def _extract_box2d(
path: str, sequence: str,
box2d_catalog: Box2DSubcatalog) -> Dict[int, List[LabeledBox2D]]:
attributes = box2d_catalog.attributes
category_names = box2d_catalog.categories.keys()
out_of_view_level = attributes["out_of_view"].enum
occlusion_level = attributes["occlusion"].enum
ground_truth_path = os.path.join(path, "UAV-benchmark-MOTD_v1.0", "GT")
frame_id_ground_truth_map = defaultdict(list)
with open(os.path.join(ground_truth_path, f"{sequence}_gt_whole.txt"),
encoding="utf-8") as fp:
for elements in csv.DictReader(fp, fieldnames=_FIELDNAMES):
box2d = LabeledBox2D.from_xywh(
*(int(elements[key]) for key in _XYWH_KEYS),
category=category_names[int(elements["object_category"]) - 1],
attributes={
"out_of_view":
out_of_view_level[int(elements["out_of_view"]) - 1],
"occlusion":
occlusion_level[int(elements["occlusion"]) - 1],
},
instance=elements["target_id"],
)
frame_id = int(elements["frame_index"])
frame_id_ground_truth_map[frame_id].append(box2d)
return frame_id_ground_truth_map
def _parse_xml(xml_path: str) -> Dict[int, List[LabeledBox2D]]:
dom = parse(xml_path)
frames = dom.documentElement.getElementsByTagName("frame")
labels = defaultdict(list)
for frame in frames:
index = int(frame.getAttribute("number"))
objects = frame.getElementsByTagName("objectlist")[0]
for obj in objects.getElementsByTagName("object"):
box = obj.getElementsByTagName("box")[0]
box_h = int(box.getAttribute("h"))
box_w = int(box.getAttribute("w"))
box_xc = int(box.getAttribute("xc"))
box_yc = int(box.getAttribute("yc"))
labels[index].append(
LabeledBox2D.from_xywh(
x=box_xc - box_w // 2,
y=box_yc - box_h // 2,
width=box_w,
height=box_h,
category=obj.getElementsByTagName("subtype")
[0].firstChild.data,
))
return labels
def _get_data(mat: Any, name: Any, bbox: Any, file_path: str) -> Data:
image_path = "".join(chr(v[0]) for v in mat[name[0]])
data = Data(os.path.join(file_path, image_path),
target_remote_path=image_path.zfill(10))
data.label.box2d = []
mat_bbox = mat[bbox[0]]
labeled_box = ({key: [value[0][0]]
for key, value in mat_bbox.items()}
if mat_bbox["label"].shape[0] == 1 else {
key: [mat[value[0]][0][0] for value in values]
for key, values in mat_bbox.items()
})
for x, y, w, h, e in zip(
labeled_box["left"],
labeled_box["top"],
labeled_box["width"],
labeled_box["height"],
labeled_box["label"],
):
data.label.box2d.append(
LabeledBox2D.from_xywh(x,
y,
w,
h,
category="0" if e == 10 else str(int(e))))
return data
def _get_instance_label(instances_annotations: Dict[int, Any], image_id: int,
categories: Dict[int, str]) -> Label:
label: Label = Label()
label.box2d = []
label.multi_polygon = []
label.rle = []
if image_id not in instances_annotations:
return label
for annotation in instances_annotations[image_id]:
category = categories[annotation["category_id"]]
label.box2d.append(
LabeledBox2D.from_xywh(*annotation["bbox"], category=category))
if annotation["iscrowd"] == 0:
points = [
chunked(coordinates, 2)
for coordinates in annotation["segmentation"]
]
label.multi_polygon.append(
LabeledMultiPolygon(points, category=category))
else:
label.rle.append(
LabeledRLE(annotation["segmentation"]["counts"],
category=category))
return label
def _get_data_part1(root_path: str, aniamls: Iterable[str]) -> Iterator[Data]:
try:
import xmltodict # pylint: disable=import-outside-toplevel
except ModuleNotFoundError as error:
raise ModuleImportError(module_name=error.name) from error
for animal in aniamls:
for image_path in glob(
os.path.join(root_path, "keypoint_image_part1", animal,
"*.jpg")):
data = Data(
image_path,
target_remote_path=f"{animal}/{os.path.basename(image_path)}")
for annotation_path in glob(
os.path.join(
root_path,
"PASCAL2011_animal_annotation",
animal,
f"{os.path.splitext(os.path.basename(image_path))[0]}_*.xml",
)):
with open(annotation_path, encoding="utf-8") as fp:
labels: Any = xmltodict.parse(fp.read())
box2d = labels["annotation"]["visible_bounds"]
data.label.box2d = [
LabeledBox2D.from_xywh(
x=float(box2d["@xmin"]),
y=float(box2d["@ymin"]),
width=float(box2d["@width"]),
height=float(box2d["@height"]),
category=animal,
)
]
keypoints2d: List[Tuple[float, float, int]] = [
()
