Add VGGFace2 format support

CHANGELOG.md

@@ -10,6 +10,7 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 ### Added
 - `WiderFace` dataset format (<https://github.com/openvinotoolkit/datumaro/pull/65>)
 - Function to transform annotations to labels (<https://github.com/openvinotoolkit/datumaro/pull/66>)
+- `VGGFace2` dataset format (<https://github.com/openvinotoolkit/datumaro/pull/69>)
 
 ### Changed
 -

README.md

@@ -112,6 +112,7 @@ CVAT annotations                             ---> Publication, statistics etc.
   - [YOLO](https://github.com/AlexeyAB/darknet#how-to-train-pascal-voc-data) (`bboxes`)
   - [TF Detection API](https://github.com/tensorflow/models/blob/master/research/object_detection/g3doc/using_your_own_dataset.md) (`bboxes`, `masks`)
   - [WIDER Face](http://shuoyang1213.me/WIDERFACE/) (`bboxes`)
+  - [VGGFace2](https://github.com/ox-vgg/vgg_face2) (`landmarks`, `bboxes`)
   - [MOT sequences](https://arxiv.org/pdf/1906.04567.pdf)
   - [MOTS PNG](https://www.vision.rwth-aachen.de/page/mots)
   - [ImageNet](http://image-net.org/)

