OpenCV基础入门【C++语言】
- OpenCV3.4.6安装包(含contrib)：https://pan.baidu.com/s/1KBD-fAO63p0s5ANYa5XcEQ 提取码：p7j0
- resources资源：https://pan.baidu.com/s/1nkQ6iVV7IeeP4gTXvM_DyQ 提取码：ypvt
Chapter1 读取图片/视频/摄像头
从文件读取图片
模块功能imgcodecsImage file reading and writingimgprocImage ProcssinghighguiHigh-level GUI
Mat cv::imread(const String &filename, int flags = IMREAD_COLOR)
从文件加载图像。函数
imread从指定文件加载图像并返回。 如果无法读取图像（由于缺少文件、权限不正确、格式不受支持或无效），该函数将返回一个空矩阵（
Mat::data==NULL）。在彩色图像的情况下，解码图像的通道将以 B G R 顺序存储。
void cv::imshow(cosnst String &winnanme, InputArray mat)
在指定窗口中显示图像。这个函数后面应该是
cv::waitKey函数，它显示指定毫秒的图像。否则，它不会显示图像。例如，
waitKey(0)将无限显示窗口，直到有任何按键（适用于图像显示）。
waitKey(25)将显示一帧 25 毫秒，之后显示将自动关闭。（如果你把它放在一个循环中读取视频，它会逐帧显示视频）
int cv::waitKey(int delay = 0)
等待按下的键。函数
waitKey无限等待按键事件（当
delay≤0时）或延迟毫秒，当它为正时。由于操作系统在切换线程之间有最短时间，因此该函数不会完全等待延迟毫秒，它会至少等待延迟毫秒，具体取决于当时您计算机上正在运行的其他内容。如果在指定的时间过去之前没有按下任何键，则返回被按下键的代码或 -1。
#include<opencv2/imgcodecs.hpp>#include<opencv2/highgui.hpp>#include<opencv2/imgproc.hpp>#include<iostream>usingnamespace cv;usingnamespace std;intmain(){
string path ="Resources/test.png";
Mat img =imread(path);imshow("Image", img);waitKey(0);//显示图片不会一闪而过return0;}
从文件读取视频
要捕获视频，需要创建一个VideoCapture对象。它的参数可以是视频文件的名称或设备索引。
OpenCV3.4.6中
VideoCapture
类构造函数及成员函数
cv::VideoCapture::VideoCapture()cv::VideoCapture::VideoCapture(const String &filename)cv::VideoCapture::VideoCapture(const String &filename, int apiPreference)cv::VideoCapture::VideoCapture(int index)cv::VideoCapture::VideoCapture(int index, int apiPreference)
打开视频文件或捕获设备或 IP 视频流进行视频捕获。
virtual bool cv::VideoCapture::isOpened() const
如果视频捕获已经初始化，则返回true。如果先前对
VideoCapture构造函数或
VideoCapture::open()的调用成功，则该方法返回 true。
virtual bool cv::VideoCapture::read(OutputArray image)
抓取、解码并返回下一个视频帧。
virtual double cv::VideoCapture::get(int proId) const
返回指定的
VideoCapture属性。
virtual double cv::VideoCapture::set(int proId, double value)
在
VideoCapture中设置一个属性。
#include<opencv2/imgcodecs.hpp>#include<opencv2/highgui.hpp>#include<opencv2/imgproc.hpp>#include<iostream>usingnamespace cv;usingnamespace std;intmain(){
string path ="Resources/test_video.mp4";
VideoCapture cap(path);//视频捕捉对象
Mat img;while(true){
cap.read(img);imshow("Image", img);waitKey(1);}return0;}
读摄像头
#include<opencv2/imgcodecs.hpp>#include<opencv2/highgui.hpp>#include<opencv2/imgproc.hpp>#include<iostream>usingnamespace cv;usingnamespace std;intmain(){
VideoCapture cap(0);
Mat img;while(true){
cap.read(img);imshow("Image", img);waitKey(1);}return0;}
Chapter2 基础函数
void cv::cvtColor(InputArray src, OutputArray dst, int code, int dstCn = 0)
将图像从一种颜色空间转换为另一种颜色空间。该函数将输入图像从一种颜色空间转换为另一种颜色空间。在从 RGB 颜色空间转换的情况下，应明确指定通道的顺序（RGB 或 BGR）。man请注意，OpenCV 中的默认颜色格式通常称为 RGB，但实际上是 BGR（字节反转）。因此，标准（24 位）彩色图像中的第一个字节将是 8 位蓝色分量，第二个字节将是绿色，第三个字节将是红色。 然后第四、第五和第六个字节将是第二个像素（蓝色，然后是绿色，然后是红色），依此类推。
