Release 0.1.4 (#64)

* Relax importer for Pascal VOC dataset (search in subdirectories) (#50)

In some cases developers don't want to specify the exact path to Pascal VOC.
Now you have to specify VOCtrainval_11-May-2012/VOCdevkit/VOC2012/. After the
patch it will be possible to specify VOCtrainval_11-May-2012/.

* Allow missing supercategory in COCO annotations (#54)

Now it is possible to load coco_instances dataset even if the annotation file doesn't have supercategory

* Add CamVid format support (#55)

Co-authored-by: Maxim Zhiltsov <[email protected]>

* Fix CamVid format (#57)

* Fix ImageNet format

* Fix CamVid format

* ability to install opencv-python-headless instead opencv-python (#62)

Allow to choose `opencv=python-headless` as dependency with `DATUMARO_HEADLESS=1` env. variable when installing

* Release 0.1.4 (#63)

* update version

* update changelog

Co-authored-by: Nikita Manovich <[email protected]>
Co-authored-by: Anastasia Yasakova <[email protected]>
Co-authored-by: Andrey Zhavoronkov <[email protected]>

Author: 4 people

CHANGELOG.md

@@ -25,6 +25,28 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 ### Security
 -
 
+## 12/10/2020 - Release v0.1.4
+### Added
+- `CamVid` dataset format (<https://github.com/openvinotoolkit/datumaro/pull/57>)
+- Ability to install `opencv-python-headless` dependency with `DATUMARO_HEADLESS=1`
+  enviroment variable instead of `opencv-python` (<https://github.com/openvinotoolkit/datumaro/pull/62>)
+
+### Changed
+- Allow empty supercategory in COCO (<https://github.com/openvinotoolkit/datumaro/pull/54>)
+- Allow Pascal VOC to search in subdirectories (<https://github.com/openvinotoolkit/datumaro/pull/50>)
+
+### Deprecated
+-
+
+### Removed
+-
+
+### Fixed
+-
+
+### Security
+-
+
 ## 10/28/2020 - Release v0.1.3
 ### Added
 - `ImageNet` and `ImageNetTxt` dataset formats (<https://github.com/openvinotoolkit/datumaro/pull/41>)

README.md

@@ -114,6 +114,7 @@ CVAT annotations                             ---> Publication, statistics etc.
   - [MOT sequences](https://arxiv.org/pdf/1906.04567.pdf)
   - [MOTS PNG](https://www.vision.rwth-aachen.de/page/mots)
   - [ImageNet](http://image-net.org/)
+  - [CamVid](http://mi.eng.cam.ac.uk/research/projects/VideoRec/CamVid/)
   - [CVAT](https://github.com/opencv/cvat/blob/develop/cvat/apps/documentation/xml_format.md)
   - [LabelMe](http://labelme.csail.mit.edu/Release3.0)
 - Dataset building

