opencv template matching (cv2.matchTemplate, cv2.minMaxLoc)

1, the target matching function: cv2.matchTemplate ()

res=cv2.matchTemplate(image, templ, method, result=None, mask=None)

image: Image to be searched
templ: Template image
result: Match result
method: The method of calculating the degree of matching, there are the following

method	meaning
CV_TM_SQDIFF	Squared difference matching method: The method uses matching square difference; best match is 0; matching worse, the larger a matching value.
CV_TM_CCORR	Related matching method: The method uses the multiplication operation; A greater value indicates better degree of matching.
CV_TM_CCOEFF	Correlation matching method: 1 indicates a perfect match; -1 indicates the worst match.
CV_TM_SQDIFF_NORMED	Computing a normalized square is different, the calculated value is closer to 0, the more relevant
CV_TM_CCORR_NORMED	Calcd normalized correlation calculated closer to 1, the more relevant
CV_TM_CCOEFF_NORMED	Normalized correlation coefficient is calculated, the calculated value is closer to 1, the more relevant

2, obtaining results matching function: cv2.minMaxLoc ()

min_val, max_val, min_loc, max_loc = cv2.minMaxLoc(ret) 

Parameters: min_val, max_val, min_loc, max_loc represent the minimum, maximum, and minimum and maximum values ​​of the corresponding position in the image, that is, RET cv2.matchTemplate () function returns a matrix

# 模板匹配
img = cv2.imread('lena.jpg', 0)
template = cv2.imread('face.jpg', 0)
h, w = template.shape[:2] 
methods = ['cv2.TM_CCOEFF', 'cv2.TM_CCOEFF_NORMED', 'cv2.TM_CCORR',
           'cv2.TM_CCORR_NORMED', 'cv2.TM_SQDIFF', 'cv2.TM_SQDIFF_NORMED']

res = cv2.matchTemplate(img, template, cv2.TM_SQDIFF)
min_val, max_val, min_loc, max_loc = cv2.minMaxLoc(res)

for meth in methods:
    img2 = img.copy()

    # 匹配方法的真值
    method = eval(meth)
    res = cv2.matchTemplate(img, template, method)
    min_val, max_val, min_loc, max_loc = cv2.minMaxLoc(res)

    # 如果是平方差匹配TM_SQDIFF或归一化平方差匹配TM_SQDIFF_NORMED，取最小值
    if method in [cv2.TM_SQDIFF, cv2.TM_SQDIFF_NORMED]:
        top_left = min_loc
    else:
        top_left = max_loc
    bottom_right = (top_left[0] + w, top_left[1] + h)

    # 画矩形
    cv2.rectangle(img2, top_left, bottom_right, 255, 2)

    plt.subplot(121), plt.imshow(res, cmap='gray')
    plt.xticks([]), plt.yticks([])  # 隐藏坐标轴
    plt.subplot(122), plt.imshow(img2, cmap='gray')
    plt.xticks([]), plt.yticks([])
    plt.suptitle(meth)
    plt.show()

Multi-object matching: We are matching the figure of gold, read two pictures are original and gold template

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.imshow('img_rgb', img_rgb)
cv2.waitKey(0)