void cv::GaussianBlur(InputArray src, OutputArray dst, Size ksize, double sigmaX, doube sigmaY = 0, int borderType = BORDER_DEFAULT)
使用高斯滤波器模糊图像。该函数将源图像与指定的高斯核进行卷积。
void cv::Canny(InputArray image, OutputArray edges, double threshold1, double threshold2, int apertureSize = 3, bool L2gradient = false)
使用 Canny 算法在图像中查找边缘。
Mat cv::getStructuringElement(int shape, Size ksize, Point anchor = Point(-1, -1))
返回指定大小和形状的结构元素，用于形态学操作。该函数构造并返回可以进一步传递给腐蚀、扩张或形态学的结构元素。 但是您也可以自己构建任意二进制掩码并将其用作结构元素。
void cv::dilate(InputArray src, OutputArray dst, InuputArray kernel, Point anchor = Point(-1, -1), int iterations = 1, int borderType = BORDER_CONSTANT, const Scalar &borderValue = morphologyDefaultBorderValue())
使用特定的结构元素膨胀图像。
void cv::erode(InputArray src, OutputArray dst, InuputArray kernel, Point anchor = Point(-1, -1), int iterations = 1, int borderType = BORDER_CONSTANT, const Scalar &borderValue = morphologyDefaultBorderValue())
使用特定的结构元素腐蚀图像。
#include<opencv2/imgcodecs.hpp>#include<opencv2/highgui.hpp>#include<opencv2/imgproc.hpp>#include<iostream>usingnamespace cv;usingnamespace std;intmain(){
string path ="resources/test.png";
Mat img =imread(path);
Mat imgGray, imgBlur, imgCanny, imgDil, imgErode;cvtColor(img, imgGray, COLOR_BGR2GRAY);//灰度化GaussianBlur(img, imgBlur,Size(3,3),3,0);//高斯模糊Canny(imgBlur, imgCanny,25,75);//边缘检测
Mat kernel =getStructuringElement(MORPH_RECT,Size(3,3));dilate(imgCanny, imgDil, kernel);erode(imgDil, imgErode, kernel);imshow("Image", img);imshow("ImageGray", imgGray);imshow("ImageBlur", imgBlur);imshow("ImageCanny", imgCanny);imshow("ImageDilation", imgDil);imshow("ImageErode", imgErode);waitKey(0);return0;}
Chapter3 调整和剪裁
void cv::resize(InputArray src, OutputArray dst, Size dsize, double fx=0, double fy=0, int interpolation = INTER_LINEAR)
调整图像的大小。函数
resize将图像
src的大小缩小到或最大到指定的大小。请注意，不考虑初始
dst类型或大小。相反，大小和类型是从
src、dsize、fx 和 fy派生的。
#include<opencv2/imgcodecs.hpp>#include<opencv2/highgui.hpp>#include<opencv2/imgproc.hpp>#include<iostream>usingnamespace cv;usingnamespace std;intmain(){
string path ="resources/test.png";
Mat img =imread(path);
Mat imgResize, imgCrop;
cout << img.size()<< endl;resize(img, imgResize,Size(),0.5,0.5);
Rect roi(200,100,300,300);
imgCrop =img(roi);imshow("Image", img);imshow("ImageResieze", imgResize);imshow("ImageCrop", imgCrop);waitKey(0);return0;}
Chapter4 绘制形状和文字
Mat(int rows, int cols, int type, const Scalar &s)
重载的构造函数
void cv::circle(InputOutputArray img, Point center, int radius, const Scalar &color, int thickness=1, int lineType=LINE_8, int shift=0)
函数
cv::circle用给定的中心和半径绘制一个简单的或实心圆。
void cv::rectangle(InputOutputArray img, Point pt1, Point pt2, const Scalar &color, int thickness=1, int lineType=LINE_8, int shift=0)void cv::rectangle(Mat &img, Rect rec, const Scalar &color, int thickness=1, int lineType=LINE_8, int shift=0)
