - @classmethod - def spawn(cls, path): - spawn = not osp.isdir(cls._git_dir(path)) - repo = git.Repo.init(path=path) - if spawn: - repo.config_writer().set_value("user", "name", "User") \ - .set_value("user", "email", "user@nowhere.com") \ - .release() - # gitpython does not support init, use git directly - repo.git.init() - repo.git.commit('-m', 'Initial commit', '--allow-empty') - return repo - - def init(self, path): - self.repo = self.spawn(path) - return self.repo - - def is_initialized(self): - return self.repo is not None - - def create_submodule(self, name, dst_dir, **kwargs): - self.repo.create_submodule(name, dst_dir, **kwargs) - - def has_submodule(self, name): - return name in [submodule.name for submodule in self.repo.submodules] - - def remove_submodule(self, name, **kwargs): - return self.repo.submodule(name).remove(**kwargs) - -def load_project_as_dataset(url): - # symbol forward declaration - raise NotImplementedError() - -class Environment: - _builtin_plugins = None - PROJECT_EXTRACTOR_NAME = 'datumaro_project' - - def __init__(self, config=None): - config = Config(config, - fallback=PROJECT_DEFAULT_CONFIG, schema=PROJECT_SCHEMA) - - self.models = ModelRegistry(config) - self.sources = SourceRegistry(config) - - self.git = GitWrapper(config) - - env_dir = osp.join(config.project_dir, config.env_dir) - builtin = self._load_builtin_plugins() - custom = self._load_plugins2(osp.join(env_dir, config.plugins_dir)) - select = lambda seq, t: [e for e in seq if issubclass(e, t)] - from datumaro.components.extractor import Transform - from datumaro.components.extractor import SourceExtractor - from datumaro.components.extractor import Importer - from datumaro.components.converter import Converter - from datumaro.components.launcher import Launcher - self.extractors = PluginRegistry( - builtin=select(builtin, SourceExtractor), - local=select(custom, SourceExtractor) - ) - self.extractors.register(self.PROJECT_EXTRACTOR_NAME, - load_project_as_dataset) - - self.importers = PluginRegistry( - builtin=select(builtin, Importer), - local=select(custom, Importer) - ) - self.launchers = PluginRegistry( - builtin=select(builtin, Launcher), - local=select(custom, Launcher) - ) - self.converters = PluginRegistry( - builtin=select(builtin, Converter), - local=select(custom, Converter) - ) - self.transforms = PluginRegistry( - builtin=select(builtin, Transform), - local=select(custom, Transform) - ) - - @staticmethod - def _find_plugins(plugins_dir): - plugins = [] - if not osp.exists(plugins_dir): - return plugins - - for plugin_name in os.listdir(plugins_dir): - p = osp.join(plugins_dir, plugin_name) - if osp.isfile(p) and p.endswith('.py'): - plugins.append((plugins_dir, plugin_name, None)) - elif osp.isdir(p): - plugins += [(plugins_dir, - osp.splitext(plugin_name)[0] + '.' + osp.basename(p), - osp.splitext(plugin_name)[0] - ) - for p in glob(osp.join(p, '*.py'))] - return plugins - - @classmethod - def _import_module(cls, module_dir, module_name, types, package=None): - module = import_foreign_module(osp.splitext(module_name)[0], module_dir, - package=package) - - exports = [] - if hasattr(module, 'exports'): - exports = module.exports - else: - for symbol in dir(module): - if symbol.startswith('_'): - continue - exports.append(getattr(module, symbol)) - - exports = [s for s in exports - if inspect.isclass(s) and issubclass(s, types) and not s in types] - - return exports - - @classmethod - def _load_plugins(cls, plugins_dir, types): - types = tuple(types) - - plugins = cls._find_plugins(plugins_dir) - - all_exports = [] - for module_dir, module_name, package in plugins: - try: - exports = cls._import_module(module_dir, module_name, types, - package) - except Exception as e: - module_search_error = ImportError - try: - module_search_error = ModuleNotFoundError # python 3.6+ - except NameError: - pass - - message = ["Failed to import module '%s': %s", module_name, e] - if isinstance(e, module_search_error): - log.debug(*message) - else: - log.warning(*message) - continue - - log.debug("Imported the following symbols from %s: %s" % \ - ( - module_name, - ', '.join(s.__name__ for s in exports) - ) - ) - all_exports.extend(exports) - - return all_exports - - @classmethod - def _load_builtin_plugins(cls): - if not cls._builtin_plugins: - plugins_dir = osp.join( - __file__[: __file__.rfind(osp.join('datumaro', 'components'))], - osp.join('datumaro', 'plugins') - ) - assert osp.isdir(plugins_dir), plugins_dir - cls._builtin_plugins = cls._load_plugins2(plugins_dir) - return cls._builtin_plugins - - @classmethod - def _load_plugins2(cls, plugins_dir): - from datumaro.components.extractor import Transform - from datumaro.components.extractor import SourceExtractor - from datumaro.components.extractor import Importer - from datumaro.components.converter import Converter - from datumaro.components.launcher import Launcher - types = [SourceExtractor, Converter, Importer, Launcher, Transform] - - return cls._load_plugins(plugins_dir, types) - - def make_extractor(self, name, *args, **kwargs): - return self.extractors.get(name)(*args, **kwargs) - - def make_importer(self, name, *args, **kwargs): - return self.importers.get(name)(*args, **kwargs) - - def make_launcher(self, name, *args, **kwargs): - return self.launchers.get(name)(*args, **kwargs) - - def make_converter(self, name, *args, **kwargs): - return self.converters.get(name)(*args, **kwargs) - - def register_model(self, name, model): - self.models.register(name, model) - - def unregister_model(self, name): - self.models.unregister(name) - - -class Dataset(Extractor): - class Subset(Extractor): - def __init__(self, parent): - self.parent = parent - self.items = OrderedDict() - - def __iter__(self): - yield from self.items.values() - - def __len__(self): - return len(self.items) - - def categories(self): - return self.parent.categories() - - @classmethod - def from_iterable(cls, iterable: Iterable[DatasetItem], - categories: Union[Dict, List[str]] = None): - if isinstance(categories, list): - categories = { AnnotationType.label: - LabelCategories.from_iterable(categories) - } - - if not categories: - categories = {} - - class _extractor(Extractor): - def __iter__(self): - return iter(iterable) - - def categories(self): - return categories - - return cls.from_extractors(_extractor()) - - @classmethod - def from_extractors(cls, *sources): - categories = cls._merge_categories(s.categories() for s in sources) - dataset = Dataset(categories=categories) - - # merge items - subsets = defaultdict(lambda: cls.Subset(dataset)) - for source in sources: - for item in source: - existing_item = subsets[item.subset].items.get(item.id) - if existing_item is not None: - path = existing_item.path - if item.path != path: - path = None - item = cls._merge_items(existing_item, item, path=path) - - subsets[item.subset].items[item.id] = item - - dataset._subsets = dict(subsets) - return dataset - - def __init__(self, categories=None): - super().__init__() - - self._subsets = {} - - if not categories: - categories = {} - self._categories = categories - - def __iter__(self): - for subset in self._subsets.values(): - for item in subset: - yield item - - def __len__(self): - if self._length is None: - self._length = reduce(lambda s, x: s + len(x), - self._subsets.values(), 0) - return self._length - - def get_subset(self, name): - return self._subsets[name] - - def subsets(self): - return list(self._subsets) - - def categories(self): - return self._categories - - def get(self, item_id, subset=None, path=None): - if path: - raise KeyError("Requested dataset item path is not found") - item_id = str(item_id) - subset = subset or '' - subset = self._subsets[subset] - return subset.items[item_id] - - def put(self, item, item_id=None, subset=None, path=None): - if path: - raise KeyError("Requested dataset item path is not found") - - if item_id is None: - item_id = item.id - if subset is None: - subset = item.subset - - item = item.wrap(id=item_id, subset=subset, path=None) - if subset not in self._subsets: - self._subsets[subset] = self.Subset(self) - self._subsets[subset].items[item_id] = item - self._length = None - - return item - - def filter(self, expr, filter_annotations=False, remove_empty=False): - if filter_annotations: - return self.transform(XPathAnnotationsFilter, expr, remove_empty) - else: - return self.transform(XPathDatasetFilter, expr) - - def update(self, items): - for item in items: - self.put(item) - return self - - def define_categories(self, categories): - assert not self._categories - self._categories = categories - - @staticmethod - def _lazy_image(item): - # NOTE: avoid https://docs.python.org/3/faq/programming.html#why-do-lambdas-defined-in-a-loop-with-different-values-all-return-the-same-result - return lambda: item.image - - @classmethod - def _merge_items(cls, existing_item, current_item, path=None): - return existing_item.wrap(path=path, - image=cls._merge_images(existing_item, current_item), - annotations=cls._merge_anno( - existing_item.annotations, current_item.annotations)) - - @staticmethod - def _merge_images(existing_item, current_item): - image = None - if existing_item.has_image and current_item.has_image: - if existing_item.image.has_data: - image = existing_item.image - else: - image = current_item.image - - if existing_item.image.path != current_item.image.path: - if not existing_item.image.path: - image._path = current_item.image.path - - if all([existing_item.image._size, current_item.image._size]): - assert existing_item.image._size == current_item.image._size, "Image info differs for item '%s'" % existing_item.id - elif existing_item.image._size: - image._size = existing_item.image._size - else: - image._size = current_item.image._size - elif existing_item.has_image: - image = existing_item.image - else: - image = current_item.image - - return image - - @staticmethod - def _merge_anno(a, b): - # TODO: implement properly with merging and annotations remapping - from .operations import merge_annotations_equal - return merge_annotations_equal(a, b) - - @staticmethod - def _merge_categories(sources): - # TODO: implement properly with merging and annotations remapping - from .operations import merge_categories - return merge_categories(sources) - -class ProjectDataset(Dataset): - def __init__(self, project): - super().__init__() - - self._project = project - config = self.config - env = self.env - - sources = {} - for s_name, source in config.sources.items(): - s_format = source.format or env.PROJECT_EXTRACTOR_NAME - options = {} - options.update(source.options) - - url = source.url - if not source.url: - url = osp.join(config.project_dir, config.sources_dir, s_name) - sources[s_name] = env.make_extractor(s_format, url, **options) - self._sources = sources - - own_source = None - own_source_dir = osp.join(config.project_dir, config.dataset_dir) - if config.project_dir and osp.isdir(own_source_dir): - log.disable(log.INFO) - own_source = env.make_importer(DEFAULT_FORMAT)(own_source_dir) \ - .make_dataset() - log.disable(log.NOTSET) - - # merge categories - # TODO: implement properly with merging and annotations remapping - categories = self._merge_categories(s.categories() - for s in self._sources.values()) - # ovewrite with own categories - if own_source is not None and (not categories or len(own_source) != 0): - categories.update(own_source.categories()) - self._categories = categories - - # merge items - subsets = defaultdict(lambda: self.Subset(self)) - for source_name, source in self._sources.items(): - log.debug("Loading '%s' source contents..." % source_name) - for item in source: - existing_item = subsets[item.subset].items.get(item.id) - if existing_item is not None: - path = existing_item.path - if item.path != path: - path = None # NOTE: move to our own dataset - item = self._merge_items(existing_item, item, path=path) - else: - s_config = config.sources[source_name] - if s_config and \ - s_config.format != env.PROJECT_EXTRACTOR_NAME: - # NOTE: consider imported sources as our own dataset - path = None - else: - path = [source_name] + (item.path or []) - item = item.wrap(path=path) - - subsets[item.subset].items[item.id] = item - - # override with our items, fallback to existing images - if own_source is not None: - log.debug("Loading own dataset...") - for item in own_source: - existing_item = subsets[item.subset].items.get(item.id) - if existing_item is not None: - item = item.wrap(path=None, - image=self._merge_images(existing_item, item)) - - subsets[item.subset].items[item.id] = item - - # TODO: implement subset remapping when needed - subsets_filter = config.subsets - if len(subsets_filter) != 0: - subsets = { k: v for k, v in subsets.items() if k in subsets_filter} - self._subsets = dict(subsets) - - self._length = None - - def iterate_own(self): - return self.select(lambda item: not item.path) - - def get(self, item_id, subset=None, path=None): - if path: - source = path[0] - rest_path = path[1:] - return self._sources[source].get( - item_id=item_id, subset=subset, path=rest_path) - return super().get(item_id, subset) - - def put(self, item, item_id=None, subset=None, path=None): - if path is None: - path = item.path - - if path: - source = path[0] - rest_path = path[1:] - # TODO: reverse remapping - self._sources[source].put(item, - item_id=item_id, subset=subset, path=rest_path) - - if item_id is None: - item_id = item.id - if subset is None: - subset = item.subset - - item = item.wrap(path=path) - if subset not in self._subsets: - self._subsets[subset] = self.Subset(self) - self._subsets[subset].items[item_id] = item - self._length = None - - return item - - def save(self, save_dir=None, merge=False, recursive=True, - save_images=False): - if save_dir is None: - assert self.config.project_dir - save_dir = self.config.project_dir - project = self._project - else: - merge = True - - if merge: - project = Project(Config(self.config)) - project.config.remove('sources') - - save_dir = osp.abspath(save_dir) - dataset_save_dir = osp.join(save_dir, project.config.dataset_dir) - - converter_kwargs = { - 'save_images': save_images, - } - - save_dir_existed = osp.exists(save_dir) - try: - os.makedirs(save_dir, exist_ok=True) - os.makedirs(dataset_save_dir, exist_ok=True) - - if merge: - # merge and save the resulting dataset - self.env.converters.get(DEFAULT_FORMAT).convert( - self, dataset_save_dir, **converter_kwargs) - else: - if recursive: - # children items should already be updated - # so we just save them recursively - for source in self._sources.values(): - if isinstance(source, ProjectDataset): - source.save(**converter_kwargs) - - self.env.converters.get(DEFAULT_FORMAT).convert( - self.iterate_own(), dataset_save_dir, **converter_kwargs) - - project.save(save_dir) - except BaseException: - if not save_dir_existed and osp.isdir(save_dir): - shutil.rmtree(save_dir, ignore_errors=True) - raise - - @property - def env(self): - return self._project.env - - @property - def config(self): - return self._project.config - - @property - def sources(self): - return self._sources - - def _save_branch_project(self, extractor, save_dir=None): - extractor = Dataset.from_extractors(extractor) # apply lazy transforms - - # NOTE: probably this function should be in the ViewModel layer - save_dir = osp.abspath(save_dir) - if save_dir: - dst_project = Project() - else: - if not self.config.project_dir: - raise Exception("Either a save directory or a project " - "directory should be specified") - save_dir = self.config.project_dir - - dst_project = Project(Config(self.config)) - dst_project.config.remove('project_dir') - dst_project.config.remove('sources') - dst_project.config.project_name = osp.basename(save_dir) - - dst_dataset = dst_project.make_dataset() - dst_dataset.define_categories(extractor.categories()) - dst_dataset.update(extractor) - - dst_dataset.save(save_dir=save_dir, merge=True) - - def transform_project(self, method, save_dir=None, **method_kwargs): - # NOTE: probably this function should be in the ViewModel layer - if isinstance(method, str): - method = self.env.make_transform(method) - - transformed = self.transform(method, **method_kwargs) - self._save_branch_project(transformed, save_dir=save_dir) - - def apply_model(self, model, save_dir=None, batch_size=1): - # NOTE: probably this function should be in the ViewModel layer - if isinstance(model, str): - launcher = self._project.make_executable_model(model) - - self.transform_project(ModelTransform, launcher=launcher, - save_dir=save_dir, batch_size=batch_size) - - def export_project(self, save_dir, converter, - filter_expr=None, filter_annotations=False, remove_empty=False): - # NOTE: probably this function should be in the ViewModel layer - dataset = self - if filter_expr: - dataset = dataset.filter(filter_expr, - filter_annotations=filter_annotations, - remove_empty=remove_empty) - - save_dir = osp.abspath(save_dir) - save_dir_existed = osp.exists(save_dir) - try: - os.makedirs(save_dir, exist_ok=True) - converter(dataset, save_dir) - except BaseException: - if not save_dir_existed: - shutil.rmtree(save_dir) - raise - - def filter_project(self, filter_expr, filter_annotations=False, - save_dir=None, remove_empty=False): - # NOTE: probably this function should be in the ViewModel layer - dataset = self - if filter_expr: - dataset = dataset.filter(filter_expr, - filter_annotations=filter_annotations, - remove_empty=remove_empty) - self._save_branch_project(dataset, save_dir=save_dir) - -class Project: - @classmethod - def load(cls, path): - path = osp.abspath(path) - config_path = osp.join(path, PROJECT_DEFAULT_CONFIG.env_dir, - PROJECT_DEFAULT_CONFIG.project_filename) - config = Config.parse(config_path) - config.project_dir = path - config.project_filename = osp.basename(config_path) - return Project(config) - - def save(self, save_dir=None): - config = self.config - - if save_dir is None: - assert config.project_dir - project_dir = config.project_dir - else: - project_dir = save_dir - - env_dir = osp.join(project_dir, config.env_dir) - save_dir = osp.abspath(env_dir) - - project_dir_existed = osp.exists(project_dir) - env_dir_existed = osp.exists(env_dir) - try: - os.makedirs(save_dir, exist_ok=True) - - config_path = osp.join(save_dir, config.project_filename) - config.dump(config_path) - except BaseException: - if not env_dir_existed: - shutil.rmtree(save_dir, ignore_errors=True) - if not project_dir_existed: - shutil.rmtree(project_dir, ignore_errors=True) - raise - - @staticmethod - def generate(save_dir, config=None): - config = Config(config) - config.project_dir = save_dir - project = Project(config) - project.save(save_dir) - return project - - @staticmethod - def import_from(path, dataset_format, env=None, **kwargs): - if env is None: - env = Environment() - importer = env.make_importer(dataset_format) - return importer(path, **kwargs) - - def __init__(self, config=None): - self.config = Config(config, - fallback=PROJECT_DEFAULT_CONFIG, schema=PROJECT_SCHEMA) - self.env = Environment(self.config) - - def make_dataset(self): - return ProjectDataset(self) - - def add_source(self, name, value=None): - if value is None or isinstance(value, (dict, Config)): - value = Source(value) - self.config.sources[name] = value - self.env.sources.register(name, value) - - def remove_source(self, name): - self.config.sources.remove(name) - self.env.sources.unregister(name) - - def get_source(self, name): - try: - return self.config.sources[name] - except KeyError: - raise KeyError("Source '%s' is not found" % name) - - def get_subsets(self): - return self.config.subsets - - def set_subsets(self, value): - if not value: - self.config.remove('subsets') - else: - self.config.subsets = value - - def add_model(self, name, value=None): - if value is None or isinstance(value, (dict, Config)): - value = Model(value) - self.env.register_model(name, value) - self.config.models[name] = value - - def get_model(self, name): - try: - return self.env.models.get(name) - except KeyError: - raise KeyError("Model '%s' is not found" % name) - - def remove_model(self, name): - self.config.models.remove(name) - self.env.unregister_model(name) - - def make_executable_model(self, name): - model = self.get_model(name) - return self.env.make_launcher(model.launcher, - **model.options, model_dir=self.local_model_dir(name)) - - def make_source_project(self, name): - source = self.get_source(name) - - config = Config(self.config) - config.remove('sources') - config.remove('subsets') - project = Project(config) - project.add_source(name, source) - return project - - def local_model_dir(self, model_name): - return osp.join( - self.config.env_dir, self.config.models_dir, model_name) - - def local_source_dir(self, source_name): - return osp.join(self.config.sources_dir, source_name) - -# pylint: disable=function-redefined -def load_project_as_dataset(url): - # implement the function declared above - return Project.load(url).make_dataset() -# pylint: enable=function-redefined diff --git a/datumaro/datumaro/plugins/__init__.py b/datumaro/datumaro/plugins/__init__.py deleted file mode 100644 index e69de29b..00000000 diff --git a/datumaro/datumaro/plugins/accuracy_checker_plugin/__init__.py b/datumaro/datumaro/plugins/accuracy_checker_plugin/__init__.py deleted file mode 100644 index fdd6d291..00000000 --- a/datumaro/datumaro/plugins/accuracy_checker_plugin/__init__.py +++ /dev/null @@ -1,4 +0,0 @@ -# Copyright (C) 2020 Intel Corporation -# -# SPDX-License-Identifier: MIT - diff --git a/datumaro/datumaro/plugins/accuracy_checker_plugin/details/ac.py b/datumaro/datumaro/plugins/accuracy_checker_plugin/details/ac.py deleted file mode 100644 index 4fc2ffb5..00000000 --- a/datumaro/datumaro/plugins/accuracy_checker_plugin/details/ac.py +++ /dev/null @@ -1,116 +0,0 @@ - -# Copyright (C) 2020 Intel Corporation -# -# SPDX-License-Identifier: MIT - -from datumaro.util.tf_util import import_tf -import_tf() # prevent TF loading and potential interpeter crash - -from itertools import groupby - -from accuracy_checker.adapters import create_adapter -from accuracy_checker.data_readers import DataRepresentation -from accuracy_checker.launcher import InputFeeder, create_launcher -from accuracy_checker.postprocessor import PostprocessingExecutor -from accuracy_checker.preprocessor import PreprocessingExecutor -from accuracy_checker.utils import extract_image_representations - -from datumaro.components.extractor import AnnotationType, LabelCategories - -from .representation import import_predictions - - -class _FakeDataset: - def __init__(self, metadata=None): - self.metadata = metadata or {} - -class GenericAcLauncher: - @staticmethod - def from_config(config): - launcher_config = config['launcher'] - launcher = create_launcher(launcher_config) - - dataset = _FakeDataset() - adapter_config = config.get('adapter') or launcher_config.get('adapter') - label_config = adapter_config.get('labels') \ - if isinstance(adapter_config, dict) else None - if label_config: - assert isinstance(label_config, (list, dict)) - if isinstance(label_config, list): - label_config = dict(enumerate(label_config)) - - dataset.metadata = {'label_map': { - int(key): label for key, label in label_config.items() - }} - adapter = create_adapter(adapter_config, launcher, dataset) - - preproc_config = config.get('preprocessing') - preproc = None - if preproc_config: - preproc = PreprocessingExecutor(preproc_config, - dataset_meta=dataset.metadata, - input_shapes=launcher.inputs_info_for_meta() - ) - - postproc_config = config.get('postprocessing') - postproc = None - if postproc_config: - postproc = PostprocessingExecutor(postproc_config, - dataset_meta=dataset.metadata, - ) - - return __class__(launcher, - adapter=adapter, preproc=preproc, postproc=postproc) - - def __init__(self, launcher, adapter=None, - preproc=None, postproc=None, input_feeder=None): - self._launcher = launcher - self._input_feeder = input_feeder or InputFeeder( - launcher.config.get('inputs', []), launcher.inputs, - launcher.fit_to_input, launcher.default_layout - ) - self._adapter = adapter - self._preproc = preproc - self._postproc = postproc - - self._categories = self._init_categories() - - def launch_raw(self, inputs): - ids = range(len(inputs)) - inputs = [DataRepresentation(inp, identifier=id) - for id, inp in zip(ids, inputs)] - _, batch_meta = extract_image_representations(inputs) - - if self._preproc: - inputs = self._preproc.process(inputs) - - inputs = self._input_feeder.fill_inputs(inputs) - outputs = self._launcher.predict(inputs, batch_meta) - - if self._adapter: - outputs = self._adapter.process(outputs, ids, batch_meta) - - if self._postproc: - outputs = self._postproc.process(outputs) - - return outputs - - def launch(self, inputs): - outputs = self.launch_raw(inputs) - return [import_predictions(g) for _, g in - groupby(outputs, key=lambda o: o.identifier)] - - def categories(self): - return self._categories - - def _init_categories(self): - if self._adapter is None or self._adapter.label_map is None: - return None - - label_map = sorted(self._adapter.label_map.items(), key=lambda e: e[0]) - - label_cat = LabelCategories() - for _, label in label_map: - label_cat.add(label) - - return { AnnotationType.label: label_cat } diff --git a/datumaro/datumaro/plugins/accuracy_checker_plugin/details/representation.py b/datumaro/datumaro/plugins/accuracy_checker_plugin/details/representation.py deleted file mode 100644 index d7007806..00000000 --- a/datumaro/datumaro/plugins/accuracy_checker_plugin/details/representation.py +++ /dev/null @@ -1,62 +0,0 @@ - -# Copyright (C) 2020 Intel Corporation -# -# SPDX-License-Identifier: MIT - -from datumaro.util.tf_util import import_tf -import_tf() # prevent TF loading and potential interpeter crash - -import accuracy_checker.representation as ac - -import datumaro.components.extractor as dm -from datumaro.util.annotation_util import softmax - -def import_predictions(predictions): - # Convert Accuracy checker predictions to Datumaro annotations - - anns = [] - - for pred in predictions: - anns.extend(import_prediction(pred)) - - return anns - -def import_prediction(pred): - if isinstance(pred, ac.ClassificationPrediction): - scores = softmax(pred.scores) - return (dm.Label(label_id, attributes={'score': float(score)}) - for label_id, score in enumerate(scores)) - elif isinstance(pred, ac.ArgMaxClassificationPrediction): - return (dm.Label(int(pred.label)), ) - elif isinstance(pred, ac.CharacterRecognitionPrediction): - return (dm.Label(int(pred.label)), ) - elif isinstance(pred, (ac.DetectionPrediction, ac.ActionDetectionPrediction)): - return (dm.Bbox(x0, y0, x1 - x0, y1 - y0, int(label_id), - attributes={'score': float(score)}) - for label, score, x0, y0, x1, y1 in zip(pred.labels, pred.scores, - pred.x_mins, pred.y_mins, pred.x_maxs, pred.y_maxs) - ) - elif isinstance(pred, ac.DepthEstimationPrediction): - return (dm.Mask(pred.depth_map), ) # 2d floating point mask - # elif isinstance(pred, ac.HitRatioPrediction): - # - - elif isinstance(pred, ac.ImageInpaintingPrediction): - return (dm.Mask(pred.value), ) # an image - # elif isinstance(pred, ac.MultiLabelRecognitionPrediction): - # - - # elif isinstance(pred, ac.MachineTranslationPrediction): - # - - # elif isinstance(pred, ac.QuestionAnsweringPrediction): - # - - # elif isinstance(pred, ac.PoseEstimation3dPrediction): - # - - # elif isinstance(pred, ac.PoseEstimationPrediction): - # - - # elif isinstance(pred, ac.RegressionPrediction): - # - - else: - raise NotImplementedError("Can't convert %s" % type(pred)) - - - - diff --git a/datumaro/datumaro/plugins/accuracy_checker_plugin/launcher.py b/datumaro/datumaro/plugins/accuracy_checker_plugin/launcher.py deleted file mode 100644 index 15251108..00000000 --- a/datumaro/datumaro/plugins/accuracy_checker_plugin/launcher.py +++ /dev/null @@ -1,37 +0,0 @@ - -# Copyright (C) 2020 Intel Corporation -# -# SPDX-License-Identifier: MIT - -import os.path as osp -import yaml - -from datumaro.components.cli_plugin import CliPlugin -from datumaro.components.launcher import Launcher - -from .details.ac import GenericAcLauncher as _GenericAcLauncher - - -class AcLauncher(Launcher, CliPlugin): - """ - Generic model launcher with Accuracy Checker backend. - """ - - @classmethod - def build_cmdline_parser(cls, **kwargs): - parser = super().build_cmdline_parser(**kwargs) - parser.add_argument('-c', '--config', type=osp.abspath, required=True, - help="Path to the launcher configuration file (.yml)") - return parser - - def __init__(self, config, model_dir=None): - model_dir = model_dir or '' - with open(osp.join(model_dir, config), 'r') as f: - config = yaml.safe_load(f) - self._launcher = _GenericAcLauncher.from_config(config) - - def launch(self, inputs): - return self._launcher.launch(inputs) - - def categories(self): - return self._launcher.categories() diff --git a/datumaro/datumaro/plugins/coco_format/__init__.py b/datumaro/datumaro/plugins/coco_format/__init__.py deleted file mode 100644 index e69de29b..00000000 diff --git a/datumaro/datumaro/plugins/coco_format/converter.py b/datumaro/datumaro/plugins/coco_format/converter.py deleted file mode 100644 index 27cdd087..00000000 --- a/datumaro/datumaro/plugins/coco_format/converter.py +++ /dev/null @@ -1,596 +0,0 @@ - -# Copyright (C) 2020 Intel Corporation -# -# SPDX-License-Identifier: MIT - -import json -import logging as log -import os -import os.path as osp -from enum import Enum -from itertools import groupby - -import pycocotools.mask as mask_utils - -import datumaro.util.annotation_util as anno_tools -import datumaro.util.mask_tools as mask_tools -from datumaro.components.converter import Converter -from datumaro.components.extractor import (_COORDINATE_ROUNDING_DIGITS, - DEFAULT_SUBSET_NAME, AnnotationType, Points) -from datumaro.util import cast, find, str_to_bool - -from .format import CocoPath, CocoTask - -SegmentationMode = Enum('SegmentationMode', ['guess', 'polygons', 'mask']) - -class _TaskConverter: - def __init__(self, context): - self._min_ann_id = 1 - self._context = context - - data = { - 'licenses': [], - 'info': {}, - 'categories': [], - 'images': [], - 'annotations': [] - } - - data['licenses'].append({ - 'name': '', - 'id': 0, - 'url': '' - }) - - data['info'] = { - 'contributor': '', - 'date_created': '', - 'description': '', - 'url': '', - 'version': '', - 'year': '' - } - self._data = data - - def is_empty(self): - return len(self._data['annotations']) == 0 - - def _get_image_id(self, item): - return self._context._get_image_id(item) - - def save_image_info(self, item, filename): - if item.has_image: - h, w = item.image.size - else: - h = 0 - w = 0 - - self._data['images'].append({ - 'id': self._get_image_id(item), - 'width': int(w), - 'height': int(h), - 'file_name': cast(filename, str, ''), - 'license': 0, - 'flickr_url': '', - 'coco_url': '', - 'date_captured': 0, - }) - - def save_categories(self, dataset): - raise NotImplementedError() - - def save_annotations(self, item): - raise NotImplementedError() - - def write(self, path): - next_id = self._min_ann_id - for ann in self.annotations: - if ann['id'] is None: - ann['id'] = next_id - next_id += 1 - - with open(path, 'w') as outfile: - json.dump(self._data, outfile) - - @property - def annotations(self): - return self._data['annotations'] - - @property - def categories(self): - return self._data['categories'] - - def _get_ann_id(self, annotation): - ann_id = annotation.id - if ann_id: - self._min_ann_id = max(ann_id, self._min_ann_id) - return ann_id - - @staticmethod - def _convert_attributes(ann): - return { k: v for k, v in ann.attributes.items() - if k not in {'is_crowd', 'score'} - } - -class _ImageInfoConverter(_TaskConverter): - def is_empty(self): - return len(self._data['images']) == 0 - - def save_categories(self, dataset): - pass - - def save_annotations(self, item): - pass - -class _CaptionsConverter(_TaskConverter): - def save_categories(self, dataset): - pass - - def save_annotations(self, item): - for ann_idx, ann in enumerate(item.annotations): - if ann.type != AnnotationType.caption: - continue - - elem = { - 'id': self._get_ann_id(ann), - 'image_id': self._get_image_id(item), - 'category_id': 0, # NOTE: workaround for a bug in cocoapi - 'caption': ann.caption, - } - if 'score' in ann.attributes: - try: - elem['score'] = float(ann.attributes['score']) - except Exception as e: - log.warning("Item '%s', ann #%s: failed to convert " - "attribute 'score': %e" % (item.id, ann_idx, e)) - if self._context._allow_attributes: - elem['attributes'] = self._convert_attributes(ann) - - self.annotations.append(elem) - -class _InstancesConverter(_TaskConverter): - def save_categories(self, dataset): - label_categories = dataset.categories().get(AnnotationType.label) - if label_categories is None: - return - - for idx, cat in enumerate(label_categories.items): - self.categories.append({ - 'id': 1 + idx, - 'name': cast(cat.name, str, ''), - 'supercategory': cast(cat.parent, str, ''), - }) - - @classmethod - def crop_segments(cls, instances, img_width, img_height): - instances = sorted(instances, key=lambda x: x[0].z_order) - - segment_map = [] - segments = [] - for inst_idx, (_, polygons, mask, _) in enumerate(instances): - if polygons: - segment_map.extend(inst_idx for p in polygons) - segments.extend(polygons) - elif mask is not None: - segment_map.append(inst_idx) - segments.append(mask) - - segments = mask_tools.crop_covered_segments( - segments, img_width, img_height) - - for inst_idx, inst in enumerate(instances): - new_segments = [s for si_id, s in zip(segment_map, segments) - if si_id == inst_idx] - - if not new_segments: - inst[1] = [] - inst[2] = None - continue - - if inst[1]: - inst[1] = sum(new_segments, []) - else: - mask = mask_tools.merge_masks(new_segments) - inst[2] = mask_tools.mask_to_rle(mask) - - return instances - - def find_instance_parts(self, group, img_width, img_height): - boxes = [a for a in group if a.type == AnnotationType.bbox] - polygons = [a for a in group if a.type == AnnotationType.polygon] - masks = [a for a in group if a.type == AnnotationType.mask] - - anns = boxes + polygons + masks - leader = anno_tools.find_group_leader(anns) - bbox = anno_tools.max_bbox(anns) - mask = None - polygons = [p.points for p in polygons] - - if self._context._segmentation_mode == SegmentationMode.guess: - use_masks = True == leader.attributes.get('is_crowd', - find(masks, lambda x: x.label == leader.label) is not None) - elif self._context._segmentation_mode == SegmentationMode.polygons: - use_masks = False - elif self._context._segmentation_mode == SegmentationMode.mask: - use_masks = True - else: - raise NotImplementedError("Unexpected segmentation mode '%s'" % \ - self._context._segmentation_mode) - - if use_masks: - if polygons: - mask = mask_tools.rles_to_mask(polygons, img_width, img_height) - - if masks: - if mask is not None: - masks += [mask] - mask = mask_tools.merge_masks([m.image for m in masks]) - - if mask is not None: - mask = mask_tools.mask_to_rle(mask) - polygons = [] - else: - if masks: - mask = mask_tools.merge_masks([m.image for m in masks]) - polygons += mask_tools.mask_to_polygons(mask) - mask = None - - return [leader, polygons, mask, bbox] - - @staticmethod - def find_instance_anns(annotations): - return [a for a in annotations - if a.type in { AnnotationType.bbox, - AnnotationType.polygon, AnnotationType.mask } - ] - - @classmethod - def find_instances(cls, annotations): - return anno_tools.find_instances(cls.find_instance_anns(annotations)) - - def save_annotations(self, item): - instances = self.find_instances(item.annotations) - if not instances: - return - - if not item.has_image: - log.warn("Item '%s': skipping writing instances " - "since no image info available" % item.id) - return - h, w = item.image.size - instances = [self.find_instance_parts(i, w, h) for i in instances] - - if self._context._crop_covered: - instances = self.crop_segments(instances, w, h) - - for instance in instances: - elem = self.convert_instance(instance, item) - if elem: - self.annotations.append(elem) - - def convert_instance(self, instance, item): - ann, polygons, mask, bbox = instance - - is_crowd = mask is not None - if is_crowd: - segmentation = { - 'counts': list(int(c) for c in mask['counts']), - 'size': list(int(c) for c in mask['size']) - } - else: - segmentation = [list(map(float, p)) for p in polygons] - - area = 0 - if segmentation: - if item.has_image: - h, w = item.image.size - else: - # NOTE: here we can guess the image size as - # it is only needed for the area computation - w = bbox[0] + bbox[2] - h = bbox[1] + bbox[3] - - rles = mask_utils.frPyObjects(segmentation, h, w) - if is_crowd: - rles = [rles] - else: - rles = mask_utils.merge(rles) - area = mask_utils.area(rles) - else: - _, _, w, h = bbox - segmentation = [] - area = w * h - - elem = { - 'id': self._get_ann_id(ann), - 'image_id': self._get_image_id(item), - 'category_id': cast(ann.label, int, -1) + 1, - 'segmentation': segmentation, - 'area': float(area), - 'bbox': [round(float(n), _COORDINATE_ROUNDING_DIGITS) for n in bbox], - 'iscrowd': int(is_crowd), - } - if 'score' in ann.attributes: - try: - elem['score'] = float(ann.attributes['score']) - except Exception as e: - log.warning("Item '%s': failed to convert attribute " - "'score': %e" % (item.id, e)) - if self._context._allow_attributes: - elem['attributes'] = self._convert_attributes(ann) - - return elem - -class _KeypointsConverter(_InstancesConverter): - def save_categories(self, dataset): - label_categories = dataset.categories().get(AnnotationType.label) - if label_categories is None: - return - point_categories = dataset.categories().get(AnnotationType.points) - - for idx, label_cat in enumerate(label_categories.items): - cat = { - 'id': 1 + idx, - 'name': cast(label_cat.name, str, ''), - 'supercategory': cast(label_cat.parent, str, ''), - 'keypoints': [], - 'skeleton': [], - } - - if point_categories is not None: - kp_cat = point_categories.items.get(idx) - if kp_cat is not None: - cat.update({ - 'keypoints': [str(l) for l in kp_cat.labels], - 'skeleton': [list(map(int, j)) for j in kp_cat.joints], - }) - self.categories.append(cat) - - def save_annotations(self, item): - point_annotations = [a for a in item.annotations - if a.type == AnnotationType.points] - if not point_annotations: - return - - # Create annotations for solitary keypoints annotations - for points in self.find_solitary_points(item.annotations): - instance = [points, [], None, points.get_bbox()] - elem = super().convert_instance(instance, item) - elem.update(self.convert_points_object(points)) - self.annotations.append(elem) - - # Create annotations for complete instance + keypoints annotations - super().save_annotations(item) - - @classmethod - def find_solitary_points(cls, annotations): - annotations = sorted(annotations, key=lambda a: a.group) - solitary_points = [] - - for g_id, group in groupby(annotations, lambda a: a.group): - if not g_id or g_id and not cls.find_instance_anns(group): - group = [a for a in group if a.type == AnnotationType.points] - solitary_points.extend(group) - - return solitary_points - - @staticmethod - def convert_points_object(ann): - keypoints = [] - points = ann.points - visibility = ann.visibility - for index in range(0, len(points), 2): - kp = points[index : index + 2] - state = visibility[index // 2].value - keypoints.extend([*kp, state]) - - num_annotated = len([v for v in visibility \ - if v != Points.Visibility.absent]) - - return { - 'keypoints': keypoints, - 'num_keypoints': num_annotated, - } - - def convert_instance(self, instance, item): - points_ann = find(item.annotations, lambda x: \ - x.type == AnnotationType.points and \ - instance[0].group and x.group == instance[0].group) - if not points_ann: - return None - - elem = super().convert_instance(instance, item) - elem.update(self.convert_points_object(points_ann)) - - return elem - -class _LabelsConverter(_TaskConverter): - def save_categories(self, dataset): - label_categories = dataset.categories().get(AnnotationType.label) - if label_categories is None: - return - - for idx, cat in enumerate(label_categories.items): - self.categories.append({ - 'id': 1 + idx, - 'name': cast(cat.name, str, ''), - 'supercategory': cast(cat.parent, str, ''), - }) - - def save_annotations(self, item): - for ann in item.annotations: - if ann.type != AnnotationType.label: - continue - - elem = { - 'id': self._get_ann_id(ann), - 'image_id': self._get_image_id(item), - 'category_id': int(ann.label) + 1, - } - if 'score' in ann.attributes: - try: - elem['score'] = float(ann.attributes['score']) - except Exception as e: - log.warning("Item '%s': failed to convert attribute " - "'score': %e" % (item.id, e)) - if self._context._allow_attributes: - elem['attributes'] = self._convert_attributes(ann) - - self.annotations.append(elem) - -class CocoConverter(Converter): - @staticmethod - def _split_tasks_string(s): - return [CocoTask[i.strip()] for i in s.split(',')] - - @classmethod - def build_cmdline_parser(cls, **kwargs): - parser = super().build_cmdline_parser(**kwargs) - parser.add_argument('--segmentation-mode', - choices=[m.name for m in SegmentationMode], - default=SegmentationMode.guess.name, - help=""" - Save mode for instance segmentation:|n - - '{sm.guess.name}': guess the mode for each instance,|n - |s|suse 'is_crowd' attribute as hint|n - - '{sm.polygons.name}': save polygons,|n - |s|smerge and convert masks, prefer polygons|n - - '{sm.mask.name}': save masks,|n - |s|smerge and convert polygons, prefer masks|n - Default: %(default)s. - """.format(sm=SegmentationMode)) - parser.add_argument('--crop-covered', action='store_true', - help="Crop covered segments so that background objects' " - "segmentation was more accurate (default: %(default)s)") - parser.add_argument('--allow-attributes', - type=str_to_bool, default=True, - help="Allow export of attributes (default: %(default)s)") - parser.add_argument('--tasks', type=cls._split_tasks_string, - help="COCO task filter, comma-separated list of {%s} " - "(default: all)" % ', '.join(t.name for t in CocoTask)) - return parser - - DEFAULT_IMAGE_EXT = CocoPath.IMAGE_EXT - - _TASK_CONVERTER = { - CocoTask.image_info: _ImageInfoConverter, - CocoTask.instances: _InstancesConverter, - CocoTask.person_keypoints: _KeypointsConverter, - CocoTask.captions: _CaptionsConverter, - CocoTask.labels: _LabelsConverter, - } - - def __init__(self, extractor, save_dir, - tasks=None, segmentation_mode=None, crop_covered=False, - allow_attributes=True, **kwargs): - super().