catalogue

1, Image reading and display

2, Image preprocessing

Principle and algorithm of Gaussian blur

Canny edge detection

3, Image clipping

4, Draw shapes and add text

5, Perspective transformation

6, Color detection

7, Shape detection and contour detection

8, Face recognition

1, Image reading and display

#include<opencv2/imgcodecs.hpp>
#include<opencv2/highgui.hpp>
#include<opencv2/imgproc.hpp>
#include<iostream>
 
using namespace cv;
using namespace std;
 
int main()
{
	string path = "Resources/lambo.png";//Pathname of the picture
	Mat img = imread(path);//Assign a value to the image variable img after loading the image
    //if (path.empty()) { cout << "file not loaded" << endl; }
    //Check whether the file is open, and execute the print statement when it is not open
    //namedWindow("Image", WINDOW_FREERATIO);// Create an adjustable window called image
	imshow("Image", img);//Create a window to display the image img
	waitKey(0);//Constantly refresh images
	return 0;
}
waitKey()The function refreshes the image continuously with a frequency of delay，Unit is ms. 
delay When it is 0, this frame is always displayed.
delay If it is not 0, the program waits after displaying a frame of image“ delay"ms The next frame of image is displayed.

2, Image preprocessing

#include <opencv2/imgcodecs.hpp>
#include <opencv2/highgui.hpp>
#include <opencv2/imgproc.hpp>
#include <iostream>
 
using namespace cv;
using namespace std;
 
void main() {
 
	string path = "Resources/test.png";
	Mat img = imread(path);
	Mat imgGray,imgBlur,imgCanny,imgDil,imgErode;
	//Convert photos to grayscale
	cvtColor(img, imgGray, COLOR_BGR2GRAY);
	//Gaussian blur
	GaussianBlur(imgGray, imgBlur, Size(3, 3), 3, 0);
	//Canny edge detector usually does some fuzzy processing before using Canny edge detector
	Canny(imgBlur, imgCanny, 25, 75);
	//Create a kernel that can use bloat
	Mat kernel = getStructuringElement(MORPH_RECT, Size(3, 3));
	//Image expansion
	dilate(imgCanny, imgDil, kernel);
	//Image erosion
	erode(imgDil, imgErode, kernel);
    //Result presentation
	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);
}

Principle and algorithm of Gaussian blur

The first parameter is the image to be Gaussian blurred; The second parameter is the image output after processing; The third parameter is the size of Gaussian kernel. The first value is wide and the second value is high. The two values can be different, but they must be positive odd numbers; The fourth and fifth parameters are the standard deviation of the Gaussian kernel function in the X direction and Y direction respectively. If y is 0, the function will automatically set the value on y to be the same as X. if x and y are both 0, these two values will be calculated from the two values of the Gaussian kernel.

Canny edge detection

    Canny(imgBlur, imgCanny, 25, 75);

The third and fourth parameters represent the bottom threshold and high threshold respectively, where the bottom threshold is often 1 / 2 or 1 / 3 of the high threshold

//Create a kernel that can use bloat
	Mat kernel = getStructuringElement(MORPH_RECT, Size(3, 3));
	//Image expansion
	dilate(imgCanny, imgDil, kernel);
	//Image erosion
	erode(imgDil, imgErode, kernel);
​```
![Insert picture description here](https://img-blog.csdnimg.cn/f9792a6b2634421ba9d5f134c5fec130.png#pic_center)