绘制一个简单的、粗的或填充的右上矩形。函数
cv::rectangle绘制一个矩形轮廓或两个对角为 pt1 和 pt2 的填充矩形。
void cv::line (InputOutputArray img, Point pt1, Point pt2, const Scalar &color, int thickness=1, int lineType=LINE_8, int shift=0)
绘制连接两点的线段。函数
line绘制图像中 pt1 和 pt2 点之间的线段。
void cv::putText (InputOutputArray img, const String &text, Point org, int fontFace, double fontScale, Scalar color, int thickness=1, int lineType=LINE_8, bool bottomLeftOrigin=false)
绘制一个文本字符串。函数
cv::putText在图像中呈现指定的文本字符串。无法使用指定字体呈现的符号将替换为问号。
#include<opencv2/imgcodecs.hpp>#include<opencv2/highgui.hpp>#include<opencv2/imgproc.hpp>#include<iostream>usingnamespace cv;usingnamespace std;intmain(){//Blank Image
Mat img(512,512, CV_8UC3,Scalar(255,255,255));circle(img,Point(256,256),155,Scalar(0,69,255), FILLED);rectangle(img,Point(130,226),Point(382,286),Scalar(255,255,255),-1);line(img,Point(130,296),Point(382,296),Scalar(255,255,255),2);putText(img,"SJN's Workshop",Point(137,262), FONT_HERSHEY_DUPLEX,0.95,Scalar(0,69,255),2);imshow("Image", img);waitKey(0);return0;}
Chapter5 透视变换
Mat cv::getPerspectiveTransform (const Point2f src[], const Point2f dst[])
返回相应 4 个点对的 3x3 透视变换。
void cv::warpPerspective (InputArray src, OutputArray dst, InputArray M, Size dsize, int flags=INTER_LINEAR, int borderMode=BORDER_CONSTANT, const Scalar &borderValue=Scalar())
对图像应用透视变换。
#include<opencv2/imgcodecs.hpp>#include<opencv2/highgui.hpp>#include<opencv2/imgproc.hpp>#include<iostream>usingnamespace cv;usingnamespace std;float w =250, h =350;
Mat matrix, imgWarp;intmain(){
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);warpPerspective(img, imgWarp, matrix,Point(w, h));for(int i =0; i <4; i++){circle(img, src[i],10,Scalar(0,0,255), FILLED);}imshow("Image", img);imshow("ImageWarp", imgWarp);waitKey(0);return0;}
注:文档扫描用到这种变换技术
Chapter6 颜色检测
void cv::inRange (InputArray src, InputArray lowerb, InputArray upperb, OutputArray dst)
检查数组元素是否位于其他两个数组的元素之间。
void cv::namedWindow (const String &winname, int flags = WINDOW_AUTOSIZE)
创建一个窗口。函数
namedWindow创建一个可用作图像和轨迹栏占位符的窗口。创建的窗口由它们的名称引用。如果同名的窗口已经存在，则该函数不执行任何操作。
int cv::createTrackbar (const String &trackbarname, const String &winname, int *value, int count, TrackbarCallback onChange = 0, void *userdata = 0)
创建一个
trackbar并将其附加到指定窗口。函数
createTrackbar创建一个具有指定名称和范围的
trackbar（滑块或范围控件），分配一个变量值作为与
trackbar同步的位置，并指定回调函数
onChange为 在跟踪栏位置变化时被调用。创建的轨迹栏显示在指定的窗口
winname中。
#include<opencv2/imgcodecs.hpp>#include<opencv2/highgui.hpp>#include<opencv2/imgproc.hpp>#include<iostream>usingnamespace cv;usingnamespace std;
Mat imgHSV, mask;int hmin =0, smin =110, vmin =153;int hmax =19, smax =240, vmax =255;intmain(){
string path ="resources/lambo.png";
