C++ opencv (一)：基础代码入门_opencv c++入门

opencv学习资料声明

对于C++ 不熟悉的伙伴，强推这版学习资源！
B站学习资源：4h上手C++版Opencv
安装opencv包以及添加包含目录、库目录和附加依赖项看视频即可
以下是我的学习B站视频和知乎大神笔记整理与自己所在领域——医学图像处理相关的实践和学习笔记！

1.基础知识

1.1 Read Images Videos and Webcams

示例：

#include <opencv2/imgcodecs.hpp>
#include <opencv2/highgui.hpp>
#include <opencv2/imgproc.hpp>
#include <iostream>//输入输出流

using namespace cv;
using namespace std;


/  Images  //

void main() {

	string path = "Resources/test.png";
	//1.读取：imread读取到Mat类型的 img中
	Mat img = imread(path);  
	//检测image有无数据，无数据 img.empty()返回真
	if (img.empty())  
	{
		cout << "Could not open or find the image" << endl;
		return -1;
	}
	cout << "图像的行数为： " << image1.rows << endl;  //获取图像的高度，行数；
	cout << "图像的列数为： " << image1.cols << endl;  //获取图像的宽度，列数；
	cout << "图像的通道数为： " << image1.channels() << endl;  //获取图像的通道数，彩色图=3，灰度图=1；
	cout << "图像的尺寸为： " << image1.size << endl;  //获取图像的尺寸，行*列；
	//2.保存：imwrite保存图像为png格式
	imwrite("Resources/test_save.png", img); 
	//3.显示：imshow窗口显示图像
	imshow("Image", img);   
	waitKey(0);

}

///  Video  //

//void main() {
//
//	string path = "Resources/test_video.mp4";
//	VideoCapture cap(path);
//	Mat img;
//
//	while (true) {
//
//		cap.read(img);
//		imshow("Image", img);
//		waitKey(20);
//	}
//}

/  Webcam  //

//void main() {
//
//	VideoCapture cap(0);
//	Mat img;
//
//	while (true) {
//
//		cap.read(img);
//		imshow("Image", img);
//		waitKey(1);
//	}
//}
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imread读取图像的方式有三种：
① 彩色：

imread(path, IMREAD_COLOR);
or
imread(path, 1);
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②灰度：

imread(path,  IMREAD_GRAYSCALE);
or
imread(path, 0);
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③alpha通道：

为实现图形的透明效果，采取在图形文件的处理与存储中附加上另一个8位信息的方法，这个附加的代表图形中各个素点透明度的通道信息就被叫做Alpha通道。Alpha通道使用8位二进制数，就可以表示256级灰度，即256级的透明度。

imread(path,  IMREAD_UNCHANGED);
or
imread(path, -1);
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1.2 Basic Functions

基本的图像处理操作（依次增加操作）：
图像转换（cvtColor）
图像模糊（GaussianBlur）
图像边缘检测（Canny）
图像边缘扩大（dilate）
图像边缘侵蚀（erode）\

#include <opencv2/imgcodecs.hpp>
#include <opencv2/highgui.hpp>
#include <opencv2/imgproc.hpp>
#include <iostream>

using namespace cv;
using namespace std;

///  Basic Functions  //

void main() {

	string path = "Resources/test.png";
	Mat img = imread(path);
	Mat imgGray, imgBlur, imgCanny, imgDil, imgErode;

	cvtColor(img, imgGray, COLOR_BGR2GRAY); //BGR——GRAY
	GaussianBlur(imgGray, imgBlur, Size(7, 7), 5, 0); // 高斯模糊 Size(7, 7), 5, 控制模糊程度
	Canny(imgBlur, imgCanny, 25,75); // 边缘检测，使用前常进行模糊处理 ,75 控制边缘数（越大边缘越少）
    //Q:边缘检测后部分边缘没有被完全填充
	Mat kernel = getStructuringElement(MORPH_RECT, Size(3, 3)); //创建膨胀内核
	dilate(imgCanny, imgDil, kernel); //扩大，增加厚度，
	erode(imgDil, imgErode, kernel); //侵蚀，减小厚度，
	imshow("Image", img);
	imshow("Image Gray", imgGray);
	imshow("Image Blur", imgBlur);
	imshow("Image Canny", imgCanny);
	imshow("Image Dilation", imgDil);
	imshow("Image Erode", imgErode);
	waitKey(0);
}
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imshow图像展示：

