【笔记】c++中opencv的使用
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【笔记】c++中opencv的使用
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記錄一下代碼。
標準霍夫變換 HoughLines() HoughLines(midImage, lines, 1, CV_PI/180, 150, 0, 0 );
函數輸入圖像為8位單通道二進制
經過調用HoughLines函數后儲存了霍夫線變換檢測到線條的輸出矢量。每一條線由具有兩個元素的矢量（ρ，θ）表示，其中，ρ是離坐標原點（0，0）（也就是圖像的左上角）的距離，θ是弧度線條旋轉角度（0度表示垂直線，π/2度表示水平線）。 第三個參數，double類型的rho，以像素為單位的距離精度。另一種表述方式是直線搜索時的進步尺寸的單位半徑。（Latex中/rho即表示ρ） 第四個參數，double類型的theta，以弧度為單位的角度精度。另一種表述方式是直線搜索時的進步尺寸的單位角度。 第五個參數，int類型的threshold，累加平面的閾值參數，即識別某部分為圖中的一條直線時它在累加平面中必須達到的值。大于閾值threshold的線段才可以被檢測通過并返回到結果中。
累計概率霍夫變換：HoughLinesP（）函數
此函數在HoughLines的基礎上，在末尾加了一個代表Probabilistic（概率）的P，表明它可以采用累計概率霍夫變換（PPHT）來找出二值圖像中的直線。 C++: void HoughLinesP(InputArray image, OutputArray lines, double rho, double theta, int threshold, double minLineLength=0, double maxLineGap=0 ) 第一個參數，InputArray類型的image，輸入圖像，即源圖像。需為8位的單通道二進制圖像，可以將任意的源圖載入進來后由函數修改成此格式后，再填在這里。 第二個參數，InputArray類型的lines，經過調用HoughLinesP函數后存儲了檢測到的線條的輸出矢量，每一條線由具有4個元素的矢量（x_1，y_1，x_2，y_2）表示，其中，（x_1，y_1）和（x_2，y_2）是是每個檢測到的線段的結束點。 第三個參數，double類型的rho，以像素為單位的距離精度。另一種表述方式是直線搜索時的進步尺寸的單位半徑。 第四個參數，double類型的theta，以弧度為單位的角度精度。另一種表述方式是直線搜索時的進步尺寸的單位角度。 第五個參數，int類型的threshold，累加平面的閾值參數，即識別某部分為圖中的一條直線時它在累加平面中必須達到的值。大于閾值threshold的線段才可以被檢測通過并返回到結果中。 第六個參數，double類型的minLineLength，有默認值0，表示最低線段的長度，比這個設定參數短的線段就不能被顯現出來。 第七個參數，double類型的maxLineGap，有默認值0，允許將同一行點與點之間連接起來的最大的距離。
#include "opencv2/core.hpp" #include <opencv2/imgproc.hpp> #include "opencv2/video.hpp" #include "opencv2/videoio.hpp" #include "opencv2/highgui.hpp" #include"opencv2/imgproc/imgproc.hpp" #include<opencv2/opencv.hpp> #include <iostream>using namespace std; using namespace cv;int main() {//【1】載入原始圖和Mat變量定義Mat srcImage = imread("/home/heziyi/圖片/6.jpg"); //工程目錄下應該有一張名為1.jpg的素材圖Mat midImage,dstImage;//臨時變量和目標圖的定義//【2】進行邊緣檢測和轉化為灰度圖Canny(srcImage, midImage, 50, 200, 3);//進行一此canny邊緣檢測cvtColor(midImage,dstImage, COLOR_GRAY2BGR);//轉化邊緣檢測后的圖為灰度圖//【3】進行霍夫線變換vector<Vec4i> lines;//定義一個矢量結構lines用于存放得到的線段矢量集合HoughLinesP(midImage, lines, 1, CV_PI/180, 80, 50, 10 );//【4】依次在圖中繪制出每條線段for( size_t i = 0; i < lines.size(); i++ ){Vec4i l = lines[i];//此句代碼的OpenCV2版為：//line( dstImage, Point(l[0], l[1]), Point(l[2], l[3]), Scalar(186,88,255), 1, CV_AA);//此句代碼的OpenCV3版為：line( dstImage, Point(l[0], l[1]), Point(l[2], l[3]), Scalar(186,88,255), 1, LINE_AA);}//【5】顯示原始圖imshow("【原始圖】", srcImage);//【6】邊緣檢測后的圖imshow("【邊緣檢測后的圖】", midImage);//【7】顯示效果圖imshow("【效果圖】", dstImage);waitKey(0); }原圖沒有什么直線，檢測效果不明顯。
findContours（）函數用于在二值圖像中尋找輪廓。 C++: void findContours(InputOutputArray image, OutputArrayOfArrays contours, OutputArray hierarchy, int mode, int method, Point offset=Point()) 第一個參數，InputArray類型的image，輸入圖像，即源圖像，填Mat類的對象即可，且需為8位單通道圖像。 第三個參數，OutputArray類型的hierarchy，可選的輸出向量，包含圖像的拓撲信息。其作為輪廓數量的表示，包含了許多元素。每個輪廓contours[i]對應4個hierarchy元素hierarchy[i][0]～hierarchy[i][3]，分別表示后一個輪廓、前一個輪廓、父輪廓、內嵌輪廓的索引編號。如果沒有對應項，對應的hierarchy[i]值設置為負數。 第四個參數，int類型的mode，第五個參數，int類型的method，為輪廓的近似辦法，