Mat img =imread(path);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,2555);while(true){
Scalar lower(hmin, smin, vmin);
Scalar upper(hmax, smax, vmax);inRange(imgHSV, lower, upper, mask);imshow("Image", img);imshow("Image HSV", imgHSV);imshow("Image Mask", mask);waitKey(1);}return0;}
Chapter7 形状/轮廓检测
void cv::findContours(InputOutputArray image, OutputArrayOfArrays contours, OutputArray hierarchy, int mode, int method, Point offset = Point())
在二值图像中查找轮廓。从OpenCV3.2开始源图像不会这个函数被修改。
参数含义image二值输入图像contours检测到的轮廓，每个轮廓都存储为点向量（例如
std::vector<std::vector<cv::Point> >
）hierarchy可选的输出向量（例如
std::vector<cv::Vec4i>
），包含有关图像拓扑的信息mode轮廓检索模式method轮廓近似方式offset每个轮廓点移动的可选偏移量
double cv::contourArea(InputArray contour, bool oriented=false)
计算轮廓区域
double cv::arcLength(InputArray curve, bool closed)
计算曲线长度或闭合轮廓周长
void cv::approxPolyDP(InputArray curve, OutputArray approxCurve, double epsilon, bool closed)
函数
cv::approxPolyDP用另一个具有较少顶点的曲线/多边形来逼近一条曲线或多边形，以使它们之间的距离小于或等于指定的精度。
Rect cv::boundingRect(InputArray array)
计算并返回指定点集或灰度图像非零像素的最小上边界矩形。
void cv::drawContours(InputOutputArray image, InputArrayOfArrays contours, int contourIdx, const Scalar &color, int thickness = 1, int lineType = LINE_8, InputArray hierarchy = noArray(), int maxLevel = INT_MAX, Point offset = Point())
绘制轮廓轮廓或填充轮廓。如果厚度≥0，该函数在图像中绘制轮廓轮廓，如果厚度<0，则填充轮廓所包围的区域。
Point_< _Tp > tl() const
左上角
Point_< _Tp > br() const
右下角
//recttemplate<typename_Tp>classcv::Rect_< _Tp >typedef Rect_<int> cv::Rect2i
typedef Rect2i cv::Rect
//pointtemplate<typename_Tp>classcv::Point_< _Tp >typedef Point_<int> cv::Point2i
typedef Point2i cv::Point
cv::Rect_< _Tp >
类属性含义height矩形高度width矩形宽度x左上角的 x 坐标y左上角的 y 坐标
#include<opencv2/imgcodecs.hpp>#include<opencv2/highgui.hpp>#include<opencv2/imgproc.hpp>#include<iostream>usingnamespace cv;usingnamespace std;voidgetContours(Mat imgDil, Mat img){
vector<vector<Point>> contours;//轮廓数据
vector<Vec4i> hierarchy;findContours(imgDil, contours, hierarchy, RETR_EXTERNAL, CHAIN_APPROX_SIMPLE);//通过预处理的二值图像找到所有轮廓contours//drawContours(img, contours, -1, Scalar(255, 0, 255), 2); //绘制所有轮廓for(int i =0; i < contours.size(); i++){double area =contourArea(contours[i]);//计算每个轮廓区域
cout << area << endl;
vector<vector<Point>>conPoly(contours.size());
vector<Rect>boundRect(contours.size());
string objectType;if(area >1000)//过滤噪声{//找轮廓的近似多边形或曲线double peri =arcLength(contours[i],true);approxPolyDP(contours[i], conPoly[i],0.02* peri,true);
cout << conPoly[i].size()<< endl;
boundRect[i]=boundingRect(conPoly[i]);//找每个近似曲线的最小上边界矩形int objCor =(int)conPoly[i].size();if(objCor ==3){ objectType ="Tri";}if(objCor ==4){float aspRatio =(float)boundRect[i].width / boundRect[i].height;//宽高比
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);//绘制滤除噪声后的所有轮廓rectangle(img, boundRect[i].tl(), boundRect[i].br(),Scalar(0,255,0),5);//绘制边界框putText(img, objectType,{ boundRect[i].x, boundRect[i].y -5}, FONT_HERSHEY_PLAIN,1,Scalar(0,69,255),1);}}}intmain(){