## 3, Image clipping
```c
#include <opencv2/imgcodecs.hpp>
#include <opencv2/highgui.hpp>
#include <opencv2/imgproc.hpp>
#include <iostream>
 
using namespace cv;
using namespace std;
 
void main() {
 
	string path = "Resources/test.png";
	Mat img = imread(path);
	Mat imgResize,imgCrop;
	//Resize image
	//cout << img. size() << endl;// View the size of the original image
	//resize(img, imgResize, Size(640, 480));// Scale by custom width and height
	resize(img, imgResize, Size(),0.5,0.5);//Scale
	//Image clipping
	Rect roi(200, 100, 300, 300);
    //The first two parameters are the distance between the X direction and the y direction from the upper left origin, and the last two parameters are the extended x and y lengths
	imgCrop = img(roi);
 
	imshow("Image", img);
	imshow("Image Resize", imgResize);
	imshow("Image Crop", imgCrop);
	waitKey(0);
 
}

4, Draw shapes and add text

#include <opencv2/imgcodecs.hpp>
#include <opencv2/highgui.hpp>
#include <opencv2/imgproc.hpp>
#include <iostream>
 
using namespace cv;
using namespace std;
//Draw shapes and add text
void main() {
	//Create a blank image
	Mat img(512, 512, CV_8UC3, Scalar(255, 255, 255));
 
	//Draw circle
	//circle(img, Point(256, 256), 155, Scalar(0, 69, 255),10);
	circle(img, Point(256, 256), 155, Scalar(0, 69, 255),FILLED);
    //The function parameters are output to the image img, circle center, radius, color and thickness (FILLED indicates full)
 
    //draw rectangle
	rectangle(img, Point(130, 226), Point(382, 286), Scalar(255, 255, 255), FILLED);
    //The function parameters are output to the image img, the vertex coordinates of the upper left corner of the rectangle, the vertex coordinates of the lower right corner, color and thickness
 
    //Draw line segments
	line(img, Point(130, 296), Point(382, 296), Scalar(255, 255, 255), 2);
    //The function parameters are output to the image img, two endpoint coordinates, color and thickness
 
	//Add text
	putText(img, "Murtaza's Workshop", Point(137, 262), FONT_HERSHEY_DUPLEX, 0.75, Scalar(0, 69, 255), 2);
    //The function parameters are output to image img, text content, starting point, font, size, color and thickness
 
	imshow("Image", img);
 
	waitKey(0);
}

5, Perspective transformation

#include <opencv2/imgcodecs.hpp>
#include <opencv2/highgui.hpp>
#include <opencv2/imgproc.hpp>
#include <iostream>
 
using namespace cv;
using namespace std;
 
float w = 250, h = 350;
Mat matrix, imgWarp;
// Perspective transformation
void main() {
 
	string path = "Resources/cards.jpg";
	Mat img = imread(path);
 
	Point2f src[4] = { {529,142},{771,190},{405,395},{674,457} };
	Point2f dst[4] = { {0.0f,0.0f},{w,0.0f},{0.0f,h},{w,h} };
 
	matrix = getPerspectiveTransform(src, dst);//Get perspective transformation matrix
    //src is the quadrilateral vertex coordinates of the source image, and dst is the quadrilateral vertex coordinates corresponding to the target image
	warpPerspective(img, imgWarp, matrix, Point(w, h));
    //The parameters are input image, output image, perspective transformation matrix and image size
 
	for (int i = 0; i < 4; i++)
	{
		circle(img, src[i], 10, Scalar(0, 0, 255), FILLED);
	}//Mark the target vertex in the original image
 
	imshow("Image", img);
	imshow("Image Warp", imgWarp);
 
	waitKey(0);
}

6, Color detection

[OpenCV] HSV color recognition - HSV basic color component range_ Taily Lao Duan's column - CSDN blog_ hsv

Color space conversion function cvtColor

#include <opencv2/imgcodecs.hpp>
#include <opencv2/highgui.hpp>
#include <opencv2/imgproc.hpp>
#include <iostream>
 
using namespace cv;
using namespace std;
 
Mat imgHSV,mask;
int hmin = 0, smin = 0, vmin = 0;
int hmax =179, smax = 255, vmax = 255;
 
void main() {
 
	string path = "Resources/shapes.png";
	Mat img = imread(path);
	cvtColor(img, imgHSV, COLOR_BGR2HSV);
    //HSV color space H (hue): 0 ~ 180 s (saturation): 0 ~ 255 V (brightness): 0 ~ 255
 
	namedWindow("Trackbars", (640, 200));//Create a window named Trackbars with a size of 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);
    //The createTrackbar function creates a track bar,
    //The four parameters are the name of the track bar, the output window, a pointer to an integer to represent the current value and the maximum value that can be reached
 
	while (true)
	{
		//Detect the color we want and set a mask in the range of colors