1.3 Resize and Crop

#include <opencv2/imgcodecs.hpp>
#include <opencv2/highgui.hpp>
#include <opencv2/imgproc.hpp>
#include <iostream>

using namespace cv;
using namespace std;


///  Resize and Crop //

void main() {

	string path = "Resources/test.png";
	Mat img = imread(path);
	Mat imgResize, imgCrop;

	//cout << img.size() << endl; //在终端打印图像宽高
	resize(img, imgResize, Size(),0.5,0.5); //可按比例resize，也可以给实际的数字

	Rect roi(200, 100, 300, 300); //矩形数据类型 rectangle，定位左上角的点，并给出矩形size
	imgCrop = img(roi);

	imshow("Image", img);
	imshow("Image Resize", imgResize);
	imshow("Image Crop", imgCrop);
	waitKey(0);
}

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1.4 Draw Shapes and Text

圆(circle)
框(rectangle)
线(line)
显示内容(putText)\

#include <opencv2/imgcodecs.hpp>
#include <opencv2/highgui.hpp>
#include <opencv2/imgproc.hpp>
#include <iostream>

using namespace cv;
using namespace std;

//  Draw Shapes and Text //

void main() {

	// Blank Image 
	Mat img(512, 512, CV_8UC3, Scalar(255, 255, 255));

	circle(img, Point(256, 256), 155, Scalar(0, 69, 255),FILLED); //Point表示圆心；155表示圆的半径； FILLED填满，数字表示线的粗细
	rectangle(img, Point(130, 226), Point(382, 286), Scalar(255, 255, 255), FILLED); //定义左上角和右下角
	line(img, Point(130, 296), Point(382, 296), Scalar(255, 255, 255), 2);
	
	putText(img, "Murtaza's Workshop", Point(137, 262), FONT_HERSHEY_DUPLEX, 0.75, Scalar(0, 69, 255),2);//0.75字体大小，2字体粗细

	imshow("Image", img);
	waitKey(0);
}
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1.5 Warp Images

透视转换

#include <opencv2/imgcodecs.hpp>
#include <opencv2/highgui.hpp>
#include <opencv2/imgproc.hpp>
#include <iostream>

using namespace cv;
using namespace std;

///  Warp Images  //

void main() {

	string path = "Resources/cards.jpg";
	Mat img = imread(path);
	Mat matrix, imgWarp;
	float w = 250, h = 350; // 目标转换大小
	
	Point2f src[4] = { {529,142},{771,190},{405,395},{674,457} }; //定位目标点
	Point2f dst[4] = { {0.0f,0.0f},{w,0.0f},{0.0f,h},{w,h} }; //定义目标点顺序

	matrix = getPerspectiveTransform(src, dst);
	warpPerspective(img, imgWarp, matrix, Point(w, h));

	for (int i = 0; i < 4; i++)
	{
		circle(img, src[i], 10, Scalar(0, 0, 255), FILLED);
	} //实心圆圈注释四个角

	imshow("Image", img);
	imshow("Image Warp", imgWarp);
	waitKey(0);

}
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1.6 Color Detection

图像转换为HSV空间，更容易查找颜色
创建色调可调节滑动窗口

#include <opencv2/imgcodecs.hpp>
#include <opencv2/highgui.hpp>
#include <opencv2/imgproc.hpp>
#include <iostream>

using namespace cv;
using namespace std;

