open-edge-platform · zhiltsov-max · Dec 28, 2021 · Dec 16, 2021 · Dec 17, 2021 · Dec 17, 2021
diff --git a/CHANGELOG.md b/CHANGELOG.md
@@ -26,6 +26,8 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
   (<https://github.com/openvinotoolkit/datumaro/pull/573>)
 - Image resizing transform
   (<https://github.com/openvinotoolkit/datumaro/pull/581>)
+- Import for MPII Human Pose Dataset
+  (<https://github.com/openvinotoolkit/datumaro/pull/584>)
 
 ### Changed
 - The following formats can now be detected unambiguously:

diff --git a/datumaro/plugins/mpii_format.py b/datumaro/plugins/mpii_format.py
@@ -0,0 +1,113 @@
+# Copyright (C) 2021 Intel Corporation
+#
+# SPDX-License-Identifier: MIT
+
+import json
+import os.path as osp
+
+import numpy as np
+
+from datumaro.components.annotation import Bbox, Points, PointsCategories
+from datumaro.components.extractor import (
+    AnnotationType, DatasetItem, Importer, SourceExtractor,
+)
+from datumaro.components.format_detection import FormatDetectionContext
+from datumaro.components.media import Image
+
+
+class MpiiPath:
+    ANNOTATION_FILE = 'mpii_annotations.json'
+    HEADBOXES_FILE = 'mpii_headboxes.npy'
+    VISIBILITY_FILE = 'jnt_visible.npy'
+    POS_GT_FILE = 'mpii_pos_gt.npy'
+
+MpiiPointsCategories = [(0, ['r_ankle']), (1, ['r_knee']), (2, ['r_hip']),
+    (3, ['l_hip']), (4, ['l_knee']), (5, ['l_ankle']), (6, ['pelvis']),
+    (7, ['thorax']), (8, ['upper_neck']), (9, ['head top']), (10, ['r_wrist']),
+    (11, ['r_elbow']), (12, ['r_shoulder']), (13, ['l_shoulder']),
+    (14, ['l_elbow']), (15, ['l_wrist'])]
+
+class MpiiExtractor(SourceExtractor):
+    def __init__(self, path):
+        if not osp.isfile(path):
+            raise FileNotFoundError("Can't read annotation file '%s'" % path)
+
+        super().__init__()
+
+        self._categories = { AnnotationType.points:
+            PointsCategories.from_iterable(MpiiPointsCategories) }
+
+        self._items = list(self._load_items(path).values())
+
+    def _load_items(self, path):
+        items = {}
+
+        root_dir = osp.dirname(path)
+
+        hb_path = osp.join(root_dir, MpiiPath.HEADBOXES_FILE)
+        if osp.isfile(hb_path):
+            headboxes = np.load(hb_path)
+        else:
+            headboxes = []
+
+        vis_path = osp.join(root_dir, MpiiPath.VISIBILITY_FILE)
+        if osp.isfile(vis_path):
+            visibility = np.load(vis_path).T
+        else:
+            visibility = []
+
+        pos_gt_path = osp.join(root_dir, MpiiPath.POS_GT_FILE)
+        if osp.isfile(pos_gt_path):
+            gt_pose = np.transpose(np.load(pos_gt_path), (2, 0, 1))
+        else:
+            gt_pose = []
+
+        with open(path) as f:
+            for i, ann in enumerate(json.load(f)):
+                item_id = osp.splitext(ann.get('img_paths'))[0]
+
+                center = ann.get('objpos')
+                scale = float(ann.get('scale_provided'))
+
+                if np.size(gt_pose):
+                    points = gt_pose[i]
+                    points = points.reshape(points.shape[0] * points.shape[1])
+
+                    if np.size(visibility):
+                        vis = visibility[i]
+                    else:
+                        vis = np.ones(len(points) // 2, dtype=np.int8)
+                else:
+                    keypoints = np.array(ann.get('joint_self'))
+                    keypoints = keypoints.reshape(keypoints.shape[0] * keypoints.shape[1])
+                    points = [p for i, p in enumerate(keypoints) if i % 3 != 2]
+
+                    vis = keypoints[2::3]
+                    if np.size(visibility):
+                        vis = visibility[i]
+
+                vis = [int(val) for val in vis]
+
+                annotations = [Points(points, vis, attributes={'center': center,
+                    'scale': scale})]
+
+                if np.size(headboxes):
+                    bbox = headboxes[:, :, i]
+                    annotations.append(Bbox(bbox[0][0], bbox[0][1],
+                        bbox[1][0] - bbox[0][0], bbox[1][1] - bbox[0][1]))
+
+
+                items[item_id] = DatasetItem(id=item_id, subset=self._subset,
+                    image=Image(path=osp.join(root_dir, ann.get('img_paths'))),
+                    annotations=annotations)
+
+        return items
+
+class MpiiImporter(Importer):
+    @classmethod
+    def find_sources(cls, path):
+        return cls._find_sources_recursive(path, '.json', 'mpii')
+
+    @classmethod
+    def detect(cls, context: FormatDetectionContext) -> None:
+        context.require_file(MpiiPath.ANNOTATION_FILE)
diff --git a/site/content/en/docs/formats/mpii.md b/site/content/en/docs/formats/mpii.md
@@ -0,0 +1,88 @@
+---
+title: 'MPII Human Pose Dataset'
+linkTitle: 'MPII Human Pose Dataset'
+description: ''
+weight: 1
+---
+
+## Format specification
+
+The original MPII Human Pose Dataset is available
+[here](http://human-pose.mpi-inf.mpg.de/#overview).
