in models/vision/detection/awsdet/core/evaluation/mean_ap.py [0:0]
def tpfp_imagenet(det_bboxes,
gt_bboxes,
gt_bboxes_ignore=None,
default_iou_thr=0.5,
area_ranges=None):
"""Check if detected bboxes are true positive or false positive.
Args:
det_bbox (ndarray): Detected bboxes of this image, of shape (m, 5).
gt_bboxes (ndarray): GT bboxes of this image, of shape (n, 4).
gt_bboxes_ignore (ndarray): Ignored gt bboxes of this image,
of shape (k, 4). Default: None
default_iou_thr (float): IoU threshold to be considered as matched for
medium and large bboxes (small ones have special rules).
Default: 0.5.
area_ranges (list[tuple] | None): Range of bbox areas to be evaluated,
in the format [(min1, max1), (min2, max2), ...]. Default: None.
Returns:
tuple[np.ndarray]: (tp, fp) whose elements are 0 and 1. The shape of
each array is (num_scales, m).
"""
# an indicator of ignored gts
gt_ignore_inds = np.concatenate(
(np.zeros(gt_bboxes.shape[0], dtype=np.bool),
np.ones(gt_bboxes_ignore.shape[0], dtype=np.bool)))
# stack gt_bboxes and gt_bboxes_ignore for convenience
gt_bboxes = np.vstack((gt_bboxes, gt_bboxes_ignore))
num_dets = det_bboxes.shape[0]
num_gts = gt_bboxes.shape[0]
if area_ranges is None:
area_ranges = [(None, None)]
num_scales = len(area_ranges)
# tp and fp are of shape (num_scales, num_gts), each row is tp or fp
# of a certain scale.
tp = np.zeros((num_scales, num_dets), dtype=np.float32)
fp = np.zeros((num_scales, num_dets), dtype=np.float32)
if gt_bboxes.shape[0] == 0:
if area_ranges == [(None, None)]:
fp[...] = 1
else:
det_areas = (det_bboxes[:, 2] - det_bboxes[:, 0]) * (
det_bboxes[:, 3] - det_bboxes[:, 1])
for i, (min_area, max_area) in enumerate(area_ranges):
fp[i, (det_areas >= min_area) & (det_areas < max_area)] = 1
return tp, fp
ious = bbox_overlaps(det_bboxes, gt_bboxes - 1)
gt_w = gt_bboxes[:, 2] - gt_bboxes[:, 0]
gt_h = gt_bboxes[:, 3] - gt_bboxes[:, 1]
iou_thrs = np.minimum((gt_w * gt_h) / ((gt_w + 10.0) * (gt_h + 10.0)),
default_iou_thr)
# sort all detections by scores in descending order
sort_inds = np.argsort(-det_bboxes[:, -1])
for k, (min_area, max_area) in enumerate(area_ranges):
gt_covered = np.zeros(num_gts, dtype=bool)
# if no area range is specified, gt_area_ignore is all False
if min_area is None:
gt_area_ignore = np.zeros_like(gt_ignore_inds, dtype=bool)
else:
gt_areas = gt_w * gt_h
gt_area_ignore = (gt_areas < min_area) | (gt_areas >= max_area)
for i in sort_inds:
max_iou = -1
matched_gt = -1
# find best overlapped available gt
for j in range(num_gts):
# different from PASCAL VOC: allow finding other gts if the
# best overlaped ones are already matched by other det bboxes
if gt_covered[j]:
continue
elif ious[i, j] >= iou_thrs[j] and ious[i, j] > max_iou:
max_iou = ious[i, j]
matched_gt = j
# there are 4 cases for a det bbox:
# 1. it matches a gt, tp = 1, fp = 0
# 2. it matches an ignored gt, tp = 0, fp = 0
# 3. it matches no gt and within area range, tp = 0, fp = 1
# 4. it matches no gt but is beyond area range, tp = 0, fp = 0
if matched_gt >= 0:
gt_covered[matched_gt] = 1
if not (gt_ignore_inds[matched_gt]
or gt_area_ignore[matched_gt]):
tp[k, i] = 1
elif min_area is None:
fp[k, i] = 1
else:
bbox = det_bboxes[i, :4]
area = (bbox[2] - bbox[0]) * (bbox[3] - bbox[1])
if area >= min_area and area < max_area:
fp[k, i] = 1
return tp, fp