图像拼接工件[OpenCV Python]

Question

我正在使用OpenCV缝合多个图像。它开始起作用了，但我有一件事有问题。在cv2.warpPerspective图像具有"软"边界之后，这意味着计算的掩模太大了一个像素。

我的代码：

    # apply a perspective warp to stitch the images
    # together
    result = cv2.warpPerspective(imageA, H,
        (imageA.shape[1] + imageB.shape[1], imageA.shape[0]))
    # Now create a mask of logo and create its inverse mask also
    img2gray = cv2.cvtColor(result,cv2.COLOR_BGR2GRAY)
    ret, mask = cv2.threshold(img2gray, 0, 255, cv2.THRESH_BINARY)
    mask_inv = cv2.bitwise_not(mask)
    resizedB = np.zeros((result.shape[0],result.shape[1],3), np.uint8)
    resizedB[0:imageB.shape[0], 0:imageB.shape[1]] = imageB
    difference = cv2.bitwise_or(resizedB,result, mask=mask_inv)
    result = cv2.add(result,difference)
    cv2.imwrite('result .jpg', result)

我不得不使用cv2.bitwise_or，因为使用cv2.add添加两个图像会使其过于明亮，导致连接处几乎出现黑线。你知道怎么解决这个问题吗？也许有一种方法可以修改蒙版，使其缩小1像素？

Answer 1

通过使用几个逻辑运算的组合，我终于解决了这个问题。解决方案如下：

    h1,w1 = imageB.shape[:2]
    h2,w2 = imageA.shape[:2]
    pts1 = np.float32([[0,0],[0,h1],[w1,h1],[w1,0]]).reshape(-1,1,2)
    pts2 = np.float32([[0,0],[0,h2],[w2,h2],[w2,0]]).reshape(-1,1,2)
    pts2_ = cv2.perspectiveTransform(pts2, H)
    pts = np.concatenate((pts1, pts2_), axis=0)
    # print("pts:", pts)
    [xmin, ymin] = np.int32(pts.min(axis=0).ravel() - 0.5)
    [xmax, ymax] = np.int32(pts.max(axis=0).ravel() + 0.5)
    t = [-xmin,-ymin]
    Ht = np.array([[1,0,t[0]],[0,1,t[1]],[0,0,1]]) # translate
    result = cv2.warpPerspective(imageA, Ht.dot(H), (xmax-xmin, ymax-ymin))
    resizedB = np.zeros((result.shape[0], result.shape[1], 3), np.uint8)
    resizedB[t[1]:t[1]+h1,t[0]:w1+t[0]] = imageB
    # Now create a mask of logo and create its inverse mask also
    img2gray = cv2.cvtColor(result,cv2.COLOR_BGR2GRAY)
    ret, mask = cv2.threshold(img2gray, 0, 255, cv2.THRESH_BINARY)
    kernel = np.ones((5,5),np.uint8)
    k1 = (kernel == 1).astype('uint8')
    mask = cv2.erode(mask, k1, borderType=cv2.BORDER_CONSTANT)
    mask_inv = cv2.bitwise_not(mask)
    difference = cv2.bitwise_or(resizedB, resizedB, mask=mask_inv)
    result2 = cv2.bitwise_and(result, result, mask=mask)
    result = cv2.add(result2, difference)