Stereo Calibration Opencv Python and Disparity Map

Question

I'm keen on discovering the different map of a scene. In the first place, I did sound system adjustment/calibration utilizing the accompanying code (I composed it myself with a little assistance from Google, in the wake of neglecting to track down any accommodating instructional exercises for a similar written in python for OpenCV 2.4.10).

I took pictures of a chessboard at the same time on the two cameras and saved them as left*.jpg and right*.jpg.

import numpy as np

import cv2

import glob

# termination criteria

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((6*9,3), np.float32)

objp[:,:2] = np.mgrid[0:9,0:6].T.reshape(-1,2)

# Arrays to store object points and image points from all the images.

objpointsL = [] # 3d point in real world space

imgpointsL = [] # 2d points in image plane.

objpointsR = []

imgpointsR = []

images = glob.glob('left*.jpg')

for fname in images:

    img = cv2.imread(fname)

    grayL = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)

    # Find the chess board corners

    ret, cornersL = cv2.findChessboardCorners(grayL, (9,6),None)

    # If found, add object points, image points (after refining them)

    if ret == True:

        objpointsL.append(objp)

        cv2.cornerSubPix(grayL,cornersL,(11,11),(-1,-1),criteria)

        imgpointsL.append(cornersL)

images = glob.glob('right*.jpg')

for fname in images:

    img = cv2.imread(fname)

    grayR = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)

    # Find the chess board corners

    ret, cornersR = cv2.findChessboardCorners(grayR, (9,6),None)

    # If found, add object points, image points (after refining them)

    if ret == True:

        objpointsR.append(objp)

        cv2.cornerSubPix(grayR,cornersR,(11,11),(-1,-1),criteria)

        imgpointsR.append(cornersR)

retval,cameraMatrix1, distCoeffs1, cameraMatrix2, distCoeffs2, R, T, E, F = cv2.stereoCalibrate(objpointsL, imgpointsL, imgpointsR, (320,240))

How would I correct the pictures? What different advances would it be advisable for me to do prior to proceeding to discover the disparity map? I read someplace that while ascertaining the uniqueness map, the features detected on the two frames should lie on a similar flat line. Kindly assist me with trip. Any assistance would be quite valued.'

Answer 1

You want cameraMatrix1,  distCoeffs2, distCoeffs1, cameraMatrix2 and "newCameraMatrix" also, for cv2.undistort()

With the help of cv2.getOptimalNewCameraMatrix(), you can also get "newCameraMatrix".

Kindly check the below code:

# Assuming you have left01.jpg and right01.jpg that you want to rectify

lFrame = cv2.imread('left01.jpg')

rFrame = cv2.imread('right01.jpg')

w, h = lFrame.shape[:2] # both frames should be of same shape

frames = [lFrame, rFrame]

# Params from camera calibration

camMats = [cameraMatrix1, cameraMatrix2]

distCoeffs = [distCoeffs1, distCoeffs2]

camSources = [0,1]

for src in camSources:

    distCoeffs[src][0][4] = 0.0 # use only the first 2 values in distCoeffs

# The rectification process

newCams = [0,0]

roi = [0,0]

for src in camSources:

    newCams[src], roi[src] = cv2.getOptimalNewCameraMatrix(cameraMatrix = camMats[src], 

                                                           distCoeffs = distCoeffs[src], 

                                                           imageSize = (w,h), 

                                                           alpha = 0)

rectFrames = [0,0]

for src in camSources:

        rectFrames[src] = cv2.undistort(frames[src], 

                                        camMats[src], 

                                        distCoeffs[src])

# See the results

view = np.hstack([frames[0], frames[1]])    

rectView = np.hstack([rectFrames[0], rectFrames[1]])

cv2.imshow('view', view)

cv2.imshow('rectView', rectView)

# Wait indefinitely for any keypress

cv2.waitKey(0)

Hope you got your answer, Next step is just to calculate "disparity maps"

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