string path ="resources/shapes.png";
Mat img =imread(path);
Mat imgGray, imgBlur, imgCanny, imgDil;// PreprocessingcvtColor(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);return0;}
Chapter8 人脸检测
涉及模块
objdetect:Object Detection
class cv::CascadeClassifier
用于对象检测的级联分类器类。
bool load (const String &filename)
从文件加载分类器。
bool empty() const
检查分类器是否已加载。
void detectMultiScale(InputArray image, std::vector<Rect> &objects, double scaleFactor=1.1, int minNeighbors=3, int flags=0, Size minSize=Size(), Size maxSize=Size())
检测输入图像中不同大小的对象。检测到的对象作为矩形列表返回。
#include<opencv2/imgcodecs.hpp>#include<opencv2/highgui.hpp>#include<opencv2/imgproc.hpp>#include<opencv2/objdetect.hpp>#include<iostream>usingnamespace cv;usingnamespace std;intmain(){
string path ="Resources/test.png";
Mat img =imread(path);
CascadeClassifier faceCascade;
faceCascade.load("Resources/haarcascade_frontalface_default.xml");if(faceCascade.empty()){ cout <<"XML file not loaded"<< endl;}
vector<Rect> faces;
faceCascade.detectMultiScale(img, faces,1.1,10);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);return0;}
Project1 虚拟画家
颜色选择器：先找出待检测颜色的HSV阈值
#include<opencv2/imgcodecs.hpp>#include<opencv2/highgui.hpp>#include<opencv2/imgproc.hpp>#include<iostream>usingnamespace cv;usingnamespace std;intmain(){
VideoCapture cap(1);
Mat img;
Mat imgHSV, mask, imgColor;int hmin =0, smin =0, vmin =0;int hmax =179, smax =255, vmax =255;namedWindow("Trackbars",(640,200));// Create WindowcreateTrackbar("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){
cap.read(img);cvtColor(img, imgHSV, COLOR_BGR2HSV);
Scalar lower(hmin, smin, vmin);
Scalar upper(hmax, smax, vmax);inRange(imgHSV, lower, upper, mask);// hmin, smin, vmin, hmax, smax, vmax;
cout << hmin <<", "<< smin <<", "<< vmin <<", "<< hmax <<", "<< smax <<", "<< vmax << endl;imshow("Image", img);imshow("Mask", mask);waitKey(1);}}
利用检测到颜色的矩形框上边界中点开始虚拟作画
#include<opencv2/imgcodecs.hpp>#include<opencv2/highgui.hpp>#include<opencv2/imgproc.hpp>#include<iostream>usingnamespace cv;usingnamespace std;
Mat img;
vector<vector<int>> newPoints;
vector<vector<int>> myColors{{124,48,117,143,170,255},//purple{68,72,156,102,126,255}};//green
vector<Scalar> myColorValues{{255,0,255},//purple{0,255,0}};//green
Point getContours(Mat imgDil){
vector<vector<Point>> contours;//轮廓数据
vector<Vec4i> hierarchy;findContours(imgDil, contours, hierarchy, RETR_EXTERNAL, CHAIN_APPROX_SIMPLE);//通过预处理的二值图像找到所有轮廓contours//drawContours(img, contours, -1, Scalar(255, 0, 255), 2); //绘制所有轮廓(不滤除噪声)
vector<vector<Point>>conPoly(contours.size());
vector<Rect>boundRect(contours.size());