datumaro/plugins/camvid_format.py

@@ -0,0 +1,344 @@
+
+# Copyright (C) 2020 Intel Corporation
+#
+# SPDX-License-Identifier: MIT
+
+import os
+import os.path as osp
+from collections import OrderedDict
+from enum import Enum
+from glob import glob
+
+import numpy as np
+from datumaro.components.converter import Converter
+from datumaro.components.extractor import (AnnotationType, CompiledMask,
+    DatasetItem, Importer, LabelCategories, Mask,
+    MaskCategories, SourceExtractor)
+from datumaro.util import find, str_to_bool
+from datumaro.util.image import save_image
+from datumaro.util.mask_tools import lazy_mask, paint_mask, generate_colormap
+
+
+CamvidLabelMap = OrderedDict([
+    ('Void', (0, 0, 0)),
+    ('Animal', (64, 128, 64)),
+    ('Archway', (192, 0, 128)),
+    ('Bicyclist', (0, 128, 192)),
+    ('Bridge', (0, 128, 64)),
+    ('Building', (128, 0, 0)),
+    ('Car', (64, 0, 128)),
+    ('CartLuggagePram', (64, 0, 192)),
+    ('Child', (192, 128, 64)),
+    ('Column_Pole', (192, 192, 128)),
+    ('Fence', (64, 64, 128)),
+    ('LaneMkgsDriv', (128, 0, 192)),
+    ('LaneMkgsNonDriv', (192, 0, 64)),
+    ('Misc_Text', (128, 128, 64)),
+    ('MotorcycycleScooter', (192, 0, 192)),
+    ('OtherMoving', (128, 64, 64)),
+    ('ParkingBlock', (64, 192, 128)),
+    ('Pedestrian', (64, 64, 0)),
+    ('Road', (128, 64, 128)),
+    ('RoadShoulder', (128, 128, 192)),
+    ('Sidewalk', (0, 0, 192)),
+    ('SignSymbol', (192, 128, 128)),
+    ('Sky', (128, 128, 128)),
+    ('SUVPickupTruck', (64, 128, 192)),
+    ('TrafficCone', (0, 0, 64)),
+    ('TrafficLight', (0, 64, 64)),
+    ('Train', (192, 64, 128)),
+    ('Tree', (128, 128, 0)),
+    ('Truck_Bus', (192, 128, 192)),
+    ('Tunnel', (64, 0, 64)),
+    ('VegetationMisc', (192, 192, 0)),
+    ('Wall', (64, 192, 0))
+])
+
+class CamvidPath:
+    LABELMAP_FILE = 'label_colors.txt'
+    SEGM_DIR = "annot"
+    IMAGE_EXT = '.png'
+
+
+def parse_label_map(path):
+    if not path:
+        return None
+
+    label_map = OrderedDict()
+    with open(path, 'r') as f:
+        for line in f:
+            # skip empty and commented lines
+            line = line.strip()
+            if not line or line and line[0] == '#':
+                continue
+
+            # color, name
+            label_desc = line.strip().split()
+
+            if 2 < len(label_desc):
+                name = label_desc[3]
+                color = tuple([int(c) for c in label_desc[:-1]])
+            else:
+                name = label_desc[0]
+                color = None
+
+            if name in label_map:
+                raise ValueError("Label '%s' is already defined" % name)
+
+            label_map[name] = color
+    return label_map
+
+def write_label_map(path, label_map):
+    with open(path, 'w') as f:
+        for label_name, label_desc in label_map.items():
+            if label_desc:
+                color_rgb = ' '.join(str(c) for c in label_desc)
+            else:
+                color_rgb = ''
+            f.write('%s %s\n' % (color_rgb, label_name))
+
+def make_camvid_categories(label_map=None):
+    if label_map is None:
+        label_map = CamvidLabelMap
+
+    # There must always be a label with color (0, 0, 0) at index 0
+    bg_label = find(label_map.items(), lambda x: x[1] == (0, 0, 0))
+    if bg_label is not None:
+        bg_label = bg_label[0]
+    else:
+        bg_label = 'background'
+        if bg_label not in label_map:
+            has_colors = any(v is not None for v in label_map.values())
+            color = (0, 0, 0) if has_colors else None
+            label_map[bg_label] = color
+    label_map.move_to_end(bg_label, last=False)
+
+    categories = {}
+    label_categories = LabelCategories()
+    for label, desc in label_map.items():
+        label_categories.add(label)
+    categories[AnnotationType.label] = label_categories
+
+    has_colors = any(v is not None for v in label_map.values())
+    if not has_colors: # generate new colors
+        colormap = generate_colormap(len(label_map))
+    else: # only copy defined colors
+        label_id = lambda label: label_categories.find(label)[0]
+        colormap = { label_id(name): (desc[0], desc[1], desc[2])
+            for name, desc in label_map.items() }
+    mask_categories = MaskCategories(colormap)
+    mask_categories.inverse_colormap # pylint: disable=pointless-statement
+    categories[AnnotationType.mask] = mask_categories
+    return categories
+
+
+class CamvidExtractor(SourceExtractor):
+    def __init__(self, path):