__init__(extractor, save_dir, **kwargs) - - assert tasks is None or isinstance(tasks, (CocoTask, list, str)) - if isinstance(tasks, CocoTask): - tasks = [tasks] - elif isinstance(tasks, str): - tasks = [CocoTask[tasks]] - elif tasks: - for i, t in enumerate(tasks): - if isinstance(t, str): - tasks[i] = CocoTask[t] - else: - assert t in CocoTask, t - self._tasks = tasks - - assert segmentation_mode is None or \ - isinstance(segmentation_mode, str) or \ - segmentation_mode in SegmentationMode - if segmentation_mode is None: - segmentation_mode = SegmentationMode.guess - if isinstance(segmentation_mode, str): - segmentation_mode = SegmentationMode[segmentation_mode] - self._segmentation_mode = segmentation_mode - - self._crop_covered = crop_covered - self._allow_attributes = allow_attributes - - self._image_ids = {} - - def _make_dirs(self): - self._images_dir = osp.join(self._save_dir, CocoPath.IMAGES_DIR) - os.makedirs(self._images_dir, exist_ok=True) - - self._ann_dir = osp.join(self._save_dir, CocoPath.ANNOTATIONS_DIR) - os.makedirs(self._ann_dir, exist_ok=True) - - def _make_task_converter(self, task): - if task not in self._TASK_CONVERTER: - raise NotImplementedError() - return self._TASK_CONVERTER[task](self) - - def _make_task_converters(self): - return { task: self._make_task_converter(task) - for task in (self._tasks or self._TASK_CONVERTER) } - - def _get_image_id(self, item): - image_id = self._image_ids.get(item.id) - if image_id is None: - image_id = cast(item.attributes.get('id'), int, - len(self._image_ids) + 1) - self._image_ids[item.id] = image_id - return image_id - - def _save_image(self, item, path=None): - super()._save_image(item, - osp.join(self._images_dir, self._make_image_filename(item))) - - def apply(self): - self._make_dirs() - - for subset_name in self._extractor.subsets() or [None]: - if subset_name: - subset = self._extractor.get_subset(subset_name) - else: - subset_name = DEFAULT_SUBSET_NAME - subset = self._extractor - - task_converters = self._make_task_converters() - for task_conv in task_converters.values(): - task_conv.save_categories(subset) - for item in subset: - if self._save_images: - if item.has_image: - self._save_image(item) - else: - log.debug("Item '%s' has no image info", item.id) - for task_conv in task_converters.values(): - task_conv.save_image_info(item, - self._make_image_filename(item)) - task_conv.save_annotations(item) - - for task, task_conv in task_converters.items(): - if task_conv.is_empty() and not self._tasks: - continue - task_conv.write(osp.join(self._ann_dir, - '%s_%s.json' % (task.name, subset_name))) - -class CocoInstancesConverter(CocoConverter): - def __init__(self, *args, **kwargs): - kwargs['tasks'] = CocoTask.instances - super().__init__(*args, **kwargs) - -class CocoImageInfoConverter(CocoConverter): - def __init__(self, *args, **kwargs): - kwargs['tasks'] = CocoTask.image_info - super().__init__(*args, **kwargs) - -class CocoPersonKeypointsConverter(CocoConverter): - def __init__(self, *args, **kwargs): - kwargs['tasks'] = CocoTask.person_keypoints - super().__init__(*args, **kwargs) - -class CocoCaptionsConverter(CocoConverter): - def __init__(self, *args, **kwargs): - kwargs['tasks'] = CocoTask.captions - super().__init__(*args, **kwargs) - -class CocoLabelsConverter(CocoConverter): - def __init__(self, *args, **kwargs): - kwargs['tasks'] = CocoTask.labels - super().__init__(*args, **kwargs) diff --git a/datumaro/datumaro/plugins/coco_format/extractor.py b/datumaro/datumaro/plugins/coco_format/extractor.py deleted file mode 100644 index 73e78820..00000000 --- a/datumaro/datumaro/plugins/coco_format/extractor.py +++ /dev/null @@ -1,261 +0,0 @@ - -# Copyright (C) 2019-2020 Intel Corporation -# -# SPDX-License-Identifier: MIT - -from collections import OrderedDict -import logging as log -import os.path as osp - -from pycocotools.coco import COCO -import pycocotools.mask as mask_utils - -from datumaro.components.extractor import (SourceExtractor, - DEFAULT_SUBSET_NAME, DatasetItem, - AnnotationType, Label, RleMask, Points, Polygon, Bbox, Caption, - LabelCategories, PointsCategories -) -from datumaro.util.image import Image - -from .format import CocoTask, CocoPath - - -class _CocoExtractor(SourceExtractor): - def __init__(self, path, task, merge_instance_polygons=False): - assert osp.isfile(path), path - - subset = osp.splitext(osp.basename(path))[0].rsplit('_', maxsplit=1) - subset = subset[1] if len(subset) == 2 else None - super().__init__(subset=subset) - - rootpath = '' - if path.endswith(osp.join(CocoPath.ANNOTATIONS_DIR, osp.basename(path))): - rootpath = path.rsplit(CocoPath.ANNOTATIONS_DIR, maxsplit=1)[0] - images_dir = '' - if rootpath and osp.isdir(osp.join(rootpath, CocoPath.IMAGES_DIR)): - images_dir = osp.join(rootpath, CocoPath.IMAGES_DIR) - if osp.isdir(osp.join(images_dir, subset or DEFAULT_SUBSET_NAME)): - images_dir = osp.join(images_dir, subset or DEFAULT_SUBSET_NAME) - self._images_dir = images_dir - self._task = task - - self._merge_instance_polygons = merge_instance_polygons - - loader = self._make_subset_loader(path) - self._load_categories(loader) - self._items = self._load_items(loader) - - def categories(self): - return self._categories - - def __iter__(self): - for item in self._items.values(): - yield item - - def __len__(self): - return len(self._items) - - @staticmethod - def _make_subset_loader(path): - # COCO API has an 'unclosed file' warning - coco_api = COCO() - with open(path, 'r') as f: - import json - dataset = json.load(f) - - coco_api.dataset = dataset - coco_api.createIndex() - return coco_api - - def _load_categories(self, loader): - self._categories = {} - - if self._task in [CocoTask.instances, CocoTask.labels, - CocoTask.person_keypoints, - # TODO: Task.stuff, CocoTask.panoptic - ]: - label_categories, label_map = self._load_label_categories(loader) - self._categories[AnnotationType.label] = label_categories - self._label_map = label_map - - if self._task == CocoTask.person_keypoints: - person_kp_categories = self._load_person_kp_categories(loader) - self._categories[AnnotationType.points] = person_kp_categories - - # pylint: disable=no-self-use - def _load_label_categories(self, loader): - catIds = loader.getCatIds() - cats = loader.loadCats(catIds) - - categories = LabelCategories() - label_map = {} - for idx, cat in enumerate(cats): - label_map[cat['id']] = idx - categories.add(name=cat['name'], parent=cat['supercategory']) - - return categories, label_map - # pylint: enable=no-self-use - - def _load_person_kp_categories(self, loader): - catIds = loader.getCatIds() - cats = loader.loadCats(catIds) - - categories = PointsCategories() - for cat in cats: - label_id = self._label_map[cat['id']] - categories.add(label_id=label_id, - labels=cat['keypoints'], joints=cat['skeleton'] - ) - - return categories - - def _load_items(self, loader): - items = OrderedDict() - - for img_id in loader.getImgIds(): - image_info = loader.loadImgs(img_id)[0] - image_path = osp.join(self._images_dir, image_info['file_name']) - image_size = (image_info.get('height'), image_info.get('width')) - if all(image_size): - image_size = (int(image_size[0]), int(image_size[1])) - else: - image_size = None - image = Image(path=image_path, size=image_size) - - anns = loader.getAnnIds(imgIds=img_id) - anns = loader.loadAnns(anns) - anns = sum((self._load_annotations(a, image_info) for a in anns), []) - - items[img_id] = DatasetItem( - id=osp.splitext(image_info['file_name'])[0], - subset=self._subset, image=image, annotations=anns, - attributes={'id': img_id}) - - return items - - def _get_label_id(self, ann): - cat_id = ann.get('category_id') - if cat_id in [0, None]: - return None - return self._label_map[cat_id] - - def _load_annotations(self, ann, image_info=None): - parsed_annotations = [] - - ann_id = ann.get('id') - - attributes = {} - if 'attributes' in ann: - try: - attributes.update(ann['attributes']) - except Exception as e: - log.debug("item #%s: failed to read annotation attributes: %s", - image_info['id'], e) - if 'score' in ann: - attributes['score'] = ann['score'] - - group = ann_id # make sure all tasks' annotations are merged - - if self._task in [CocoTask.instances, CocoTask.person_keypoints]: - x, y, w, h = ann['bbox'] - label_id = self._get_label_id(ann) - - is_crowd = bool(ann['iscrowd']) - attributes['is_crowd'] = is_crowd - - if self._task is CocoTask.person_keypoints: - keypoints = ann['keypoints'] - points = [p for i, p in enumerate(keypoints) if i % 3 != 2] - visibility = keypoints[2::3] - parsed_annotations.append( - Points(points, visibility, label=label_id, - id=ann_id, attributes=attributes, group=group) - ) - - segmentation = ann.get('segmentation') - if segmentation and segmentation != [[]]: - rle = None - - if isinstance(segmentation, list): - if not self._merge_instance_polygons: - # polygon - a single object can consist of multiple parts - for polygon_points in segmentation: - parsed_annotations.append(Polygon( - points=polygon_points, label=label_id, - id=ann_id, attributes=attributes, group=group - )) - else: - # merge all parts into a single mask RLE - img_h = image_info['height'] - img_w = image_info['width'] - rles = mask_utils.frPyObjects(segmentation, img_h, img_w) - rle = mask_utils.merge(rles) - elif isinstance(segmentation['counts'], list): - # uncompressed RLE - img_h = image_info['height'] - img_w = image_info['width'] - mask_h, mask_w = segmentation['size'] - if img_h == mask_h and img_w == mask_w: - rle = mask_utils.frPyObjects( - [segmentation], mask_h, mask_w)[0] - else: - log.warning("item #%s: mask #%s " - "does not match image size: %s vs. %s. " - "Skipping this annotation.", - image_info['id'], ann_id, - (mask_h, mask_w), (img_h, img_w) - ) - else: - # compressed RLE - rle = segmentation - - if rle is not None: - parsed_annotations.append(RleMask(rle=rle, label=label_id, - id=ann_id, attributes=attributes, group=group - )) - else: - parsed_annotations.append( - Bbox(x, y, w, h, label=label_id, - id=ann_id, attributes=attributes, group=group) - ) - elif self._task is CocoTask.labels: - label_id = self._get_label_id(ann) - parsed_annotations.append( - Label(label=label_id, - id=ann_id, attributes=attributes, group=group) - ) - elif self._task is CocoTask.captions: - caption = ann['caption'] - parsed_annotations.append( - Caption(caption, - id=ann_id, attributes=attributes, group=group) - ) - else: - raise NotImplementedError() - - return parsed_annotations - -class CocoImageInfoExtractor(_CocoExtractor): - def __init__(self, path, **kwargs): - kwargs['task'] = CocoTask.image_info - super().__init__(path, **kwargs) - -class CocoCaptionsExtractor(_CocoExtractor): - def __init__(self, path, **kwargs): - kwargs['task'] = CocoTask.captions - super().__init__(path, **kwargs) - -class CocoInstancesExtractor(_CocoExtractor): - def __init__(self, path, **kwargs): - kwargs['task'] = CocoTask.instances - super().__init__(path, **kwargs) - -class CocoPersonKeypointsExtractor(_CocoExtractor): - def __init__(self, path, **kwargs): - kwargs['task'] = CocoTask.person_keypoints - super().__init__(path, **kwargs) - -class CocoLabelsExtractor(_CocoExtractor): - def __init__(self, path, **kwargs): - kwargs['task'] = CocoTask.labels - super().__init__(path, **kwargs) diff --git a/datumaro/datumaro/plugins/coco_format/format.py b/datumaro/datumaro/plugins/coco_format/format.py deleted file mode 100644 index 5129d49d..00000000 --- a/datumaro/datumaro/plugins/coco_format/format.py +++ /dev/null @@ -1,23 +0,0 @@ - -# Copyright (C) 2019-2020 Intel Corporation -# -# SPDX-License-Identifier: MIT - -from enum import Enum - - -CocoTask = Enum('CocoTask', [ - 'instances', - 'person_keypoints', - 'captions', - 'labels', # extension, does not exist in the original COCO format - 'image_info', - # 'panoptic', - # 'stuff', -]) - -class CocoPath: - IMAGES_DIR = 'images' - ANNOTATIONS_DIR = 'annotations' - - IMAGE_EXT = '.jpg' diff --git a/datumaro/datumaro/plugins/coco_format/importer.py b/datumaro/datumaro/plugins/coco_format/importer.py deleted file mode 100644 index 3896b725..00000000 --- a/datumaro/datumaro/plugins/coco_format/importer.py +++ /dev/null @@ -1,95 +0,0 @@ - -# Copyright (C) 2019-2020 Intel Corporation -# -# SPDX-License-Identifier: MIT - -from collections import defaultdict -from glob import glob -import logging as log -import os.path as osp - -from datumaro.components.extractor import Importer -from datumaro.util.log_utils import logging_disabled - -from .format import CocoTask - - -class CocoImporter(Importer): - _COCO_EXTRACTORS = { - CocoTask.instances: 'coco_instances', - CocoTask.person_keypoints: 'coco_person_keypoints', - CocoTask.captions: 'coco_captions', - CocoTask.labels: 'coco_labels', - CocoTask.image_info: 'coco_image_info', - } - - @classmethod - def detect(cls, path): - with logging_disabled(log.WARN): - return len(cls.find_subsets(path)) != 0 - - def __call__(self, path, **extra_params): - from datumaro.components.project import Project # cyclic import - project = Project() - - subsets = self.find_subsets(path) - - if len(subsets) == 0: - raise Exception("Failed to find 'coco' dataset at '%s'" % path) - - # TODO: should be removed when proper label merging is implemented - conflicting_types = {CocoTask.instances, - CocoTask.person_keypoints, CocoTask.labels} - ann_types = set(t for s in subsets.values() for t in s) \ - & conflicting_types - if 1 <= len(ann_types): - selected_ann_type = sorted(ann_types, key=lambda x: x.name)[0] - if 1 < len(ann_types): - log.warning("Not implemented: " - "Found potentially conflicting source types with labels: %s. " - "Only one type will be used: %s" \ - % (", ".join(t.name for t in ann_types), selected_ann_type.name)) - - for ann_files in subsets.values(): - for ann_type, ann_file in ann_files.items(): - if ann_type in conflicting_types: - if ann_type is not selected_ann_type: - log.warning("Not implemented: " - "conflicting source '%s' is skipped." % ann_file) - continue - log.info("Found a dataset at '%s'" % ann_file) - - source_name = osp.splitext(osp.basename(ann_file))[0] - project.add_source(source_name, { - 'url': ann_file, - 'format': self._COCO_EXTRACTORS[ann_type], - 'options': dict(extra_params), - }) - - return project - - @staticmethod - def find_subsets(path): - if path.endswith('.json') and osp.isfile(path): - subset_paths = [path] - else: - subset_paths = glob(osp.join(path, '**', '*_*.json'), - recursive=True) - - subsets = defaultdict(dict) - for subset_path in subset_paths: - name_parts = osp.splitext(osp.basename(subset_path))[0] \ - .rsplit('_', maxsplit=1) - - ann_type = name_parts[0] - try: - ann_type = CocoTask[ann_type] - except KeyError: - log.warn("Skipping '%s': unknown subset " - "type '%s', the only known are: %s" % \ - (subset_path, ann_type, - ', '.join([e.name for e in CocoTask]))) - continue - subset_name = name_parts[1] - subsets[subset_name][ann_type] = subset_path - return dict(subsets) diff --git a/datumaro/datumaro/plugins/cvat_format/__init__.py b/datumaro/datumaro/plugins/cvat_format/__init__.py deleted file mode 100644 index e69de29b..00000000 diff --git a/datumaro/datumaro/plugins/cvat_format/converter.py b/datumaro/datumaro/plugins/cvat_format/converter.py deleted file mode 100644 index 4849619b..00000000 --- a/datumaro/datumaro/plugins/cvat_format/converter.py +++ /dev/null @@ -1,331 +0,0 @@ - -# Copyright (C) 2019-2020 Intel Corporation -# -# SPDX-License-Identifier: MIT - -import logging as log -import os -import os.path as osp -from collections import OrderedDict -from xml.sax.saxutils import XMLGenerator - -from datumaro.components.converter import Converter -from datumaro.components.extractor import DEFAULT_SUBSET_NAME, AnnotationType -from datumaro.util import cast, pairs - -from .format import CvatPath - - -class XmlAnnotationWriter: - VERSION = '1.1' - - def __init__(self, f): - self.xmlgen = XMLGenerator(f, 'utf-8') - self._level = 0 - - def _indent(self, newline = True): - if newline: - self.xmlgen.ignorableWhitespace('\n') - self.xmlgen.ignorableWhitespace(' ' * self._level) - - def _add_version(self): - self._indent() - self.xmlgen.startElement('version', {}) - self.xmlgen.characters(self.VERSION) - self.xmlgen.endElement('version') - - def open_root(self): - self.xmlgen.startDocument() - self.xmlgen.startElement('annotations', {}) - self._level += 1 - self._add_version() - - def _add_meta(self, meta): - self._level += 1 - for k, v in meta.items(): - if isinstance(v, OrderedDict): - self._indent() - self.xmlgen.startElement(k, {}) - self._add_meta(v) - self._indent() - self.xmlgen.endElement(k) - elif isinstance(v, list): - self._indent() - self.xmlgen.startElement(k, {}) - for tup in v: - self._add_meta(OrderedDict([tup])) - self._indent() - self.xmlgen.endElement(k) - else: - self._indent() - self.xmlgen.startElement(k, {}) - self.xmlgen.characters(v) - self.xmlgen.endElement(k) - self._level -= 1 - - def write_meta(self, meta): - self._indent() - self.xmlgen.startElement('meta', {}) - self._add_meta(meta) - self._indent() - self.xmlgen.endElement('meta') - - def open_track(self, track): - self._indent() - self.xmlgen.startElement('track', track) - self._level += 1 - - def open_image(self, image): - self._indent() - self.xmlgen.startElement('image', image) - self._level += 1 - - def open_box(self, box): - self._indent() - self.xmlgen.startElement('box', box) - self._level += 1 - - def open_polygon(self, polygon): - self._indent() - self.xmlgen.startElement('polygon', polygon) - self._level += 1 - - def open_polyline(self, polyline): - self._indent() - self.xmlgen.startElement('polyline', polyline) - self._level += 1 - - def open_points(self, points): - self._indent() - self.xmlgen.startElement('points', points) - self._level += 1 - - def open_tag(self, tag): - self._indent() - self.xmlgen.startElement("tag", tag) - self._level += 1 - - def add_attribute(self, attribute): - self._indent() - self.xmlgen.startElement('attribute', {'name': attribute['name']}) - self.xmlgen.characters(attribute['value']) - self.xmlgen.endElement('attribute') - - def _close_element(self, element): - self._level -= 1 - self._indent() - self.xmlgen.endElement(element) - - def close_box(self): - self._close_element('box') - - def close_polygon(self): - self._close_element('polygon') - - def close_polyline(self): - self._close_element('polyline') - - def close_points(self): - self._close_element('points') - - def close_tag(self): - self._close_element('tag') - - def close_image(self): - self._close_element('image') - - def close_track(self): - self._close_element('track') - - def close_root(self): - self._close_element('annotations') - self.xmlgen.endDocument() - -class _SubsetWriter: - def __init__(self, file, name, extractor, context): - self._writer = XmlAnnotationWriter(file) - self._name = name - self._extractor = extractor - self._context = context - - def write(self): - self._writer.open_root() - self._write_meta() - - for index, item in enumerate(self._extractor): - self._write_item(item, index) - - self._writer.close_root() - - def _write_item(self, item, index): - image_info = OrderedDict([ - ("id", str(cast(item.attributes.get('frame'), int, index))), - ]) - filename = item.id + CvatPath.IMAGE_EXT - image_info["name"] = filename - if item.has_image: - size = item.image.size - if size: - h, w = size - image_info["width"] = str(w) - image_info["height"] = str(h) - - if self._context._save_images: - self._context._save_image(item, - osp.join(self._context._images_dir, filename)) - else: - log.debug("Item '%s' has no image info", item.id) - self._writer.open_image(image_info) - - for ann in item.annotations: - if ann.type in {AnnotationType.points, AnnotationType.polyline, - AnnotationType.polygon, AnnotationType.bbox}: - self._write_shape(ann) - elif ann.type == AnnotationType.label: - self._write_tag(ann) - else: - continue - - self._writer.close_image() - - def _write_meta(self): - label_cat = self._extractor.categories()[AnnotationType.label] - meta = OrderedDict([ - ("task", OrderedDict([ - ("id", ""), - ("name", self._name), - ("size", str(len(self._extractor))), - ("mode", "annotation"), - ("overlap", ""), - ("start_frame", "0"), - ("stop_frame", str(len(self._extractor))), - ("frame_filter", ""), - ("z_order", "True"), - - ("labels", [ - ("label", OrderedDict([ - ("name", label.name), - ("attributes", [ - ("attribute", OrderedDict([ - ("name", attr), - ("mutable", "True"), - ("input_type", "text"), - ("default_value", ""), - ("values", ""), - ])) for attr in label.attributes - ]) - ])) for label in label_cat.items - ]), - ])), - ]) - self._writer.write_meta(meta) - - def _get_label(self, label_id): - label_cat = self._extractor.categories()[AnnotationType.label] - return label_cat.items[label_id] - - def _write_shape(self, shape): - if shape.label is None: - return - - shape_data = OrderedDict([ - ("label", self._get_label(shape.label).name), - ("occluded", str(int(shape.attributes.get('occluded', False)))), - ]) - - if shape.type == AnnotationType.bbox: - shape_data.update(OrderedDict([ - ("xtl", "{:.2f}".format(shape.points[0])), - ("ytl", "{:.2f}".format(shape.points[1])), - ("xbr", "{:.2f}".format(shape.points[2])), - ("ybr", "{:.2f}".format(shape.points[3])) - ])) - else: - shape_data.update(OrderedDict([ - ("points", ';'.join(( - ','.join(( - "{:.2f}".format(x), - "{:.2f}".format(y) - )) for x, y in pairs(shape.points)) - )), - ])) - - shape_data['z_order'] = str(int(shape.z_order)) - if shape.group: - shape_data['group_id'] = str(shape.group) - - if shape.type == AnnotationType.bbox: - self._writer.open_box(shape_data) - elif shape.type == AnnotationType.polygon: - self._writer.open_polygon(shape_data) - elif shape.type == AnnotationType.polyline: - self._writer.open_polyline(shape_data) - elif shape.type == AnnotationType.points: - self._writer.open_points(shape_data) - else: - raise NotImplementedError("unknown shape type") - - for attr_name, attr_value in shape.attributes.items(): - if isinstance(attr_value, bool): - attr_value = 'true' if attr_value else 'false' - if attr_name in self._get_label(shape.label).attributes: - self._writer.add_attribute(OrderedDict([ - ("name", str(attr_name)), - ("value", str(attr_value)), - ])) - - if shape.type == AnnotationType.bbox: - self._writer.close_box() - elif shape.type == AnnotationType.polygon: - self._writer.close_polygon() - elif shape.type == AnnotationType.polyline: - self._writer.close_polyline() - elif shape.type == AnnotationType.points: - self._writer.close_points() - else: - raise NotImplementedError("unknown shape type") - - def _write_tag(self, label): - if label.label is None: - return - - tag_data = OrderedDict([ - ('label', self._get_label(label.label).name), - ]) - if label.group: - tag_data['group_id'] = str(label.group) - self._writer.open_tag(tag_data) - - for attr_name, attr_value in label.attributes.items(): - if isinstance(attr_value, bool): - attr_value = 'true' if attr_value else 'false' - if attr_name in self._get_label(label.label).attributes: - self._writer.add_attribute(OrderedDict([ - ("name", str(attr_name)), - ("value", str(attr_value)), - ])) - - self._writer.close_tag() - -class CvatConverter(Converter): - DEFAULT_IMAGE_EXT = CvatPath.IMAGE_EXT - - def apply(self): - images_dir = osp.join(self._save_dir, CvatPath.IMAGES_DIR) - os.makedirs(images_dir, exist_ok=True) - self._images_dir = images_dir - - subsets = self._extractor.subsets() - if len(subsets) == 0: - subsets = [ None ] - - for subset_name in subsets: - if subset_name: - subset = self._extractor.get_subset(subset_name) - else: - subset_name = DEFAULT_SUBSET_NAME - subset = self._extractor - - with open(osp.join(self._save_dir, '%s.xml' % subset_name), 'w') as f: - writer = _SubsetWriter(f, subset_name, subset, self) - writer.write() diff --git a/datumaro/datumaro/plugins/cvat_format/extractor.py b/datumaro/datumaro/plugins/cvat_format/extractor.py deleted file mode 100644 index 7e37c2dd..00000000 --- a/datumaro/datumaro/plugins/cvat_format/extractor.py +++ /dev/null @@ -1,316 +0,0 @@ - -# Copyright (C) 2019-2020 Intel Corporation -# -# SPDX-License-Identifier: MIT - -from collections import OrderedDict -import os.path as osp -from defusedxml import ElementTree - -from datumaro.components.extractor import (SourceExtractor, DatasetItem, - AnnotationType, Points, Polygon, PolyLine, Bbox, Label, - LabelCategories -) -from datumaro.util.image import Image - -from .format import CvatPath - - -class CvatExtractor(SourceExtractor): - _SUPPORTED_SHAPES = ('box', 'polygon', 'polyline', 'points') - - def __init__(self, path): - assert osp.isfile(path), path - rootpath = osp.dirname(path) - images_dir = '' - if osp.isdir(osp.join(rootpath, CvatPath.IMAGES_DIR)): - images_dir = osp.join(rootpath, CvatPath.IMAGES_DIR) - self._images_dir = images_dir - self._path = path - - super().__init__(subset=osp.splitext(osp.basename(path))[0]) - - items, categories = self._parse(path) - self._items = self._load_items(items) - self._categories = categories - - def categories(self): - return self._categories - - def __iter__(self): - for item in self._items.values(): - yield item - - def __len__(self): - return len(self._items) - - @classmethod - def _parse(cls, path): - context = ElementTree.iterparse(path, events=("start", "end")) - context = iter(context) - - categories, frame_size = cls._parse_meta(context) - - items = OrderedDict() - - track = None - shape = None - tag = None - attributes = None - image = None - for ev, el in context: - if ev == 'start': - if el.tag == 'track': - track = { - 'id': el.attrib['id'], - 'label': el.attrib.get('label'), - 'group': int(el.attrib.get('group_id', 0)), - 'height': frame_size[0], - 'width': frame_size[1], - } - elif el.tag == 'image': - image = { - 'name': el.attrib.get('name'), - 'frame': el.attrib['id'], - 'width': el.attrib.get('width'), - 'height': el.attrib.get('height'), - } - elif el.tag in cls._SUPPORTED_SHAPES and (track or image): - attributes = {} - shape = { - 'type': None, - 'attributes': attributes, - } - if track: - shape.update(track) - shape['track_id'] = int(track['id']) - if image: - shape.update(image) - elif el.tag == 'tag' and image: - attributes = {} - tag = { - 'frame': image['frame'], - 'attributes': attributes, - 'group': int(el.attrib.get('group_id', 0)), - 'label': el.attrib['label'], - } - elif ev == 'end': - if el.tag == 'attribute' and attributes is not None: - attr_value = el.text - if el.text in ['true', 'false']: - attr_value = attr_value == 'true' - else: - try: - attr_value = float(attr_value) - except ValueError: - pass - attributes[el.attrib['name']] = attr_value - elif el.tag in cls._SUPPORTED_SHAPES: - if track is not None: - shape['frame'] = el.attrib['frame'] - shape['outside'] = (el.attrib.get('outside') == '1') - shape['keyframe'] = (el.attrib.get('keyframe') == '1') - if image is not None: - shape['label'] = el.attrib.get('label') - shape['group'] = int(el.attrib.get('group_id', 0)) - - shape['type'] = el.tag - shape['occluded'] = (el.attrib.get('occluded') == '1') - shape['z_order'] = int(el.attrib.get('z_order', 0)) - - if el.tag == 'box': - shape['points'] = list(map(float, [ - el.attrib['xtl'], el.attrib['ytl'], - el.attrib['xbr'], el.attrib['ybr'], - ])) - else: - shape['points'] = [] - for pair in el.attrib['points'].split(';'): - shape['points'].extend(map(float, pair.split(','))) - - frame_desc = items.get(shape['frame'], {'annotations': []}) - frame_desc['annotations'].append( - cls._parse_shape_ann(shape, categories)) - items[shape['frame']] = frame_desc - shape = None - - elif el.tag == 'tag': - frame_desc = items.get(tag['frame'], {'annotations': []}) - frame_desc['annotations'].append( - cls._parse_tag_ann(tag, categories)) - items[tag['frame']] = frame_desc - tag = None - elif el.tag == 'track': - track = None - elif el.tag == 'image': - frame_desc = items.get(image['frame'], {'annotations': []}) - frame_desc.update({ - 'name': image.get('name'), - 'height': image.get('height'), - 'width': image.get('width'), - }) - items[image['frame']] = frame_desc - image = None - el.clear() - - return items, categories - - @staticmethod - def _parse_meta(context): - ev, el = next(context) - if not (ev == 'start' and el.tag == 'annotations'): - raise Exception("Unexpected token ") - - categories = {} - - frame_size = None - mode = None - labels = OrderedDict() - label = None - - # Recursive descent parser - el = None - states = ['annotations'] - def accepted(expected_state, tag, next_state=None): - state = states[-1] - if state == expected_state and el is not None and el.tag == tag: - if not next_state: - next_state = tag - states.append(next_state) - return True - return False - def consumed(expected_state, tag): - state = states[-1] - if state == expected_state and el is not None and el.tag == tag: - states.pop() - return True - return False - - for ev, el in context: - if ev == 'start': - if accepted('annotations', 'meta'): pass - elif accepted('meta', 'task'): pass - elif accepted('task', 'mode'): pass - elif accepted('task', 'original_size'): - frame_size = [None, None] - elif accepted('original_size', 'height', next_state='frame_height'): pass - elif accepted('original_size', 'width', next_state='frame_width'): pass - elif accepted('task', 'labels'): pass - elif accepted('labels', 'label'): - label = { 'name': None, 'attributes': set() } - elif accepted('label', 'name', next_state='label_name'): pass - elif accepted('label', 'attributes'): pass - elif accepted('attributes', 'attribute'): pass - elif accepted('attribute', 'name', next_state='attr_name'): pass - elif accepted('annotations', 'image') or \ - accepted('annotations', 'track') or \ - accepted('annotations', 'tag'): - break - else: - pass - elif ev == 'end': - if consumed('meta', 'meta'): - break - elif consumed('task', 'task'): pass - elif consumed('mode', 'mode'): - mode = el.text - elif consumed('original_size', 'original_size'): pass - elif consumed('frame_height', 'height'): - frame_size[0] = int(el.text) - elif consumed('frame_width', 'width'): - frame_size[1] = int(el.text) - elif consumed('label_name', 'name'): - label['name'] = el.text - elif consumed('attr_name', 'name'): - label['attributes'].add(el.text) - elif consumed('attribute', 'attribute'): pass - elif consumed('attributes', 'attributes'): pass - elif consumed('label', 'label'): - labels[label['name']] = label['attributes'] - label = None - elif consumed('labels', 'labels'): pass - else: - pass - - assert len(states) == 1 and states[0] == 'annotations', \ - "Expected 'meta' section in the annotation file, path: %s" % states - - common_attrs = ['occluded'] - if mode == 'interpolation': - common_attrs.append('keyframe') - common_attrs.append('outside') - common_attrs.append('track_id') - - label_cat = LabelCategories(attributes=common_attrs) - for label, attrs in labels.items(): - label_cat.add(label, attributes=attrs) - - categories[AnnotationType.label] = label_cat - - return categories, frame_size - - @classmethod - def _parse_shape_ann(cls, ann, categories): - ann_id = ann.get('id', 0) - ann_type = ann['type'] - - attributes = ann.get('attributes') or {} - if 'occluded' in categories[AnnotationType.label].attributes: - attributes['occluded'] = ann.get('occluded', False) - if 'outside' in ann: - attributes['outside'] = ann['outside'] - if 'keyframe' in ann: - attributes['keyframe'] = ann['keyframe'] - if 'track_id' in ann: - attributes['track_id'] = ann['track_id'] - - group = ann.get('group') - - label = ann.get('label') - label_id = categories[AnnotationType.label].find(label)[0] - - z_order = ann.get('z_order', 0) - points = ann.get('points', []) - - if ann_type == 'polyline': - return PolyLine(points, label=label_id, z_order=z_order, - id=ann_id, attributes=attributes, group=group) - - elif ann_type == 'polygon': - return Polygon(points, label=label_id, z_order=z_order, - id=ann_id, attributes=attributes, group=group) - - elif ann_type == 'points': - return Points(points, label=label_id, z_order=z_order, - id=ann_id, attributes=attributes, group=group) - - elif ann_type == 'box': - x, y = points[0], points[1] - w, h = points[2] - x, points[3] - y - return Bbox(x, y, w, h, label=label_id, z_order=z_order, - id=ann_id, attributes=attributes, group=group) - - else: - raise NotImplementedError("Unknown annotation type '%s'" % ann_type) - - @classmethod - def _parse_tag_ann(cls, ann, categories): - label = ann.get('label') - label_id = categories[AnnotationType.label].find(label)[0] - group = ann.get('group') - attributes = ann.get('attributes') - return Label(label_id, attributes=attributes, group=group) - - def _load_items(self, parsed): - for frame_id, item_desc in parsed.items(): - name = item_desc.get('name', 'frame_%06d.png' % int(frame_id)) - image = osp.join(self._images_dir, name) - image_size = (item_desc.get('height'), item_desc.get('width')) - if all(image_size): - image = Image(path=image, size=tuple(map(int, image_size))) - - parsed[frame_id] = DatasetItem(id=osp.splitext(name)[0], - subset=self._subset, image=image, - annotations=item_desc.get('annotations'), - attributes={'frame': int(frame_id)}) - return parsed diff --git a/datumaro/datumaro/plugins/cvat_format/format.py b/datumaro/datumaro/plugins/cvat_format/format.py deleted file mode 100644 index e5572a89..00000000 --- a/datumaro/datumaro/plugins/cvat_format/format.py +++ /dev/null @@ -1,9 +0,0 @@ - -# Copyright (C) 2019-2020 Intel Corporation -# -# SPDX-License-Identifier: MIT - -class CvatPath: - IMAGES_DIR = 'images' - - IMAGE_EXT = '.jpg' diff --git a/datumaro/datumaro/plugins/cvat_format/importer.py b/datumaro/datumaro/plugins/cvat_format/importer.py deleted file mode 100644 index a3a83757..00000000 --- a/datumaro/datumaro/plugins/cvat_format/importer.py +++ /dev/null @@ -1,51 +0,0 @@ - -# Copyright (C) 2019-2020 Intel Corporation -# -# SPDX-License-Identifier: MIT - -from glob import glob -import logging as log -import os.path as osp - -from datumaro.components.extractor import Importer - - -class CvatImporter(Importer): - EXTRACTOR_NAME = 'cvat' - - @classmethod - def detect(cls, path): - return len(cls.find_subsets(path)) != 0 - - def __call__(self, path, **extra_params): - from datumaro.components.project import Project # cyclic import - project = Project() - - subset_paths = self.find_subsets(path) - - if len(subset_paths) == 0: - raise Exception("Failed to find 'cvat' dataset at '%s'" % path) - - for subset_path in subset_paths: - if not osp.isfile(subset_path): - continue - - log.info("Found a dataset at '%s'" % subset_path) - - subset_name = osp.splitext(osp.basename(subset_path))[0] - - project.add_source(subset_name, { - 'url': subset_path, - 'format': self.EXTRACTOR_NAME, - 'options': dict(extra_params), - }) - - return project - - @staticmethod - def find_subsets(path): - if path.endswith('.xml') and osp.isfile(path): - subset_paths = [path] - else: - subset_paths = glob(osp.join(path, '**', '*.xml'), recursive=True) - return subset_paths \ No newline at end of file diff --git a/datumaro/datumaro/plugins/datumaro_format/__init__.py b/datumaro/datumaro/plugins/datumaro_format/__init__.py deleted file mode 100644 index e69de29b..00000000 diff --git a/datumaro/datumaro/plugins/datumaro_format/converter.py b/datumaro/datumaro/plugins/datumaro_format/converter.py deleted file mode 100644 index 2d862094..00000000 --- a/datumaro/datumaro/plugins/datumaro_format/converter.py +++ /dev/null @@ -1,261 +0,0 @@ - -# Copyright (C) 2019-2020 Intel Corporation -# -# SPDX-License-Identifier: MIT - -# pylint: disable=no-self-use - -import json -import numpy as np -import os -import os.path as osp - -from datumaro.components.converter import Converter -from datumaro.components.extractor import ( - DEFAULT_SUBSET_NAME, Annotation, _Shape, - Label, Mask, RleMask, Points, Polygon, PolyLine, Bbox, Caption, - LabelCategories, MaskCategories, PointsCategories -) -from datumaro.util import cast -import pycocotools.mask as mask_utils - -from .format import DatumaroPath - - -class _SubsetWriter: - def __init__(self, name, context): - self._name = name - self._context = context - - self._data = { - 'info': {}, - 'categories': {}, - 'items': [], - } - - @property - def categories(self): - return self._data['categories'] - - @property - def items(self): - return self._data['items'] - - def write_item(self, item): - annotations = [] - item_desc = { - 'id': item.id, - 'annotations': annotations, - } - if item.attributes: - item_desc['attr'] = item.attributes - if item.path: - item_desc['path'] = item.path - if item.has_image: - path = item.image.path - if self._context._save_images: - path = self._context._make_image_filename(item) - self._context._save_image(item, path) - - item_desc['image'] = { - 'size': item.image.size, - 'path': path, - } - self.items.append(item_desc) - - for ann in item.annotations: - if isinstance(ann, Label): - converted_ann = self._convert_label_object(ann) - elif isinstance(ann, Mask): - converted_ann = self._convert_mask_object(ann) - elif isinstance(ann, Points): - converted_ann = self._convert_points_object(ann) - elif isinstance(ann, PolyLine): - converted_ann = self._convert_polyline_object(ann) - elif isinstance(ann, Polygon): - converted_ann = self._convert_polygon_object(ann) - elif isinstance(ann, Bbox): - converted_ann = self._convert_bbox_object(ann) - elif isinstance(ann, Caption): - converted_ann = self._convert_caption_object(ann) - else: - raise NotImplementedError() - annotations.append(converted_ann) - - def write_categories(self, categories): - for ann_type, desc in categories.items(): - if isinstance(desc, LabelCategories): - converted_desc = self._convert_label_categories(desc) - elif isinstance(desc, MaskCategories): - converted_desc = self._convert_mask_categories(desc) - elif isinstance(desc, PointsCategories): - converted_desc = self._convert_points_categories(desc) - else: - raise NotImplementedError() - self.categories[ann_type.name] = converted_desc - - def write(self, save_dir): - with open(osp.join(save_dir, '%s.json' % (self._name)), 'w') as f: - json.dump(self._data, f) - - def _convert_annotation(self, obj): - assert isinstance(obj, Annotation) - - ann_json = { - 'id': cast(obj.id, int), - 'type': cast(obj.type.name, str), - 'attributes': obj.attributes, - 'group': cast(obj.group, int, 0), - } - return ann_json - - def _convert_label_object(self, obj): - converted = self._convert_annotation(obj) - - converted.update({ - 'label_id': cast(obj.label, int), - }) - return converted - - def _convert_mask_object(self, obj): - converted = self._convert_annotation(obj) - - if isinstance(obj, RleMask): - rle = obj.rle - else: - rle = mask_utils.encode( - np.require(obj.image, dtype=np.uint8, requirements='F')) - - converted.update({ - 'label_id': cast(obj.label, int), - 'rle': { - # serialize as compressed COCO mask - 'counts': rle['counts'].decode('ascii'), - 'size': list(int(c) for c in rle['size']), - }, - 'z_order': obj.z_order, - }) - return converted - - def _convert_shape_object(self, obj): - assert isinstance(obj, _Shape) - converted = self._convert_annotation(obj) - - converted.update({ - 'label_id': cast(obj.label, int), - 'points': [float(p) for p in obj.points], - 'z_order': obj.z_order, - }) - return converted - - def _convert_polyline_object(self, obj): - return self._convert_shape_object(obj) - - def _convert_polygon_object(self, obj): - return self._convert_shape_object(obj) - - def _convert_bbox_object(self, obj): - converted = self._convert_shape_object(obj) - converted.pop('points', None) - converted['bbox'] = [float(p) for p in obj.get_bbox()] - return converted - - def _convert_points_object(self, obj): - converted = self._convert_shape_object(obj) - - converted.update({ - 'visibility': [int(v.value) for v in obj.visibility], - }) - return converted - - def _convert_caption_object(self, obj): - converted = self._convert_annotation(obj) - - converted.update({ - 'caption': cast(obj.caption, str), - }) - return converted - - def _convert_label_categories(self, obj): - converted = { - 'labels': [], - } - for label in obj.items: - converted['labels'].append({ - 'name': cast(label.name, str), - 'parent': cast(label.parent, str), - }) - return converted - - def _convert_mask_categories(self, obj): - converted = { - 'colormap': [], - } - for label_id, color in obj.colormap.items(): - converted['colormap'].append({ - 'label_id': int(label_id), - 'r': int(color[0]), - 'g': int(color[1]), - 'b': int(color[2]), - }) - return converted - - def _convert_points_categories(self, obj): - converted = { - 'items': [], - } - for label_id, item in obj.items.items(): - converted['items'].append({ - 'label_id': int(label_id), - 'labels': [cast(label, str) for label in item.labels], - 'joints': [list(map(int, j)) for j in item.joints], - }) - return converted - -class DatumaroConverter(Converter): - DEFAULT_IMAGE_EXT = DatumaroPath.IMAGE_EXT - - def apply(self): - os.makedirs(self._save_dir, exist_ok=True) - - images_dir = osp.join(self._save_dir, DatumaroPath.IMAGES_DIR) - os.makedirs(images_dir, exist_ok=True) - self._images_dir = images_dir - - annotations_dir = osp.join(self._save_dir, DatumaroPath.ANNOTATIONS_DIR) - os.makedirs(annotations_dir, exist_ok=True) - self._annotations_dir = annotations_dir - - subsets = self._extractor.subsets() or [None] - subsets = [n or DEFAULT_SUBSET_NAME for n in subsets] - subsets = { name: _SubsetWriter(name, self) for name in subsets } - - for subset, writer in subsets.items(): - writer.write_categories(self._extractor.categories()) - - for item in self._extractor: - subset = item.subset or DEFAULT_SUBSET_NAME - writer = subsets[subset] - - writer.write_item(item) - - for subset, writer in subsets.items(): - writer.write(annotations_dir) - - def _save_image(self, item, path=None): - super()._save_image(item, - osp.join(self._images_dir, self._make_image_filename(item))) - -class DatumaroProjectConverter(Converter): - @classmethod - def convert(cls, extractor, save_dir, **kwargs): - os.makedirs(save_dir, exist_ok=True) - - from datumaro.components.project import Project - project = Project.generate(save_dir, - config=kwargs.pop('project_config', None)) - - DatumaroConverter.convert(extractor, - save_dir=osp.join( - project.config.project_dir, project.config.dataset_dir), - **kwargs) \ No newline at end of file diff --git a/datumaro/datumaro/plugins/datumaro_format/extractor.py b/datumaro/datumaro/plugins/datumaro_format/extractor.py deleted file mode 100644 index c1ae40d4..00000000 --- a/datumaro/datumaro/plugins/datumaro_format/extractor.py +++ /dev/null @@ -1,157 +0,0 @@ - -# Copyright (C) 2019-2020 Intel Corporation -# -# SPDX-License-Identifier: MIT - -import json -import os.path as osp - -from datumaro.components.extractor import (SourceExtractor, DatasetItem, - AnnotationType, Label, RleMask, Points, Polygon, PolyLine, Bbox, Caption, - LabelCategories, MaskCategories, PointsCategories -) -from datumaro.util.image import Image - -from .format import DatumaroPath - - -class DatumaroExtractor(SourceExtractor): - def __init__(self, path): - assert osp.isfile(path), path - rootpath = '' - if path.endswith(osp.join(DatumaroPath.ANNOTATIONS_DIR, osp.basename(path))): - rootpath = path.rsplit(DatumaroPath.ANNOTATIONS_DIR, maxsplit=1)[0] - images_dir = '' - if rootpath and osp.isdir(osp.join(rootpath, DatumaroPath.IMAGES_DIR)): - images_dir = osp.join(rootpath, DatumaroPath.IMAGES_DIR) - self._images_dir = images_dir - - super().