def eval_recall(args):
f = open(args.det_file, "r")
dets, det_bboxes = cp.load(f)
f.close()
f = open(args.gt_file, "r")
all_gts, all_gt_bboxes = cp.load(f)
f.close()
num_img = len(dets)
tp = []
fp = []
score = []
total_num_gts = 0
for i in xrange(num_img):
gts = all_gts[i]
gt_bboxes = all_gt_bboxes[i]
gt_ubbs = []
num_gts = gts.shape[0]
for j in xrange(num_gts):
gt_ubbs.append(getUnionBB(gt_bboxes[j, 0, :], gt_bboxes[j, 1, :]))
total_num_gts += num_gts
gt_detected = np.zeros(num_gts)
if dets[i].shape[0] > 0:
det_score = np.log(dets[i][:, 0]) + np.log(dets[i][:, 1]) + np.log(
dets[i][:, 2])
inds = np.argsort(det_score)[::-1]
if args.num_dets > 0 and args.num_dets < len(inds):
inds = inds[:args.num_dets]
top_dets = dets[i][inds, 3:]
top_scores = det_score[inds]
top_det_bboxes = det_bboxes[i][inds, :]
top_det_ubbs = []
num_dets = len(inds)
for j in xrange(num_dets):
top_det_ubbs.append(
getUnionBB(top_det_bboxes[j, 0, :], top_det_bboxes[j,
1, :]))
for j in xrange(num_dets):
ov_max = 0
arg_max = -1
for k in xrange(num_gts):
if gt_detected[k] == 0 and top_dets[j, 0] == gts[
k, 0] and top_dets[j, 1] == gts[k, 1] and top_dets[
j, 2] == gts[k, 2]:
ov = computeIoU(top_det_ubbs[j], gt_ubbs[k])
if ov >= args.ov_thresh and ov > ov_max:
ov_max = ov
arg_max = k
if arg_max != -1:
gt_detected[arg_max] = 1
tp.append(1)
fp.append(0)
else:
tp.append(0)
fp.append(1)
score.append(top_scores[j])
score = np.array(score)
tp = np.array(tp)
fp = np.array(fp)
inds = np.argsort(score)
inds = inds[::-1]
tp = tp[inds]
fp = fp[inds]
tp = np.cumsum(tp)
fp = np.cumsum(fp)
recall = (tp + 0.0) / total_num_gts
top_recall = recall[-1]
print top_recall
def computeOverlap(detBBs, gtBBs):
aIoU = computeIoU(detBBs[0, :], gtBBs[0, :])
bIoU = computeIoU(detBBs[1, :], gtBBs[1, :])
return min(aIoU, bIoU)