opencv 模板匹配(在大图中找小图)

# -*-encoding:utf-8-*-
import pytesseract
from PIL import Image
from PIL import ImageFilter
from PIL import ImageFont
from PIL import ImageDraw
import numpy as np
from PIL import Image

import cv2


def main():
    # 使用模板匹配在图像中寻找物体
    # OpenCV函数：cv2.matchTemplate(), cv2.minMaxLoc()
    # 模板匹配就是用来在大图中找小图，也就是说在一副图像中寻找另外一张模板图像的位置

    #  =================================模板匹配
    img = cv2.imread('learn.jpg', 0)
    template = cv2.imread('learn_1.jpg', 0)
    h, w = template.shape[:2]  # rows->h, cols->w
    print("h = " ,h )
    print("w = ", w)

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

    # 平方差匹配CV_TM_SQDIFF：用两者的平方差来匹配，最好的匹配值为0
    # 归一化平方差匹配CV_TM_SQDIFF_NORMED
    # 相关匹配CV_TM_CCORR：用两者的乘积匹配，数值越大表明匹配程度越好
    # 归一化相关匹配CV_TM_CCORR_NORMED
    # 相关系数匹配CV_TM_CCOEFF：用两者的相关系数匹配，1
    # 表示完美的匹配，-1
    # 表示最差的匹配
    # 归一化相关系数匹配CV_TM_CCOEFF_NORMED

    min_val, max_val, min_loc, max_loc = cv2.minMaxLoc(res)
    #cv2.minMaxLoc()函数可以得到最大匹配值的坐标，以这个点为左上角角点，模板的宽和高画矩形就是匹配的位置了
    print("min_val = ", min_val)
    print("max_val = ", max_val)#最大匹配值
    print("min_loc = ", min_loc)
    print("max_loc = ", max_loc)#最大左上角坐标

    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

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

if __name__ == '__main__':
    main()

# -*-encoding:utf-8-*-
import pytesseract
from PIL import Image
from PIL import ImageFilter
from PIL import ImageFont
from PIL import ImageDraw
import numpy as np
from PIL import Image

import cv2


def main():
    # 使用模板匹配在图像中寻找物体
    # OpenCV函数：cv2.matchTemplate(), cv2.minMaxLoc()
    # 模板匹配就是用来在大图中找小图，也就是说在一副图像中寻找另外一张模板图像的位置

    #  =================================匹配多个物体,模板匹配
    # 1.读入原图和模板
    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]
    # 2.标准相关模板匹配
    res = cv2.matchTemplate(img_gray, template, cv2.TM_CCOEFF_NORMED)
    threshold = 0.8
    # 3.这边是Python/Numpy的知识，后面解释
    loc = np.where(res >= threshold)  # 匹配程度大于%80的坐标y,x,loc是先y坐标再x坐标

    for pt in zip(*loc[::-1]):  # *号表示可选参数,，所以用loc[::-1]翻转一下，然后再用zip函数拼接在一起。
        right_bottom = (pt[0] + w, pt[1] + h)
        cv2.rectangle(img_rgb, pt, right_bottom, (0, 0, 255), 2)

    cv2.imshow('img_rgb', img_rgb)
    cv2.waitKey(0)
    
if __name__ == '__main__':
    main()