User documentation for Pascal VOC format

CHANGELOG.md

@@ -12,6 +12,7 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 - Support for Segmentation Splitting (<https://github.com/openvinotoolkit/datumaro/pull/223>)
 - Support for CIFAR-10/100 dataset format (<https://github.com/openvinotoolkit/datumaro/pull/225>)
 - Support COCO panoptic and stuff format (<https://github.com/openvinotoolkit/datumaro/pull/210>)
+- Documentation file and integration tests for Pascal VOC format (<https://github.com/openvinotoolkit/datumaro/pull/228>)
 
 ### Changed
 - LabelMe format saves dataset items with their relative paths by subsets without changing names (<https://github.com/openvinotoolkit/datumaro/pull/200>)

docs/pascal_voc_user_manual.md

@@ -0,0 +1,317 @@
+# Pascal VOC user manual
+
+## Contents
+- [Format specification](#format-specification)
+- [Load Pascal VOC dataset](#load-pascal-voc-dataset)
+- [Export to other formats](#export-to-other-formats)
+- [Export to Pascal VOC](#export-to-pascal-VOC)
+- [Particular use cases](#particular-use-cases)
+
+## Format specification
+
+- Pascal VOC format specification available
+[here](http://host.robots.ox.ac.uk/pascal/VOC/voc2012/devkit_doc.pdf).
+
+- Original Pascal VOC dataset format support the followoing types of annotations:
+    - `Labels` (for classification tasks);
+    - `Bounding boxes` (for detection, action detection and person layout tasks);
+    - `Masks` (for segmentations tasks).
+
+- Supported attributes:
+    - `occluded`: indicates that a significant portion of the object within the
+    bounding box is occluded by another object;
+    - `truncated`: indicates that the bounding box specified for the object does
+    not correspond to the full extent of the object;
+    - `difficult`: indicates that the object is considered difficult to recognize;
+    - action attributes (`jumping`, `reading`, `phoning` and
+    [more](http://host.robots.ox.ac.uk/pascal/VOC/voc2012/actionexamples/index.html)).
+
+## Load Pascal VOC dataset
+
+The Pascal VOC dataset is available for free download
+[here](http://host.robots.ox.ac.uk/pascal/VOC/voc2012/index.html#devkit)
+
+There are two ways to create Datumaro project and add Pascal VOC dataset to it:
+
+``` bash
+datum import --format voc --input-path <path/to/dataset>
+# or
+datum create
+datum add path -f voc <path/to/dataset>
+```
+
+It is possible to specify project name and project directory run
+`datum create --help` for more information.
+Pascal VOC dataset directory should have the following structure:
+
+<!--lint disable fenced-code-flag-->
+```
+└─ Dataset/
+   ├── label_map.txt # list of non-pascal labels (optional)
+   ├── Annotations/
+   │     ├── ann1.xml # Pascal VOC format annotation file
+   │     ├── ann2.xml
+   │     ├── ...
+   ├── JPEGImages/
+   │    ├── img1.jpg
+   │    ├── img2.jpg
+   │    ├── ...
+   ├── SegmentationClass/ # directory with semantic segmentation masks
+   │    ├── img1.png
+   │    ├── img2.png
+   │    ├── ...
+   ├── SegmentationObject/ # directory with instance segmentation masks
+   │    ├── img1.png
+   │    ├── img2.png
+   │    ├── ...
+   ├── ImageSets/
+   │    ├── Main/ # directory with list of images for detection and classification task
+   │    │   ├── test.txt  # list of image names in test subset  (without extension)
+   |    |   ├── train.txt # list of image names in train subset (without extension)
+   |    |   ├── ...
+   │    ├── Layout/ # directory with list of images for person layout task
+   │    │   ├── test.txt
+   |    |   ├── train.txt
+   |    |   ├── ...
+   │    ├── Action/ # directory with list of images for action classification task
+   │    │   ├── test.txt
+   |    |   ├── train.txt
+   |    |   ├── ...
+   │    ├── Segmentation/ # directory with list of images for segmentation task
+   │    │   ├── test.txt
+   |    |   ├── train.txt
+   |    |   ├── ...
+```
+
+The `ImageSets` directory should contain at least one of the directories:
+`Main`, `Layout`, `Action`, `Segmentation`.
+These directories contain `.txt` files
+with a list of images in a subset, the subset name is the same as the `.txt` file name.
+
+In `label_map.txt` you can define custom color map and non-pascal labels, for example:
+
+```
+# label_map [label : color_rgb : parts : actions]
+helicopter:::
+elephant:0:124:134:head,ear,foot:
+```
+It is also possible to import grayscale (1-channel) PNG masks.
+For grayscale masks provide a list of labels with the number of lines
+equal to the maximum color index on images. The lines must be in the
+right order so that line index is equal to the color index. Lines can
+have arbitrary, but different, colors. If there are gaps in the used
+color indices in the annotations, they must be filled with arbitrary
+dummy labels. Example:
+
+```
+car:0,128,0:: # color index 0
+aeroplane:10,10,128:: # color index 1
+_dummy2:2,2,2:: # filler for color index 2
+_dummy3:3,3,3:: # filler for color index 3
+boat:108,0,100:: # color index 3
+...
+_dummy198:198,198,198:: # filler for color index 198
+_dummy199:199,199,199:: # filler for color index 199
+the_last_label:12,28,0:: # color index 200
+```
+
+You can import dataset for specific tasks
+of Pascal VOC dataset instead of the whole dataset,
+for example:
+
+``` bash
+datum add path -f voc_detection <path/to/dataset/ImageSets/Main/train.txt>
+```
+
+Datumaro supports the following Pascal VOC tasks:
+- Image classification (`voc_classification`)
+- Object detection (`voc_detection`)
+- Action classification (`voc_action`)
+- Class and instance segmentation (`voc_segmentation`)
+- Person layout detection (`voc_layout`)
+
+To make sure that the selected dataset has been added to the project, you can run
+`datum info`, which will display the project and dataset information.
+
+## Export to other formats
+
+Datumaro can convert Pascal VOC dataset into any other format
+[Datumaro supports](../docs/user_manual.md#supported-formats).
