非極大值抑制——NMS實例

輸入：左上角和右下角座標
輸出：留下的boxes的索引

import numpy as np
import cv2
import matplotlib.pyplot as plt
import random
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)
#從大到小排列，取index
    order = scores.argsort()[::-1]  #逆序排序
#keep爲最後保留的邊框
    keep = []
    while order.size > 0:
#order[0]是當前分數最大的窗口，之前沒有被過濾掉，肯定是要保留的
        i = order[0]
        keep.append(i)

#計算窗口i與其他所以窗口的交疊部分的面積，矩陣計算
        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/並得到iou值
        ovr = inter / (areas[i] + areas[order[1:]] - inter)
#ind爲所有與窗口i的iou值小於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]
#下一次計算前要把窗口i去除，所有i對應的在order裏的位置是0，所以剩下的加1
        order = order[inds + 1]
    return keep

boxes = 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(boxes, 0.3)
img = cv2.imread('model.jpg')
color =['red','blue','green','black']

fig, ax = plt.subplots(figsize=(12, 12))
ax.imshow(img, aspect='equal')
for box in boxes[y]:
  ax.add_patch(
              plt.Rectangle((box[0], box[1]),
                            box[2] - box[0],
                            box[3] - box[1], fill=False,
                            edgecolor=random.choice(color), linewidth=1)
              )


plt.axis('off')
plt.show()

原始框：

經NMS後的框