使用usb相机以及内参标定相关

使用opencv标定

参考链接

opencv4.0 中文文档 https://apachecn.github.io/opencv-doc-zh/#/docs/4.0.0/7.1-tutorial_py_calibration 使用的是python版本

原文档https://docs.opencv.org/4.x/d4/d94/tutorial_camera_calibration.html

http://t.csdnimg.cn/HKECW

标定

棋盘https://docs.opencv.org/4.x/pattern.png

程序

#include <opencv2/opencv.hpp>
#include <stdio.h>
#include <iostream>

using namespace std;
using namespace cv;

// Defining the dimensions of checkerboard
// 定义棋盘格的尺寸
int CHECKERBOARD[2] {6,9};

int main()
{
	// Creating vector to store vectors of 3D points for each checkerboard image
	// 创建矢量以存储每个棋盘图像的三维点矢量
	std::vector<std::vector<cv::Point3f> > objpoints;

	// Creating vector to store vectors of 2D points for each checkerboard image
	// 创建矢量以存储每个棋盘图像的二维点矢量
	std::vector<std::vector<cv::Point2f> > imgpoints;

	// Defining the world coordinates for 3D points
	// 为三维点定义世界坐标系
	std::vector<cv::Point3f> objp;
	for (int i{ 0 }; i < CHECKERBOARD[1]; i++)
	{
		for (int j{ 0 }; j < CHECKERBOARD[0]; j++)
		{
			objp.push_back(cv::Point3f(j, i, 0));
		}
	}

	// Extracting path of individual image stored in a given directory
	// 提取存储在给定目录中的单个图像的路径
	std::vector<cv::String> images;

	// Path of the folder containing checkerboard images
	// 包含棋盘图像的文件夹的路径
	std::string path = "../images/CameraCalibration/*.jpg";

	// 使用glob函数读取所有图像的路径
	cv::glob(path, images);
	cout << images[0] << endl;
	cv::Mat frame, gray;
	// vector to store the pixel coordinates of detected checker board corners
	// 存储检测到的棋盘转角像素坐标的矢量
	std::vector<cv::Point2f> corner_pts;
	bool success;

	frame = cv::imread(images[0]);
	cv::cvtColor(frame,gray,cv::COLOR_BGR2GRAY);

	// Looping over all the images in the directory
	// 循环读取图像
	for (int i{ 0 }; i < images.size(); i++)
	{
		frame = cv::imread(images[i]);
		if (frame.empty())
		{
			continue;
		}
		if (i == 40)
		{
			int b = 1;
		}
		cout << "the current image is " << i << "th" << endl;
		cv::cvtColor(frame, gray, cv::COLOR_BGR2GRAY);       // COLOR_BGR2GRAY 从BGR 转换到灰度图
		cv::resize(gray, gray, cv::Size(), 0.125, 0.125, cv::INTER_LINEAR);
		cv::imshow("gray", gray);
		cv::waitKey(1);
		// Finding checker board corners
		// 寻找角点
		// If desired number of corners are found in the image then success = true
		// 如果在图像中找到所需数量的角，则success = true
		// opencv4以下版本，flag参数为CV_CALIB_CB_ADAPTIVE_THRESH | CV_CALIB_CB_FAST_CHECK | CV_CALIB_CB_NORMALIZE_IMAGE
		success = cv::findChessboardCorners(gray, cv::Size(CHECKERBOARD[0], CHECKERBOARD[1]), corner_pts, CALIB_CB_ADAPTIVE_THRESH | CALIB_CB_FAST_CHECK | CALIB_CB_NORMALIZE_IMAGE);

		/*
		 * If desired number of corner are detected,
		 * we refine the pixel coordinates and display
		 * them on the images of checker board
		*/
		// 如果检测到所需数量的角点，我们将细化像素坐标并将其显示在棋盘图像上
		if (success)
		{
			// 如果是OpenCV4以下版本，第一个参数为CV_TERMCRIT_EPS | CV_TERMCRIT_ITER
			cv::TermCriteria criteria(TermCriteria::EPS | TermCriteria::Type::MAX_ITER, 30, 0.001);

			// refining pixel coordinates for given 2d points.
			// 为给定的二维点细化像素坐标
			cv::cornerSubPix(gray, corner_pts, cv::Size(11, 11), cv::Size(-1, -1), criteria);

			// Displaying the detected corner points on the checker board
			// 在棋盘上显示检测到的角点
			cv::drawChessboardCorners(frame, cv::Size(CHECKERBOARD[0], CHECKERBOARD[1]), corner_pts, success);

			objpoints.push_back(objp);
			imgpoints.push_back(corner_pts);
		}

		//cv::imshow("Image", frame);
		//cv::waitKey(0);
	}

	cv::destroyAllWindows();

	cv::Mat cameraMatrix, distCoeffs, R, T;

	/*
	 * Performing camera calibration by
	 * passing the value of known 3D points (objpoints)
	 * and corresponding pixel coordinates of the
	 * detected corners (imgpoints)
	*/
	// 通过传递已知3D点（objpoints）的值和检测到的角点（imgpoints）的相应像素坐标来执行相机校准
	cv::calibrateCamera(objpoints, imgpoints, cv::Size(gray.rows, gray.cols), cameraMatrix, distCoeffs, R, T);

