import numpy as np # ---------------------------- NMS ---------------------------- ## basic NMS def nms(bboxes, scores, nms_thresh): """"Pure Python NMS.""" x1 = bboxes[:, 0] #xmin y1 = bboxes[:, 1] #ymin x2 = bboxes[:, 2] #xmax y2 = bboxes[:, 3] #ymax areas = (x2 - x1) * (y2 - y1) order = scores.argsort()[::-1] keep = [] while order.size > 0: i = order[0] keep.append(i) # compute iou xx1 = np.maximum(x1[i], x1[order[1:]]) yy1 = np.maximum(y1[i], y1[order[1:]]) xx2 = np.minimum(x2[i], x2[order[1:]]) yy2 = np.minimum(y2[i], y2[order[1:]]) w = np.maximum(1e-10, xx2 - xx1) h = np.maximum(1e-10, yy2 - yy1) inter = w * h iou = inter / (areas[i] + areas[order[1:]] - inter + 1e-14) #reserve all the boundingbox whose ovr less than thresh inds = np.where(iou <= nms_thresh)[0] order = order[inds + 1] return keep ## class-agnostic NMS def multiclass_nms_class_agnostic(scores, labels, bboxes, nms_thresh): # nms keep = nms(bboxes, scores, nms_thresh) scores = scores[keep] labels = labels[keep] bboxes = bboxes[keep] return scores, labels, bboxes ## class-aware NMS def multiclass_nms_class_aware(scores, labels, bboxes, nms_thresh, num_classes): # nms keep = np.zeros(len(bboxes), dtype=np.int32) for i in range(num_classes): inds = np.where(labels == i)[0] if len(inds) == 0: continue c_bboxes = bboxes[inds] c_scores = scores[inds] c_keep = nms(c_bboxes, c_scores, nms_thresh) keep[inds[c_keep]] = 1 keep = np.where(keep > 0) scores = scores[keep] labels = labels[keep] bboxes = bboxes[keep] return scores, labels, bboxes ## multi-class NMS def multiclass_nms(scores, labels, bboxes, nms_thresh, num_classes, class_agnostic=False): if class_agnostic: return multiclass_nms_class_agnostic(scores, labels, bboxes, nms_thresh) else: return multiclass_nms_class_aware(scores, labels, bboxes, nms_thresh, num_classes)