vector<vector> contours;
findContours(image,
contours, //輪廓數組
CV_RETR_EXTERNAL, //獲取外輪廓
CV_CHAIN_APPROX_NONE); // 獲取每個輪廓的每個像素
例子：
#include "opencv2/core.hpp" #include <opencv2/imgproc.hpp> #include "opencv2/video.hpp" #include "opencv2/videoio.hpp" #include "opencv2/highgui.hpp" #include"opencv2/imgproc/imgproc.hpp" #include<opencv2/opencv.hpp> #include <iostream>using namespace std; using namespace cv;int main() {//【1】載入原始圖和Mat變量定義Mat srcImage = imread("/home/heziyi/圖片/6.jpg"); //工程目錄下應該有一張名為1.jpg的素材圖imshow("原始圖",srcImage);//【2】初始化結果圖Mat dstImage; Mat midImage;//【3】srcImage取大于閾值119的那部分srcImage = srcImage > 119;imshow( "取閾值后的原始圖", srcImage );//【4】定義輪廓和層次結構vector<vector<Point> > contours;vector<Vec4i> hierarchy;//【5】查找輪廓//此句代碼的OpenCV2版為：//findContours( srcImage, contours, hierarchy,CV_RETR_CCOMP, CV_CHAIN_APPROX_SIMPLE );//此句代碼的OpenCV3版為：// Canny(srcImage, midImage, 50, 200, 3);//進行一此canny邊緣檢測cvtColor(srcImage,dstImage, COLOR_BGR2GRAY);//轉化邊緣檢測后的圖為灰度圖findContours( dstImage, contours, hierarchy,RETR_TREE, CHAIN_APPROX_SIMPLE );// 【6】遍歷所有頂層的輪廓， 以隨機顏色繪制出每個連接組件顏色int index = 0;for( ; index >= 0; index = hierarchy[index][0] ){Scalar color( rand()&255, rand()&255, rand()&255 );//此句代碼的OpenCV2版為：//drawContours( dstImage, contours, index, color, CV_FILLED, 8, hierarchy );//此句代碼的OpenCV3版為：drawContours( dstImage, contours, index, color, FILLED, 8, hierarchy ); }//【7】顯示最后的輪廓圖imshow( "輪廓圖", dstImage );waitKey(0); }grabcut函數（官方
#include "opencv2/imgcodecs.hpp" #include "opencv2/highgui.hpp" #include "opencv2/imgproc.hpp"#include <iostream>using namespace std; using namespace cv;static void help() {cout << "\nThis program demonstrates GrabCut segmentation -- select an object in a region\n""and then grabcut will attempt to segment it out.\n""Call:\n""./grabcut <image_name>\n""\nSelect a rectangular area around the object you want to segment\n" <<"\nHot keys: \n""\tESC - quit the program\n""\tr - restore the original image\n""\tn - next iteration\n""\n""\tleft mouse button - set rectangle\n""\n""\tCTRL+left mouse button - set GC_BGD pixels\n""\tSHIFT+left mouse button - set GC_FGD pixels\n""\n""\tCTRL+right mouse button - set GC_PR_BGD pixels\n""\tSHIFT+right mouse button - set GC_PR_FGD pixels\n" << endl; }const Scalar RED = Scalar(0,0,255); const Scalar PINK = Scalar(230,130,255); const Scalar BLUE = Scalar(255,0,0); const Scalar LIGHTBLUE = Scalar(255,255,160); const Scalar GREEN = Scalar(0,255,0);const int BGD_KEY = EVENT_FLAG_CTRLKEY; const int FGD_KEY = EVENT_FLAG_SHIFTKEY;static void getBinMask( const Mat& comMask, Mat& binMask ) {if( comMask.empty() || comMask.type()!=CV_8UC1 )CV_Error( Error::StsBadArg, "comMask is empty or has incorrect type (not CV_8UC1)" );if( binMask.empty() || binMask.rows!=comMask.rows || binMask.cols!