Point myPoint(0,0);for(int i =0; i < contours.size(); i++){double area =contourArea(contours[i]);//计算每个轮廓区域
cout << area << endl;if(area >1000)//过滤噪声{//找轮廓的近似多边形或曲线double peri =arcLength(contours[i],true);approxPolyDP(contours[i], conPoly[i],0.02* peri,true);
cout << conPoly[i].size()<< endl;
boundRect[i]=boundingRect(conPoly[i]);//找每个近似曲线的最小上边界矩形
myPoint.x = boundRect[i].x + boundRect[i].width /2;
myPoint.y = boundRect[i].y;//drawContours(img, conPoly, i, Scalar(255, 0, 255), 2); //绘制滤除噪声后的所有轮廓//rectangle(img, boundRect[i].tl(), boundRect[i].br(), Scalar(0, 255, 0), 5); //绘制边界框}}return myPoint;//返回矩形框上边界中点坐标}
vector<vector<int>>findColor(Mat img){
Mat imgHSV, mask;cvtColor(img, imgHSV, COLOR_BGR2HSV);for(int i =0; i < myColors.size(); i++){
Scalar lower(myColors[i][0], myColors[i][1], myColors[i][2]);
Scalar upper(myColors[i][3], myColors[i][4], myColors[i][5]);inRange(imgHSV, lower, upper, mask);//imshow(to_string(i), mask);
Point myPoint =getContours(mask);//根据mask得到检测到当前颜色矩形框的上边界中点坐标if(myPoint.x !=0&& myPoint.y !=0){
newPoints.push_back({ myPoint.x, myPoint.y, i });//得到当前帧检测颜色的目标点}}return newPoints;}voiddrawOnCanvas(vector<vector<int>> newPoints, vector<Scalar> myColorValues){for(int i =0; i < newPoints.size(); i++){circle(img,Point(newPoints[i][0], newPoints[i][1]),10, myColorValues[newPoints[i][2]], FILLED);}}intmain(){
VideoCapture cap(0);while(true){
cap.read(img);
newPoints =findColor(img);drawOnCanvas(newPoints, myColorValues);imshow("Image", img);waitKey(1);}return0;}
Project2 文档扫描
#include<opencv2/imgcodecs.hpp>#include<opencv2/highgui.hpp>#include<opencv2/imgproc.hpp>#include<iostream>usingnamespace cv;usingnamespace std;
Mat imgOriginal, imgGray, imgBlur,imgCanny, imgThre, imgDil, imgErode, imgWarp, imgCrop;
vector<Point> initialPoints, docPoints;float w =420, h =596;
Mat preProcessing(Mat img){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);//erode(imgDil, imgErode, kernel);return imgDil;}
vector<Point>getContours(Mat imgDil){
vector<vector<Point>> contours;//轮廓数据
vector<Vec4i> hierarchy;findContours(imgDil, contours, hierarchy, RETR_EXTERNAL, CHAIN_APPROX_SIMPLE);//通过预处理的二值图像找到所有轮廓contours//drawContours(img, contours, -1, Scalar(255, 0, 255), 2); //绘制所有轮廓(不滤除噪声)
vector<vector<Point>>conPoly(contours.size());
vector<Point> biggest;int maxArea =0;for(int i =0; i < contours.size(); i++){double area =contourArea(contours[i]);//计算每个轮廓区域
cout << area << endl;if(area >1000)//过滤噪声{//找轮廓的近似多边形或曲线double peri =arcLength(contours[i],true);approxPolyDP(contours[i], conPoly[i],0.02* peri,true);if(area > maxArea && conPoly[i].size()==4){//drawContours(imgOriginal, conPoly, i, Scalar(255, 0, 255), 5); //绘制滤除噪声后的所有轮廓
biggest ={ conPoly[i][0], conPoly[i][1], conPoly[i][2], conPoly[i][3]};
