雙目標(biāo)定

參考博客 OpenCV相機(jī)標(biāo)定全過程
[OpenCV實(shí)戰(zhàn)]38 基于OpenCV的相機(jī)標(biāo)定
opencv立體標(biāo)定函數(shù) stereoCalibrate()

主要使用的函數(shù)

findChessboardCorners() #棋盤格角點(diǎn)檢測
cornerSubPix() #亞像素檢測
calibrateCamera() #單目標(biāo)定 求解攝像機(jī)的內(nèi)在參數(shù)和外在參數(shù)
stereoCalibrate() #雙目標(biāo)定 求解兩個(gè)攝像頭的內(nèi)外參數(shù)矩陣，以及兩個(gè)攝像頭的位置關(guān)系R,T

代碼

import cv2
import os
import numpy as np

leftpath = 'images/left'
rightpath = 'images/right'
CHECKERBOARD = (11,12)  #棋盤格內(nèi)角點(diǎn)數(shù)
square_size = (30,30)   #棋盤格大小，單位mm
criteria = (cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_MAX_ITER, 30, 0.001)
imgpoints_l = []    #存放左圖像坐標(biāo)系下角點(diǎn)位置
imgpoints_r = []    #存放左圖像坐標(biāo)系下角點(diǎn)位置
objpoints = []   #存放世界坐標(biāo)系下角點(diǎn)位置
objp = np.zeros((1, CHECKERBOARD[0]*CHECKERBOARD[1], 3), np.float32)
objp[0,:,:2] = np.mgrid[0:CHECKERBOARD[0], 0:CHECKERBOARD[1]].T.reshape(-1, 2)
objp[0,:,0] *= square_size[0]
objp[0,:,1] *= square_size[1]


for ii in os.listdir(leftpath):
    img_l = cv2.imread(os.path.join(leftpath,ii))
    gray_l = cv2.cvtColor(img_l,cv2.COLOR_BGR2GRAY)
    img_r = cv2.imread(os.path.join(rightpath,ii))
    gray_r = cv2.cvtColor(img_r,cv2.COLOR_BGR2GRAY)
    ret_l, corners_l = cv2.findChessboardCorners(gray_l, CHECKERBOARD)   #檢測棋盤格內(nèi)角點(diǎn)
    ret_r, corners_r = cv2.findChessboardCorners(gray_r, CHECKERBOARD)
    if ret_l and ret_r:
        objpoints.append(objp)
        corners2_l = cv2.cornerSubPix(gray_l,corners_l,(11,11),(-1,-1),criteria) 
        imgpoints_l.append(corners2_l)
        corners2_r = cv2.cornerSubPix(gray_r,corners_r,(11,11),(-1,-1),criteria)
        imgpoints_r.append(corners2_r)
        #img = cv2.drawChessboardCorners(img, CHECKERBOARD, corners2,ret)
        #cv2.imwrite('./ChessboardCornersimg.jpg', img)
ret, mtx_l, dist_l, rvecs_l, tvecs_l = cv2.calibrateCamera(objpoints, imgpoints_l, gray_l.shape[::-1],None,None)  #先分別做單目標(biāo)定
ret, mtx_r, dist_r, rvecs_r, tvecs_r = cv2.calibrateCamera(objpoints, imgpoints_r, gray_r.shape[::-1],None,None)

retval, cameraMatrix1, distCoeffs1, cameraMatrix2, distCoeffs2, R, T, E, F = \
    cv2.stereoCalibrate(objpoints, imgpoints_l, imgpoints_r, mtx_l, dist_l, mtx_r, dist_r, gray_l.shape[::-1])   #再做雙目標(biāo)定

print("stereoCalibrate : \n")
print("Camera matrix left : \n")
print(cameraMatrix1)
print("distCoeffs left  : \n")
print(distCoeffs1)
print("cameraMatrix left : \n")
print(cameraMatrix2)
print("distCoeffs left : \n")
print(distCoeffs2)
print("R : \n")
print(R)
print("T : \n")
print(T)
print("E : \n")
print(E)
print("F : \n")
print(F)