///  Color Detection  //

void main() {

	string path = "Resources/lambo.png";
	Mat img = imread(path);
	Mat imgHSV, mask;
	int hmin = 0, smin = 110, vmin = 153;
	int hmax = 19, smax = 240, vmax = 255;

	cvtColor(img, imgHSV, COLOR_BGR2HSV);

	namedWindow("Trackbars", (640, 200));
	createTrackbar("Hue Min", "Trackbars", &hmin, 179);
	createTrackbar("Hue Max", "Trackbars", &hmax, 179);
	createTrackbar("Sat Min", "Trackbars", &smin, 255);
	createTrackbar("Sat Max", "Trackbars", &smax, 255);
	createTrackbar("Val Min", "Trackbars", &vmin, 255);
	createTrackbar("Val Max", "Trackbars", &vmax, 255);

	while (true) {

		Scalar lower(hmin, smin, vmin);
		Scalar upper(hmax, smax, vmax); //定义标量
		inRange(imgHSV, lower, upper, mask); //

		imshow("Image", img);
		imshow("Image HSV", imgHSV);
		imshow("Image Mask", mask);
		waitKey(1);
	}
}
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1.7 Shape/Contour Detection

使用颜色进行对象检测和跟踪
存在问题：图像在放大后像素间存在间隙（为什么在1.2小节中进行膨胀的原因）

#include <opencv2/imgcodecs.hpp>
#include <opencv2/highgui.hpp>
#include <opencv2/imgproc.hpp>
#include <iostream>

using namespace cv;
using namespace std;

///  Color Detection  //

void getContours(Mat imgDil, Mat img) {

	vector<vector<Point>> contours;
	vector<Vec4i> hierarchy;

	findContours(imgDil, contours, hierarchy, RETR_EXTERNAL, CHAIN_APPROX_SIMPLE);
	//drawContours(img, contours, -1, Scalar(255, 0, 255), 2);
    //扩张，轮廓，层次，方法  
	vector<vector<Point>> conPoly(contours.size());
	vector<Rect> boundRect(contours.size());
	 
	for (int i = 0; i < contours.size(); i++)
	{
		int area = contourArea(contours[i]);
		cout << area << endl;
		string objectType;

		if (area > 1000) 
		{
			float peri = arcLength(contours[i], true);
			approxPolyDP(contours[i], conPoly[i], 0.02 * peri, true);
			cout << conPoly[i].size() << endl;
			boundRect[i] = boundingRect(conPoly[i]);
		
			int objCor = (int)conPoly[i].size();

			if (objCor == 3) { objectType = "Tri"; }
			else if (objCor == 4)
			{ 
				float aspRatio = (float)boundRect[i].width / (float)boundRect[i].height;
				cout << aspRatio << endl;
				if (aspRatio> 0.95 && aspRatio< 1.05){ objectType = "Square"; }
				else { objectType = "Rect";}
			}
			else if (objCor > 4) { objectType = "Circle"; }

			drawContours(img, conPoly, i, Scalar(255, 0, 255), 2);
			rectangle(img, boundRect[i].tl(), boundRect[i].br(), Scalar(0, 255, 0), 5);
			putText(img, objectType, { boundRect[i].x,boundRect[i].y - 5 }, FONT_HERSHEY_PLAIN,1, Scalar(0, 69, 255), 2);
		}
	}
}


void main() {

	string path = "Resources/shapes.png";
	Mat img = imread(path);
	Mat imgGray, imgBlur, imgCanny, imgDil, imgErode;

	// Preprocessing
	cvtColor(img, imgGray, COLOR_BGR2GRAY);
	GaussianBlur(imgGray, imgBlur, Size(3, 3), 3, 0);
	Canny(imgBlur, imgCanny, 25, 75);
	Mat kernel = getStructuringElement(MORPH_RECT, Size(3, 3));
	dilate(imgCanny, imgDil, kernel);

	getContours(imgDil,img);

	imshow("Image", img);
	//imshow("Image Gray", imgGray);
	//imshow("Image Blur", imgBlur);
	//imshow("Image Canny", imgCanny);
	//imshow("Image Dil", imgDil);

	waitKey(0);

}

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