zhiyuanyou/calibration-zhangzhengyou-method

Calibrate the camera with ZhangZhengyou method (in both distortion case and no distortion case)

Hi, there, I’m Zhiyuan You (尤志远) 👋 Homepage

• 🔭 I am currently a Ph.D. candidate in MMLab at The Chinese University of Hong Kong. I received my B.Eng degree (with honor) and M.Eng degree (with honor) from Shanghai Jiao Tong University in Jun. 2020 and Mar. 2023, respectively.
• 🤔 My research interests lie primarily in Deep Learning in Computer Vision, especially Anomaly Detection and Few-Shot Learning.
• 🤝🏻 Contact with me via my email [email protected].

Experience

• [2022.12-2023.3] Software Engineer (perception algorithm for autonomous driving) in Horizon Robotics.
• [2020.12-2022.11] Research Internship (anomaly detection & few-shot learning) in SenseTime.

calibration-zhangzhengyou-method's Issues

1

矫正过的图片显示不完整

您好，我想问一下为什么我标定过后得到的矫正图像只显示了图像的一个角落，不全，该如何解决，谢谢。

Wrong Output

Amazing Output. Why it becomes a circle? :(

一些关于标定图片选取的问题

就是我用dalao你的代码跑了一下我自己手机拍的图片，可结果误差在0.7左右，不太理想。
我输入的照片是拿一部旧手机（红米5A）的单摄像头拍摄的，分辨率是31204160，一共26张。
dalao我看到你用的输入图片貌似是640480的，想问一下dalao'在拍摄的时候使用的是什么设备，有没有经过什么预处理，还有就是在调用opencv的库的时候有没有一些特别要调的参数（我好像没看出来）。另外就是如果我自己拿着那台手机标定的话，拍照的时候有没有什么技巧可以减小误差。
dalao有空的时候可以麻烦帮忙看一下，感激不尽。

Photos in JPEG format

Can I ask for the internal reference of the phone based on the JPEG format photos taken by Apple 12?

[Dedistortion] 修正畸变后RGB图像输出异常

Enviroment:

Windows 10
Python 3.10.10
Conda 23.3.1
Opencv-python 4.7.0.72

新建文件夹: ./pic/out

添加一行:

