Created
May 10, 2020 00:27
-
-
Save sagnikdas98/2f0c293c5d5df56291af8eeeb45061fe to your computer and use it in GitHub Desktop.
Trying Epipolar geo with orb slam
This file contains hidden or bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
| import cv2 | |
| import numpy as np | |
| from matplotlib import pyplot as plt | |
| img1 = cv2.imread('myleft.jpg',0) #queryimage # left image | |
| img2 = cv2.imread('myright.jpg',0) #trainimage # right image | |
| sift = cv2.ORB_create() | |
| # find the keypoints and descriptors with SIFT | |
| kp1= sift.detect(img1,None) | |
| kp2= sift.detect(img2,None) | |
| kp1, des1 = sift.compute(img1, kp1) | |
| kp2, des2 = sift.compute(img2, kp2) | |
| print('ORB done') | |
| des2 = np.float32(des2) | |
| des1 = np.float32(des1) | |
| # if ( des1.empty()): | |
| # cv2.cvError(0,"MatchFinder","1st descriptor empty",__FILE__,__LINE__) | |
| # if ( des2.empty() ): | |
| # cv2.cvError(0,"MatchFinder","2nd descriptor empty",__FILE__,__LINE__) | |
| # FLANN parameters | |
| FLANN_INDEX_KDTREE = 0 | |
| index_params = dict(algorithm = FLANN_INDEX_KDTREE, trees = 5) | |
| search_params = dict(checks=50) | |
| flann = cv2.FlannBasedMatcher(index_params,search_params) | |
| matches = flann.knnMatch(des1,des2,k=2) | |
| good = [] | |
| pts1 = [] | |
| pts2 = [] | |
| # ratio test as per Lowe's paper | |
| for i,(m,n) in enumerate(matches): | |
| if m.distance < 0.8*n.distance: | |
| good.append(m) | |
| pts2.append(kp2[m.trainIdx].pt) | |
| pts1.append(kp1[m.queryIdx].pt) | |
| pts1 = np.int32(pts1) | |
| pts2 = np.int32(pts2) | |
| F, mask = cv2.findFundamentalMat(pts1,pts2,cv2.FM_LMEDS) | |
| # We select only inlier points | |
| pts1 = pts1[mask.ravel()==1] | |
| pts2 = pts2[mask.ravel()==1] | |
| def drawlines(img1,img2,lines,pts1,pts2): | |
| ''' img1 - image on which we draw the epilines for the points in img2 | |
| lines - corresponding epilines ''' | |
| r,c = img1.shape | |
| img1 = cv2.cvtColor(img1,cv2.COLOR_GRAY2BGR) | |
| img2 = cv2.cvtColor(img2,cv2.COLOR_GRAY2BGR) | |
| for r,pt1,pt2 in zip(lines,pts1,pts2): | |
| color = tuple(np.random.randint(0,255,3).tolist()) | |
| x0,y0 = map(int, [0, -r[2]/r[1] ]) | |
| x1,y1 = map(int, [c, -(r[2]+r[0]*c)/r[1] ]) | |
| img1 = cv2.line(img1, (x0,y0), (x1,y1), color,1) | |
| img1 = cv2.circle(img1,tuple(pt1),5,color,-1) | |
| img2 = cv2.circle(img2,tuple(pt2),5,color,-1) | |
| return img1,img2 | |
| # Find epilines corresponding to points in right image (second image) and | |
| # drawing its lines on left image | |
| lines1 = cv2.computeCorrespondEpilines(pts2.reshape(-1,1,2), 2,F) | |
| lines1 = lines1.reshape(-1,3) | |
| img5,img6 = drawlines(img1,img2,lines1,pts1,pts2) | |
| # Find epilines corresponding to points in left image (first image) and | |
| # drawing its lines on right image | |
| lines2 = cv2.computeCorrespondEpilines(pts1.reshape(-1,1,2), 1,F) | |
| lines2 = lines2.reshape(-1,3) | |
| img3,img4 = drawlines(img2,img1,lines2,pts2,pts1) | |
| plt.subplot(121),plt.imshow(img5) | |
| plt.subplot(122),plt.imshow(img3) | |
| plt.show() |
Sign up for free
to join this conversation on GitHub.
Already have an account?
Sign in to comment