Release v0.6.1

CHANGELOG.md

@@ -4,6 +4,22 @@ All notable changes to this project will be documented in this file.
 The format is based on [Keep a Changelog](https://keepachangelog.com/en/1.0.0/),
 and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0.html).
 
+## [0.6.1] - 2020-03-21
+### Changed
+- VOC task export now does not use official label map by default, but takes one
+  from the source task to avoid primary-class and class part name
+  clashing ([#1275](https://github.com/opencv/cvat/issues/1275))
+
+### Fixed
+- File names in LabelMe format export are no longer truncated ([#1259](https://github.com/opencv/cvat/issues/1259))
+- `occluded` and `z_order` annotation attributes are now correctly passed to Datumaro ([#1271](https://github.com/opencv/cvat/pull/1271))
+- Annotation-less tasks now can be exported as empty datasets in COCO ([#1277](https://github.com/opencv/cvat/issues/1277))
+- Frame name matching for video annotations import -
+  allowed `frame_XXXXXX[.ext]` format ([#1274](https://github.com/opencv/cvat/pull/1274))
+
+### Security
+- Bump acorn from 6.3.0 to 6.4.1 in /cvat-ui ([#1270](https://github.com/opencv/cvat/pull/1270))
+
 ## [0.6.0] - 2020-03-15
 ### Added
 - Server only support for projects. Extend REST API v1 (/api/v1/projects*)

CONTRIBUTING.md

@@ -15,7 +15,7 @@ Next steps should work on clear Ubuntu 18.04.
 - Install necessary dependencies:
 
 ```sh
-$ sudo apt update && apt install -y nodejs npm curl redis-server python3-dev python3-pip python3-venv libldap2-dev libsasl2-dev
+$ sudo apt-get update && sudo apt-get --no-install-recommends install -y ffmpeg build-essential nodejs npm curl redis-server python3-dev python3-pip python3-venv libldap2-dev libsasl2-dev
 ```
 
 -   Install [Visual Studio Code](https://code.visualstudio.com/docs/setup/linux#_debian-and-ubuntu-based-distributions)
@@ -28,7 +28,7 @@ git clone https://github.com/opencv/cvat
 cd cvat && mkdir logs keys
 python3 -m venv .env
 . .env/bin/activate
-pip install -U pip wheel
+pip install -U pip wheel setuptools
 pip install -r cvat/requirements/development.txt
 pip install -r datumaro/requirements.txt
 python manage.py migrate

cvat/__init__.py

@@ -4,6 +4,6 @@
 
 from cvat.utils.version import get_version
 
-VERSION = (0, 6, 0, 'final', 0)
+VERSION = (0, 6, 1, 'final', 0)
 
 __version__ = get_version(VERSION)

cvat/apps/annotation/labelme.py

@@ -107,17 +107,17 @@ def dump_frame_anno(frame_annotation):
     return ET.tostring(root_elem, encoding='unicode', pretty_print=True)
 
 def dump_as_labelme_annotation(file_object, annotations):
+    import os.path as osp
     from zipfile import ZipFile, ZIP_DEFLATED
 
     with ZipFile(file_object, 'w', compression=ZIP_DEFLATED) as output_zip:
         for frame_annotation in annotations.group_by_frame():
             xml_data = dump_frame_anno(frame_annotation)
-            filename = frame_annotation.name
-            filename = filename[ : filename.rfind('.')] + '.xml'
+            filename = osp.splitext(frame_annotation.name)[0] + '.xml'
             output_zip.writestr(filename, xml_data)
 
 def parse_xml_annotations(xml_data, annotations, input_zip):
-    from cvat.apps.annotation.coco import mask_to_polygon
+    from datumaro.util.mask_tools import mask_to_polygons
     from io import BytesIO
     from lxml import etree as ET
     import numpy as np
@@ -229,7 +229,7 @@ def parse_attributes(attributes_string):
             mask = input_zip.read(osp.join(_MASKS_DIR, mask_file))
             mask = np.asarray(Image.open(BytesIO(mask)).convert('L'))
             mask = (mask != 0)
-            polygons = mask_to_polygon(mask)
+            polygons = mask_to_polygons(mask)
 
