def test_polygon_area(self):
# Draw polygon boxes
for d in [5.0, 10.0, 1000.0]:
polygon = PolygonMasks([[[0, 0, 0, d, d, d, d, 0]]])
area = polygon.area()[0]
target = d**2
self.assertEqual(area, target)
# Draw polygon triangles
for d in [5.0, 10.0, 1000.0]:
polygon = PolygonMasks([[[0, 0, 0, d, d, d]]])
area = polygon.area()[0]
target = d**2 / 2
self.assertEqual(area, target)
def convert_to_coco_dict(dataset_name):
"""
Convert an instance detection/segmentation or keypoint detection dataset
in detectron2's standard format into COCO json format.
Generic dataset description can be found here:
https://detectron2.readthedocs.io/tutorials/datasets.html#register-a-dataset
COCO data format description can be found here:
http://cocodataset.org/#format-data
Args:
dataset_name (str):
name of the source dataset
Must be registered in DatastCatalog and in detectron2's standard format.
Must have corresponding metadata "thing_classes"
Returns:
coco_dict: serializable dict in COCO json format
"""
dataset_dicts = DatasetCatalog.get(dataset_name)
metadata = MetadataCatalog.get(dataset_name)
# unmap the category mapping ids for COCO
if hasattr(metadata, "thing_dataset_id_to_contiguous_id"):
reverse_id_mapping = {v: k for k, v in metadata.thing_dataset_id_to_contiguous_id.items()}
reverse_id_mapper = lambda contiguous_id: reverse_id_mapping[contiguous_id] # noqa
else:
reverse_id_mapper = lambda contiguous_id: contiguous_id # noqa
# categories = [
# {"id": reverse_id_mapper(id), "name": name}
# for id, name in enumerate(metadata.thing_classes)
# ]
categories = [ {"id": 1, "name": 'lesion'} ]
logger.info("Converting dataset dicts into COCO format")
coco_images = []
coco_annotations = []
for image_id, image_dict in enumerate(dataset_dicts):
coco_image = {
"id": image_dict.get("image_id", image_id),
"width": int(image_dict["width"]),
"height": int(image_dict["height"]),
"file_name": str(image_dict["file_name"]),
}
coco_images.append(coco_image)
anns_per_image = image_dict.get("annotations", [])
for annotation in anns_per_image:
# create a new dict with only COCO fields
coco_annotation = {}
# COCO requirement: XYWH box format for axis-align and XYWHA for rotated
bbox = annotation["bbox"]
if isinstance(bbox, np.ndarray):
if bbox.ndim != 1:
raise ValueError(f"bbox has to be 1-dimensional. Got shape={bbox.shape}.")
bbox = bbox.tolist()
if len(bbox) not in [4, 5]:
raise ValueError(f"bbox has to has length 4 or 5. Got {bbox}.")
from_bbox_mode = annotation["bbox_mode"]
to_bbox_mode = BoxMode.XYWH_ABS if len(bbox) == 4 else BoxMode.XYWHA_ABS
bbox = BoxMode.convert(bbox, from_bbox_mode, to_bbox_mode)
# COCO requirement: instance area
if "segmentation" in annotation:
# Computing areas for instances by counting the pixels
segmentation = annotation["segmentation"]
# TODO: check segmentation type: RLE, BinaryMask or Polygon
if isinstance(segmentation, list):
polygons = PolygonMasks([segmentation])
area = polygons.area()[0].item()
elif isinstance(segmentation, dict): # RLE
area = mask_util.area(segmentation).item()
else:
raise TypeError(f"Unknown segmentation type {type(segmentation)}!")