__init__(subset=osp.splitext(osp.basename(path))[0]) - - with open(path, 'r') as f: - parsed_anns = json.load(f) - self._categories = self._load_categories(parsed_anns) - self._items = self._load_items(parsed_anns) - - def categories(self): - return self._categories - - def __iter__(self): - for item in self._items: - yield item - - def __len__(self): - return len(self._items) - - @staticmethod - def _load_categories(parsed): - categories = {} - - parsed_label_cat = parsed['categories'].get(AnnotationType.label.name) - if parsed_label_cat: - label_categories = LabelCategories() - for item in parsed_label_cat['labels']: - label_categories.add(item['name'], parent=item['parent']) - - categories[AnnotationType.label] = label_categories - - parsed_mask_cat = parsed['categories'].get(AnnotationType.mask.name) - if parsed_mask_cat: - colormap = {} - for item in parsed_mask_cat['colormap']: - colormap[int(item['label_id'])] = \ - (item['r'], item['g'], item['b']) - - mask_categories = MaskCategories(colormap=colormap) - categories[AnnotationType.mask] = mask_categories - - parsed_points_cat = parsed['categories'].get(AnnotationType.points.name) - if parsed_points_cat: - point_categories = PointsCategories() - for item in parsed_points_cat['items']: - point_categories.add(int(item['label_id']), - item['labels'], joints=item['joints']) - - categories[AnnotationType.points] = point_categories - - return categories - - def _load_items(self, parsed): - items = [] - for item_desc in parsed['items']: - item_id = item_desc['id'] - - image = None - image_info = item_desc.get('image') - if image_info: - image_path = image_info.get('path') or \ - item_id + DatumaroPath.IMAGE_EXT - image_path = osp.join(self._images_dir, image_path) - image = Image(path=image_path, size=image_info.get('size')) - - annotations = self._load_annotations(item_desc) - - item = DatasetItem(id=item_id, subset=self._subset, - annotations=annotations, image=image, - attributes=item_desc.get('attr')) - - items.append(item) - - return items - - @staticmethod - def _load_annotations(item): - parsed = item['annotations'] - loaded = [] - - for ann in parsed: - ann_id = ann.get('id') - ann_type = AnnotationType[ann['type']] - attributes = ann.get('attributes') - group = ann.get('group') - - label_id = ann.get('label_id') - z_order = ann.get('z_order') - points = ann.get('points') - - if ann_type == AnnotationType.label: - loaded.append(Label(label=label_id, - id=ann_id, attributes=attributes, group=group)) - - elif ann_type == AnnotationType.mask: - rle = ann['rle'] - rle['counts'] = rle['counts'].encode('ascii') - loaded.append(RleMask(rle=rle, label=label_id, - id=ann_id, attributes=attributes, group=group, - z_order=z_order)) - - elif ann_type == AnnotationType.polyline: - loaded.append(PolyLine(points, label=label_id, - id=ann_id, attributes=attributes, group=group, - z_order=z_order)) - - elif ann_type == AnnotationType.polygon: - loaded.append(Polygon(points, label=label_id, - id=ann_id, attributes=attributes, group=group, - z_order=z_order)) - - elif ann_type == AnnotationType.bbox: - x, y, w, h = ann['bbox'] - loaded.append(Bbox(x, y, w, h, label=label_id, - id=ann_id, attributes=attributes, group=group, - z_order=z_order)) - - elif ann_type == AnnotationType.points: - loaded.append(Points(points, label=label_id, - id=ann_id, attributes=attributes, group=group, - z_order=z_order)) - - elif ann_type == AnnotationType.caption: - caption = ann.get('caption') - loaded.append(Caption(caption, - id=ann_id, attributes=attributes, group=group)) - - else: - raise NotImplementedError() - - return loaded diff --git a/datumaro/datumaro/plugins/datumaro_format/format.py b/datumaro/datumaro/plugins/datumaro_format/format.py deleted file mode 100644 index 501c100b..00000000 --- a/datumaro/datumaro/plugins/datumaro_format/format.py +++ /dev/null @@ -1,12 +0,0 @@ - -# Copyright (C) 2019-2020 Intel Corporation -# -# SPDX-License-Identifier: MIT - -class DatumaroPath: - IMAGES_DIR = 'images' - ANNOTATIONS_DIR = 'annotations' - MASKS_DIR = 'masks' - - IMAGE_EXT = '.jpg' - MASK_EXT = '.png' diff --git a/datumaro/datumaro/plugins/datumaro_format/importer.py b/datumaro/datumaro/plugins/datumaro_format/importer.py deleted file mode 100644 index dbb90f86..00000000 --- a/datumaro/datumaro/plugins/datumaro_format/importer.py +++ /dev/null @@ -1,56 +0,0 @@ - -# Copyright (C) 2019-2020 Intel Corporation -# -# SPDX-License-Identifier: MIT - -from glob import glob -import logging as log -import os.path as osp - -from datumaro.components.extractor import Importer - -from .format import DatumaroPath - - -class DatumaroImporter(Importer): - EXTRACTOR_NAME = 'datumaro' - - @classmethod - def detect(cls, path): - return len(cls.find_subsets(path)) != 0 - - def __call__(self, path, **extra_params): - from datumaro.components.project import Project # cyclic import - project = Project() - - subset_paths = self.find_subsets(path) - if len(subset_paths) == 0: - raise Exception("Failed to find 'datumaro' dataset at '%s'" % path) - - for subset_path in subset_paths: - if not osp.isfile(subset_path): - continue - - log.info("Found a dataset at '%s'" % subset_path) - - subset_name = osp.splitext(osp.basename(subset_path))[0] - - project.add_source(subset_name, { - 'url': subset_path, - 'format': self.EXTRACTOR_NAME, - 'options': dict(extra_params), - }) - - return project - - @staticmethod - def find_subsets(path): - if path.endswith('.json') and osp.isfile(path): - subset_paths = [path] - else: - subset_paths = glob(osp.join(path, '*.json')) - - if osp.basename(osp.normpath(path)) != DatumaroPath.ANNOTATIONS_DIR: - path = osp.join(path, DatumaroPath.ANNOTATIONS_DIR) - subset_paths += glob(osp.join(path, '*.json')) - return subset_paths \ No newline at end of file diff --git a/datumaro/datumaro/plugins/image_dir.py b/datumaro/datumaro/plugins/image_dir.py deleted file mode 100644 index 062387e1..00000000 --- a/datumaro/datumaro/plugins/image_dir.py +++ /dev/null @@ -1,76 +0,0 @@ - -# Copyright (C) 2019-2020 Intel Corporation -# -# SPDX-License-Identifier: MIT - -import logging as log -import os -import os.path as osp - -from datumaro.components.extractor import DatasetItem, SourceExtractor, Importer -from datumaro.components.converter import Converter -from datumaro.util.image import Image - - -class ImageDirImporter(Importer): - EXTRACTOR_NAME = 'image_dir' - - def __call__(self, path, **extra_params): - from datumaro.components.project import Project # cyclic import - project = Project() - - if not osp.isdir(path): - raise Exception("Can't find a directory at '%s'" % path) - - source_name = osp.basename(osp.normpath(path)) - project.add_source(source_name, { - 'url': source_name, - 'format': self.EXTRACTOR_NAME, - 'options': dict(extra_params), - }) - - return project - - -class ImageDirExtractor(SourceExtractor): - def __init__(self, url): - super().__init__() - - assert osp.isdir(url), url - - items = [] - for dirpath, _, filenames in os.walk(url): - for name in filenames: - path = osp.join(dirpath, name) - try: - image = Image(path) - # force loading - image.data # pylint: disable=pointless-statement - except Exception: - continue - - item_id = osp.relpath(osp.splitext(path)[0], url) - items.append(DatasetItem(id=item_id, image=image)) - - self._items = items - - def __iter__(self): - for item in self._items: - yield item - - def __len__(self): - return len(self._items) - - -class ImageDirConverter(Converter): - DEFAULT_IMAGE_EXT = '.jpg' - - def apply(self): - os.makedirs(self._save_dir, exist_ok=True) - - for item in self._extractor: - if item.has_image: - self._save_image(item, - osp.join(self._save_dir, self._make_image_filename(item))) - else: - log.debug("Item '%s' has no image info", item.id) \ No newline at end of file diff --git a/datumaro/datumaro/plugins/labelme_format.py b/datumaro/datumaro/plugins/labelme_format.py deleted file mode 100644 index e037afba..00000000 --- a/datumaro/datumaro/plugins/labelme_format.py +++ /dev/null @@ -1,437 +0,0 @@ -# Copyright (C) 2020 Intel Corporation -# -# SPDX-License-Identifier: MIT - -from collections import defaultdict -from defusedxml import ElementTree -import logging as log -import numpy as np -import os -import os.path as osp - -from datumaro.components.extractor import (SourceExtractor, DEFAULT_SUBSET_NAME, - DatasetItem, AnnotationType, Mask, Bbox, Polygon, LabelCategories -) -from datumaro.components.extractor import Importer -from datumaro.components.converter import Converter -from datumaro.util.image import Image, save_image -from datumaro.util.mask_tools import load_mask, find_mask_bbox - - -class LabelMePath: - MASKS_DIR = 'Masks' - IMAGE_EXT = '.jpg' - -class LabelMeExtractor(SourceExtractor): - def __init__(self, path, subset_name=None): - assert osp.isdir(path), path - super().__init__(subset=subset_name) - - items, categories = self._parse(path) - self._categories = categories - self._items = items - - def categories(self): - return self._categories - - def __iter__(self): - for item in self._items: - yield item - - def __len__(self): - return len(self._items) - - def _parse(self, path): - categories = { - AnnotationType.label: LabelCategories(attributes={ - 'occluded', 'username' - }) - } - - items = [] - for p in sorted(p for p in os.listdir(path) if p.endswith('.xml')): - root = ElementTree.parse(osp.join(path, p)) - - image_path = osp.join(path, root.find('filename').text) - image_size = None - imagesize_elem = root.find('imagesize') - if imagesize_elem is not None: - width_elem = imagesize_elem.find('ncols') - height_elem = imagesize_elem.find('nrows') - image_size = (int(height_elem.text), int(width_elem.text)) - image = Image(path=image_path, size=image_size) - - annotations = self._parse_annotations(root, path, categories) - - items.append(DatasetItem(id=osp.splitext(p)[0], - subset=self._subset, image=image, annotations=annotations)) - return items, categories - - @classmethod - def _parse_annotations(cls, xml_root, dataset_root, categories): - def parse_attributes(attr_str): - parsed = [] - if not attr_str: - return parsed - - for attr in [a.strip() for a in attr_str.split(',') if a.strip()]: - if '=' in attr: - name, value = attr.split('=', maxsplit=1) - if value.lower() in {'true', 'false'}: - value = value.lower() == 'true' - else: - try: - value = float(value) - except ValueError: - pass - parsed.append((name, value)) - else: - parsed.append((attr, True)) - - return parsed - - label_cat = categories[AnnotationType.label] - def get_label_id(label): - if not label: - return None - idx, _ = label_cat.find(label) - if idx is None: - idx = label_cat.add(label) - return idx - - image_annotations = [] - - parsed_annotations = dict() - group_assignments = dict() - root_annotations = set() - for obj_elem in xml_root.iter('object'): - obj_id = int(obj_elem.find('id').text) - - ann_items = [] - - label = get_label_id(obj_elem.find('name').text) - - attributes = [] - attributes_elem = obj_elem.find('attributes') - if attributes_elem is not None and attributes_elem.text: - attributes = parse_attributes(attributes_elem.text) - - occluded = False - occluded_elem = obj_elem.find('occluded') - if occluded_elem is not None and occluded_elem.text: - occluded = (occluded_elem.text == 'yes') - attributes.append(('occluded', occluded)) - - deleted = False - deleted_elem = obj_elem.find('deleted') - if deleted_elem is not None and deleted_elem.text: - deleted = bool(int(deleted_elem.text)) - - user = '' - - poly_elem = obj_elem.find('polygon') - segm_elem = obj_elem.find('segm') - type_elem = obj_elem.find('type') # the only value is 'bounding_box' - if poly_elem is not None: - user_elem = poly_elem.find('username') - if user_elem is not None and user_elem.text: - user = user_elem.text - attributes.append(('username', user)) - - points = [] - for point_elem in poly_elem.iter('pt'): - x = float(point_elem.find('x').text) - y = float(point_elem.find('y').text) - points.append(x) - points.append(y) - - if type_elem is not None and type_elem.text == 'bounding_box': - xmin = min(points[::2]) - xmax = max(points[::2]) - ymin = min(points[1::2]) - ymax = max(points[1::2]) - ann_items.append(Bbox(xmin, ymin, xmax - xmin, ymax - ymin, - label=label, attributes=attributes, id=obj_id, - )) - else: - ann_items.append(Polygon(points, - label=label, attributes=attributes, id=obj_id, - )) - elif segm_elem is not None: - user_elem = segm_elem.find('username') - if user_elem is not None and user_elem.text: - user = user_elem.text - attributes.append(('username', user)) - - mask_path = osp.join(dataset_root, LabelMePath.MASKS_DIR, - segm_elem.find('mask').text) - if not osp.isfile(mask_path): - raise Exception("Can't find mask at '%s'" % mask_path) - mask = load_mask(mask_path) - mask = np.any(mask, axis=2) - ann_items.append(Mask(image=mask, label=label, id=obj_id, - attributes=attributes)) - - if not deleted: - parsed_annotations[obj_id] = ann_items - - # Find parents and children - parts_elem = obj_elem.find('parts') - if parts_elem is not None: - children_ids = [] - hasparts_elem = parts_elem.find('hasparts') - if hasparts_elem is not None and hasparts_elem.text: - children_ids = [int(c) for c in hasparts_elem.text.split(',')] - - parent_ids = [] - ispartof_elem = parts_elem.find('ispartof') - if ispartof_elem is not None and ispartof_elem.text: - parent_ids = [int(c) for c in ispartof_elem.text.split(',')] - - if children_ids and not parent_ids and hasparts_elem.text: - root_annotations.add(obj_id) - group_assignments[obj_id] = [None, children_ids] - - # assign single group to all grouped annotations - current_group_id = 0 - annotations_to_visit = list(root_annotations) - while annotations_to_visit: - ann_id = annotations_to_visit.pop() - ann_assignment = group_assignments[ann_id] - group_id, children_ids = ann_assignment - if group_id: - continue - - if ann_id in root_annotations: - current_group_id += 1 # start a new group - - group_id = current_group_id - ann_assignment[0] = group_id - - # continue with children - annotations_to_visit.extend(children_ids) - - assert current_group_id == len(root_annotations) - - for ann_id, ann_items in parsed_annotations.items(): - group_id = 0 - if ann_id in group_assignments: - ann_assignment = group_assignments[ann_id] - group_id = ann_assignment[0] - - for ann_item in ann_items: - if group_id: - ann_item.group = group_id - - image_annotations.append(ann_item) - - return image_annotations - - -class LabelMeImporter(Importer): - _EXTRACTOR_NAME = 'label_me' - - @classmethod - def detect(cls, path): - if not osp.isdir(path): - return False - return len(cls.find_subsets(path)) != 0 - - def __call__(self, path, **extra_params): - from datumaro.components.project import Project # cyclic import - project = Project() - - subset_paths = self.find_subsets(path) - if len(subset_paths) == 0: - raise Exception("Failed to find 'label_me' dataset at '%s'" % path) - - for subset_path, subset_name in subset_paths: - params = {} - if subset_name: - params['subset_name'] = subset_name - params.update(extra_params) - - source_name = osp.splitext(osp.basename(subset_path))[0] - project.add_source(source_name, { - 'url': subset_path, - 'format': self._EXTRACTOR_NAME, - 'options': params, - }) - - return project - - @staticmethod - def find_subsets(path): - subset_paths = [] - if not osp.isdir(path): - raise Exception("Expected directory path, got '%s'" % path) - - path = osp.normpath(path) - - def has_annotations(d): - return len([p for p in os.listdir(d) if p.endswith('.xml')]) != 0 - - if has_annotations(path): - subset_paths = [(path, None)] - else: - for d in os.listdir(path): - subset = d - d = osp.join(path, d) - if osp.isdir(d) and has_annotations(d): - subset_paths.append((d, subset)) - return subset_paths - - -class LabelMeConverter(Converter): - DEFAULT_IMAGE_EXT = LabelMePath.IMAGE_EXT - - def apply(self): - for subset_name in self._extractor.subsets() or [None]: - if subset_name: - subset = self._extractor.get_subset(subset_name) - else: - subset_name = DEFAULT_SUBSET_NAME - subset = self._extractor - - subset_dir = osp.join(self._save_dir, subset_name) - os.makedirs(subset_dir, exist_ok=True) - os.makedirs(osp.join(subset_dir, LabelMePath.MASKS_DIR), - exist_ok=True) - - for item in subset: - self._save_item(item, subset_dir) - - def _get_label(self, label_id): - if label_id is None: - return '' - return self._extractor.categories()[AnnotationType.label] \ - .items[label_id].name - - def _save_item(self, item, subset_dir): - from lxml import etree as ET - - log.debug("Converting item '%s'", item.id) - - if '/' in item.id: - raise Exception("Can't export item '%s': " - "LabelMe format only supports flat image layout" % item.id) - - image_filename = self._make_image_filename(item) - if self._save_images: - if item.has_image and item.image.has_data: - self._save_image(item, osp.join(subset_dir, image_filename)) - else: - log.debug("Item '%s' has no image", item.id) - - root_elem = ET.Element('annotation') - ET.SubElement(root_elem, 'filename').text = image_filename - ET.SubElement(root_elem, 'folder').text = '' - - source_elem = ET.SubElement(root_elem, 'source') - ET.SubElement(source_elem, 'sourceImage').text = '' - ET.SubElement(source_elem, 'sourceAnnotation').text = 'Datumaro' - - if item.has_image: - image_elem = ET.SubElement(root_elem, 'imagesize') - image_size = item.image.size - ET.SubElement(image_elem, 'nrows').text = str(image_size[0]) - ET.SubElement(image_elem, 'ncols').text = str(image_size[1]) - - groups = defaultdict(list) - - obj_id = 0 - for ann in item.annotations: - if not ann.type in { AnnotationType.polygon, - AnnotationType.bbox, AnnotationType.mask }: - continue - - obj_elem = ET.SubElement(root_elem, 'object') - ET.SubElement(obj_elem, 'name').text = self._get_label(ann.label) - ET.SubElement(obj_elem, 'deleted').text = '0' - ET.SubElement(obj_elem, 'verified').text = '0' - ET.SubElement(obj_elem, 'occluded').text = \ - 'yes' if ann.attributes.pop('occluded', '') == True else 'no' - ET.SubElement(obj_elem, 'date').text = '' - ET.SubElement(obj_elem, 'id').text = str(obj_id) - - parts_elem = ET.SubElement(obj_elem, 'parts') - if ann.group: - groups[ann.group].append((obj_id, parts_elem)) - else: - ET.SubElement(parts_elem, 'hasparts').text = '' - ET.SubElement(parts_elem, 'ispartof').text = '' - - if ann.type == AnnotationType.bbox: - ET.SubElement(obj_elem, 'type').text = 'bounding_box' - - poly_elem = ET.SubElement(obj_elem, 'polygon') - x0, y0, x1, y1 = ann.points - points = [ (x0, y0), (x1, y0), (x1, y1), (x0, y1) ] - for x, y in points: - point_elem = ET.SubElement(poly_elem, 'pt') - ET.SubElement(point_elem, 'x').text = '%.2f' % x - ET.SubElement(point_elem, 'y').text = '%.2f' % y - - ET.SubElement(poly_elem, 'username').text = \ - str(ann.attributes.pop('username', '')) - elif ann.type == AnnotationType.polygon: - poly_elem = ET.SubElement(obj_elem, 'polygon') - for x, y in zip(ann.points[::2], ann.points[1::2]): - point_elem = ET.SubElement(poly_elem, 'pt') - ET.SubElement(point_elem, 'x').text = '%.2f' % x - ET.SubElement(point_elem, 'y').text = '%.2f' % y - - ET.SubElement(poly_elem, 'username').text = \ - str(ann.attributes.pop('username', '')) - elif ann.type == AnnotationType.mask: - mask_filename = '%s_mask_%s.png' % (item.id, obj_id) - save_image(osp.join(subset_dir, LabelMePath.MASKS_DIR, - mask_filename), - self._paint_mask(ann.image)) - - segm_elem = ET.SubElement(obj_elem, 'segm') - ET.SubElement(segm_elem, 'mask').text = mask_filename - - bbox = find_mask_bbox(ann.image) - box_elem = ET.SubElement(segm_elem, 'box') - ET.SubElement(box_elem, 'xmin').text = '%.2f' % bbox[0] - ET.SubElement(box_elem, 'ymin').text = '%.2f' % bbox[1] - ET.SubElement(box_elem, 'xmax').text = \ - '%.2f' % (bbox[0] + bbox[2]) - ET.SubElement(box_elem, 'ymax').text = \ - '%.2f' % (bbox[1] + bbox[3]) - - ET.SubElement(segm_elem, 'username').text = \ - str(ann.attributes.pop('username', '')) - else: - raise NotImplementedError("Unknown shape type '%s'" % ann.type) - - attrs = [] - for k, v in ann.attributes.items(): - attrs.append('%s=%s' % (k, v)) - ET.SubElement(obj_elem, 'attributes').text = ', '.join(attrs) - - obj_id += 1 - - for _, group in groups.items(): - leader_id, leader_parts_elem = group[0] - leader_parts = [str(o_id) for o_id, _ in group[1:]] - ET.SubElement(leader_parts_elem, 'hasparts').text = \ - ','.join(leader_parts) - ET.SubElement(leader_parts_elem, 'ispartof').text = '' - - for obj_id, parts_elem in group[1:]: - ET.SubElement(parts_elem, 'hasparts').text = '' - ET.SubElement(parts_elem, 'ispartof').text = str(leader_id) - - xml_path = osp.join(subset_dir, '%s.xml' % item.id) - with open(xml_path, 'w', encoding='utf-8') as f: - xml_data = ET.tostring(root_elem, encoding='unicode', - pretty_print=True) - f.write(xml_data) - - @staticmethod - def _paint_mask(mask): - # TODO: check if mask colors are random - return np.array([[0, 0, 0, 0], [255, 203, 0, 153]], - dtype=np.uint8)[mask.astype(np.uint8)] \ No newline at end of file diff --git a/datumaro/datumaro/plugins/mot_format.py b/datumaro/datumaro/plugins/mot_format.py deleted file mode 100644 index 12d3d07c..00000000 --- a/datumaro/datumaro/plugins/mot_format.py +++ /dev/null @@ -1,314 +0,0 @@ -# Copyright (C) 2020 Intel Corporation -# -# SPDX-License-Identifier: MIT - -# The Multiple Object Tracking Benchmark challenge format support -# Format description: https://arxiv.org/pdf/1906.04567.pdf -# Another description: https://motchallenge.net/instructions - -from collections import OrderedDict -import csv -from enum import Enum -import logging as log -import os -import os.path as osp - -from datumaro.components.extractor import (SourceExtractor, - DatasetItem, AnnotationType, Bbox, LabelCategories -) -from datumaro.components.extractor import Importer -from datumaro.components.converter import Converter -from datumaro.util import cast -from datumaro.util.image import Image - - -MotLabel = Enum('MotLabel', [ - ('pedestrian', 1), - ('person on vehicle', 2), - ('car', 3), - ('bicycle', 4), - ('motorbike', 5), - ('non motorized vehicle', 6), - ('static person', 7), - ('distractor', 8), - ('occluder', 9), - ('occluder on the ground', 10), - ('occluder full', 11), - ('reflection', 12), -]) - -class MotPath: - IMAGE_DIR = 'img1' - SEQINFO_FILE = 'seqinfo.ini' - LABELS_FILE = 'labels.txt' - GT_FILENAME = 'gt.txt' - DET_FILENAME = 'det.txt' - - IMAGE_EXT = '.jpg' - - FIELDS = [ - 'frame_id', - 'track_id', - 'x', - 'y', - 'w', - 'h', - 'confidence', # or 'not ignored' flag for GT anns - 'class_id', - 'visibility' - ] - - -class MotSeqExtractor(SourceExtractor): - def __init__(self, path, labels=None, occlusion_threshold=0, is_gt=None): - super().__init__() - - assert osp.isfile(path) - seq_root = osp.dirname(osp.dirname(path)) - self._image_dir = '' - if osp.isdir(osp.join(seq_root, MotPath.IMAGE_DIR)): - self._image_dir = osp.join(seq_root, MotPath.IMAGE_DIR) - - seq_info = osp.join(seq_root, MotPath.SEQINFO_FILE) - if osp.isfile(seq_info): - seq_info = self._parse_seq_info(seq_info) - self._image_dir = osp.join(seq_root, seq_info['imdir']) - else: - seq_info = None - self._seq_info = seq_info - - self._occlusion_threshold = float(occlusion_threshold) - - assert is_gt in {None, True, False} - if is_gt is None: - if osp.basename(path) == MotPath.DET_FILENAME: - is_gt = False - else: - is_gt = True - self._is_gt = is_gt - - if labels is None: - labels = osp.join(osp.dirname(path), MotPath.LABELS_FILE) - if not osp.isfile(labels): - labels = [lbl.name for lbl in MotLabel] - if isinstance(labels, str): - labels = self._parse_labels(labels) - elif isinstance(labels, list): - assert all(isinstance(lbl, str) for lbl in labels), labels - else: - raise TypeError("Unexpected type of 'labels' argument: %s" % labels) - self._categories = self._load_categories(labels) - self._items = self._load_items(path) - - def categories(self): - return self._categories - - def __iter__(self): - for item in self._items.values(): - yield item - - def __len__(self): - return len(self._items) - - @staticmethod - def _parse_labels(path): - with open(path, encoding='utf-8') as labels_file: - return [s.strip() for s in labels_file] - - def _load_categories(self, labels): - attributes = ['track_id'] - if self._is_gt: - attributes += ['occluded', 'visibility', 'ignored'] - else: - attributes += ['score'] - label_cat = LabelCategories(attributes=attributes) - for label in labels: - label_cat.add(label) - - return { AnnotationType.label: label_cat } - - def _load_items(self, path): - labels_count = len(self._categories[AnnotationType.label].items) - items = OrderedDict() - - if self._seq_info: - for frame_id in range(self._seq_info['seqlength']): - items[frame_id] = DatasetItem( - id=frame_id, - subset=self._subset, - image=Image( - path=osp.join(self._image_dir, - '%06d%s' % (frame_id, self._seq_info['imext'])), - size=(self._seq_info['imheight'], self._seq_info['imwidth']) - ) - ) - elif osp.isdir(self._image_dir): - for p in os.listdir(self._image_dir): - if p.endswith(MotPath.IMAGE_EXT): - frame_id = int(osp.splitext(p)[0]) - items[frame_id] = DatasetItem( - id=frame_id, - subset=self._subset, - image=osp.join(self._image_dir, p), - ) - - with open(path, newline='', encoding='utf-8') as csv_file: - # NOTE: Different MOT files have different count of fields - # (7, 9 or 10). This is handled by reader: - # - all extra fields go to a separate field - # - all unmet fields have None values - for row in csv.DictReader(csv_file, fieldnames=MotPath.FIELDS): - frame_id = int(row['frame_id']) - item = items.get(frame_id) - if item is None: - item = DatasetItem(id=frame_id, subset=self._subset) - annotations = item.annotations - - x, y = float(row['x']), float(row['y']) - w, h = float(row['w']), float(row['h']) - label_id = row.get('class_id') - if label_id and label_id != '-1': - label_id = int(label_id) - 1 - assert label_id < labels_count, label_id - else: - label_id = None - - attributes = {} - - # Annotations for detection task are not related to any track - track_id = int(row['track_id']) - if 0 < track_id: - attributes['track_id'] = track_id - - confidence = cast(row.get('confidence'), float, 1) - visibility = cast(row.get('visibility'), float, 1) - if self._is_gt: - attributes['visibility'] = visibility - attributes['occluded'] = \ - visibility <= self._occlusion_threshold - attributes['ignored'] = confidence == 0 - else: - attributes['score'] = float(confidence) - - annotations.append(Bbox(x, y, w, h, label=label_id, - attributes=attributes)) - - items[frame_id] = item - return items - - @classmethod - def _parse_seq_info(cls, path): - fields = {} - with open(path, encoding='utf-8') as f: - for line in f: - entry = line.lower().strip().split('=', maxsplit=1) - if len(entry) == 2: - fields[entry[0]] = entry[1] - cls._check_seq_info(fields) - for k in { 'framerate', 'seqlength', 'imwidth', 'imheight' }: - fields[k] = int(fields[k]) - return fields - - @staticmethod - def _check_seq_info(seq_info): - assert set(seq_info) == {'name', 'imdir', 'framerate', 'seqlength', 'imwidth', 'imheight', 'imext'}, seq_info - -class MotSeqImporter(Importer): - _EXTRACTOR_NAME = 'mot_seq' - - @classmethod - def detect(cls, path): - return len(cls.find_subsets(path)) != 0 - - def __call__(self, path, **extra_params): - from datumaro.components.project import Project # cyclic import - project = Project() - - subsets = self.find_subsets(path) - if len(subsets) == 0: - raise Exception("Failed to find 'mot' dataset at '%s'" % path) - - for ann_file in subsets: - log.info("Found a dataset at '%s'" % ann_file) - - source_name = osp.splitext(osp.basename(ann_file))[0] - project.add_source(source_name, { - 'url': ann_file, - 'format': self._EXTRACTOR_NAME, - 'options': extra_params, - }) - - return project - - @staticmethod - def find_subsets(path): - subsets = [] - if path.endswith('.txt') and osp.isfile(path): - subsets = [path] - elif osp.isdir(path): - p = osp.join(path, 'gt', MotPath.GT_FILENAME) - if osp.isfile(p): - subsets.append(p) - return subsets - -class MotSeqGtConverter(Converter): - DEFAULT_IMAGE_EXT = MotPath.IMAGE_EXT - - def apply(self): - extractor = self._extractor - - images_dir = osp.join(self._save_dir, MotPath.IMAGE_DIR) - os.makedirs(images_dir, exist_ok=True) - self._images_dir = images_dir - - anno_dir = osp.join(self._save_dir, 'gt') - os.makedirs(anno_dir, exist_ok=True) - anno_file = osp.join(anno_dir, MotPath.GT_FILENAME) - with open(anno_file, 'w', encoding="utf-8") as csv_file: - writer = csv.DictWriter(csv_file, fieldnames=MotPath.FIELDS) - - track_id_mapping = {-1: -1} - for idx, item in enumerate(extractor): - log.debug("Converting item '%s'", item.id) - - frame_id = cast(item.id, int, 1 + idx) - - for anno in item.annotations: - if anno.type != AnnotationType.bbox: - continue - - track_id = int(anno.attributes.get('track_id', -1)) - if track_id not in track_id_mapping: - track_id_mapping[track_id] = len(track_id_mapping) - track_id = track_id_mapping[track_id] - - writer.writerow({ - 'frame_id': frame_id, - 'track_id': track_id, - 'x': anno.x, - 'y': anno.y, - 'w': anno.w, - 'h': anno.h, - 'confidence': int(anno.attributes.get('ignored') != True), - 'class_id': 1 + cast(anno.label, int, -2), - 'visibility': float( - anno.attributes.get('visibility', - 1 - float( - anno.attributes.get('occluded', False) - ) - ) - ) - }) - - if self._save_images: - if item.has_image and item.image.has_data: - self._save_image(item, osp.join(self._images_dir, - '%06d%s' % (frame_id, self._find_image_ext(item)))) - else: - log.debug("Item '%s' has no image", item.id) - - labels_file = osp.join(anno_dir, MotPath.LABELS_FILE) - with open(labels_file, 'w', encoding='utf-8') as f: - f.write('\n'.join(l.name - for l in extractor.categories()[AnnotationType.label].items) - ) diff --git a/datumaro/datumaro/plugins/openvino_launcher.py b/datumaro/datumaro/plugins/openvino_launcher.py deleted file mode 100644 index abdaa0fc..00000000 --- a/datumaro/datumaro/plugins/openvino_launcher.py +++ /dev/null @@ -1,188 +0,0 @@ - -# Copyright (C) 2019-2020 Intel Corporation -# -# SPDX-License-Identifier: MIT - -# pylint: disable=exec-used - -import cv2 -import logging as log -import numpy as np -import os.path as osp -import shutil - -from openvino.inference_engine import IECore - -from datumaro.components.cli_plugin import CliPlugin -from datumaro.components.launcher import Launcher - - -class OpenVinoImporter(CliPlugin): - @classmethod - def build_cmdline_parser(cls, **kwargs): - parser = super().build_cmdline_parser(**kwargs) - parser.add_argument('-d', '--description', required=True, - help="Path to the model description file (.xml)") - parser.add_argument('-w', '--weights', required=True, - help="Path to the model weights file (.bin)") - parser.add_argument('-i', '--interpreter', required=True, - help="Path to the network output interprter script (.py)") - parser.add_argument('--device', default='CPU', - help="Target device (default: %(default)s)") - return parser - - @staticmethod - def copy_model(model_dir, model): - shutil.copy(model['description'], - osp.join(model_dir, osp.basename(model['description']))) - model['description'] = osp.basename(model['description']) - - shutil.copy(model['weights'], - osp.join(model_dir, osp.basename(model['weights']))) - model['weights'] = osp.basename(model['weights']) - - shutil.copy(model['interpreter'], - osp.join(model_dir, osp.basename(model['interpreter']))) - model['interpreter'] = osp.basename(model['interpreter']) - - -class InterpreterScript: - def __init__(self, path): - with open(path, 'r') as f: - script = f.read() - - context = {} - exec(script, context, context) - - process_outputs = context.get('process_outputs') - if not callable(process_outputs): - raise Exception("Can't find 'process_outputs' function in " - "the interpreter script") - self.__dict__['process_outputs'] = process_outputs - - get_categories = context.get('get_categories') - assert get_categories is None or callable(get_categories) - if get_categories: - self.__dict__['get_categories'] = get_categories - - @staticmethod - def get_categories(): - return None - - @staticmethod - def process_outputs(inputs, outputs): - raise NotImplementedError( - "Function should be implemented in the interpreter script") - - -class OpenVinoLauncher(Launcher): - cli_plugin = OpenVinoImporter - - def __init__(self, description, weights, interpreter, - plugins_path=None, device=None, model_dir=None): - model_dir = model_dir or '' - if not osp.isfile(description): - description = osp.join(model_dir, description) - if not osp.isfile(description): - raise Exception('Failed to open model description file "%s"' % \ - (description)) - - if not osp.isfile(weights): - weights = osp.join(model_dir, weights) - if not osp.isfile(weights): - raise Exception('Failed to open model weights file "%s"' % \ - (weights)) - - if not osp.isfile(interpreter): - interpreter = osp.join(model_dir, interpreter) - if not osp.isfile(interpreter): - raise Exception('Failed to open model interpreter script file "%s"' % \ - (interpreter)) - - self._interpreter = InterpreterScript(interpreter) - - self._device = device or 'CPU' - - self._ie = IECore() - if hasattr(self._ie, 'read_network'): - self._network = self._ie.read_network(description, weights) - else: # backward compatibility - from openvino.inference_engine import IENetwork - self._network = IENetwork.from_ir(description, weights) - self._check_model_support(self._network, self._device) - self._load_executable_net() - - def _check_model_support(self, net, device): - supported_layers = set(self._ie.query_network(net, device)) - not_supported_layers = set(net.layers) - supported_layers - if len(not_supported_layers) != 0: - log.error("The following layers are not supported " \ - "by the plugin for device '%s': %s." % \ - (device, ', '.join(not_supported_layers))) - raise NotImplementedError( - "Some layers are not supported on the device") - - def _load_executable_net(self, batch_size=1): - network = self._network - - iter_inputs = iter(network.inputs) - self._input_blob_name = next(iter_inputs) - self._output_blob_name = next(iter(network.outputs)) - - # NOTE: handling for the inclusion of `image_info` in OpenVino2019 - self._require_image_info = 'image_info' in network.inputs - if self._input_blob_name == 'image_info': - self._input_blob_name = next(iter_inputs) - - input_type = network.inputs[self._input_blob_name] - self._input_layout = input_type if isinstance(input_type, list) else input_type.shape - - self._input_layout[0] = batch_size - network.reshape({self._input_blob_name: self._input_layout}) - self._batch_size = batch_size - - self._net = self._ie.load_network(network=network, num_requests=1, - device_name=self._device) - - def infer(self, inputs): - assert len(inputs.shape) == 4, \ - "Expected an input image in (N, H, W, C) format, got %s" % \ - (inputs.shape) - assert inputs.shape[3] == 3, "Expected BGR input, got %s" % inputs.shape - - n, c, h, w = self._input_layout - if inputs.shape[1:3] != (h, w): - resized_inputs = np.empty((n, h, w, c), dtype=inputs.dtype) - for inp, resized_input in zip(inputs, resized_inputs): - cv2.resize(inp, (w, h), resized_input) - inputs = resized_inputs - inputs = inputs.transpose((0, 3, 1, 2)) # NHWC to NCHW - inputs = {self._input_blob_name: inputs} - if self._require_image_info: - info = np.zeros([1, 3]) - info[0, 0] = h - info[0, 1] = w - info[0, 2] = 1.0 # scale - inputs['image_info'] = info - - results = self._net.infer(inputs) - if len(results) == 1: - return results[self._output_blob_name] - else: - return results - - def launch(self, inputs): - batch_size = len(inputs) - if self._batch_size < batch_size: - self._load_executable_net(batch_size) - - outputs = self.infer(inputs) - results = self.process_outputs(inputs, outputs) - return results - - def categories(self): - return self._interpreter.get_categories() - - def process_outputs(self, inputs, outputs): - return self._interpreter.process_outputs(inputs, outputs) - diff --git a/datumaro/datumaro/plugins/tf_detection_api_format/__init__.py b/datumaro/datumaro/plugins/tf_detection_api_format/__init__.py deleted file mode 100644 index e69de29b..00000000 diff --git a/datumaro/datumaro/plugins/tf_detection_api_format/converter.py b/datumaro/datumaro/plugins/tf_detection_api_format/converter.py deleted file mode 100644 index a178bdba..00000000 --- a/datumaro/datumaro/plugins/tf_detection_api_format/converter.py +++ /dev/null @@ -1,217 +0,0 @@ - -# Copyright (C) 2019-2020 Intel Corporation -# -# SPDX-License-Identifier: MIT - -import codecs -from collections import OrderedDict -import hashlib -import logging as log -import os -import os.path as osp -import string - -from datumaro.components.extractor import (AnnotationType, DEFAULT_SUBSET_NAME, - LabelCategories -) -from datumaro.components.converter import Converter -from datumaro.util.image import encode_image -from datumaro.util.annotation_util import (max_bbox, - find_group_leader, find_instances) -from datumaro.util.mask_tools import merge_masks -from datumaro.util.tf_util import import_tf as _import_tf - -from .format import DetectionApiPath -tf = _import_tf() - - -# filter out non-ASCII characters, otherwise training will crash -_printable = set(string.printable) -def _make_printable(s): - return ''.join(filter(lambda x: x in _printable, s)) - -def int64_feature(value): - return tf.train.Feature(int64_list=tf.train.Int64List(value=[value])) - -def int64_list_feature(value): - return tf.train.Feature(int64_list=tf.train.Int64List(value=value)) - -def bytes_feature(value): - return tf.train.Feature(bytes_list=tf.train.BytesList(value=[value])) - -def bytes_list_feature(value): - return tf.train.Feature(bytes_list=tf.train.BytesList(value=value)) - -def float_list_feature(value): - return tf.train.Feature(float_list=tf.train.FloatList(value=value)) - -class TfDetectionApiConverter(Converter): - DEFAULT_IMAGE_EXT = DetectionApiPath.DEFAULT_IMAGE_EXT - - @classmethod - def build_cmdline_parser(cls, **kwargs): - parser = super().build_cmdline_parser(**kwargs) - parser.add_argument('--save-masks', action='store_true', - help="Include instance masks (default: %(default)s)") - return parser - - def __init__(self, extractor, save_dir, save_masks=False, **kwargs): - super().