             for polygon in polygons:
                 ann_items.append(annotations.LabeledShape(

cvat/apps/annotation/pascal_voc.py

@@ -74,7 +74,7 @@ def dump(file_object, annotations):
     extractor = CvatAnnotationsExtractor('', annotations)
     extractor = extractor.transform(id_from_image)
     extractor = Dataset.from_extractors(extractor) # apply lazy transforms
-    converter = env.make_converter('voc')
+    converter = env.make_converter('voc', label_map='source')
     with TemporaryDirectory() as temp_dir:
         converter(extractor, save_dir=temp_dir)
         make_zip_archive(temp_dir, file_object)

cvat/apps/dataset_manager/bindings.py

@@ -91,7 +91,9 @@ def categories(self):
     @staticmethod
     def _load_categories(cvat_anno):
         categories = {}
-        label_categories = datumaro.LabelCategories()
+
+        label_categories = datumaro.LabelCategories(
+            attributes=['occluded', 'z_order'])
 
         for _, label in cvat_anno.meta['task']['labels']:
             label_categories.add(label['name'])
@@ -144,6 +146,8 @@ def convert_attrs(label, cvat_attrs):
             anno_group = shape_obj.group
             anno_label = map_label(shape_obj.label)
             anno_attr = convert_attrs(shape_obj.label, shape_obj.attributes)
+            anno_attr['occluded'] = shape_obj.occluded
+            anno_attr['z_order'] = shape_obj.z_order
 
             anno_points = shape_obj.points
             if shape_obj.type == ShapeType.POINTS:
@@ -177,6 +181,8 @@ def __init__(self, url, db_task, user):
 
 
 def match_frame(item, cvat_task_anno):
+    is_video = cvat_task_anno.meta['task']['mode'] == 'interpolation'
+
     frame_number = None
     if frame_number is None:
         try:
@@ -193,6 +199,8 @@ def match_frame(item, cvat_task_anno):
             frame_number = int(item.id)
         except Exception:
             pass
+    if frame_number is None and is_video and item.id.startswith('frame_'):
+        frame_number = int(item.id[len('frame_'):])
     if not frame_number in cvat_task_anno.frame_info:
         raise Exception("Could not match item id: '%s' with any task frame" %
             item.id)
@@ -234,7 +242,7 @@ def import_dm_annotations(dm_dataset, cvat_task_anno):
                     frame=frame_number,
                     label=label_cat.items[ann.label].name,
                     points=ann.points,
-                    occluded=False,
+                    occluded=ann.attributes.get('occluded') == True,
                     group=group_map.get(ann.group, 0),
                     attributes=[cvat_task_anno.Attribute(name=n, value=str(v))
                         for n, v in ann.attributes.items()],

cvat/apps/engine/tests/test_rest_api.py

@@ -2655,6 +2655,15 @@ def _get_initial_annotation(annotation_format):
                 "points": [20.0, 0.1, 10, 3.22, 4, 7, 10, 30, 1, 2, 4.44, 5.55],
                 "type": "polygon",
                 "occluded": True
+            },
+            {
+                "frame": 2,
+                "label_id": task["labels"][1]["id"],
+                "group": 1,
+                "attributes": [],
+                "points": [4, 7, 10, 30, 4, 5.55],
+                "type": "polygon",
+                "occluded": False
             }]
 
             tags_wo_attrs = [{
@@ -2711,6 +2720,12 @@ def _get_initial_annotation(annotation_format):
             elif annotation_format == "MOT CSV 1.0":
                 annotations["tracks"] = rectangle_tracks_wo_attrs
 