else:
# Computing areas using bounding boxes
if to_bbox_mode == BoxMode.XYWH_ABS:
bbox_xy = BoxMode.convert(bbox, to_bbox_mode, BoxMode.XYXY_ABS)
area = Boxes([bbox_xy]).area()[0].item()
else:
area = RotatedBoxes([bbox]).area()[0].item()
if "keypoints" in annotation:
keypoints = annotation["keypoints"] # list[int]
for idx, v in enumerate(keypoints):
if idx % 3 != 2:
# COCO's segmentation coordinates are floating points in [0, H or W],
# but keypoint coordinates are integers in [0, H-1 or W-1]
# For COCO format consistency we substract 0.5
# https://github.com/facebookresearch/detectron2/pull/175#issuecomment-551202163
keypoints[idx] = v - 0.5
if "num_keypoints" in annotation:
num_keypoints = annotation["num_keypoints"]
else:
num_keypoints = sum(kp > 0 for kp in keypoints[2::3])
# COCO requirement:
# linking annotations to images
# "id" field must start with 1
coco_annotation["id"] = len(coco_annotations) + 1
coco_annotation["image_id"] = coco_image["id"]
coco_annotation["bbox"] = [round(float(x), 3) for x in bbox]
coco_annotation["area"] = float(area)
coco_annotation["iscrowd"] = int(annotation.get("iscrowd", 0))
coco_annotation["category_id"] = int(reverse_id_mapper(annotation["category_id"]))
# Add optional fields
if "keypoints" in annotation:
coco_annotation["keypoints"] = keypoints
coco_annotation["num_keypoints"] = num_keypoints
if "segmentation" in annotation:
seg = coco_annotation["segmentation"] = annotation["segmentation"]
if isinstance(seg, dict): # RLE
counts = seg["counts"]
if not isinstance(counts, str):
# make it json-serializable
seg["counts"] = counts.decode("ascii")
coco_annotations.append(coco_annotation)
logger.info(
"Conversion finished, "
f"#images: {len(coco_images)}, #annotations: {len(coco_annotations)}"
)
info = {
"date_created": str(datetime.datetime.now()),
"description": "Automatically generated COCO json file for Detectron2.",
}
coco_dict = {"info": info, "images": coco_images, "categories": categories, "licenses": None}
if len(coco_annotations) > 0:
coco_dict["annotations"] = coco_annotations
return coco_dict
def convert_to_coco_dict(dataset_name):
"""
Convert a dataset in detectron2's standard format into COCO json format
Generic dataset description can be found here:
https://detectron2.readthedocs.io/tutorials/datasets.html#register-a-dataset
COCO data format description can be found here:
http://cocodataset.org/#format-data
Args:
dataset_name:
name of the source dataset
must be registered in DatastCatalog and in detectron2's standard format
Returns:
coco_dict: serializable dict in COCO json format
"""
dataset_dicts = DatasetCatalog.get(dataset_name)
categories = [{
"id": id,
"name": name
} for id, name in enumerate(
MetadataCatalog.get(dataset_name).thing_classes)]
logger.info("Converting dataset dicts into COCO format")
coco_images = []
coco_annotations = []
for image_dict in dataset_dicts:
coco_image = {
"id": image_dict["image_id"],
"width": image_dict["width"],
"height": image_dict["height"],
"file_name": image_dict["file_name"],
}
coco_images.append(coco_image)
anns_per_image = image_dict["annotations"]
for annotation in anns_per_image:
# create a new dict with only COCO fields
coco_annotation = {}
# COCO requirement: XYWH box format
bbox = annotation["bbox"]
bbox_mode = annotation["bbox_mode"]
bbox = BoxMode.convert(bbox, bbox_mode, BoxMode.XYWH_ABS)
# COCO requirement: instance area
if "segmentation" in annotation:
# Computing areas for instances by counting the pixels
segmentation = annotation["segmentation"]
# TODO: check segmentation type: RLE, BinaryMask or Polygon
polygons = PolygonMasks([segmentation])