+
+Such conversion will only be successful if the output
+format can represent the type of dataset you want to convert,
+e.g. image classification annotations can be
+saved in `ImageNet` format, but no as `COCO keypoints`.
+
+There are few ways to convert Pascal VOC dataset to other dataset format:
+
+``` bash
+datum import -f voc -i <path/to/voc>
+datum export -f coco -o <path/to/output/dir>
+# or
+datum convert -if voc -i <path/to/voc> -f coco -o <path/to/output/dir>
+
+```
+
+Some formats provide extra options for conversion.
+These options are passed after double dash (`--`) in the command line.
+To get information about them, run
+
+`datum export -f <FORMAT> -- -h`
+
+## Export to Pascal VOC
+
+There are few ways to convert an existing dataset to Pascal VOC format:
+
+``` bash
+# export dataset into Pascal VOC format (classification) from existing project
+datum export -p <path/to/project> -f voc -o <path/to/export/dir> -- --tasks classification
+
+# converting to Pascal VOC format from other format
+datum convert -if imagenet -i <path/to/imagenet/dataset> \
+    -f voc -o <path/to/export/dir> \
+    -- --label_map voc --save-images
+```
+
+Extra options for export to Pascal VOC format:
+
+- `--save-images` allow to export dataset with saving images
+(by default `False`);
+
+- `--image-ext IMAGE_EXT` allow to specify image extension
+for exporting dataset (by default use original or `.jpg` if none);
+
+- `--apply-colormap APPLY_COLORMAP` allow to use colormap for class
+and instance masks (by default `True`);
+
+- `--allow-attributes ALLOW_ATTRIBUTES` allow export of attributes
+(by default `True`);
+
+- `--tasks TASKS` allow to specify tasks for export dataset,
+by default Datumaro uses all tasks. Example:
+
+```bash
+datum import -o project -f voc -i ./VOC2012
+datum export -p project -f voc -- --tasks detection,classification
+```
+
+- `--label_map` allow to define a custom colormap. Example
+
+``` bash
+# mycolormap.txt [label : color_rgb : parts : actions]:
+# cat:0,0,255::
+# person:255,0,0:head:
+datum export -f voc_segmentation -- --label-map mycolormap.txt
+
+# or you can use original voc colomap:
+datum export -f voc_segmentation -- --label-map voc
+```
+
+## Particular use cases
+
+Datumaro supports filtering, transformation, merging etc. for all formats
+and for the Pascal VOC format in particular. Follow
+[user manual](../docs/user_manual.md)
+to get more information about these operations.
+
+There are few examples of using Datumaro operations to solve
+particular problems with Pascal VOC dataset:
+
+### Example 1. How to prepare an original dataset for training.
+In this example, preparing the original dataset to train the semantic segmentation model includes:
+loading,
+checking duplicate images,
+setting the number of images,
+splitting into subsets,
+export the result to Pascal VOC format.
+
+```bash
+datum create -o project
+datum add path -p project -f voc_segmentation ./VOC2012/ImageSets/Segmentation/trainval.txt
+datum stats -p project # check statisctics.json -> repeated images
+datum transform -p project -o ndr_project -t ndr -- -w trainval -k 2500
+datum filter -p ndr_project -o trainval2500 -e '/item[subset="trainval"]'
+datum transform -p trainval2500 -o final_project -t random_split -- -s train:.8 -s val:.2
+datum export -p final_project -o dataset -f voc -- --label-map voc --save-images
+```
+
+### Example 2. How to create custom dataset
+
+```python
+from datumaro.components.dataset import Dataset
+from datumaro.util.image import Image
+from datumaro.components.extractor import Bbox, Polygon, Label, DatasetItem
+
+dataset = Dataset.from_iterable([
+    DatasetItem(id='image1', image=Image(path='image1.jpg', size=(10, 20)),
+       annotations=[Label(3),
+           Bbox(1.0, 1.0, 10.0, 8.0, label=0, attributes={'difficult': True, 'running': True}),
+           Polygon([1, 2, 3, 2, 4, 4], label=2, attributes={'occluded': True}),
+           Polygon([6, 7, 8, 8, 9, 7, 9, 6], label=2),
+        ]
+    ),
+], categories=['person', 'sky', 'water', 'lion'])
+
+dataset.transform('polygons_to_masks')
+dataset.export('./mydataset', format='voc', label_map='my_labelmap.txt')
+
+"""
+my_labelmap.txt:
+# label:color_rgb:parts:actions
+person:0,0,255:hand,foot:jumping,running
+sky:128,0,0::
+water:0,128,0::
+lion:255,128,0::
+"""
+```
+
+### Example 3. Load, filter and convert from code
+Load Pascal VOC dataset, and export train subset with items
+which has `jumping` attribute:
+
+```python
+from datumaro.components.dataset import Dataset
+
+dataset = Dataset.import_from('./VOC2012', format='voc')
+
+train_dataset = dataset.get_subset('train').as_dataset()
+
+def only_jumping(item):
+    for ann in item.annotations:
+        if ann.attributes.get('jumping'):
+            return True
+    return False
+
+train_dataset.select(only_jumping)
+
+train_dataset.export('./jumping_label_me', format='label_me', save_images=True)
+```
+
+### Example 4. Get information about items in Pascal VOC 2012 dataset for segmentation task:
+
+```python
+from datumaro.components.dataset import Dataset
+from datumaro.components.extractor import AnnotationType
+
+dataset = Dataset.import_from('./VOC2012', format='voc')
+
+def has_mask(item):
+    for ann in item.annotations:
+        if ann.type == AnnotationType.mask:
+            return True
+    return False
+
+dataset.select(has_mask)
+
+print("Pascal VOC 2012 has %s images for segmentation task:" % len(dataset))
+for subset_name, subset in dataset.subsets().items():
+    for item in subset:
+        print(item.id, subset_name, end=";")
+```
+
+After executing this code, we can see that there are 5826 images
+in Pascal VOC 2012 has for segmentation task and this result is the same as the
+[official documentation](http://host.robots.ox.ac.uk/pascal/VOC/voc2012/dbstats.html)
+
+Some examples of working with Pascal VOC dataset from code you can found in
+[tests](../tests/test_voc_format.py)

