我正在尝试通过执行精明的边缘检测来提取图像的一部分。我已经成功地创建了该对象的掩码。但是，当我对原始图像执行bitwise_and操作以提取前景部分时，会出现以下错误。OpenCV Error: Assertion failed ((mtype == CV_8U || mtype == CV_8S) && _mask.sameSize(*psrc1)) in cv::binary_op, file C:\projects\opencv-python\opencv\modules\core\src\arithm.cpp, line 241

Traceback (most recent call last):

File "C:\Users\Boudhayan Dev\Desktop\extraction.py", line 37, in

new_image = cv2.bitwise_and(img_rgb,img_rgb,mask=mask)

cv2.error: C:\projects\opencv-python\opencv\modules\core\src\arithm.cpp:241: error: (-215) (mtype == CV_8U || mtype == CV_8S) && _mask.sameSize(*psrc1) in function cv::binary_op

我的代码如下-import cv2

import numpy as np

img_rgb = cv2.imread("3.jpg")

cv2.namedWindow("Original Image",cv2.WINDOW_NORMAL)

img = cv2.cvtColor(img_rgb,cv2.COLOR_RGB2HSV)

img = cv2.bilateralFilter(img,9,105,105)

r,g,b=cv2.split(img)

equalize1= cv2.equalizeHist(r)

equalize2= cv2.equalizeHist(g)

equalize3= cv2.equalizeHist(b)

equalize=cv2.merge((r,g,b))

equalize = cv2.cvtColor(equalize,cv2.COLOR_RGB2GRAY)

ret,thresh_image = cv2.threshold(equalize,0,255,cv2.THRESH_OTSU+cv2.THRESH_BINARY)

equalize= cv2.equalizeHist(thresh_image)

canny_image = cv2.Canny(equalize,250,255)

canny_image = cv2.convertScaleAbs(canny_image)

kernel = np.ones((3,3), np.uint8)

dilated_image = cv2.dilate(canny_image,kernel,iterations=1)

new,contours, hierarchy = cv2.findContours(dilated_image, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)

contours= sorted(contours, key = cv2.contourArea, reverse = True)[:10]

c=contours[0]

print(cv2.contourArea(c))

final = cv2.drawContours(img, [c], -1, (255,0, 0), 3)

mask = np.zeros(img_rgb.shape,np.uint8)

new_image = cv2.drawContours(mask,[c],0,255,-1,)

new_image = cv2.bitwise_and(img_rgb,img_rgb,mask=mask)

cv2.namedWindow("new",cv2.WINDOW_NORMAL)

cv2.imshow("new",new_image)

cv2.imshow("Original Image",img)

cv2.waitKey()

注意：-如果我尝试用图像的灰度版本执行bitwise_and，则代码可以正常工作。但是，RGB、HSV或任何其他颜色空间都会导致上述错误。

请帮忙。

编辑1-

有问题的图像是这样的-

编辑2-

下面是使用Numpy方法后的结果。如您所见，提取的图像与橙色的大小相同，但它不包含橙色，而是包含遮罩本身。

编辑3-@DanMašek和@lightalchemist，我终于可以提取任何前景图像。

谢谢你