area = polygons.area()[0].item()
else:
# Computing areas using bounding boxes
area = Boxes([bbox]).area()[0].item()
if "keypoints" in annotation:
keypoints = annotation["keypoints"] # list[int]
for idx, v in enumerate(keypoints):
if idx % 3 != 2:
# COCO's segmentation coordinates are floating points in [0, H or W],
# but keypoint coordinates are integers in [0, H-1 or W-1]
# For COCO format consistency we substract 0.5
# https://github.com/facebookresearch/detectron2/pull/175#issuecomment-551202163
keypoints[idx] = v - 0.5
if "num_keypoints" in annotation:
num_keypoints = annotation["num_keypoints"]
else:
num_keypoints = sum(kp > 0 for kp in keypoints[2::3])
# COCO requirement:
# linking annotations to images
# "id" field must start with 1
coco_annotation["id"] = len(coco_annotations) + 1
coco_annotation["image_id"] = image_dict["image_id"]
coco_annotation["bbox"] = bbox
coco_annotation["area"] = area
coco_annotation["category_id"] = annotation["category_id"]
coco_annotation["iscrowd"] = annotation.get("iscrowd", 0)
# Add optional fields
if "keypoints" in annotation:
coco_annotation["keypoints"] = keypoints
coco_annotation["num_keypoints"] = num_keypoints
if "segmentation" in annotation:
coco_annotation["segmentation"] = annotation["segmentation"]
coco_annotations.append(coco_annotation)
logger.info(
"Conversion finished, "
f"num images: {len(coco_images)}, num annotations: {len(coco_annotations)}"
)
info = {
"date_created": str(datetime.datetime.now()),
"description":
"Automatically generated COCO json file for Detectron2.",
}
coco_dict = {
"info": info,
"images": coco_images,
"annotations": coco_annotations,
"categories": categories,
"licenses": None,
}
return coco_dict
def convert_to_coco_dict_from_detdict(dataset_dicts, metadata):
"""
See `convert_to_coco_dict`.
"""
# dataset_dicts = DatasetCatalog.get(dataset_name)
# metadata = MetadataCatalog.get(dataset_name)
# unmap the category mapping ids for COCO
if hasattr(metadata, "thing_dataset_id_to_contiguous_id"):
reverse_id_mapping = {v: k for k, v in metadata.thing_dataset_id_to_contiguous_id.items()}
reverse_id_mapper = lambda contiguous_id: reverse_id_mapping[contiguous_id] # noqa
else:
reverse_id_mapper = lambda contiguous_id: contiguous_id # noqa
categories = [
{"id": reverse_id_mapper(id), "name": name}
for id, name in enumerate(metadata.thing_classes)
]
logger.info("Converting dataset dicts into COCO format")
coco_images = []
coco_annotations = []
for image_id, image_dict in enumerate(dataset_dicts):
coco_image = {
"id": image_dict.get("image_id", image_id),
"width": image_dict["width"],
"height": image_dict["height"],
"file_name": image_dict["file_name"],
}
coco_images.append(coco_image)
anns_per_image = image_dict["annotations"]
for annotation in anns_per_image:
# create a new dict with only COCO fields
coco_annotation = {}
# COCO requirement: XYWH box format
bbox = annotation["bbox"]
bbox_mode = annotation["bbox_mode"]
bbox = BoxMode.convert(bbox, bbox_mode, BoxMode.XYWH_ABS)
# COCO requirement: instance area
if "segmentation" in annotation:
# Computing areas for instances by counting the pixels
segmentation = annotation["segmentation"]
# TODO: check segmentation type: RLE, BinaryMask or Polygon
polygons = PolygonMasks([segmentation])
area = polygons.area()[0].item()
else:
# Computing areas using bounding boxes
bbox_xy = BoxMode.convert(bbox, BoxMode.XYWH_ABS, BoxMode.XYXY_ABS)
area = Boxes([bbox_xy]).area()[0].item()
if "keypoints" in annotation:
keypoints = annotation["keypoints"] # list[int]
for idx, v in enumerate(keypoints):
if idx % 3 != 2:
# COCO's segmentation coordinates are floating points in [0, H or W],
# but keypoint coordinates are integers in [0, H-1 or W-1]
# For COCO format consistency we substract 0.5
# https://github.com/facebookresearch/detectron2/pull/175#issuecomment-551202163
keypoints[idx] = v - 0.5
if "num_keypoints" in annotation:
num_keypoints = annotation["num_keypoints"]
else:
num_keypoints = sum(kp > 0 for kp in keypoints[2::3])
# COCO requirement:
# linking annotations to images
# "id" field must start with 1
coco_annotation["id"] = len(coco_annotations) + 1
coco_annotation["image_id"] = coco_image["id"]
coco_annotation["bbox"] = [round(float(x), 3) for x in bbox]
coco_annotation["area"] = area
coco_annotation["iscrowd"] = annotation.get("iscrowd", 0)
coco_annotation["category_id"] = reverse_id_mapper(annotation["category_id"])
# Add optional fields
if "keypoints" in annotation:
coco_annotation["keypoints"] = keypoints
coco_annotation["num_keypoints"] = num_keypoints
if "segmentation" in annotation:
coco_annotation["segmentation"] = annotation["segmentation"]
coco_annotations.append(coco_annotation)
logger.info(
"Conversion finished, "
f"num images: {len(coco_images)}, num annotations: {len(coco_annotations)}"
)
info = {
"date_created": str(datetime.datetime.now()),
"description": "Automatically generated COCO json file for Detectron2.",
}
coco_dict = {
"info": info,
"images": coco_images,
"annotations": coco_annotations,
"categories": categories,
"licenses": None,
}
return coco_dict
def convert_to_coco_dict(dataset_name):
"""
Convert a dataset in detectron2's standard format into COCO json format
Generic dataset description can be found here:
https://detectron2.readthedocs.io/tutorials/datasets.html#register-a-dataset
COCO data format description can be found here:
http://cocodataset.org/#format-data
Args:
dataset_name:
name of the source dataset
must be registered in DatastCatalog and in detectron2's standard format
Returns:
coco_dict: serializable dict in COCO json format
"""
dataset_dicts = DatasetCatalog.get(dataset_name)
metadata = MetadataCatalog.get(dataset_name)
# unmap the category mapping ids for COCO
if hasattr(metadata, "thing_dataset_id_to_contiguous_id"):
reverse_id_mapping = {
v: k
for k, v in metadata.thing_dataset_id_to_contiguous_id.items()
}
reverse_id_mapper = lambda contiguous_id: reverse_id_mapping[
contiguous_id] # noqa
else:
reverse_id_mapper = lambda contiguous_id: contiguous_id # noqa
categories = [{
"id": reverse_id_mapper(id),
"name": name
} for id, name in enumerate(metadata.thing_classes)]
logger.info("Converting dataset dicts into COCO format")
coco_images = []
coco_annotations = []
for image_id, image_dict in enumerate(dataset_dicts):
coco_image = {
"id": image_dict.get("image_id", image_id),
"width": image_dict["width"],
"height": image_dict["height"],
"file_name": image_dict["file_name"],
}
coco_images.append(coco_image)
anns_per_image = image_dict["annotations"]
for annotation in anns_per_image:
# create a new dict with only COCO fields
coco_annotation = {}
# COCO requirement: XYWH box format
bbox = annotation["bbox"]
bbox_mode = annotation["bbox_mode"]
bbox = BoxMode.convert(bbox, bbox_mode, BoxMode.XYWH_ABS)
# COCO requirement: instance area
if "segmentation" in annotation:
# Computing areas for instances by counting the pixels
segmentation = annotation["segmentation"]
# TODO: check segmentation type: RLE, BinaryMask or Polygon
polygons = PolygonMasks([segmentation])
area = polygons.area()[0].item()
else:
# Computing areas using bounding boxes
bbox_xy = BoxMode.convert(bbox, BoxMode.XYWH_ABS,
BoxMode.XYXY_ABS)
area = Boxes([bbox_xy]).area()[0].item()