docs/user_manual.md

@@ -92,6 +92,7 @@ List of supported formats:
 - 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)
+  - [Format documentation](./pascal_voc_user_manual.md)
 - YOLO (`bboxes`)
   - [Format specification](https://github.com/AlexeyAB/darknet#how-to-train-pascal-voc-data)
   - [Dataset example](../tests/assets/yolo_dataset)

tests/assets/voc_dataset/voc_dataset2/Annotations/a.xml

@@ -0,0 +1,22 @@
+<annotation>
+  <folder></folder>
+  <filename>a.jpg</filename>
+  <source>
+    <database>Unknown</database>
+    <annotation>Unknown</annotation>
+    <image>Unknown</image>
+  </source>
+  <segmented>0</segmented>
+  <object>
+    <name>background</name>
+    <truncated>0</truncated>
+    <occluded>1</occluded>
+    <difficult>0</difficult>
+    <bndbox>
+      <xmin>1.0</xmin>
+      <ymin>2.0</ymin>
+      <xmax>4.0</xmax>
+      <ymax>6.0</ymax>
+    </bndbox>
+  </object>
+</annotation>

tests/assets/voc_dataset/voc_dataset2/Annotations/b.xml

@@ -0,0 +1,22 @@
+<annotation>
+  <folder></folder>
+  <filename>b.jpg</filename>
+  <source>
+    <database>Unknown</database>
+    <annotation>Unknown</annotation>
+    <image>Unknown</image>
+  </source>
+  <segmented>0</segmented>
+  <object>
+    <name>aeroplane</name>
+    <truncated>0</truncated>
+    <occluded>1</occluded>
+    <difficult>0</difficult>
+    <bndbox>
+      <xmin>2.0</xmin>
+      <ymin>2.0</ymin>
+      <xmax>7.0</xmax>
+      <ymax>6.0</ymax>
+    </bndbox>
+  </object>
+</annotation>