datumaro/plugins/vgg_face2_format.py

@@ -0,0 +1,139 @@
+# Copyright (C) 2020 Intel Corporation
+#
+# SPDX-License-Identifier: MIT
+
+import csv
+import os
+import os.path as osp
+from glob import glob
+
+from datumaro.components.converter import Converter
+from datumaro.components.extractor import (AnnotationType, Bbox, DatasetItem,
+    Importer, Points, LabelCategories, SourceExtractor)
+
+
+class VggFace2Path:
+    ANNOTATION_DIR = "bb_landmark"
+    IMAGE_EXT = '.jpg'
+    BBOXES_FILE = 'loose_bb_'
+    LANDMARKS_FILE = 'loose_landmark_'
+
+class VggFace2Extractor(SourceExtractor):
+    def __init__(self, path):
+        if not osp.isfile(path):
+            raise Exception("Can't read .csv annotation file '%s'" % path)
+        self._path = path
+        self._dataset_dir = osp.dirname(osp.dirname(path))
+
+        subset = osp.splitext(osp.basename(path))[0]
+        if subset.startswith(VggFace2Path.LANDMARKS_FILE):
+            subset = subset.split('_')[2]
+        super().__init__(subset=subset)
+
+        self._load_categories()
+        self._items = list(self._load_items(path).values())
+
+    def _load_categories(self):
+        self._categories[AnnotationType.label] = LabelCategories()
+
+    def _load_items(self, path):
+        items = {}
+        with open(path) as content:
+            landmarks_table = list(csv.DictReader(content))
+
+        for row in landmarks_table:
+            item_id = row['NAME_ID']
+            image_path = osp.join(self._dataset_dir, self._subset,
+                item_id + VggFace2Path.IMAGE_EXT)
+            annotations = []
+            if len([p for p in row if row[p] == '']) == 0 and len(row) == 11:
+                annotations.append(Points(
+                    [float(row[p]) for p in row if p != 'NAME_ID']))
+            if item_id in items and 0 < len(annotations):
+                annotation = items[item_id].annotations
+                annotation.append(annotations[0])
+            else:
+                items[item_id] = DatasetItem(id=item_id, subset=self._subset,
+                    image=image_path, annotations=annotations)
+
+        bboxes_path = osp.join(self._dataset_dir, VggFace2Path.ANNOTATION_DIR,
+            VggFace2Path.BBOXES_FILE + self._subset + '.csv')
+        if osp.isfile(bboxes_path):
+            with open(bboxes_path) as content:
+                bboxes_table = list(csv.DictReader(content))
+            for row in bboxes_table:
+                if len([p for p in row if row[p] == '']) == 0 and len(row) == 5:
+                    item_id = row['NAME_ID']
+                    annotations = items[item_id].annotations
+                    annotations.append(Bbox(int(row['X']), int(row['Y']),
+                        int(row['W']), int(row['H'])))
+        return items
+
+class VggFace2Importer(Importer):
+    @classmethod
+    def find_sources(cls, path):
+        subset_paths = [p for p in glob(osp.join(path,
+            VggFace2Path.ANNOTATION_DIR, '**.csv'), recursive=True)
+            if not osp.basename(p).startswith(VggFace2Path.BBOXES_FILE)]
+        sources = []
+        for subset_path in subset_paths:
+            sources += cls._find_sources_recursive(
+                subset_path, '.csv', 'vgg_face2')
+        return sources
+
+class VggFace2Converter(Converter):
+    DEFAULT_IMAGE_EXT = '.jpg'
+
+    def apply(self):
+        save_dir = self._save_dir
+
+        os.makedirs(save_dir, exist_ok=True)
+        for subset_name, subset in self._extractor.subsets().items():
+            subset_dir = osp.join(save_dir, subset_name)
+            bboxes_table = []
+            landmarks_table = []
+            for item in subset:
+                if item.has_image and self._save_images:
+                    self._save_image(item, osp.join(save_dir, subset_dir,
+                        item.id + VggFace2Path.IMAGE_EXT))
+
+                landmarks = [a for a in item.annotations
+                    if a.type == AnnotationType.points]
+                if landmarks:
+                    for landmark in landmarks:
+                        points = landmark.points
+                        landmarks_table.append({'NAME_ID': item.id,
+                            'P1X': points[0], 'P1Y': points[1],
+                            'P2X': points[2], 'P2Y': points[3],
+                            'P3X': points[4], 'P3Y': points[5],
+                            'P4X': points[6], 'P4Y': points[7],
+                            'P5X': points[8], 'P5Y': points[9]})
+                else:
+                    landmarks_table.append({'NAME_ID': item.id})
+
+                bboxes = [a for a in item.annotations
+                    if a.type == AnnotationType.bbox]
+                if bboxes:
+                    for bbox in bboxes:
+                        bboxes_table.append({'NAME_ID': item.id, 'X': int(bbox.x),
+                            'Y': int(bbox.y), 'W': int(bbox.w), 'H': int(bbox.h)})
+
+            landmarks_path = osp.join(save_dir, VggFace2Path.ANNOTATION_DIR,
+                VggFace2Path.LANDMARKS_FILE + subset_name + '.csv')
+            os.makedirs(osp.dirname(landmarks_path), exist_ok=True)
+            with open(landmarks_path, 'w', newline='') as file:
+                columns = ['NAME_ID', 'P1X', 'P1Y', 'P2X', 'P2Y',
+                    'P3X', 'P3Y', 'P4X', 'P4Y', 'P5X', 'P5Y']
+                writer = csv.DictWriter(file, fieldnames=columns)
+                writer.writeheader()
+                writer.writerows(landmarks_table)
+
+            if bboxes_table:
+                bboxes_path = osp.join(save_dir, VggFace2Path.ANNOTATION_DIR,
+                    VggFace2Path.BBOXES_FILE + subset_name + '.csv')
+                os.makedirs(osp.dirname(bboxes_path), exist_ok=True)
+                with open(bboxes_path, 'w', newline='') as file:
+                    columns = ['NAME_ID', 'X', 'Y', 'W', 'H']
+                    writer = csv.DictWriter(file, fieldnames=columns)
+                    writer.writeheader()
+                    writer.writerows(bboxes_table)

docs/user_manual.md

@@ -94,6 +94,9 @@ List of supported formats:
 - WIDER Face (`bboxes`)
   - [Format specification](http://shuoyang1213.me/WIDERFACE/)
   - [Dataset example](../tests/assets/wider_dataset)
+- VGGFace2 (`landmarks`, `bboxes`)
+  - [Format specification](https://github.com/ox-vgg/vgg_face2)
+  - [Dataset example](../tests/assets/vgg_face2_dataset)
 - MOT sequences
   - [Format specification](https://arxiv.org/pdf/1906.04567.pdf)
   - [Dataset example](../tests/assets/mot_dataset)

tests/assets/vgg_face2_dataset/bb_landmark/loose_bb_train.csv

@@ -0,0 +1,3 @@
+NAME_ID,X,Y,W,H
+n000001/0001_01,2,2,1,2
+n000002/0002_01,1,3,1,1

tests/assets/vgg_face2_dataset/bb_landmark/loose_landmark_train.csv

@@ -0,0 +1,3 @@
+NAME_ID,P1X,P1Y,P2X,P2Y,P3X,P3Y,P4X,P4Y,P5X,P5Y
+n000001/0001_01,2.787,2.898,2.965,2.79,2.8,2.456,2.81,2.32,2.89,2.3
+n000002/0002_01,1.2,3.8,1.8,3.82,1.51,3.634,1.43,3.34,1.65,3.32