+
+Supported annotation types:
+- `Bbox`
+- `Points`
+
+Supported attributes:
+- `center` (list)
+- `scale` (float)
+
+## Import MPII Human Pose Dataset
+
+Datumaro does not support `MATLAB` annotation files. Instead,
+`JSON` and `NUMPY` files are supported.
+
+A Datumaro project with a MPII Human Pose Dataset source can be
+created in the following way:
+
+```bash
+datum create
+datum import --format mpii <path/to/dataset>
+```
+
+It is also possible to import the dataset using Python API:
+
+```python
+from datumaro.components.dataset import Dataset
+
+mpii_dataset = Dataset.import_from('<path/to/dataset>', 'mpii')
+```
+
+MPII Human Pose Dataset directory should have the following structure:
+
+<!--lint disable fenced-code-flag-->
+```
+dataset/
+├── jnt_visible.npy # optional
+├── mpii_annotations.json
+├── mpii_headboxes.npy # optional
+├── mpii_pos_gt.npy # optional
+├── 000000001.jpg
+├── 000000002.jpg
+├── 000000003.jpg
+└── ...
+```
+
+## Export to other formats
+
+Datumaro can convert a MPII Human Pose Dataset into any other format
+[Datumaro supports](/docs/user-manual/supported_formats/).
+To get the expected result, convert the dataset to a format
+that supports bounding boxes or points.
+
+There are several ways to convert a MPII Human Pose Dataset
+to other dataset formats using CLI:
+
+```bash
+datum create
+datum import -f mpii <path/to/dataset>
+datum export -f voc -o ./save_dir -- --save-images
+# or
+datum convert -if mpii -i <path/to/dataset> \
+    -f voc -o <output/dir> -- --save-images
+```
+
+Or, using Python API:
+
+```python
+from datumaro.components.dataset import Dataset
+
+dataset = Dataset.import_from('<path/to/dataset>', 'mpii')
+dataset.export('save_dir', 'voc')
+```
+
+## Examples
+
+Examples of using this format from the code can be found in
+[the format tests](https://github.com/openvinotoolkit/datumaro/blob/develop/tests/test_mpii_format.py)
diff --git a/site/content/en/docs/user-manual/supported_formats.md b/site/content/en/docs/user-manual/supported_formats.md
@@ -108,6 +108,10 @@ List of supported formats:
   - [Format specification](https://github.com/microsoft/VoTT)
   - [Dataset example](https://github.com/openvinotoolkit/datumaro/tree/develop/tests/assets/vott_json_dataset)
   - [Format documentation](/docs/formats/vott_json)
+- MPII Human Pose Dataset (`detection`, `pose estimation`)
+  - [Format specification](http://human-pose.mpi-inf.mpg.de/#overview)
+  - [Dataset example](https://github.com/openvinotoolkit/datumaro/tree/develop/tests/assets/mpii_dataset)
+  - [Format documentation](/docs/formats/mpii)
 
 ### Supported annotation types <a id="annotation-types"></a>
 

diff --git a/tests/assets/mpii_dataset/dataset_with_numpy_files/000000001.jpg b/tests/assets/mpii_dataset/dataset_with_numpy_files/000000001.jpg
diff --git a/tests/assets/mpii_dataset/dataset_with_numpy_files/000000002.jpg b/tests/assets/mpii_dataset/dataset_with_numpy_files/000000002.jpg
diff --git a/tests/assets/mpii_dataset/dataset_with_numpy_files/000000003.jpg b/tests/assets/mpii_dataset/dataset_with_numpy_files/000000003.jpg
diff --git a/tests/assets/mpii_dataset/dataset_with_numpy_files/jnt_visible.npy b/tests/assets/mpii_dataset/dataset_with_numpy_files/jnt_visible.npy
diff --git a/tests/assets/mpii_dataset/dataset_with_numpy_files/mpii_annotations.json b/tests/assets/mpii_dataset/dataset_with_numpy_files/mpii_annotations.json
@@ -0,0 +1 @@