將打印的結(jié)果保存到標(biāo)定文件中即可

極線校正

參考博客 機(jī)器視覺學(xué)習(xí)筆記（8）——基于OpenCV的Bouguet立體校正
小白視角之Bouguet雙目立體校正原理

主要使用的函數(shù)

stereoRectify() #計(jì)算旋轉(zhuǎn)矩陣和投影矩陣
initUndistortRectifyMap() #計(jì)算校正查找映射表
remap() #重映射

代碼

import cv2
import numpy as np

def cat2images(limg, rimg):
    HEIGHT = limg.shape[0]
    WIDTH = limg.shape[1]
    imgcat = np.zeros((HEIGHT, WIDTH*2+20,3))
    imgcat[:,:WIDTH,:] = limg
    imgcat[:,-WIDTH:,:] = rimg
    for i in range(int(HEIGHT / 32)):
        imgcat[i*32,:,:] = 255 
    return imgcat

left_image = cv2.imread("images/left/268.jpg")
right_image = cv2.imread("images/right/268.jpg")

imgcat_source = cat2images(left_image,right_image)
HEIGHT = left_image.shape[0]
WIDTH = left_image.shape[1]
cv2.imwrite('imgcat_source.jpg', imgcat_source )

camera_matrix0 = np.array([[1.30991855e+03, 0.00000000e+00, 5.90463086e+02],
                            [0.00000000e+00, 1.31136722e+03, 3.33464608e+02],
                            [0.00000000e+00, 0.00000000e+00, 1.00000000e+00]]
                        ) .reshape((3,3)) #即上文標(biāo)定得到的 cameraMatrix1
                        
distortion0 = np.array([-4.88890701e-01,  3.27964225e-01, -2.72130825e-04,  1.28030208e-03, -1.85964828e-01]) #即上文標(biāo)定得到的 distCoeffs1

camera_matrix1 = np.array([[1.30057467e+03, 0.00000000e+00, 6.28445749e+02],
                            [0.00000000e+00, 1.30026325e+03, 3.90475091e+02],
                            [0.00000000e+00, 0.00000000e+00, 1.00000000e+00]]
                        ) .reshape((3,3)) #即上文標(biāo)定得到的 cameraMatrix2
distortion1 = np.array([-4.95938411e-01,  2.70207629e-01,  1.81014753e-04, -4.58891345e-04, 4.41327829e-01]) #即上文標(biāo)定得到的 distCoeffs2

R = np.array([[ 0.99989348,  0.01340678, -0.00576869], 
              [-0.01338004,  0.99989967,  0.00465071], 
              [ 0.00583046, -0.00457303,  0.99997255]]
            ) #即上文標(biāo)定得到的 R
T = np.array([-244.28272039, 3.84124178, 2.0963191]) #即上文標(biāo)定得到的T


(R_l, R_r, P_l, P_r, Q, validPixROI1, validPixROI2) = \
    cv2.stereoRectify(camera_matrix0, distortion0, camera_matrix1, distortion1, np.array([WIDTH,HEIGHT]), R, T) #計(jì)算旋轉(zhuǎn)矩陣和投影矩陣

(map1, map2) = \
    cv2.initUndistortRectifyMap(camera_matrix0, distortion0, R_l, P_l, np.array([WIDTH,HEIGHT]), cv2.CV_32FC1) #計(jì)算校正查找映射表

rect_left_image = cv2.remap(left_image, map1, map2, cv2.INTER_CUBIC) #重映射

#左右圖需要分別計(jì)算校正查找映射表以及重映射
(map1, map2) = \
    cv2.initUndistortRectifyMap(camera_matrix1, distortion1, R_r, P_r, np.array([WIDTH,HEIGHT]), cv2.CV_32FC1)
    
rect_right_image = cv2.remap(right_image, map1, map2, cv2.INTER_CUBIC)

imgcat_out = cat2images(rect_left_image,rect_right_image)
cv2.imwrite('imgcat_out.jpg', imgcat_out)

效果圖

校正前
左圖

右圖

校正后
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