这是我在stackoverflow上的第一篇文章。请为我的英语和编程知识感到抱歉,如果他们不知何故令人不安。在
好吧,我正在尝试用windows8.1操作系统中的opencv2.4.9进行摄像机校准(ubuntu操作系统不能解决这个问题)
问题:我正在使用下面的代码来校准我的相机,但似乎如果我的样本图像(带有检查板模式)的数量大于2,那么newcameramtx的roi=cv2。getoptiminaNewCameraMatrix(mtx,dist,(w,h),1,(w,h))结果为[0,0,0,0]。样本数如何与结果相关联?(在此之前,在对该代码进行某些更改之前,最大样本数为12)。在
我所说的最大样本数是指从我的相机中采集到的棋盘图案的图像,如果数量超过最大数量,roi就不能给出好的结果。在
角点检测效果很好。你可以找到我的示例图片here。在
# -*- coding: utf-8 -*-
"""
Created on Fri May 16 15:23:00 2014
@author: kakarot
"""
import numpy as np
import cv2
#import os
#import time
from matplotlib import pyplot as plt
LeftorRight = 'L'
numer = 12
chx = 6
chy = 9
chd = 25
# termination criteria
criteria = (cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_MAX_ITER, numer, 0.001)
# prepare object points, like (0,0,0), (1,0,0), (2,0,0) ....,(6,5,0)
objp = np.zeros((chy*chx,3), np.float32)
objp[:,:2] = np.mgrid[0:chy,0:chx].T.reshape(-1,2)
# Arrays to store object points and image points from all the images.
objpoints = [] # 3d point in real world space, (x25mm)
imgpoints = [] # 2d points in image plane.
enum = 1
while(enum<=numer):
img=cv2.imread('1280x720p/BestAsPerMatlab/calib_'+str(LeftorRight)+str(enum)+'.jpg')
gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
# Find the chess board corners
ret, corners = cv2.findChessboardCorners(gray, (chy,chx),None)
#cv2.imshow('Calibration',img)
# If found, add object points, image points (after refining them)
if ret == True and enum <= numer:
objpoints.append(objp*chd)
cv2.cornerSubPix(gray,corners,(11,11),(-1,-1),criteria)
imgpoints.append(corners)
# Draw and display the corners
cv2.drawChessboardCorners(img, (chy,chx),corners,ret)
cv2.imshow('Calibration',img)
cv2.imwrite('1280x720p/Chessboard/calibrated_L{0}.jpg'.format(enum),img)
print enum
#time.sleep(2)
if enum == numer:
ret, mtx, dist, rvecs, tvecs = cv2.calibrateCamera(objpoints, imgpoints, gray.shape[::-1],None,None)
img = cv2.imread('1280x720p/BestAsPerMatlab/calib_'+str(LeftorRight)+'7.jpg')
gray = cv2.cvtColor(img,cv2.COLOR_RGB2GRAY)
h, w = img.shape[:2] #a (1 to see the whole picture)
newcameramtx, roi=cv2.getOptimalNewCameraMatrix(mtx,dist,(w,h),1,(w,h))
if (np.size(roi) == 4 and np.mean(roi) != 0):
# undistort
mapx,mapy = cv2.initUndistortRectifyMap(mtx,dist,None,newcameramtx,(w,h),5)
dst = cv2.remap(img,mapx,mapy,cv2.INTER_LINEAR)
# crop the image
x,y,w,h = roi
dst = dst[y:y+h, x:x+w]
dst = cv2.cvtColor(dst,cv2.COLOR_RGB2BGR)
plt.imshow(dst)
#cv2.imwrite('result.jpg',dst)
#np.savetxt('mtxL.txt',mtx)
#np.savetxt('distL.txt',dist)
else:
np.disp('Something Went Wrong')
enum += 1
'''
k = cv2.waitKey(1) & 0xFF
if k == 27:
break
'''
cv2.destroyAllWindows()
编辑:我使用的是两个便宜的usb摄像头。我发现其中一个相机的样品集还可以,我可以使用超过19个样品而没有问题。但当使用其他相机的校准样本时,最大样本图像数为2。(如果我再做一组样品,数量会有所不同)。总的来说,校准矩阵的产生似乎是有问题的。但这很奇怪。在
最后,我使用鱼眼相机,相信在每次拍摄结束时削减足够的像素,我会模拟普通相机。。。也许这就是给我带来麻烦的原因!在
你应该把距离改成
然后打电话给
^{pr2}$它对我有用。在
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