+[{"dataset": "MPI","isValidation": 0.000,"img_paths": "000000001.jpg","img_width": 1280.000,"img_height": 720.000,"objpos": [594.000,257.000],"joint_self": [[620.000,394.000,1.000],[616.000,269.000,1.000],[573.000,185.000,1.000],[647.000,188.000,0.000],[661.000,221.000,1.000],[656.000,231.000,1.000],[610.000,187.000,0.000],[647.000,176.000,1.000],[637.020,189.818,1.000],[695.980,108.182,1.000],[606.000,217.000,1.000],[553.000,161.000,1.000],[601.000,167.000,1.000],[692.000,185.000,1.000],[693.000,240.000,1.000],[688.000,313.000,1.000]],"scale_provided": 3.021}, {"dataset": "MPI","isValidation": 0.000,"img_paths": "000000002.jpg","img_width": 1280.000,"img_height": 720.000,"objpos": [624.000,287.000],"joint_self": [[650.000,424.000,1.000],[646.000,309.000,1.000],[603.000,215.000,1.000],[677.000,218.000,1.000],[691.000,251.000,0.000],[686.000,261.000,1.000],[640.000,217.000,0.000],[677.000,216.000,1.000],[667.020,219.818,1.000],[725.980,138.182,1.000],[636.000,247.000,1.000],[583.000,191.000,1.000],[631.000,197.000,1.000],[722.000,215.000,1.000],[723.000,270.000,1.000],[718.000,343.000,1.000]],"scale_provided": 3.7}, {"dataset": "MPI","isValidation": 0.000,"img_paths": "000000003.jpg","img_width": 1280.000,"img_height": 720.000,"objpos": [564.000,227.000],"joint_self": [[590.000,364.000,1.000],[586.000,239.000,1.000],[533.000,155.000,1.000],[617.000,158.000,1.000],[631.000,191.000,1.000],[626.000,201.000,1.000],[580.000,157.000,1.000],[617.000,146.000,1.000],[607.020,159.818,1.000],[645.980,68.182,1.000],[576.000,187.000,1.000],[532.000,131.000,1.000],[571.000,137.000,1.000],[662.000,155.000,0.000],[663.000,210.000,1.000],[658.000,283.000,1.000]],"scale_provided": 3.2}]
diff --git a/tests/assets/mpii_dataset/dataset_with_numpy_files/mpii_headboxes.npy b/tests/assets/mpii_dataset/dataset_with_numpy_files/mpii_headboxes.npy
diff --git a/tests/assets/mpii_dataset/dataset_with_numpy_files/mpii_pos_gt.npy b/tests/assets/mpii_dataset/dataset_with_numpy_files/mpii_pos_gt.npy
diff --git a/tests/assets/mpii_dataset/dataset_wo_numpy_files/000000001.jpg b/tests/assets/mpii_dataset/dataset_wo_numpy_files/000000001.jpg
diff --git a/tests/assets/mpii_dataset/dataset_wo_numpy_files/000000002.jpg b/tests/assets/mpii_dataset/dataset_wo_numpy_files/000000002.jpg
diff --git a/tests/assets/mpii_dataset/dataset_wo_numpy_files/000000003.jpg b/tests/assets/mpii_dataset/dataset_wo_numpy_files/000000003.jpg
diff --git a/tests/assets/mpii_dataset/dataset_wo_numpy_files/mpii_annotations.json b/tests/assets/mpii_dataset/dataset_wo_numpy_files/mpii_annotations.json
@@ -0,0 +1 @@
+[{"dataset": "MPI","isValidation": 0.000,"img_paths": "000000001.jpg","img_width": 1280.000,"img_height": 720.000,"objpos": [594.000,257.000],"joint_self": [[620.000,394.000,1.000],[616.000,269.000,1.000],[573.000,185.000,1.000],[647.000,188.000,0.000],[661.000,221.000,1.000],[656.000,231.000,1.000],[610.000,187.000,0.000],[647.000,176.000,1.000],[637.020,189.818,1.000],[695.980,108.182,1.000],[606.000,217.000,1.000],[553.000,161.000,1.000],[601.000,167.000,1.000],[692.000,185.000,1.000],[693.000,240.000,1.000],[688.000,313.000,1.000]],"scale_provided": 3.021}, {"dataset": "MPI","isValidation": 0.000,"img_paths": "000000002.jpg","img_width": 1280.000,"img_height": 720.000,"objpos": [624.000,287.000],"joint_self": [[650.000,424.000,1.000],[646.000,309.000,1.000],[603.000,215.000,1.000],[677.000,218.000,1.000],[691.000,251.000,0.000],[686.000,261.000,1.000],[640.000,217.000,0.000],[677.000,216.000,1.000],[667.020,219.818,1.000],[725.980,138.182,1.000],[636.000,247.000,1.000],[583.000,191.000,1.000],[631.000,197.000,1.000],[722.000,215.000,1.000],[723.000,270.000,1.000],[718.000,343.000,1.000]],"scale_provided": 3.7}, {"dataset": "MPI","isValidation": 0.000,"img_paths": "000000003.jpg","img_width": 1280.000,"img_height": 720.000,"objpos": [564.000,227.000],"joint_self": [[590.000,364.000,1.000],[586.000,239.000,1.000],[533.000,155.000,1.000],[617.000,158.000,1.000],[631.000,191.000,1.000],[626.000,201.000,1.000],[580.000,157.000,1.000],[617.000,146.000,1.000],[607.020,159.818,1.000],[645.980,68.182,1.000],[576.000,187.000,1.000],[532.000,131.000,1.000],[571.000,137.000,1.000],[662.000,155.000,0.000],[663.000,210.000,1.000],[658.000,283.000,1.000]],"scale_provided": 3.2}]
