#StackBounty: #python #image #opencv #image-processing #computer-vision image stitching problem using python and opencv

Bounty: 50

I got output like below after stitching result of 24 stitched images to next 25th image. Before that stitching was good.

enter image description here

Is anyone aware of why/when output of stitching comes like this? What are the possibilities of output coming like that? What may be the reason of that?

Stitching code is following standard stitching steps like finding keypoints, descriptors then matching points, calculating homography and then wraping of images. But I am not understanding why that output is coming.

Core part of stitching is like below:

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:

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)

Please help me out. Thanks.


Below image shows match drawing while stitching 25 to result until 24 images.
enter image description here

And before that match drawing was like below:
enter image description here

I have total 97 images to stitch. If I stitch 24 and 25 image separately they stitches properly. If I start stitching from 23rd image onwards then also stitching is good but it gives me problem when I stitches starting from 1st image. I am not able to understand the problem.

Get this bounty!!!

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