TM_SQDIFF:计算平方不同,计算出来的值越小,越相关 TM_CCORR:计算相关性,计算出来的值越大,越相关 TM_CCOEFF:计算相关系数,计算出来的值越大,越想关 TM_SQDIFF_NORMED:计算归一平方不同,计算出来的结果越接近0,越相关 TM_CCORR_NORMED:计算归一化相关性,计算出来的结果越接近1,越相关 TM_CCOEFF_NORMED:计算归...
TM_CCORR_NORMED接近1好 TM_CCOEF_NORMED接近1好 image = cv.imread('D:/3.jpg') #得到原数据 ima_template = image.copy()[200:300, 200:300] #得到模板数据 th, tw = ima_template.shape[:2] #得到模板的长、宽 res = cv.matchTemplate(image, ima_template, cv.TM_CCORR_NORMED) #进行模板匹...
CV_TM_SQDIFF 平方差匹配法:该方法采用平方差来进行匹配;最好的匹配值为0;匹配越差,匹配值越大。 CV_TM_CCORR 相关匹配法:该方法采用乘法操作;数值越大表明匹配程度越好。 CV_TM_CCOEFF 相关系数匹配法:1表示完美的匹配;-1表示最差的匹配。 CV_TM_SQDIFF_NORMED 归一化平方差匹配法 CV_TM_CCORR_NORMED 归...
cv2.TM_CCOEFF_NORMED:归一化互相关 cv2.TM_CCORR:相关匹配 cv2.TM_CCORR_NORMED:归一化相关匹配 cv2.TM_SQDIFF:平方差匹配 cv2.TM_SQDIFF_NORMED:归一化平方差匹配 示例:python # 使用归一化互相关方法进行匹配 result = cv2.matchTemplate(image, templ, cv2.TM_CCOEFF_NORMED) ...
methods = ['cv2.TM_CCOEFF_NORMED', 'cv2.TM_CCOEFF', 'cv2.TM_SQDIFF_NORMED', 'cv2.TM_SQDIFF', 'cv2.TM_CCORR', 'cv2.TM_CCORR_NORMED'] for method in methods: draw_img = img.copy() op = eval(method) ret = cv2.matchTemplate(img, template, op) ...
'cv2.TM_CCORR', 'cv2.TM_CCORR_NORMED'] ret = cv2.matchTemplate(img, template, cv2.TM_CCOEFF_NORMED) min_val, max_val, min_loc, max_loc = cv2.minMaxLoc(ret) draw_img = original.copy() ret = cv2.rectangle(draw_img, max_loc, (max_loc[0]+w, max_loc[1]+h), (0, 0, 255)...
Static TM_CCORR := 2 Static TM_CCORR_NORMED := 3 Static TM_CCOEFF := 4 Static TM_CCOEFF_NORMED := 5; ColormapTypes Static COLORMAP_AUTUMN := 0 Static COLORMAP_BONE := 1 Static COLORMAP_JET := 2 Static COLORMAP_WINTER := 3 ...
学习计算机视觉最重要的能力应该就是编程了，为了帮助小伙伴尽快入门计算机视觉，小白准备了【OpenCV入门】...
TemplateMatchModes Static TM_SQDIFF := 0 Static TM_SQDIFF_NORMED := 1 Static TM_CCORR := 2 Static TM_CCORR_NORMED := 3 Static TM_CCOEFF := 4 Static TM_CCOEFF_NORMED := 5 ; ColormapTypes Static COLORMAP_AUTUMN := 0 Static COLORMAP_BONE := 1 Static COLORMAP_JET := 2 Static COL...
cv::TM_CCORR=2, cv::TM_CCORR_NORMED=3, cv::TM_CCOEFF=4, cv::TM_CCOEFF_NORMED=5} 2. 如何选用模板方法 通常情况是:如果想要精确或接近精确的匹配,请使SSD。它较快,而且肯定按照你所要求的那样,试图最小化模板图片和目标图像之间的差异。在这种情况下,是不需要归一化的,那只是徒增开销。