def _add_gt_annotations(self, entry):
"""Add ground truth annotation metadata to an roidb entry."""
count = 0
#for k in self.category_to_id_map:
# imgs = self.COCO.getImgIds(catIds=(self.category_to_id_map[k]))
# count += len(imgs)
ann_ids = self.COCO.getAnnIds(imgIds=entry['id'], iscrowd=None)
objs = self.COCO.loadAnns(ann_ids)
# Sanitize bboxes -- some are invalid
valid_objs = []
valid_segms = []
width = entry['width']
height = entry['height']
for obj in objs:
if isinstance(obj['segmentation'], list):
# Valid polygons have >= 3 points, so require >= 6 coordinates
obj['segmentation'] = [
p for p in obj['segmentation'] if len(p) >= 6
]
if obj['area'] < cfg.TRAIN.gt_min_area:
continue
if 'ignore' in obj and obj['ignore'] == 1:
continue
# Convert form (x1, y1, w, h) to (x1, y1, x2, y2)
x1, y1, x2, y2 = box_utils.xywh_to_xyxy(obj['bbox'])
x1, y1, x2, y2 = box_utils.clip_xyxy_to_image(
x1, y1, x2, y2, height, width)
# Require non-zero seg area and more than 1x1 box size
if obj['area'] > 0 and x2 > x1 and y2 > y1:
obj['clean_bbox'] = [x1, y1, x2, y2]
valid_objs.append(obj)
valid_segms.append(obj['segmentation'])
num_valid_objs = len(valid_objs)
gt_boxes = np.zeros((num_valid_objs, 4), dtype=entry['gt_boxes'].dtype)
gt_id = np.zeros((num_valid_objs), dtype=np.int32)
gt_classes = np.zeros((num_valid_objs),
dtype=entry['gt_classes'].dtype)
is_crowd = np.zeros((num_valid_objs), dtype=entry['is_crowd'].dtype)
for ix, obj in enumerate(valid_objs):
cls = self.json_category_id_to_contiguous_id[obj['category_id']]
gt_boxes[ix, :] = obj['clean_bbox']
gt_classes[ix] = cls
gt_id[ix] = np.int32(obj['id'])
is_crowd[ix] = obj['iscrowd']
entry['gt_boxes'] = np.append(entry['gt_boxes'], gt_boxes, axis=0)
entry['gt_classes'] = np.append(entry['gt_classes'], gt_classes)
entry['gt_id'] = np.append(entry['gt_id'], gt_id)
entry['is_crowd'] = np.append(entry['is_crowd'], is_crowd)
entry['segms'].extend(valid_segms)
def _add_gt_annotations(self, entry):
"""Add ground truth annotation metadata to an roidb entry"""
ann_ids = self.COCO.getAnnIds(imgIds=entry['id'], iscrowd=None)
objs = self.COCO.loadAnns(ann_ids)
valid_objs = []
width = entry['width']
height = entry['height']
for obj in objs:
if obj['area'] < cfg.TRAIN.gt_min_area:
continue
if 'ignore' in obj and obj['ignore'] == 1:
continue
x1, y1, x2, y2 = box_utils.xywh_to_xyxy(obj['bbox'])
x1, y1, x2, y2 = box_utils.clip_xyxy_to_image(
x1, y1, x2, y2, height, width)
# require non-zero seg area and more than 1x1 box size
if obj['area'] > 0 and x2 > x1 and y2 > y1:
obj['clean_bbox'] = [x1, y1, x2, y2]
valid_objs.append(obj)
num_valid_objs = len(valid_objs)
gt_boxes = np.zeros((num_valid_objs, 4), dtype=entry['gt_boxes'].dtype)
gt_id = np.zeros((num_valid_objs), dtype=np.int64)
gt_classes = np.zeros((num_valid_objs),
dtype=entry['gt_classes'].dtype)
is_crowd = np.zeros((num_valid_objs), dtype=entry['is_crowd'].dtype)
for ix, obj in enumerate(valid_objs):
cls = self.json_category_id_to_contiguous_id[obj['category_id']]
gt_boxes[ix, :] = obj['clean_bbox']
gt_classes[ix] = cls
gt_id[ix] = np.int64(obj['id'])
is_crowd[ix] = obj['iscrowd']
entry['gt_boxes'] = np.append(entry['gt_boxes'], gt_boxes, axis=0)
entry['gt_classes'] = np.append(entry['gt_classes'], gt_classes)
entry['gt_id'] = np.append(entry['gt_id'], gt_id)
entry['is_crowd'] = np.append(entry['is_crowd'], is_crowd)