	// 内参矩阵
	std::cout << "cameraMatrix : " << cameraMatrix << std::endl;
	// 透镜畸变系数
	std::cout << "distCoeffs : " << distCoeffs << std::endl;
	// rvecs
	std::cout << "Rotation vector : " << R << std::endl;
	// tvecs
	std::cout << "Translation vector : " << T << std::endl;

	return 0;
}

CMakeLists.txt

cmake_minimum_required(VERSION 2.8)
project(CameraCalibration)

set(CMAKE_BUILD_TYPE "Debug")
set(CMAKE_CXX_FLAGS "-std=c++11")
set(LIBRARY_OUTPUT_PATH ${PROJECT_NAME_DIR}/lib)

find_package(OpenCV 4.0 REQUIRED)
include_directories(${OpenCV_INCLUDE_DIR})
include_directories(${PROJECT_NAME_DIR}/include)
add_subdirectory(${PROJECT_SOURCE_DIR}/src)

add_executable(CameraCalibration src/camera_calibration.cpp)
target_link_libraries(CameraCalibration ${OpenCV_LIBS})

结果

报错，

terminate called after throwing an instance of 'cv::Exception'
  what():  OpenCV(4.2.0) ../modules/calib3d/src/calibration.cpp:3681: error: (-215:Assertion failed) nimages > 0 in function 'calibrateCameraRO'

不知道为啥，

猜测是因为ipone拍摄的相机像素太高，减小尺寸试试

在for循环中加入

		cv::resize(gray, gray, cv::Size(), 0.125, 0.125, cv::INTER_LINEAR);

可以出结果了

使用usb摄像头，并标定

安装usb_cam

ubuntu20.04

ros版本 noetic

• 查看usb连接设备，显示有camera

  lsusb
  

• 安装usb_cam包

  sudo apt-get install ros-noetic-usb-cam
  

• 安装image-view

  sudo apt-get install ros-noetic-image-view
  

• 启动roscore

• 运行launch文件, 启动 usb_cam_node 节点

  roslaunch usb_cam usb_cam-test.launch
  

在rviz中显示

• 启动 rviz
• 点击 Add ， 添加image 不是camera

相机标定

本文使用ros链接https://wiki.ros.org/camera_calibration

opencv链接https://docs.opencv.org/4.x/dc/dbb/tutorial_py_calibration.html

校准使用棋盘的内部顶点，因此“10x7”棋盘使用内部顶点参数“9x6”

• 查看话题 rostopic list，， 打印话题信息 rostopic echo

• 新开终端

  rosrun camera_calibration cameracalibrator.py --size 9x6 --square 0.24 image:=/usb_cam/image_raw camera:=/head_camera --no-service-check
  

  • 参数

    –size 9x6 为棋盘内部角点的个数，方格几列几行(需要减1)，比如我的标定板方格是10X8，则siez为9x6
    –square 0.24为每个棋盘格的边长
    image:=/usb_cam/image_raw 为当前订阅的图像来自名为/usb_cam/image_raw的topic
    camera:=/head_camera为摄像机名
    

• 移动标定板

  • In order to get a good calibration you will need to move the checkerboard around in the camera frame such that:
    • checkerboard on the camera's left, right, top and bottom of field of view
      • X bar - left/right in field of view
      • Y bar - top/bottom in field of view
      • Size bar - toward/away and tilt from the camera
    • checkerboard filling the whole field of view
    • checkerboard tilted to the left, right, top and bottom (Skew)
  • 直到条形变为绿色。当calibration按钮亮起时，代表已经有足够的数据进行摄像头的标定，此时请按下calibration并等待一分钟左右，标定界面会变成灰色，无法进行操作，属于正常情况。

• 标定结果将在终端显示，参数如下

  camera matrix：摄像头的内部参数矩阵
  distortion：畸变系数矩阵
  rectification：矫正矩阵，一般为单位阵
  projection：外部世界坐标到像平面的投影矩阵
  

• 点击save按钮

• 如果对结果满意，click COMMIT to send the calibration parameters to the camera for permanent storage

Simply loading a calibration file does not rectify the image. For rectification, use the image_proc package.

运行后警告

Camera calibration file

[ WARN] [1719882910.689573001]: Camera calibration file /home/wenming/.ros/camera_info/head_camera.yaml not found.

参考http://t.csdnimg.cn/E0XA4，ROS下采用camera_calibration进行单目相机标定

[ WARN] [1719882910.877910518]: unknown control 'white_balance_temperature_auto'

[ WARN] [1719882910.884822220]: unknown control 'focus_auto'

https://blog.csdn.net/newbeixue/article/details/102796474

说是将下面launch文件中关闭了

<param name="autoexposure" value="false" /> 
 <param name="auto_whitebalance" value="false" /> 
 <param name="auto_focus" value="false" /> 
 <param name="auto_brigthness" value="false" />