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)
# Sanitize bboxes -- some are invalid
valid_objs = []
valid_segms = []
width = entry['width']
height = entry['height']
for obj in objs:
# crowd regions are RLE encoded and stored as dicts
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)
boxes = np.zeros((num_valid_objs, 4), dtype=entry['boxes'].dtype)
gt_classes = np.zeros((num_valid_objs),
dtype=entry['gt_classes'].dtype)
gt_overlaps = np.zeros((num_valid_objs, self.num_classes),
dtype=entry['gt_overlaps'].dtype)
seg_areas = np.zeros((num_valid_objs), dtype=entry['seg_areas'].dtype)
is_crowd = np.zeros((num_valid_objs), dtype=entry['is_crowd'].dtype)
box_to_gt_ind_map = np.zeros((num_valid_objs),
dtype=entry['box_to_gt_ind_map'].dtype)
if self.keypoints is not None:
gt_keypoints = np.zeros((num_valid_objs, 3, self.num_keypoints),
dtype=entry['gt_keypoints'].dtype)
im_has_visible_keypoints = False
for ix, obj in enumerate(valid_objs):
cls = self.json_category_id_to_contiguous_id[obj['category_id']]
boxes[ix, :] = obj['clean_bbox']
gt_classes[ix] = cls
seg_areas[ix] = obj['area']
is_crowd[ix] = obj['iscrowd']
box_to_gt_ind_map[ix] = ix
if self.keypoints is not None:
gt_keypoints[ix, :, :] = self._get_gt_keypoints(obj)
if np.sum(gt_keypoints[ix, 2, :]) > 0:
im_has_visible_keypoints = True
if obj['iscrowd']:
# Set overlap to -1 for all classes for crowd objects
# so they will be excluded during training
gt_overlaps[ix, :] = -1.0
else:
gt_overlaps[ix, cls] = 1.0
entry['boxes'] = np.append(entry['boxes'], boxes, axis=0)
entry['segms'].extend(valid_segms)
# To match the original implementation:
# entry['boxes'] = np.append(
# entry['boxes'], boxes.astype(np.int).astype(np.float), axis=0)
entry['gt_classes'] = np.append(entry['gt_classes'], gt_classes)
entry['seg_areas'] = np.append(entry['seg_areas'], seg_areas)
entry['gt_overlaps'] = np.append(entry['gt_overlaps'].toarray(),
gt_overlaps,
axis=0)
entry['gt_overlaps'] = scipy.sparse.csr_matrix(entry['gt_overlaps'])
entry['is_crowd'] = np.append(entry['is_crowd'], is_crowd)
entry['box_to_gt_ind_map'] = np.append(entry['box_to_gt_ind_map'],
box_to_gt_ind_map)
if self.keypoints is not None:
entry['gt_keypoints'] = np.append(entry['gt_keypoints'],
gt_keypoints,
axis=0)
entry['has_visible_keypoints'] = im_has_visible_keypoints
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)
# Sanitize bboxes -- some are invalid
valid_objs = []
valid_segms = []
####
valid_dp_x = []
valid_dp_y = []
valid_dp_I = []
valid_dp_U = []
valid_dp_V = []
valid_dp_masks = []
####
width = entry['width']
height = entry['height']
for obj in objs:
# crowd regions are RLE encoded and stored as dicts
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'])
###
if 'dp_x' in obj.keys():
valid_dp_x.append(obj['dp_x'])
valid_dp_y.append(obj['dp_y'])
valid_dp_I.append(obj['dp_I'])
valid_dp_U.append(obj['dp_U'])
valid_dp_V.append(obj['dp_V'])
valid_dp_masks.append(obj['dp_masks'])
else:
valid_dp_x.append([])
valid_dp_y.append([])
valid_dp_I.append([])
valid_dp_U.append([])
valid_dp_V.append([])
valid_dp_masks.append([])
###
num_valid_objs = len(valid_objs)
##
boxes = np.zeros((num_valid_objs, 4), dtype=entry['boxes'].dtype)
gt_classes = np.zeros((num_valid_objs), dtype=entry['gt_classes'].dtype)
gt_overlaps = np.zeros(
(num_valid_objs, self.num_classes),
dtype=entry['gt_overlaps'].dtype
)
seg_areas = np.zeros((num_valid_objs), dtype=entry['seg_areas'].dtype)
is_crowd = np.zeros((num_valid_objs), dtype=entry['is_crowd'].dtype)
box_to_gt_ind_map = np.zeros(
(num_valid_objs), dtype=entry['box_to_gt_ind_map'].dtype
)
if self.keypoints is not None:
gt_keypoints = np.zeros(
(num_valid_objs, 3, self.num_keypoints),
dtype=entry['gt_keypoints'].dtype
)
if cfg.MODEL.BODY_UV_ON:
ignore_UV_body = np.zeros((num_valid_objs))
#Box_image_body = [None]*num_valid_objs
im_has_visible_keypoints = False