diff --git a/tests/requirements.py b/tests/requirements.py
@@ -29,6 +29,7 @@ class Requirements:
     DATUM_475 = "Support import for CelebA dataset"
     DATUM_497 = "Support import for SYNTHIA dataset"
     DATUM_542 = "Images missing after merging two datasets"
+    DATUM_580 = "Import for MPII Human Pose Dataset"
 
     # GitHub issues (bugs)
     # https://github.com/openvinotoolkit/datumaro/issues

diff --git a/tests/test_mpii_format.py b/tests/test_mpii_format.py
@@ -0,0 +1,122 @@
+from unittest import TestCase
+import os.path as osp
+
+import numpy as np
+
+from datumaro.components.annotation import Bbox, Points, PointsCategories
+from datumaro.components.dataset import Dataset
+from datumaro.components.environment import Environment
+from datumaro.components.extractor import AnnotationType, DatasetItem
+from datumaro.plugins.mpii_format import MpiiImporter, MpiiPointsCategories
+from datumaro.util.test_utils import compare_datasets
+
+from .requirements import Requirements, mark_requirement
+
+DUMMY_DATASET_DIR_WITH_NUMPY_FILES = osp.join(osp.dirname(__file__), 'assets',
+    'mpii_dataset', 'dataset_with_numpy_files')
+DUMMY_DATASET_DIR_WO_NUMPY_FILES = osp.join(osp.dirname(__file__), 'assets',
+    'mpii_dataset', 'dataset_wo_numpy_files')
+
+class MpiiImporterTest(TestCase):
+    @mark_requirement(Requirements.DATUM_580)
+    def test_can_import_dataset_witn_numpy_files(self):
+        expected_dataset = Dataset.from_iterable([
+            DatasetItem(id='000000001', image=np.ones((5, 5, 3)),
+                annotations=[
+                    Points([620.0, 394.0, 616.0, 269.0, 573.0, 185.0, 647.0,
+                        188.0, 661.0, 221.0, 656.0, 231.0, 610.0, 187.0,
+                        647.0, 176.0, 637.02, 189.818, 695.98, 108.182,
+                        606.0, 217.0, 553.0, 161.0, 601.0, 167.0, 692.0,
+                        185.0, 693.0, 240.0, 688.0, 313.0],
+                        [1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1],
+                        attributes={'center': [594.000,257.000], 'scale': 3.021}),
+                    Bbox(615, 218.65, 288.4, 286.95)
+                ]
+            ),
+            DatasetItem(id='000000002', image=np.ones((5, 5, 3)),
+                annotations=[
+                    Points([650.0, 424.0, 646.0, 309.0, 603.0, 215.0, 677.0,
+                        218.0, 691.0, 251.0, 686.0, 261.0, 640.0, 217.0,
+                        677.0, 216.0, 667.02, 219.818, 725.98, 138.182,
+                        636.0, 247.0, 583.0, 191.0, 631.0, 197.0, 722.0,
+                        215.0, 723.0, 270.0, 718.0, 343.0],
+                        [1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1],
+                        attributes={'center': [624.000,287.000], 'scale': 3.7}),
+                    Bbox(101.1, 33.3, 113.9, 81.4)
+                ]
+            ),
+            DatasetItem(id='000000003', image=np.ones((5, 5, 3)),
+                annotations=[
+                    Points([590.0, 364.0, 586.0, 239.0, 533.0, 155.0, 617.0,
+                        158.0, 631.0, 191.0, 626.0, 201.0, 580.0, 157.0,
+                        617.0, 146.0, 607.02, 159.818, 645.98, 68.182,
+                        576.0, 187.0, 532.0, 131.0, 571.0, 137.0, 662.0,
