利用MatLab+OpenCV進行相機畸變矯正

pre:

        關於矯正的數學原理這裏不再贅述，可以參考openCV官方文檔和https://github.com/Nocami/PythonComputerVision-6-CameraCalibration

1.準備數據

        step1:去openCV下載pattern.jpg

顯示在屏幕上即可。

        step2:用需要標定的相機進行拍照，各個角度拍4-5張。共20張左右。

        step3:需要記錄拍照屏幕的方格的大小，推薦使用PS像素轉釐米，如圖在我PC上的長度

2.MATLAB上場

        step1: 新建文件

J = (checkerboard(300,4,5)>0.5);
figure, imshow(J);

        step2:

在控制檯輸入 cameraCalibrator 

上傳你拍的圖
 

之後會出現

這個時候就需要測量的長度參數值 單位mm

        step3: 等待......

        step4:矯正（請忽略雜亂有序的實驗室）

        step5:導出參數         在工作區看到了參數

 

3.Python矯正

 

這裏面的參數值有的是不需要的所以要把需要的提取出來

這裏需要的是

1.

 

總共有五個，徑向畸變3個（k1,k2,k3）和切向畸變2個(p1,p2)。沒的添上0。

2.

 

對比着加到代碼裏。

 

大概就這樣

C++版

// correct_camera.cpp : 此文件包含 "main" 函數。程序執行將在此處開始並結束。
//

#include "pch.h"
#include "opencv2/opencv.hpp"
#include <iostream>

using namespace cv;
using namespace std;

int main()
{
	VideoCapture inputVideo(0);
	if (!inputVideo.isOpened())
	{
		cout << "Could not open the input video: " << endl;
		return -1;
	}
	Mat frame;
	Mat frameCalibration;

	inputVideo >> frame;
	Mat cameraMatrix = Mat::eye(3, 3, CV_64F);
	cameraMatrix.at<double>(0, 0) = 4.450537506243416e+02;
	cameraMatrix.at<double>(0, 1) = 0.192095145445498;
	cameraMatrix.at<double>(0, 2) = 3.271489590204837e+02;
	cameraMatrix.at<double>(1, 1) = 4.473690628394497e+02;
	cameraMatrix.at<double>(1, 2) = 2.442734958206504e+02;

	Mat distCoeffs = Mat::zeros(5, 1, CV_64F);
	distCoeffs.at<double>(0, 0) = -0.320311439187776;
	distCoeffs.at<double>(1, 0) = 0.117708464407889;
	distCoeffs.at<double>(2, 0) = -0.00548954846049678;
	distCoeffs.at<double>(3, 0) = 0.00141925006352090;
	distCoeffs.at<double>(4, 0) = 0;

	Mat view, rview, map1, map2;
	Size imageSize;
	imageSize = frame.size();
	initUndistortRectifyMap(cameraMatrix, distCoeffs, Mat(),
		getOptimalNewCameraMatrix(cameraMatrix, distCoeffs, imageSize, 1, imageSize, 0),
		imageSize, CV_16SC2, map1, map2);


	while (1) //Show the image captured in the window and repeat
	{
		inputVideo >> frame;              // read
		if (frame.empty()) break;         // check if at end
		remap(frame, frameCalibration, map1, map2, INTER_LINEAR);
		imshow("Origianl", frame);
		imshow("Calibration", frameCalibration);
		char key = waitKey(1);
		if (key == 27 || key == 'q' || key == 'Q')
			break;
	}
	return 0;
}

python版

代碼是我超哥整的，@超哥 https://blog.csdn.net/qq_41170600/article/details/103037028

import cv2
import numpy as np
cap = cv2.VideoCapture(0)
 
def undistort(frame):
    fx = 1311.94228326091
    cx = 937.984968117315
    fy = 1310.63631268594
    cy = 514.783585422419
    k1, k2, p1, p2, k3 = -0.469785052535390, 0.274212670963307, 0.0, 0.0, 0.0
 
    # 相機座標系到像素座標系的轉換矩陣
    k = np.array([
        [fx, 0, cx],
        [0, fy, cy],
        [0, 0, 1]
    ])
    # 畸變係數
    d = np.array([
        k1, k2, p1, p2, k3
    ])
    h, w = frame.shape[:2]
    mapx, mapy = cv2.initUndistortRectifyMap(k, d, None, k, (w, h), 5)
    return cv2.remap(frame, mapx, mapy, cv2.INTER_LINEAR)
 
 
while(cap.isOpened()):
    ret, frame = cap.read()
   # frame =
    cv2.imshow('frame', undistort(frame))
 
    if cv2.waitKey(1) & 0xFF == ord('q'):
        break
cap.release()
cv2.destroyAllWindows()