tensorflow学习——NMS的python实现

1、NMS（Non-Maximum Suppression）
根据框的概率和IOU进行抑制

import numpy as np  def py_cpu_nms(dets, thresh):      """Pure Python NMS baseline."""      x1 = dets[:, 0]      y1 = dets[:, 1]      x2 = dets[:, 2]      y2 = dets[:, 3]      scores = dets[:, 4]      areas = (x2 - x1 + 1) * (y2 - y1 + 1)  #从大到小排列，取index      order = scores.argsort()[::-1]  #keep为最后保留的边框      keep = []      while order.size > 0:  #order[0]是当前分数最大的窗口，之前没有被过滤掉，肯定是要保留的          i = order[0]          keep.append(i)  #计算窗口i与其他所以窗口的交叠部分的面积，矩阵计算        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)          h = np.maximum(0.0, yy2 - yy1 + 1)          inter = w * h  #交/并得到iou值          ovr = inter / (areas[i] + areas[order[1:]] - inter)  #ind为所有与窗口i的iou值小于threshold值的窗口的index，其他窗口此次都被窗口i吸收          inds = np.where(ovr <= thresh)[0]  #下一次计算前要把窗口i去除，所有i对应的在order里的位置是0，所以剩下的加1          order = order[inds + 1]      return keepdef main():    x = np.array([[3,6,9,11,0.9],[6,3,8,7,0.6],[3,7,10,12,0.7],[1,4,13,7,0.2]])    y = py_cpu_nms(x, 0.3)if __name__ == '__main__':    main()

2、转自网上教程
首先比较二者的参数部分：

np.max：(a, axis=None, out=None, keepdims=False)    求序列的最值    最少接收一个参数    axis：默认为列向（也即 axis=0），axis = 1 时为行方向的最值；np.maximum：(X, Y, out=None)    X 与 Y 逐位比较取其大者；    最少接收两个参数

>> np.max([-2, -1, 0, 1, 2])2>> np.maximum([-2, -1, 0, 1, 2], 0)array([0, 0, 0, 1, 2])        # 当然 np.maximum 接受的两个参数，也可以大小一致        # 或者更为准确地说，第二个参数只是一个单独的值时，其实是用到了维度的 broadcast 机制；

3、计算IOU

def calcIOU(x1, y1, w1, h1, x2, y2, w2, h2):    if((abs(x1 - x2) < ((w1 + w2)/ 2.0)) and (abs(y1-y2) < ((h1 + h2)/2.0))):        left = max((x1 - (w1 / 2.0)), (x2 - (w2 / 2.0)))        upper = max((y1 - (h1 / 2.0)), (y2 - (h2 / 2.0)))        right = min((x1 + (w1 / 2.0)), (x2 + (w2 / 2.0)))        bottom = min((y1 + (h1 / 2.0)), (y2 + (h2 / 2.0)))        inter_w = abs(left - right)        inter_h = abs(upper - bottom)        inter_square = inter_w * inter_h        union_square = (w1 * h1)+(w2 * h2)-inter_square        calcIOU = inter_square/union_square * 1.0        print("calcIOU:", calcIOU)    else:        print("No intersection!")    return calcIOUdef main():    calcIOU(1, 2, 2, 2, 2, 1, 2, 2)if __name__ == '__main__':    main()