im_has_any_body_uv = False
for ix, obj in enumerate(valid_objs):
cls = self.json_category_id_to_contiguous_id[obj['category_id']]
boxes[ix, :] = obj['clean_bbox']
gt_classes[ix] = cls
seg_areas[ix] = obj['area']
is_crowd[ix] = obj['iscrowd']
box_to_gt_ind_map[ix] = ix
if self.keypoints is not None:
gt_keypoints[ix, :, :] = self._get_gt_keypoints(obj)
if np.sum(gt_keypoints[ix, 2, :]) > 0:
im_has_visible_keypoints = True
if cfg.MODEL.BODY_UV_ON:
if 'dp_x' in obj:
ignore_UV_body[ix] = False
im_has_any_body_uv = True
else:
ignore_UV_body[ix] = True
if obj['iscrowd']:
# Set overlap to -1 for all classes for crowd objects
# so they will be excluded during training
gt_overlaps[ix, :] = -1.0
else:
gt_overlaps[ix, cls] = 1.0
entry['boxes'] = np.append(entry['boxes'], boxes, axis=0)
entry['segms'].extend(valid_segms)
entry['dp_x'].extend(valid_dp_x)
entry['dp_y'].extend(valid_dp_y)
entry['dp_I'].extend(valid_dp_I)
entry['dp_U'].extend(valid_dp_U)
entry['dp_V'].extend(valid_dp_V)
entry['dp_masks'].extend(valid_dp_masks)
entry['gt_classes'] = np.append(entry['gt_classes'], gt_classes)
entry['seg_areas'] = np.append(entry['seg_areas'], seg_areas)
entry['gt_overlaps'] = np.append(
entry['gt_overlaps'].toarray(), gt_overlaps, axis=0
)
entry['gt_overlaps'] = scipy.sparse.csr_matrix(entry['gt_overlaps'])
entry['is_crowd'] = np.append(entry['is_crowd'], is_crowd)
entry['box_to_gt_ind_map'] = np.append(
entry['box_to_gt_ind_map'], box_to_gt_ind_map
)
if self.keypoints is not None:
entry['gt_keypoints'] = np.append(
entry['gt_keypoints'], gt_keypoints, axis=0
)
entry['has_visible_keypoints'] = im_has_visible_keypoints
if cfg.MODEL.BODY_UV_ON:
entry['ignore_UV_body'] = np.append(entry['ignore_UV_body'], ignore_UV_body)
#entry['Box_image_links_body'].extend(Box_image_body)
entry['has_body_uv'] = im_has_any_body_uv
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)
# Sanitize bboxes -- some are invalid
valid_objs = []
valid_segms = []
width = entry['width']
height = entry['height']
for obj in objs:
# crowd regions are RLE encoded
if segm_utils.is_poly(obj['segmentation']):
# 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)
boxes = np.zeros((num_valid_objs, 4), dtype=entry['boxes'].dtype)
gt_classes = np.zeros((num_valid_objs), dtype=entry['gt_classes'].dtype)
gt_overlaps = np.zeros(
(num_valid_objs, self.num_classes),
dtype=entry['gt_overlaps'].dtype
)
seg_areas = np.zeros((num_valid_objs), dtype=entry['seg_areas'].dtype)
is_crowd = np.zeros((num_valid_objs), dtype=entry['is_crowd'].dtype)
box_to_gt_ind_map = np.zeros(
(num_valid_objs), dtype=entry['box_to_gt_ind_map'].dtype
)
if self.keypoints is not None:
gt_keypoints = np.zeros(
(num_valid_objs, 3, self.num_keypoints),
dtype=entry['gt_keypoints'].dtype
)
im_has_visible_keypoints = False
for ix, obj in enumerate(valid_objs):
cls = self.json_category_id_to_contiguous_id[obj['category_id']]
boxes[ix, :] = obj['clean_bbox']
gt_classes[ix] = cls
seg_areas[ix] = obj['area']
is_crowd[ix] = obj['iscrowd']
box_to_gt_ind_map[ix] = ix
if self.keypoints is not None:
gt_keypoints[ix, :, :] = self._get_gt_keypoints(obj)
if np.sum(gt_keypoints[ix, 2, :]) > 0:
im_has_visible_keypoints = True
if obj['iscrowd']:
# Set overlap to -1 for all classes for crowd objects
# so they will be excluded during training
gt_overlaps[ix, :] = -1.0
else:
gt_overlaps[ix, cls] = 1.0
entry['boxes'] = np.append(entry['boxes'], boxes, axis=0)
entry['segms'].extend(valid_segms)
# To match the original implementation:
# entry['boxes'] = np.append(
# entry['boxes'], boxes.astype(np.int).astype(np.float), axis=0)
entry['gt_classes'] = np.append(entry['gt_classes'], gt_classes)
entry['seg_areas'] = np.append(entry['seg_areas'], seg_areas)
entry['gt_overlaps'] = np.append(
entry['gt_overlaps'].toarray(), gt_overlaps, axis=0
)
entry['gt_overlaps'] = scipy.sparse.csr_matrix(entry['gt_overlaps'])
