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Fix import annotations for frames with dots in name #3091

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merged 10 commits into from
Apr 20, 2021
Merged

Fix import annotations for frames with dots in name #3091

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fc636d2
fix match dm item for no image frame
Apr 15, 2021
6366b99
test
Apr 15, 2021
d7be042
fix import dm annotations
Apr 15, 2021
cbcb620
fix incorrect solution
Apr 16, 2021
43fe33f
remove incorrect tests
Apr 16, 2021
aad580b
add image path in load annotations for cvat format
Apr 16, 2021
5d5a330
add image with dots in filename for test rest api
Apr 16, 2021
54b7cc3
delete filenames with dots in test rest api
Apr 19, 2021
be35a52
add test
Apr 19, 2021
b36c5f2
Merge branch 'develop' into sk/fix-import-annotations-for-YOLO-format
Apr 19, 2021
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7 changes: 4 additions & 3 deletions cvat/apps/dataset_manager/bindings.py
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Original file line number Diff line number Diff line change
Expand Up @@ -435,8 +435,9 @@ def db_task(self):
def _get_filename(path):
return osp.splitext(path)[0]

def match_frame(self, path, root_hint=None):
path = self._get_filename(path)
def match_frame(self, path, root_hint=None, path_has_ext=True):
if path_has_ext:
path = self._get_filename(path)
match = self._frame_mapping.get(path)
if not match and root_hint and not path.startswith(root_hint):
path = osp.join(root_hint, path)
Expand Down Expand Up @@ -611,7 +612,7 @@ def match_dm_item(item, task_data, root_hint=None):
if frame_number is None and item.has_image:
frame_number = task_data.match_frame(item.id + item.image.ext, root_hint)
if frame_number is None:
frame_number = task_data.match_frame(item.id, root_hint)
frame_number = task_data.match_frame(item.id, root_hint, path_has_ext=False)
if frame_number is None:
frame_number = cast(item.attributes.get('frame', item.id), int)
if frame_number is None and is_video:
Expand Down
5 changes: 3 additions & 2 deletions cvat/apps/dataset_manager/formats/cvat.py
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Original file line number Diff line number Diff line change
Expand Up @@ -441,8 +441,9 @@ def load(file_object, annotations):
elif el.tag == 'image':
image_is_opened = True
frame_id = annotations.abs_frame_id(match_dm_item(
DatasetItem(id=el.attrib['name'],
attributes={'frame': el.attrib['id']}
DatasetItem(id=osp.splitext(el.attrib['name'])[0],
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attributes={'frame': el.attrib['id']},
image=el.attrib['name']
),
task_data=annotations
))
Expand Down
325 changes: 325 additions & 0 deletions cvat/apps/dataset_manager/tests/test_formats.py
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Original file line number Diff line number Diff line change
Expand Up @@ -496,6 +496,7 @@ def test_frames_outside_are_not_generated(self):
self.assertTrue(frame.frame in range(6, 10))
self.assertEqual(i + 1, 4)


class FrameMatchingTest(_DbTestBase):
def _generate_task_images(self, paths): # pylint: disable=no-self-use
f = BytesIO()
Expand Down Expand Up @@ -586,3 +587,327 @@ def test_dataset_root(self):

root = find_dataset_root(dataset, task_data)
self.assertEqual(expected, root)

class TaskAnnotationsImportTest(_DbTestBase):
def _generate_custom_annotations(self, annotations, task):
self._put_api_v1_task_id_annotations(task["id"], annotations)
return annotations

def _generate_task_images(self, count, name="image"):
images = {
"client_files[%d]" % i: generate_image_file("image_%d.jpg" % i)
for i in range(count)
}
images["image_quality"] = 75
return images

def _generate_task(self, images, annotation_format, **overrides):
labels = []
if annotation_format in ["ICDAR Recognition 1.0",
"ICDAR Localization 1.0"]:
labels = [{
"name": "icdar",
"attributes": [{
"name": "text",
"mutable": False,
"input_type": "text",
"values": ["word1", "word2"]
}]
}]
elif annotation_format == "ICDAR Segmentation 1.0":
labels = [{
"name": "icdar",
"attributes": [
{
"name": "text",
"mutable": False,
"input_type": "text",
"values": ["word_1", "word_2", "word_3"]
},
{
"name": "index",
"mutable": False,
"input_type": "number",
"values": ["0", "1", "2"]
},
{
"name": "color",
"mutable": False,
"input_type": "text",
"values": ["100 110 240", "10 15 20", "120 128 64"]
},
{
"name": "center",
"mutable": False,
"input_type": "text",
"values": ["1 2", "2 4", "10 45"]
},
]
}]
elif annotation_format == "Market-1501 1.0":
labels = [{
"name": "market-1501",
"attributes": [
{
"name": "query",
"mutable": False,
"input_type": "select",
"values": ["True", "False"]
},
{
"name": "camera_id",
"mutable": False,
"input_type": "number",
"values": ["0", "1", "2", "3"]
},
{
"name": "person_id",
"mutable": False,
"input_type": "number",
"values": ["1", "2", "3"]
},
]
}]
else:
labels = [
{
"name": "car",
"attributes": [
{
"name": "model",
"mutable": False,
"input_type": "select",
"default_value": "mazda",
"values": ["bmw", "mazda", "renault"]
},
{
"name": "parked",
"mutable": True,
"input_type": "checkbox",
"default_value": False
}
]
},
{"name": "person"}
]

task = {
"name": "my task #1",
"overlap": 0,
"segment_size": 100,
"labels": labels
}
task.update(overrides)
return self._create_task(task, images)

def _generate_annotations(self, task, annotation_format):
shapes = []
tracks = []
tags = []