] * 20 # type: ignore[list-item]
for keypoint in labels["annotation"]["keypoints"]["keypoint"]:
keypoints2d[_KEYPOINT_TO_INDEX[keypoint["@name"]]] = (
float(keypoint["@x"]),
float(keypoint["@y"]),
int(keypoint["@visible"]),
)
data.label.keypoints2d = [
LabeledKeypoints2D(keypoints2d, category=animal)
]
yield data
def _load_box_labels(file_path: str) -> List[LabeledBox2D]:
box_labels = []
with open(file_path, encoding="utf-8") as fp:
for line in fp:
center_x, center_y, width, height, occlusion, blur = map(int, line.strip().split())
attributes = {"occlusion-level": _OCCLUSION_MAP[occlusion], "blur-level": bool(blur)}
box_labels.append(
LabeledBox2D.from_xywh(
x=center_x - width / 2,
y=center_y - height / 2,
width=width,
height=height,
attributes=attributes,
)
)
return box_labels
def _get_panoptic_box2d(panoptic_annotations: Dict[int, Any], image_id: int,
categories: Dict[int, str]) -> List[LabeledBox2D]:
if image_id not in panoptic_annotations:
return []
box2d: List[LabeledBox2D] = []
for annotation in panoptic_annotations[image_id]:
for segment_info in annotation["segments_info"]:
# category_id 1-91 are thing categories from the detection task
# category_id 92-200 are stuff categories from the stuff task
if segment_info["category_id"] > 91:
category = categories[segment_info["category_id"]]
box2d.append(
LabeledBox2D.from_xywh(*segment_info["bbox"],
category=category))
return box2d
def _get_data_part2(root_path: str, aniamls: Iterable[str]) -> Iterator[Data]:
try:
import xmltodict # pylint: disable=import-outside-toplevel
except ModuleNotFoundError as error:
raise ModuleImportError(module_name=error.name) from error
for animal in aniamls:
for image_path in glob(
os.path.join(root_path, "animalpose_image_part2", animal,
"*.jpeg")):
data = Data(
image_path,
target_remote_path=f"{animal}/{os.path.basename(image_path)}")
annotation_path = os.path.join(
root_path,
"animalpose_anno2",
animal,
f"{os.path.splitext(os.path.basename(image_path))[0]}.xml",
)
with open(annotation_path, encoding="utf-8") as fp:
labels: Any = xmltodict.parse(fp.read())
box2d = labels["annotation"]["visible_bounds"]
data.label.box2d = [
LabeledBox2D.from_xywh(
x=float(box2d["@xmin"]),
y=float(
box2d["@xmax"]), # xmax means ymin in the annotation
width=float(box2d["@width"]),
height=float(box2d["@height"]),
category=animal,
)
]
keypoints2d = LabeledKeypoints2D(category=animal)
for keypoint in labels["annotation"]["keypoints"]["keypoint"]:
keypoints2d.append(
Keypoint2D(float(keypoint["@x"]), float(keypoint["@y"]),
int(keypoint["@visible"])))
data.label.keypoints2d = [keypoints2d]
yield data
def test_from_xywh(self):
x, y, width, height = 1, 2, 3, 4
xmin, xmax, ymin, ymax = 1, 2, 4, 6
labeledbox2d = LabeledBox2D.from_xywh(
x,
y,
width,
height,
category="cat",
attributes={"gender": "male"},
instance="12345",
)
assert labeledbox2d.category == "cat"
assert labeledbox2d.attributes == {"gender": "male"}
assert labeledbox2d.instance == "12345"
assert labeledbox2d[0] == xmin
assert labeledbox2d[1] == xmax
assert labeledbox2d[2] == ymin
assert labeledbox2d[3] == ymax
def _generate_data(image_path: str, labels: Dict[str, Any]) -> Data:
data = Data(image_path)
data.label.box2d = []
image_id = labels["image_name_id_map"][os.path.basename(image_path)]
image_annotations_map = labels["image_annotations_map"]
if image_id not in image_annotations_map:
return data
annotations = labels["annotations"]
poses = labels["poses"]
categories = labels["categories"]
for annotation_id in image_annotations_map[image_id]:
annotation = annotations[annotation_id]
x_top, y_top, width, height = annotation["bbox"]
attributes = {
"occluded": annotation["occluded"],
"difficult": annotation["difficult"],
"pose": poses[annotation["pose_id"] - 1]["name"],
"truncated": annotation["truncated"],
}
data.label.box2d.append(
LabeledBox2D.from_xywh(
x=x_top,
y=y_top,
width=width,
height=height,
category=categories[annotation["category_id"]]["name"],
attributes=attributes,
instance=str(annotation["tracking_id"]),
))
return data
def _get_box2ds(label_path: str) -> Dict[str, LabeledBox2D]:
all_box2d_attributes: DefaultDict[str, AttributeType] = defaultdict(dict)