# run_calib_RGB.py
calibrator.dedistortion("./pic/out")

执行 python run_calib_RGB.py

查看 ./pic/out 文件夹下的图片，均为全黑

执行 python run_calib_IR.py 则能够输出正常的结果

其中程序的输出结果如下

Details

ret: 0.3589505528504342
intrinsic matrix: 
 [[614.63662496   0.         325.54644338]        
 [  0.         614.54453172 240.49048682]
 [  0.           0.           1.        ]]        
distortion cofficients:
 [[ 2.89444147e-02  7.25547149e-01  6.47776530e-04 -1.59124348e-03
  -2.80534645e+00]]
rotation vectors:
 (array([[ 0.32921655],
       [ 0.38902304],
       [-0.20588054]]), array([[ 0.38716799],     
       [ 0.40542893],
       [-0.26872124]]), array([[0.52825573],      
       [0.03181425],
       [0.13114434]]), array([[0.27360081],       
       [0.01991948],
       [0.13535478]]), array([[ 0.43303876],      
       [-0.27686533],
       [ 0.36988017]]), array([[ 0.21465947],     
       [-0.01082683],
       [ 0.17857765]]), array([[-0.0661331 ],     
       [-0.01557704],
       [ 0.18464881]]), array([[-0.30854543],     
       [-0.0197305 ],
       [ 0.18493629]]), array([[-0.14762718],     
       [ 0.42775415],
       [-0.01521786]]), array([[ 0.04796106],     
       [ 0.71108914],
       [-0.31425717]]), array([[0.326922  ],      
       [0.04472084],
       [0.19863675]]), array([[ 0.19164687],      
       [-0.25118224],
       [ 0.41130588]]), array([[ 0.28943137],     
       [-0.37064556],
       [-0.22450606]]), array([[ 0.35871117],     
       [-0.30563564],
       [ 0.2050959 ]]), array([[ 0.34292959],     
       [-0.38990777],
       [-0.03399842]]), array([[ 0.0856877 ],     
       [-0.20301109],
       [ 0.77778982]]), array([[-0.10815291],     
       [ 0.2541726 ],
       [ 0.44638983]]), array([[ 0.0912108 ],     
       [ 0.53356457],
       [-0.31557546]]), array([[ 0.25483896],     
       [ 0.21970949],
       [-0.13260016]]), array([[ 4.40326540e-01], 
       [-5.18032933e-03],
       [ 1.83173056e-04]]), array([[0.04656733],  
       [0.0114811 ],
       [0.00560543]]), array([[-0.00464196],      
       [ 0.00656343],
       [ 0.6464811 ]]), array([[ 0.1347218 ],     
       [ 0.18266664],
       [-0.42291462]]), array([[ 0.18311092],     
       [ 0.15985598],
       [-0.85733209]]), array([[-0.31269128],     
       [ 0.25520988],
       [-0.12085377]]), array([[-0.47211653],     
       [-0.16431945],
       [ 0.42903134]]), array([[-0.0879517 ],     
       [ 0.53908854],
       [ 0.06455465]]), array([[ 0.05134532],     
       [-0.24394424],
       [ 0.4972588 ]]), array([[ 0.03991402],     
       [-0.01407845],
       [ 0.45767241]]), array([[ 0.06846588],     
       [-0.22402447],
       [ 0.52355169]]), array([[-0.02728241],     
       [-0.12328669],
       [ 0.45263374]]), array([[-0.27999083],     
       [ 0.01653011],
       [ 0.46882008]]), array([[-0.39115654],     
       [ 0.2358753 ],
       [ 0.36959138]]), array([[-0.38434591],     
       [ 0.14323832],
       [ 0.72358758]]), array([[-0.32175643],     
       [ 0.36040757],
       [ 0.53319573]]), array([[-0.21193196],     
       [ 0.65000888],
       [ 0.31975973]]), array([[ 0.31133633],     
       [-0.47068566],
       [ 1.09654404]]), array([[ 0.23688829],     
       [-0.27529049],
       [-0.05870385]]), array([[ 0.34118001],     
       [-0.04129057],
       [-1.21766241]]), array([[ 0.31936745],     
       [ 0.04037847],
       [-0.71533848]]), array([[-0.04758352],     
       [ 0.15711423],
       [ 0.48740468]]))
translation vectors:
 (array([[-0.08964413],
       [ 0.00137993],
       [ 0.64135769]]), array([[-0.15740514],     
       [-0.03448781],
       [ 0.64333063]]), array([[-0.11740165],     
       [-0.17543351],
       [ 0.57855342]]), array([[-0.14019627],     
       [ 0.03695148],
       [ 0.68282344]]), array([[-0.11572036],     
       [-0.10769263],
       [ 0.61620667]]), array([[-0.11716945],     
       [ 0.07828635],
       [ 0.72484508]]), array([[-0.0946633 ],     
       [-0.09533591],
       [ 0.59207157]]), array([[-0.09180288],     
       [-0.10579107],
       [ 0.64376994]]), array([[ 0.00471511],     
       [-0.10604917],
       [ 0.67653162]]), array([[-0.01160595],     
       [-0.0294887 ],
       [ 0.81938646]]), array([[-0.01885689],     
       [-0.11208433],
       [ 0.67303087]]), array([[-0.03864911],     
       [ 0.0129961 ],
       [ 0.64930531]]), array([[-0.18293709],     
       [-0.04363913],
       [ 0.55969339]]), array([[-0.07035979],     
       [-0.12267593],
       [ 0.5602048 ]]), array([[-0.07138411],     
       [-0.11927577],
       [ 0.56752116]]), array([[-0.17764517],     
       [-0.05314887],
       [ 0.65497619]]), array([[-0.16441606],     
       [ 0.0112919 ],
       [ 0.82890839]]), array([[-0.04454502],     
       [ 0.10851272],
       [ 0.80304172]]), array([[-0.15621002],     
       [ 0.03486986],
       [ 0.53750776]]), array([[-0.16437662],     
       [-0.08038394],
       [ 0.39959859]]), array([[-0.15964207],     
       [-0.10617654],
       [ 0.53262168]]), array([[ 0.04853947],     
       [-0.14111229],
       [ 0.55137814]]), array([[-0.09906887],     
       [-0.05759885],
       [ 0.57027707]]), array([[-0.18172496],     
       [ 0.02946714],
       [ 0.57018081]]), array([[-0.0184784 ],     
       [-0.10334359],
       [ 0.65438968]]), array([[-0.13730065],     
       [-0.0115269 ],
       [ 0.65234034]]), array([[-0.00427519],     
       [-0.07250393],
       [ 0.76821021]]), array([[-0.14417319],     
       [-0.12393955],
       [ 0.52813531]]), array([[-0.0453171 ],     
       [-0.080869  ],
       [ 0.44136214]]), array([[-0.08647411],     
       [-0.09308957],
       [ 0.40471387]]), array([[-0.13809794],     
       [-0.08190234],
       [ 0.61967713]]), array([[ 0.00900247],     
       [-0.0719825 ],
       [ 0.52961237]]), array([[ 0.02464785],     
       [-0.01638459],
       [ 0.61696854]]), array([[ 0.08858164],     
       [-0.06494174],
       [ 0.59532646]]), array([[ 0.04952573],     
       [-0.11860169],
       [ 0.64033163]]), array([[0.07251157],      
       [0.03138613],
       [0.84235581]]), array([[-0.00629761],      
       [ 0.50943599]]), array([[-0.17357948],     
       [-0.03641907],
       [ 0.10617503],
       [ 0.51301293]]), array([[-0.12490969],     
       [-0.00432736],
       [ 0.51326929]]), array([[-0.18098651],     
       [-0.1633446 ],
       [ 0.80059424]]))
Average error of reproject: 0.033731469750518175  
Dedistorted images have been saved to: ./pic/out