+        assert osp.isfile(path), path
+        self._path = path
+        self._dataset_dir = osp.dirname(path)
+        super().__init__(subset=osp.splitext(osp.basename(path))[0])
+
+        self._categories = self._load_categories(self._dataset_dir)
+        self._items = list(self._load_items(path).values())
+
+    def _load_categories(self, path):
+        label_map = None
+        label_map_path = osp.join(path, CamvidPath.LABELMAP_FILE)
+        if osp.isfile(label_map_path):
+            label_map = parse_label_map(label_map_path)
+        else:
+            label_map = CamvidLabelMap
+        self._labels = [label for label in label_map]
+        return make_camvid_categories(label_map)
+
+    def _load_items(self, path):
+        items = {}
+        with open(path, encoding='utf-8') as f:
+            for line in f:
+                objects = line.split()
+                image = objects[0]
+                item_id = ('/'.join(image.split('/')[2:]))[:-len(CamvidPath.IMAGE_EXT)]
+                image_path = osp.join(self._dataset_dir,
+                    (image, image[1:])[image[0] == '/'])
+                item_annotations = []
+                if 1 < len(objects):
+                    gt = objects[1]
+                    gt_path = osp.join(self._dataset_dir,
+                        (gt, gt[1:]) [gt[0] == '/'])
+                    inverse_cls_colormap = \
+                        self._categories[AnnotationType.mask].inverse_colormap
+                    mask = lazy_mask(gt_path, inverse_cls_colormap)
+                    # loading mask through cache
+                    mask = mask()
+                    classes = np.unique(mask)
+                    labels = self._categories[AnnotationType.label]._indices
+                    labels = { labels[label_name]: label_name
+                        for label_name in labels }
+                    for label_id in classes:
+                        if labels[label_id] in self._labels:
+                            image = self._lazy_extract_mask(mask, label_id)
+                            item_annotations.append(Mask(image=image, label=label_id))
+                items[item_id] = DatasetItem(id=item_id, subset=self._subset,
+                    image=image_path, annotations=item_annotations)
+        return items
+
+    @staticmethod
+    def _lazy_extract_mask(mask, c):
+        return lambda: mask == c
+
+
+class CamvidImporter(Importer):
+    @classmethod
+    def find_sources(cls, path):
+        subset_paths = [p for p in glob(osp.join(path, '**.txt'), recursive=True)
+            if osp.basename(p) != CamvidPath.LABELMAP_FILE]
+        sources = []
+        for subset_path in subset_paths:
+            sources += cls._find_sources_recursive(
+                subset_path, '.txt', 'camvid')
+        return sources
+
+
+LabelmapType = Enum('LabelmapType', ['camvid', 'source'])
+
+class CamvidConverter(Converter):
+    DEFAULT_IMAGE_EXT = '.png'
+
+    @classmethod
+    def build_cmdline_parser(cls, **kwargs):
+        parser = super().build_cmdline_parser(**kwargs)
+
+        parser.add_argument('--apply-colormap', type=str_to_bool, default=True,
+            help="Use colormap for class masks (default: %(default)s)")
+        parser.add_argument('--label-map', type=cls._get_labelmap, default=None,
+            help="Labelmap file path or one of %s" % \
+                ', '.join(t.name for t in LabelmapType))
+
+    def __init__(self, extractor, save_dir,
+            apply_colormap=True, label_map=None, **kwargs):
+        super().__init__(extractor, save_dir, **kwargs)
+
+        self._apply_colormap = apply_colormap
+
+        if label_map is None:
+            label_map = LabelmapType.source.name
+        self._load_categories(label_map)
+
+    def apply(self):
+        subset_dir = self._save_dir
+        os.makedirs(subset_dir, exist_ok=True)
+
+        for subset_name, subset in self._extractor.subsets().items():
+            segm_list = {}
+            for item in subset:
+                masks = [a for a in item.annotations
+                    if a.type == AnnotationType.mask]
+
+                if masks:
+                    compiled_mask = CompiledMask.from_instance_masks(masks,
+                        instance_labels=[self._label_id_mapping(m.label)
+                            for m in masks])
+
+                    self.save_segm(osp.join(subset_dir,
+                            subset_name + CamvidPath.SEGM_DIR,
+                            item.id + CamvidPath.IMAGE_EXT),
