在将24幅缝合图像的结果拼接到下一幅25幅图像后，我得到了如下输出。在那之前，缝线很好。

有人知道为什么/什么时候缝合输出是这样的吗？产出可能是这样的吗？原因可能是什么？
缝合代码遵循标准的缝合步骤，如查找关键点、描述符、匹配点、计算单应性，然后 Package 图像。但我不明白为什么会有这样的产出。
缝合的核心部分如下所示：

detector = cv2.SIFT_create(400)

# find the keypoints and descriptors with SIFT

gray1 = cv2.cvtColor(image1,cv2.COLOR_BGR2GRAY)
ret1, mask1 = cv2.threshold(gray1,1,255,cv2.THRESH_BINARY)
kp1, descriptors1 = detector.detectAndCompute(gray1,mask1)

gray2 = cv2.cvtColor(image2,cv2.COLOR_BGR2GRAY)
ret2, mask2 = cv2.threshold(gray2,1,255,cv2.THRESH_BINARY)
kp2, descriptors2 = detector.detectAndCompute(gray2,mask2)

keypoints1Im = cv2.drawKeypoints(image1, kp1, outImage = cv2.DRAW_MATCHES_FLAGS_DEFAULT, color=(0,0,255))
keypoints2Im = cv2.drawKeypoints(image2, kp2, outImage = cv2.DRAW_MATCHES_FLAGS_DEFAULT, color=(0,0,255))

# BFMatcher with default params

matcher = cv2.BFMatcher()
matches = matcher.knnMatch(descriptors2,descriptors1, k=2)

# Apply ratio test

good = []
for m, n in matches:
    if m.distance < 0.75 * n.distance:
        good.append(m)

print (str(len(good)) + " Matches were Found")

if len(good) <= 10:
    return image1

matches = copy.copy(good)

matchDrawing = util.drawMatches(gray2,kp2,gray1,kp1,matches)

# Aligning the images

src_pts = np.float32([ kp2[m.queryIdx].pt for m in matches ]).reshape(-1,1,2)
dst_pts = np.float32([ kp1[m.trainIdx].pt for m in matches ]).reshape(-1,1,2)

H = cv2.findHomography(src_pts, dst_pts, cv2.RANSAC,5.0)[0]

h1,w1 = image1.shape[:2]
h2,w2 = image2.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(image2, 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]] = image1

# 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)

请帮帮我。谢谢