立体声校准Opencv Python和视差图 [英] Stereo Calibration Opencv Python and Disparity Map

问题描述

我有兴趣寻找场景的视差图。首先，我使用下面的代码进行立体声校准（我自己写了一些来自谷歌的帮助，没有找到任何有用的教程同样写在python的OpenCV 2.4.10）。

I am interested in finding the disparity map of a scene. To start with, I did stereo calibration using the following code (I wrote it myself with a little help from Google, after failing to find any helpful tutorials for the same written in python for OpenCV 2.4.10).

我在两台相机上同时拍摄了一个棋盘的图像，并将它们保存为左* .jpg和右* .jpg。

I took images of a chessboard simultaneously on both 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))

如何纠正图像？在继续查找视差图之前，我还需要做什么其他步骤？我读某处，在计算视差图时，在两个帧上检测到的特征应该位于同一水平线上。请帮我在这里。任何帮助将不胜感激。

How do I rectify the images? What other steps should I do before going on to find the disparity map? I read somewhere that while calculating the disparity map, the features detected on both frames should lie on the same horizontal line. Please help me out here. Any help would be much appreciated.

推荐答案

您需要 cameraMatrix1 ， distCoeffs1 ，<$ hc $ c>，， cameraMatrix2 ， distCoeffs2 和newCameraMatrix docs.opencv.org/modules/imgproc/doc/geometric_transformations.html#undistortrel =nofollow> cv2.undistort（）

you need cameraMatrix1, distCoeffs1, cameraMatrix2, distCoeffs2 and "newCameraMatrix" for cv2.undistort()

您可以使用 cv2.getOptimalNewCameraMatrix（）获取newCameraMatrix

you can get "newCameraMatrix" using cv2.getOptimalNewCameraMatrix()

所以在脚本的剩余部分粘贴：

so in the remainder of your script paste this:

# 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)

希望可以帮助您计算下一个可能计算视差图的事情）;

hope that gets you on your way to the next thing which might be calculating "disparity maps" ;)

参考：

http://www.janeriksolem.net/2014/05/how-to-calibrate-camera-with-opencv-and.html

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