I am working on stereo camera calibration with OpenCV according to the standard tutorial given by http://docs.opencv.org/2.4.11/modules/calib3d/doc/camera_calibration_and_3d_reconstruction.html#stereorectify. However, the calibrated output is not good and the rms value is 78.26. I already tried any available solutions I can find from Google, while none of them can work.
Detail implementation: I use 13 image pairs to find object points and image point with the below code.
def getCalibrateParams(leftImgPath, rightImgPath):
# termination criteria
w = 9
h = 7
chess_size = (9, 7)
chess_size_r = (7,9)
criteria = (cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_MAX_ITER, 30, 0.001)
# prepare object points, like (0,0,0), (1,0,0), (2,0,0) ....,(6,5,0)
#objp = np.zeros((np.prod(chess_size),3), np.float32)
#objp[:,:2] = np.indices(chess_size).T.reshape(-1,2)
objp = np.zeros((w*h, 3), np.float32)
objp[:,:2] = np.mgrid[0:w, 0:h].T.reshape(-1,2)
# Arrays to store object points and image points from all the images.
objpoints = [] # 3d point in real world space
leftImgpoints = [] # 2d points in image plane.
rightImgPoints = []
leftImg = glob.glob(leftImgPath)
rightImg = glob.glob(rightImgPath)
for fname in leftImg:
img = cv2.imread(fname)
gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
# Find the chess board corners
ret, corners = cv2.findChessboardCorners(gray, (w,h), None)
if not ret:
raise ChessboardNotFoundError('No chessboard could be found!')
else:
objpoints.append(objp)
#increase the accuracy of seeking for corners
cv2.cornerSubPix(gray,corners,(11,11),(-1,-1),criteria)
leftImgpoints.append(corners)
# Draw and display the corners
#cv2.drawChessboardCorners(img, chess_size, corners,ret)
#cv2.imshow('img',img)
#cv2.waitKey()
for fname in rightImg:
img = cv2.imread(fname)
gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
ret, corners = cv2.findChessboardCorners(gray, chess_size_r)
if not ret:
raise ChessboardNotFoundError('No chessboard could be found!')
else:
#increase the accuracy of seeking for corners
cv2.cornerSubPix(gray,corners,(11,11),(-1,-1),criteria)
rightImgPoints.append(corners)
return objpoints,leftImgpoints,rightImgPoints
After that, I try to calibrate an image pair with the below code:
objectPoints, imagePoints1, imagePoints2 = getCalibrateParams(leftImgPath, rightImgPath) #use any image to find the size img = cv2.imread('/home/wuyang/vr/img/test/test_1_01_02.jpg') gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) h, w = img.shape[:2]
#single camera calibration to fetch a more accurate camera matrix
ret1, cameraMatrix1, distCoeffs1, rvecs1, tvecs1 = cv2.calibrateCamera(objectPoints, imagePoints1, gray.shape[::-1],None, None)
ret2, cameraMatrix2, distCoeffs2, rvecs2, tvecs2 = cv2.calibrateCamera(objectPoints, imagePoints2, gray.shape[::-1],None, None)
print ret1, ret2
stereo_criteria = (cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_MAX_ITER, 30, 0.001)
stereo_flags = cv2.CALIB_FIX_INTRINSIC
rms, cameraMatrix1,distCoeffs1, cameraMatrix2, distCoeffs2, R, T = cv2.stereoCalibrate(objectPoints, imagePoints1,
imagePoints2, imageSize = (w,h),
cameraMatrix1 = cameraMatrix1, distCoeffs1 = distCoeffs1,
cameraMatrix2 = cameraMatrix2, distCoeffs2 = distCoeffs2,
criteria = stereo_criteria, flags = stereo_flags)[:-2]
print 'stereo calibration result: ',rms
#print cv2.CALIB_FIX_INTRINSIC 256
#print cv2.CALIB_USE_INTRINSIC_GUESS 1
#print cv2.CALIB_FIX_PRINCIPAL_POINT 4
#print cv2.CALIB_FIX_FOCAL_LENGTH 16
#print cv2.CALIB_FIX_ASPECT_RATIO 2
#print cv2.CALIB_SAME_FOCAL_LENGTH 512
#print cv2.CALIB_RATIONAL_MODEL 16384
#print cv2.CALIB_ZERO_TANGENT_DIST 8
#print cv2.CALIB_FIX_K1 32
#print cv2.CALIB_FIX_K2 64
#print cv2.CALIB_FIX_K3 128
#print cv2.CALIB_FIX_K4 2048
#print cv2.CALIB_FIX_K5 4096
#print cv2.CALIB_FIX_K6 8192
'''
print 'rms value:', rms
print 'cameraMatrix1:\n', cameraMatrix1
print 'cameraMatrix2:\n', cameraMatrix2
print 'disCoeffs1:\n', distCoeffs1
print 'disCoeffs2:\n', distCoeffs2
print 'rotation vector:\n', R
print 'translation vector:\n', T
'''
#left camera calibration test
'''
computeReprojectionError(objectPoints, imagePoints1, rvecs1, tvecs1, cameraMatrix1, distCoeffs1)
newcameramtx1, roi1 = getCameraMatrix(img, cameraMatrix1, distCoeffs1)
undistort(img, cameraMatrix1, distCoeffs1, newcameramtx1, roi1)
'''
R1, R2, P1, P2, Q = cv2.stereoRectify(cameraMatrix1, distCoeffs1, cameraMatrix2, distCoeffs2,
(w,h), R, T, flags = 0, alpha = -1)[:-2]
# distort images
undistort_map1, rectify_map1 = cv2.initUndistortRectifyMap(cameraMatrix1, distCoeffs1, R1, P1, (w,h), cv2.CV_32FC1)
undistort_map2, rectify_map2 = cv2.initUndistortRectifyMap(cameraMatrix2, distCoeffs2, R2, P2, (w,h), cv2.CV_32FC1)
lpath = '/home/wuyang/vr/img/test/test_2_01_01.jpg'
rpath = '/home/wuyang/vr/img/test/test_2_01_02.jpg'
lImg = cv2.imread(lpath)
rImg = cv2.imread(rpath)
#undistor_output1 = cv2.undistort(test,undistort_map1, rectify_map1, None, newcameramtx)
undistor_output1 = cv2.remap(lImg, undistort_map1, rectify_map1, cv2.INTER_LINEAR)
undistor_output2 = cv2.remap(rImg, undistort_map2, rectify_map2, cv2.INTER_LINEAR)
cv2.imwrite('ss.jpg', undistor_output1)
The flow is quite standard while the output is not good. The left image to be calibrated: http://imgur.com/8WvzTvc The calibrated result: enter link description here
Please help to see how to get a reasonable good calibrated result. Thanks a lot!