+            elif annotation_format == "LabelMe ZIP 3.0 for images":
+                annotations["shapes"] = rectangle_shapes_with_attrs + \
+                                        rectangle_shapes_wo_attrs + \
+                                        polygon_shapes_wo_attrs + \
+                                        polygon_shapes_with_attrs
+
             return annotations
 
         response = self._get_annotation_formats(annotator)

cvat/requirements/development.txt

@@ -7,7 +7,7 @@ pylint==2.3.1
 pylint-django==0.9.4
 pylint-plugin-utils==0.2.6
 rope==0.11
-wrapt==1.10.11
+wrapt==1.11.1
 django-extensions==2.0.6
 Werkzeug==0.15.3
 snakeviz==0.4.2

datumaro/datumaro/plugins/coco_format/converter.py

@@ -329,20 +329,24 @@ def save_categories(self, dataset):
         label_categories = dataset.categories().get(AnnotationType.label)
         if label_categories is None:
             return
-        points_categories = dataset.categories().get(AnnotationType.points)
-        if points_categories is None:
-            return
-
-        for idx, kp_cat in points_categories.items.items():
-            label_cat = label_categories.items[idx]
+        point_categories = dataset.categories().get(AnnotationType.points)
 
+        for idx, label_cat in enumerate(label_categories.items):
             cat = {
                 'id': 1 + idx,
                 'name': _cast(label_cat.name, str, ''),
                 'supercategory': _cast(label_cat.parent, str, ''),
-                'keypoints': [str(l) for l in kp_cat.labels],
-                'skeleton': [int(i) for i in kp_cat.adjacent],
+                'keypoints': [],
+                'skeleton': [],
             }
+
+            if point_categories is not None:
+                kp_cat = point_categories.items.get(idx)
+                if kp_cat is not None:
+                    cat.update({
+                        'keypoints': [str(l) for l in kp_cat.labels],
+                        'skeleton': [int(i) for i in kp_cat.adjacent],
+                    })
             self.categories.append(cat)
 
     def save_annotations(self, item):
@@ -447,14 +451,19 @@ class _Converter:
     def __init__(self, extractor, save_dir,
             tasks=None, save_images=False, segmentation_mode=None,
             crop_covered=False):
-        assert tasks is None or isinstance(tasks, (CocoTask, list))
+        assert tasks is None or isinstance(tasks, (CocoTask, list, str))
         if tasks is None:
             tasks = list(self._TASK_CONVERTER)
         elif isinstance(tasks, CocoTask):
             tasks = [tasks]
+        elif isinstance(tasks, str):
+            tasks = [CocoTask[tasks]]
         else:
-            for t in tasks:
-                assert t in CocoTask
+            for i, t in enumerate(tasks):
+                if isinstance(t, str):
+                    tasks[i] = CocoTask[t]
+                else:
+                    assert t in CocoTask, t
         self._tasks = tasks
 
         self._extractor = extractor
@@ -546,9 +555,8 @@ def convert(self):
                     task_conv.save_annotations(item)
 
             for task, task_conv in task_converters.items():
-                if not task_conv.is_empty():
-                    task_conv.write(osp.join(self._ann_dir,
-                        '%s_%s.json' % (task.name, subset_name)))
+                task_conv.write(osp.join(self._ann_dir,
+                    '%s_%s.json' % (task.name, subset_name)))
 
 class CocoConverter(Converter, CliPlugin):
     @staticmethod

datumaro/datumaro/plugins/yolo_format/extractor.py

@@ -90,7 +90,9 @@ def __init__(self, config_path, image_info=None):
             subset = YoloExtractor.Subset(subset_name, self)
             with open(list_path, 'r') as f:
                 subset.items = OrderedDict(
-                    (osp.splitext(osp.basename(p))[0], p.strip()) for p in f)
+                    (osp.splitext(osp.basename(p.strip()))[0], p.strip())
+                    for p in f
+                )
 
             for item_id, image_path in subset.items.items():
                 image_path = self._make_local_path(image_path)

datumaro/tests/test_coco_format.py

@@ -626,10 +626,13 @@ def __iter__(self):
 
             def categories(self):
                 label_cat = LabelCategories()
+                point_cat = PointsCategories()
                 for label in range(10):
                     label_cat.add('label_' + str(label))
+                    point_cat.add(label)
                 return {
                     AnnotationType.label: label_cat,
+                    AnnotationType.points: point_cat,
                 }
 
         with TestDir() as test_dir:
@@ -645,4 +648,4 @@ def __iter__(self):
 
         with TestDir() as test_dir:
             self._test_save_and_load(TestExtractor(),
-                CocoConverter(), test_dir)
+                CocoConverter(tasks='image_info'), test_dir)