3d重建和距离测量

时间：2014-04-25 12:55:19

标签： python opencv

大家好，我一直在使用opencv库和两个网络摄像头在python中编写一个小程序，以便我可以测量最后一根丝束与它们前面的物体之间的距离（使用视差图），所以当我最后运行程序我通常在矩阵中得到结果但我得到的不仅是一个数字（应该是距离）而是许多不同的数字，除了我总是得到一个不改变的数字即使我改变视图，任何身体都可以告诉我为什么？！

这是代码：

import numpy as np
import cv2 as cv
import cv2.cv as cv1
from VideoCapture import Device
import os


def caliLeftCam():    
    args, img_mask = getopt.getopt(sys.argv[1:], '', ['save=', 'debug=',       'square_size='])
    args = dict(args)
    try: img_mask = img_mask[0]
    except: img_mask = '../cpp/img*.jpg'
    img_names = glob(img_mask)
    debug_dir = args.get('--debug')
    square_size = float(args.get('--square_size', 1.0))

    pattern_size = (7, 5)
    pattern_points = np.zeros( (np.prod(pattern_size), 3), np.float32 )
    pattern_points[:,:2] = np.indices(pattern_size).T.reshape(-1, 2)
    pattern_points *= square_size

   obj_points = []
   img_pointsL = []
   h, w = 0, 0
    for fn in img_names:
    print "processing %s..." % fn,
    imgL = cv.imread(fn, 0)
    h, w = imgL.shape[:2]
    found, corners = cv.findChessboardCorners(imgL, pattern_size)
    if found:
        term = ( cv.TERM_CRITERIA_EPS + cv.TERM_CRITERIA_COUNT, 30, 0.1 )
        cv.cornerSubPix(imgL, corners, (5, 5), (-1, -1), term)
    if debug_dir:
        vis = cv.cvtColor(imgL, cv.COLOR_GRAY2BGR)
        cv.drawChessboardCorners(vis, pattern_size, corners, found)
        path, name, ext = splitfn(fn)
        cv.imwrite('%s/%s_chess.bmp' % (debug_dir, name), vis)
    if not found:
        print "chessboard not found"
        continue
    img_pointsL.append(corners.reshape(-1, 2))
    obj_points.append(pattern_points)

    print 'ok'

rmsL, cameraL_matrix, dist_coefsL, rvecsL, tvecsL = cv.calibrateCamera(obj_points, img_pointsL, (w, h))
print "RMSL:", rmsL
print "Left camera matrix:\n", cameraL_matrix
print "distortion coefficients: ", dist_coefsL.ravel()

newcameramtxL, roi=cv.getOptimalNewCameraMatrix(cameraL_matrix,dist_coefsL,(w,h),1,(w,h))
#undistort
mapxL,mapyL = cv.initUndistortRectifyMap(cameraL_matrix,dist_coefsL,None,newcameramtxL,(w,h),5)
dstL = cv.remap(imgL,mapxL,mapyL,cv.INTER_LINEAR)
return img_pointsL, cameraL_matrix, dist_coefsL
def caliRightCam():

args, img_mask = getopt.getopt(sys.argv[1:], '', ['save=', 'debug=', 'square_size='])
args = dict(args)
try: img_mask = img_mask[0]
except: img_mask = '../cpp/ph*.jpg'
img_names = glob(img_mask)
debug_dir = args.get('--debug')
square_size = float(args.get('--square_size', 1.0))

pattern_size = (7, 5)
pattern_points = np.zeros( (np.prod(pattern_size), 3), np.float32 )
pattern_points[:,:2] = np.indices(pattern_size).T.reshape(-1, 2)
pattern_points *= square_size

obj_points = []
img_pointsR = []
h, w = 0, 0
for fn in img_names:
    print "processing %s..." % fn,
    imgR = cv.imread(fn, 0)
    h, w = imgR.shape[:2]
    found, corners = cv.findChessboardCorners(imgR, pattern_size)
    if found:
        term = ( cv.TERM_CRITERIA_EPS + cv.TERM_CRITERIA_COUNT, 30, 0.1 )
        cv.cornerSubPix(imgR, corners, (5, 5), (-1, -1), term)
    if debug_dir:
        vis = cv.cvtColor(img, cv2.COLOR_GRAY2BGR)
        cv.drawChessboardCorners(vis, pattern_size, corners, found)
        path, name, ext = splitfn(fn)
        cv.imwrite('%s/%s_chess.bmp' % (debug_dir, name), vis)

    if not found:
        print "chessboard not found"
        continue
    img_pointsR.append(corners.reshape(-1, 2))
    obj_points.append(pattern_points)

    print 'ok'