+                        155.0, 663.0, 210.0, 658.0, 283.0],
+                        [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1],
+                        attributes={'center': [564.000,227.000], 'scale': 3.2}),
+                    Bbox(313.3, 512.43, 220.7, 121.57)
+                ]
+            )
+        ], categories={
+            AnnotationType.points: PointsCategories.from_iterable(MpiiPointsCategories)
+        })
+
+        dataset = Dataset.import_from(DUMMY_DATASET_DIR_WITH_NUMPY_FILES, 'mpii')
+
+        compare_datasets(self, expected_dataset, dataset, require_images=True)
+
+    @mark_requirement(Requirements.DATUM_580)
+    def test_can_import_dataset_wo_numpy_files(self):
+        expected_dataset = Dataset.from_iterable([
+            DatasetItem(id='000000001', image=np.ones((5, 5, 3)),
+                annotations=[
+                    Points([620.0, 394.0, 616.0, 269.0, 573.0, 185.0, 647.0,
+                        188.0, 661.0, 221.0, 656.0, 231.0, 610.0, 187.0,
+                        647.0, 176.0, 637.02, 189.818, 695.98, 108.182,
+                        606.0, 217.0, 553.0, 161.0, 601.0, 167.0, 692.0,
+                        185.0, 693.0, 240.0, 688.0, 313.0],
+                        [1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1],
+                        attributes={'center': [594.000,257.000], 'scale': 3.021})
+                ]
+            ),
+            DatasetItem(id='000000002', image=np.ones((5, 5, 3)),
+                annotations=[
+                    Points([650.0, 424.0, 646.0, 309.0, 603.0, 215.0, 677.0,
+                        218.0, 691.0, 251.0, 686.0, 261.0, 640.0, 217.0,
+                        677.0, 216.0, 667.02, 219.818, 725.98, 138.182,
+                        636.0, 247.0, 583.0, 191.0, 631.0, 197.0, 722.0,
+                        215.0, 723.0, 270.0, 718.0, 343.0],
+                        [1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1],
+                        attributes={'center': [624.000,287.000], 'scale': 3.7})
+                ]
+            ),
+            DatasetItem(id='000000003', image=np.ones((5, 5, 3)),
+                annotations=[
+                    Points([590.0, 364.0, 586.0, 239.0, 533.0, 155.0, 617.0,
+                        158.0, 631.0, 191.0, 626.0, 201.0, 580.0, 157.0,
+                        617.0, 146.0, 607.02, 159.818, 645.98, 68.182,
+                        576.0, 187.0, 532.0, 131.0, 571.0, 137.0, 662.0,
+                        155.0, 663.0, 210.0, 658.0, 283.0],
+                        [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1],
+                        attributes={'center': [564.000,227.000], 'scale': 3.2})
+                ]
+            )
+        ], categories={
+            AnnotationType.points: PointsCategories.from_iterable(MpiiPointsCategories)
+        })
+
+        dataset = Dataset.import_from(DUMMY_DATASET_DIR_WO_NUMPY_FILES, 'mpii')
+
+        compare_datasets(self, expected_dataset, dataset, require_images=True)
+
+    @mark_requirement(Requirements.DATUM_580)
+    def test_can_detect_dataset_with_numpy_files(self):
+        detected_formats = Environment().detect_dataset(
+            DUMMY_DATASET_DIR_WITH_NUMPY_FILES)
+        self.assertEqual([MpiiImporter.NAME], detected_formats)
+
+    @mark_requirement(Requirements.DATUM_580)
+    def test_can_detect_dataset_wo_numpy_files(self):
+        detected_formats = Environment().detect_dataset(
+            DUMMY_DATASET_DIR_WO_NUMPY_FILES)
+        self.assertEqual([MpiiImporter.NAME], detected_formats)
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