Pts = np.concatenate((pts1, pts2_), axis=0) Pts2_ = cv2.perspectiveTransform(pts2, H) H = cv2.findHomography(src_pts, dst_pts, cv2.RANSAC,5.0) Src_pts = np.float32(.pt for m in matches ]).reshape(-1,1,2)ĭst_pts = np.float32(.pt for m in matches ]).reshape(-1,1,2) MatchDrawing = util.drawMatches(gray2,kp2,gray1,kp1,matches) Print (str(len(good)) + " Matches were Found")
Matches = matcher.knnMatch(descriptors2,descriptors1, k=2)
Keypoints2Im = cv2.drawKeypoints(image2, kp2, outImage = cv2.DRAW_MATCHES_FLAGS_DEFAULT, color=(0,0,255)) Keypoints1Im = cv2.drawKeypoints(image1, kp1, outImage = cv2.DRAW_MATCHES_FLAGS_DEFAULT, color=(0,0,255)) Kp2, descriptors2 = tectAndCompute(gray2,mask2) Gray2 = cv2.cvtColor(image2,cv2.COLOR_BGR2GRAY) Kp1, descriptors1 = tectAndCompute(gray1,mask1) Gray1 = cv2.cvtColor(image1,cv2.COLOR_BGR2GRAY) # find the keypoints and descriptors with SIFT But I am not understanding why that output is coming.Ĭore part of stitching is like below: detector = cv2.SIFT_create(400)
Panorama stitcher for four images python open cv python code#
Stitching code is following standard stitching steps like finding keypoints, descriptors then matching points, calculating homography and then warping of images. 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? Matches2to3 = bf.I got output like below after stitching result of 24 stitched images to next 25th image. Matches1to2 = bf.knnMatch(des1, des2, k=2) Kpts3, des3 = orb.detectAndCompute(img3, None)īf = cv.BFMatcher_create(cv.NORM_HAMMING)
Kpts2, des2 = orb.detectAndCompute(img2, None) Kpts1, des1 = orb.detectAndCompute(img1, None) # find the keypoints and compute the descriptors with ORB for images # Initiate ORB detector, that it will be our detector object. Img3 = cv.cvtColor(img3, cv.COLOR_BGR2GRAY) Img2 = cv.cvtColor(img2, cv.COLOR_BGR2GRAY) Img1 = cv.cvtColor(img1, cv.COLOR_BGR2GRAY) Transformation_rigid_matrix = np.vstack((transformation_rigid_matrix, affine_row)) Transformation_rigid_matrix, rigid_mask = cv.estimateAffinePartial2D(src_pts, dst_pts) # Compute a rigid transformation (without depth, only scale + rotation + translation) /affine transformation Src_pts = np.float32(.pt for m in matches]).reshape(-1, 1, 2)ĭst_pts = np.float32(.pt for m in matches]).reshape(-1, 1, 2) # Transforming keypoints to list of points # Find an Homography matrix between two pictures New_img:h1 + translation_dist, translation_dist:w1 + translation_dist] = img1ĭef find_homography(kpt1, kpt2, matches): New_img = cv.warpPerspective(img2, H_translation.dot(M), (x_max - x_min, y_max - y_min)) ListOfPoints = np.concatenate((pts_corners_src, pts_corners_dst), axis=0)
Pts_corners_dst = cv.perspectiveTransform(pts_corners_temp, M) # perform perspective tranform using previously calculated matrix and the corners of # When we have established a homography we need to warp perspective # get the corner coordinates of the "query" and "train" image # Create a blank image with the size of the first image + second image Here is my code where i print panorama between img1 and img2, and between img2 and img3: import numpy as npįrom matplotlib.pyplot import imshow, show, subplot, title, axisĭef draw_matches(img1, kpt1, img2, kpt2, matches): Someone can help me please to implement what i need please ? I didn't find a good explanation or an enough good explanation for me in order to compute and fine the panoramic view of theses 3 pictures. So, I have to of course choose image 2 as the center of the panorama And i don't know to do that ( panoramic view for 3 pictures. Then i know to compute and find the panoramic view between only 2 images, and i do it between img1 and img2, and between img2 and img3.īut Then for the last step, i want to find the panoramic view of theses 3 pictures using affine tranformation with Ransac algorithm from opencv. I need to make a panoramic view from serie of pictures (3 pictures).Īfter that i created orb, detected and computed keypoints and descriptors for the three picture, i matched the most likely similars keypoints between:
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