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...
在使用OpenCV进行模板匹配时,如果你发现cv2.TM_CCORR方法无法识别到图像中的多个目标,可以考虑以下方法来改进: 检查模板匹配算法的参数设置: 确认你使用的参数是否适合识别多个目标。对于cv2.TM_CCORR,它计算的是模板和图像区域之间的互相关。这个方法对光照变化比较敏感,可能不适合所有场景。你可以尝试使用其他模板匹配方...
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...
根据“bhattacharya系数”一书，它与标准化的交叉关联完全不同，但当我尝试这段代码时，每个image.Can的...
methods = [cv.TM_SQDIFF_NORMED, cv.TM_CCORR_NORMED, cv.TM_CCOEFF_NORMED] th, tw = tpl.shape[:2] for md in methods: print(md) result = cv.matchTemplate(target, tpl, md) min_val, max_val, min_loc, max_loc = cv.minMaxLoc(result) ...
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) ...
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 import cv2 def findpic(self, target='background.png', template='slider.png'): """ :param target: 背景图路径 :param template: 滑块图片路...
cv.CV_TM_CCORR_NORMED) (min_val, max_val, minloc, maxloc) = cv2.minMaxLoc(result) if len(region_center): x = int(maxloc[0]+region_center[0]-source_width/2) y = int(maxloc[1]+region_center[1]-source_height/2) else: [x,y] = maxloc return max_val, [x,y] ...
result = cv2.matchTemplate(image, template, cv2.TM_SQDIFF) ``` 2.标准化平方差匹配(`cv2.TM_SQDIFF_NORMED`): -类似于平方差匹配,但是对结果进行了标准化,使匹配值在0到1之间。 ```python result = cv2.matchTemplate(image, template, cv2.TM_SQDIFF_NORMED) ``` 3.相关性匹配(`cv2.TM_CCORR`):...
'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)...