		Scalar lower(hmin, smin, vmin);//Minimum value of HSV range
		Scalar upper(hmax, smax, vmax);//Maximum value of HSV range
		inRange(imgHSV, lower, upper, mask);//Input, low value, high value, output
//inRange is to set the pixel value within the threshold interval to white (255), while the pixel value not within the threshold interval is set to black (0)
 
		imshow("Image", img);
		imshow("Image HSV", imgHSV);
		imshow("Image Mask", mask);
 
		waitKey(1);
	}
}

7, Shape detection and contour detection

#include <opencv2/imgcodecs.hpp>
#include <opencv2/highgui.hpp>
#include <opencv2/imgproc.hpp>
#include <iostream>
 
using namespace cv;
using namespace std;
 
Mat imgGray, imgBlur, imgCanny, imgDil, imgErode;
//Define a contour processing function
void getContours(Mat imgDil,Mat img) {
 
	vector<vector<Point>> contours;//{ {Point(20,30),Point(50,60)},{}, {}}
	vector<Vec4i>hierarchy;//Four variables of type int are placed in the vector
	findContours(imgDil, contours, hierarchy, RETR_EXTERNAL, CHAIN_APPROX_SIMPLE);
	//drawContours(img, contours, -1, Scalar(255, 0, 255), 2);
	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;//Area to be filtered correctly (noise filtering)
 
		string objectType;
        //Judge shape
		if (area>1000)
		{
			float peri = arcLength(contours[i], true);
			approxPolyDP(contours[i], conPoly[i], 0.02 * peri, true);//Find approximate value
			
			cout << conPoly[i].size() << endl;
			boundRect[i] = boundingRect(conPoly[i]);//Boundary rectangle
			
			int objCor = (int)conPoly[i].size();
 
			if (objCor == 3) { objectType = "Tri"; }
			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"; 
				}
			}
			if (objCor > 4) { objectType = "Circle"; }
 
			drawContours(img, conPoly, i, Scalar(255, 0, 255), 2);//Draw count outline
			rectangle(img, boundRect[i].tl(), boundRect[i].br(), Scalar(0, 255, 0), 5);//Draw a rectangle
			//The name of the printed drawing
			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);
 
//Image preprocessing
	//1. Convert photos to grayscale
	cvtColor(img, imgGray, COLOR_BGR2GRAY);
	//2. Gaussian blur
	GaussianBlur(imgGray, imgBlur, Size(3, 3), 3, 0);
	//3.Canny edge detector
	Canny(imgBlur, imgCanny, 25, 75);
	//4. Create a kernel that can use expansion
	Mat kernel = getStructuringElement(MORPH_RECT, Size(3, 3));
	//5. Image expansion
	dilate(imgCanny, imgDil, kernel);
 
	getContours(imgDil,img);
 
	imshow("Image", img);
	/*imshow("Image Gray", imgGray);
	imshow("Image Blur", imgBlur);
	imshow("Image Canny", imgCanny);
	imshow("Image Dilation", imgDil);*/
 
	waitKey(0);
}

reference resources:

Common data types of opencv (explanation of vector and contour)_ One rabbit blog - CSDN blog_ The data type of contours is

Detailed explanation of findcontours function parameters_ Makino's blog - CSDN blog_ findcontours function

8, Face recognition

#include <opencv2/imgcodecs.hpp>
#include <opencv2/highgui.hpp>
#include <opencv2/imgproc.hpp>
#include <opencv2/objdetect.hpp>
#include <iostream>
 
using namespace cv;
using namespace std;
 
void main() {
 
	string path = "Resources/test.png";
	Mat img = imread(path);
 
	CascadeClassifier faceCascade;//Create cascade classifier
    //Load training model
	faceCascade.load("Resources/haarcascade_frontalface_default.xml");
 
	if(faceCascade.empty()){cout<<"XML file not loaded"<<endl; }
    //Check whether the file is open, and execute the print statement when it is not open
 
	vector<Rect>faces;//Create a vector for storing faces
	faceCascade.detectMultiScale(img, faces, 1.1, 10);
//The detectMultiScale function can detect all faces in the picture, and save the coordinates and size of each face with vector
 
    //Draw a rectangular border of the face in the original image
	for (int i = 0; i < faces.size(); i++)
	{
		rectangle(img, faces[i].tl(),faces[i].br(), Scalar(255, 0, 255), 3);
	}
 
	imshow("Image", img);
 
	waitKey(0);
}

reference resources:

Usage of cv::Rect rectangle class in openCV_ sinat_38102206 blog - CSDN blog_ cv rect

Introduction to OpenCV detectMultiScale() function parameters_ zhuyue_66-CSDN blog_ The meaning of the parameters of the detectmultiscale function

Result display:

Learning source: 4h starting C + + Opencv_ Beep beep beep_ bilibili
The article is reproduced in https://blog.csdn.net/qq_62330330/article/details/122800959 , is an excellent blogger (my classmate). He himself has agreed to reprint it