+                        compiled_mask.class_mask)
+                    segm_list[item.id] = True
+                else:
+                    segm_list[item.id] = False
+
+                if self._save_images:
+                    self._save_image(item, osp.join(subset_dir, subset_name,
+                        item.id + CamvidPath.IMAGE_EXT))
+
+            self.save_segm_lists(subset_name, segm_list)
+        self.save_label_map()
+
+    def save_segm(self, path, mask, colormap=None):
+        if self._apply_colormap:
+            if colormap is None:
+                colormap = self._categories[AnnotationType.mask].colormap
+            mask = paint_mask(mask, colormap)
+        save_image(path, mask, create_dir=True)
+
+    def save_segm_lists(self, subset_name, segm_list):
+        if not segm_list:
+            return
+
+        ann_file = osp.join(self._save_dir, subset_name + '.txt')
+        with open(ann_file, 'w') as f:
+            for item in segm_list:
+                if segm_list[item]:
+                    path_mask = '/%s/%s' % (subset_name + CamvidPath.SEGM_DIR,
+                        item + CamvidPath.IMAGE_EXT)
+                else:
+                    path_mask = ''
+                f.write('/%s/%s %s\n' % (subset_name,
+                    item + CamvidPath.IMAGE_EXT, path_mask))
+
+    def save_label_map(self):
+        path = osp.join(self._save_dir, CamvidPath.LABELMAP_FILE)
+        labels = self._extractor.categories()[AnnotationType.label]._indices
+        if len(self._label_map) > len(labels):
+            self._label_map.pop('background')
+        write_label_map(path, self._label_map)
+
+    def _load_categories(self, label_map_source):
+        if label_map_source == LabelmapType.camvid.name:
+            # use the default Camvid colormap
+            label_map = CamvidLabelMap
+
+        elif label_map_source == LabelmapType.source.name and \
+                AnnotationType.mask not in self._extractor.categories():
+            # generate colormap for input labels
+            labels = self._extractor.categories() \
+                .get(AnnotationType.label, LabelCategories())
+            label_map = OrderedDict((item.name, None)
+                for item in labels.items)
+
+        elif label_map_source == LabelmapType.source.name and \
+                AnnotationType.mask in self._extractor.categories():
+            # use source colormap
+            labels = self._extractor.categories()[AnnotationType.label]
+            colors = self._extractor.categories()[AnnotationType.mask]
+            label_map = OrderedDict()
+            for idx, item in enumerate(labels.items):
+                color = colors.colormap.get(idx)
+                if color is not None:
+                    label_map[item.name] = color
+
+        elif isinstance(label_map_source, dict):
+            label_map = OrderedDict(
+                sorted(label_map_source.items(), key=lambda e: e[0]))
+
+        elif isinstance(label_map_source, str) and osp.isfile(label_map_source):
+            label_map = parse_label_map(label_map_source)
+
+        else:
+            raise Exception("Wrong labelmap specified, "
+                "expected one of %s or a file path" % \
+                ', '.join(t.name for t in LabelmapType))
+
+        self._categories = make_camvid_categories(label_map)
+        self._label_map = label_map
+        self._label_id_mapping = self._make_label_id_map()
+
+    def _make_label_id_map(self):
+        source_labels = {
+            id: label.name for id, label in
+            enumerate(self._extractor.categories().get(
+                AnnotationType.label, LabelCategories()).items)
+        }
+        target_labels = {
+            label.name: id for id, label in
+            enumerate(self._categories[AnnotationType.label].items)
+        }
+        id_mapping = {
+            src_id: target_labels.get(src_label, 0)
+            for src_id, src_label in source_labels.items()
+        }
+
+        def map_id(src_id):
+            return id_mapping.get(src_id, 0)
+        return map_id

datumaro/plugins/coco_format/extractor.py

@@ -81,7 +81,7 @@ def _load_label_categories(self, loader):
         label_map = {}
         for idx, cat in enumerate(cats):
             label_map[cat['id']] = idx
-            categories.add(name=cat['name'], parent=cat['supercategory'])
+            categories.add(name=cat['name'], parent=cat.get('supercategory'))
 
         return categories, label_map
     # pylint: enable=no-self-use