# The normal format of each line of the file is
# n000002/0032_01.jpg 0
# n000002/0039_01.jpg 0
# n000002/0090_01.jpg 0
# ...
for file_path in glob(os.path.join(label_path, "attributes", "*.txt")):
attribute_name = os.path.basename(file_path).rstrip(".txt").split(
"-", 1)[1]
with open(file_path, encoding="utf-8") as fp:
for line in fp:
name, attribute_value = line.rstrip("\n").split("\t", 1)
all_box2d_attributes[name.rstrip(
".jpg")][attribute_name] = bool(int(attribute_value))
all_boxes = {}
for file_path in (
os.path.join(label_path, "loose_bb_test.csv"),
os.path.join(label_path, "loose_bb_train.csv"),
):
# The normal format of each line of the file is
# NAME_ID,X,Y,W,H
# "n000001/0001_01",60,60,79,109
# "n000001/0002_01",134,81,207,295
# "n000001/0003_01",58,32,75,103
# ...
with open(file_path, encoding="utf-8") as fp:
for row in islice(csv.reader(fp), 1, None):
name_id = row.pop(0).strip('"')
box = LabeledBox2D.from_xywh(*map(float, row))
box2d_attribute = all_box2d_attributes.get(name_id)
if box2d_attribute:
box.attributes = box2d_attribute
all_boxes[name_id] = box
return all_boxes
def _load_data(path: str, attribute_map: _ATTRIBUTE_MAP_TYPE, segment_name: str) -> Iterator[Data]:
"""Loads the box2d and classification label in to data.
The train and val label file context should be like::
0--Parade/0_Parade_marchingband_1_849.jpg
1
449 330 122 149 0 0 0 0 0 0
0--Parade/0_Parade_Parade_0_452.jpg
0
0 0 0 0 0 0 0 0 0 0
0--Parade/0_Parade_marchingband_1_799.jpg
21
78 221 7 8 2 0 0 0 0 0
78 238 14 17 2 0 0 0 0 0
113 212 11 15 2 0 0 0 0 0
134 260 15 15 2 0 0 0 0 0
Arguments:
path: The path of label file.
attribute_map: A attribute value enum table.
segment_name: Name of the segment.
Yields:
Data with loaded lables.
"""
is_test = segment_name == "test"
with open(path, encoding="utf-8") as fp:
for image_path in fp:
event, file_name = image_path.split("/")
# translate directory name to category. like 0--Parade -> Parade
category = "_".join(event.split("--")[1:])
root_path = path.rsplit(os.sep, 2)[0]
data = Data(
os.path.join(root_path, f"WIDER_{segment_name}", "images", event, file_name)
)
data.label.classification = Classification(category)
if not is_test:
label_num = int(fp.readline())
# when the label num is 0, a line of "0 0 0 0 0 0 0 0 0 0" also given
if label_num == 0:
fp.readline()
data.label.box2d = []
for line in islice(fp, label_num):
labels = line.strip().split()
attributes = {
key: mapping[int(value)]
for (key, mapping), value in zip(attribute_map.items(), labels[4:10])
}
data.label.box2d.append(
LabeledBox2D.from_xywh(
x=int(labels[0]),
y=int(labels[1]),
width=int(labels[2]),
height=int(labels[3]),
attributes=attributes,
)
)
yield data