def from_dict(cls, ann_dict: dict, strict: bool = True) -> COCO_Annotation:
if strict:
check_required_keys(ann_dict,
required_keys=[
'segmentation', 'num_keypoints', 'area',
'iscrowd', 'keypoints', 'image_id', 'bbox',
'category_id', 'id'
])
return COCO_Annotation(
segmentation=Segmentation.from_list(ann_dict['segmentation'],
demarcation=False),
num_keypoints=ann_dict['num_keypoints'],
area=ann_dict['area'],
iscrowd=ann_dict['iscrowd'],
keypoints=Keypoint2D_List.from_list(ann_dict['keypoints'],
demarcation=False),
image_id=ann_dict['image_id'],
bbox=BBox.from_list(ann_dict['bbox'], input_format='pminsize'),
category_id=ann_dict['category_id'],
id=ann_dict['id'],
keypoints_3d=Keypoint3D_List.from_list(
ann_dict['keypoints_3d'], demarcation=False)
if 'keypoints_3d' in ann_dict else None,
camera=Camera.from_dict(ann_dict['camera_params'])
if 'camera_params' in ann_dict else None)
else:
check_required_keys(
ann_dict, required_keys=['id', 'category_id', 'image_id'])
return COCO_Annotation(
segmentation=Segmentation.from_list(ann_dict['segmentation'],
demarcation=False)
if 'segmentation' in ann_dict else None,
num_keypoints=ann_dict['num_keypoints']
if 'num_keypoints' in ann_dict else None,
area=ann_dict['area'] if 'area' in ann_dict else None,
iscrowd=ann_dict['iscrowd'] if 'iscrowd' in ann_dict else None,
keypoints=Keypoint2D_List.from_list(ann_dict['keypoints'],
demarcation=False)
if 'keypoints' in ann_dict else None,
image_id=ann_dict['image_id'],
bbox=BBox.from_list(ann_dict['bbox'], input_format='pminsize')
if 'bbox' in ann_dict else None,
category_id=ann_dict['category_id'],
id=ann_dict['id'],
keypoints_3d=Keypoint3D_List.from_list(
ann_dict['keypoints_3d'], demarcation=False)
if 'keypoints_3d' in ann_dict else None,
camera=Camera.from_dict(ann_dict['camera_params'])
if 'camera_params' in ann_dict else None)
def from_dict(cls, ann_dict: dict) -> Detectron2_Annotation:
check_required_keys(
ann_dict,
required_keys=['iscrowd', 'bbox', 'category_id', 'bbox_mode'])
if ann_dict['bbox_mode'] == BoxMode.XYWH_ABS:
bbox = BBox.from_list(ann_dict['bbox'], input_format='pminsize')
elif ann_dict['bbox_mode'] == BoxMode.XYXY_ABS:
bbox = BBox.from_list(ann_dict['bbox'], input_format='pminpmax')
else:
raise NotImplementedError
if 'keypoints' in ann_dict:
keypoints = Keypoint2D_List.from_list(
value_list=ann_dict['keypoints'], demarcation=False)
else:
keypoints = Keypoint2D_List()
return Detectron2_Annotation(iscrowd=ann_dict['iscrowd'],
bbox=bbox,
keypoints=keypoints,
category_id=ann_dict['category_id'],
segmentation=Segmentation.from_list(
points_list=ann_dict['segmentation']
if 'segmentation' in ann_dict else [],
demarcation=False),
bbox_mode=ann_dict['bbox_mode'])
def generate_visualizations(self, do_sort: bool = False):
delete_dir_if_exists(self.visualization_dump_dir)
make_dir(self.visualization_dump_dir)
if do_sort:
self.parser.images.image_list.sort(
key=operator.attrgetter('file_name'))
for coco_image in self.parser.images.image_list:
coco_image = COCO_Image.buffer(coco_image)
img_path = f"{self.img_dir}/{coco_image.file_name}"
img = cv2.imread(img_path)
coco_annotation_list = self.parser.annotations.get_annotations_from_imgIds(
imgIds=[coco_image.id])
bbox_list = []
kpt_list_list = []