tests/test_vgg_face2_format.py

@@ -0,0 +1,99 @@
+import os.path as osp
+from unittest import TestCase
+
+import numpy as np
+from datumaro.components.extractor import Bbox, DatasetItem, Points
+from datumaro.components.project import Dataset, Project
+from datumaro.plugins.vgg_face2_format import (VggFace2Converter,
+    VggFace2Importer)
+from datumaro.util.test_utils import TestDir, compare_datasets
+
+
+class VggFace2FormatTest(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),
+                    Points([3.2, 3.12, 4.11, 3.2, 2.11,
+                        2.5, 3.5, 2.11, 3.8, 2.13]),
+                ]
+            ),
+            DatasetItem(id='2', subset='train', image=np.ones((10, 10, 3)),
+                annotations=[
+                    Points([4.23, 4.32, 5.34, 4.45, 3.54,
+                        3.56, 4.52, 3.51, 4.78, 3.34]),
+                ]
+            ),
+            DatasetItem(id='3', subset='val', image=np.ones((8, 8, 3))),
+            DatasetItem(id='4', subset='val', image=np.ones((10, 10, 3)),
+                annotations=[
+                    Bbox(0, 2, 4, 2),
+                    Points([3.2, 3.12, 4.11, 3.2, 2.11,
+                        2.5, 3.5, 2.11, 3.8, 2.13]),
+                    Bbox(2, 2, 1, 2),
+                    Points([2.787, 2.898, 2.965, 2.79, 2.8,
+                        2.456, 2.81, 2.32, 2.89, 2.3]),
+                ]
+            ),
+            DatasetItem(id='5', subset='val', image=np.ones((8, 8, 3)),
+                annotations=[
+                    Bbox(2, 2, 2, 2),
+                ]
+            ),
+        ], categories=[])
+
+        with TestDir() as test_dir:
+            VggFace2Converter.convert(source_dataset, test_dir, save_images=True)
+            parsed_dataset = VggFace2Importer()(test_dir).make_dataset()
+
+            compare_datasets(self, source_dataset, parsed_dataset)
+
+    def test_can_save_dataset_with_no_subsets(self):
+        source_dataset = Dataset.from_iterable([
+            DatasetItem(id='a/b/1', image=np.ones((8, 8, 3)),
+                annotations=[
+                    Bbox(0, 2, 4, 2),
+                    Points([4.23, 4.32, 5.34, 4.45, 3.54,
+                        3.56, 4.52, 3.51, 4.78, 3.34]),
+                ]
+            ),
+        ], categories=[])
+
+        with TestDir() as test_dir:
+            VggFace2Converter.convert(source_dataset, test_dir, save_images=True)
+            parsed_dataset = VggFace2Importer()(test_dir).make_dataset()
+
+            compare_datasets(self, source_dataset, parsed_dataset)
+
+
+DUMMY_DATASET_DIR = osp.join(osp.dirname(__file__), 'assets', 'vgg_face2_dataset')
+
+class VggFace2ImporterTest(TestCase):
+    def test_can_detect(self):
+        self.assertTrue(VggFace2Importer.detect(DUMMY_DATASET_DIR))
+
+    def test_can_import(self):
+        expected_dataset = Dataset.from_iterable([
+            DatasetItem(id='n000001/0001_01', subset='train',
+                image=np.ones((10, 15, 3)),
+                annotations=[
+                    Bbox(2, 2, 1, 2),
+                    Points([2.787, 2.898, 2.965, 2.79, 2.8,
+                        2.456, 2.81, 2.32, 2.89, 2.3]),
+                ]
+            ),
+            DatasetItem(id='n000002/0002_01', subset='train',
+                image=np.ones((10, 15, 3)),
+                annotations=[
+                    Bbox(1, 3, 1, 1),
+                    Points([1.2, 3.8, 1.8, 3.82, 1.51,
+                        3.634, 1.43, 3.34, 1.65, 3.32])
+                ]
+            ),
+        ], categories=[])
+
+        dataset = Project.import_from(DUMMY_DATASET_DIR, 'vgg_face2') \
+            .make_dataset()
+
+        compare_datasets(self, expected_dataset, dataset)