entry['is_crowd'] = np.append(entry['is_crowd'], is_crowd)
entry['box_to_gt_ind_map'] = np.append(
entry['box_to_gt_ind_map'], box_to_gt_ind_map
)
if self.keypoints is not None:
entry['gt_keypoints'] = np.append(
entry['gt_keypoints'], gt_keypoints, axis=0
)
entry['has_visible_keypoints'] = im_has_visible_keypoints
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)
# Sanitize bboxes -- some are invalid
valid_objs = []
valid_segms = []
####
valid_dp_x = []
valid_dp_y = []
valid_dp_I = []
valid_dp_U = []
valid_dp_V = []
valid_dp_masks = []
####
width = entry['width']
height = entry['height']
for obj in objs:
# crowd regions are RLE encoded
if segm_utils.is_poly(obj['segmentation']):
# 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'])
###
if 'dp_x' in obj:
valid_dp_x.append(obj['dp_x'])
valid_dp_y.append(obj['dp_y'])
valid_dp_I.append(obj['dp_I'])
valid_dp_U.append(obj['dp_U'])
valid_dp_V.append(obj['dp_V'])
valid_dp_masks.append(obj['dp_masks'])
else:
valid_dp_x.append([])
valid_dp_y.append([])
valid_dp_I.append([])
valid_dp_U.append([])
valid_dp_V.append([])
valid_dp_masks.append([])
###
num_valid_objs = len(valid_objs)
boxes = np.zeros((num_valid_objs, 4), dtype=entry['boxes'].dtype)
gt_classes = np.zeros((num_valid_objs),
dtype=entry['gt_classes'].dtype)
gt_overlaps = np.zeros((num_valid_objs, self.num_classes),
dtype=entry['gt_overlaps'].dtype)
seg_areas = np.zeros((num_valid_objs), dtype=entry['seg_areas'].dtype)
is_crowd = np.zeros((num_valid_objs), dtype=entry['is_crowd'].dtype)
box_to_gt_ind_map = np.zeros((num_valid_objs),
dtype=entry['box_to_gt_ind_map'].dtype)
if self.keypoints is not None:
gt_keypoints = np.zeros((num_valid_objs, 3, self.num_keypoints),
dtype=entry['gt_keypoints'].dtype)
if cfg.MODEL.BODY_UV_ON:
ignore_UV_body = np.zeros((num_valid_objs),
dtype=entry['ignore_UV_body'].dtype)
#Box_image_body = [None]*num_valid_objs
im_has_visible_keypoints = False
im_has_any_body_uv = False
for ix, obj in enumerate(valid_objs):
cls = self.json_category_id_to_contiguous_id[obj['category_id']]
boxes[ix, :] = obj['clean_bbox']
gt_classes[ix] = cls
seg_areas[ix] = obj['area']
is_crowd[ix] = obj['iscrowd']
box_to_gt_ind_map[ix] = ix
if self.keypoints is not None:
gt_keypoints[ix, :, :] = self._get_gt_keypoints(obj)
if np.sum(gt_keypoints[ix, 2, :]) > 0:
im_has_visible_keypoints = True
if cfg.MODEL.BODY_UV_ON:
if 'dp_x' in obj:
ignore_UV_body[ix] = False
im_has_any_body_uv = True
else:
ignore_UV_body[ix] = True
if obj['iscrowd']:
# Set overlap to -1 for all classes for crowd objects
# so they will be excluded during training
gt_overlaps[ix, :] = -1.0
else:
gt_overlaps[ix, cls] = 1.0
entry['boxes'] = np.append(entry['boxes'], boxes, axis=0)
entry['segms'].extend(valid_segms)
entry['dp_x'].extend(valid_dp_x)
entry['dp_y'].extend(valid_dp_y)
entry['dp_I'].extend(valid_dp_I)
entry['dp_U'].extend(valid_dp_U)
entry['dp_V'].extend(valid_dp_V)
entry['dp_masks'].extend(valid_dp_masks)
entry['gt_classes'] = np.append(entry['gt_classes'], gt_classes)
entry['seg_areas'] = np.append(entry['seg_areas'], seg_areas)
entry['gt_overlaps'] = np.append(entry['gt_overlaps'].toarray(),
gt_overlaps,
axis=0)
entry['gt_overlaps'] = scipy.sparse.csr_matrix(entry['gt_overlaps'])
entry['is_crowd'] = np.append(entry['is_crowd'], is_crowd)
entry['box_to_gt_ind_map'] = np.append(entry['box_to_gt_ind_map'],
box_to_gt_ind_map)
if self.keypoints is not None:
entry['gt_keypoints'] = np.append(entry['gt_keypoints'],
gt_keypoints,
axis=0)
entry['has_visible_keypoints'] = im_has_visible_keypoints
if cfg.MODEL.BODY_UV_ON:
entry['ignore_UV_body'] = np.append(entry['ignore_UV_body'],
ignore_UV_body)
#entry['Box_image_links_body'].extend(Box_image_body)
entry['has_body_uv'] = im_has_any_body_uv
def _add_gt_annotations(self, entry):
"""Add ground truth annotation metadata to an roidb entry."""