__init__(extractor, save_dir, **kwargs) - - self._save_masks = save_masks - - def apply(self): - os.makedirs(self._save_dir, exist_ok=True) - - label_categories = self._extractor.categories().get(AnnotationType.label, - LabelCategories()) - get_label = lambda label_id: label_categories.items[label_id].name \ - if label_id is not None else '' - label_ids = OrderedDict((label.name, 1 + idx) - for idx, label in enumerate(label_categories.items)) - map_label_id = lambda label_id: label_ids.get(get_label(label_id), 0) - self._get_label = get_label - self._get_label_id = map_label_id - - subsets = self._extractor.subsets() - if len(subsets) == 0: - subsets = [ None ] - - for subset_name in subsets: - if subset_name: - subset = self._extractor.get_subset(subset_name) - else: - subset_name = DEFAULT_SUBSET_NAME - subset = self._extractor - - labelmap_path = osp.join(self._save_dir, DetectionApiPath.LABELMAP_FILE) - with codecs.open(labelmap_path, 'w', encoding='utf8') as f: - for label, idx in label_ids.items(): - f.write( - 'item {\n' + - ('\tid: %s\n' % (idx)) + - ("\tname: '%s'\n" % (label)) + - '}\n\n' - ) - - anno_path = osp.join(self._save_dir, '%s.tfrecord' % (subset_name)) - with tf.io.TFRecordWriter(anno_path) as writer: - for item in subset: - tf_example = self._make_tf_example(item) - writer.write(tf_example.SerializeToString()) - - @staticmethod - def _find_instances(annotations): - return find_instances(a for a in annotations - if a.type in { AnnotationType.bbox, AnnotationType.mask }) - - def _find_instance_parts(self, group, img_width, img_height): - boxes = [a for a in group if a.type == AnnotationType.bbox] - masks = [a for a in group if a.type == AnnotationType.mask] - - anns = boxes + masks - leader = find_group_leader(anns) - bbox = max_bbox(anns) - - mask = None - if self._save_masks: - mask = merge_masks([m.image for m in masks]) - - return [leader, mask, bbox] - - def _export_instances(self, instances, width, height): - xmins = [] # List of normalized left x coordinates of bounding boxes (1 per box) - xmaxs = [] # List of normalized right x coordinates of bounding boxes (1 per box) - ymins = [] # List of normalized top y coordinates of bounding boxes (1 per box) - ymaxs = [] # List of normalized bottom y coordinates of bounding boxes (1 per box) - classes_text = [] # List of class names of bounding boxes (1 per box) - classes = [] # List of class ids of bounding boxes (1 per box) - masks = [] # List of PNG-encoded instance masks (1 per box) - - for leader, mask, box in instances: - label = _make_printable(self._get_label(leader.label)) - classes_text.append(label.encode('utf-8')) - classes.append(self._get_label_id(leader.label)) - - xmins.append(box[0] / width) - xmaxs.append((box[0] + box[2]) / width) - ymins.append(box[1] / height) - ymaxs.append((box[1] + box[3]) / height) - - if self._save_masks: - if mask is not None: - mask = encode_image(mask, '.png') - else: - mask = b'' - masks.append(mask) - - result = {} - if classes: - result = { - 'image/object/bbox/xmin': float_list_feature(xmins), - 'image/object/bbox/xmax': float_list_feature(xmaxs), - 'image/object/bbox/ymin': float_list_feature(ymins), - 'image/object/bbox/ymax': float_list_feature(ymaxs), - 'image/object/class/text': bytes_list_feature(classes_text), - 'image/object/class/label': int64_list_feature(classes), - } - if masks: - result['image/object/mask'] = bytes_list_feature(masks) - return result - - def _make_tf_example(self, item): - features = { - 'image/source_id': bytes_feature( - str(item.attributes.get('source_id') or '').encode('utf-8') - ), - } - - filename = self._make_image_filename(item) - features['image/filename'] = bytes_feature(filename.encode('utf-8')) - - if not item.has_image: - raise Exception("Failed to export dataset item '%s': " - "item has no image info" % item.id) - height, width = item.image.size - - features.update({ - 'image/height': int64_feature(height), - 'image/width': int64_feature(width), - }) - - features.update({ - 'image/encoded': bytes_feature(b''), - 'image/format': bytes_feature(b''), - 'image/key/sha256': bytes_feature(b''), - }) - if self._save_images: - if item.has_image and item.image.has_data: - buffer, fmt = self._save_image(item, filename) - key = hashlib.sha256(buffer).hexdigest() - - features.update({ - 'image/encoded': bytes_feature(buffer), - 'image/format': bytes_feature(fmt.encode('utf-8')), - 'image/key/sha256': bytes_feature(key.encode('utf8')), - }) - else: - log.warning("Item '%s' has no image" % item.id) - - instances = self._find_instances(item.annotations) - instances = [self._find_instance_parts(i, width, height) for i in instances] - features.update(self._export_instances(instances, width, height)) - - tf_example = tf.train.Example( - features=tf.train.Features(feature=features)) - - return tf_example - - def _save_image(self, item, path=None): - dst_ext = osp.splitext(osp.basename(path))[1] - fmt = DetectionApiPath.IMAGE_EXT_FORMAT.get(dst_ext) - if not fmt: - log.warning("Item '%s': can't find format string for the '%s' " - "image extension, the corresponding field will be empty." % \ - (item.id, dst_ext)) - buffer = encode_image(item.image.data, dst_ext) - return buffer, fmt \ No newline at end of file diff --git a/datumaro/datumaro/plugins/tf_detection_api_format/extractor.py b/datumaro/datumaro/plugins/tf_detection_api_format/extractor.py deleted file mode 100644 index 6962d3c0..00000000 --- a/datumaro/datumaro/plugins/tf_detection_api_format/extractor.py +++ /dev/null @@ -1,195 +0,0 @@ - -# Copyright (C) 2019-2020 Intel Corporation -# -# SPDX-License-Identifier: MIT - -from collections import OrderedDict -import numpy as np -import os.path as osp -import re - -from datumaro.components.extractor import (SourceExtractor, DatasetItem, - AnnotationType, Bbox, Mask, LabelCategories -) -from datumaro.util.image import Image, decode_image, lazy_image -from datumaro.util.tf_util import import_tf as _import_tf - -from .format import DetectionApiPath -tf = _import_tf() - - -def clamp(value, _min, _max): - return max(min(_max, value), _min) - -class TfDetectionApiExtractor(SourceExtractor): - def __init__(self, path): - assert osp.isfile(path), path - images_dir = '' - root_dir = osp.dirname(osp.abspath(path)) - if osp.basename(root_dir) == DetectionApiPath.ANNOTATIONS_DIR: - root_dir = osp.dirname(root_dir) - images_dir = osp.join(root_dir, DetectionApiPath.IMAGES_DIR) - if not osp.isdir(images_dir): - images_dir = '' - - super().__init__(subset=osp.splitext(osp.basename(path))[0]) - - items, labels = self._parse_tfrecord_file(path, self._subset, images_dir) - self._items = items - self._categories = self._load_categories(labels) - - def categories(self): - return self._categories - - def __iter__(self): - for item in self._items: - yield item - - def __len__(self): - return len(self._items) - - @staticmethod - def _load_categories(labels): - label_categories = LabelCategories() - labels = sorted(labels.items(), key=lambda item: item[1]) - for label, _ in labels: - label_categories.add(label) - return { - AnnotationType.label: label_categories - } - - @classmethod - def _parse_labelmap(cls, text): - id_pattern = r'(?:id\s*:\s*(?P\d+))' - name_pattern = r'(?:name\s*:\s*[\'\"](?P.*?)[\'\"])' - entry_pattern = r'(\{(?:[\s\n]*(?:%(id)s|%(name)s)[\s\n]*){2}\})+' % \ - {'id': id_pattern, 'name': name_pattern} - matches = re.finditer(entry_pattern, text) - - labelmap = {} - for match in matches: - label_id = match.group('id') - label_name = match.group('name') - if label_id is not None and label_name is not None: - labelmap[label_name] = int(label_id) - - return labelmap - - @classmethod - def _parse_tfrecord_file(cls, filepath, subset, images_dir): - dataset = tf.data.TFRecordDataset(filepath) - features = { - 'image/filename': tf.io.FixedLenFeature([], tf.string), - 'image/source_id': tf.io.FixedLenFeature([], tf.string), - 'image/height': tf.io.FixedLenFeature([], tf.int64), - 'image/width': tf.io.FixedLenFeature([], tf.int64), - 'image/encoded': tf.io.FixedLenFeature([], tf.string), - 'image/format': tf.io.FixedLenFeature([], tf.string), - - # use varlen to avoid errors when this field is missing - 'image/key/sha256': tf.io.VarLenFeature(tf.string), - - # Object boxes and classes. - 'image/object/bbox/xmin': tf.io.VarLenFeature(tf.float32), - 'image/object/bbox/xmax': tf.io.VarLenFeature(tf.float32), - 'image/object/bbox/ymin': tf.io.VarLenFeature(tf.float32), - 'image/object/bbox/ymax': tf.io.VarLenFeature(tf.float32), - 'image/object/class/label': tf.io.VarLenFeature(tf.int64), - 'image/object/class/text': tf.io.VarLenFeature(tf.string), - 'image/object/mask': tf.io.VarLenFeature(tf.string), - } - - dataset_labels = OrderedDict() - labelmap_path = osp.join(osp.dirname(filepath), - DetectionApiPath.LABELMAP_FILE) - if osp.exists(labelmap_path): - with open(labelmap_path, 'r', encoding='utf-8') as f: - labelmap_text = f.read() - dataset_labels.update({ label: id - 1 - for label, id in cls._parse_labelmap(labelmap_text).items() - }) - - dataset_items = [] - - for record in dataset: - parsed_record = tf.io.parse_single_example(record, features) - frame_id = parsed_record['image/source_id'].numpy().decode('utf-8') - frame_filename = \ - parsed_record['image/filename'].numpy().decode('utf-8') - frame_height = tf.cast( - parsed_record['image/height'], tf.int64).numpy().item() - frame_width = tf.cast( - parsed_record['image/width'], tf.int64).numpy().item() - frame_image = parsed_record['image/encoded'].numpy() - xmins = tf.sparse.to_dense( - parsed_record['image/object/bbox/xmin']).numpy() - ymins = tf.sparse.to_dense( - parsed_record['image/object/bbox/ymin']).numpy() - xmaxs = tf.sparse.to_dense( - parsed_record['image/object/bbox/xmax']).numpy() - ymaxs = tf.sparse.to_dense( - parsed_record['image/object/bbox/ymax']).numpy() - label_ids = tf.sparse.to_dense( - parsed_record['image/object/class/label']).numpy() - labels = tf.sparse.to_dense( - parsed_record['image/object/class/text'], - default_value=b'').numpy() - masks = tf.sparse.to_dense( - parsed_record['image/object/mask'], - default_value=b'').numpy() - - for label, label_id in zip(labels, label_ids): - label = label.decode('utf-8') - if not label: - continue - if label_id <= 0: - continue - if label in dataset_labels: - continue - dataset_labels[label] = label_id - 1 - - item_id = osp.splitext(frame_filename)[0] - - annotations = [] - for shape_id, shape in enumerate( - np.dstack((labels, xmins, ymins, xmaxs, ymaxs))[0]): - label = shape[0].decode('utf-8') - - mask = None - if len(masks) != 0: - mask = masks[shape_id] - - if mask is not None: - if isinstance(mask, bytes): - mask = lazy_image(mask, decode_image) - annotations.append(Mask(image=mask, - label=dataset_labels.get(label) - )) - else: - x = clamp(shape[1] * frame_width, 0, frame_width) - y = clamp(shape[2] * frame_height, 0, frame_height) - w = clamp(shape[3] * frame_width, 0, frame_width) - x - h = clamp(shape[4] * frame_height, 0, frame_height) - y - annotations.append(Bbox(x, y, w, h, - label=dataset_labels.get(label) - )) - - image_size = None - if frame_height and frame_width: - image_size = (frame_height, frame_width) - - image_params = {} - if frame_image: - image_params['data'] = lazy_image(frame_image, decode_image) - if frame_filename: - image_params['path'] = osp.join(images_dir, frame_filename) - - image = None - if image_params: - image = Image(**image_params, size=image_size) - - dataset_items.append(DatasetItem(id=item_id, subset=subset, - image=image, annotations=annotations, - attributes={'source_id': frame_id})) - - return dataset_items, dataset_labels diff --git a/datumaro/datumaro/plugins/tf_detection_api_format/format.py b/datumaro/datumaro/plugins/tf_detection_api_format/format.py deleted file mode 100644 index f4a879a6..00000000 --- a/datumaro/datumaro/plugins/tf_detection_api_format/format.py +++ /dev/null @@ -1,13 +0,0 @@ - -# Copyright (C) 2019-2020 Intel Corporation -# -# SPDX-License-Identifier: MIT - -class DetectionApiPath: - IMAGES_DIR = 'images' - ANNOTATIONS_DIR = 'annotations' - - DEFAULT_IMAGE_EXT = '.jpg' - IMAGE_EXT_FORMAT = {'.jpg': 'jpeg', '.png': 'png'} - - LABELMAP_FILE = 'label_map.pbtxt' \ No newline at end of file diff --git a/datumaro/datumaro/plugins/tf_detection_api_format/importer.py b/datumaro/datumaro/plugins/tf_detection_api_format/importer.py deleted file mode 100644 index b3d8a47d..00000000 --- a/datumaro/datumaro/plugins/tf_detection_api_format/importer.py +++ /dev/null @@ -1,52 +0,0 @@ - -# Copyright (C) 2019-2020 Intel Corporation -# -# SPDX-License-Identifier: MIT - -from glob import glob -import logging as log -import os.path as osp - -from datumaro.components.extractor import Importer - - -class TfDetectionApiImporter(Importer): - EXTRACTOR_NAME = 'tf_detection_api' - - @classmethod - def detect(cls, path): - return len(cls.find_subsets(path)) != 0 - - def __call__(self, path, **extra_params): - from datumaro.components.project import Project # cyclic import - project = Project() - - subset_paths = self.find_subsets(path) - if len(subset_paths) == 0: - raise Exception( - "Failed to find 'tf_detection_api' dataset at '%s'" % path) - - for subset_path in subset_paths: - if not osp.isfile(subset_path): - continue - - log.info("Found a dataset at '%s'" % subset_path) - - subset_name = osp.splitext(osp.basename(subset_path))[0] - - project.add_source(subset_name, { - 'url': subset_path, - 'format': self.EXTRACTOR_NAME, - 'options': dict(extra_params), - }) - - return project - - @staticmethod - def find_subsets(path): - if path.endswith('.tfrecord') and osp.isfile(path): - subset_paths = [path] - else: - subset_paths = glob(osp.join(path, '**', '*.tfrecord'), - recursive=True) - return subset_paths \ No newline at end of file diff --git a/datumaro/datumaro/plugins/transforms.py b/datumaro/datumaro/plugins/transforms.py deleted file mode 100644 index 7e7cea8b..00000000 --- a/datumaro/datumaro/plugins/transforms.py +++ /dev/null @@ -1,524 +0,0 @@ -# Copyright (C) 2020 Intel Corporation -# -# SPDX-License-Identifier: MIT - -from enum import Enum -import logging as log -import os.path as osp -import random -import re - -import pycocotools.mask as mask_utils - -from datumaro.components.extractor import (Transform, AnnotationType, - RleMask, Polygon, Bbox, - LabelCategories, MaskCategories, PointsCategories -) -from datumaro.components.cli_plugin import CliPlugin -import datumaro.util.mask_tools as mask_tools -from datumaro.util.annotation_util import find_group_leader, find_instances - - -class CropCoveredSegments(Transform, CliPlugin): - def transform_item(self, item): - annotations = [] - segments = [] - for ann in item.annotations: - if ann.type in {AnnotationType.polygon, AnnotationType.mask}: - segments.append(ann) - else: - annotations.append(ann) - if not segments: - return item - - if not item.has_image: - raise Exception("Image info is required for this transform") - h, w = item.image.size - segments = self.crop_segments(segments, w, h) - - annotations += segments - return self.wrap_item(item, annotations=annotations) - - @classmethod - def crop_segments(cls, segment_anns, img_width, img_height): - segment_anns = sorted(segment_anns, key=lambda x: x.z_order) - - segments = [] - for s in segment_anns: - if s.type == AnnotationType.polygon: - segments.append(s.points) - elif s.type == AnnotationType.mask: - if isinstance(s, RleMask): - rle = s.rle - else: - rle = mask_tools.mask_to_rle(s.image) - segments.append(rle) - - segments = mask_tools.crop_covered_segments( - segments, img_width, img_height) - - new_anns = [] - for ann, new_segment in zip(segment_anns, segments): - fields = {'z_order': ann.z_order, 'label': ann.label, - 'id': ann.id, 'group': ann.group, 'attributes': ann.attributes - } - if ann.type == AnnotationType.polygon: - if fields['group'] is None: - fields['group'] = cls._make_group_id( - segment_anns + new_anns, fields['id']) - for polygon in new_segment: - new_anns.append(Polygon(points=polygon, **fields)) - else: - rle = mask_tools.mask_to_rle(new_segment) - rle = mask_utils.frPyObjects(rle, *rle['size']) - new_anns.append(RleMask(rle=rle, **fields)) - - return new_anns - - @staticmethod - def _make_group_id(anns, ann_id): - if ann_id: - return ann_id - max_gid = max(anns, default=0, key=lambda x: x.group) - return max_gid + 1 - -class MergeInstanceSegments(Transform, CliPlugin): - """ - Replaces instance masks and, optionally, polygons with a single mask. - """ - - @classmethod - def build_cmdline_parser(cls, **kwargs): - parser = super().build_cmdline_parser(**kwargs) - parser.add_argument('--include-polygons', action='store_true', - help="Include polygons") - return parser - - def __init__(self, extractor, include_polygons=False): - super().__init__(extractor) - - self._include_polygons = include_polygons - - def transform_item(self, item): - annotations = [] - segments = [] - for ann in item.annotations: - if ann.type in {AnnotationType.polygon, AnnotationType.mask}: - segments.append(ann) - else: - annotations.append(ann) - if not segments: - return item - - if not item.has_image: - raise Exception("Image info is required for this transform") - h, w = item.image.size - instances = self.find_instances(segments) - segments = [self.merge_segments(i, w, h, self._include_polygons) - for i in instances] - segments = sum(segments, []) - - annotations += segments - return self.wrap_item(item, annotations=annotations) - - @classmethod - def merge_segments(cls, instance, img_width, img_height, - include_polygons=False): - polygons = [a for a in instance if a.type == AnnotationType.polygon] - masks = [a for a in instance if a.type == AnnotationType.mask] - if not polygons and not masks: - return [] - - leader = find_group_leader(polygons + masks) - instance = [] - - # Build the resulting mask - mask = None - - if include_polygons and polygons: - polygons = [p.points for p in polygons] - mask = mask_tools.rles_to_mask(polygons, img_width, img_height) - else: - instance += polygons # keep unused polygons - - if masks: - masks = [m.image for m in masks] - if mask is not None: - masks += [mask] - mask = mask_tools.merge_masks(masks) - - if mask is None: - return instance - - mask = mask_tools.mask_to_rle(mask) - mask = mask_utils.frPyObjects(mask, *mask['size']) - instance.append( - RleMask(rle=mask, label=leader.label, z_order=leader.z_order, - id=leader.id, attributes=leader.attributes, group=leader.group - ) - ) - return instance - - @staticmethod - def find_instances(annotations): - return find_instances(a for a in annotations - if a.type in {AnnotationType.polygon, AnnotationType.mask}) - -class PolygonsToMasks(Transform, CliPlugin): - def transform_item(self, item): - annotations = [] - for ann in item.annotations: - if ann.type == AnnotationType.polygon: - if not item.has_image: - raise Exception("Image info is required for this transform") - h, w = item.image.size - annotations.append(self.convert_polygon(ann, h, w)) - else: - annotations.append(ann) - - return self.wrap_item(item, annotations=annotations) - - @staticmethod - def convert_polygon(polygon, img_h, img_w): - rle = mask_utils.frPyObjects([polygon.points], img_h, img_w)[0] - - return RleMask(rle=rle, label=polygon.label, z_order=polygon.z_order, - id=polygon.id, attributes=polygon.attributes, group=polygon.group) - -class BoxesToMasks(Transform, CliPlugin): - def transform_item(self, item): - annotations = [] - for ann in item.annotations: - if ann.type == AnnotationType.bbox: - if not item.has_image: - raise Exception("Image info is required for this transform") - h, w = item.image.size - annotations.append(self.convert_bbox(ann, h, w)) - else: - annotations.append(ann) - - return self.wrap_item(item, annotations=annotations) - - @staticmethod - def convert_bbox(bbox, img_h, img_w): - rle = mask_utils.frPyObjects([bbox.as_polygon()], img_h, img_w)[0] - - return RleMask(rle=rle, label=bbox.label, z_order=bbox.z_order, - id=bbox.id, attributes=bbox.attributes, group=bbox.group) - -class MasksToPolygons(Transform, CliPlugin): - def transform_item(self, item): - annotations = [] - for ann in item.annotations: - if ann.type == AnnotationType.mask: - polygons = self.convert_mask(ann) - if not polygons: - log.debug("[%s]: item %s: " - "Mask conversion to polygons resulted in too " - "small polygons, which were discarded" % \ - (self._get_name(__class__), item.id)) - annotations.extend(polygons) - else: - annotations.append(ann) - - return self.wrap_item(item, annotations=annotations) - - @staticmethod - def convert_mask(mask): - polygons = mask_tools.mask_to_polygons(mask.image) - - return [ - Polygon(points=p, label=mask.label, z_order=mask.z_order, - id=mask.id, attributes=mask.attributes, group=mask.group) - for p in polygons - ] - -class ShapesToBoxes(Transform, CliPlugin): - def transform_item(self, item): - annotations = [] - for ann in item.annotations: - if ann.type in { AnnotationType.mask, AnnotationType.polygon, - AnnotationType.polyline, AnnotationType.points, - }: - annotations.append(self.convert_shape(ann)) - else: - annotations.append(ann) - - return self.wrap_item(item, annotations=annotations) - - @staticmethod - def convert_shape(shape): - bbox = shape.get_bbox() - return Bbox(*bbox, label=shape.label, z_order=shape.z_order, - id=shape.id, attributes=shape.attributes, group=shape.group) - -class Reindex(Transform, CliPlugin): - @classmethod - def build_cmdline_parser(cls, **kwargs): - parser = super().build_cmdline_parser(**kwargs) - parser.add_argument('-s', '--start', type=int, default=1, - help="Start value for item ids") - return parser - - def __init__(self, extractor, start=1): - super().__init__(extractor) - - self._start = start - - def __iter__(self): - for i, item in enumerate(self._extractor): - yield self.wrap_item(item, id=i + self._start) - -class MapSubsets(Transform, CliPlugin): - @staticmethod - def _mapping_arg(s): - parts = s.split(':') - if len(parts) != 2: - import argparse - raise argparse.ArgumentTypeError() - return parts - - @classmethod - def build_cmdline_parser(cls, **kwargs): - parser = super().build_cmdline_parser(**kwargs) - parser.add_argument('-s', '--subset', action='append', - type=cls._mapping_arg, dest='mapping', - help="Subset mapping of the form: 'src:dst' (repeatable)") - return parser - - def __init__(self, extractor, mapping=None): - super().__init__(extractor) - - if mapping is None: - mapping = {} - elif not isinstance(mapping, dict): - mapping = dict(tuple(m) for m in mapping) - self._mapping = mapping - - def transform_item(self, item): - return self.wrap_item(item, - subset=self._mapping.get(item.subset, item.subset)) - -class RandomSplit(Transform, CliPlugin): - """ - Joins all subsets into one and splits the result into few parts. - It is expected that item ids are unique and subset ratios sum up to 1.|n - |n - Example:|n - |s|s%(prog)s --subset train:.67 --subset test:.33 - """ - - @staticmethod - def _split_arg(s): - parts = s.split(':') - if len(parts) != 2: - import argparse - raise argparse.ArgumentTypeError() - return (parts[0], float(parts[1])) - - @classmethod - def build_cmdline_parser(cls, **kwargs): - parser = super().build_cmdline_parser(**kwargs) - parser.add_argument('-s', '--subset', action='append', - type=cls._split_arg, dest='splits', - default=[('train', 0.67), ('test', 0.33)], - help="Subsets in the form of: ':' (repeatable)") - parser.add_argument('--seed', type=int, help="Random seed") - return parser - - def __init__(self, extractor, splits, seed=None): - super().__init__(extractor) - - assert 0 < len(splits), "Expected at least one split" - assert all(0.0 <= r and r <= 1.0 for _, r in splits), \ - "Ratios are expected to be in the range [0; 1], but got %s" % splits - - total_ratio = sum(s[1] for s in splits) - if not abs(total_ratio - 1.0) <= 1e-7: - raise Exception( - "Sum of ratios is expected to be 1, got %s, which is %s" % - (splits, total_ratio)) - - dataset_size = len(extractor) - indices = list(range(dataset_size)) - - random.seed(seed) - random.shuffle(indices) - parts = [] - s = 0 - for subset, ratio in splits: - s += ratio - boundary = int(s * dataset_size) - parts.append((boundary, subset)) - - self._parts = parts - - def _find_split(self, index): - for boundary, subset in self._parts: - if index < boundary: - return subset - return subset # all the possible remainder goes to the last split - - def __iter__(self): - for i, item in enumerate(self._extractor): - yield self.wrap_item(item, subset=self._find_split(i)) - -class IdFromImageName(Transform, CliPlugin): - def transform_item(self, item): - if item.has_image and item.image.path: - name = osp.splitext(osp.basename(item.image.path))[0] - return self.wrap_item(item, id=name) - else: - log.debug("Can't change item id for item '%s': " - "item has no image info" % item.id) - return item - -class Rename(Transform, CliPlugin): - """ - Renames items in the dataset. Supports regular expressions. - The first character in the expression is a delimiter for - the pattern and replacement parts. Replacement part can also - contain string.format tokens with 'item' object available.|n - |n - Examples:|n - - Replace 'pattern' with 'replacement':|n - |s|srename -e '|pattern|replacement|'|n - - Remove 'frame_' from item ids:|n - |s|srename -e '|frame_(\d+)|\\1|' - """ - - @classmethod - def build_cmdline_parser(cls, **kwargs): - parser = super().build_cmdline_parser(**kwargs) - parser.add_argument('-e', '--regex', - help="Regex for renaming.") - return parser - - def __init__(self, extractor, regex): - super().__init__(extractor) - - assert regex and isinstance(regex, str) - parts = regex.split(regex[0], maxsplit=3) - regex, sub = parts[1:3] - self._re = re.compile(regex) - self._sub = sub - - def transform_item(self, item): - return self.wrap_item(item, id=self._re.sub(self._sub, item.id) \ - .format(item=item)) - -class RemapLabels(Transform, CliPlugin): - """ - Changes labels in the dataset.|n - Examples:|n - - Rename 'person' to 'car' and 'cat' to 'dog', keep 'bus', remove others:|n - |s|sremap_labels -l person:car -l bus:bus -l cat:dog --default delete - """ - - DefaultAction = Enum('DefaultAction', ['keep', 'delete']) - - @staticmethod - def _split_arg(s): - parts = s.split(':') - if len(parts) != 2: - import argparse - raise argparse.ArgumentTypeError() - return (parts[0], parts[1]) - - @classmethod - def build_cmdline_parser(cls, **kwargs): - parser = super().build_cmdline_parser(**kwargs) - parser.add_argument('-l', '--label', action='append', - type=cls._split_arg, dest='mapping', - help="Label in the form of: ':' (repeatable)") - parser.add_argument('--default', - choices=[a.name for a in cls.DefaultAction], - default=cls.DefaultAction.keep.name, - help="Action for unspecified labels (default: %(default)s)") - return parser - - def __init__(self, extractor, mapping, default=None): - super().__init__(extractor) - - assert isinstance(default, (str, self.DefaultAction)) - if isinstance(default, str): - default = self.DefaultAction[default] - - assert isinstance(mapping, (dict, list)) - if isinstance(mapping, list): - mapping = dict(mapping) - - self._categories = {} - - src_label_cat = self._extractor.categories().get(AnnotationType.label) - if src_label_cat is not None: - self._make_label_id_map(src_label_cat, mapping, default) - - src_mask_cat = self._extractor.categories().get(AnnotationType.mask) - if src_mask_cat is not None: - assert src_label_cat is not None - dst_mask_cat = MaskCategories(attributes=src_mask_cat.attributes) - dst_mask_cat.colormap = { - id: src_mask_cat.colormap[id] - for id, _ in enumerate(src_label_cat.items) - if self._map_id(id) or id == 0 - } - self._categories[AnnotationType.mask] = dst_mask_cat - - src_points_cat = self._extractor.categories().get(AnnotationType.points) - if src_points_cat is not None: - assert src_label_cat is not None - dst_points_cat = PointsCategories(attributes=src_points_cat.attributes) - dst_points_cat.items = { - id: src_points_cat.items[id] - for id, item in enumerate(src_label_cat.items) - if self._map_id(id) or id == 0 - } - self._categories[AnnotationType.points] = dst_points_cat - - def _make_label_id_map(self, src_label_cat, label_mapping, default_action): - dst_label_cat = LabelCategories(attributes=src_label_cat.attributes) - id_mapping = {} - for src_index, src_label in enumerate(src_label_cat.items): - dst_label = label_mapping.get(src_label.name) - if not dst_label and default_action == self.DefaultAction.keep: - dst_label = src_label.name # keep unspecified as is - if not dst_label: - continue - - dst_index = dst_label_cat.find(dst_label)[0] - if dst_index is None: - dst_index = dst_label_cat.add(dst_label, - src_label.parent, src_label.attributes) - id_mapping[src_index] = dst_index - - if log.getLogger().isEnabledFor(log.DEBUG): - log.debug("Label mapping:") - for src_id, src_label in enumerate(src_label_cat.items): - if id_mapping.get(src_id): - log.debug("#%s '%s' -> #%s '%s'", - src_id, src_label.name, id_mapping[src_id], - dst_label_cat.items[id_mapping[src_id]].name - ) - else: - log.debug("#%s '%s' -> ", src_id, src_label.name) - - self._map_id = lambda src_id: id_mapping.get(src_id, None) - self._categories[AnnotationType.label] = dst_label_cat - - def categories(self): - return self._categories - - def transform_item(self, item): - annotations = [] - for ann in item.annotations: - if ann.type in { AnnotationType.label, AnnotationType.mask, - AnnotationType.points, AnnotationType.polygon, - AnnotationType.polyline, AnnotationType.bbox - } and ann.label is not None: - conv_label = self._map_id(ann.label) - if conv_label is not None: - annotations.append(ann.wrap(label=conv_label)) - else: - annotations.append(ann.wrap()) - return item.wrap(annotations=annotations) \ No newline at end of file diff --git a/datumaro/datumaro/plugins/voc_format/__init__.py b/datumaro/datumaro/plugins/voc_format/__init__.py deleted file mode 100644 index e69de29b..00000000 diff --git a/datumaro/datumaro/plugins/voc_format/converter.py b/datumaro/datumaro/plugins/voc_format/converter.py deleted file mode 100644 index 65e586d8..00000000 --- a/datumaro/datumaro/plugins/voc_format/converter.py +++ /dev/null @@ -1,590 +0,0 @@ - -# Copyright (C) 2020 Intel Corporation -# -# SPDX-License-Identifier: MIT - -import logging as log -import os -import os.path as osp -from collections import OrderedDict, defaultdict -from enum import Enum -from itertools import chain - -from lxml import etree as ET - -from datumaro.components.converter import Converter -from datumaro.components.extractor import (DEFAULT_SUBSET_NAME, AnnotationType, - CompiledMask, LabelCategories) -from datumaro.util import find, str_to_bool -from datumaro.util.image import save_image -from datumaro.util.mask_tools import paint_mask, remap_mask - -from .format import (VocTask, VocPath, VocInstColormap, - parse_label_map, make_voc_label_map, make_voc_categories, write_label_map -) - - -def _convert_attr(name, attributes, type_conv, default=None, warn=True): - d = object() - value = attributes.get(name, d) - if value is d: - return default - - try: - return type_conv(value) - except Exception as e: - log.warning("Failed to convert attribute '%s'='%s': %s" % \ - (name, value, e)) - return default - -def _write_xml_bbox(bbox, parent_elem): - x, y, w, h = bbox - bbox_elem = ET.SubElement(parent_elem, 'bndbox') - ET.SubElement(bbox_elem, 'xmin').text = str(x) - ET.SubElement(bbox_elem, 'ymin').text = str(y) - ET.SubElement(bbox_elem, 'xmax').text = str(x + w) - ET.SubElement(bbox_elem, 'ymax').text = str(y + h) - return bbox_elem - - -LabelmapType = Enum('LabelmapType', ['voc', 'source']) - -class VocConverter(Converter): - DEFAULT_IMAGE_EXT = VocPath.IMAGE_EXT - - @staticmethod - def _split_tasks_string(s): - return [VocTask[i.strip()] for i in s.split(',')] - - @staticmethod - def _get_labelmap(s): - if osp.isfile(s): - return s - try: - return LabelmapType[s].name - except KeyError: - import argparse - raise argparse.ArgumentTypeError() - - @classmethod - def build_cmdline_parser(cls, **kwargs): - parser = super().build_cmdline_parser(**kwargs) - - parser.add_argument('--apply-colormap', type=str_to_bool, default=True, - help="Use colormap for class and instance masks " - "(default: %(default)s)") - parser.add_argument('--label-map', type=cls._get_labelmap, default=None, - help="Labelmap file path or one of %s" % \ - ', '.join(t.name for t in LabelmapType)) - parser.add_argument('--allow-attributes', - type=str_to_bool, default=True, - help="Allow export of attributes (default: %(default)s)") - parser.add_argument('--tasks', type=cls._split_tasks_string, - help="VOC task filter, comma-separated list of {%s} " - "(default: all)" % ', '.join(t.name for t in VocTask)) - - return parser - - def __init__(self, extractor, save_dir, - tasks=None, apply_colormap=True, label_map=None, - allow_attributes=True, **kwargs): - super().__init__(extractor, save_dir, **kwargs) - - assert tasks is None or isinstance(tasks, (VocTask, list, set)) - if tasks is None: - tasks = set(VocTask) - elif isinstance(tasks, VocTask): - tasks = {tasks} - else: - tasks = set(t if t in VocTask else VocTask[t] for t in tasks) - self._tasks = tasks - - self._apply_colormap = apply_colormap - self._allow_attributes = allow_attributes - - if label_map is None: - label_map = LabelmapType.source - self._load_categories(label_map) - - def apply(self): - self.make_dirs() - self.save_subsets() - self.save_label_map() - - def make_dirs(self): - save_dir = self._save_dir - subsets_dir = osp.join(save_dir, VocPath.SUBSETS_DIR) - cls_subsets_dir = osp.join(subsets_dir, - VocPath.TASK_DIR[VocTask.classification]) - action_subsets_dir = osp.join(subsets_dir, - VocPath.TASK_DIR[VocTask.action_classification]) - layout_subsets_dir = osp.join(subsets_dir, - VocPath.TASK_DIR[VocTask.person_layout]) - segm_subsets_dir = osp.join(subsets_dir, - VocPath.TASK_DIR[VocTask.segmentation]) - ann_dir = osp.join(save_dir, VocPath.ANNOTATIONS_DIR) - img_dir = osp.join(save_dir, VocPath.IMAGES_DIR) - segm_dir = osp.join(save_dir, VocPath.SEGMENTATION_DIR) - inst_dir = osp.join(save_dir, VocPath.INSTANCES_DIR) - images_dir = osp.join(save_dir, VocPath.IMAGES_DIR) - - os.makedirs(subsets_dir, exist_ok=True) - os.makedirs(ann_dir, exist_ok=True) - os.makedirs(img_dir, exist_ok=True) - os.makedirs(segm_dir, exist_ok=True) - os.makedirs(inst_dir, exist_ok=True) - os.makedirs(images_dir, exist_ok=True) - - self._subsets_dir = subsets_dir - self._cls_subsets_dir = cls_subsets_dir - self._action_subsets_dir = action_subsets_dir - self._layout_subsets_dir = layout_subsets_dir - self._segm_subsets_dir = segm_subsets_dir - self._ann_dir = ann_dir - self._img_dir = img_dir - self._segm_dir = segm_dir - self._inst_dir = inst_dir - self._images_dir = images_dir - - def get_label(self, label_id): - return self._extractor. \ - categories()[AnnotationType.label].items[label_id].name - - def save_subsets(self): - for subset_name in self._extractor.subsets() or [None]: - if subset_name: - subset = self._extractor.get_subset(subset_name) - else: - subset_name = DEFAULT_SUBSET_NAME - subset = self._extractor - - class_lists = OrderedDict() - clsdet_list = OrderedDict() - action_list = OrderedDict() - layout_list = OrderedDict() - segm_list = OrderedDict() - - for item in subset: - log.debug("Converting item '%s'", item.id) - - image_filename = self._make_image_filename(item) - if self._save_images: - if item.has_image and item.image.has_data: - self._save_image(item, - osp.join(self._images_dir, image_filename)) - else: - log.debug("Item '%s' has no image", item.id) - - labels = [] - bboxes = [] - masks = [] - for a in item.annotations: - if a.type == AnnotationType.label: - labels.append(a) - elif a.type == AnnotationType.bbox: - bboxes.append(a) - elif a.type == AnnotationType.mask: - masks.append(a) - - if self._tasks is None and bboxes or \ - self._tasks & {VocTask.detection, VocTask.person_layout, - VocTask.action_classification}: - root_elem = ET.Element('annotation') - if '_' in item.id: - folder = item.id[ : item.id.find('_')] - else: - folder = '' - ET.SubElement(root_elem, 'folder').text = folder - ET.SubElement(root_elem, 'filename').text = image_filename - - source_elem = ET.SubElement(root_elem, 'source') - ET.SubElement(source_elem, 'database').text = 'Unknown' - ET.SubElement(source_elem, 'annotation').text = 'Unknown' - ET.SubElement(source_elem, 'image').text = 'Unknown' - - if item.has_image: - h, w = item.image.size - if item.image.has_data: - image_shape = item.image.data.shape - c = 1 if len(image_shape) == 2 else image_shape[2] - else: - c = 3 - size_elem = ET.SubElement(root_elem, 'size') - ET.SubElement(size_elem, 'width').text = str(w) - ET.SubElement(size_elem, 'height').text = str(h) - ET.SubElement(size_elem, 'depth').text = str(c) - - item_segmented = 0 < len(masks) - ET.SubElement(root_elem, 'segmented').text = \ - str(int(item_segmented)) - - objects_with_parts = [] - objects_with_actions = defaultdict(dict) - - main_bboxes = [] - layout_bboxes = [] - for bbox in bboxes: - label = self.get_label(bbox.label) - if self._is_part(label): - layout_bboxes.append(bbox) - elif self._is_label(label): - main_bboxes.append(bbox) - - for new_obj_id, obj in enumerate(main_bboxes): - attr = obj.attributes - - obj_elem = ET.SubElement(root_elem, 'object') - - obj_label = self.get_label(obj.label) - ET.SubElement(obj_elem, 'name').text = obj_label - - if 'pose' in attr: - ET.SubElement(obj_elem, 'pose').text = \ - str(attr['pose']) - - if 'truncated' in attr: - truncated = _convert_attr('truncated', attr, int, 0) - ET.SubElement(obj_elem, 'truncated').text = \ - '%d' % truncated - - if 'difficult' in attr: - difficult = _convert_attr('difficult', attr, int, 0) - ET.SubElement(obj_elem, 'difficult').text = \ - '%d' % difficult - - if 'occluded' in attr: - occluded = _convert_attr('occluded', attr, int, 0) - ET.SubElement(obj_elem, 'occluded').text = \ - '%d' % occluded - - bbox = obj.get_bbox() - if bbox is not None: - _write_xml_bbox(bbox, obj_elem) - - for part_bbox in filter( - lambda x: obj.group and obj.group == x.group, - layout_bboxes): - part_elem = ET.SubElement(obj_elem, 'part') - ET.SubElement(part_elem, 'name').text = \ - self.get_label(part_bbox.label) - _write_xml_bbox(part_bbox.get_bbox(), part_elem) - - objects_with_parts.append(new_obj_id) - - label_actions = self._get_actions(obj_label) - actions_elem = ET.Element('actions') - for action in label_actions: - present = 0 - if action in attr: - present = _convert_attr(action, attr, - lambda v: int(v == True), 0) - ET.SubElement(actions_elem, action).text = \ - '%d' % present - - objects_with_actions[new_obj_id][action] = present - if len(actions_elem) != 0: - obj_elem.append(actions_elem) - - if self._allow_attributes: - native_attrs = {'difficult', 'pose', - 'truncated', 'occluded' } - native_attrs.update(label_actions) - - attrs_elem = ET.Element('attributes') - for k, v in attr.items(): - if k in native_attrs: - continue - attr_elem = ET.SubElement(attrs_elem, 'attribute') - ET.SubElement(attr_elem, 'name').text = str(k) - ET.SubElement(attr_elem, 'value').text = str(v) - if len(attrs_elem): - obj_elem.append(attrs_elem) - - if self._tasks & {VocTask.detection, VocTask.person_layout, - VocTask.action_classification}: - ann_path = osp.join(self._ann_dir, item.id + '.xml') - os.makedirs(osp.dirname(ann_path), exist_ok=True) - with open(ann_path, 'w') as f: - f.write(ET.tostring(root_elem, - encoding='unicode', pretty_print=True)) - - clsdet_list[item.id] = True - layout_list[item.id] = objects_with_parts - action_list[item.id] = objects_with_actions - - for label_ann in labels: - label = self.get_label(label_ann.label) - if not self._is_label(label): - continue - class_list = class_lists.get(item.id, set()) - class_list.add(label_ann.label) - class_lists[item.id] = class_list - - clsdet_list[item.id] = True - - if masks: - compiled_mask = CompiledMask.from_instance_masks(masks, - instance_labels=[self._label_id_mapping(m.label) - for m in masks]) - - self.save_segm( - osp.join(self._segm_dir, item.id + VocPath.SEGM_EXT), - compiled_mask.class_mask) - self.save_segm( - osp.join(self._inst_dir, item.id + VocPath.SEGM_EXT), - compiled_mask.instance_mask, - colormap=VocInstColormap) - - segm_list[item.id] = True - - if len(item.annotations) == 0: - clsdet_list[item.id] = None - layout_list[item.id] = None - action_list[item.id] = None - segm_list[item.id] = None - - if self._tasks & {VocTask.classification, VocTask.detection, - VocTask.action_classification, VocTask.person_layout}: - self.save_clsdet_lists(subset_name, clsdet_list) - if self._tasks & {VocTask.classification}: - self.save_class_lists(subset_name, class_lists) - if self._tasks & {VocTask.action_classification}: - self.save_action_lists(subset_name, action_list) - if self._tasks & {VocTask.person_layout}: - self.save_layout_lists(subset_name, layout_list) - if self._tasks & {VocTask.segmentation}: - self.save_segm_lists(subset_name, segm_list) - - def save_action_lists(self, subset_name, action_list): - if not action_list: - return - - os.makedirs(self._action_subsets_dir, exist_ok=True) - - ann_file = osp.join(self._action_subsets_dir, subset_name + '.txt') - with open(ann_file, 'w') as f: - for item in action_list: - f.write('%s\n' % item) - - if len(action_list) == 0: - return - - all_actions = set(chain(*(self._get_actions(l) - for l in self._label_map))) - for action in all_actions: - ann_file = osp.join(self._action_subsets_dir, - '%s_%s.txt' % (action, subset_name)) - with open(ann_file, 'w') as f: - for item, objs in action_list.items(): - if not objs: - continue - for obj_id, obj_actions in objs.items(): - presented = obj_actions[action] - f.write('%s %s % d\n' % \ - (item, 1 + obj_id, 1 if presented else -1)) - - def save_class_lists(self, subset_name, class_lists): - if not class_lists: - return - - os.makedirs(self._cls_subsets_dir, exist_ok=True) - - for label in self._label_map: - ann_file = osp.join(self._cls_subsets_dir, - '%s_%s.txt' % (label, subset_name)) - with open(ann_file, 'w') as f: - for item, item_labels in class_lists.items(): - if not item_labels: - continue - item_labels = [self.get_label(l) for l in item_labels] - presented = label in item_labels - f.write('%s % d\n' % (item, 1 if presented else -1)) - - def save_clsdet_lists(self, subset_name, clsdet_list): - if not clsdet_list: - return - - os.makedirs(self._cls_subsets_dir, exist_ok=True) - - ann_file = osp.join(self._cls_subsets_dir, subset_name + '.txt') - with open(ann_file, 'w') as f: - for item in clsdet_list: - f.write('%s\n' % item) - - def save_segm_lists(self, subset_name, segm_list): - if not segm_list: - return - - os.makedirs(self._segm_subsets_dir, exist_ok=True) - - ann_file = osp.join(self._segm_subsets_dir, subset_name + '.txt') - with open(ann_file, 'w') as f: - for item in segm_list: - f.write('%s\n' % item) - - def save_layout_lists(self, subset_name, layout_list): - if not layout_list: - return - - os.makedirs(self._layout_subsets_dir, exist_ok=True) - - ann_file = osp.join(self._layout_subsets_dir, subset_name + '.txt') - with open(ann_file, 'w') as f: - for item, item_layouts in layout_list.items(): - if item_layouts: - for obj_id in item_layouts: - f.write('%s % d\n' % (item, 1 + obj_id)) - else: - f.write('%s\n' % (item)) - - def save_segm(self, path, mask, colormap=None): - if self._apply_colormap: - if colormap is None: - colormap = self._categories[AnnotationType.mask].colormap - mask = paint_mask(mask, colormap) - save_image(path, mask, create_dir=True) - - def save_label_map(self): - path = osp.join(self._save_dir, VocPath.LABELMAP_FILE) - write_label_map(path, self._label_map) - - def _load_categories(self, label_map_source): - if label_map_source == LabelmapType.voc.name: - # use the default VOC colormap - label_map = make_voc_label_map() - - elif label_map_source == LabelmapType.source.name and \ - AnnotationType.mask not in self._extractor.categories(): - # generate colormap for input labels - labels = self._extractor.categories() \ - .get(AnnotationType.label, LabelCategories()) - label_map = OrderedDict((item.name, [None, [], []]) - for item in labels.items) - - elif label_map_source == LabelmapType.source.name and \ - AnnotationType.mask in self._extractor.categories(): - # use source colormap - labels = self._extractor.categories()[AnnotationType.label] - colors = self._extractor.categories()[AnnotationType.mask] - label_map = OrderedDict() - for idx, item in enumerate(labels.items): - color = colors.colormap.get(idx) - if color is not None: - label_map[item.name] = [color, [], []] - - elif isinstance(label_map_source, dict): - label_map = OrderedDict( - sorted(label_map_source.items(), key=lambda e: e[0])) - - elif isinstance(label_map_source, str) and osp.isfile(label_map_source): - label_map = parse_label_map(label_map_source) - - else: - raise Exception("Wrong labelmap specified, " - "expected one of %s or a file path" % \ - ', '.join(t.name for t in LabelmapType)) - - # There must always be a label with color (0, 0, 0) at index 0 - bg_label = find(label_map.items(), lambda x: x[1][0] == (0, 0, 0)) - if bg_label is not None: - bg_label = bg_label[0] - else: - bg_label = 'background' - if bg_label not in label_map: - has_colors = any(v[0] is not None for v in label_map.values()) - color = (0, 0, 0) if has_colors else None - label_map[bg_label] = [color, [], []] - label_map.move_to_end(bg_label, last=False) - - self._categories = make_voc_categories(label_map) - - # Update colors with assigned values - colormap = self._categories[AnnotationType.mask].colormap - for label_id, color in colormap.items(): - label_desc = label_map[ - self._categories[AnnotationType.label].items[label_id].name] - label_desc[0] = color - - self._label_map = label_map - self._label_id_mapping = self._make_label_id_map() - - def _is_label(self, s): - return self._label_map.get(s) is not None - - def _is_part(self, s): - for label_desc in self._label_map.values(): - if s in label_desc[1]: - return True - return False - - def _is_action(self, label, s): - return s in self._get_actions(label) - - def _get_actions(self, label): - label_desc = self._label_map.get(label) - if not label_desc: - return [] - return label_desc[2] - - def _make_label_id_map(self): - source_labels = { - id: label.name for id, label in - enumerate(self._extractor.categories().get( - AnnotationType.label, LabelCategories()).items) - } - target_labels = { - label.name: id for id, label in - enumerate(self._categories[AnnotationType.label].items) - } - id_mapping = { - src_id: target_labels.get(src_label, 0) - for src_id, src_label in source_labels.items() - } - - void_labels = [src_label for src_id, src_label in source_labels.items() - if src_label not in target_labels] - if void_labels: - log.warning("The following labels are remapped to background: %s" % - ', '.join(void_labels)) - log.debug("Saving segmentations with the following label mapping: \n%s" % - '\n'.join(["#%s '%s' -> #%s '%s'" % - ( - src_id, src_label, id_mapping[src_id], - self._categories[AnnotationType.label] \ - .items[id_mapping[src_id]].name - ) - for src_id, src_label in source_labels.items() - ]) - ) - - def map_id(src_id): - return id_mapping.get(src_id, 0) - return map_id - - def _remap_mask(self, mask): - return remap_mask(mask, self._label_id_mapping) - -class VocClassificationConverter(VocConverter): - def __init__(self, *args, **kwargs): - kwargs['tasks'] = VocTask.classification - super().__init__(*args, **kwargs) - -class VocDetectionConverter(VocConverter): - def __init__(self, *args, **kwargs): - kwargs['tasks'] = VocTask.detection - super().__init__(*args, **kwargs) - -class VocLayoutConverter(VocConverter): - def __init__(self, *args, **kwargs): - kwargs['tasks'] = VocTask.person_layout - super().__init__(*args, **kwargs) - -class VocActionConverter(VocConverter): - def __init__(self, *args, **kwargs): - kwargs['tasks'] = VocTask.action_classification - super().__init__(*args, **kwargs) - -class VocSegmentationConverter(VocConverter): - def __init__(self, *args, **kwargs): - kwargs['tasks'] = VocTask.segmentation - super().__init__(*args, **kwargs) diff --git a/datumaro/datumaro/plugins/voc_format/extractor.py b/datumaro/datumaro/plugins/voc_format/extractor.py deleted file mode 100644 index 0fe667d3..00000000 --- a/datumaro/datumaro/plugins/voc_format/extractor.py +++ /dev/null @@ -1,302 +0,0 @@ - -# Copyright (C) 2019-2020 Intel Corporation -# -# SPDX-License-Identifier: MIT - -from collections import defaultdict -import logging as log -import numpy as np -import os.path as osp -from defusedxml import ElementTree - -from datumaro.components.extractor import (SourceExtractor, DatasetItem, - AnnotationType, Label, Mask, Bbox, CompiledMask -) -from datumaro.util import dir_items -from datumaro.util.image import Image -from datumaro.util.mask_tools import lazy_mask, invert_colormap - -from .format import ( - VocTask, VocPath, VocInstColormap, parse_label_map, make_voc_categories -) - - -_inverse_inst_colormap = invert_colormap(VocInstColormap) - -class _VocExtractor(SourceExtractor): - def __init__(self, path): - assert osp.isfile(path), path - self._path = path - self._dataset_dir = osp.dirname(osp.dirname(osp.dirname(path))) - - super().__init__(subset=osp.splitext(osp.basename(path))[0]) - - self._categories = self._load_categories(self._dataset_dir) - - label_color = lambda label_idx: \ - self._categories[AnnotationType.mask].colormap.get(label_idx, None) - log.debug("Loaded labels: %s" % ', '.join( - "'%s' %s" % (l.name, ('(%s, %s, %s)' % c) if c else '') - for i, l, c in ((i, l, label_color(i)) for i, l in enumerate( - self._categories[AnnotationType.label].items - )) - )) - self._items = self._load_subset_list(path) - - def categories(self): - return self._categories - - def __len__(self): - return len(self._items) - - def _get_label_id(self, label): - label_id, _ = self._categories[AnnotationType.label].find(label) - assert label_id is not None, label - return label_id - - @staticmethod - def _load_categories(dataset_path): - label_map = None - label_map_path = osp.join(dataset_path, VocPath.LABELMAP_FILE) - if osp.isfile(label_map_path): - label_map = parse_label_map(label_map_path) - return make_voc_categories(label_map) - - @staticmethod - def _load_subset_list(subset_path): - with open(subset_path) as f: - return [line.split()[0] for line in f] - -class VocClassificationExtractor(_VocExtractor): - def __iter__(self): - raw_anns = self._load_annotations() - for item_id in self._items: - log.debug("Reading item '%s'" % item_id) - image = osp.join(self._dataset_dir, VocPath.IMAGES_DIR, - item_id + VocPath.IMAGE_EXT) - anns = self._parse_annotations(raw_anns, item_id) - yield DatasetItem(id=item_id, subset=self._subset, - image=image, annotations=anns) - - def _load_annotations(self): - annotations = defaultdict(list) - task_dir = osp.dirname(self._path) - anno_files = [s for s in dir_items(task_dir, '.txt') - if s.endswith('_' + osp.basename(self._path))] - for ann_filename in anno_files: - with open(osp.join(task_dir, ann_filename)) as f: - label = ann_filename[:ann_filename.rfind('_')] - label_id = self._get_label_id(label) - for line in f: - item, present = line.split() - if present == '1': - annotations[item].append(label_id) - - return dict(annotations) - - @staticmethod - def _parse_annotations(raw_anns, item_id): - return [Label(label_id) for label_id in raw_anns.get(item_id, [])] - -class _VocXmlExtractor(_VocExtractor): - def __init__(self, path, task): - super().__init__(path) - self._task = task - - def __iter__(self): - anno_dir = osp.join(self._dataset_dir, VocPath.ANNOTATIONS_DIR) - - for item_id in self._items: - log.debug("Reading item '%s'" % item_id) - image = item_id + VocPath.IMAGE_EXT - height, width = 0, 0 - - anns = [] - ann_file = osp.join(anno_dir, item_id + '.xml') - if osp.isfile(ann_file): - root_elem = ElementTree.parse(ann_file) - height = root_elem.find('size/height') - if height is not None: - height = int(height.text) - width = root_elem.find('size/width') - if width is not None: - width = int(width.text) - filename_elem = root_elem.find('filename') - if filename_elem is not None: - image = filename_elem.text - anns = self._parse_annotations(root_elem) - - image = osp.join(self._dataset_dir, VocPath.IMAGES_DIR, image) - if height and width: - image = Image(path=image, size=(height, width)) - - yield DatasetItem(id=item_id, subset=self._subset, - image=image, annotations=anns) - - def _parse_annotations(self, root_elem): - item_annotations = [] - - for obj_id, object_elem in enumerate(root_elem.findall('object')): - obj_id += 1 - attributes = {} - group = obj_id - - obj_label_id = None - label_elem = object_elem.find('name') - if label_elem is not None: - obj_label_id = self._get_label_id(label_elem.text) - - obj_bbox = self._parse_bbox(object_elem) - - if obj_label_id is None or obj_bbox is None: - continue - - difficult_elem = object_elem.find('difficult') - attributes['difficult'] = difficult_elem is not None and \ - difficult_elem.text == '1' - - truncated_elem = object_elem.find('truncated') - attributes['truncated'] = truncated_elem is not None and \ - truncated_elem.text == '1' - - occluded_elem = object_elem.find('occluded') - attributes['occluded'] = occluded_elem is not None and \ - occluded_elem.text == '1' - - pose_elem = object_elem.find('pose') - if pose_elem is not None: - attributes['pose'] = pose_elem.text - - point_elem = object_elem.find('point') - if point_elem is not None: - point_x = point_elem.find('x') - point_y = point_elem.find('y') - point = [float(point_x.text), float(point_y.text)] - attributes['point'] = point - - actions_elem = object_elem.find('actions') - actions = {a: False - for a in self._categories[AnnotationType.label] \ - .items[obj_label_id].attributes} - if actions_elem is not None: - for action_elem in actions_elem: - actions[action_elem.tag] = (action_elem.text == '1') - for action, present in actions.items(): - attributes[action] = present - - has_parts = False - for part_elem in object_elem.findall('part'): - part = part_elem.find('name').text - part_label_id = self._get_label_id(part) - part_bbox = self._parse_bbox(part_elem) - - if self._task is not VocTask.person_layout: - break - if part_bbox is None: - continue - has_parts = True - item_annotations.append(Bbox(*part_bbox, label=part_label_id, - group=group)) - - attributes_elem = object_elem.find('attributes') - if attributes_elem is not None: - for attr_elem in attributes_elem.iter('attribute'): - attributes[attr_elem.find('name').text] = \ - attr_elem.find('value').text - - if self._task is VocTask.person_layout and not has_parts: - continue - if self._task is VocTask.action_classification and not actions: - continue - - item_annotations.append(Bbox(*obj_bbox, label=obj_label_id, - attributes=attributes, id=obj_id, group=group)) - - return item_annotations - - @staticmethod - def _parse_bbox(object_elem): - bbox_elem = object_elem.find('bndbox') - xmin = float(bbox_elem.find('xmin').text) - xmax = float(bbox_elem.find('xmax').text) - ymin = float(bbox_elem.find('ymin').text) - ymax = float(bbox_elem.find('ymax').text) - return [xmin, ymin, xmax - xmin, ymax - ymin] - -class VocDetectionExtractor(_VocXmlExtractor): - def __init__(self, path): - super().__init__(path, task=VocTask.detection) - -class VocLayoutExtractor(_VocXmlExtractor): - def __init__(self, path): - super().__init__(path, task=VocTask.person_layout) - -class VocActionExtractor(_VocXmlExtractor): - def __init__(self, path): - super().__init__(path, task=VocTask.action_classification) - -class VocSegmentationExtractor(_VocExtractor): - def __iter__(self): - for item_id in self._items: - log.debug("Reading item '%s'" % item_id) - image = osp.join(self._dataset_dir, VocPath.IMAGES_DIR, - item_id + VocPath.IMAGE_EXT) - anns = self._load_annotations(item_id) - yield DatasetItem(id=item_id, subset=self._subset, - image=image, annotations=anns) - - @staticmethod - def _lazy_extract_mask(mask, c): - return lambda: mask == c - - def _load_annotations(self, item_id): - item_annotations = [] - - class_mask = None - segm_path = osp.join(self._dataset_dir, VocPath.SEGMENTATION_DIR, - item_id + VocPath.SEGM_EXT) - if osp.isfile(segm_path): - inverse_cls_colormap = \ - self._categories[AnnotationType.mask].inverse_colormap - class_mask = lazy_mask(segm_path, inverse_cls_colormap) - - instances_mask = None - inst_path = osp.join(self._dataset_dir, VocPath.INSTANCES_DIR, - item_id + VocPath.SEGM_EXT) - if osp.isfile(inst_path): - instances_mask = lazy_mask(inst_path, _inverse_inst_colormap) - - if instances_mask is not None: - compiled_mask = CompiledMask(class_mask, instances_mask) - - if class_mask is not None: - label_cat = self._categories[AnnotationType.label] - instance_labels = compiled_mask.get_instance_labels() - else: - instance_labels = {i: None - for i in range(compiled_mask.instance_count)} - - for instance_id, label_id in instance_labels.items(): - image = compiled_mask.lazy_extract(instance_id) - - attributes = {} - if label_id is not None: - actions = {a: False - for a in label_cat.items[label_id].attributes - } - attributes.update(actions) - - item_annotations.append(Mask( - image=image, label=label_id, - attributes=attributes, group=instance_id - )) - elif class_mask is not None: - log.warn("item '%s': has only class segmentation, " - "instance masks will not be available" % item_id) - class_mask = class_mask() - classes = np.unique(class_mask) - for label_id in classes: - image = self._lazy_extract_mask(class_mask, label_id) - item_annotations.append(Mask(image=image, label=label_id)) - - return item_annotations diff --git a/datumaro/datumaro/plugins/voc_format/format.py b/datumaro/datumaro/plugins/voc_format/format.py deleted file mode 100644 index a03446d5..00000000 --- a/datumaro/datumaro/plugins/voc_format/format.py +++ /dev/null @@ -1,206 +0,0 @@ - -# Copyright (C) 2019-2020 Intel Corporation -# -# SPDX-License-Identifier: MIT - -from collections import OrderedDict -from enum import Enum -from itertools import chain -import numpy as np - -from datumaro.components.extractor import (AnnotationType, - LabelCategories, MaskCategories -) - - -VocTask = Enum('VocTask', [ - 'classification', - 'detection', - 'segmentation', - 'action_classification', - 'person_layout', -]) - -VocLabel = Enum('VocLabel', [ - ('background', 0), - ('aeroplane', 1), - ('bicycle', 2), - ('bird', 3), - ('boat', 4), - ('bottle', 5), - ('bus', 6), - ('car', 7), - ('cat', 8), - ('chair', 9), - ('cow', 10), - ('diningtable', 11), - ('dog', 12), - ('horse', 13), - ('motorbike', 14), - ('person', 15), - ('pottedplant', 16), - ('sheep', 17), - ('sofa', 18), - ('train', 19), - ('tvmonitor', 20), - ('ignored', 255), -]) - -VocPose = Enum('VocPose', [ - 'Unspecified', - 'Left', - 'Right', - 'Frontal', - 'Rear', -]) - -VocBodyPart = Enum('VocBodyPart', [ - 'head', - 'hand', - 'foot', -]) - -VocAction = Enum('VocAction', [ - 'other', - 'jumping', - 'phoning', - 'playinginstrument', - 'reading', - 'ridingbike', - 'ridinghorse', - 'running', - 'takingphoto', - 'usingcomputer', - 'walking', -]) - -def generate_colormap(length=256): - def get_bit(number, index): - return (number >> index) & 1 - - colormap = np.zeros((length, 3), dtype=int) - indices = np.arange(length, dtype=int) - - for j in range(7, -1, -1): - for c in range(3): - colormap[:, c] |= get_bit(indices, c) << j - indices >>= 3 - - return OrderedDict( - (id, tuple(color)) for id, color in enumerate(colormap) - ) - -VocColormap = {id: color for id, color in generate_colormap(256).items() - if id in [l.value for l in VocLabel]} -VocInstColormap = generate_colormap(256) - -class VocPath: - IMAGES_DIR = 'JPEGImages' - ANNOTATIONS_DIR = 'Annotations' - SEGMENTATION_DIR = 'SegmentationClass' - INSTANCES_DIR = 'SegmentationObject' - SUBSETS_DIR = 'ImageSets' - IMAGE_EXT = '.jpg' - SEGM_EXT = '.png' - LABELMAP_FILE = 'labelmap.txt' - - TASK_DIR = { - VocTask.classification: 'Main', - VocTask.detection: 'Main', - VocTask.segmentation: 'Segmentation', - VocTask.action_classification: 'Action', - VocTask.person_layout: 'Layout', - } - - -def make_voc_label_map(): - labels = sorted(VocLabel, key=lambda l: l.value) - label_map = OrderedDict( - (label.name, [VocColormap[label.value], [], []]) for label in labels) - label_map[VocLabel.person.name][1] = [p.name for p in VocBodyPart] - label_map[VocLabel.person.name][2] = [a.name for a in VocAction] - return label_map - -def parse_label_map(path): - if not path: - return None - - label_map = OrderedDict() - with open(path, 'r') as f: - for line in f: - # skip empty and commented lines - line = line.strip() - if not line or line and line[0] == '#': - continue - - # name, color, parts, actions - label_desc = line.strip().split(':') - name = label_desc[0] - - if name in label_map: - raise ValueError("Label '%s' is already defined" % name) - - if 1 < len(label_desc) and len(label_desc[1]) != 0: - color = label_desc[1].split(',') - assert len(color) == 3, \ - "Label '%s' has wrong color, expected 'r,g,b', got '%s'" % \ - (name, color) - color = tuple([int(c) for c in color]) - else: - color = None - - if 2 < len(label_desc) and len(label_desc[2]) != 0: - parts = label_desc[2].split(',') - else: - parts = [] - - if 3 < len(label_desc) and len(label_desc[3]) != 0: - actions = label_desc[3].split(',') - else: - actions = [] - - label_map[name] = [color, parts, actions] - return label_map - -def write_label_map(path, label_map): - with open(path, 'w') as f: - f.write('# label:color_rgb:parts:actions\n') - for label_name, label_desc in label_map.items(): - if label_desc[0]: - color_rgb = ','.join(str(c) for c in label_desc[0]) - else: - color_rgb = '' - - parts = ','.join(str(p) for p in label_desc[1]) - actions = ','.join(str(a) for a in label_desc[2]) - - f.write('%s\n' % ':'.join([label_name, color_rgb, parts, actions])) - -def make_voc_categories(label_map=None): - if label_map is None: - label_map = make_voc_label_map() - - categories = {} - - label_categories = LabelCategories() - label_categories.attributes.update(['difficult', 'truncated', 'occluded']) - - for label, desc in label_map.items(): - label_categories.add(label, attributes=desc[2]) - for part in OrderedDict((k, None) for k in chain( - *(desc[1] for desc in label_map.values()))): - label_categories.add(part) - categories[AnnotationType.label] = label_categories - - has_colors = any(v[0] is not None for v in label_map.values()) - if not has_colors: # generate new colors - colormap = generate_colormap(len(label_map)) - else: # only copy defined colors - label_id = lambda label: label_categories.find(label)[0] - colormap = { label_id(name): desc[0] - for name, desc in label_map.items() if desc[0] is not None } - mask_categories = MaskCategories(colormap) - mask_categories.inverse_colormap # pylint: disable=pointless-statement - categories[AnnotationType.mask] = mask_categories - - return categories diff --git a/datumaro/datumaro/plugins/voc_format/importer.py b/datumaro/datumaro/plugins/voc_format/importer.py deleted file mode 100644 index e9354e6c..00000000 --- a/datumaro/datumaro/plugins/voc_format/importer.py +++ /dev/null @@ -1,56 +0,0 @@ - -# Copyright (C) 2019-2020 Intel Corporation -# -# SPDX-License-Identifier: MIT - -from glob import glob -import os.path as osp - -from datumaro.components.extractor import Importer - -from .format import VocTask, VocPath - - -class VocImporter(Importer): - _TASKS = [ - (VocTask.classification, 'voc_classification', 'Main'), - (VocTask.detection, 'voc_detection', 'Main'), - (VocTask.segmentation, 'voc_segmentation', 'Segmentation'), - (VocTask.person_layout, 'voc_layout', 'Layout'), - (VocTask.action_classification, 'voc_action', 'Action'), - ] - - @classmethod - def detect(cls, path): - return len(cls.find_subsets(path)) != 0 - - def __call__(self, path, **extra_params): - from datumaro.components.project import Project # cyclic import - project = Project() - - subset_paths = self.find_subsets(path) - if len(subset_paths) == 0: - raise Exception("Failed to find 'voc' dataset at '%s'" % path) - - for task, extractor_type, subset_path in subset_paths: - project.add_source('%s-%s' % - (task.name, osp.splitext(osp.basename(subset_path))[0]), - { - 'url': subset_path, - 'format': extractor_type, - 'options': dict(extra_params), - }) - - return project - - @staticmethod - def find_subsets(path): - subset_paths = [] - for task, extractor_type, task_dir in __class__._TASKS: - task_dir = osp.join(path, VocPath.SUBSETS_DIR, task_dir) - if not osp.isdir(task_dir): - continue - task_subsets = [p for p in glob(osp.join(task_dir, '*.txt')) - if '_' not in osp.basename(p)] - subset_paths += [(task, extractor_type, p) for p in task_subsets] - return subset_paths diff --git a/datumaro/datumaro/plugins/yolo_format/__init__.py b/datumaro/datumaro/plugins/yolo_format/__init__.py deleted file mode 100644 index e69de29b..00000000 diff --git a/datumaro/datumaro/plugins/yolo_format/converter.py b/datumaro/datumaro/plugins/yolo_format/converter.py deleted file mode 100644 index 9217c774..00000000 --- a/datumaro/datumaro/plugins/yolo_format/converter.py +++ /dev/null @@ -1,108 +0,0 @@ - -# Copyright (C) 2019-2020 Intel Corporation -# -# SPDX-License-Identifier: MIT - -import logging as log -import os -import os.path as osp -from collections import OrderedDict - -from datumaro.components.converter import Converter -from datumaro.components.extractor import AnnotationType - -from .format import YoloPath - - -def _make_yolo_bbox(img_size, box): - # https://github.com/pjreddie/darknet/blob/master/scripts/voc_label.py - # - values relative to width and height of image - # - are center of rectangle - x = (box[0] + box[2]) / 2 / img_size[0] - y = (box[1] + box[3]) / 2 / img_size[1] - w = (box[2] - box[0]) / img_size[0] - h = (box[3] - box[1]) / img_size[1] - return x, y, w, h - -class YoloConverter(Converter): - # https://github.com/AlexeyAB/darknet#how-to-train-to-detect-your-custom-objects - DEFAULT_IMAGE_EXT = '.jpg' - - def apply(self): - extractor = self._extractor - save_dir = self._save_dir - - os.makedirs(save_dir, exist_ok=True) - - label_categories = extractor.categories()[AnnotationType.label] - label_ids = {label.name: idx - for idx, label in enumerate(label_categories.items)} - with open(osp.join(save_dir, 'obj.names'), 'w') as f: - f.writelines('%s\n' % l[0] - for l in sorted(label_ids.items(), key=lambda x: x[1])) - - subset_lists = OrderedDict() - - for subset_name in extractor.subsets() or [None]: - if subset_name and subset_name in YoloPath.SUBSET_NAMES: - subset = extractor.get_subset(subset_name) - elif not subset_name: - subset_name = YoloPath.DEFAULT_SUBSET_NAME - subset = extractor - else: - log.warn("Skipping subset export '%s'. " - "If specified, the only valid names are %s" % \ - (subset_name, ', '.join( - "'%s'" % s for s in YoloPath.SUBSET_NAMES))) - continue - - subset_dir = osp.join(save_dir, 'obj_%s_data' % subset_name) - os.makedirs(subset_dir, exist_ok=True) - - image_paths = OrderedDict() - - for item in subset: - if not item.has_image: - raise Exception("Failed to export item '%s': " - "item has no image info" % item.id) - height, width = item.image.size - - image_name = self._make_image_filename(item) - if self._save_images: - if item.has_image and item.image.has_data: - self._save_image(item, osp.join(subset_dir, image_name)) - else: - log.warning("Item '%s' has no image" % item.id) - image_paths[item.id] = osp.join('data', - osp.basename(subset_dir), image_name) - - yolo_annotation = '' - for bbox in item.annotations: - if bbox.type is not AnnotationType.bbox: - continue - if bbox.label is None: - continue - - yolo_bb = _make_yolo_bbox((width, height), bbox.points) - yolo_bb = ' '.join('%.6f' % p for p in yolo_bb) - yolo_annotation += '%s %s\n' % (bbox.label, yolo_bb) - - annotation_path = osp.join(subset_dir, '%s.txt' % item.id) - os.makedirs(osp.dirname(annotation_path), exist_ok=True) - with open(annotation_path, 'w') as f: - f.write(yolo_annotation) - - subset_list_name = '%s.txt' % subset_name - subset_lists[subset_name] = subset_list_name - with open(osp.join(save_dir, subset_list_name), 'w') as f: - f.writelines('%s\n' % s for s in image_paths.values()) - - with open(osp.join(save_dir, 'obj.data'), 'w') as f: - f.write('classes = %s\n' % len(label_ids)) - - for subset_name, subset_list_name in subset_lists.items(): - f.write('%s = %s\n' % (subset_name, - osp.join('data', subset_list_name))) - - f.write('names = %s\n' % osp.join('data', 'obj.names')) - f.write('backup = backup/\n') diff --git a/datumaro/datumaro/plugins/yolo_format/extractor.py b/datumaro/datumaro/plugins/yolo_format/extractor.py deleted file mode 100644 index c8c39c42..00000000 --- a/datumaro/datumaro/plugins/yolo_format/extractor.py +++ /dev/null @@ -1,201 +0,0 @@ - -# Copyright (C) 2019-2020 Intel Corporation -# -# SPDX-License-Identifier: MIT - -from collections import OrderedDict -import os.path as osp -import re - -from datumaro.components.extractor import (SourceExtractor, Extractor, - DatasetItem, AnnotationType, Bbox, LabelCategories -) -from datumaro.util import split_path -from datumaro.util.image import Image - -from .format import YoloPath - - -class YoloExtractor(SourceExtractor): - class Subset(Extractor): - def __init__(self, name, parent): - super().__init__() - self._name = name - self._parent = parent - self.items = OrderedDict() - - def __iter__(self): - for item_id in self.items: - yield self._parent._get(item_id, self._name) - - def __len__(self): - return len(self.items) - - def categories(self): - return self._parent.categories() - - def __init__(self, config_path, image_info=None): - super().__init__() - - if not osp.isfile(config_path): - raise Exception("Can't read dataset descriptor file '%s'" % - config_path) - - rootpath = osp.dirname(config_path) - self._path = rootpath - - assert image_info is None or isinstance(image_info, (str, dict)) - if image_info is None: - image_info = osp.join(rootpath, YoloPath.IMAGE_META_FILE) - if not osp.isfile(image_info): - image_info = {} - if isinstance(image_info, str): - if not osp.isfile(image_info): - raise Exception("Can't read image meta file '%s'" % image_info) - with open(image_info) as f: - image_info = {} - for line in f: - image_name, h, w = line.strip().split() - image_info[image_name] = (int(h), int(w)) - self._image_info = image_info - - with open(config_path, 'r') as f: - config_lines = f.readlines() - - subsets = OrderedDict() - names_path = None - - for line in config_lines: - match = re.match(r'(\w+)\s*=\s*(.+)$', line) - if not match: - continue - - key = match.group(1) - value = match.group(2) - if key == 'names': - names_path = value - elif key in YoloPath.SUBSET_NAMES: - subsets[key] = value - else: - continue - - if not names_path: - raise Exception("Failed to parse labels path from '%s'" % \ - config_path) - - for subset_name, list_path in subsets.items(): - list_path = osp.join(self._path, self.localize_path(list_path)) - if not osp.isfile(list_path): - raise Exception("Not found '%s' subset list file" % subset_name) - - subset = YoloExtractor.Subset(subset_name, self) - with open(list_path, 'r') as f: - subset.items = OrderedDict( - (self.name_from_path(p), self.localize_path(p)) - for p in f - ) - subsets[subset_name] = subset - - self._subsets = subsets - - self._categories = { - AnnotationType.label: - self._load_categories( - osp.join(self._path, self.localize_path(names_path))) - } - - @staticmethod - def localize_path(path): - path = path.strip() - default_base = osp.join('data', '') - if path.startswith(default_base): # default path - path = path[len(default_base) : ] - return path - - @classmethod - def name_from_path(cls, path): - path = cls.localize_path(path) - parts = split_path(path) - if 1 < len(parts) and not osp.isabs(path): - # NOTE: when path is like [data/]/ - # drop everything but - # can be , so no just basename() - path = osp.join(*parts[1:]) - return osp.splitext(path)[0] - - def _get(self, item_id, subset_name): - subset = self._subsets[subset_name] - item = subset.items[item_id] - - if isinstance(item, str): - image_size = self._image_info.get(item_id) - image = Image(path=osp.join(self._path, item), size=image_size) - - anno_path = osp.splitext(image.path)[0] + '.txt' - annotations = self._parse_annotations(anno_path, image) - - item = DatasetItem(id=item_id, subset=subset_name, - image=image, annotations=annotations) - subset.items[item_id] = item - - return item - - @staticmethod - def _parse_annotations(anno_path, image): - lines = [] - with open(anno_path, 'r') as f: - for line in f: - line = line.strip() - if line: - lines.append(line) - - annotations = [] - if lines: - size = image.size # use image info as late as possible - if size is None: - raise Exception("Can't find image info for '%s'" % image.path) - image_height, image_width = size - for line in lines: - label_id, xc, yc, w, h = line.split() - label_id = int(label_id) - w = float(w) - h = float(h) - x = float(xc) - w * 0.5 - y = float(yc) - h * 0.5 - annotations.append(Bbox( - round(x * image_width, 1), round(y * image_height, 1), - round(w * image_width, 1), round(h * image_height, 1), - label=label_id - )) - - return annotations - - @staticmethod - def _load_categories(names_path): - label_categories = LabelCategories() - - with open(names_path, 'r') as f: - for label in f: - label_categories.add(label.strip()) - - return label_categories - - def categories(self): - return self._categories - - def __iter__(self): - for subset in self._subsets.values(): - for item in subset: - yield item - - def __len__(self): - length = 0 - for subset in self._subsets.values(): - length += len(subset) - return length - - def subsets(self): - return list(self._subsets) - - def get_subset(self, name): - return self._subsets[name] \ No newline at end of file diff --git a/datumaro/datumaro/plugins/yolo_format/format.py b/datumaro/datumaro/plugins/yolo_format/format.py deleted file mode 100644 index 02a07669..00000000 --- a/datumaro/datumaro/plugins/yolo_format/format.py +++ /dev/null @@ -1,11 +0,0 @@ - -# Copyright (C) 2019-2020 Intel Corporation -# -# SPDX-License-Identifier: MIT - - -class YoloPath: - DEFAULT_SUBSET_NAME = 'train' - SUBSET_NAMES = ['train', 'valid'] - - IMAGE_META_FILE = 'images.meta' \ No newline at end of file diff --git a/datumaro/datumaro/plugins/yolo_format/importer.py b/datumaro/datumaro/plugins/yolo_format/importer.py deleted file mode 100644 index a040ea4e..00000000 --- a/datumaro/datumaro/plugins/yolo_format/importer.py +++ /dev/null @@ -1,46 +0,0 @@ - -# Copyright (C) 2019-2020 Intel Corporation -# -# SPDX-License-Identifier: MIT - -from glob import glob -import logging as log -import os.path as osp - -from datumaro.components.extractor import Importer - - -class YoloImporter(Importer): - @classmethod - def detect(cls, path): - return len(cls.find_configs(path)) != 0 - - def __call__(self, path, **extra_params): - from datumaro.components.project import Project # cyclic import - project = Project() - - config_paths = self.find_configs(path) - if len(config_paths) == 0: - raise Exception("Failed to find 'yolo' dataset at '%s'" % path) - - for config_path in config_paths: - log.info("Found a dataset at '%s'" % config_path) - - source_name = '%s_%s' % ( - osp.basename(osp.dirname(config_path)), - osp.splitext(osp.basename(config_path))[0]) - project.add_source(source_name, { - 'url': config_path, - 'format': 'yolo', - 'options': dict(extra_params), - }) - - return project - - @staticmethod - def find_configs(path): - if path.endswith('.data') and osp.isfile(path): - config_paths = [path] - else: - config_paths = glob(osp.join(path, '**', '*.data'), recursive=True) - return config_paths \ No newline at end of file diff --git a/datumaro/datumaro/util/__init__.py b/datumaro/datumaro/util/__init__.py deleted file mode 100644 index 0a75756b..00000000 --- a/datumaro/datumaro/util/__init__.py +++ /dev/null @@ -1,93 +0,0 @@ - -# Copyright (C) 2019-2020 Intel Corporation -# -# SPDX-License-Identifier: MIT - -import os -import os.path as osp -from itertools import islice - - -def find(iterable, pred=lambda x: True, default=None): - return next((x for x in iterable if pred(x)), default) - -def dir_items(path, ext, truncate_ext=False): - items = [] - for f in os.listdir(path): - ext_pos = f.rfind(ext) - if ext_pos != -1: - if truncate_ext: - f = f[:ext_pos] - items.append(f) - return items - -def split_path(path): - path = osp.normpath(path) - parts = [] - - while True: - path, part = osp.split(path) - if part: - parts.append(part) - else: - if path: - parts.append(path) - break - parts.reverse() - - return parts - -def cast(value, type_conv, default=None): - if value is None: - return default - try: - return type_conv(value) - except Exception: - return default - -def to_snake_case(s): - if not s: - return '' - - name = [s[0].lower()] - for idx, char in enumerate(s[1:]): - idx = idx + 1 - if char.isalpha() and char.isupper(): - prev_char = s[idx - 1] - if not (prev_char.isalpha() and prev_char.isupper()): - # avoid "HTML" -> "h_t_m_l" - name.append('_') - name.append(char.lower()) - else: - name.append(char) - return ''.join(name) - -def pairs(iterable): - a = iter(iterable) - return zip(a, a) - -def take_by(iterable, count): - """ - Returns elements from the input iterable by batches of N items. - ('abcdefg', 3) -> ['a', 'b', 'c'], ['d', 'e', 'f'], ['g'] - """ - - it = iter(iterable) - while True: - batch = list(islice(it, count)) - if len(batch) == 0: - break - - yield batch - -def str_to_bool(s): - t = s.lower() - if t in {'true', '1', 'ok', 'yes', 'y'}: - return True - elif t in {'false', '0', 'no', 'n'}: - return False - else: - raise ValueError("Can't convert value '%s' to bool" % s) - -def filter_dict(d, exclude_keys): - return { k: v for k, v in d.items() if k not in exclude_keys } \ No newline at end of file diff --git a/datumaro/datumaro/util/annotation_util.py b/datumaro/datumaro/util/annotation_util.py deleted file mode 100644 index 3daa313f..00000000 --- a/datumaro/datumaro/util/annotation_util.py +++ /dev/null @@ -1,212 +0,0 @@ -# Copyright (C) 2020 Intel Corporation -# -# SPDX-License-Identifier: MIT - -from itertools import groupby - -import numpy as np - -from datumaro.components.extractor import _Shape, Mask, AnnotationType, RleMask -from datumaro.util.mask_tools import mask_to_rle - - -def find_instances(instance_anns): - instance_anns = sorted(instance_anns, key=lambda a: a.group) - ann_groups = [] - for g_id, group in groupby(instance_anns, lambda a: a.group): - if not g_id: - ann_groups.extend(([a] for a in group)) - else: - ann_groups.append(list(group)) - - return ann_groups - -def find_group_leader(group): - return max(group, key=lambda x: x.get_area()) - -def _get_bbox(ann): - if isinstance(ann, (_Shape, Mask)): - return ann.get_bbox() - else: - return ann - -def max_bbox(annotations): - boxes = [_get_bbox(ann) for ann in annotations] - x0 = min((b[0] for b in boxes), default=0) - y0 = min((b[1] for b in boxes), default=0) - x1 = max((b[0] + b[2] for b in boxes), default=0) - y1 = max((b[1] + b[3] for b in boxes), default=0) - return [x0, y0, x1 - x0, y1 - y0] - -def mean_bbox(annotations): - le = len(annotations) - boxes = [_get_bbox(ann) for ann in annotations] - mlb = sum(b[0] for b in boxes) / le - mtb = sum(b[1] for b in boxes) / le - mrb = sum(b[0] + b[2] for b in boxes) / le - mbb = sum(b[1] + b[3] for b in boxes) / le - return [mlb, mtb, mrb - mlb, mbb - mtb] - -def softmax(x): - return np.exp(x) / sum(np.exp(x)) - -def nms(segments, iou_thresh=0.5): - """ - Non-maxima suppression algorithm. - """ - - indices = np.argsort([b.attributes['score'] for b in segments]) - ious = np.array([[iou(a, b) for b in segments] for a in segments]) - - predictions = [] - while len(indices) != 0: - i = len(indices) - 1 - pred_idx = indices[i] - to_remove = [i] - predictions.append(segments[pred_idx]) - for i, box_idx in enumerate(indices[:i]): - if iou_thresh < ious[pred_idx, box_idx]: - to_remove.append(i) - indices = np.delete(indices, to_remove) - - return predictions - -def bbox_iou(a, b): - """ - IoU computations for simple cases with bounding boxes - """ - bbox_a = _get_bbox(a) - bbox_b = _get_bbox(b) - - aX, aY, aW, aH = bbox_a - bX, bY, bW, bH = bbox_b - in_right = min(aX + aW, bX + bW) - in_left = max(aX, bX) - in_top = max(aY, bY) - in_bottom = min(aY + aH, bY + bH) - - in_w = max(0, in_right - in_left) - in_h = max(0, in_bottom - in_top) - intersection = in_w * in_h - if not intersection: - return -1 - - a_area = aW * aH - b_area = bW * bH - union = a_area + b_area - intersection - return intersection / union - -def segment_iou(a, b): - """ - Generic IoU computation with masks, polygons, and boxes. - Returns -1 if no intersection, [0; 1] otherwise - """ - from pycocotools import mask as mask_utils - - a_bbox = a.get_bbox() - b_bbox = b.get_bbox() - - is_bbox = AnnotationType.bbox in [a.type, b.type] - if is_bbox: - a = [a_bbox] - b = [b_bbox] - else: - w = max(a_bbox[0] + a_bbox[2], b_bbox[0] + b_bbox[2]) - h = max(a_bbox[1] + a_bbox[3], b_bbox[1] + b_bbox[3]) - - def _to_rle(ann): - if ann.type == AnnotationType.polygon: - return mask_utils.frPyObjects([ann.points], h, w) - elif isinstance(ann, RleMask): - return [ann.rle] - elif ann.type == AnnotationType.mask: - return mask_utils.frPyObjects([mask_to_rle(ann.image)], h, w) - else: - raise TypeError("Unexpected arguments: %s, %s" % (a, b)) - a = _to_rle(a) - b = _to_rle(b) - return float(mask_utils.iou(a, b, [not is_bbox])) - -def PDJ(a, b, eps=None, ratio=0.05, bbox=None): - """ - Percentage of Detected Joints metric. - Counts the number of matching points. - """ - - assert eps is not None or ratio is not None - - p1 = np.array(a.points).reshape((-1, 2)) - p2 = np.array(b.points).reshape((-1, 2)) - if len(p1) != len(p2): - return 0 - - if not eps: - if bbox is None: - bbox = mean_bbox([a, b]) - - diag = (bbox[2] ** 2 + bbox[3] ** 2) ** 0.5 - eps = ratio * diag - - dists = np.linalg.norm(p1 - p2, axis=1) - return np.sum(dists < eps) / len(p1) - -def OKS(a, b, sigma=None, bbox=None, scale=None): - """ - Object Keypoint Similarity metric. - https://cocodataset.org/#keypoints-eval - """ - - p1 = np.array(a.points).reshape((-1, 2)) - p2 = np.array(b.points).reshape((-1, 2)) - if len(p1) != len(p2): - return 0 - - if not sigma: - sigma = 0.1 - else: - assert len(sigma) == len(p1) - - if not scale: - if bbox is None: - bbox = mean_bbox([a, b]) - scale = bbox[2] * bbox[3] - - dists = np.linalg.norm(p1 - p2, axis=1) - return np.sum(np.exp(-(dists ** 2) / (2 * scale * (2 * sigma) ** 2))) - -def smooth_line(points, segments): - assert 2 <= len(points) // 2 and len(points) % 2 == 0 - - if len(points) // 2 == segments: - return points - - points = list(points) - if len(points) == 2: - points.extend(points) - points = np.array(points).reshape((-1, 2)) - - lengths = np.linalg.norm(points[1:] - points[:-1], axis=1) - dists = [0] - for l in lengths: - dists.append(dists[-1] + l) - - step = dists[-1] / segments - - new_points = np.zeros((segments + 1, 2)) - new_points[0] = points[0] - - old_segment = 0 - for new_segment in range(1, segments + 1): - pos = new_segment * step - while dists[old_segment + 1] < pos and old_segment + 2 < len(dists): - old_segment += 1 - - segment_start = dists[old_segment] - segment_len = lengths[old_segment] - prev_p = points[old_segment] - next_p = points[old_segment + 1] - r = (pos - segment_start) / segment_len - - new_points[new_segment] = prev_p * (1 - r) + next_p * r - - return new_points, step diff --git a/datumaro/datumaro/util/attrs_util.py b/datumaro/datumaro/util/attrs_util.py deleted file mode 100644 index e631f35a..00000000 --- a/datumaro/datumaro/util/attrs_util.py +++ /dev/null @@ -1,33 +0,0 @@ -# Copyright (C) 2020 Intel Corporation -# -# SPDX-License-Identifier: MIT - -import attr - -_NOTSET = object() - -def not_empty(inst, attribute, x): - assert len(x) != 0, x - -def default_if_none(conv): - def validator(inst, attribute, value): - default = attribute.default - if value is None: - if callable(default): - value = default() - elif isinstance(default, attr.Factory): - value = default.factory() - else: - value = default - elif not isinstance(value, attribute.type or conv): - value = conv(value) - setattr(inst, attribute.name, value) - return validator - -def ensure_cls(c): - def converter(arg): - if isinstance(arg, c): - return arg - else: - return c(**arg) - return converter \ No newline at end of file diff --git a/datumaro/datumaro/util/command_targets.py b/datumaro/datumaro/util/command_targets.py deleted file mode 100644 index 50c854f2..00000000 --- a/datumaro/datumaro/util/command_targets.py +++ /dev/null @@ -1,113 +0,0 @@ - -# Copyright (C) 2019-2020 Intel Corporation -# -# SPDX-License-Identifier: MIT - -import argparse -from enum import Enum - -from datumaro.components.project import Project -from datumaro.util.image import load_image - - -TargetKinds = Enum('TargetKinds', - ['project', 'source', 'external_dataset', 'inference', 'image']) - -def is_project_name(value, project): - return value == project.config.project_name - -def is_project_path(value): - if value: - try: - Project.load(value) - return True - except Exception: - pass - return False - -def is_project(value, project=None): - if is_project_path(value): - return True - elif project is not None: - return is_project_name(value, project) - - return False - -def is_source(value, project=None): - if project is not None: - try: - project.get_source(value) - return True - except KeyError: - pass - - return False - -def is_external_source(value): - return False - -def is_inference_path(value): - return False - -def is_image_path(value): - try: - return load_image(value) is not None - except Exception: - return False - - -class Target: - def __init__(self, kind, test, is_default=False, name=None): - self.kind = kind - self.test = test - self.is_default = is_default - self.name = name - - def _get_fields(self): - return [self.kind, self.test, self.is_default, self.name] - - def __str__(self): - return self.name or str(self.kind) - - def __len__(self): - return len(self._get_fields()) - - def __iter__(self): - return iter(self._get_fields()) - -def ProjectTarget(kind=TargetKinds.project, test=None, - is_default=False, name='project name or path', - project=None): - if test is None: - test = lambda v: is_project(v, project=project) - return Target(kind, test, is_default, name) - -def SourceTarget(kind=TargetKinds.source, test=None, - is_default=False, name='source name', - project=None): - if test is None: - test = lambda v: is_source(v, project=project) - return Target(kind, test, is_default, name) - -def ExternalDatasetTarget(kind=TargetKinds.external_dataset, - test=is_external_source, - is_default=False, name='external dataset path'): - return Target(kind, test, is_default, name) - -def InferenceTarget(kind=TargetKinds.inference, test=is_inference_path, - is_default=False, name='inference path'): - return Target(kind, test, is_default, name) - -def ImageTarget(kind=TargetKinds.image, test=is_image_path, - is_default=False, name='image path'): - return Target(kind, test, is_default, name) - - -def target_selector(*targets): - def selector(value): - for (kind, test, is_default, _) in targets: - if (is_default and (value == '' or value is None)) or test(value): - return (kind, value) - raise argparse.ArgumentTypeError('Value should be one of: %s' \ - % (', '.join([str(t) for t in targets]))) - return selector diff --git a/datumaro/datumaro/util/image.py b/datumaro/datumaro/util/image.py deleted file mode 100644 index 626d8499..00000000 --- a/datumaro/datumaro/util/image.py +++ /dev/null @@ -1,246 +0,0 @@ - -# Copyright (C) 2019-2020 Intel Corporation -# -# SPDX-License-Identifier: MIT - -# pylint: disable=unused-import - -from enum import Enum -from io import BytesIO -import numpy as np -import os -import os.path as osp - -_IMAGE_BACKENDS = Enum('_IMAGE_BACKENDS', ['cv2', 'PIL']) -_IMAGE_BACKEND = None -try: - import cv2 - _IMAGE_BACKEND = _IMAGE_BACKENDS.cv2 -except ImportError: - import PIL - _IMAGE_BACKEND = _IMAGE_BACKENDS.PIL - -from datumaro.util.image_cache import ImageCache as _ImageCache - - -def load_image(path): - """ - Reads an image in the HWC Grayscale/BGR(A) float [0; 255] format. - """ - - if _IMAGE_BACKEND == _IMAGE_BACKENDS.cv2: - import cv2 - image = cv2.imread(path, cv2.IMREAD_UNCHANGED) - image = image.astype(np.float32) - elif _IMAGE_BACKEND == _IMAGE_BACKENDS.PIL: - from PIL import Image - image = Image.open(path) - image = np.asarray(image, dtype=np.float32) - if len(image.shape) == 3 and image.shape[2] in {3, 4}: - image[:, :, :3] = image[:, :, 2::-1] # RGB to BGR - else: - raise NotImplementedError() - - assert len(image.shape) in {2, 3} - if len(image.shape) == 3: - assert image.shape[2] in {3, 4} - return image - -def save_image(path, image, create_dir=False, **kwargs): - # NOTE: Check destination path for existence - # OpenCV silently fails if target directory does not exist - dst_dir = osp.dirname(path) - if dst_dir: - if create_dir: - os.makedirs(dst_dir, exist_ok=True) - elif not osp.isdir(dst_dir): - raise FileNotFoundError("Directory does not exist: '%s'" % dst_dir) - - if not kwargs: - kwargs = {} - - if _IMAGE_BACKEND == _IMAGE_BACKENDS.cv2: - import cv2 - - params = [] - - ext = path[-4:] - if ext.upper() == '.JPG': - params = [ - int(cv2.IMWRITE_JPEG_QUALITY), kwargs.get('jpeg_quality', 75) - ] - - image = image.astype(np.uint8) - cv2.imwrite(path, image, params=params) - elif _IMAGE_BACKEND == _IMAGE_BACKENDS.PIL: - from PIL import Image - - params = {} - params['quality'] = kwargs.get('jpeg_quality') - if kwargs.get('jpeg_quality') == 100: - params['subsampling'] = 0 - - image = image.astype(np.uint8) - if len(image.shape) == 3 and image.shape[2] in {3, 4}: - image[:, :, :3] = image[:, :, 2::-1] # BGR to RGB - image = Image.fromarray(image) - image.save(path, **params) - else: - raise NotImplementedError() - -def encode_image(image, ext, **kwargs): - if not kwargs: - kwargs = {} - - if _IMAGE_BACKEND == _IMAGE_BACKENDS.cv2: - import cv2 - - params = [] - - if not ext.startswith('.'): - ext = '.' + ext - - if ext.upper() == '.JPG': - params = [ - int(cv2.IMWRITE_JPEG_QUALITY), kwargs.get('jpeg_quality', 75) - ] - - image = image.astype(np.uint8) - success, result = cv2.imencode(ext, image, params=params) - if not success: - raise Exception("Failed to encode image to '%s' format" % (ext)) - return result.tobytes() - elif _IMAGE_BACKEND == _IMAGE_BACKENDS.PIL: - from PIL import Image - - if ext.startswith('.'): - ext = ext[1:] - - params = {} - params['quality'] = kwargs.get('jpeg_quality') - if kwargs.get('jpeg_quality') == 100: - params['subsampling'] = 0 - - image = image.astype(np.uint8) - if len(image.shape) == 3 and image.shape[2] in {3, 4}: - image[:, :, :3] = image[:, :, 2::-1] # BGR to RGB - image = Image.fromarray(image) - with BytesIO() as buffer: - image.save(buffer, format=ext, **params) - return buffer.getvalue() - else: - raise NotImplementedError() - -def decode_image(image_bytes): - if _IMAGE_BACKEND == _IMAGE_BACKENDS.cv2: - import cv2 - image = np.frombuffer(image_bytes, dtype=np.uint8) - image = cv2.imdecode(image, cv2.IMREAD_UNCHANGED) - image = image.astype(np.float32) - elif _IMAGE_BACKEND == _IMAGE_BACKENDS.PIL: - from PIL import Image - image = Image.open(BytesIO(image_bytes)) - image = np.asarray(image, dtype=np.float32) - if len(image.shape) == 3 and image.shape[2] in {3, 4}: - image[:, :, :3] = image[:, :, 2::-1] # RGB to BGR - else: - raise NotImplementedError() - - assert len(image.shape) in {2, 3} - if len(image.shape) == 3: - assert image.shape[2] in {3, 4} - return image - - -class lazy_image: - def __init__(self, path, loader=None, cache=None): - if loader is None: - loader = load_image - self.path = path - self.loader = loader - - # Cache: - # - False: do not cache - # - None: use the global cache - # - object: an object to be used as cache - assert cache in {None, False} or isinstance(cache, object) - self.cache = cache - - def __call__(self): - image = None - image_id = hash(self) # path is not necessary hashable or a file path - - cache = self._get_cache(self.cache) - if cache is not None: - image = cache.get(image_id) - - if image is None: - image = self.loader(self.path) - if cache is not None: - cache.push(image_id, image) - return image - - @staticmethod - def _get_cache(cache): - if cache is None: - cache = _ImageCache.get_instance() - elif cache == False: - return None - return cache - - def __hash__(self): - return hash((id(self), self.path, self.loader)) - -class Image: - def __init__(self, data=None, path=None, loader=None, cache=None, - size=None): - assert size is None or len(size) == 2 - if size is not None: - assert len(size) == 2 and 0 < size[0] and 0 < size[1], size - size = tuple(size) - self._size = size # (H, W) - - assert path is None or isinstance(path, str) - if path is None: - path = '' - self._path = path - - assert data is not None or path or loader, "Image can not be empty" - if data is None and (path or loader): - if osp.isfile(path) or loader: - data = lazy_image(path, loader=loader, cache=cache) - self._data = data - - @property - def path(self): - return self._path - - @property - def data(self): - if callable(self._data): - return self._data() - return self._data - - @property - def has_data(self): - return self._data is not None - - @property - def size(self): - if self._size is None: - data = self.data - if data is not None: - self._size = data.shape[:2] - return self._size - - def __eq__(self, other): - if isinstance(other, np.ndarray): - return self.has_data and np.array_equal(self.data, other) - - if not isinstance(other, __class__): - return False - return \ - (np.array_equal(self.size, other.size)) and \ - (self.has_data == other.has_data) and \ - (self.has_data and np.array_equal(self.data, other.data) or \ - not self.has_data) \ No newline at end of file diff --git a/datumaro/datumaro/util/image_cache.py b/datumaro/datumaro/util/image_cache.py deleted file mode 100644 index 08f02582..00000000 --- a/datumaro/datumaro/util/image_cache.py +++ /dev/null @@ -1,42 +0,0 @@ -# Copyright (C) 2019-2020 Intel Corporation -# -# SPDX-License-Identifier: MIT - -from collections import OrderedDict - - -_instance = None - -DEFAULT_CAPACITY = 2 - -class ImageCache: - @staticmethod - def get_instance(): - global _instance - if _instance is None: - _instance = ImageCache() - return _instance - - def __init__(self, capacity=DEFAULT_CAPACITY): - self.capacity = int(capacity) - self.items = OrderedDict() - - def push(self, item_id, image): - if self.capacity <= len(self.items): - self.items.popitem(last=True) - self.items[item_id] = image - - def get(self, item_id): - default = object() - item = self.items.get(item_id, default) - if item is default: - return None - - self.items.move_to_end(item_id, last=False) # naive splay tree - return item - - def size(self): - return len(self.items) - - def clear(self): - self.items.clear() \ No newline at end of file diff --git a/datumaro/datumaro/util/log_utils.py b/datumaro/datumaro/util/log_utils.py deleted file mode 100644 index 6c8d8421..00000000 --- a/datumaro/datumaro/util/log_utils.py +++ /dev/null @@ -1,16 +0,0 @@ - -# Copyright (C) 2020 Intel Corporation -# -# SPDX-License-Identifier: MIT - -from contextlib import contextmanager -import logging - -@contextmanager -def logging_disabled(max_level=logging.CRITICAL): - previous_level = logging.root.manager.disable - logging.disable(max_level) - try: - yield - finally: - logging.disable(previous_level) \ No newline at end of file diff --git a/datumaro/datumaro/util/mask_tools.py b/datumaro/datumaro/util/mask_tools.py deleted file mode 100644 index 95c8633a..00000000 --- a/datumaro/datumaro/util/mask_tools.py +++ /dev/null @@ -1,289 +0,0 @@ - -# Copyright (C) 2019-2020 Intel Corporation -# -# SPDX-License-Identifier: MIT - -import numpy as np - -from datumaro.util.image import lazy_image, load_image - - -def generate_colormap(length=256): - """ - Generates colors using PASCAL VOC algorithm. - - Returns index -> (R, G, B) mapping. - """ - - def get_bit(number, index): - return (number >> index) & 1 - - colormap = np.zeros((length, 3), dtype=int) - indices = np.arange(length, dtype=int) - - for j in range(7, -1, -1): - for c in range(3): - colormap[:, c] |= get_bit(indices, c) << j - indices >>= 3 - - return { - id: tuple(color) for id, color in enumerate(colormap) - } - -def invert_colormap(colormap): - return { - tuple(a): index for index, a in colormap.items() - } - -def check_is_mask(mask): - assert len(mask.shape) in {2, 3} - if len(mask.shape) == 3: - assert mask.shape[2] == 1 - -_default_colormap = generate_colormap() -_default_unpaint_colormap = invert_colormap(_default_colormap) - -def unpaint_mask(painted_mask, inverse_colormap=None): - # Covert color mask to index mask - - # mask: HWC BGR [0; 255] - # colormap: (R, G, B) -> index - assert len(painted_mask.shape) == 3 - if inverse_colormap is None: - inverse_colormap = _default_unpaint_colormap - - if callable(inverse_colormap): - map_fn = lambda a: inverse_colormap( - (a >> 16) & 255, (a >> 8) & 255, a & 255 - ) - else: - map_fn = lambda a: inverse_colormap[( - (a >> 16) & 255, (a >> 8) & 255, a & 255 - )] - - painted_mask = painted_mask.astype(int) - painted_mask = painted_mask[:, :, 0] + \ - (painted_mask[:, :, 1] << 8) + \ - (painted_mask[:, :, 2] << 16) - uvals, unpainted_mask = np.unique(painted_mask, return_inverse=True) - palette = np.array([map_fn(v) for v in uvals], dtype=np.float32) - unpainted_mask = palette[unpainted_mask].reshape(painted_mask.shape[:2]) - - return unpainted_mask - -def paint_mask(mask, colormap=None): - # Applies colormap to index mask - - # mask: HW(C) [0; max_index] mask - # colormap: index -> (R, G, B) - check_is_mask(mask) - - if colormap is None: - colormap = _default_colormap - if callable(colormap): - map_fn = colormap - else: - map_fn = lambda c: colormap.get(c, (-1, -1, -1)) - palette = np.array([map_fn(c)[::-1] for c in range(256)], dtype=np.float32) - - mask = mask.astype(np.uint8) - painted_mask = palette[mask].reshape((*mask.shape[:2], 3)) - return painted_mask - -def remap_mask(mask, map_fn): - # Changes mask elements from one colormap to another - - # mask: HW(C) [0; max_index] mask - check_is_mask(mask) - - return np.array([map_fn(c) for c in range(256)], dtype=np.uint8)[mask] - -def make_index_mask(binary_mask, index): - return np.choose(binary_mask, np.array([0, index], dtype=np.uint8)) - -def make_binary_mask(mask): - return np.nonzero(mask) - - -def load_mask(path, inverse_colormap=None): - mask = load_image(path) - mask = mask.astype(np.uint8) - if inverse_colormap is not None: - if len(mask.shape) == 3 and mask.shape[2] != 1: - mask = unpaint_mask(mask, inverse_colormap) - return mask - -def lazy_mask(path, inverse_colormap=None): - return lazy_image(path, lambda path: load_mask(path, inverse_colormap)) - -def mask_to_rle(binary_mask): - # walk in row-major order as COCO format specifies - bounded = binary_mask.ravel(order='F') - - # add borders to sequence - # find boundary positions for sequences and compute their lengths - difs = np.diff(bounded, prepend=[1 - bounded[0]], append=[1 - bounded[-1]]) - counts, = np.where(difs != 0) - - # start RLE encoding from 0 as COCO format specifies - if bounded[0] != 0: - counts = np.diff(counts, prepend=[0]) - else: - counts = np.diff(counts) - - return { - 'counts': counts, - 'size': list(binary_mask.shape) - } - -def mask_to_polygons(mask, tolerance=1.0, area_threshold=1): - """ - Convert an instance mask to polygons - - Args: - mask: a 2d binary mask - tolerance: maximum distance from original points of - a polygon to the approximated ones - area_threshold: minimal area of generated polygons - - Returns: - A list of polygons like [[x1,y1, x2,y2 ...], [...]] - """ - from pycocotools import mask as mask_utils - from skimage import measure - - polygons = [] - - # pad mask with 0 around borders - padded_mask = np.pad(mask, pad_width=1, mode='constant', constant_values=0) - contours = measure.find_contours(padded_mask, 0.5) - # Fix coordinates after padding - contours = np.subtract(contours, 1) - - for contour in contours: - if not np.array_equal(contour[0], contour[-1]): - contour = np.vstack((contour, contour[0])) # make polygon closed - - contour = measure.approximate_polygon(contour, tolerance) - if len(contour) <= 2: - continue - - contour = np.flip(contour, axis=1).flatten().clip(0) # [x0, y0, ...] - - # Check if the polygon is big enough - rle = mask_utils.frPyObjects([contour], mask.shape[0], mask.shape[1]) - area = sum(mask_utils.area(rle)) - if area_threshold <= area: - polygons.append(contour) - return polygons - -def crop_covered_segments(segments, width, height, - iou_threshold=0.0, ratio_tolerance=0.001, area_threshold=1, - return_masks=False): - """ - Find all segments occluded by others and crop them to the visible part only. - Input segments are expected to be sorted from background to foreground. - - Args: - segments: 1d list of segment RLEs (in COCO format) - width: width of the image - height: height of the image - iou_threshold: IoU threshold for objects to be counted as intersected - By default is set to 0 to process any intersected objects - ratio_tolerance: an IoU "handicap" value for a situation - when an object is (almost) fully covered by another one and we - don't want make a "hole" in the background object - area_threshold: minimal area of included segments - - Returns: - A list of input segments' parts (in the same order as input): - [ - [[x1,y1, x2,y2 ...], ...], # input segment #0 parts - mask1, # input segment #1 mask (if source segment is mask) - [], # when source segment is too small - ... - ] - """ - from pycocotools import mask as mask_utils - - segments = [[s] for s in segments] - input_rles = [mask_utils.frPyObjects(s, height, width) for s in segments] - - for i, rle_bottom in enumerate(input_rles): - area_bottom = sum(mask_utils.area(rle_bottom)) - if area_bottom < area_threshold: - segments[i] = [] if not return_masks else None - continue - - rles_top = [] - for j in range(i + 1, len(input_rles)): - rle_top = input_rles[j] - iou = sum(mask_utils.iou(rle_bottom, rle_top, [0, 0]))[0] - - if iou <= iou_threshold: - continue - - area_top = sum(mask_utils.area(rle_top)) - area_ratio = area_top / area_bottom - - # If a segment is fully inside another one, skip this segment - if abs(area_ratio - iou) < ratio_tolerance: - continue - - # Check if the bottom segment is fully covered by the top one. - # There is a mistake in the annotation, keep the background one - if abs(1 / area_ratio - iou) < ratio_tolerance: - rles_top = [] - break - - rles_top += rle_top - - if not rles_top and not isinstance(segments[i][0], dict) \ - and not return_masks: - continue - - rle_bottom = rle_bottom[0] - bottom_mask = mask_utils.decode(rle_bottom).astype(np.uint8) - - if rles_top: - rle_top = mask_utils.merge(rles_top) - top_mask = mask_utils.decode(rle_top).astype(np.uint8) - - bottom_mask -= top_mask - bottom_mask[bottom_mask != 1] = 0 - - if not return_masks and not isinstance(segments[i][0], dict): - segments[i] = mask_to_polygons(bottom_mask, - area_threshold=area_threshold) - else: - segments[i] = bottom_mask - - return segments - -def rles_to_mask(rles, width, height): - from pycocotools import mask as mask_utils - - rles = mask_utils.frPyObjects(rles, height, width) - rles = mask_utils.merge(rles) - mask = mask_utils.decode(rles) - return mask - -def find_mask_bbox(mask): - cols = np.any(mask, axis=0) - rows = np.any(mask, axis=1) - x0, x1 = np.where(cols)[0][[0, -1]] - y0, y1 = np.where(rows)[0][[0, -1]] - return [x0, y0, x1 - x0, y1 - y0] - -def merge_masks(masks): - """ - Merges masks into one, mask order is responsible for z order. - """ - if not masks: - return None - - merged_mask = masks[0] - for m in masks[1:]: - merged_mask = np.where(m != 0, m, merged_mask) - - return merged_mask \ No newline at end of file diff --git a/datumaro/datumaro/util/os_util.py b/datumaro/datumaro/util/os_util.py deleted file mode 100644 index b4d05e37..00000000 --- a/datumaro/datumaro/util/os_util.py +++ /dev/null @@ -1,17 +0,0 @@ - -# Copyright (C) 2020 Intel Corporation -# -# SPDX-License-Identifier: MIT - -import subprocess - - -def check_instruction_set(instruction): - return instruction == str.strip( - # Let's ignore a warning from bandit about using shell=True. - # In this case it isn't a security issue and we use some - # shell features like pipes. - subprocess.check_output( - 'lscpu | grep -o "%s" | head -1' % instruction, - shell=True).decode('utf-8') # nosec - ) \ No newline at end of file diff --git a/datumaro/datumaro/util/test_utils.py b/datumaro/datumaro/util/test_utils.py deleted file mode 100644 index db2767db..00000000 --- a/datumaro/datumaro/util/test_utils.py +++ /dev/null @@ -1,121 +0,0 @@ - -# Copyright (C) 2019-2020 Intel Corporation -# -# SPDX-License-Identifier: MIT - -import inspect -import os -import os.path as osp -import shutil -import tempfile - -from datumaro.components.extractor import AnnotationType -from datumaro.util import find - - -def current_function_name(depth=1): - return inspect.getouterframes(inspect.currentframe())[depth].function - -class FileRemover: - def __init__(self, path, is_dir=False, ignore_errors=False): - self.path = path - self.is_dir = is_dir - self.ignore_errors = ignore_errors - - def __enter__(self): - return self.path - - # pylint: disable=redefined-builtin - def __exit__(self, type=None, value=None, traceback=None): - if self.is_dir: - shutil.rmtree(self.path, ignore_errors=self.ignore_errors) - else: - os.remove(self.path) - # pylint: enable=redefined-builtin - -class TestDir(FileRemover): - def __init__(self, path=None, ignore_errors=False): - if path is None: - path = osp.abspath('temp_%s-' % current_function_name(2)) - path = tempfile.mkdtemp(dir=os.getcwd(), prefix=path) - else: - os.makedirs(path, exist_ok=ignore_errors) - - super().__init__(path, is_dir=True, ignore_errors=ignore_errors) - -def compare_categories(test, expected, actual): - test.assertEqual( - sorted(expected, key=lambda t: t.value), - sorted(actual, key=lambda t: t.value) - ) - - if AnnotationType.label in expected: - test.assertEqual( - expected[AnnotationType.label].items, - actual[AnnotationType.label].items, - ) - if AnnotationType.mask in expected: - test.assertEqual( - expected[AnnotationType.mask].colormap, - actual[AnnotationType.mask].colormap, - ) - if AnnotationType.points in expected: - test.assertEqual( - expected[AnnotationType.points].items, - actual[AnnotationType.points].items, - ) - -def _compare_annotations(expected, actual, ignored_attrs=None): - if not ignored_attrs: - return expected == actual - - a_attr = expected.attributes - b_attr = actual.attributes - - expected.attributes = {k:v for k,v in a_attr.items() if k not in ignored_attrs} - actual.attributes = {k:v for k,v in b_attr.items() if k not in ignored_attrs} - r = expected == actual - - expected.attributes = a_attr - actual.attributes = b_attr - return r - -def compare_datasets(test, expected, actual, ignored_attrs=None): - compare_categories(test, expected.categories(), actual.categories()) - - test.assertEqual(sorted(expected.subsets()), sorted(actual.subsets())) - test.assertEqual(len(expected), len(actual)) - for item_a in expected: - item_b = find(actual, lambda x: x.id == item_a.id and \ - x.subset == item_a.subset) - test.assertFalse(item_b is None, item_a.id) - test.assertEqual(item_a.attributes, item_b.attributes) - test.assertEqual(len(item_a.annotations), len(item_b.annotations)) - for ann_a in item_a.annotations: - # We might find few corresponding items, so check them all - ann_b_matches = [x for x in item_b.annotations - if x.type == ann_a.type] - test.assertFalse(len(ann_b_matches) == 0, 'ann id: %s' % ann_a.id) - - ann_b = find(ann_b_matches, lambda x: - _compare_annotations(x, ann_a, ignored_attrs=ignored_attrs)) - if ann_b is None: - test.fail('ann %s, candidates %s' % (ann_a, ann_b_matches)) - item_b.annotations.remove(ann_b) # avoid repeats - -def compare_datasets_strict(test, expected, actual): - # Compares datasets for strong equality - - test.assertEqual(expected.categories(), actual.categories()) - - test.assertListEqual(sorted(expected.subsets()), sorted(actual.subsets())) - test.assertEqual(len(expected), len(actual)) - - for subset_name in expected.subsets(): - e_subset = expected.get_subset(subset_name) - a_subset = actual.get_subset(subset_name) - test.assertEqual(len(e_subset), len(a_subset)) - for idx, (item_a, item_b) in enumerate(zip(e_subset, a_subset)): - test.assertEqual(item_a, item_b, - '%s:\n%s\nvs.\n%s\n' % \ - (idx, item_a, item_b)) \ No newline at end of file diff --git a/datumaro/datumaro/util/tf_util.py b/datumaro/datumaro/util/tf_util.py deleted file mode 100644 index 9eda97ba..00000000 --- a/datumaro/datumaro/util/tf_util.py +++ /dev/null @@ -1,80 +0,0 @@ - -# Copyright (C) 2019-2020 Intel Corporation -# -# SPDX-License-Identifier: MIT - - -def check_import(): - # Workaround for checking import availability: - # Official TF builds include AVX instructions. Once we try to import, - # the program crashes. We raise an exception instead. - - import subprocess - import sys - - from .os_util import check_instruction_set - - result = subprocess.run([sys.executable, '-c', 'import tensorflow'], - timeout=60, - universal_newlines=True, # use text mode for output stream - stdout=subprocess.PIPE, stderr=subprocess.PIPE) # capture output - - if result.returncode != 0: - message = result.stderr - if not message: - message = "Can't import tensorflow. " \ - "Test process exit code: %s." % result.returncode - if not check_instruction_set('avx'): - # The process has probably crashed for AVX unavalability - message += " This is likely because your CPU does not " \ - "support AVX instructions, " \ - "which are required for tensorflow." - - raise ImportError(message) - -def import_tf(check=True): - import sys - - not_found = object() - tf = sys.modules.get('tensorflow', not_found) - if tf is None: - import tensorflow as tf # emit default error - elif tf is not not_found: - return tf - - # Reduce output noise, https://stackoverflow.com/questions/38073432/how-to-suppress-verbose-tensorflow-logging - import os - os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2' - - if check: - try: - check_import() - except Exception: - sys.modules['tensorflow'] = None # prevent further import - raise - - import tensorflow as tf - - try: - tf.get_logger().setLevel('WARNING') - except AttributeError: - pass - try: - tf.compat.v1.logging.set_verbosity(tf.compat.v1.logging.WARN) - except AttributeError: - pass - - # Enable eager execution in early versions to unlock dataset operations - eager_enabled = False - try: - tf.compat.v1.enable_eager_execution() - eager_enabled = True - except AttributeError: - pass - try: - if not eager_enabled: - tf.enable_eager_execution() - except AttributeError: - pass - - return tf diff --git a/datumaro/datumaro/version.py b/datumaro/datumaro/version.py deleted file mode 100644 index 8589c063..00000000 --- a/datumaro/datumaro/version.py +++ /dev/null @@ -1 +0,0 @@ -VERSION = '0.1.0' \ No newline at end of file diff --git a/datumaro/docs/cli_design.mm b/datumaro/docs/cli_design.mm deleted file mode 100644 index 0ff17cb2..00000000 --- a/datumaro/docs/cli_design.mm +++ /dev/null @@ -1,65 +0,0 @@ - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - diff --git a/datumaro/docs/design.md b/datumaro/docs/design.md deleted file mode 100644 index 528b2adf..00000000 --- a/datumaro/docs/design.md +++ /dev/null @@ -1,185 +0,0 @@ -# Datumaro - - - -## Table of contents - -- [Concept](#concept) -- [RC 1 vision](#rc-1-vision) - -## Concept - -Datumaro is: -- a tool to build composite datasets and iterate over them -- a tool to create and maintain datasets - - Version control of annotations and images - - Publication (with removal of sensitive information) - - Editing - - Joining and splitting - - Exporting, format changing - - Image preprocessing -- a dataset storage -- a tool to debug datasets - - A network can be used to generate - informative data subsets (e.g. with false-positives) - to be analyzed further - -### Requirements - -- User interfaces - - a library - - a console tool with visualization means -- Targets: single datasets, composite datasets, single images / videos -- Built-in support for well-known annotation formats and datasets: - CVAT, COCO, PASCAL VOC, Cityscapes, ImageNet -- Extensibility with user-provided components -- Lightweightness - it should be easy to start working with Datumaro - - Minimal dependency on environment and configuration - - It should be easier to use Datumaro than writing own code - for computation of statistics or dataset manipulations - -### Functionality and ideas - -- Blur sensitive areas on dataset images -- Dataset annotation filters, relabelling etc. -- Dataset augmentation -- Calculation of statistics: - - Mean & std, custom stats -- "Edit" command to modify annotations -- Versioning (for images, annotations, subsets, sources etc., comparison) -- Documentation generation -- Provision of iterators for user code -- Dataset downloading -- Dataset generation -- Dataset building (export in a specific format, indexation, statistics, documentation) -- Dataset exporting to other formats -- Dataset debugging (run inference, generate dataset slices, compute statistics) -- "Explainable AI" - highlight network attention areas ([paper](https://arxiv.org/abs/1901.04592)) - - Black-box approach - - Classification, Detection, Segmentation, Captioning - - White-box approach - -### Research topics - -- exploration of network prediction uncertainty (aka Bayessian approach) - Use case: explanation of network "quality", "stability", "certainty" -- adversarial attacks on networks -- dataset minification / reduction - Use case: removal of redundant information to reach the same network quality with lesser training time -- dataset expansion and filtration of additions - Use case: add only important data -- guidance for key frame selection for tracking ([paper](https://arxiv.org/abs/1903.11779)) - Use case: more effective annotation, better predictions - -## RC 1 vision - -In the first version Datumaro should be a project manager for CVAT. -It should only consume data from CVAT. The collected dataset -can be downloaded by user to be operated on with Datumaro CLI. - - -``` - User - | - v - +------------------+ - | CVAT | - +--------v---------+ +------------------+ +--------------+ - | Datumaro module | ----> | Datumaro project | <---> | Datumaro CLI | <--- User - +------------------+ +------------------+ +--------------+ -``` - - -### Interfaces - -- [x] Python API for user code - - [x] Installation as a package -- [x] A command-line tool for dataset manipulations - -### Features - -- Dataset format support (reading, writing) - - [x] Own format - - [x] CVAT - - [x] COCO - - [x] PASCAL VOC - - [x] YOLO - - [x] TF Detection API - - [ ] Cityscapes - - [ ] ImageNet - -- Dataset visualization (`show`) - - [ ] Ability to visualize a dataset - - [ ] with TensorBoard - -- Calculation of statistics for datasets - - [x] Pixel mean, std - - [x] Object counts (detection scenario) - - [x] Image-Class distribution (classification scenario) - - [x] Pixel-Class distribution (segmentation scenario) - - [ ] Image similarity clusters - - [ ] Custom statistics - -- Dataset building - - [x] Composite dataset building - - [x] Class remapping - - [x] Subset splitting - - [x] Dataset filtering (`extract`) - - [x] Dataset merging (`merge`) - - [ ] Dataset item editing (`edit`) - -- Dataset comparison (`diff`) - - [x] Annotation-annotation comparison - - [x] Annotation-inference comparison - - [x] Annotation quality estimation (for CVAT) - - Provide a simple method to check - annotation quality with a model and generate summary - -- Dataset and model debugging - - [x] Inference explanation (`explain`) - - [x] Black-box approach ([RISE paper](https://arxiv.org/abs/1806.07421)) - - [x] Ability to run a model on a dataset and read the results - -- CVAT-integration features - - [x] Task export - - [x] Datumaro project export - - [x] Dataset export - - [x] Original raw data (images, a video file) can be downloaded (exported) - together with annotations or just have links - on CVAT server (in future, support S3, etc) - - [x] Be able to use local files instead of remote links - - [ ] Specify cache directory - - [x] Use case "annotate for model training" - - create a task - - annotate - - export the task - - convert to a training format - - train a DL model - - [x] Use case "annotate - reannotate problematic images - merge" - - [x] Use case "annotate and estimate quality" - - create a task - - annotate - - estimate quality of annotations - -### Optional features - -- Dataset publishing - - [ ] Versioning (for annotations, subsets, sources, etc.) - - [ ] Blur sensitive areas on images - - [ ] Tracking of legal information - - [ ] Documentation generation - -- Dataset building - - [ ] Dataset minification / Extraction of the most representative subset - - Use case: generate low-precision calibration dataset - -- Dataset and model debugging - - [ ] Training visualization - - [ ] Inference explanation (`explain`) - - [ ] White-box approach - -### Properties - -- Lightweightness -- Modularity -- Extensibility diff --git a/datumaro/docs/developer_guide.md b/datumaro/docs/developer_guide.md deleted file mode 100644 index e2fd101d..00000000 --- a/datumaro/docs/developer_guide.md +++ /dev/null @@ -1,200 +0,0 @@ -## Basics - -The center part of the library is the `Dataset` class, which allows to iterate -over its elements. `DatasetItem`, an element of a dataset, represents a single -dataset entry with annotations - an image, video sequence, audio track etc. -It can contain only annotated data or meta information, only annotations, or -all of this. - -Basic library usage and data flow: - -```lang-none -Extractors -> Dataset -> Converter - | - Filtration - Transformations - Statistics - Merging - Inference - Quality Checking - Comparison - ... -``` - -1. Data is read (or produced) by one or many `Extractor`s and merged - into a `Dataset` -1. A dataset is processed in some way -1. A dataset is saved with a `Converter` - -Datumaro has a number of dataset and annotation features: -- iteration over dataset elements -- filtering of datasets and annotations by a custom criteria -- working with subsets (e.g. `train`, `val`, `test`) -- computing of dataset statistics -- comparison and merging of datasets -- various annotation operations - -```python -from datumaro.components.project import Environment - -# Import and save a dataset -env = Environment() -dataset = env.make_importer('voc')('src/dir').make_dataset() -env.converters.get('coco').convert(dataset, save_dir='dst/dir') -``` - -## Library contents - -### Dataset Formats - -Dataset reading is supported by `Extractor`s and `Importer`s: -- An `Extractor` produces a list of `DatasetItem`s corresponding -to the dataset. -- An `Importer` creates a project from the data source location. - -It is possible to add custom Extractors and Importers. To do this, you need -to put an `Extractor` and `Importer` implementations to a plugin directory. - -Dataset writing is supported by `Converter`s. -A Converter produces a dataset of a specific format from dataset items. -It is possible to add custom `Converter`s. To do this, you need to put a -Converter implementation script to a plugin directory. - -### Dataset Conversions ("Transforms") - -A `Transform` is a function for altering a dataset and producing a new one. -It can update dataset items, annotations, classes, and other properties. -A list of available transforms for dataset conversions can be extended by -adding a `Transform` implementation script into a plugin directory. - -### Model launchers - -A list of available launchers for model execution can be extended by -adding a `Launcher` implementation script into a plugin directory. - -## Plugins - -Datumaro comes with a number of built-in formats and other tools, -but it also can be extended by plugins. Plugins are optional components, -which dependencies are not installed by default. -In Datumaro there are several types of plugins, which include: -- `extractor` - produces dataset items from data source -- `importer` - recognizes dataset type and creates project -- `converter` - exports dataset to a specific format -- `transformation` - modifies dataset items or other properties -- `launcher` - executes models - -A plugin is a regular Python module. It must be present in a plugin directory: -- `/.datumaro/plugins` for project-specific plugins -- `/plugins` for global plugins - -A plugin can be used either via the `Environment` class instance, -or by regular module importing: - -```python -from datumaro.components.project import Environment, Project -from datumaro.plugins.yolo_format.converter import YoloConverter - -# Import a dataset -dataset = Environment().make_importer('voc')(src_dir).make_dataset() - -# Load an existing project, save the dataset in some project-specific format -project = Project.load('project/dir') -project.env.converters.get('custom_format').convert(dataset, save_dir=dst_dir) - -# Save the dataset in some built-in format -Environment().converters.get('yolo').convert(dataset, save_dir=dst_dir) -YoloConverter.convert(dataset, save_dir=dst_dir) -``` - -### Writing a plugin - -A plugin is a Python module with any name, which exports some symbols. -To export a symbol, inherit it from one of special classes: - -```python -from datumaro.components.extractor import Importer, SourceExtractor, Transform -from datumaro.components.launcher import Launcher -from datumaro.components.converter import Converter -``` - -The `exports` list of the module can be used to override default behaviour: -```python -class MyComponent1: ... -class MyComponent2: ... -exports = [MyComponent2] # exports only MyComponent2 -``` - -There is also an additional class to modify plugin appearance in command line: - -```python -from datumaro.components.cli_plugin import CliPlugin -``` - -#### Plugin example - - - -``` -datumaro/plugins/ -- my_plugin1/file1.py -- my_plugin1/file2.py -- my_plugin2.py -``` - - - -`my_plugin1/file2.py` contents: - -```python -from datumaro.components.extractor import Transform, CliPlugin -from .file1 import something, useful - -class MyTransform(Transform, CliPlugin): - NAME = "custom_name" # could be generated automatically - - """ - Some description. The text will be displayed in the command line output. - """ - - @classmethod - def build_cmdline_parser(cls, **kwargs): - parser = super().build_cmdline_parser(**kwargs) - parser.add_argument('-q', help="Very useful parameter") - return parser - - def __init__(self, extractor, q): - super().