# COCO requirement:
# linking annotations to images
# "id" field must start with 1
coco_annotation["id"] = len(coco_annotations) + 1
coco_annotation["image_id"] = coco_image["id"]
coco_annotation["bbox"] = [round(float(x), 3) for x in bbox]
coco_annotation["area"] = area
coco_annotation["iscrowd"] = annotation.get("iscrowd", 0)
coco_annotation["category_id"] = reverse_id_mapper(
annotation["category_id"])
if "segmentation" in annotation:
coco_annotation["segmentation"] = annotation["segmentation"]
coco_annotations.append(coco_annotation)
logger.info(
"Conversion finished, "
f"num images: {len(coco_images)}, num annotations: {len(coco_annotations)}"
)
info = {
"date_created": str(datetime.datetime.now()),
"description":
"Automatically generated COCO json file for Detectron2.",
}
coco_dict = {
"info": info,
"images": coco_images,
"annotations": coco_annotations,
"categories": categories,
"licenses": None,
}
return coco_dict
def convert_to_coco_dict(dataset_name):
"""
Convert a generic dataset into COCO json format
Generic dataset description can be found here:
https://github.com/facebookresearch/detectron2/blob/master/docs/tutorials/datasets.md#register-a-dataset
COCO data format description can be found here:
http://cocodataset.org/#format-data
Args:
dataset_name: name of the source dataset
Returns:
coco_dict: serializable dict in COCO json format
"""
dataset_dicts = DatasetCatalog.get(dataset_name)
categories = [{
"id": id,
"name": name
} for id, name in enumerate(
MetadataCatalog.get(dataset_name).thing_classes)]
logger.info("Converting dataset dicts into COCO format")
images = []
annotations = []
# just for logging purposes
_annotation_keys = Counter()
for image_dict in dataset_dicts:
image = {
"id": image_dict["image_id"],
"width": image_dict["width"],
"height": image_dict["height"],
"file_name": image_dict["file_name"],
}
images.append(image)
# deep-copying various annotations from the original format
# can be bbox, segmentation, keypoint, etc.
anns_per_image = deepcopy(image_dict["annotations"])
for annotation in anns_per_image:
# COCO requirement: linking annotations to images
annotation["id"] = len(annotations) + 1
annotation["image_id"] = image_dict["image_id"]
# COCO requirement: XYWH box format
bbox = annotation["bbox"]
bbox_mode = annotation["bbox_mode"]
bbox = BoxMode.convert(bbox, bbox_mode, BoxMode.XYWH_ABS)
del annotation["bbox_mode"]
# TODO: make BBOX_MODE serializable, otherwise remove it
annotation["bbox"] = bbox
annotation["iscrowd"] = 0
# COCO requirement: instance area
if "segmentation" in annotation:
# Computing areas for instances by counting the pixels
segmentation = annotation["segmentation"]
# TODO: check segmentation type: RLE, BinaryMask or Polygon
polygons = PolygonMasks([segmentation])
area = polygons.area()[0]
else:
# Computing areas using bounding boxes
area = Boxes([bbox]).area()[0]
annotation["area"] = float(area)
# Keeping track of fields present in instances
_annotation_keys.update(annotation.keys())
annotations.append(annotation)
logger.info(
"Conversion finished, "
f"num images: {len(images)}, num annotations: {len(annotations)}")
logger.info(f"Annotation fields: {_annotation_keys}")
if len(_annotation_keys.most_common()) != len(_annotation_keys):
logger.warning(
f"Annotation fields are not homogenous between instances")
info = {
"date_created": str(datetime.datetime.now()),
"description":
"Automatically generated COCO json file for Detectron2.",
}
coco_dict = {
"info": info,
"images": images,
"annotations": annotations,
"categories": categories,
"licenses": None,
}
return coco_dict
