Template Matching

理论

模板匹配是一种在较大的图像中搜索和查找模板图像位置的方法。OpenCV带有一个函数cv2.matchTemplate()用于此目的.它只是简单地将模板图像放在输入图像上(就像在2D卷积中那样)，并在模板图像下对输入图像的模板和补丁进行比较,在OpenCV中实现了几种比较方法,它返回一个灰度图像，每个像素表示该像素区域与模板的匹配程度.

如果输入图像的大小（W x H）且模板图像的大小（w x h），则输出图像的大小为（W-w + 1，H-h + 1）.获得结果后，可以使用cv.minMaxLoc（）函数查找最大/最小值的位置。将其作为矩形的左上角，并将（w，h）作为矩形的宽度和高度.

OpenCV中的模板匹配

import cv2
import numpy as np
from matplotlib import pyplot as plt

img = cv2.imread('img.jpg',0)
img2 = img.copy()
template = cv2.imread('img_roi.png',0)
w, h = template.shape[::-1]

# All the 6 methods for comparison in a list
methods = ['cv2.TM_CCOEFF', 'cv2.TM_CCOEFF_NORMED', 'cv2.TM_CCORR',
            'cv2.TM_CCORR_NORMED', 'cv2.TM_SQDIFF', 'cv2.TM_SQDIFF_NORMED']

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

    # Apply template Matching
    res = cv2.matchTemplate(img,template,method)
    min_val, max_val, min_loc, max_loc = cv2.minMaxLoc(res)

    # If the method is TM_SQDIFF or TM_SQDIFF_NORMED, take minimum
    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)

    cv2.rectangle(img,top_left, bottom_right, 255, 2)

    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.suptitle(meth)

    plt.show()

与多个对象匹配的模板

cv.minMaxLoc()将不会提供所有的位置.在这种情况下，我们将使用阈值.

import cv2
import numpy as np
from matplotlib import pyplot as plt

img_rgb = cv2.imread('img5.png')
img_gray = cv2.cvtColor(img_rgb, cv2.COLOR_BGR2GRAY)
template = cv2.imread('img_roi1.png',0)
w, h = template.shape[::-1]

res = cv2.matchTemplate(img_gray,template,cv2.TM_CCOEFF_NORMED)
threshold = 0.8
loc = np.where( res >= threshold)
for pt in zip(*loc[::-1]):
    cv2.rectangle(img_rgb, pt, (pt[0] + w, pt[1] + h), (0,0,255), 2)

cv2.imshow('res',img_rgb)