=comMask.cols )binMask.create( comMask.size(), CV_8UC1 );binMask = comMask & 1; }class GCApplication { public:enum{ NOT_SET = 0, IN_PROCESS = 1, SET = 2 };static const int radius = 2;static const int thickness = -1;void reset();void setImageAndWinName( const Mat& _image, const string& _winName );void showImage() const;void mouseClick( int event, int x, int y, int flags, void* param );int nextIter();int getIterCount() const { return iterCount; } private:void setRectInMask();void setLblsInMask( int flags, Point p, bool isPr );const string* winName;const Mat* image;Mat mask;Mat bgdModel, fgdModel;uchar rectState, lblsState, prLblsState;bool isInitialized;Rect rect;vector<Point> fgdPxls, bgdPxls, prFgdPxls, prBgdPxls;int iterCount; };void GCApplication::reset() {if( !mask.empty() )mask.setTo(Scalar::all(GC_BGD));bgdPxls.clear(); fgdPxls.clear();prBgdPxls.clear(); prFgdPxls.clear();isInitialized = false;rectState = NOT_SET;lblsState = NOT_SET;prLblsState = NOT_SET;iterCount = 0; }void GCApplication::setImageAndWinName( const Mat& _image, const string& _winName ) {if( _image.empty() || _winName.empty() )return;image = &_image;winName = &_winName;mask.create( image->size(), CV_8UC1);reset(); }void GCApplication::showImage() const {if( image->empty() || winName->empty() )return;Mat res;Mat binMask;if( !isInitialized )image->copyTo( res );else{getBinMask( mask, binMask );image->copyTo( res, binMask );}vector<Point>::const_iterator it;for( it = bgdPxls.begin(); it != bgdPxls.end(); ++it )circle( res, *it, radius, BLUE, thickness );for( it = fgdPxls.begin(); it != fgdPxls.end(); ++it )circle( res, *it, radius, RED, thickness );for( it = prBgdPxls.begin(); it != prBgdPxls.end(); ++it )circle( res, *it, radius, LIGHTBLUE, thickness );for( it = prFgdPxls.begin(); it != prFgdPxls.end(); ++it )circle( res, *it, radius, PINK, thickness );if( rectState == IN_PROCESS || rectState == SET )rectangle( res, Point( rect.x, rect.y ), Point(rect.x + rect.width, rect.y + rect.height ), GREEN, 2);imshow( *winName, res ); }void GCApplication::setRectInMask() {CV_Assert( !mask.empty() );mask.setTo( GC_BGD );rect.x = max(0, rect.x);rect.y = max(0, rect.y);rect.width = min(rect.width, image->cols-rect.x);rect.height = min(rect.height, image->rows-rect.y);(mask(rect)).setTo( Scalar(GC_PR_FGD) ); }void GCApplication::setLblsInMask( int flags, Point p, bool isPr ) {vector<Point> *bpxls, *fpxls;uchar bvalue, fvalue;if( !isPr ){bpxls = &bgdPxls;fpxls = &fgdPxls;bvalue = GC_BGD;fvalue = GC_FGD;}else{bpxls = &prBgdPxls;fpxls = &prFgdPxls;bvalue = GC_PR_BGD;fvalue = GC_PR_FGD;}if( flags & BGD_KEY ){bpxls->push_back(p);circle( mask, p, radius, bvalue, thickness );}if( flags & FGD_KEY ){fpxls->push_back(p);circle( mask, p, radius, fvalue, thickness );} }void GCApplication::mouseClick( int event, int x, int y, int flags, void* ) {// TODO add bad args checkswitch( event ){case EVENT_LBUTTONDOWN: // set rect or GC_BGD(GC_FGD) labels{bool isb = (flags & BGD_KEY) != 0,isf = (flags & FGD_KEY) != 0;if( rectState == NOT_SET && !isb && !isf ){rectState = IN_PROCESS;rect = Rect( x, y, 1, 1 );}if ( (isb || isf) && rectState == SET )lblsState = IN_PROCESS;}break;case EVENT_RBUTTONDOWN: // set GC_PR_BGD(GC_PR_FGD) labels{bool isb = (flags & BGD_KEY) != 0,isf = (flags & FGD_KEY) != 0;if ( (isb || isf) && rectState == SET )prLblsState = IN_PROCESS;}break;case EVENT_LBUTTONUP:if( rectState == IN_PROCESS ){rect = Rect( Point(rect.x, rect.y), Point(x,y) );rectState = SET;setRectInMask();CV_Assert( bgdPxls.empty() && fgdPxls.empty() && prBgdPxls.empty() && prFgdPxls.empty() );showImage();}if( lblsState == IN_PROCESS ){setLblsInMask(flags, Point(x,y), false);lblsState = SET;showImage();}break;case EVENT_RBUTTONUP:if( prLblsState == IN_PROCESS ){setLblsInMask(flags, Point(x,y), true);prLblsState = SET;showImage();}break;case EVENT_MOUSEMOVE:if( rectState == IN_PROCESS ){rect = Rect( Point(rect.x, rect.y), Point(x,y) );CV_Assert( bgdPxls.empty() && fgdPxls.empty() && prBgdPxls.empty() && prFgdPxls.empty() );showImage();}else if( lblsState == IN_PROCESS ){setLblsInMask(flags, Point(x,y), false);showImage();}else if( prLblsState == IN_PROCESS ){setLblsInMask(flags, Point(x,y), true);showImage();}break;} }int GCApplication::nextIter() {if( isInitialized )grabCut( *image, mask, rect, bgdModel, fgdModel, 1 );else{if( rectState != SET )return iterCount;if( lblsState == SET || prLblsState == SET )grabCut( *image, mask, rect, bgdModel, fgdModel, 1, GC_INIT_WITH_MASK );elsegrabCut( *image, mask, rect, bgdModel, fgdModel, 1, GC_INIT_WITH_RECT );isInitialized = true;}iterCount++;bgdPxls.clear(); fgdPxls.clear();prBgdPxls.clear(); prFgdPxls.clear();return iterCount; }GCApplication gcapp;static void on_mouse( int event, int x, int y, int flags, void* param ) {gcapp.mouseClick( event, x, y, flags, param ); }int main( int argc, char** argv ) {cv::CommandLineParser parser(argc, argv, "{@input| /home/heziyi/圖片/6.jpg |}");help();string filename = parser.get<string>("@input");if( filename.empty() ){cout << "\nDurn, empty filename" << endl;return 1;}Mat image = imread(samples::findFile(filename), IMREAD_COLOR);if( image.empty() ){cout << "\n Durn, couldn't read image filename " << filename << endl;return 1;}const string winName = "image";namedWindow( winName, WINDOW_AUTOSIZE );setMouseCallback( winName, on_mouse, 0 );gcapp.setImageAndWinName( image, winName );gcapp.showImage();for(;;){char c = (char)waitKey(0);switch( c ){case '\x1b':cout << "Exiting ..." << endl;goto exit_main;case 'r':cout << endl;gcapp.reset();gcapp.showImage();break;case 'n':int iterCount = gcapp.getIterCount();cout << "<" << iterCount << "... ";int newIterCount = gcapp.nextIter();if( newIterCount > iterCount ){gcapp.showImage();cout << iterCount << ">" << endl;}elsecout << "rect must be determined>" << endl;break;}}exit_main:destroyWindow( winName );return 0; }總結
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