__init__(extractor) - self.q = q - - def transform_item(self, item): - return item -``` - -`my_plugin2.py` contents: - -```python -from datumaro.components.extractor import SourceExtractor - -class MyFormat: ... -class MyFormatExtractor(SourceExtractor): ... - -exports = [MyFormat] # explicit exports declaration -# MyFormatExtractor won't be exported -``` - -## Command-line - -Basically, the interface is divided on contexts and single commands. -Contexts are semantically grouped commands, related to a single topic or target. -Single commands are handy shorter alternatives for the most used commands -and also special commands, which are hard to be put into any specific context. -[Docker](https://www.docker.com/) is an example of similar approach. - -![cli-design-image](images/cli_design.png) - -- The diagram above was created with [FreeMind](http://freemind.sourceforge.net/wiki/index.php/Main_Page) - -Model-View-ViewModel (MVVM) UI pattern is used. - -![mvvm-image](images/mvvm.png) diff --git a/datumaro/docs/images/cli_design.png b/datumaro/docs/images/cli_design.png deleted file mode 100644 index f83b1430ec53a28546cd82d9c2d4b2031474d01d..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 35845 zcma%j1yod9|27ROB_JUw(#;^I3Uactzp~H!8Ro zd#~S-POqExSgRMR#=D%ZdG21;6keJxoE~2xu=$Xo_>c)a)J4HW5XM9>A_4vq#*B4= zJ`h%1dwlmVSOU{Hbu@Y5a`U6E_FyTYW9A$FRB#bO?_2`hw|)3Db%OfijBmpgQHVzK z$%uL%6tI0h_df4*A56hQ-E+Sz3K!lCRw-b(qUa_xnm-)qj1-*F6jth{LK1hroh&h4&RIEW(4X2KEPnfV_K(Zih+ zHD={I8PkZr+c>6KK?8HA_i@G z{ni*3QY?L7Q5irwqL044I z4&}~N9aZSoZF4wVWgx+}?#4ov${5`CC+O#=7KhdM&flVJsek+?B7$G9Yq6rI$`Lau zLfl-IDhkZcSeMo}R4d~UW!I0B2O2IuO?F_Dt%V=`A2Rryi$YO_Hr{0I 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z-aCqAPgm!=Z$^4EHb84gAwhJck$1vvY(Dw@e1u45@a!N5*N3ikxr1EPgjIplH(P2H z{iV31#YDltO!)vdKeA#YsFCAf6Pv^eGx!Gy&%T diff --git a/datumaro/docs/user_manual.md b/datumaro/docs/user_manual.md deleted file mode 100644 index 9e68f8f9..00000000 --- a/datumaro/docs/user_manual.md +++ /dev/null @@ -1,1003 +0,0 @@ -# User manual - -## Contents - -- [Installation](#installation) -- [Interfaces](#interfaces) -- [Supported dataset formats and annotations](#supported-formats) -- [Command line workflow](#command-line-workflow) - - [Project structure](#project-structure) -- [Command reference](#command-reference) - - [Convert datasets](#convert-datasets) - - [Create project](#create-project) - - [Add and remove data](#add-and-remove-data) - - [Import project](#import-project) - - [Filter project](#filter-project) - - [Update project (merge)](#update-project) - - [Merge projects](#merge-projects) - - [Export project](#export-project) - - [Compare projects](#compare-projects) - - [Obtaining project info](#get-project-info) - - [Obtaining project statistics](#get-project-statistics) - - [Register model](#register-model) - - [Run inference](#run-inference) - - [Run inference explanation](#explain-inference) - - [Transform project](#transform-project) -- [Extending](#extending) -- [Links](#links) - -## Installation - -### Prerequisites - -- Python (3.5+) -- OpenVINO (optional) - -### Installation steps - -Optionally, set up a virtual environment: - -``` bash -python -m pip install virtualenv -python -m virtualenv venv -. venv/bin/activate -``` - -Install: -``` bash -pip install 'git+https://github.com/opencv/cvat#egg=datumaro&subdirectory=datumaro' -``` - -> You can change the installation branch with `.../cvat@#egg...` -> Also note `--force-reinstall` parameter in this case. - -## Interfaces - -As a standalone tool: - -``` bash -datum --help -``` - -As a python module: -> The directory containing Datumaro should be in the `PYTHONPATH` -> environment variable or `cvat/datumaro/` should be the current directory. - -``` bash -python -m datumaro --help -python datumaro/ --help -python datum.py --help -``` - -As a python library: - -``` python -import datumaro -``` - -## Supported Formats - -List of supported formats: -- MS COCO (`image_info`, `instances`, `person_keypoints`, `captions`, `labels`*) - - [Format specification](http://cocodataset.org/#format-data) - - [Dataset example](../tests/assets/coco_dataset) - - `labels` are our extension - like `instances` with only `category_id` -- PASCAL VOC (`classification`, `detection`, `segmentation` (class, instances), `action_classification`, `person_layout`) - - [Format specification](http://host.robots.ox.ac.uk/pascal/VOC/voc2012/htmldoc/index.html) - - [Dataset example](../tests/assets/voc_dataset) -- YOLO (`bboxes`) - - [Format specification](https://github.com/AlexeyAB/darknet#how-to-train-pascal-voc-data) - - [Dataset example](../tests/assets/yolo_dataset) -- TF Detection API (`bboxes`, `masks`) - - Format specifications: [bboxes](https://github.com/tensorflow/models/blob/master/research/object_detection/g3doc/using_your_own_dataset.md), [masks](https://github.com/tensorflow/models/blob/master/research/object_detection/g3doc/instance_segmentation.md) - - [Dataset example](../tests/assets/tf_detection_api_dataset) -- MOT sequences - - [Format specification](https://arxiv.org/pdf/1906.04567.pdf) - - [Dataset example](../tests/assets/mot_dataset) -- CVAT - - [Format specification](https://github.com/opencv/cvat/blob/develop/cvat/apps/documentation/xml_format.md) - - [Dataset example](../tests/assets/cvat_dataset) -- LabelMe - - [Format specification](http://labelme.csail.mit.edu/Release3.0) - - [Dataset example](../tests/assets/labelme_dataset) - -List of supported annotation types: -- Labels -- Bounding boxes -- Polygons -- Polylines -- (Segmentation) Masks -- (Key-)Points -- Captions - -## Command line workflow - -The key object is a project, so most CLI commands operate on projects. -However, there are few commands operating on datasets directly. -A project is a combination of a project's own dataset, a number of -external data sources and an environment. -An empty Project can be created by `project create` command, -an existing dataset can be imported with `project import` command. -A typical way to obtain projects is to export tasks in CVAT UI. - -If you want to interact with models, you need to add them to project first. - -### Project structure - - -``` -└── project/ - ├── .datumaro/ - | ├── config.yml - │   ├── .git/ - │   ├── models/ - │   └── plugins/ - │   ├── plugin1/ - │   | ├── file1.py - │   | └── file2.py - │   ├── plugin2.py - │   ├── custom_extractor1.py - │   └── ... - ├── dataset/ - └── sources/ - ├── source1 - └── ... -``` - - -## Command reference - -> **Note**: command invocation syntax is subject to change, -> **always refer to command --help output** - -Available CLI commands: -![CLI design doc](images/cli_design.png) - -### Convert datasets - -This command allows to convert a dataset from one format into another. In fact, this -command is a combination of `project import` and `project export` and just provides a simpler -way to obtain the same result when no extra options is needed. A list of supported -formats can be found in the `--help` output of this command. - -Usage: - -``` bash -datum convert --help - -datum convert \ - -i \ - -if \ - -o \ - -f \ - -- [extra parameters for output format] -``` - -Example: convert a VOC-like dataset to a COCO-like one: - -``` bash -datum convert --input-format voc --input-path \ - --output-format coco -``` - -### Import project - -This command creates a Project from an existing dataset. - -Supported formats are listed in the command help. Check [extending tips](#extending) -for information on extra format support. - -Usage: - -``` bash -datum project import --help - -datum project import \ - -i \ - -o \ - -f -``` - -Example: create a project from COCO-like dataset - -``` bash -datum project import \ - -i /home/coco_dir \ - -o /home/project_dir \ - -f coco -``` - -An _MS COCO_-like dataset should have the following directory structure: - - -``` -COCO/ -├── annotations/ -│   ├── instances_val2017.json -│   ├── instances_train2017.json -├── images/ -│   ├── val2017 -│   ├── train2017 -``` - - -Everything after the last `_` is considered a subset name in the COCO format. - -### Create project - -The command creates an empty project. Once a Project is created, there are -a few options to interact with it. - -Usage: - -``` bash -datum project create --help - -datum project create \ - -o -``` - -Example: create an empty project `my_dataset` - -``` bash -datum project create -o my_dataset/ -``` - -### Add and remove data - -A Project can contain a number of external Data Sources. Each Data Source -describes a way to produce dataset items. A Project combines dataset items from -all the sources and its own dataset into one composite dataset. You can manage -project sources by commands in the `source` command line context. - -Datasets come in a wide variety of formats. Each dataset -format defines its own data structure and rules on how to -interpret the data. For example, the following data structure -is used in COCO format: - -``` -/dataset/ -- /images/.jpg -- /annotations/ -``` - - -Supported formats are listed in the command help. Check [extending tips](#extending) -for information on extra format support. - -Usage: - -``` bash -datum source add --help -datum source remove --help - -datum source add \ - path \ - -p \ - -n - -datum source remove \ - -p \ - -n -``` - -Example: create a project from a bunch of different annotations and images, -and generate TFrecord for TF Detection API for model training - -``` bash -datum project create -# 'default' is the name of the subset below -datum source add path -f coco_instances -datum source add path -f cvat -datum source add path -f voc_detection -datum source add path -f datumaro -datum source add path -f image_dir -datum project export -f tf_detection_api -``` - -### Filter project - -This command allows to create a sub-Project from a Project. The new project -includes only items satisfying some condition. [XPath](https://devhints.io/xpath) -is used as a query format. - -There are several filtering modes available (`-m/--mode` parameter). -Supported modes: -- `i`, `items` -- `a`, `annotations` -- `i+a`, `a+i`, `items+annotations`, `annotations+items` - -When filtering annotations, use the `items+annotations` -mode to point that annotation-less dataset items should be -removed. To select an annotation, write an XPath that -returns `annotation` elements (see examples). - -Usage: - -``` bash -datum project filter --help - -datum project filter \ - -p \ - -e '' -``` - -Example: extract a dataset with only images which `width` < `height` - -``` bash -datum project filter \ - -p test_project \ - -e '/item[image/width < image/height]' -``` - -Example: extract a dataset with only large annotations of class `cat` and any non-`persons` - -``` bash -datum project filter \ - -p test_project \ - --mode annotations -e '/item/annotation[(label="cat" and area > 99.5) or label!="person"]' -``` - -Example: extract a dataset with only occluded annotations, remove empty images - -``` bash -datum project filter \ - -p test_project \ - -m i+a -e '/item/annotation[occluded="True"]' -``` - -Item representations are available with `--dry-run` parameter: - -``` xml - - 290768 - minival2014 - - 612 - 612 - 3 - - - 80154 - bbox - 39 - 264.59 - 150.25 - 11.199999999999989 - 42.31 - 473.87199999999956 - - - 669839 - bbox - 41 - 163.58 - 191.75 - 76.98999999999998 - 73.63 - 5668.773699999998 - - ... - -``` - -### Update project - -This command updates items in a project from another one -(check [Merge Projects](#merge-projects) for complex merging). - -Usage: - -``` bash -datum project merge --help - -datum project merge \ - -p \ - -o \ - -``` - -Example: update annotations in the `first_project` with annotations -from the `second_project` and save the result as `merged_project` - -``` bash -datum project merge \ - -p first_project \ - -o merged_project \ - second_project -``` - -### Merge projects - -This command merges items from 2 or more projects and checks annotations for errors. - -Spatial annotations are compared by distance and intersected, labels and attributes -are selected by voting. -Merge conflicts, missing items and annotations, other errors are saved into a `.json` file. - -Usage: - -``` bash -datum merge --help - -datum merge -``` - -Example: merge 4 (partially-)intersecting projects, -- consider voting succeeded when there are 3+ same votes -- consider shapes intersecting when IoU >= 0.6 -- check annotation groups to have `person`, `hand`, `head` and `foot` (`?` for optional) - -``` bash -datum merge project1/ project2/ project3/ project4/ \ - --quorum 3 \ - -iou 0.6 \ - --groups 'person,hand?,head,foot?' -``` - -### Export project - -This command exports a Project as a dataset in some format. - -Supported formats are listed in the command help. Check [extending tips](#extending) -for information on extra format support. - -Usage: - -``` bash -datum project export --help - -datum project export \ - -p \ - -o \ - -f \ - -- [additional format parameters] -``` - -Example: save project as VOC-like dataset, include images, convert images to `PNG` - -``` bash -datum project export \ - -p test_project \ - -o test_project-export \ - -f voc \ - -- --save-images --image-ext='.png' -``` - -### Get project info - -This command outputs project status information. - -Usage: - -``` bash -datum project info --help - -datum project info \ - -p -``` - -Example: - -``` bash -datum project info -p /test_project - -Project: - name: test_project - location: /test_project -Sources: - source 'instances_minival2014': - format: coco_instances - url: /coco_like/annotations/instances_minival2014.json -Dataset: - length: 5000 - categories: label - label: - count: 80 - labels: person, bicycle, car, motorcycle (and 76 more) - subsets: minival2014 - subset 'minival2014': - length: 5000 - categories: label - label: - count: 80 - labels: person, bicycle, car, motorcycle (and 76 more) -``` - -### Get project statistics - -This command computes various project statistics, such as: -- image mean and std. dev. -- class and attribute balance -- mask pixel balance -- segment area distribution - -Usage: - -``` bash -datum project stats --help - -datum project stats \ - -p -``` - -Example: - -
- -``` bash -datum project stats -p /test_project - -{ - "annotations": { - "labels": { - "attributes": { - "gender": { - "count": 358, - "distribution": { - "female": [ - 149, - 0.41620111731843573 - ], - "male": [ - 209, - 0.5837988826815642 - ] - }, - "values count": 2, - "values present": [ - "female", - "male" - ] - }, - "view": { - "count": 340, - "distribution": { - "__undefined__": [ - 4, - 0.011764705882352941 - ], - "front": [ - 54, - 0.1588235294117647 - ], - "left": [ - 14, - 0.041176470588235294 - ], - "rear": [ - 235, - 0.6911764705882353 - ], - "right": [ - 33, - 0.09705882352941177 - ] - }, - "values count": 5, - "values present": [ - "__undefined__", - "front", - "left", - "rear", - "right" - ] - } - }, - "count": 2038, - "distribution": { - "car": [ - 340, - 0.16683022571148184 - ], - "cyclist": [ - 194, - 0.09519136408243375 - ], - "head": [ - 354, - 0.17369970559371933 - ], - "ignore": [ - 100, - 0.04906771344455348 - ], - "left_hand": [ - 238, - 0.11678115799803729 - ], - "person": [ - 358, - 0.17566241413150147 - ], - "right_hand": [ - 77, - 0.037782139352306184 - ], - "road_arrows": [ - 326, - 0.15996074582924436 - ], - "traffic_sign": [ - 51, - 0.025024533856722278 - ] - } - }, - "segments": { - "area distribution": [ - { - "count": 1318, - "max": 11425.1, - "min": 0.0, - "percent": 0.9627465303140978 - }, - { - "count": 1, - "max": 22850.2, - "min": 11425.1, - "percent": 0.0007304601899196494 - }, - { - "count": 0, - "max": 34275.3, - "min": 22850.2, - "percent": 0.0 - }, - { - "count": 0, - "max": 45700.4, - "min": 34275.3, - "percent": 0.0 - }, - { - "count": 0, - "max": 57125.5, - "min": 45700.4, - "percent": 0.0 - }, - { - "count": 0, - "max": 68550.6, - "min": 57125.5, - "percent": 0.0 - }, - { - "count": 0, - "max": 79975.7, - "min": 68550.6, - "percent": 0.0 - }, - { - "count": 0, - "max": 91400.8, - "min": 79975.7, - "percent": 0.0 - }, - { - "count": 0, - "max": 102825.90000000001, - "min": 91400.8, - "percent": 0.0 - }, - { - "count": 50, - "max": 114251.0, - "min": 102825.90000000001, - "percent": 0.036523009495982466 - } - ], - "avg. area": 5411.624543462382, - "pixel distribution": { - "car": [ - 13655, - 0.0018431496518735067 - ], - "cyclist": [ - 939005, - 0.12674674030446592 - ], - "head": [ - 0, - 0.0 - ], - "ignore": [ - 5501200, - 0.7425510702956085 - ], - "left_hand": [ - 0, - 0.0 - ], - "person": [ - 954654, - 0.12885903974805205 - ], - "right_hand": [ - 0, - 0.0 - ], - "road_arrows": [ - 0, - 0.0 - ], - "traffic_sign": [ - 0, - 0.0 - ] - } - } - }, - "annotations by type": { - "bbox": { - "count": 548 - }, - "caption": { - "count": 0 - }, - "label": { - "count": 0 - }, - "mask": { - "count": 0 - }, - "points": { - "count": 669 - }, - "polygon": { - "count": 821 - }, - "polyline": { - "count": 0 - } - }, - "annotations count": 2038, - "dataset": { - "image mean": [ - 107.06903686941979, - 79.12831698580979, - 52.95829558185416 - ], - "image std": [ - 49.40237673503467, - 43.29600731496902, - 35.47373007603151 - ], - "images count": 100 - }, - "images count": 100, - "subsets": {}, - "unannotated images": [ - "img00051", - "img00052", - "img00053", - "img00054", - "img00055", - ], - "unannotated images count": 5 -} -``` - -
- -### Register model - -Supported models: -- OpenVINO -- Custom models via custom `launchers` - -Usage: - -``` bash -datum model add --help -``` - -Example: register an OpenVINO model - -A model consists of a graph description and weights. There is also a script -used to convert model outputs to internal data structures. - -``` bash -datum project create -datum model add \ - -n openvino \ - -d -w -i -``` - -Interpretation script for an OpenVINO detection model (`convert.py`): - -``` python -from datumaro.components.extractor import * - -max_det = 10 -conf_thresh = 0.1 - -def process_outputs(inputs, outputs): - # inputs = model input, array or images, shape = (N, C, H, W) - # outputs = model output, shape = (N, 1, K, 7) - # results = conversion result, [ [ Annotation, ... ], ... ] - results = [] - for input, output in zip(inputs, outputs): - input_height, input_width = input.shape[:2] - detections = output[0] - image_results = [] - for i, det in enumerate(detections): - label = int(det[1]) - conf = det[2] - if conf <= conf_thresh: - continue - - x = max(int(det[3] * input_width), 0) - y = max(int(det[4] * input_height), 0) - w = min(int(det[5] * input_width - x), input_width) - h = min(int(det[6] * input_height - y), input_height) - image_results.append(Bbox(x, y, w, h, - label=label, attributes={'score': conf} )) - - results.append(image_results[:max_det]) - - return results - -def get_categories(): - # Optionally, provide output categories - label map etc. - # Example: - label_categories = LabelCategories() - label_categories.add('person') - label_categories.add('car') - return { AnnotationType.label: label_categories } -``` - -### Run model - -This command applies model to dataset images and produces a new project. - -Usage: - -``` bash -datum model run --help - -datum model run \ - -p \ - -m \ - -o -``` - -Example: launch inference on a dataset - -``` bash -datum project import <...> -datum model add mymodel <...> -datum model run -m mymodel -o inference -``` - -### Compare projects - -The command compares two datasets and saves the results in the -specified directory. The current project is considered to be -"ground truth". - -``` bash -datum project diff --help - -datum project diff -o -``` - -Example: compare a dataset with model inference - -``` bash -datum project import <...> -datum model add mymodel <...> -datum project transform <...> -o inference -datum project diff inference -o diff -``` - -### Explain inference - -Usage: - -``` bash -datum explain --help - -datum explain \ - -m \ - -o \ - -t \ - \ - -``` - -Example: run inference explanation on a single image with visualization - -``` bash -datum project create <...> -datum model add mymodel <...> -datum explain \ - -m mymodel \ - -t 'image.png' \ - rise \ - -s 1000 --progressive -``` - -### Transform Project - -This command allows to modify images or annotations in a project all at once. - -``` bash -datum project transform --help - -datum project transform \ - -p \ - -o \ - -t \ - -- [extra transform options] -``` - -Example: split a dataset randomly to `train` and `test` subsets, ratio is 2:1 - -``` bash -datum project transform -t random_split -- --subset train:.67 --subset test:.33 -``` - -Example: convert polygons to masks, masks to boxes etc.: - -``` bash -datum project transform -t boxes_to_masks -datum project transform -t masks_to_polygons -datum project transform -t polygons_to_masks -datum project transform -t shapes_to_boxes -``` - -Example: remap dataset labels, `person` to `car` and `cat` to `dog`, keep `bus`, remove others - -``` bash -datum project transform -t remap_labels -- \ - -l person:car -l bus:bus -l cat:dog \ - --default delete -``` - -Example: rename dataset items by a regular expression -- Replace `pattern` with `replacement` -- Remove `frame_` from item ids - -``` bash -datum project transform -t rename -- -e '|pattern|replacement|' -datum project transform -t rename -- -e '|frame_(\d+)|\\1|' -``` - -## Extending - -There are few ways to extend and customize Datumaro behaviour, which is supported by plugins. -Check [our contribution guide](../CONTRIBUTING.md) for details on plugin implementation. -In general, a plugin is a Python code file. It must be put into a plugin directory: -- `/.datumaro/plugins` for project-specific plugins -- `/plugins` for global plugins - -### Dataset Formats - -Dataset reading is supported by Extractors and Importers. -An Extractor produces a list of dataset items corresponding -to the dataset. An Importer creates a project from the data source location. -It is possible to add custom Extractors and Importers. To do this, you need -to put an Extractor and Importer implementation scripts to a plugin directory. - -Dataset writing is supported by Converters. -A Converter produces a dataset of a specific format from dataset items. -It is possible to add custom Converters. To do this, you need to put a Converter -implementation script to a plugin directory. - -### Dataset Conversions ("Transforms") - -A Transform is a function for altering a dataset and producing a new one. It can update -dataset items, annotations, classes, and other properties. -A list of available transforms for dataset conversions can be extended by adding a Transform -implementation script into a plugin directory. - -### Model launchers - -A list of available launchers for model execution can be extended by adding a Launcher -implementation script into a plugin directory. - -## Links -- [TensorFlow detection model zoo](https://github.com/tensorflow/models/blob/master/research/object_detection/g3doc/detection_model_zoo.md) -- [How to convert model to OpenVINO format](https://docs.openvinotoolkit.org/latest/_docs_MO_DG_prepare_model_convert_model_tf_specific_Convert_Object_Detection_API_Models.html) -- [Model conversion script example](https://github.com/opencv/cvat/blob/3e09503ba6c6daa6469a6c4d275a5a8b168dfa2c/components/tf_annotation/install.sh#L23) diff --git a/datumaro/requirements.txt b/datumaro/requirements.txt deleted file mode 100644 index 6bc3c7ee..00000000 --- a/datumaro/requirements.txt +++ /dev/null @@ -1,12 +0,0 @@ -attrs>=19.3.0 -Cython>=0.27.3 # include before pycocotools -defusedxml>=0.6.0 -GitPython>=3.0.8 -lxml>=4.4.1 -matplotlib>=3.3.1 -opencv-python-headless>=4.1.0.25 -Pillow>=6.1.0 -pycocotools>=2.0.0 -PyYAML>=5.3.1 -scikit-image>=0.15.0 -tensorboardX>=1.8 diff --git a/datumaro/setup.py b/datumaro/setup.py deleted file mode 100644 index cf6d0433..00000000 --- a/datumaro/setup.py +++ /dev/null @@ -1,73 +0,0 @@ - -# Copyright (C) 2019-2020 Intel Corporation -# -# SPDX-License-Identifier: MIT - -import os.path as osp -import re -import setuptools - - -def find_version(file_path=None): - if not file_path: - file_path = osp.join(osp.dirname(osp.abspath(__file__)), - 'datumaro', 'version.py') - - with open(file_path, 'r') as version_file: - version_text = version_file.read() - - # PEP440: - # https://www.python.org/dev/peps/pep-0440/#appendix-b-parsing-version-strings-with-regular-expressions - pep_regex = r'([1-9]\d*!)?(0|[1-9]\d*)(\.(0|[1-9]\d*))*((a|b|rc)(0|[1-9]\d*))?(\.post(0|[1-9]\d*))?(\.dev(0|[1-9]\d*))?' - version_regex = r'VERSION\s*=\s*.(' + pep_regex + ').' - match = re.match(version_regex, version_text) - if not match: - raise RuntimeError("Failed to find version string in '%s'" % file_path) - - version = version_text[match.start(1) : match.end(1)] - return version - - -with open('README.md', 'r') as fh: - long_description = fh.read() - -setuptools.setup( - name="datumaro", - version=find_version(), - author="Intel", - author_email="maxim.zhiltsov@intel.com", - description="Dataset Management Framework (Datumaro)", - long_description=long_description, - long_description_content_type="text/markdown", - url="https://github.com/opencv/cvat/datumaro", - packages=setuptools.find_packages(exclude=['tests*']), - classifiers=[ - "Programming Language :: Python :: 3", - "License :: OSI Approved :: MIT License", - "Operating System :: OS Independent", - ], - python_requires='>=3.5', - install_requires=[ - 'attrs', - 'defusedxml', - 'GitPython', - 'lxml', - 'matplotlib', - 'numpy', - 'opencv-python', - 'Pillow', - 'pycocotools', - 'PyYAML', - 'scikit-image', - 'tensorboardX', - ], - extras_require={ - 'tf': ['tensorflow'], - 'tf-gpu': ['tensorflow-gpu'], - }, - entry_points={ - 'console_scripts': [ - 'datum=datumaro.cli.__main__:main', - ], - }, -) diff --git a/datumaro/tests/__init__.py b/datumaro/tests/__init__.py deleted file mode 100644 index e69de29b..00000000 diff --git a/datumaro/tests/assets/coco_dataset/annotations/instances_val.json b/datumaro/tests/assets/coco_dataset/annotations/instances_val.json deleted file mode 100644 index b5d9bd86..00000000 --- a/datumaro/tests/assets/coco_dataset/annotations/instances_val.json +++ /dev/null @@ -1,59 +0,0 @@ -{ - "licenses": [ - { - "name": "", - "id": 0, - "url": "" - } - ], - "info": { - "contributor": "", - "date_created": "", - "description": "", - "url": "", - "version": "", - "year": "" - }, - "categories": [ 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applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -""" - -import torch.nn as nn -import torch.nn.functional as F - - -class SampLeNet(nn.Module): - def __init__(self): - super(SampLeNet, self).__init__() - self.conv1 = nn.Conv2d(3, 6, 5) - self.pool = nn.MaxPool2d(2, 2) - self.conv2 = nn.Conv2d(6, 16, 5) - self.fc1 = nn.Linear(16 * 5 * 5, 120) - self.fc2 = nn.Linear(120, 84) - self.fc3 = nn.Linear(84, 10) - - def forward(self, x): - x = self.pool(F.relu(self.conv1(x))) - x = self.pool(F.relu(self.conv2(x))) - x = x.view(-1, 16 * 5 * 5) - x = F.relu(self.fc1(x)) - x = F.relu(self.fc2(x)) - x = self.fc3(x) - return x diff --git a/datumaro/tests/assets/tf_detection_api_dataset/label_map.pbtxt 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--git a/datumaro/tests/assets/voc_dataset/ImageSets/Layout/test.txt b/datumaro/tests/assets/voc_dataset/ImageSets/Layout/test.txt deleted file mode 100644 index c9fdc251..00000000 --- a/datumaro/tests/assets/voc_dataset/ImageSets/Layout/test.txt +++ /dev/null @@ -1 +0,0 @@ -2007_000002 diff --git a/datumaro/tests/assets/voc_dataset/ImageSets/Layout/train.txt b/datumaro/tests/assets/voc_dataset/ImageSets/Layout/train.txt deleted file mode 100644 index 640b0d53..00000000 --- a/datumaro/tests/assets/voc_dataset/ImageSets/Layout/train.txt +++ /dev/null @@ -1 +0,0 @@ -2007_000001 diff --git a/datumaro/tests/assets/voc_dataset/ImageSets/Main/aeroplane_train.txt b/datumaro/tests/assets/voc_dataset/ImageSets/Main/aeroplane_train.txt deleted file mode 100644 index a3decd42..00000000 --- a/datumaro/tests/assets/voc_dataset/ImageSets/Main/aeroplane_train.txt +++ /dev/null @@ -1 +0,0 @@ -2007_000001 1 diff --git a/datumaro/tests/assets/voc_dataset/ImageSets/Main/background_train.txt b/datumaro/tests/assets/voc_dataset/ImageSets/Main/background_train.txt deleted file mode 100644 index d4385b69..00000000 --- a/datumaro/tests/assets/voc_dataset/ImageSets/Main/background_train.txt +++ /dev/null @@ -1 +0,0 @@ -2007_000001 -1 diff --git a/datumaro/tests/assets/voc_dataset/ImageSets/Main/bicycle_train.txt b/datumaro/tests/assets/voc_dataset/ImageSets/Main/bicycle_train.txt deleted file mode 100644 index d4385b69..00000000 --- a/datumaro/tests/assets/voc_dataset/ImageSets/Main/bicycle_train.txt +++ /dev/null @@ -1 +0,0 @@ -2007_000001 -1 diff --git a/datumaro/tests/assets/voc_dataset/ImageSets/Main/bird_train.txt b/datumaro/tests/assets/voc_dataset/ImageSets/Main/bird_train.txt deleted file mode 100644 index a3decd42..00000000 --- a/datumaro/tests/assets/voc_dataset/ImageSets/Main/bird_train.txt +++ /dev/null @@ -1 +0,0 @@ -2007_000001 1 diff --git a/datumaro/tests/assets/voc_dataset/ImageSets/Main/boat_train.txt b/datumaro/tests/assets/voc_dataset/ImageSets/Main/boat_train.txt deleted file mode 100644 index d4385b69..00000000 --- a/datumaro/tests/assets/voc_dataset/ImageSets/Main/boat_train.txt +++ /dev/null @@ -1 +0,0 @@ -2007_000001 -1 diff --git a/datumaro/tests/assets/voc_dataset/ImageSets/Main/bottle_train.txt b/datumaro/tests/assets/voc_dataset/ImageSets/Main/bottle_train.txt deleted file mode 100644 index a3decd42..00000000 --- a/datumaro/tests/assets/voc_dataset/ImageSets/Main/bottle_train.txt +++ /dev/null @@ -1 +0,0 @@ -2007_000001 1 diff --git a/datumaro/tests/assets/voc_dataset/ImageSets/Main/bus_train.txt b/datumaro/tests/assets/voc_dataset/ImageSets/Main/bus_train.txt deleted file mode 100644 index d4385b69..00000000 --- a/datumaro/tests/assets/voc_dataset/ImageSets/Main/bus_train.txt +++ /dev/null @@ -1 +0,0 @@ -2007_000001 -1 diff --git a/datumaro/tests/assets/voc_dataset/ImageSets/Main/car_train.txt b/datumaro/tests/assets/voc_dataset/ImageSets/Main/car_train.txt deleted file mode 100644 index a3decd42..00000000 --- a/datumaro/tests/assets/voc_dataset/ImageSets/Main/car_train.txt +++ /dev/null @@ -1 +0,0 @@ -2007_000001 1 diff --git a/datumaro/tests/assets/voc_dataset/ImageSets/Main/cat_train.txt b/datumaro/tests/assets/voc_dataset/ImageSets/Main/cat_train.txt deleted file mode 100644 index d4385b69..00000000 --- a/datumaro/tests/assets/voc_dataset/ImageSets/Main/cat_train.txt +++ /dev/null @@ -1 +0,0 @@ -2007_000001 -1 diff --git a/datumaro/tests/assets/voc_dataset/ImageSets/Main/chair_train.txt b/datumaro/tests/assets/voc_dataset/ImageSets/Main/chair_train.txt deleted file mode 100644 index a3decd42..00000000 --- a/datumaro/tests/assets/voc_dataset/ImageSets/Main/chair_train.txt +++ /dev/null @@ -1 +0,0 @@ -2007_000001 1 diff --git a/datumaro/tests/assets/voc_dataset/ImageSets/Main/cow_train.txt b/datumaro/tests/assets/voc_dataset/ImageSets/Main/cow_train.txt deleted file mode 100644 index d4385b69..00000000 --- a/datumaro/tests/assets/voc_dataset/ImageSets/Main/cow_train.txt +++ /dev/null @@ -1 +0,0 @@ -2007_000001 -1 diff --git a/datumaro/tests/assets/voc_dataset/ImageSets/Main/diningtable_train.txt b/datumaro/tests/assets/voc_dataset/ImageSets/Main/diningtable_train.txt deleted file mode 100644 index a3decd42..00000000 --- a/datumaro/tests/assets/voc_dataset/ImageSets/Main/diningtable_train.txt +++ /dev/null @@ -1 +0,0 @@ -2007_000001 1 diff --git a/datumaro/tests/assets/voc_dataset/ImageSets/Main/dog_train.txt b/datumaro/tests/assets/voc_dataset/ImageSets/Main/dog_train.txt deleted file mode 100644 index d4385b69..00000000 --- a/datumaro/tests/assets/voc_dataset/ImageSets/Main/dog_train.txt +++ /dev/null @@ -1 +0,0 @@ -2007_000001 -1 diff --git a/datumaro/tests/assets/voc_dataset/ImageSets/Main/horse_train.txt b/datumaro/tests/assets/voc_dataset/ImageSets/Main/horse_train.txt deleted file mode 100644 index a3decd42..00000000 --- a/datumaro/tests/assets/voc_dataset/ImageSets/Main/horse_train.txt +++ /dev/null @@ -1 +0,0 @@ -2007_000001 1 diff --git a/datumaro/tests/assets/voc_dataset/ImageSets/Main/ignored_train.txt b/datumaro/tests/assets/voc_dataset/ImageSets/Main/ignored_train.txt deleted file mode 100644 index a3decd42..00000000 --- a/datumaro/tests/assets/voc_dataset/ImageSets/Main/ignored_train.txt +++ /dev/null @@ -1 +0,0 @@ -2007_000001 1 diff --git a/datumaro/tests/assets/voc_dataset/ImageSets/Main/motorbike_train.txt b/datumaro/tests/assets/voc_dataset/ImageSets/Main/motorbike_train.txt deleted file mode 100644 index d4385b69..00000000 --- a/datumaro/tests/assets/voc_dataset/ImageSets/Main/motorbike_train.txt +++ /dev/null @@ -1 +0,0 @@ -2007_000001 -1 diff --git a/datumaro/tests/assets/voc_dataset/ImageSets/Main/person_train.txt b/datumaro/tests/assets/voc_dataset/ImageSets/Main/person_train.txt deleted file mode 100644 index a3decd42..00000000 --- a/datumaro/tests/assets/voc_dataset/ImageSets/Main/person_train.txt +++ /dev/null @@ -1 +0,0 @@ -2007_000001 1 diff --git a/datumaro/tests/assets/voc_dataset/ImageSets/Main/pottedplant_train.txt b/datumaro/tests/assets/voc_dataset/ImageSets/Main/pottedplant_train.txt deleted file mode 100644 index d4385b69..00000000 --- a/datumaro/tests/assets/voc_dataset/ImageSets/Main/pottedplant_train.txt +++ /dev/null @@ -1 +0,0 @@ -2007_000001 -1 diff --git a/datumaro/tests/assets/voc_dataset/ImageSets/Main/sheep_train.txt b/datumaro/tests/assets/voc_dataset/ImageSets/Main/sheep_train.txt deleted file mode 100644 index a3decd42..00000000 --- a/datumaro/tests/assets/voc_dataset/ImageSets/Main/sheep_train.txt +++ /dev/null @@ -1 +0,0 @@ -2007_000001 1 diff --git a/datumaro/tests/assets/voc_dataset/ImageSets/Main/sofa_train.txt b/datumaro/tests/assets/voc_dataset/ImageSets/Main/sofa_train.txt deleted file mode 100644 index d4385b69..00000000 --- a/datumaro/tests/assets/voc_dataset/ImageSets/Main/sofa_train.txt +++ /dev/null @@ -1 +0,0 @@ -2007_000001 -1 diff --git a/datumaro/tests/assets/voc_dataset/ImageSets/Main/test.txt b/datumaro/tests/assets/voc_dataset/ImageSets/Main/test.txt deleted file mode 100644 index c9fdc251..00000000 --- a/datumaro/tests/assets/voc_dataset/ImageSets/Main/test.txt +++ /dev/null @@ -1 +0,0 @@ -2007_000002 diff --git a/datumaro/tests/assets/voc_dataset/ImageSets/Main/train.txt b/datumaro/tests/assets/voc_dataset/ImageSets/Main/train.txt deleted file mode 100644 index 640b0d53..00000000 --- a/datumaro/tests/assets/voc_dataset/ImageSets/Main/train.txt +++ /dev/null @@ -1 +0,0 @@ -2007_000001 diff --git a/datumaro/tests/assets/voc_dataset/ImageSets/Main/train_train.txt b/datumaro/tests/assets/voc_dataset/ImageSets/Main/train_train.txt deleted file mode 100644 index a3decd42..00000000 --- a/datumaro/tests/assets/voc_dataset/ImageSets/Main/train_train.txt +++ /dev/null @@ -1 +0,0 @@ -2007_000001 1 diff --git a/datumaro/tests/assets/voc_dataset/ImageSets/Main/tvmonitor_train.txt b/datumaro/tests/assets/voc_dataset/ImageSets/Main/tvmonitor_train.txt deleted file mode 100644 index d4385b69..00000000 --- a/datumaro/tests/assets/voc_dataset/ImageSets/Main/tvmonitor_train.txt +++ /dev/null @@ -1 +0,0 @@ -2007_000001 -1 diff --git a/datumaro/tests/assets/voc_dataset/ImageSets/Segmentation/test.txt b/datumaro/tests/assets/voc_dataset/ImageSets/Segmentation/test.txt deleted file mode 100644 index c9fdc251..00000000 --- a/datumaro/tests/assets/voc_dataset/ImageSets/Segmentation/test.txt +++ /dev/null @@ -1 +0,0 @@ -2007_000002 diff --git a/datumaro/tests/assets/voc_dataset/ImageSets/Segmentation/train.txt b/datumaro/tests/assets/voc_dataset/ImageSets/Segmentation/train.txt deleted file mode 100644 index 640b0d53..00000000 --- a/datumaro/tests/assets/voc_dataset/ImageSets/Segmentation/train.txt +++ /dev/null @@ -1 +0,0 @@ -2007_000001 diff --git a/datumaro/tests/assets/voc_dataset/JPEGImages/2007_000002.jpg b/datumaro/tests/assets/voc_dataset/JPEGImages/2007_000002.jpg deleted file mode 100644 index 3c81296b31dcb791dacede93e4be1f2d08b98347..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 635 zcmex=^(PF6}rMnOeST|r4lSw=>~TvNxu(8R<W=16jCP7AKLB{__803NOWMu>c1}I=;VrF4wW9Q)H;sz?% zD!{d!pzFb!U9xX3zTPI5o8roG<0MW4oqZMDikqloVbuf*=gfJ(V&YTRE(2~ znmD<{#3dx9RMpfqG__1j&CD$#!v`*nMGYWq|NkZcN!rf* diff --git a/datumaro/tests/assets/voc_dataset/SegmentationClass/2007_000001.png b/datumaro/tests/assets/voc_dataset/SegmentationClass/2007_000001.png deleted file mode 100644 index 0b92051452392a7827f09a27d7b5a179f93b3748..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 87 zcmeAS@N?(olHy`uVBq!ia0vp^AT}!p6OjDO_R1be$$7dshH%K%9%N($@|qNC?|qTu iFly>iP(Hz7bcpfk8>Tj~#tAclatxlXelF{r5}E++^A#un diff --git a/datumaro/tests/assets/voc_dataset/SegmentationObject/2007_000001.png b/datumaro/tests/assets/voc_dataset/SegmentationObject/2007_000001.png deleted file mode 100644 index ebbeee61dd687d6cb27a7bfcef77b503095d10d2..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 82 zcmeAS@N?(olHy`uVBq!ia0vp^AT}!pkYI@9SK0!kBt2amLpWqj4;nH8d508U#^1_5 ba0m#B7;6}~8DEFI0x9-%^>bP0l+XkKf*}+L diff --git a/datumaro/tests/assets/yolo_dataset/obj.data b/datumaro/tests/assets/yolo_dataset/obj.data deleted file mode 100644 index 16ca4090..00000000 --- a/datumaro/tests/assets/yolo_dataset/obj.data +++ /dev/null @@ -1,4 +0,0 @@ -classes = 10 -train = data/train.txt -names = data/obj.names -backup = backup/ diff --git a/datumaro/tests/assets/yolo_dataset/obj.names b/datumaro/tests/assets/yolo_dataset/obj.names deleted file mode 100644 index b24c644d..00000000 --- a/datumaro/tests/assets/yolo_dataset/obj.names +++ /dev/null @@ -1,10 +0,0 @@ -label_0 -label_1 -label_2 -label_3 -label_4 -label_5 -label_6 -label_7 -label_8 -label_9 diff --git a/datumaro/tests/assets/yolo_dataset/obj_train_data/1.jpg b/datumaro/tests/assets/yolo_dataset/obj_train_data/1.jpg deleted file mode 100644 index 8689b956311969f2efc9e3334f375c0ad65e24f1..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 631 zcmex=^(PF6}rMnOeST|r4lSw=>~TvNxu(8R<c1}I=;VrF4wW9Q)H;sz?% zD!