- 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)
- 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)
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 <projectdir>
```
Example:
<details>
``` 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
}
```
</details>
### Register model
### Register model
Supported models:
Supported models:
@ -557,6 +896,85 @@ datum explain \
-s 1000 --progressive
-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 <project_dir> \
-o <output_dir> \
-t <transform_name> \
-- [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:
- `<project_dir>/.datumaro/plugins` for project-specific plugins
- `<datumaro_dir>/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
## Links
- [TensorFlow detection model zoo](https://github.com/tensorflow/models/blob/master/research/object_detection/g3doc/detection_model_zoo.md)
- [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)
- [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)
Maxim Zhiltsov
6 years ago
committed by
GitHub