rmsR, cameraR_matrix, dist_coefsR, rvecsR, tvecsR = cv.calibrateCamera(obj_points, img_pointsR, (w, h))
print "RMSR:", rmsR
print "Right camera matrix:\n", cameraR_matrix
print "distortion coefficients: ", dist_coefsR.ravel()
newcameramtxR, roi=cv.getOptimalNewCameraMatrix(cameraR_matrix,dist_coefsR,(w,h),1,(w,h))
# undistort
mapxR,mapyR = cv.initUndistortRectifyMap(cameraR_matrix,dist_coefsR,None,newcameramtxR,(w,h),5)
dstR = cv.remap(imgR,mapxR,mapyR,cv.INTER_LINEAR)
return img_pointsR,obj_points,  cameraR_matrix, dist_coefsR
def Pics():
vc = cv.VideoCapture(2)
retVal, frame = vc.read();
while True :
    if frame is not None:
        imgray = cv.cvtColor(frame,cv.COLOR_BGR2GRAY)
        ret,thresh = cv.threshold(imgray,127,255,1)
        contours, hierarchy = cv.findContours(thresh,cv.RETR_EXTERNAL,cv.CHAIN_APPROX_SIMPLE)
        cv.namedWindow("threshold")
        cv.namedWindow("Camera")     
        #cv2.drawContours(frame, contours, -1, (0,255,0), 2)
        cv.imshow("Camera", frame)
        cv.imshow("threshold", thresh)
    rval, frame = vc.read()
    if cv.waitKey(1) & 0xFF == 27:
        break
cv1.DestroyAllWindows()
def LeftCap():
cam = Device(2)
cam.saveSnapshot('imageL.jpg')
fn = 'C:\opencv2.4.8\sources\samples\python2\imageL.jpg'
return fn
def RightCap():
cam = Device(0)
cam.saveSnapshot('imageR.jpg')
fn = 'C:\opencv2.4.8\sources\samples\python2\imageR.jpg'
return fn
def Calculate(Li, Ri, Mat):
img_L = cv.pyrDown( cv.imread(Li) )
img_R = cv.pyrDown( cv.imread(Ri) )
window_size = 3
min_disp = 16
num_disp = 112-min_disp
stereo = cv.StereoSGBM(minDisparity = min_disp,
    numDisparities = num_disp,
    SADWindowSize = window_size,
    uniquenessRatio = 10,
    speckleWindowSize = 100,
    speckleRange = 32,
    disp12MaxDiff = 1,
    P1 = 8*3*window_size**2,
    P2 = 32*3*window_size**2,
    fullDP = False
)
print "computing disparity..."
disp = stereo.compute(img_L, img_R).astype(np.float32) / 16.0

print "generating 3d point cloud..."
h, w = img_L.shape[:2]
f = 0.8*w                          # guess for focal length
points = cv.reprojectImageTo3D(disp, Mat)
colors = cv.cvtColor(img_L, cv.COLOR_BGR2RGB)
mask = disp > disp.min()

cv.imshow('left', img_L)
cv.imshow('disparity', (disp-min_disp)/num_disp)

b=6.50
D=b*f/disp
print "The Distance =", D
cv.waitKey()
cv1.DestroyAllWindows()

if __name__ == '__main__':
import sys, getopt
from glob import glob

Img_pointsL, Cam_MatL, DisL = caliLeftCam()
Img_pointsR,obj_points,  Cam_MatR, DisR = caliRightCam()

print "Running stereo calibration..."
retval, Cam_MatL, DisL, Cam_MatR, DisR, R, T, E, F= cv.stereoCalibrate(obj_points, Img_pointsL, Img_pointsR,(384,288))
print"running rectifation..."
RL, Rr, PL, PR, Q, validRoiL, validRoiR = cv.stereoRectify(Cam_MatL, DisL, Cam_MatR, DisR,(384,288), R, T)
Pics()
Li = LeftCap()
Ri = RightCap()
Calculate(Li, Ri, Q)

1 个答案:

答案 0 :(得分：0)

不确定这是否有问题，但您使用cv2.StereoRectify（）而不是cv2.cv.StefeoRectify（）调用cv.StereoRectify（）函数，并且您提供了错误的参数。 来自文档：

  

cv.StereoRectify（cameraMatrix1，cameraMatrix2，distCoeffs1，distCoeffs2，imageSize，R，T，R1，R2，P1，P2，Q = None，flags = CV_CALIB_ZERO_DISPARITY，alpha = -1，newImageSize =（0,0））

你做错了（错误的）方式：

  

cv.stereoRectify（cameraMatrix1，distCoeffs1，cameraMatrix2，distCoeffs2 ...）

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