(gray_image, template, cv2.TM_CCOEFF_NORMED) threshold = 0.8 loc = np.where(res >= threshold) # 绘制矩形框标记匹配结果 for pt in zip(*loc[::-1]): cv2.rectangle(image, pt, (pt[0] + w, pt[1] + h), (0, 255, 0), 2) cv2.imshow('Template Matching', image) cv2.waitKey(...
result = cv2.matchTemplate(img1_processed, img2_processed, cv2.TM_CCOEFF_NORMED) # print(result) similarity_scores[i, j] = np.max(result) # 计算每个字符与其他字符的平均相似度 print(similarity_scores) average_similarity = np.mean(...
②:标准平方差匹配 method=CV_TM_SQDIFF_NORMED ③:相关匹配 method=CV_TM_CCORR 这类方法采用模板和图像间的乘法操作,所以较大的数表示匹配程度较高,0标识最坏的匹配效果. ④:标准相关匹配 method=CV_TM_CCORR_NORMED ⑤:相关匹配 method=CV_TM_CCOEFF 这类方法将模版对其均值的相对值与图像对其均值的相关值...
②:标准平方差匹配 method=CV_TM_SQDIFF_NORMED ③:相关匹配 method=CV_TM_CCORR 这类方法采用模板和图像间的乘法操作,所以较大的数表示匹配程度较高,0标识最坏的匹配效果. ④:标准相关匹配 method=CV_TM_CCORR_NORMED ⑤:相关匹配 method=CV_TM_CCOEFF 这类方法将模版对其均值的相对值与图像对其均值的相关值...
result = cv2.matchTemplate(img1_processed, img2_processed, cv2.TM_CCOEFF_NORMED) # print(result) similarity_scores[i, j] = np.max(result) # 计算每个字符与其他字符的平均相似度 print(similarity_scores) average_similarity = np.mean(similarity_scores, axis=1) ...
TM_CCORR_NORMED:计算归一化相关性,计算出来的值越接近1,越相关 TM_CCOEFF_NORMED:计算归一化相关系数,计算出来的值越接近1,越相关 #模板匹配img=cv.imread("E:\\Pec\\lida.jpg",0)template=cv.imread("E:\\Pec\\face.jpg",0)#cv_show("lida",img)#cv_show("tem",template)h,w=template.shape[:...
result = cv2.matchTemplate(img1_processed, img2_processed, cv2.TM_CCOEFF_NORMED) # print(result) similarity_scores[i, j] = np.max(result) # 计算每个字符与其他字符的平均相似度 print(similarity_scores) average_similarity = np.mean(similarity_scores, axis=1) ...
②:标准平方差匹配 method=CV_TM_SQDIFF_NORMED ③:相关匹配 method=CV_TM_CCORR 这类方法采用模板和图像间的乘法操作,所以较大的数表示匹配程度较高,0标识最坏的匹配效果. ④:标准相关匹配 method=CV_TM_CCORR_NORMED ⑤:相关匹配 method=CV_TM_CCOEFF ...
问EmguCV - MatchTemplateEN由于您使用的是Emgu.CV.CvEnum.TM_TYPE.CV_TM_CCOEFF_NORMED，因此结果取决...
TM_CCOEFF_NORMED:计算归一化相关系数,计算出来的值越接近1,越相关 #模板匹配 img=cv.imread("E:\\Pec\\lida.jpg",0) template=cv.imread("E:\\Pec\\face.jpg",0) #cv_show("lida",img) #cv_show("tem",template) h,w=template.shape[:2] ...