目标检测 - 非极大值抑制(Non Maximum Suppression)

# --------------------------------------------------------
# Fast R-CNN
# Copyright (c) 2015 Microsoft
# Licensed under The MIT License [see LICENSE for details]
# Written by Ross Girshick
# --------------------------------------------------------

import numpy as np

# dets: 检测的 boxes 及对应的 scores；
# thresh: 设定的阈值


def nms(dets, thresh):
    # boxes 位置
    x1 = dets[:, 0]  
    y1 = dets[:, 1] 
    x2 = dets[:, 2]
    y2 = dets[:, 3]
    # boxes scores
    scores = dets[:, 4]

    areas = (x2 - x1 + 1) * (y2 - y1 + 1) # 各 box 的面积
    order = scores.argsort()[::-1] # boxes 的按照 score 排序

    keep = [] # 记录保留下的 boxes
    while order.size > 0:
        i = order[0] # score 最大的 box 对应的 index
        keep.append(i) # 将本轮 score 最大的 box 的 index 保留

        # 计算剩余 boxes 与当前 box 的重叠程度 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(0.0, xx2 - xx1 + 1) # IoU
        h = np.maximum(0.0, yy2 - yy1 + 1)
        inter = w * h
        ovr = inter / (areas[i] + areas[order[1:]] - inter)

        # 保留 IoU 小于设定阈值的 boxes
        inds = np.where(ovr <= thresh)[0]
        order = order[inds + 1]

    return keep