通过 opencv + pyautogui 精准找到目标定位， 以图找图

 

# 导入模块 cv2匹配算法 plt 显示图片

import cv2

from matplotlib import pyplot as plt

import pyautogui

import time

# 读入图片 big1.png是背景大图; small.png是需要寻找的小图（格式.jpg .png都行）

# time.sleep(3)

# pyautogui.screenshot('bg.png')

# img = cv2.imread("bg.png",0) # 0 读入灰度图

# img3 = cv2.imread("bg.png",1) # 1 读入彩色图

# img2 = img.copy()

# template = cv2.imread("entry.png",0)

# w, h = template.shape[::-1]

# 6种算法的列表

methods = ['cv2.TM_CCOEFF', 'cv2.TM_CCOEFF_NORMED', 'cv2.TM_CCORR',

            'cv2.TM_CCORR_NORMED', 'cv2.TM_SQDIFF', 'cv2.TM_SQDIFF_NORMED']

methods = ['cv2.TM_CCOEFF_NORMED']

def find_img(big_path, small_path, method='cv2.TM_CCOEFF_NORMED'):

    # Load images

    small = cv2.imread(small_path, 0)

    big = cv2.imread(big_path, 0)

    # Check if images are loaded

    if small is None:

        raise FileNotFoundError(f"Could not load image at {small_path}")

    if big is None:

        raise FileNotFoundError(f"Could not load image at {big_path}")

    # Get the dimensions of the small image

    w, h = small.shape[::-1]

    # Apply template matching with the given method

    method = eval(method)

    res = cv2.matchTemplate(big, small, method)

    # Find the location with the best match

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

    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)

    center = ((top_left[0] + bottom_right[0]) // 2, (top_left[1] + bottom_right[1]) // 2)

    return center

if __name__ == "__main__":

    time.sleep(3)

    pyautogui.screenshot('bg.png')

    small_path = 'entry.png'

    big_path = 'bg.png'

    p = find_img(big_path,small_path)

    pyautogui.click(p)