if annotation_format in ["ICDAR Recognition 1.0",
"ICDAR Localization 1.0"]:
shapes = [{
"frame": 0,
"label_id": task["labels"][0]["id"],
"group": 0,
"source": "manual",
"attributes": [
{
"spec_id": task["labels"][0]["attributes"][0]["id"],
"value": task["labels"][0]["attributes"][0]["values"][0]
},
],
"points": [1.0, 2.1, 10.6, 53.22],
"type": "rectangle",
"occluded": False,
}]
elif annotation_format == "Market-1501 1.0":
tags = [{
"frame": 1,
"label_id": task["labels"][0]["id"],
"group": 0,
"source": "manual",
"attributes": [
{
"spec_id": task["labels"][0]["attributes"][0]["id"],
"value": task["labels"][0]["attributes"][0]["values"][1]
},
{
"spec_id": task["labels"][0]["attributes"][1]["id"],
"value": task["labels"][0]["attributes"][1]["values"][2]
},
{
"spec_id": task["labels"][0]["attributes"][2]["id"],
"value": task["labels"][0]["attributes"][2]["values"][0]
}
],
}]
elif annotation_format == "ICDAR Segmentation 1.0":
shapes = [{
"frame": 0,
"label_id": task["labels"][0]["id"],
"group": 0,
"source": "manual",
"attributes": [
{
"spec_id": task["labels"][0]["attributes"][0]["id"],
"value": task["labels"][0]["attributes"][0]["values"][0]
},
{
"spec_id": task["labels"][0]["attributes"][1]["id"],
"value": task["labels"][0]["attributes"][1]["values"][0]
},
{
"spec_id": task["labels"][0]["attributes"][2]["id"],
"value": task["labels"][0]["attributes"][2]["values"][1]
},
{
"spec_id": task["labels"][0]["attributes"][3]["id"],
"value": task["labels"][0]["attributes"][3]["values"][2]
}
],
"points": [1.0, 2.1, 10.6, 53.22],
"type": "rectangle",
"occluded": False,
}]
elif annotation_format == "VGGFace2 1.0":
shapes = [{
"frame": 1,
"label_id": task["labels"][1]["id"],
"group": None,
"source": "manual",
"attributes": [],
"points": [2.0, 2.1, 40, 50.7],
"type": "rectangle",
"occluded": False
}]
else:
rectangle_shape_wo_attrs = {
"frame": 1,
"label_id": task["labels"][1]["id"],
"group": 0,
"source": "manual",
"attributes": [],
"points": [2.0, 2.1, 40, 50.7],
"type": "rectangle",
"occluded": False,
}

rectangle_shape_with_attrs = {
"frame": 0,
"label_id": task["labels"][0]["id"],
"group": 0,
"source": "manual",
"attributes": [
{
"spec_id": task["labels"][0]["attributes"][0]["id"],
"value": task["labels"][0]["attributes"][0]["values"][0]
},
{
"spec_id": task["labels"][0]["attributes"][1]["id"],
"value": task["labels"][0]["attributes"][1]["default_value"]
}
],
"points": [1.0, 2.1, 10.6, 53.22],
"type": "rectangle",
"occluded": False,
}

track_wo_attrs = {
"frame": 0,
"label_id": task["labels"][1]["id"],
"group": 0,
"source": "manual",
"attributes": [],
"shapes": [
{
"frame": 0,
"attributes": [],
"points": [1.0, 2.1, 100, 300.222],
"type": "polygon",
"occluded": False,
"outside": False
}
]
}

tag_wo_attrs = {
"frame": 0,
"label_id": task["labels"][0]["id"],
"group": None,
"attributes": []
}

tag_with_attrs = {
"frame": 1,
"label_id": task["labels"][0]["id"],
"group": 3,
"source": "manual",
"attributes": [
{
"spec_id": task["labels"][0]["attributes"][0]["id"],
"value": task["labels"][0]["attributes"][0]["values"][1]
},
{
"spec_id": task["labels"][0]["attributes"][1]["id"],
"value": task["labels"][0]["attributes"][1]["default_value"]
}
],
}

if annotation_format == "VGGFace2 1.0":
shapes = rectangle_shape_wo_attrs
elif annotation_format == "CVAT 1.1":
shapes = [rectangle_shape_wo_attrs,
rectangle_shape_with_attrs]
tags = [tag_with_attrs, tag_wo_attrs]
elif annotation_format == "MOTS PNG 1.0":
tracks = [track_wo_attrs]
else:
shapes = [rectangle_shape_wo_attrs,
rectangle_shape_with_attrs]
tags = tag_wo_attrs
tracks = track_wo_attrs

annotations = {
"version": 0,
"tags": tags,
"shapes": shapes,
"tracks": tracks
}

return self._generate_custom_annotations(annotations, task)

def _test_can_import_annotations(self, task, import_format):
with tempfile.TemporaryDirectory() as temp_dir:
file_path = osp.join(temp_dir, import_format)

export_format = import_format
if import_format == "CVAT 1.1":
export_format = "CVAT for images 1.1"

dm.task.export_task(task["id"], file_path, export_format)
expected_ann = TaskAnnotation(task["id"])
expected_ann.init_from_db()

dm.task.import_task_annotations(task["id"],
file_path, import_format)
actual_ann = TaskAnnotation(task["id"])
actual_ann.init_from_db()

self.assertEqual(len(expected_ann.data), len(actual_ann.data))

def test_can_import_annotations_for_image_with_dots_in_filename(self):
for f in dm.views.get_import_formats():
format_name = f.DISPLAY_NAME

images = self._generate_task_images(3, "img0.0.0")
task = self._generate_task(images, format_name)
self._generate_annotations(task, format_name)

with self.subTest(format=format_name):
if not f.ENABLED:
self.skipTest("Format is disabled")

self._test_can_import_annotations(task, format_name)