Add Cityscapes format (#3758)
Kirill Sizov
4 years ago
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GitHub
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4bdaf3c083
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# Copyright (C) 2021 Intel Corporation
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#
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# SPDX-License-Identifier: MIT
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import os.path as osp
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from tempfile import TemporaryDirectory
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from datumaro.components.dataset import Dataset
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from datumaro.plugins.cityscapes_format import write_label_map
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from pyunpack import Archive
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from cvat.apps.dataset_manager.bindings import (GetCVATDataExtractor,
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import_dm_annotations)
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from cvat.apps.dataset_manager.util import make_zip_archive
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from .registry import dm_env, exporter, importer
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from .utils import make_colormap
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@exporter(name='Cityscapes', ext='ZIP', version='1.0')
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def _export(dst_file, instance_data, save_images=False):
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dataset = Dataset.from_extractors(GetCVATDataExtractor(
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instance_data, include_images=save_images), env=dm_env)
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dataset.transform('polygons_to_masks')
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dataset.transform('boxes_to_masks')
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dataset.transform('merge_instance_segments')
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with TemporaryDirectory() as temp_dir:
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dataset.export(temp_dir, 'cityscapes', save_images=save_images,
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apply_colormap=True, label_map={label: info[0]
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for label, info in make_colormap(instance_data).items()})
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make_zip_archive(temp_dir, dst_file)
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@importer(name='Cityscapes', ext='ZIP', version='1.0')
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def _import(src_file, instance_data):
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with TemporaryDirectory() as tmp_dir:
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Archive(src_file.name).extractall(tmp_dir)
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labelmap_file = osp.join(tmp_dir, 'label_colors.txt')
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if not osp.isfile(labelmap_file):
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colormap = {label: info[0]
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for label, info in make_colormap(instance_data).items()}
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write_label_map(labelmap_file, colormap)
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dataset = Dataset.import_from(tmp_dir, 'cityscapes', env=dm_env)
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dataset.transform('masks_to_polygons')
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import_dm_annotations(dataset, instance_data)
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---
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linkTitle: 'Cityscapes'
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weight: 16
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---
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# [Cityscapes](https://www.cityscapes-dataset.com/login/)
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- [Format specification](https://github.com/mcordts/cityscapesScripts#the-cityscapes-dataset)
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- Supported annotations
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- Polygons (segmentation task)
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- Supported attributes
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- 'is_crowd' (boolean, should be defined for labels as `checkbox` -es)
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Specifies if the annotation label can distinguish between different instances.
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If False, the annotation id field encodes the instance id.
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# Cityscapes export
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Downloaded file: a zip archive of the following structure:
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```
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.
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├── label_color.txt
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├── gtFine
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│ ├── <subset_name>
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│ │ └── <city_name>
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│ │ ├── image_0_gtFine_instanceIds.png
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│ │ ├── image_0_gtFine_color.png
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│ │ ├── image_0_gtFine_labelIds.png
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│ │ ├── image_1_gtFine_instanceIds.png
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│ │ ├── image_1_gtFine_color.png
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│ │ ├── image_1_gtFine_labelIds.png
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│ │ ├── ...
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└── imgsFine # if saving images was requested
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└── leftImg8bit
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├── <subset_name>
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│ └── <city_name>
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│ ├── image_0_leftImg8bit.png
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│ ├── image_1_leftImg8bit.png
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│ ├── ...
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```
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- `label_color.txt` a file that describes the color for each label
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```
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# label_color.txt example
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# r g b label_name
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0 0 0 background
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0 255 0 tree
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...
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```
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- `*_gtFine_color.png` class labels encoded by its color.
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- `*_gtFine_labelIds.png` class labels are encoded by its index.
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- `*_gtFine_instanceIds.png` class and instance labels encoded
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by an instance ID. The pixel values encode class and the individual instance:
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the integer part of a division by 1000 of each ID provides class ID,
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the remainder is the instance ID. If a certain annotation describes multiple
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instances, then the pixels have the regular ID of that class
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# Cityscapes annotations import
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Uploaded file: a zip archive with the following structure:
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```
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.
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├── label_color.txt # optional
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└── gtFine
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└── <city_name>
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├── image_0_gtFine_instanceIds.png
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├── image_1_gtFine_instanceIds.png
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├── ...
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```
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# Creating task with Cityscapes dataset
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Create a task with the labels you need
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or you can use the labels and colors of the original dataset.
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To work with the Cityscapes format, you must have a black color label
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for the background.
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Original Cityscapes color map:
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<details>
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```JSON
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[
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{"name": "unlabeled", "color": "#000000", "attributes": []},
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{"name": "egovehicle", "color": "#000000", "attributes": []},
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{"name": "rectificationborder", "color": "#000000", "attributes": []},
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{"name": "outofroi", "color": "#000000", "attributes": []},
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{"name": "static", "color": "#000000", "attributes": []},
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{"name": "dynamic", "color": "#6f4a00", "attributes": []},
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{"name": "ground", "color": "#510051", "attributes": []},
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{"name": "road", "color": "#804080", "attributes": []},
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{"name": "sidewalk", "color": "#f423e8", "attributes": []},
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{"name": "parking", "color": "#faaaa0", "attributes": []},
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{"name": "railtrack", "color": "#e6968c", "attributes": []},
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{"name": "building", "color": "#464646", "attributes": []},
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{"name": "wall", "color": "#66669c", "attributes": []},
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{"name": "fence", "color": "#be9999", "attributes": []},
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{"name": "guardrail", "color": "#b4a5b4", "attributes": []},
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{"name": "bridge", "color": "#966464", "attributes": []},
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{"name": "tunnel", "color": "#96785a", "attributes": []},
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{"name": "pole", "color": "#999999", "attributes": []},
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{"name": "polegroup", "color": "#999999", "attributes": []},
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{"name": "trafficlight", "color": "#faaa1e", "attributes": []},
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{"name": "trafficsign", "color": "#dcdc00", "attributes": []},
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{"name": "vegetation", "color": "#6b8e23", "attributes": []},
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{"name": "terrain", "color": "#98fb98", "attributes": []},
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{"name": "sky", "color": "#4682b4", "attributes": []},
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{"name": "person", "color": "#dc143c", "attributes": []},
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{"name": "rider", "color": "#ff0000", "attributes": []},
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{"name": "car", "color": "#00008e", "attributes": []},
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{"name": "truck", "color": "#000046", "attributes": []},
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{"name": "bus", "color": "#003c64", "attributes": []},
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{"name": "caravan", "color": "#00005a", "attributes": []},
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{"name": "trailer", "color": "#00006e", "attributes": []},
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{"name": "train", "color": "#005064", "attributes": []},
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{"name": "motorcycle", "color": "#0000e6", "attributes": []},
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{"name": "bicycle", "color": "#770b20", "attributes": []},
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{"name": "licenseplate", "color": "#00000e", "attributes": []}
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]
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```
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<details>
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Upload images when creating a task:
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```
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images.zip/
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├── image_0.jpg
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├── image_1.jpg
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├── ...
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```
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After creating the task, upload the Cityscapes annotations as described
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in the previous section.
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