def drop_large_overlaped_boxes(detect_result, drop_iou=0.1):
drop_index = [] # record which boxes to drop
scores = detect_result[:, 4]
labels = detect_result[:, 5]
boxes = detect_result[:, :4]
num_boxes = len(boxes)
for i in range(num_boxes - 1):
for j in range(i + 1, num_boxes):
iou_ij = iou(boxes[i], boxes[j])
if iou_ij >= drop_iou:
idx = np.argmin((scores[i], scores[j]))
if idx:
drop_index.append(j)
else:
drop_index.append(i)
if not len(drop_index):
return detect_result
else:
detect_result = np.delete(detect_result, drop_index, axis=0)
return detect_result
def iou_for_debug(tracker, idx1, idx2):
box1 = tracker[idx1 + 1]["pred_position"]
box2 = tracker[idx2 + 1]["pred_position"]
return iou(box1, box2)