{d!pzFb!U9xX3zTPI5o8roG<0MW4oqZMDikqloVbuf*=gfJ(V&YTRE(2~ znmD<{#3dx9RMpfqG__1j&CD$#!v`*nMGf} 1]') - - self.assertEqual(2, len(filtered)) - - def test_annotations_filter_can_be_applied(self): - class SrcExtractor(Extractor): - def __iter__(self): - return iter([ - DatasetItem(id=0), - DatasetItem(id=1, annotations=[ - Label(0), - Label(1), - ]), - DatasetItem(id=2, annotations=[ - Label(0), - Label(2), - ]), - ]) - - class DstExtractor(Extractor): - def __iter__(self): - return iter([ - DatasetItem(id=0), - DatasetItem(id=1, annotations=[ - Label(0), - ]), - DatasetItem(id=2, annotations=[ - Label(0), - ]), - ]) - - extractor = SrcExtractor() - - filtered = XPathAnnotationsFilter(extractor, - '/item/annotation[label_id = 0]') - - self.assertListEqual(list(filtered), list(DstExtractor())) - - def test_annotations_filter_can_remove_empty_items(self): - class SrcExtractor(Extractor): - def __iter__(self): - return iter([ - DatasetItem(id=0), - DatasetItem(id=1, annotations=[ - Label(0), - Label(1), - ]), - DatasetItem(id=2, annotations=[ - Label(0), - Label(2), - ]), - ]) - - class DstExtractor(Extractor): - def __iter__(self): - return iter([ - DatasetItem(id=2, annotations=[ - Label(2), - ]), - ]) - - extractor = SrcExtractor() - - filtered = XPathAnnotationsFilter(extractor, - '/item/annotation[label_id = 2]', remove_empty=True) - - self.assertListEqual(list(filtered), list(DstExtractor())) - -class ConfigTest(TestCase): - def test_can_produce_multilayer_config_from_dict(self): - schema_low = SchemaBuilder() \ - .add('options', dict) \ - .build() - schema_mid = SchemaBuilder() \ - .add('desc', lambda: Config(schema=schema_low)) \ - .build() - schema_top = SchemaBuilder() \ - .add('container', lambda: DefaultConfig( - lambda v: Config(v, schema=schema_mid))) \ - .build() - - value = 1 - source = Config({ - 'container': { - 'elem': { - 'desc': { - 'options': { - 'k': value - } - } - } - } - }, schema=schema_top) - - self.assertEqual(value, source.container['elem'].desc.options['k']) - -class ExtractorTest(TestCase): - def test_custom_extractor_can_be_created(self): - class CustomExtractor(Extractor): - def __iter__(self): - return iter([ - DatasetItem(id=0, subset='train'), - DatasetItem(id=1, subset='train'), - DatasetItem(id=2, subset='train'), - - DatasetItem(id=3, subset='test'), - DatasetItem(id=4, subset='test'), - - DatasetItem(id=1), - DatasetItem(id=2), - DatasetItem(id=3), - ]) - - extractor_name = 'ext1' - project = Project() - project.env.extractors.register(extractor_name, CustomExtractor) - project.add_source('src1', { - 'url': 'path', - 'format': extractor_name, - }) - - dataset = project.make_dataset() - - compare_datasets(self, CustomExtractor(), dataset) - -class DatasetTest(TestCase): - def test_create_from_extractors(self): - class SrcExtractor1(Extractor): - def __iter__(self): - return iter([ - DatasetItem(id=1, subset='train', annotations=[ - Bbox(1, 2, 3, 4), - Label(4), - ]), - DatasetItem(id=1, subset='val', annotations=[ - Label(4), - ]), - ]) - - class SrcExtractor2(Extractor): - def __iter__(self): - return iter([ - DatasetItem(id=1, subset='val', annotations=[ - Label(5), - ]), - ]) - - class DstExtractor(Extractor): - def __iter__(self): - return iter([ - DatasetItem(id=1, subset='train', annotations=[ - Bbox(1, 2, 3, 4), - Label(4), - ]), - DatasetItem(id=1, subset='val', annotations=[ - Label(4), - Label(5), - ]), - ]) - - dataset = Dataset.from_extractors(SrcExtractor1(), SrcExtractor2()) - - compare_datasets(self, DstExtractor(), dataset) - - -class DatasetItemTest(TestCase): - def test_ctor_requires_id(self): - with self.assertRaises(Exception): - # pylint: disable=no-value-for-parameter - DatasetItem() - # pylint: enable=no-value-for-parameter - - @staticmethod - def test_ctors_with_image(): - for args in [ - { 'id': 0, 'image': None }, - { 'id': 0, 'image': 'path.jpg' }, - { 'id': 0, 'image': np.array([1, 2, 3]) }, - { 'id': 0, 'image': lambda f: np.array([1, 2, 3]) }, - { 'id': 0, 'image': Image(data=np.array([1, 2, 3])) }, - ]: - DatasetItem(**args) \ No newline at end of file diff --git a/datumaro/tests/test_tfrecord_format.py b/datumaro/tests/test_tfrecord_format.py deleted file mode 100644 index f2dbd160..00000000 --- a/datumaro/tests/test_tfrecord_format.py +++ /dev/null @@ -1,210 +0,0 @@ -from functools import partial -import numpy as np -import os.path as osp - -from unittest import TestCase, skipIf -from datumaro.components.project import Dataset -from datumaro.components.extractor import (Extractor, DatasetItem, - AnnotationType, Bbox, Mask, LabelCategories -) -from datumaro.components.project import Project -from datumaro.util.image import Image -from datumaro.util.test_utils import TestDir, compare_datasets -from datumaro.util.tf_util import check_import - -try: - from datumaro.plugins.tf_detection_api_format.importer import TfDetectionApiImporter - from datumaro.plugins.tf_detection_api_format.extractor import TfDetectionApiExtractor - from datumaro.plugins.tf_detection_api_format.converter import TfDetectionApiConverter - import_failed = False -except ImportError: - import_failed = True - - import importlib - module_found = importlib.util.find_spec('tensorflow') is not None - - @skipIf(not module_found, "Tensorflow package is not found") - class TfImportTest(TestCase): - def test_raises_when_crashes_on_import(self): - # Should fire if import can't be done for any reason except - # module unavailability and import crash - with self.assertRaisesRegex(ImportError, 'Test process exit code'): - check_import() - -@skipIf(import_failed, "Failed to import tensorflow") -class TfrecordConverterTest(TestCase): - def _test_save_and_load(self, source_dataset, converter, test_dir, - target_dataset=None, importer_args=None): - converter(source_dataset, test_dir) - - if importer_args is None: - importer_args = {} - parsed_dataset = TfDetectionApiImporter()(test_dir, **importer_args) \ - .make_dataset() - - if target_dataset is None: - target_dataset = source_dataset - - compare_datasets(self, expected=target_dataset, actual=parsed_dataset) - - def test_can_save_bboxes(self): - test_dataset = Dataset.from_iterable([ - DatasetItem(id=1, subset='train', - image=np.ones((16, 16, 3)), - annotations=[ - Bbox(0, 4, 4, 8, label=2), - Bbox(0, 4, 4, 4, label=3), - Bbox(2, 4, 4, 4), - ], attributes={'source_id': ''} - ), - ], categories={ - AnnotationType.label: LabelCategories.from_iterable( - 'label_' + str(label) for label in range(10)), - }) - - with TestDir() as test_dir: - self._test_save_and_load( - test_dataset, - partial(TfDetectionApiConverter.convert, save_images=True), - test_dir) - - def test_can_save_masks(self): - test_dataset = Dataset.from_iterable([ - DatasetItem(id=1, subset='train', image=np.ones((4, 5, 3)), - annotations=[ - Mask(image=np.array([ - [1, 0, 0, 1], - [0, 1, 1, 0], - [0, 1, 1, 0], - [1, 0, 0, 1], - ]), label=1), - ], - attributes={'source_id': ''} - ), - ], categories={ - AnnotationType.label: LabelCategories.from_iterable( - 'label_' + str(label) for label in range(10)), - }) - - with TestDir() as test_dir: - self._test_save_and_load( - test_dataset, - partial(TfDetectionApiConverter.convert, save_masks=True), - test_dir) - - def test_can_save_dataset_with_no_subsets(self): - test_dataset = Dataset.from_iterable([ - DatasetItem(id=1, - image=np.ones((16, 16, 3)), - annotations=[ - Bbox(2, 1, 4, 4, label=2), - Bbox(4, 2, 8, 4, label=3), - ], - attributes={'source_id': ''} - ), - - DatasetItem(id=2, - image=np.ones((8, 8, 3)) * 2, - annotations=[ - Bbox(4, 4, 4, 4, label=3), - ], - attributes={'source_id': ''} - ), - - DatasetItem(id=3, - image=np.ones((8, 4, 3)) * 3, - attributes={'source_id': ''} - ), - ], categories={ - AnnotationType.label: LabelCategories.from_iterable( - 'label_' + str(label) for label in range(10)), - }) - - with TestDir() as test_dir: - self._test_save_and_load( - test_dataset, - partial(TfDetectionApiConverter.convert, save_images=True), - test_dir) - - def test_can_save_dataset_with_image_info(self): - test_dataset = Dataset.from_iterable([ - DatasetItem(id='1/q.e', - image=Image(path='1/q.e', size=(10, 15)), - attributes={'source_id': ''} - ) - ], categories={ - AnnotationType.label: LabelCategories(), - }) - - with TestDir() as test_dir: - self._test_save_and_load(test_dataset, - TfDetectionApiConverter.convert, test_dir) - - def test_labelmap_parsing(self): - text = """ - { - id: 4 - name: 'qw1' - } - { - id: 5 name: 'qw2' - } - - { - name: 'qw3' - id: 6 - } - {name:'qw4' id:7} - """ - expected = { - 'qw1': 4, - 'qw2': 5, - 'qw3': 6, - 'qw4': 7, - } - parsed = TfDetectionApiExtractor._parse_labelmap(text) - - self.assertEqual(expected, parsed) - - -DUMMY_DATASET_DIR = osp.join(osp.dirname(__file__), - 'assets', 'tf_detection_api_dataset') - -@skipIf(import_failed, "Failed to import tensorflow") -class TfrecordImporterTest(TestCase): - def test_can_detect(self): - self.assertTrue(TfDetectionApiImporter.detect(DUMMY_DATASET_DIR)) - - def test_can_import(self): - target_dataset = Dataset.from_iterable([ - DatasetItem(id=1, subset='train', - image=np.ones((16, 16, 3)), - annotations=[ - Bbox(0, 4, 4, 8, label=2), - Bbox(0, 4, 4, 4, label=3), - Bbox(2, 4, 4, 4), - ], - attributes={'source_id': '1'} - ), - - DatasetItem(id=2, subset='val', - image=np.ones((8, 8, 3)), - annotations=[ - Bbox(1, 2, 4, 2, label=3), - ], - attributes={'source_id': '2'} - ), - - DatasetItem(id=3, subset='test', - image=np.ones((5, 4, 3)) * 3, - attributes={'source_id': '3'} - ), - ], categories={ - AnnotationType.label: LabelCategories.from_iterable( - 'label_' + str(label) for label in range(10)), - }) - - dataset = Project.import_from(DUMMY_DATASET_DIR, 'tf_detection_api') \ - .make_dataset() - - compare_datasets(self, target_dataset, dataset) diff --git a/datumaro/tests/test_transforms.py b/datumaro/tests/test_transforms.py deleted file mode 100644 index ed072a67..00000000 --- a/datumaro/tests/test_transforms.py +++ /dev/null @@ -1,415 +0,0 @@ -import logging as log -import numpy as np - -from unittest import TestCase -from datumaro.components.project import Dataset -from datumaro.components.extractor import (Extractor, DatasetItem, - Mask, Polygon, PolyLine, Points, Bbox, Label, - LabelCategories, MaskCategories, AnnotationType -) -import datumaro.util.mask_tools as mask_tools -import datumaro.plugins.transforms as transforms -from datumaro.util.test_utils import compare_datasets - - -class TransformsTest(TestCase): - def test_reindex(self): - class SrcExtractor(Extractor): - def __iter__(self): - return iter([ - DatasetItem(id=10), - DatasetItem(id=10, subset='train'), - DatasetItem(id='a', subset='val'), - ]) - - class DstExtractor(Extractor): - def __iter__(self): - return iter([ - DatasetItem(id=5), - DatasetItem(id=6, subset='train'), - DatasetItem(id=7, subset='val'), - ]) - - actual = transforms.Reindex(SrcExtractor(), start=5) - compare_datasets(self, DstExtractor(), actual) - - def test_mask_to_polygons(self): - class SrcExtractor(Extractor): - def __iter__(self): - items = [ - DatasetItem(id=1, image=np.zeros((5, 10, 3)), - annotations=[ - Mask(np.array([ - [0, 1, 1, 1, 0, 1, 1, 1, 1, 0], - [0, 0, 1, 1, 0, 1, 1, 1, 0, 0], - [0, 0, 0, 1, 0, 1, 1, 0, 0, 0], - [0, 0, 0, 0, 0, 1, 0, 0, 0, 0], - [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], - ]), - ), - ] - ), - ] - return iter(items) - - class DstExtractor(Extractor): - def __iter__(self): - return iter([ - DatasetItem(id=1, image=np.zeros((5, 10, 3)), - annotations=[ - Polygon([3.0, 2.5, 1.0, 0.0, 3.5, 0.0, 3.0, 2.5]), - Polygon([5.0, 3.5, 4.5, 0.0, 8.0, 0.0, 5.0, 3.5]), - ] - ), - ]) - - actual = transforms.MasksToPolygons(SrcExtractor()) - compare_datasets(self, DstExtractor(), actual) - - def test_mask_to_polygons_small_polygons_message(self): - source_dataset = Dataset.from_iterable([ - DatasetItem(id=1, image=np.zeros((5, 10, 3)), - annotations=[ - Mask(np.array([ - [0, 0, 0], - [0, 1, 0], - [0, 0, 0], - ]), - ), - ] - ), - ]) - - target_dataset = Dataset.from_iterable([ - DatasetItem(id=1, image=np.zeros((5, 10, 3))), ]) - - with self.assertLogs(level=log.DEBUG) as logs: - actual = transforms.MasksToPolygons(source_dataset) - - compare_datasets(self, target_dataset, actual) - self.assertRegex('\n'.join(logs.output), 'too small polygons') - - def test_polygons_to_masks(self): - source_dataset = Dataset.from_iterable([ - DatasetItem(id=1, image=np.zeros((5, 10, 3)), - annotations=[ - Polygon([0, 0, 4, 0, 4, 4]), - Polygon([5, 0, 9, 0, 5, 5]), - ] - ), - ]) - - target_dataset = Dataset.from_iterable([ - DatasetItem(id=1, image=np.zeros((5, 10, 3)), - annotations=[ - Mask(np.array([ - [0, 0, 0, 0, 0, 1, 1, 1, 1, 0], - [0, 0, 0, 0, 0, 1, 1, 1, 0, 0], - [0, 0, 0, 0, 0, 1, 1, 0, 0, 0], - [0, 0, 0, 0, 0, 1, 0, 0, 0, 0], - [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], - ]), - ), - Mask(np.array([ - [0, 1, 1, 1, 0, 0, 0, 0, 0, 0], - [0, 0, 1, 1, 0, 0, 0, 0, 0, 0], - [0, 0, 0, 1, 0, 0, 0, 0, 0, 0], - [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], - [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], - ]), - ), - ] - ), - ]) - - actual = transforms.PolygonsToMasks(source_dataset) - compare_datasets(self, target_dataset, actual) - - def test_crop_covered_segments(self): - source_dataset = Dataset.from_iterable([ - DatasetItem(id=1, image=np.zeros((5, 5, 3)), - annotations=[ - # The mask is partially covered by the polygon - Mask(np.array([ - [0, 0, 1, 1, 1], - [0, 0, 1, 1, 1], - [1, 1, 1, 1, 1], - [1, 1, 1, 0, 0], - [1, 1, 1, 0, 0]], - ), - z_order=0), - Polygon([1, 1, 4, 1, 4, 4, 1, 4], - z_order=1), - ] - ), - ]) - - target_dataset = Dataset.from_iterable([ - DatasetItem(id=1, image=np.zeros((5, 5, 3)), - annotations=[ - Mask(np.array([ - [0, 0, 1, 1, 1], - [0, 0, 0, 0, 1], - [1, 0, 0, 0, 1], - [1, 0, 0, 0, 0], - [1, 1, 1, 0, 0]], - ), - z_order=0), - Polygon([1, 1, 4, 1, 4, 4, 1, 4], - z_order=1), - ] - ), - ]) - - actual = transforms.CropCoveredSegments(source_dataset) - compare_datasets(self, target_dataset, actual) - - def test_merge_instance_segments(self): - source_dataset = Dataset.from_iterable([ - DatasetItem(id=1, image=np.zeros((5, 5, 3)), - annotations=[ - Mask(np.array([ - [0, 0, 1, 1, 1], - [0, 0, 0, 0, 1], - [1, 0, 0, 0, 1], - [1, 0, 0, 0, 0], - [1, 1, 1, 0, 0]], - ), - z_order=0, group=1), - Polygon([1, 1, 4, 1, 4, 4, 1, 4], - z_order=1, group=1), - Polygon([0, 0, 0, 2, 2, 2, 2, 0], - z_order=1), - ] - ), - ]) - - target_dataset = Dataset.from_iterable([ - DatasetItem(id=1, image=np.zeros((5, 5, 3)), - annotations=[ - Mask(np.array([ - [0, 0, 1, 1, 1], - [0, 1, 1, 1, 1], - [1, 1, 1, 1, 1], - [1, 1, 1, 1, 0], - [1, 1, 1, 0, 0]], - ), - z_order=0, group=1), - Mask(np.array([ - [1, 1, 0, 0, 0], - [1, 1, 0, 0, 0], - [0, 0, 0, 0, 0], - [0, 0, 0, 0, 0], - [0, 0, 0, 0, 0]], - ), - z_order=1), - ] - ), - ]) - - actual = transforms.MergeInstanceSegments(source_dataset, - include_polygons=True) - compare_datasets(self, target_dataset, actual) - - def test_map_subsets(self): - source_dataset = Dataset.from_iterable([ - DatasetItem(id=1, subset='a'), - DatasetItem(id=2, subset='b'), - DatasetItem(id=3, subset='c'), - ]) - - target_dataset = Dataset.from_iterable([ - DatasetItem(id=1, subset=''), - DatasetItem(id=2, subset='a'), - DatasetItem(id=3, subset='c'), - ]) - - actual = transforms.MapSubsets(source_dataset, - { 'a': '', 'b': 'a' }) - compare_datasets(self, target_dataset, actual) - - def test_shapes_to_boxes(self): - source_dataset = Dataset.from_iterable([ - DatasetItem(id=1, image=np.zeros((5, 5, 3)), - annotations=[ - Mask(np.array([ - [0, 0, 1, 1, 1], - [0, 0, 0, 0, 1], - [1, 0, 0, 0, 1], - [1, 0, 0, 0, 0], - [1, 1, 1, 0, 0]], - ), id=1), - Polygon([1, 1, 4, 1, 4, 4, 1, 4], id=2), - PolyLine([1, 1, 2, 1, 2, 2, 1, 2], id=3), - Points([2, 2, 4, 2, 4, 4, 2, 4], id=4), - ] - ), - ]) - - target_dataset = Dataset.from_iterable([ - DatasetItem(id=1, image=np.zeros((5, 5, 3)), - annotations=[ - Bbox(0, 0, 4, 4, id=1), - Bbox(1, 1, 3, 3, id=2), - Bbox(1, 1, 1, 1, id=3), - Bbox(2, 2, 2, 2, id=4), - ] - ), - ]) - - actual = transforms.ShapesToBoxes(source_dataset) - compare_datasets(self, target_dataset, actual) - - def test_id_from_image(self): - source_dataset = Dataset.from_iterable([ - DatasetItem(id=1, image='path.jpg'), - DatasetItem(id=2), - ]) - target_dataset = Dataset.from_iterable([ - DatasetItem(id='path', image='path.jpg'), - DatasetItem(id=2), - ]) - - actual = transforms.IdFromImageName(source_dataset) - compare_datasets(self, target_dataset, actual) - - def test_boxes_to_masks(self): - source_dataset = Dataset.from_iterable([ - DatasetItem(id=1, image=np.zeros((5, 5, 3)), - annotations=[ - Bbox(0, 0, 3, 3, z_order=1), - Bbox(0, 0, 3, 1, z_order=2), - Bbox(0, 2, 3, 1, z_order=3), - ] - ), - ]) - - target_dataset = Dataset.from_iterable([ - DatasetItem(id=1, image=np.zeros((5, 5, 3)), - annotations=[ - Mask(np.array([ - [1, 1, 1, 0, 0], - [1, 1, 1, 0, 0], - [1, 1, 1, 0, 0], - [0, 0, 0, 0, 0], - [0, 0, 0, 0, 0]], - ), - z_order=1), - Mask(np.array([ - [1, 1, 1, 0, 0], - [0, 0, 0, 0, 0], - [0, 0, 0, 0, 0], - [0, 0, 0, 0, 0], - [0, 0, 0, 0, 0]], - ), - z_order=2), - Mask(np.array([ - [0, 0, 0, 0, 0], - [0, 0, 0, 0, 0], - [1, 1, 1, 0, 0], - [0, 0, 0, 0, 0], - [0, 0, 0, 0, 0]], - ), - z_order=3), - ] - ), - ]) - - actual = transforms.BoxesToMasks(source_dataset) - compare_datasets(self, target_dataset, actual) - - def test_random_split(self): - source_dataset = Dataset.from_iterable([ - DatasetItem(id=1, subset="a"), - DatasetItem(id=2, subset="a"), - DatasetItem(id=3, subset="b"), - DatasetItem(id=4, subset="b"), - DatasetItem(id=5, subset="b"), - DatasetItem(id=6, subset=""), - DatasetItem(id=7, subset=""), - ]) - - actual = transforms.RandomSplit(source_dataset, splits=[ - ('train', 4.0 / 7.0), - ('test', 3.0 / 7.0), - ]) - - self.assertEqual(4, len(actual.get_subset('train'))) - self.assertEqual(3, len(actual.get_subset('test'))) - - def test_random_split_gives_error_on_wrong_ratios(self): - source_dataset = Dataset.from_iterable([DatasetItem(id=1)]) - - with self.assertRaises(Exception): - transforms.RandomSplit(source_dataset, splits=[ - ('train', 0.5), - ('test', 0.7), - ]) - - with self.assertRaises(Exception): - transforms.RandomSplit(source_dataset, splits=[]) - - with self.assertRaises(Exception): - transforms.RandomSplit(source_dataset, splits=[ - ('train', -0.5), - ('test', 1.5), - ]) - - def test_remap_labels(self): - src_dataset = Dataset.from_iterable([ - DatasetItem(id=1, annotations=[ - # Should be remapped - Label(1), - Bbox(1, 2, 3, 4, label=2), - Mask(image=np.array([1]), label=3), - - # Should be kept - Polygon([1, 1, 2, 2, 3, 4], label=4), - PolyLine([1, 3, 4, 2, 5, 6]) - ]) - ], categories={ - AnnotationType.label: LabelCategories.from_iterable( - 'label%s' % i for i in range(5)), - AnnotationType.mask: MaskCategories( - colormap=mask_tools.generate_colormap(5)), - }) - - dst_dataset = Dataset.from_iterable([ - DatasetItem(id=1, annotations=[ - Label(1), - Bbox(1, 2, 3, 4, label=0), - Mask(image=np.array([1]), label=1), - - Polygon([1, 1, 2, 2, 3, 4], label=2), - PolyLine([1, 3, 4, 2, 5, 6], label=None) - ]), - ], categories={ - AnnotationType.label: LabelCategories.from_iterable( - ['label0', 'label9', 'label4']), - AnnotationType.mask: MaskCategories(colormap={ - k: v for k, v in mask_tools.generate_colormap(5).items() - if k in { 0, 1, 3, 4 } - }) - }) - - actual = transforms.RemapLabels(src_dataset, mapping={ - 'label1': 'label9', - 'label2': 'label0', - 'label3': 'label9', - }, default='keep') - - compare_datasets(self, dst_dataset, actual) - - def test_remap_labels_delete_unspecified(self): - source_dataset = Dataset.from_iterable([ - DatasetItem(id=1, annotations=[ Label(0) ]) - ], categories=['label0']) - - target_dataset = Dataset.from_iterable([ - DatasetItem(id=1), - ], categories=[]) - - actual = transforms.RemapLabels(source_dataset, - mapping={}, default='delete') - - compare_datasets(self, target_dataset, actual) diff --git a/datumaro/tests/test_voc_format.py b/datumaro/tests/test_voc_format.py deleted file mode 100644 index e83a7430..00000000 --- a/datumaro/tests/test_voc_format.py +++ /dev/null @@ -1,677 +0,0 @@ -from collections import OrderedDict -from functools import partial -import numpy as np -import os.path as osp - -from unittest import TestCase - -from datumaro.components.extractor import (Extractor, DatasetItem, - AnnotationType, Label, Bbox, Mask, LabelCategories, -) -import datumaro.plugins.voc_format.format as VOC -from datumaro.plugins.voc_format.converter import ( - VocConverter, - VocClassificationConverter, - VocDetectionConverter, - VocLayoutConverter, - VocActionConverter, - VocSegmentationConverter, -) -from datumaro.plugins.voc_format.importer import VocImporter -from datumaro.components.project import Project -from datumaro.util.image import Image -from datumaro.util.test_utils import TestDir, compare_datasets - - -class VocFormatTest(TestCase): - def test_colormap_generator(self): - reference = np.array([ - [ 0, 0, 0], - [128, 0, 0], - [ 0, 128, 0], - [128, 128, 0], - [ 0, 0, 128], - [128, 0, 128], - [ 0, 128, 128], - [128, 128, 128], - [ 64, 0, 0], - [192, 0, 0], - [ 64, 128, 0], - [192, 128, 0], - [ 64, 0, 128], - [192, 0, 128], - [ 64, 128, 128], - [192, 128, 128], - [ 0, 64, 0], - [128, 64, 0], - [ 0, 192, 0], - [128, 192, 0], - [ 0, 64, 128], - [224, 224, 192], # ignored - ]) - - self.assertTrue(np.array_equal(reference, list(VOC.VocColormap.values()))) - - def test_can_write_and_parse_labelmap(self): - src_label_map = VOC.make_voc_label_map() - src_label_map['qq'] = [None, ['part1', 'part2'], ['act1', 'act2']] - src_label_map['ww'] = [(10, 20, 30), [], ['act3']] - - with TestDir() as test_dir: - file_path = osp.join(test_dir, 'test.txt') - - VOC.write_label_map(file_path, src_label_map) - dst_label_map = VOC.parse_label_map(file_path) - - self.assertEqual(src_label_map, dst_label_map) - -class TestExtractorBase(Extractor): - def _label(self, voc_label): - return self.categories()[AnnotationType.label].find(voc_label)[0] - - def categories(self): - return VOC.make_voc_categories() - - -DUMMY_DATASET_DIR = osp.join(osp.dirname(__file__), 'assets', 'voc_dataset') - -class VocImportTest(TestCase): - def test_can_import(self): - class DstExtractor(TestExtractorBase): - def __iter__(self): - return iter([ - DatasetItem(id='2007_000001', subset='train', - image=Image(path='2007_000001.jpg', size=(20, 10)), - annotations=[ - Label(self._label(l.name)) - for l in VOC.VocLabel if l.value % 2 == 1 - ] + [ - Bbox(1, 2, 2, 2, label=self._label('cat'), - attributes={ - 'pose': VOC.VocPose(1).name, - 'truncated': True, - 'difficult': False, - 'occluded': False, - }, - id=1, group=1, - ), - Bbox(4, 5, 2, 2, label=self._label('person'), - attributes={ - 'truncated': False, - 'difficult': False, - 'occluded': False, - **{ - a.name: a.value % 2 == 1 - for a in VOC.VocAction - } - }, - id=2, group=2, - ), - Bbox(5.5, 6, 2, 2, label=self._label( - VOC.VocBodyPart(1).name), - group=2 - ), - Mask(image=np.ones([5, 10]), - label=self._label(VOC.VocLabel(2).name), - group=1, - ), - ] - ), - DatasetItem(id='2007_000002', subset='test', - image=np.zeros((20, 10, 3))), - ]) - - dataset = Project.import_from(DUMMY_DATASET_DIR, 'voc').make_dataset() - - compare_datasets(self, DstExtractor(), dataset) - - def test_can_detect_voc(self): - self.assertTrue(VocImporter.detect(DUMMY_DATASET_DIR)) - -class VocConverterTest(TestCase): - def _test_save_and_load(self, source_dataset, converter, test_dir, - target_dataset=None, importer_args=None): - converter(source_dataset, test_dir) - - if importer_args is None: - importer_args = {} - parsed_dataset = VocImporter()(test_dir, **importer_args).make_dataset() - - if target_dataset is None: - target_dataset = source_dataset - - compare_datasets(self, expected=target_dataset, actual=parsed_dataset) - - def test_can_save_voc_cls(self): - class TestExtractor(TestExtractorBase): - def __iter__(self): - return iter([ - DatasetItem(id='a/0', subset='a', annotations=[ - Label(1), - Label(2), - Label(3), - ]), - - DatasetItem(id=1, subset='b', annotations=[ - Label(4), - ]), - ]) - - with TestDir() as test_dir: - self._test_save_and_load(TestExtractor(), - partial(VocClassificationConverter.convert, label_map='voc'), - test_dir) - - def test_can_save_voc_det(self): - class TestExtractor(TestExtractorBase): - def __iter__(self): - return iter([ - DatasetItem(id='a/1', subset='a', annotations=[ - Bbox(2, 3, 4, 5, label=2, - attributes={ 'occluded': True } - ), - Bbox(2, 3, 4, 5, label=3, - attributes={ 'truncated': True }, - ), - ]), - - DatasetItem(id=2, subset='b', annotations=[ - Bbox(5, 4, 6, 5, label=3, - attributes={ 'difficult': True }, - ), - ]), - ]) - - class DstExtractor(TestExtractorBase): - def __iter__(self): - return iter([ - DatasetItem(id='a/1', subset='a', annotations=[ - Bbox(2, 3, 4, 5, label=2, id=1, group=1, - attributes={ - 'truncated': False, - 'difficult': False, - 'occluded': True, - } - ), - Bbox(2, 3, 4, 5, label=3, id=2, group=2, - attributes={ - 'truncated': True, - 'difficult': False, - 'occluded': False, - }, - ), - ]), - - DatasetItem(id=2, subset='b', annotations=[ - Bbox(5, 4, 6, 5, label=3, id=1, group=1, - attributes={ - 'truncated': False, - 'difficult': True, - 'occluded': False, - }, - ), - ]), - ]) - - with TestDir() as test_dir: - self._test_save_and_load(TestExtractor(), - partial(VocDetectionConverter.convert, label_map='voc'), - test_dir, target_dataset=DstExtractor()) - - def test_can_save_voc_segm(self): - class TestExtractor(TestExtractorBase): - def __iter__(self): - return iter([ - DatasetItem(id='a/b/1', subset='a', annotations=[ - # overlapping masks, the first should be truncated - # the second and third are different instances - Mask(image=np.array([[0, 0, 0, 1, 0]]), label=3, - z_order=3), - Mask(image=np.array([[0, 1, 1, 1, 0]]), label=4, - z_order=1), - Mask(image=np.array([[1, 1, 0, 0, 0]]), label=3, - z_order=2), - ]), - ]) - - class DstExtractor(TestExtractorBase): - def __iter__(self): - return iter([ - DatasetItem(id='a/b/1', subset='a', annotations=[ - Mask(image=np.array([[0, 0, 1, 0, 0]]), label=4, - group=1), - Mask(image=np.array([[1, 1, 0, 0, 0]]), label=3, - group=2), - Mask(image=np.array([[0, 0, 0, 1, 0]]), label=3, - group=3), - ]), - ]) - - with TestDir() as test_dir: - self._test_save_and_load(TestExtractor(), - partial(VocSegmentationConverter.convert, label_map='voc'), - test_dir, target_dataset=DstExtractor()) - - def test_can_save_voc_segm_unpainted(self): - class TestExtractor(TestExtractorBase): - def __iter__(self): - return iter([ - DatasetItem(id=1, subset='a', annotations=[ - # overlapping masks, the first should be truncated - # the second and third are different instances - Mask(image=np.array([[0, 0, 0, 1, 0]]), label=3, - z_order=3), - Mask(image=np.array([[0, 1, 1, 1, 0]]), label=4, - z_order=1), - Mask(image=np.array([[1, 1, 0, 0, 0]]), label=3, - z_order=2), - ]), - ]) - - class DstExtractor(TestExtractorBase): - def __iter__(self): - return iter([ - DatasetItem(id=1, subset='a', annotations=[ - Mask(image=np.array([[0, 0, 1, 0, 0]]), label=4, - group=1), - Mask(image=np.array([[1, 1, 0, 0, 0]]), label=3, - group=2), - Mask(image=np.array([[0, 0, 0, 1, 0]]), label=3, - group=3), - ]), - ]) - - with TestDir() as test_dir: - self._test_save_and_load(TestExtractor(), - partial(VocSegmentationConverter.convert, - label_map='voc', apply_colormap=False), - test_dir, target_dataset=DstExtractor()) - - def test_can_save_voc_segm_with_many_instances(self): - def bit(x, y, shape): - mask = np.zeros(shape) - mask[y, x] = 1 - return mask - - class TestExtractor(TestExtractorBase): - def __iter__(self): - return iter([ - DatasetItem(id=1, subset='a', annotations=[ - Mask(image=bit(x, y, shape=[10, 10]), - label=self._label(VOC.VocLabel(3).name), - z_order=10 * y + x + 1 - ) - for y in range(10) for x in range(10) - ]), - ]) - - class DstExtractor(TestExtractorBase): - def __iter__(self): - return iter([ - DatasetItem(id=1, subset='a', annotations=[ - Mask(image=bit(x, y, shape=[10, 10]), - label=self._label(VOC.VocLabel(3).name), - group=10 * y + x + 1 - ) - for y in range(10) for x in range(10) - ]), - ]) - - with TestDir() as test_dir: - self._test_save_and_load(TestExtractor(), - partial(VocSegmentationConverter.convert, label_map='voc'), - test_dir, target_dataset=DstExtractor()) - - def test_can_save_voc_layout(self): - class TestExtractor(TestExtractorBase): - def __iter__(self): - return iter([ - DatasetItem(id='a/b/1', subset='a', annotations=[ - Bbox(2, 3, 4, 5, label=2, id=1, group=1, - attributes={ - 'pose': VOC.VocPose(1).name, - 'truncated': True, - 'difficult': False, - 'occluded': False, - } - ), - Bbox(2, 3, 1, 1, label=self._label( - VOC.VocBodyPart(1).name), group=1), - Bbox(5, 4, 3, 2, label=self._label( - VOC.VocBodyPart(2).name), group=1), - ]), - ]) - - with TestDir() as test_dir: - self._test_save_and_load(TestExtractor(), - partial(VocLayoutConverter.convert, label_map='voc'), test_dir) - - def test_can_save_voc_action(self): - class TestExtractor(TestExtractorBase): - def __iter__(self): - return iter([ - DatasetItem(id='a/b/1', subset='a', annotations=[ - Bbox(2, 3, 4, 5, label=2, - attributes={ - 'truncated': True, - VOC.VocAction(1).name: True, - VOC.VocAction(2).name: True, - } - ), - Bbox(5, 4, 3, 2, label=self._label('person'), - attributes={ - 'truncated': True, - VOC.VocAction(1).name: True, - VOC.VocAction(2).name: True, - } - ), - ]), - ]) - - class DstExtractor(TestExtractorBase): - def __iter__(self): - return iter([ - DatasetItem(id='a/b/1', subset='a', annotations=[ - Bbox(2, 3, 4, 5, label=2, - id=1, group=1, attributes={ - 'truncated': True, - 'difficult': False, - 'occluded': False, - # no attributes here in the label categories - } - ), - Bbox(5, 4, 3, 2, label=self._label('person'), - id=2, group=2, attributes={ - 'truncated': True, - 'difficult': False, - 'occluded': False, - VOC.VocAction(1).name: True, - VOC.VocAction(2).name: True, - **{ - a.name: False for a in VOC.VocAction - if a.value not in {1, 2} - } - } - ), - ]), - ]) - - with TestDir() as test_dir: - self._test_save_and_load(TestExtractor(), - partial(VocActionConverter.convert, - label_map='voc', allow_attributes=False), test_dir, - target_dataset=DstExtractor()) - - def test_can_save_dataset_with_no_subsets(self): - class TestExtractor(TestExtractorBase): - def __iter__(self): - return iter([ - DatasetItem(id=1, annotations=[ - Label(2), - Label(3), - ]), - - DatasetItem(id=2, annotations=[ - Label(3), - ]), - ]) - - with TestDir() as test_dir: - self._test_save_and_load(TestExtractor(), - partial(VocConverter.convert, label_map='voc'), test_dir) - - def test_can_save_dataset_with_images(self): - class TestExtractor(TestExtractorBase): - def __iter__(self): - return iter([ - DatasetItem(id=1, subset='a', image=np.ones([4, 5, 3])), - DatasetItem(id=2, subset='a', image=np.ones([5, 4, 3])), - - DatasetItem(id=3, subset='b', image=np.ones([2, 6, 3])), - ]) - - with TestDir() as test_dir: - self._test_save_and_load(TestExtractor(), - partial(VocConverter.convert, label_map='voc', save_images=True), - test_dir) - - def test_dataset_with_voc_labelmap(self): - class SrcExtractor(TestExtractorBase): - def __iter__(self): - yield DatasetItem(id=1, annotations=[ - Bbox(2, 3, 4, 5, label=self._label('cat'), id=1), - Bbox(1, 2, 3, 4, label=self._label('non_voc_label'), id=2), - ]) - - def categories(self): - label_cat = LabelCategories() - label_cat.add(VOC.VocLabel.cat.name) - label_cat.add('non_voc_label') - return { - AnnotationType.label: label_cat, - } - - class DstExtractor(TestExtractorBase): - def __iter__(self): - yield DatasetItem(id=1, annotations=[ - # drop non voc label - Bbox(2, 3, 4, 5, label=self._label('cat'), id=1, group=1, - attributes={ - 'truncated': False, - 'difficult': False, - 'occluded': False, - } - ), - ]) - - def categories(self): - return VOC.make_voc_categories() - - with TestDir() as test_dir: - self._test_save_and_load(SrcExtractor(), - partial(VocConverter.convert, label_map='voc'), - test_dir, target_dataset=DstExtractor()) - - def test_dataset_with_source_labelmap_undefined(self): - class SrcExtractor(TestExtractorBase): - def __iter__(self): - yield DatasetItem(id=1, annotations=[ - Bbox(2, 3, 4, 5, label=0, id=1), - Bbox(1, 2, 3, 4, label=1, id=2), - ]) - - def categories(self): - label_cat = LabelCategories() - label_cat.add('Label_1') - label_cat.add('label_2') - return { - AnnotationType.label: label_cat, - } - - class DstExtractor(TestExtractorBase): - def __iter__(self): - yield DatasetItem(id=1, annotations=[ - Bbox(2, 3, 4, 5, label=self._label('Label_1'), - id=1, group=1, attributes={ - 'truncated': False, - 'difficult': False, - 'occluded': False, - } - ), - Bbox(1, 2, 3, 4, label=self._label('label_2'), - id=2, group=2, attributes={ - 'truncated': False, - 'difficult': False, - 'occluded': False, - } - ), - ]) - - def categories(self): - label_map = OrderedDict() - label_map['background'] = [None, [], []] - label_map['Label_1'] = [None, [], []] - label_map['label_2'] = [None, [], []] - return VOC.make_voc_categories(label_map) - - with TestDir() as test_dir: - self._test_save_and_load(SrcExtractor(), - partial(VocConverter.convert, label_map='source'), - test_dir, target_dataset=DstExtractor()) - - def test_dataset_with_source_labelmap_defined(self): - class SrcExtractor(TestExtractorBase): - def __iter__(self): - yield DatasetItem(id=1, annotations=[ - Bbox(2, 3, 4, 5, label=0, id=1), - Bbox(1, 2, 3, 4, label=2, id=2), - ]) - - def categories(self): - label_map = OrderedDict() - label_map['label_1'] = [(1, 2, 3), [], []] - label_map['background'] = [(0, 0, 0), [], []] # can be not 0 - label_map['label_2'] = [(3, 2, 1), [], []] - return VOC.make_voc_categories(label_map) - - class DstExtractor(TestExtractorBase): - def __iter__(self): - yield DatasetItem(id=1, annotations=[ - Bbox(2, 3, 4, 5, label=self._label('label_1'), - id=1, group=1, attributes={ - 'truncated': False, - 'difficult': False, - 'occluded': False, - } - ), - Bbox(1, 2, 3, 4, label=self._label('label_2'), - id=2, group=2, attributes={ - 'truncated': False, - 'difficult': False, - 'occluded': False, - } - ), - ]) - - def categories(self): - label_map = OrderedDict() - label_map['background'] = [(0, 0, 0), [], []] - label_map['label_1'] = [(1, 2, 3), [], []] - label_map['label_2'] = [(3, 2, 1), [], []] - return VOC.make_voc_categories(label_map) - - with TestDir() as test_dir: - self._test_save_and_load(SrcExtractor(), - partial(VocConverter.convert, label_map='source'), - test_dir, target_dataset=DstExtractor()) - - def test_dataset_with_fixed_labelmap(self): - class SrcExtractor(TestExtractorBase): - def __iter__(self): - yield DatasetItem(id=1, annotations=[ - Bbox(2, 3, 4, 5, label=self._label('foreign_label'), id=1), - Bbox(1, 2, 3, 4, label=self._label('label'), id=2, group=2, - attributes={'act1': True}), - Bbox(2, 3, 4, 5, label=self._label('label_part1'), group=2), - Bbox(2, 3, 4, 6, label=self._label('label_part2'), group=2), - ]) - - def categories(self): - label_cat = LabelCategories() - label_cat.add('foreign_label') - label_cat.add('label', attributes=['act1', 'act2']) - label_cat.add('label_part1') - label_cat.add('label_part2') - return { - AnnotationType.label: label_cat, - } - - label_map = OrderedDict([ - ('label', [None, ['label_part1', 'label_part2'], ['act1', 'act2']]) - ]) - - dst_label_map = OrderedDict([ - ('background', [None, [], []]), - ('label', [None, ['label_part1', 'label_part2'], ['act1', 'act2']]) - ]) - - class DstExtractor(TestExtractorBase): - def __iter__(self): - yield DatasetItem(id=1, annotations=[ - Bbox(1, 2, 3, 4, label=self._label('label'), id=1, group=1, - attributes={ - 'act1': True, - 'act2': False, - 'truncated': False, - 'difficult': False, - 'occluded': False, - } - ), - Bbox(2, 3, 4, 5, label=self._label('label_part1'), group=1), - Bbox(2, 3, 4, 6, label=self._label('label_part2'), group=1), - ]) - - def categories(self): - return VOC.make_voc_categories(dst_label_map) - - with TestDir() as test_dir: - self._test_save_and_load(SrcExtractor(), - partial(VocConverter.convert, label_map=label_map), - test_dir, target_dataset=DstExtractor()) - - def test_can_save_dataset_with_image_info(self): - class TestExtractor(TestExtractorBase): - def __iter__(self): - return iter([ - DatasetItem(id=1, image=Image(path='1.jpg', size=(10, 15))), - ]) - - with TestDir() as test_dir: - self._test_save_and_load(TestExtractor(), - partial(VocConverter.convert, label_map='voc'), test_dir) - - def test_relative_paths(self): - class TestExtractor(TestExtractorBase): - def __iter__(self): - return iter([ - DatasetItem(id='1', image=np.ones((4, 2, 3))), - DatasetItem(id='subdir1/1', image=np.ones((2, 6, 3))), - DatasetItem(id='subdir2/1', image=np.ones((5, 4, 3))), - ]) - - with TestDir() as test_dir: - self._test_save_and_load(TestExtractor(), - partial(VocConverter.convert, - label_map='voc', save_images=True), - test_dir) - - def test_can_save_attributes(self): - class TestExtractor(TestExtractorBase): - def __iter__(self): - return iter([ - DatasetItem(id='a', annotations=[ - Bbox(2, 3, 4, 5, label=2, - attributes={ 'occluded': True, 'x': 1, 'y': '2' } - ), - ]), - ]) - - class DstExtractor(TestExtractorBase): - def __iter__(self): - return iter([ - DatasetItem(id='a', annotations=[ - Bbox(2, 3, 4, 5, label=2, id=1, group=1, - attributes={ - 'truncated': False, - 'difficult': False, - 'occluded': True, - 'x': '1', 'y': '2', # can only read strings - } - ), - ]), - ]) - - with TestDir() as test_dir: - self._test_save_and_load(TestExtractor(), - partial(VocConverter.convert, label_map='voc'), test_dir, - target_dataset=DstExtractor()) diff --git a/datumaro/tests/test_yolo_format.py b/datumaro/tests/test_yolo_format.py deleted file mode 100644 index 1f6425d1..00000000 --- a/datumaro/tests/test_yolo_format.py +++ /dev/null @@ -1,140 +0,0 @@ -import numpy as np -import os.path as osp - -from unittest import TestCase - -from datumaro.components.extractor import (Extractor, DatasetItem, - AnnotationType, Bbox, LabelCategories, -) -from datumaro.components.project import Project, Dataset -from datumaro.plugins.yolo_format.importer import YoloImporter -from datumaro.plugins.yolo_format.converter import YoloConverter -from datumaro.util.image import Image, save_image -from datumaro.util.test_utils import TestDir, compare_datasets - - -class YoloFormatTest(TestCase): - def test_can_save_and_load(self): - source_dataset = Dataset.from_iterable([ - DatasetItem(id=1, subset='train', image=np.ones((8, 8, 3)), - annotations=[ - Bbox(0, 2, 4, 2, label=2), - Bbox(0, 1, 2, 3, label=4), - ]), - DatasetItem(id=2, subset='train', image=np.ones((10, 10, 3)), - annotations=[ - Bbox(0, 2, 4, 2, label=2), - Bbox(3, 3, 2, 3, label=4), - Bbox(2, 1, 2, 3, label=4), - ]), - - DatasetItem(id=3, subset='valid', image=np.ones((8, 8, 3)), - annotations=[ - Bbox(0, 1, 5, 2, label=2), - Bbox(0, 2, 3, 2, label=5), - Bbox(0, 2, 4, 2, label=6), - Bbox(0, 7, 3, 2, label=7), - ]), - ], categories={ - AnnotationType.label: LabelCategories.from_iterable( - 'label_' + str(i) for i in range(10)), - }) - - with TestDir() as test_dir: - - YoloConverter.convert(source_dataset, test_dir, save_images=True) - parsed_dataset = YoloImporter()(test_dir).make_dataset() - - compare_datasets(self, source_dataset, parsed_dataset) - - def test_can_save_dataset_with_image_info(self): - source_dataset = Dataset.from_iterable([ - DatasetItem(id=1, subset='train', - image=Image(path='1.jpg', size=(10, 15)), - annotations=[ - Bbox(0, 2, 4, 2, label=2), - Bbox(3, 3, 2, 3, label=4), - ]), - ], categories={ - AnnotationType.label: LabelCategories.from_iterable( - 'label_' + str(i) for i in range(10)), - }) - - with TestDir() as test_dir: - - YoloConverter.convert(source_dataset, test_dir) - - save_image(osp.join(test_dir, 'obj_train_data', '1.jpg'), - np.ones((10, 15, 3))) # put the image for dataset - parsed_dataset = YoloImporter()(test_dir).make_dataset() - - compare_datasets(self, source_dataset, parsed_dataset) - - def test_can_load_dataset_with_exact_image_info(self): - source_dataset = Dataset.from_iterable([ - DatasetItem(id=1, subset='train', - image=Image(path='1.jpg', size=(10, 15)), - annotations=[ - Bbox(0, 2, 4, 2, label=2), - Bbox(3, 3, 2, 3, label=4), - ]), - ], categories={ - AnnotationType.label: LabelCategories.from_iterable( - 'label_' + str(i) for i in range(10)), - }) - - with TestDir() as test_dir: - - YoloConverter.convert(source_dataset, test_dir) - - parsed_dataset = YoloImporter()(test_dir, - image_info={'1': (10, 15)}).make_dataset() - - compare_datasets(self, source_dataset, parsed_dataset) - - def test_relative_paths(self): - source_dataset = Dataset.from_iterable([ - DatasetItem(id='1', subset='train', - image=np.ones((4, 2, 3))), - DatasetItem(id='subdir1/1', subset='train', - image=np.ones((2, 6, 3))), - DatasetItem(id='subdir2/1', subset='train', - image=np.ones((5, 4, 3))), - ], categories={ - AnnotationType.label: LabelCategories(), - }) - - for save_images in {True, False}: - with self.subTest(save_images=save_images): - with TestDir() as test_dir: - - YoloConverter.convert(source_dataset, test_dir, - save_images=save_images) - parsed_dataset = YoloImporter()(test_dir).make_dataset() - - compare_datasets(self, source_dataset, parsed_dataset) - - -DUMMY_DATASET_DIR = osp.join(osp.dirname(__file__), 'assets', 'yolo_dataset') - -class YoloImporterTest(TestCase): - def test_can_detect(self): - self.assertTrue(YoloImporter.detect(DUMMY_DATASET_DIR)) - - def test_can_import(self): - expected_dataset = Dataset.from_iterable([ - DatasetItem(id=1, subset='train', - image=np.ones((10, 15, 3)), - annotations=[ - Bbox(0, 2, 4, 2, label=2), - Bbox(3, 3, 2, 3, label=4), - ]), - ], categories={ - AnnotationType.label: LabelCategories.from_iterable( - 'label_' + str(i) for i in range(10)), - }) - - dataset = Project.import_from(DUMMY_DATASET_DIR, 'yolo') \ - .make_dataset() - - compare_datasets(self, expected_dataset, dataset)