def get_instance_segmentation(self,
img,
lower_thres: list = (0, 0, 0),
upper_thresh: list = (255, 255, 255)):
color_mask = cv2.inRange(src=img,
lowerb=lower_thres,
upperb=upper_thresh)
color_contours, _ = cv2.findContours(color_mask, cv2.RETR_EXTERNAL,
cv2.CHAIN_APPROX_SIMPLE)
seg = Segmentation.from_contour(contour_list=color_contours)
return seg
def get_instance_segmentation(self,
img: np.ndarray,
target_bgr: List[int] = None,
interval: int = 1,
exclude_invalid_polygons: bool = True):
target_bgr = target_bgr if target_bgr is not None else self.get_color_from_id(
)
check_list_length(target_bgr, correct_length=3)
lower_bgr = [
val - interval if val - interval >= 0 else 0 for val in target_bgr
]
upper_bgr = [
val + interval if val + interval <= 255 else 255
for val in target_bgr
]
color_mask = cv2.inRange(src=img,
lowerb=tuple(lower_bgr),
upperb=tuple(upper_bgr))
color_contours, _ = cv2.findContours(color_mask, cv2.RETR_EXTERNAL,
cv2.CHAIN_APPROX_SIMPLE)
seg = Segmentation.from_contour(
contour_list=color_contours,
exclude_invalid_polygons=exclude_invalid_polygons)
return seg
from logger import logger
from common_utils.common_types.segmentation import Segmentation, Polygon
import numpy as np
contours = [
np.array([[[0, 0]], [[1, 0]], [[1, 2]], [[0, 1]]]),
np.array([[[0, 0]], [[1, 0]], [[1, 2]], [[0, 1]]]),
np.array([[[1, 0]], [[2, 0]], [[2, 2]], [[1, 1]]]),
np.array([[[1, 1]], [[2, 1]], [[2, 3]], [[2, 10]], [[1, 2]]]),
np.array([[[50, 100]], [[100, 200]]])
]
# seg = Segmentation.from_contour(contour_list=contours)
seg = Segmentation.from_contour(contour_list=contours,
exclude_invalid_polygons=True)
logger.purple(seg)
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)