Stereo Vision (Python) - How can I improve my Stereo Rectification results?

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Hi all,

I am carrying out an undergraduate where I need to find out the depth of a stereo image, thus it is crucial to get a good disparity map for the calculations to be accurate. My rectification results are pretty mediocre at best and I have carried out the calibration countless times with no success, only minimal variations between results. I would highly appreciate experts' guidance on this issue. Like, can I try changing some parameters of my stereoRectify method? I already tried to tinker with the stereocalibrate parameters with minimal success.

I cannot post my results on this question as I have insufficient karma, but the rectified images look as if they were warped in a spiral manner and overriding the original video capture. Copy this in the URL if you wish to see a sample of my results: scontent.fmla1-2.fna.fbcdn.net/v/t35.0-12/17571001_10212528106338046_845875913_o.png?oh=3ef04efa50a3d9f4e4d253f2f57ae298&oe=58DAF735

This is my code for the calibration:

import cv,time,sys
import cv2
from itertools import izip
# python calibrate.py board_w,board_h and boards_n should be given by the user
n_boards=0  #no of boards
board_w=int(sys.argv[1])    # number of horizontal corners
board_h=int(sys.argv[2])    # number of vertical corners
n_boards=int(sys.argv[3])   # no. of views passed as pictures
board_n=board_w*board_h     # no of total corners
board_sz=(board_w,board_h)  # size of board

#   creation of memory storages for left camera
image_points0=cv.CreateMat(n_boards*board_n,2,cv.CV_32FC1)
object_points=cv.CreateMat(n_boards*board_n,3,cv.CV_32FC1)
point_counts=cv.CreateMat(n_boards,1,cv.CV_32SC1)
intrinsic_matrix0=cv.CreateMat(3,3,cv.CV_32FC1)
distortion_coefficient0=cv.CreateMat(5,1,cv.CV_32FC1)

#   creation of memory storages for right camera
image_points1=cv.CreateMat(n_boards*board_n,2,cv.CV_32FC1)
intrinsic_matrix1=cv.CreateMat(3,3,cv.CV_32FC1)
distortion_coefficient1=cv.CreateMat(5,1,cv.CV_32FC1)

R=cv.CreateMat(3,3,cv.CV_64F)
T=cv.CreateMat(3,1,cv.CV_64F)
E=cv.CreateMat(3,3,cv.CV_64F)
F=cv.CreateMat(3,3,cv.CV_64F)
#term_crit=(cv.CV_TERMCRIT_EPS+cv.CV_TERMCRIT_ITER,30,0.1)
#capture frames of specified properties and modification of matrix values
i=0
z=0     # to print number of frames
successes=0


#   capturing required number of views

found=0
with open('Left.txt','rb') as f:
    imgl= [line.strip() for line in f]
    with open('Right.txt','rb') as f1:
         imgr=[line.strip() for line in f1]
         for (image0,image1) in izip(imgl,imgr):
        if image0 =='' or image1== '' :
            break;
        else:

                Image0 = cv.LoadImage(image0)
                Image1 = cv.LoadImage(image1)

            gray_image0=cv.CreateImage(cv.GetSize(Image0),8,1)
            cv.CvtColor(Image0,gray_image0,cv.CV_BGR2GRAY)
            gray_image1=cv.CreateImage(cv.GetSize(Image1),8,1)
            cv.CvtColor(Image1,gray_image1,cv.CV_BGR2GRAY)
            (found0,corners0)=cv.FindChessboardCorners(gray_image0,board_sz,cv.CV_CALIB_CB_ADAPTIVE_THRESH| cv.CV_CALIB_CB_FILTER_QUADS)
            (found1,corners1)=cv.FindChessboardCorners(gray_image1,board_sz,cv.CV_CALIB_CB_ADAPTIVE_THRESH| cv.CV_CALIB_CB_FILTER_QUADS)
            cv.FindCornerSubPix(gray_image0,corners0,(11,11),(-1,-1),(cv.CV_TERMCRIT_EPS+cv.CV_TERMCRIT_ITER,30,0.001))
            cv.FindCornerSubPix(gray_image1,corners1,(11,11),(-1,-1),(cv.CV_TERMCRIT_EPS+cv.CV_TERMCRIT_ITER,30,0.001))     
            # if got a good ...