然后执行模板检测,接着获取最小外接矩形edged = cv2.Canny(resized,50,200)#匹配#TM_SQDIFF 匹配方法,归一化的方法更好用result = cv2.matchTemplate(edged, template, cv2.TM_CCOEFF)
cv2.imshow('Image', image) cv2.waitKey(0) cv2.destroyAllWindows() 2、模板匹配定位 OpenCV的模板匹配功能可以用于在大图中找到小图的位置。cv2.matchTemplate()函数用于执行模板匹配。 import cv2 import numpy as np large_image = cv2.imread('large_image.png') template = cv2.imread('template.png') ...
template_image_path):# 加载目标图像和模板图像target_img=cv2.imread(target_image_path)template_img=cv2.imread(template_image_path)# 获取模板的宽度和高度template_height,template_width=template_img.shape[:2]# 进行模板匹配result=cv2.matchTemplate(target_img,template_img,cv2.TM_CCOEFF_NORMED)# 设置阈...
然后执行模板检测,接着获取最小外接矩形edged = cv2.Canny(resized,50,200)#匹配#TM_SQDIFF 匹配方法,归一化的方法更好用result = cv2.matchTemplate(edged, template, cv2.TM_CCOEFF)
def match_img(image, template, value): """ :param image: 图片 :param template: 模板 :param value: 阈值 :return: 水印坐标 描述:用于获得这幅图片模板对应的位置坐标,用途:校准元素位置信息 """ res = cv2.matchTemplate(image, template, cv2.TM_CCOEFF_NORMED) threshold = value min_v, max_v,...
result = cv2.matchTemplate(screenshot_gray, template, cv2.TM_CCOEFF_NORMED) min_val, max_val, min_loc, max_loc = cv2.minMaxLoc(result) if max_val >= 0.8: # Assuming a threshold of 0.8 for match quality return max_loc else:
import cv2 # 加载目标图像 target_image = cv2.imread('path_to_target_image.png', cv2.IMREAD_GRAYSCALE) 在屏幕上搜索与目标图像匹配的区域: 为了搜索屏幕上的目标图像,你需要对屏幕进行截图,并使用模板匹配算法来找到目标图像的位置。这里可以使用pyautogui库来截图,并使用OpenCV的matchTemplate函数进行模板匹...
target = cv2.imread("target.jpg") #读取模板图片 template = cv2.imread("template.jpg") #获得模板图片的高宽尺寸 theight, twidth = template.shape[:2] #执行模板匹配,采用的匹配方式cv2.TM_SQDIFF_NORMED result = cv2.matchTemplate(target,template,cv2.TM_SQDIFF_NORMED) ...
img_back=cv2.idft(f_ishift) img_back= cv2.magnitude(img_back[:,:,0],img_back[:,:,1]) plt.subplot(121),plt.imshow(img, cmap ='gray') plt.title('Input Image'), plt.xticks([]), plt.yticks([]) plt.subplot(122),plt.imshow(img_back, cmap ='gray') ...
# Load the template image template = cv2.imread('template.png', 0) Convert the screenshot to grayscale gray_screenshot = cv2.cvtColor(screenshot, cv2.COLOR_BGR2GRAY) Perform template matching result = cv2.matchTemplate(gray_screenshot, template, cv2.TM_CCOEFF_NORMED) ...