Python OpenCV实现图像模板匹配详解

import cv2
img = cv2.imread("test.png")

print(img.shape)
# 电灯
img1 = img[20:220, 320:480, :]
# 虎牢关牌匾
img2 = img[75:150, 200:310, :]
# 青龙刀
img3 = img[170:530, 575:650, :]
# 关云长
img4 = img[270:670, 160:330, :]


cv2.imshow("img0", img)
cv2.imshow("img1", img1)
cv2.imshow("img2", img2)
cv2.imshow("img3", img3)
cv2.imshow("img4", img4)
cv2.waitKey()
cv2.destroyAllWindows()

cv2.imwrite('template_pic1.jpg', img1)
cv2.imwrite('template_pic2.jpg', img2)
cv2.imwrite('template_pic3.jpg', img3)
cv2.imwrite('template_pic4.jpg', img4)

import cv2
img = cv2.imread("test.png")

templ = cv2.imread("template_pic1.jpg")
height, width, c = templ.shape
results = cv2.matchTemplate(img, templ, cv2.TM_SQDIFF_NORMED)
# 获取匹配结果中的最小值、最大值、最小值坐标和最大值坐标
minValue, maxValue, minLoc, maxLoc = cv2.minMaxLoc(results)
resultPoint1 = minLoc
resultPoint2 = (resultPoint1[0] + width, resultPoint1[1] + height)
cv2.rectangle(img, resultPoint1, resultPoint2, (0, 0, 255), 2)
cv2.imshow("img", img)
cv2.waitKey()
cv2.destroyAllWindows()

import cv2
img = cv2.imread("test.png")
dst1 = cv2.flip(img, 1)
cv2.imshow("dst1", dst1)
cv2.waitKey()
cv2.destroyAllWindows()
cv2.imwrite('test1.png', dst1)        

import cv2

image = []
image.append(cv2.imread("test.png"))
image.append(cv2.imread("test1.png"))
templ = cv2.imread("template_pic4.jpg")
height, width, c = templ.shape

# 循环变量初始化
# 这里只是随便设定一个值，该值并无意义，只是为了定义该变量
# 使用TM_SQDIFF_NORMED计算方法，计算出的结果通常是小于1的，所以minValue可以设置为1。如果是TM_SQDIFF计算方法，则就不行了，计算出来的值会很大。代码就不再有效，需要把minMax设得更大，或者做其他修改。
index = -1
minValue = 1
minLoc1 = (0, 0)

# 遍历每幅图像
for i in range(0, len(image)):
    results = cv2.matchTemplate(image[i], templ, cv2.TM_SQDIFF_NORMED)
    min = cv2.minMaxLoc(results)[0]
    if min < minValue:
        minValue = min
        minLoc1 = cv2.minMaxLoc(results)[2]
        index = i

minLoc2 = (minLoc1[0] + width, minLoc1[1] + height)
cv2.rectangle(image[index], minLoc1, minLoc2, (0, 0, 255), 2)
cv2.imshow("result", image[index])
cv2.waitKey()
cv2.destroyAllWindows()

import cv2
img = cv2.imread("test.png")
templ = cv2.imread("template_pic1.jpg")
img[20:220, 30:190, :] = templ
cv2.imshow("img", img)
cv2.waitKey()
cv2.destroyAllWindows()
cv2.imwrite('test2.png', img)      

import cv2
img = cv2.imread("test2.png")
templ = cv2.imread("template_pic1.jpg")
height, width, c = templ.shape
# 按照标准相关系数匹配
results = cv2.matchTemplate(img, templ, cv2.TM_CCOEFF_NORMED)
for y in range(len(results)):
    for x in range(len(results[y])):
        if results[y][x] > 0.99:
            cv2.rectangle(img, (x, y), (x + width, y + height), (0, 0, 255), 2)
cv2.imshow("img", img)
cv2.waitKey()
cv2.destroyAllWindows()

import cv2


def myMatchTemplate(img, templ):
    height, width, c = templ.shape
    results = cv2.matchTemplate(img, templ, cv2.TM_CCOEFF_NORMED)
    loc = list()
    for i in range(len(results)):
        for j in range(len(results[i])):
            if results[i][j] > 0.99:
                loc.append((j, i, j + width, i + height))
    return loc

# 读取原始图像
img = cv2.imread("test.png")  
# 模板列表
templs = list()  
templs.append(cv2.imread("template_pic1.jpg"))
templs.append(cv2.imread("template_pic2.jpg"))
templs.append(cv2.imread("template_pic3.jpg"))
templs.append(cv2.imread("template_pic4.jpg"))


loc = list()  
for t in templs: 
    loc += myMatchTemplate(img, t) 

# 遍历所有红框的坐标
for i in loc:  
    cv2.rectangle(img, (i[0], i[1]), (i[2], i[3]), (0, 0, 255), 2) 

cv2.imshow("img", img) 
cv2.waitKey() 
cv2.destroyAllWindows()