maxArea = area;}}}return biggest;//返回最大轮廓四个点的坐标}voiddrawPoints(vector<Point> points, Scalar color){for(int i =0; i < points.size(); i++){circle(imgOriginal, points[i],10, color, FILLED);putText(imgOriginal,to_string(i), points[i], FONT_HERSHEY_PLAIN,4, color,4);}}
vector<Point>reorder(vector<Point> points){
vector<Point> newPoints;
vector<int> sumPoints, subPoints;for(int i =0; i <4; i++){
sumPoints.push_back(points[i].x + points[i].y);
subPoints.push_back(points[i].x - points[i].y);}
newPoints.push_back(points[min_element(sumPoints.begin(), sumPoints.end())- sumPoints.begin()]);//0
newPoints.push_back(points[max_element(subPoints.begin(), subPoints.end())- subPoints.begin()]);//1
newPoints.push_back(points[min_element(subPoints.begin(), subPoints.end())- subPoints.begin()]);//2
newPoints.push_back(points[max_element(sumPoints.begin(), sumPoints.end())- sumPoints.begin()]);//3return newPoints;}
Mat getWarp(Mat img, vector<Point> points,float w,float h){
Point2f src[4]={ points[0], points[1], points[2], points[3]};
Point2f dst[4]={{0.0f,0.0f},{w,0.0f},{0.0f, h},{w, h}};
Mat matrix =getPerspectiveTransform(src, dst);warpPerspective(img, imgWarp, matrix,Point(w, h));return imgWarp;}intmain(){
string path ="Resources/paper.jpg";
imgOriginal =imread(path);//resize(imgOriginal, imgOriginal, Size(), 0.5, 0.5);//Preprocessing
imgThre =preProcessing(imgOriginal);//Get Contours - Biggest
initialPoints =getContours(imgThre);//drawPoints(initialPoints, Scalar(0, 0, 255));
docPoints =reorder(initialPoints);//drawPoints(docPoints, Scalar(0, 255, 0));//Warp
imgWarp =getWarp(imgOriginal, docPoints, w, h);//Cropint cropValue =5;
Rect roi(cropValue, cropValue, w -(2* cropValue), h -(2* cropValue));
imgCrop =imgWarp(roi);imshow("Image", imgOriginal);imshow("Image Dilation", imgThre);imshow("Image Warp", imgWarp);imshow("Image Crop", imgCrop);waitKey(0);return0;}
Project3 车牌检测
#include<opencv2/imgcodecs.hpp>#include<opencv2/highgui.hpp>#include<opencv2/imgproc.hpp>#include<opencv2/objdetect.hpp>#include<iostream>usingnamespace cv;usingnamespace std;intmain(){
VideoCapture cap(0);
Mat img;
CascadeClassifier plateCascade;
plateCascade.load("Resources/haarcascade_russian_plate_number.xml");if(plateCascade.empty()){ cout <<"XML file not loaded"<< endl;}
vector<Rect> plates;while(true){
cap.read(img);
plateCascade.detectMultiScale(img, plates,1.1,10);for(int i =0; i < plates.size(); i++){
Mat imgCrop =img(plates[i]);imshow(to_string(i), imgCrop);imwrite("D:\\VS2019Projects\\chapter2\\chapter2\\resources\\Plates\\1.png", imgCrop);rectangle(img, plates[i].tl(), plates[i].br(),Scalar(255,0,255),3);}imshow("Image", img);waitKey(1);}return0;}
本文转载自: https://blog.csdn.net/Star_ID/article/details/122656593
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版权归原作者 Star_ID 所有， 如有侵权，请联系我们删除。