kpt_skeleton_list = []
kpt_label_list_list = []
seg_list = []
cat_name_list = []
for coco_annotation in coco_annotation_list:
coco_annotation = COCO_Annotation.buffer(coco_annotation)
bbox = coco_annotation.bounding_box
keypoint_list = coco_annotation.get_keypoint_list()
kpt_list = [[kpt.x, kpt.y] for kpt in keypoint_list]
seg = Segmentation.from_list(
points_list=coco_annotation.segmentation)
bbox_list.append(bbox)
kpt_list_list.append(kpt_list)
seg_list.append(seg)
coco_category = self.parser.categories.get_category_from_id(
id=coco_annotation.category_id)
cat_name_list.append(coco_category.name)
kpt_skeleton_list.append(coco_category.skeleton)
kpt_label_list_list.append(coco_category.keypoints)
self.save(img=img,
bbox_list=bbox_list,
kpt_list_list=kpt_list_list,
kpt_skeleton_list=kpt_skeleton_list,
kpt_label_list_list=kpt_label_list_list,
seg_list=seg_list,
cat_name_list=cat_name_list,
file_name=coco_image.file_name)
if self.viz_limit is not None and self.viz_count >= self.viz_limit:
break
def from_dict(cls, ann_dict: dict, strict: bool = True) -> COCO_Annotation:
if strict:
check_required_keys(ann_dict,
required_keys=[
'segmentation', 'num_keypoints', 'area',
'iscrowd', 'keypoints', 'image_id', 'bbox',
'category_id', 'id'
])
return COCO_Annotation(
segmentation=Segmentation.from_list(ann_dict['segmentation'],
demarcation=False),
num_keypoints=ann_dict['num_keypoints'],
area=ann_dict['area'],
iscrowd=ann_dict['iscrowd'],
keypoints=Keypoint2D_List.from_list(ann_dict['keypoints'],
demarcation=False),
image_id=ann_dict['image_id'],
bbox=BBox.from_list(ann_dict['bbox'], input_format='pminsize'),
category_id=ann_dict['category_id'],
id=ann_dict['id'],
keypoints_3d=Keypoint3D_List.from_list(
ann_dict['keypoints_3d'], demarcation=False)
if 'keypoints_3d' in ann_dict else None,
camera=Camera.from_dict(ann_dict['camera_params'])
if 'camera_params' in ann_dict else None)
else:
check_required_keys(
ann_dict, required_keys=['id', 'category_id', 'image_id'])
if 'segmentation' not in ann_dict:
seg = None
elif ann_dict['iscrowd'] == 1 and type(
ann_dict['segmentation']) == dict:
compressed_rle = mask.frPyObjects(
ann_dict['segmentation'],
ann_dict['segmentation']['size'][0],
ann_dict['segmentation']['size'][1])
seg_mask = mask.decode(compressed_rle)
contours, _ = cv2.findContours(seg_mask, cv2.RETR_EXTERNAL,
cv2.CHAIN_APPROX_SIMPLE)
seg = Segmentation.from_contour(contour_list=contours)
else:
seg = Segmentation.from_list(ann_dict['segmentation'],
demarcation=False)
return COCO_Annotation(
segmentation=seg,
num_keypoints=ann_dict['num_keypoints']
if 'num_keypoints' in ann_dict else None,
area=ann_dict['area'] if 'area' in ann_dict else None,
iscrowd=ann_dict['iscrowd'] if 'iscrowd' in ann_dict else None,
keypoints=Keypoint2D_List.from_list(ann_dict['keypoints'],
demarcation=False)
if 'keypoints' in ann_dict else None,
image_id=ann_dict['image_id'],
bbox=BBox.from_list(ann_dict['bbox'], input_format='pminsize')
if 'bbox' in ann_dict else None,
category_id=ann_dict['category_id'],
id=ann_dict['id'],
keypoints_3d=Keypoint3D_List.from_list(
ann_dict['keypoints_3d'], demarcation=False)
if 'keypoints_3d' in ann_dict else None,
camera=Camera.from_dict(ann_dict['camera_params'])
if 'camera_params' in ann_dict else None)