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[0].distance / x[1].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][0]), int(kpsA[queryIdx][1]))
ptB = (int(kpsB[trainIdx][0]) + wA, int(kpsB[trainIdx][1]))
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[1] + img_left.shape[1], img_right.shape[0]))
cvshow('result_medium', result)
# Pass the picture left to the left end of the result picture
result[0:img_left.shape[0], 0:img_left.shape[1]] = 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[1], img_right.shape[0]))
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)```