def it_uses_a_default_path_if_one_is_missing(tmp_path):
content = """
{
"image": {
"original_filename": "P49-RediPad-ProPlayLEFTY_442.jpg",
"filename": "P49-RediPad-ProPlayLEFTY_442.jpg"
},
"annotations": [
{
"keypoint": {
"x": 207.97048950195312,
"y": 449.39691162109375
},
"name": "left_knee"
}
]
}
"""
directory = tmp_path / "imports"
directory.mkdir()
import_file = directory / "darwin-file.json"
import_file.write_text(content)
annotation_file: dt.AnnotationFile = parse_darwin_json(
import_file, None)
assert annotation_file.remote_path == "/"
def split_video_annotations(self, release_name: str = "latest") -> None:
release_dir: Path = self.local_path / "releases" / release_name
annotations_path: Path = release_dir / "annotations"
for count, annotation_file in enumerate(
annotations_path.glob("*.json")):
darwin_annotation: Optional[AnnotationFile] = parse_darwin_json(
annotation_file, count)
if not darwin_annotation or not darwin_annotation.is_video:
continue
frame_annotations = split_video_annotation(darwin_annotation)
for frame_annotation in frame_annotations:
annotation = build_image_annotation(frame_annotation)
video_frame_annotations_path = annotations_path / annotation_file.stem
video_frame_annotations_path.mkdir(exist_ok=True, parents=True)
stem = Path(frame_annotation.filename).stem
output_path = video_frame_annotations_path / f"{stem}.json"
with output_path.open("w") as f:
json.dump(annotation, f)
# Finally delete video annotations
annotation_file.unlink()
# Update class list, which is used when loading local annotations in a dataset
make_class_lists(release_dir)
def it_parses_darwin_images_correctly(tmp_path):
content = """
{
"image": {
"width": 497,
"height": 778,
"original_filename": "P49-RediPad-ProPlayLEFTY_442.jpg",
"filename": "P49-RediPad-ProPlayLEFTY_442.jpg",
"url": "",
"path": "/tmp_files"
},
"annotations": [
{
"keypoint": {
"x": 207.97048950195312,
"y": 449.39691162109375
},
"name": "left_knee"
},
{
"keypoint": {
"x": 302.9606018066406,
"y": 426.13946533203125
},
"name": "left_ankle"
}
]
}
"""
directory = tmp_path / "imports"
directory.mkdir()
import_file = directory / "darwin-file.json"
import_file.write_text(content)
annotation_file: dt.AnnotationFile = parse_darwin_json(
import_file, None)
assert annotation_file.path == import_file
assert annotation_file.filename == "P49-RediPad-ProPlayLEFTY_442.jpg"
assert len(annotation_file.annotations) == 2
assert len(annotation_file.annotation_classes) == 2
assert not annotation_file.is_video
assert annotation_file.image_width == 497
assert annotation_file.image_height == 778
assert annotation_file.image_url == ""
assert not annotation_file.workview_url
assert not annotation_file.seq
assert not annotation_file.frame_urls
assert annotation_file.remote_path == "/tmp_files"
def darwin_to_dt_gen(file_paths):
count = 0
for file_path in map(Path, file_paths):
files = file_path.glob("**/*") if file_path.is_dir() else [file_path]
for f in files:
if f.suffix != ".json":
continue
data = parse_darwin_json(f, count)
if data:
if data.is_video:
for d in split_video_annotation(data):
yield d
else:
yield data
count += 1
def it_returns_None_if_no_annotations_exist(tmp_path):
content = """
{
"image": {
"width": 497,
"height": 778,
"original_filename": "P49-RediPad-ProPlayLEFTY_442.jpg",
"filename": "P49-RediPad-ProPlayLEFTY_442.jpg",
"url": "",
"path": "/tmp_files"
}
}
"""
directory = tmp_path / "imports"
directory.mkdir()
import_file = directory / "darwin-file.json"
import_file.write_text(content)
annotation_file: dt.AnnotationFile = parse_darwin_json(
import_file, None)
assert not annotation_file
def parse_file(path: Path) -> Optional[dt.AnnotationFile]:
if path.suffix != ".json":
return
return parse_darwin_json(path, 0)
def it_imports_multiple_skeletetons(tmp_path):
content = """
{
"dataset":"cars",
"image":{
"filename":"ferrari-laferrari.jpg"
},
"annotations":[
{
"bounding_box":{
"h":547.0,
"w":1709.0,
"x":96.0,
"y":437.0
},
"name":"car",
"polygon":{
"path":[
{
"x":1805.0,
"y":586.0
},
{
"x":1802.0,
"y":586.0
},
{
"x":1805.0,
"y":588.0
}
]
}
},
{
"name":"wheels",
"skeleton":{
"nodes":[
{
"name":"1",
"occluded":false,
"x":829.56,
"y":824.5
},
{
"name":"2",
"occluded":false,
"x":1670.5,
"y":741.76
}
]
}
},
{
"name":"door",
"skeleton":{
"nodes":[
{
"name":"1",
"occluded":false,
"x":867.86,
"y":637.16
},
{
"name":"2",
"occluded":false,
"x":1100.21,
"y":810.09
},
{
"name":"3",
"occluded":false,
"x":1298.45,
"y":856.56
},
{
"name":"4",
"occluded":false,
"x":1234.63,
"y":492.12
}
]
}
}
]
}
"""
directory = tmp_path / "imports"
directory.mkdir()
import_file = directory / "darwin-file.json"
import_file.write_text(content)
annotation_file: dt.AnnotationFile = parse_darwin_json(
import_file, None)
assert annotation_file.annotations[
0].annotation_class.annotation_type == "polygon"
assert annotation_file.annotations[
1].annotation_class.annotation_type == "skeleton"
assert annotation_file.annotations[
2].annotation_class.annotation_type == "skeleton"
def it_parses_darwin_videos_correctly(tmp_path):
content = """
{
"dataset": "my-dataset",
"image": {
"width": 3840,
"height": 2160,
"fps": 0.0,
"original_filename": "above tractor.mp4",
"filename": "above tractor.mp4",
"url": "https://my-website.com/api/videos/209/original",
"path": "/",
"workview_url": "https://my-website.com/workview?dataset=102&image=530",
"frame_count": 343,
"frame_urls": [
"https://my-website.com/api/videos/209/frames/0"
]
},
"annotations": [
{
"frames": {
"3": {
"bounding_box": {
"h": 547.0,
"w": 400.0,
"x": 363.0,
"y": 701.0
},
"instance_id": {
"value": 119
},
"keyframe": true,
"polygon": {
"path": [
{
"x": 748.0,
"y": 732.0
},
{
"x": 751.0,
"y": 735.0
},
{
"x": 748.0,
"y": 733.0
}
]
}
}
},
"interpolate_algorithm": "linear-1.1",
"interpolated": true,
"name": "Hand",
"segments": [
[
3,
46
]
]
}
]
}
"""
directory = tmp_path / "imports"
directory.mkdir()
import_file = directory / "darwin-file.json"
import_file.write_text(content)
annotation_file: dt.AnnotationFile = parse_darwin_json(
import_file, None)
assert annotation_file.path == import_file
assert annotation_file.filename == "above tractor.mp4"
assert len(annotation_file.annotations) == 1
assert len(annotation_file.annotation_classes) == 1
assert annotation_file.is_video
assert annotation_file.image_width == 3840
assert annotation_file.image_height == 2160
assert annotation_file.image_url == "https://my-website.com/api/videos/209/original"
assert annotation_file.workview_url == "https://my-website.com/workview?dataset=102&image=530"
assert not annotation_file.seq
assert annotation_file.frame_urls == [
"https://my-website.com/api/videos/209/frames/0"
]
assert annotation_file.remote_path == "/"
assert annotation_file.annotations == [
dt.VideoAnnotation(
annotation_class=dt.AnnotationClass(
name="Hand",
annotation_type="polygon",
annotation_internal_type=None),
frames={
3:
dt.Annotation(
annotation_class=dt.AnnotationClass(
name="Hand",
annotation_type="polygon",
annotation_internal_type=None),
data={
"path": [{
"x": 748.0,
"y": 732.0
}, {
"x": 751.0,
"y": 735.0
}, {
"x": 748.0,
"y": 733.0
}],
"bounding_box": {
"x": 363.0,
"y": 701.0,
"w": 400.0,
"h": 547.0
},
},
subs=[
dt.SubAnnotation(annotation_type="instance_id",
data=119)
],
)
},
keyframes={3: True},
segments=[[3, 46]],
interpolated=True,
)
]
