非极大值抑制（NMS）算法详解

#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
NMS function（Non-Maximum Suppression，  抑制不是极大值的元素）
        psedocode:
            1. choose the highest score element  a_1  in set B, add a_1 to the keep set C
            2. compute the IOU between the chosen element(such as a_1) and others elements in set B
            3. only keep the nums  at set B whose IOU value is less than thresholds (can be set as >=0.5), delete the nums similiar
                to a_1(the higher IOU it is , the more interseciton between a_1 and it will have)
            4. choose the highest score value a_2 left at set B  and add a_2 to set C
            5. repeat the 2-4 until  there is nothing in set B, while set C is the NMS value set

"""
import numpy as np

# boxes表示人脸框的xywh4点坐标+相关置信度
boxes = np.array([[100, 100, 210, 210, 0.72],
                  [250, 250, 420, 420, 0.8],
                  [220, 220, 320, 330, 0.92],
                  [230, 240, 325, 330, 0.81],
                  [220, 230, 315, 340, 0.9]])


def py_cpu_nms(dets, thresh):
    # dets:(m,5)  thresh:scaler

    x1 = dets[:, 0]  # [100. 250. 220. 230. 220.]
    y1 = dets[:, 1]  # [100. 250. 220. 240. 230.]
    x2 = dets[:, 2]  # [210. 420. 320. 325. 315.]
    y2 = dets[:, 3]  # [210. 420. 330. 330. 340.]

    areas = (y2 - y1 + 1) * (x2 - x1 + 1)
    scores = dets[:, 4]  # [0 1 3 4 2]
    keep = []
    # index表示按照scores从高到底的相关box的序列号
    index = scores.argsort()[::-1]  # [2 4 3 1 0]

    while index.size > 0:
        print("sorted index of boxes according to scores", index)
        # 选择得分最高的score直接加入keep列表中
        i = index[0]
        keep.append(i)
        # 计算score最高的box和其他box分别的相关交集坐标
        x11 = np.maximum(x1[i], x1[index[1:]])  # [220. 230. 250. 220.]  最高的被提走了，所以要从1开始取后 4位
        y11 = np.maximum(y1[i], y1[index[1:]])  # [230. 240. 250. 220.]
        x22 = np.minimum(x2[i], x2[index[1:]])  # [315. 320. 320. 210.]
        y22 = np.minimum(y2[i], y2[index[1:]])  # [330. 330. 330. 210.]

        print("x1 values by original order:", x1)
        print("x1 value by scores:", x1[index[:]])  #  [220. 220. 230. 250. 100.]
        print("x11 value means  replacing the less value compared" \
              " with the value by the largest score :", x11)
        # 计算交集面积
        w = np.maximum(0, x22 - x11 + 1)  # the weights of overlap
        h = np.maximum(0, y22 - y11 + 1)  # the height of overlap
        overlaps = w * h
        # 计算相关IOU值（交集面积/并集面积，表示边框重合程度，越大表示越相似，越该删除）
        # 重叠面积 /（面积1+面积2-重叠面积）
        ious = overlaps / (areas[i] + areas[index[1:]] - overlaps)
        # 只保留iou小于阈值的索引号，重复上步
        idx = np.where(ious <= thresh)[0]
        # 因为第一步index[0]已经被划走，所以需要原来的索引号需要多加一
        index = index[idx + 1]

    return keep


import matplotlib.pyplot as plt


def plot_bbox(ax, dets, c='b', title_name="title"):
    x1 = dets[:, 0]
    y1 = dets[:, 1]
    x2 = dets[:, 2]
    y2 = dets[:, 3]

    ax.plot([x1, x2], [y1, y1], c)
    ax.plot([x1, x1], [y1, y2], c)
    ax.plot([x1, x2], [y2, y2], c)
    ax.plot([x2, x2], [y1, y2], c)
    ax.set_title(title_name)


if __name__ == '__main__':
    # 1.创建画板fig
    fig = plt.figure(figsize=(12, 6))

    # 参数解释,前两个参数 1,2 表示创建了一个一行两列的框 第三个参数表示当前所在的框
    ax1 = fig.add_subplot(1, 2, 1)
    ax2 = fig.add_subplot(1, 2, 2)

    plot_bbox(ax1, boxes, 'k', title_name="before nms")  # before nms

    keep = py_cpu_nms(boxes, thresh=0.7)

    plot_bbox(ax2, boxes[keep], 'r', title_name="after nms")  # after nms
    plt.show()