def get_iou(dets_corners, track_corners, flag):
if flag == 1:
dets_standup = box_np_ops.corner_to_standup_nd(dets_corners)
track_standup = box_np_ops.corner_to_standup_nd(track_corners)
else:
dets_standup = box_np_ops.corner_to_standup_nd(dets_corners)
track_standup = dets_standup
standup_iou = box_np_ops.iou_jit(dets_standup, track_standup, eps=0.0)
standup_iou_new = standup_iou
if flag != 1:
for i in range(standup_iou.shape[0]):
standup_iou[i][i] = 0
re_cal = np.where(standup_iou > 0)
no_care = []
if re_cal[0].shape[0] == 0:
return standup_iou, standup_iou_new
for i in range(re_cal[0].shape[0]):
if re_cal[0][i] == re_cal[1][i]:
continue
if re_cal[0][i] in no_care:
continue
# # #####***** 调用模块进行IOU计算 *****#####
# IOU = intersection(dets_corners[re_cal[0][i]].tolist(), trackers_corners[re_cal[1][i]].tolist())
# #####***** 调用自定义函数进行IOU计算耗时稍微小一些 *****#####
interpoly = []
b = cal_iou.PolygonClip(dets_corners[re_cal[0][i]].tolist(),
track_corners[re_cal[1][i]].tolist(),
interpoly)
if b:
list2 = []
for num_i in range(len(interpoly)):
list2.append(interpoly[num_i])
for num_i in range(len(list2) - 1):
for num_j in range(num_i + 1, len(list2)):
if operator.eq(list2[num_i], list2[num_j]):
interpoly.pop(num_j)
area = cal_iou.CalArea(interpoly)
area1 = cal_iou.CalArea(dets_corners[re_cal[0][i]].tolist())
area2 = cal_iou.CalArea(track_corners[re_cal[1][i]].tolist())
IOU = area / (area2 + area1 - area)
IOU_new = area / area1
else:
IOU = 0
IOU_new = 0
no_care.append(re_cal[1][i])
if flag:
standup_iou[re_cal[0][i]][re_cal[1][i]] = IOU
standup_iou_new[re_cal[0][i]][re_cal[1][i]] = IOU_new
else:
standup_iou[re_cal[0][i]][re_cal[1][i]] = IOU
standup_iou[re_cal[1][i]][re_cal[0][i]] = IOU
standup_iou_new[re_cal[0][i]][re_cal[1][i]] = IOU_new
standup_iou_new[re_cal[1][i]][re_cal[0][i]] = IOU_new
return standup_iou, standup_iou_new
def fusion_box(dets_corners, track_corners, flag):
if flag == 1:
dets_standup = box_np_ops.corner_to_standup_nd(dets_corners)
track_standup = box_np_ops.corner_to_standup_nd(track_corners)
else:
dets_standup = box_np_ops.corner_to_standup_nd(dets_corners)
track_standup = dets_standup
standup_iou = box_np_ops.iou_jit(dets_standup, track_standup, eps=0.0)
if flag != 1:
for i in range(standup_iou.shape[0]):
standup_iou[i][i] = 0
re_cal = np.where(standup_iou > 0)
no_care = []
if re_cal[0].shape[0] == 0:
return np.empty((0, 2),
dtype=int), np.arange(len(dets_corners)), np.empty(
(0, 5), dtype=int), 0
for i in range(re_cal[0].shape[0]):
if re_cal[0][i] == re_cal[1][i]:
continue
if re_cal[0][i] in no_care:
continue
# # #####***** 调用模块进行IOU计算 *****#####
# IOU = intersection(dets_corners[re_cal[0][i]].tolist(), trackers_corners[re_cal[1][i]].tolist())
# #####***** 调用自定义函数进行IOU计算耗时稍微小一些 *****#####
interpoly = []
b = cal_iou.PolygonClip(dets_corners[re_cal[0][i]].tolist(),
track_corners[re_cal[1][i]].tolist(),
interpoly)
if b:
list2 = []
for num_i in range(len(interpoly)):
list2.append(interpoly[num_i])
for num_i in range(len(list2) - 1):
for num_j in range(num_i + 1, len(list2)):
if operator.eq(list2[num_i], list2[num_j]):
interpoly.pop(num_j)
area = cal_iou.CalArea(interpoly)
area1 = cal_iou.CalArea(dets_corners[re_cal[0][i]].tolist())
area2 = cal_iou.CalArea(track_corners[re_cal[1][i]].tolist())
IOU = area / (area2 + area1 - area)
else:
IOU = 0
no_care.append(re_cal[1][i])
if flag:
standup_iou[re_cal[0][i]][re_cal[1][i]] = IOU
else:
standup_iou[re_cal[0][i]][re_cal[1][i]] = IOU
standup_iou[re_cal[1][i]][re_cal[0][i]] = IOU
iou_threshold = 0.000001
matched_indices = linear_assignment(-standup_iou)
unmatched_detections = []
for d, det in enumerate(dets_corners):
if (d not in matched_indices[:, 0]):
unmatched_detections.append(d)
unmatched_trackers = []
for t, trk in enumerate(track_corners):
if (t not in matched_indices[:, 1]):
unmatched_trackers.append(t)
matches = []
for m in matched_indices:
if standup_iou[m[0], m[1]] < iou_threshold:
unmatched_detections.append(m[0])
unmatched_trackers.append(m[1])
else:
matches.append(m.reshape(1, 2))
if (len(matches) == 0):
matches = np.empty((0, 2), dtype=int)
else:
matches = np.concatenate(matches, axis=0)
return matches, np.array(unmatched_detections), np.array(
unmatched_trackers), standup_iou