Hi, I’m working on the following task:
I have 6 fisheye cameras and would like to produce a 360 degree stitched image.
After carrying out the calibration procedure with findChessboardCorners, calibrateCamera, I obtained the intrinsic and extrinsic matrix.
Starting from the 6 images with fish-eye effect, through the fisheye.initUndistortRectifyMap function, I obtained the 6 planar images.
The two planar images from above:
Now I should do the stitching to get a 360 degree image.
I tried to do this using the cv2.createStitcher function, but this doesn’t always work, also I would like to have access to the homography matrix to determine the static matrices of the system.
So I tried to calculate the homography matrix, identifying through the SIFT algorithm, the common keypoints between two images and keeping the keypoints that best match.
I then stitched the two images using the warpPerspective function.
I believe that the procedure is correct up to the calculation of the keypoints, but I do not understand why the final result is not good.
In fact, in an attempt to stitch the second image is completely deformed / changed in perspective with a loss of right image.
Here there is the code:
import cv2 import numpy as np def cvshow(name, img): cv2.imshow(name, img) cv2.waitKey(0) cv2.destroyAllWindows() cv2.destroyAllWindows() def sift_kp(image): gray_image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY) sift = cv2.xfeatures2d.SIFT_create() sift = cv2.xfeatures2d.SIFT_create() kp, des = sift.detectAndCompute(image, None) kp_image = cv2.drawKeypoints(gray_image, kp, None) return kp_image, kp, des def get_good_match(des1, des2): bf = cv2.BFMatcher() matches = bf.knnMatch(des1, des2, k=2) # des1 is the template image, des2 is the matching image matches = sorted(matches, key=lambda x: x.distance / x.distance) good =  for m, n in matches: if m.distance < 0.55 * n.distance: good.append(m) return good def drawMatches(imageA, imageB, kpsA, kpsB, matches, status): # Initialize the visualization picture, connect the A and B pictures left and right together (hA, wA) = imageA.shape[:2] (hB, wB) = imageB.shape[:2] vis = np.zeros((max(hA, hB), wA + wB, 3), dtype="uint8") vis[0:hA, 0:wA] = imageA vis[0:hB, wA:] = imageB # Joint traversal, draw matching pairs for ((trainIdx, queryIdx), s) in zip(matches, status): # When the point pair is matched successfully, draw it on the visualization if s == 1: # Draw matching pairs ptA = (int(kpsA[queryIdx]), int(kpsA[queryIdx])) ptB = (int(kpsB[trainIdx]) + wA, int(kpsB[trainIdx])) cv2.line(vis, ptA, ptB, (0, 255, 0), 1) # Return visualization results return vis # Panorama stitching def siftimg_rightlignment(img_right, img_left): _, kp1, des1 = sift_kp(img_right) _, kp2, des2 = sift_kp(img_left) goodMatch = get_good_match(des1, des2) # When the matching pairs of the filter items are greater than 4 pairs: calculate the perspective transformation matrix if len(goodMatch) > 4: # Get the point coordinates of the matching pair ptsA = np.float32([kp1[m.queryIdx].pt for m in goodMatch]).reshape(-1, 1, 2) ptsB = np.float32([kp2[m.trainIdx].pt for m in goodMatch]).reshape(-1, 1, 2) ransacReprojThreshold = 4 H, status = cv2.findHomography(ptsA, ptsB, cv2.RANSAC, ransacReprojThreshold) print(H) #H = np.array([[-3.95002617e-01,-7.49813070e-02, 4.43642683e+02], [-4.06655962e-01,5.27365057e-01, 1.20636875e+02],[-1.60149798e-03, -3.69708507e-05, 1.00000000e+00]]) # The function of this function is to first use RANSAC to select the best four sets of pairing points, and then calculate the H matrix. H is a 3*3 matrix # Change the angle of view to the right of the picture, result is the transformed picture result = cv2.warpPerspective(img_right, H, (img_right.shape + img_left.shape, img_right.shape)) cvshow('result_medium', result) # Pass the picture left to the left end of the result picture result[0:img_left.shape, 0:img_left.shape] = img_left return result # Feature matching + panoramic stitching import numpy as np import cv2 # Read the stitched pictures (note the placement of the left and right pictures) # Is to transform the graphics on the right img_left = cv2.imread(r'\planar\0.png') img_right = cv2.imread(r'\planar\5.png') img_right = cv2.resize(img_right, None, fx=0.5, fy=0.3) # Ensure that the two images are the same size img_left = cv2.resize(img_left, (img_right.shape, img_right.shape)) kpimg_right, kp1, des1 = sift_kp(img_right) kpimg_left, kp2, des2 = sift_kp(img_left) # Display the original image and the image after key point detection at the same time cvshow('img_left', np.hstack((img_left, kpimg_left))) cvshow('img_right', np.hstack((img_right, kpimg_right))) goodMatch = get_good_match(des1, des2) all_goodmatch_img = cv2.drawMatches(img_right, kp1, img_left, kp2, goodMatch, None, flags=2) # goodmatch_img Set the first goodMatch[:10] goodmatch_img = cv2.drawMatches(img_right, kp1, img_left, kp2, goodMatch[:10], None, flags=2) cvshow('Keypoint Matches1', all_goodmatch_img) cvshow('Keypoint Matches2', goodmatch_img) # Stitch the picture into a panorama result = siftimg_rightlignment(img_right, img_left) cvshow('result', result)```