Python OpenCV 图像模板匹配实战：原理、代码与NMS优化

# 相关系数匹配方法: cv2.TM_CCOEFF
res = cv2.matchTemplate(img, template, cv2.TM_CCOEFF)
min_val, max_val, min_loc, max_loc = cv2.minMaxLoc(res)

left_top = max_loc   # 左上角
right_bottom = (left_top[0] + w, left_top[1] + h)   # 右下角
cv2.rectangle(img, left_top, right_bottom, 255, 2)  # 画出矩形位置

plt.subplot(121), plt.imshow(res, cmap='gray')
plt.title('Matching Result'), plt.xticks([]), plt.yticks([])

plt.subplot(122), plt.imshow(img, cmap='gray')
plt.title('Detected Point'), plt.xticks([]), plt.yticks([])
plt.show()

import cv2
import numpy as np
img_rgb = cv2.imread('mario.jpg')
img_gray = cv2.cvtColor(img_rgb, cv2.COLOR_BGR2GRAY)
template = cv2.imread('mario_coin.jpg', 0)
h, w = template.shape[:2]
 
res = cv2.matchTemplate(img_gray, template, cv2.TM_CCOEFF_NORMED)
threshold = 0.8
# 取匹配程度大于%80的坐标
loc = np.where(res >= threshold)
#np.where返回的坐标值(x,y)是(h,w)，注意h,w的顺序
for pt in zip(*loc[::-1]):  
    bottom_right = (pt[0] + w, pt[1] + h)
    cv2.rectangle(img_rgb, pt, bottom_right, (0, 0, 255), 2)
cv2.imwrite("img.jpg",img_rgb)
cv2.imshow('img', img_rgb)
cv2.waitKey(0)

import cv2
import time
import numpy as np
 
def py_nms(dets, thresh):
    """Pure Python NMS baseline."""
    #x1、y1、x2、y2、以及score赋值
    # （x1、y1）（x2、y2）为box的左上和右下角标
    x1 = dets[:, 0]
    y1 = dets[:, 1]
    x2 = dets[:, 2]
    y2 = dets[:, 3]
    scores = dets[:, 4]
    #每一个候选框的面积
    areas = (x2 - x1 + 1) * (y2 - y1 + 1)
    #order是按照score降序排序的
    order = scores.argsort()[::-1]
    # print("order:",order)
 
    keep = []
    while order.size > 0:
        i = order[0]
        keep.append(i)
        #计算当前概率最大矩形框与其他矩形框的相交框的坐标，会用到numpy的broadcast机制，得到的是向量
        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:]])
        #计算相交框的面积,注意矩形框不相交时w或h算出来会是负数，用0代替
        w = np.maximum(0.0, xx2 - xx1 + 1)
        h = np.maximum(0.0, yy2 - yy1 + 1)
        inter = w * h
        #计算重叠度IOU：重叠面积/（面积1+面积2-重叠面积）
        ovr = inter / (areas[i] + areas[order[1:]] - inter)
        #找到重叠度不高于阈值的矩形框索引
        inds = np.where(ovr <= thresh)[0]
        # print("inds:",inds)
        #将order序列更新，由于前面得到的矩形框索引要比矩形框在原order序列中的索引小1，所以要把这个1加回来
        order = order[inds + 1]
    return keep
 
def template(img_gray,template_img,template_threshold):
    '''
    img_gray:待检测的灰度图片格式
    template_img:模板小图，也是灰度化了
    template_threshold:模板匹配的置信度
    '''
 
    h, w = template_img.shape[:2]
    res = cv2.matchTemplate(img_gray, template_img, cv2.TM_CCOEFF_NORMED)
    start_time = time.time()
    loc = np.where(res >= template_threshold)#大于模板阈值的目标坐标
    score = res[res >= template_threshold]#大于模板阈值的目标置信度
    #将模板数据坐标进行处理成左上角、右下角的格式
    xmin = np.array(loc[1])
    ymin = np.array(loc[0])
    xmax = xmin+w
    ymax = ymin+h
    xmin = xmin.reshape(-1,1)#变成n行1列维度
    xmax = xmax.reshape(-1,1)#变成n行1列维度
    ymax = ymax.reshape(-1,1)#变成n行1列维度
    ymin = ymin.reshape(-1,1)#变成n行1列维度
    score = score.reshape(-1,1)#变成n行1列维度
    data_hlist = []
    data_hlist.append(xmin)
    data_hlist.append(ymin)
    data_hlist.append(xmax)
    data_hlist.append(ymax)
    data_hlist.append(score)
    data_hstack = np.hstack(data_hlist)#将xmin、ymin、xmax、yamx、scores按照列进行拼接
    thresh = 0.3#NMS里面的IOU交互比阈值
 
    keep_dets = py_nms(data_hstack, thresh)
    print("nms time:",time.time() - start_time)#打印数据处理到nms运行时间
    dets = data_hstack[keep_dets]#最终的nms获得的矩形框
    return dets
if __name__ == "__main__":
    img_rgb = cv2.imread('mario.jpg')#需要检测的图片
    img_gray = cv2.cvtColor(img_rgb, cv2.COLOR_BGR2GRAY)#转化成灰色
    template_img = cv2.imread('mario_coin.jpg', 0)#模板小图
    template_threshold = 0.8#模板置信度
    dets = template(img_gray,template_img,template_threshold)
    count = 0
    for coord in dets:
        cv2.rectangle(img_rgb, (int(coord[0]),int(coord[1])), (int(coord[2]),int(coord[3])), (0, 0, 255), 2)
    cv2.imwrite("result.jpg",img_rgb)