objs = self.load_objs_from_index(entry)
# Sanitize bboxes -- some are invalid
valid_objs = []
valid_segms = []
width = entry['width']
height = entry['height']
for obj in objs:
x1, y1, x2, y2 = obj['bbox']
x1, y1, x2, y2 = box_utils.clip_xyxy_to_image(
x1, y1, x2, y2, height, width)
if x2 > x1 and y2 > y1:
obj['clean_bbox'] = [x1, y1, x2, y2]
valid_objs.append(obj)
num_valid_objs = len(valid_objs)
boxes = np.zeros((num_valid_objs, 4), dtype=entry['boxes'].dtype)
gt_classes = np.zeros((num_valid_objs),
dtype=entry['gt_classes'].dtype)
gt_overlaps = np.zeros((num_valid_objs, self.num_classes),
dtype=entry['gt_overlaps'].dtype)
seg_areas = np.zeros((num_valid_objs), dtype=entry['seg_areas'].dtype)
is_crowd = np.zeros((num_valid_objs), dtype=entry['is_crowd'].dtype)
box_to_gt_ind_map = np.zeros((num_valid_objs),
dtype=entry['box_to_gt_ind_map'].dtype)
if self.keypoints is not None:
gt_keypoints = np.zeros((num_valid_objs, 3, self.num_keypoints),
dtype=entry['gt_keypoints'].dtype)
im_has_visible_keypoints = False
for ix, obj in enumerate(valid_objs):
if obj['cls_name'] == 'ignore':
cls = -1
else:
cls = self._class_to_ind[obj['cls_name']]
boxes[ix, :] = obj['clean_bbox']
gt_classes[ix] = cls
seg_areas[ix] = 0
is_crowd[ix] = obj['iscrowd']
box_to_gt_ind_map[ix] = ix
if self.keypoints is not None:
gt_keypoints[ix, :, :] = self._get_gt_keypoints(obj)
if np.sum(gt_keypoints[ix, 2, :]) > 0:
im_has_visible_keypoints = True
if obj['iscrowd']:
# Set overlap to -1 for all classes for crowd objects
# so they will be excluded during training
gt_overlaps[ix, :] = -1.0
else:
gt_overlaps[ix, cls] = 1.0
entry['boxes'] = np.append(entry['boxes'], boxes, axis=0)
entry['segms'].extend(valid_segms)
# To match the original implementation:
# entry['boxes'] = np.append(
# entry['boxes'], boxes.astype(np.int).astype(np.float), axis=0)
entry['gt_classes'] = np.append(entry['gt_classes'], gt_classes)
entry['seg_areas'] = np.append(entry['seg_areas'], seg_areas)
entry['gt_overlaps'] = np.append(entry['gt_overlaps'].toarray(),
gt_overlaps,
axis=0)
entry['gt_overlaps'] = scipy.sparse.csr_matrix(entry['gt_overlaps'])
entry['is_crowd'] = np.append(entry['is_crowd'], is_crowd)
entry['box_to_gt_ind_map'] = np.append(entry['box_to_gt_ind_map'],
box_to_gt_ind_map)
if self.keypoints is not None:
entry['gt_keypoints'] = np.append(entry['gt_keypoints'],
gt_keypoints,
axis=0)
entry['has_visible_keypoints'] = im_has_visible_keypoints
