Stereo Map "Zoomed In"

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asked 6 hours ago

WannabeEngr gravatar image WannabeEngr 1 ●1 updated 1 hour ago

I am trying to produce a depth map from 2 rgb cameras using a github code from: https://github.com/LearnTechWithUs/St... (c)

I successfully finished its stereo calibration part with 50+ images. But, when i run its Main_Stereo Program, it gave me an stereo map that is somewhat "zoomed in". I tried to know which part is causing it & found out that the input (GRAYR & GRAYL) before stereo.compute is already zoomed in. I think the problem may be in its stereo rectification or maybe stereo calibration and i don't know how to fix it...i want to make a stereo map or disparity map(FILTEREDCOLORDEPTH) with the same resolution as my video.capture (CAMR & CAML)

So this is my code for stereo mapping,i highlighted the part with outputs

import numpy as np
import cv2
from sklearn.preprocessing import normalize


cv2.useOptimized()
def coords_mouse_disp(event,x,y,flags,param):
    if event == cv2.EVENT_LBUTTONDBLCLK:
        #print x,y,disp[y,x],filteredImg[y,x]
        average=0
        for u in range (-1,2):
            for v in range (-1,2):
                average += disp[y+u,x+v]
        average=average/9
        Distance= -593.97*average**(3) + 1506.8*average**(2) - 1373.1*average + 522.06
        Distance= np.around(Distance*0.01,decimals=2)
        print('Distance: '+ str(Distance)+' m')

cv2.useOptimized()

#Parameters for Distortion Calibration 

# Termination criteria
criteria =(cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_MAX_ITER, 30, 0.001)
criteria_stereo= (cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_MAX_ITER, 30, 0.001)

# Prepare object points
objp = np.zeros((9*6,3), np.float32)
objp[:,:2] = np.mgrid[0:9,0:6].T.reshape(-1,2)

# Arrays to store object points and image points from all images
objpoints= []   # 3d points in real world space
imgpointsR= []   # 2d points in image plane
imgpointsL= []

# Start calibration from the camera
print('Starting calibration for the 2 cameras... ')
# Call all saved images
for i in range(0,50):   # Put the amount of pictures you have taken for the calibration 
    t= str(i)
    ChessImaR= cv2.imread('images/chessboard-R'+t+'.png',0)    # Right side
    ChessImaL= cv2.imread('images/chessboard-L'+t+'.png',0)    # Left side
    retR, cornersR = cv2.findChessboardCorners(ChessImaR,
                                               (9,6),None)  # Define the number of chees corners
    retL, cornersL = cv2.findChessboardCorners(ChessImaL,
                                               (9,6),None)  # Left side
    if (True == retR) & (True == retL):
        objpoints.append(objp)
        cv2.cornerSubPix(ChessImaR,cornersR,(11,11),(-1,-1),criteria)
        cv2.cornerSubPix(ChessImaL,cornersL,(11,11),(-1,-1),criteria)
        imgpointsR.append(cornersR)
        imgpointsL.append(cornersL)

# Determine the new values for different parameters
#   Right Side

retR, mtxR, distR, rvecsR, tvecsR = cv2.calibrateCamera(objpoints,
                                                        imgpointsR,
                                                        ChessImaR.shape[::-1],None,None)
hR,wR= ChessImaR.shape[:2]
OmtxR, roiR= cv2.getOptimalNewCameraMatrix(mtxR,distR,
                                                   (wR,hR),1,(wR,hR))

#   Left Side
retL, mtxL, distL, rvecsL, tvecsL = cv2.calibrateCamera(objpoints,
                                                        imgpointsL,
                                                        ChessImaL.shape[::-1],None,None)
hL,wL= ChessImaL.shape[:2]
OmtxL, roiL= cv2.getOptimalNewCameraMatrix(mtxL,distL,(wL,hL),1,(wL,hL))
cv2.useOptimized()

print('Cameras Ready to use')

#Calibrate the Cameras for Stereo ...