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Create Exercises_13_1-2-11.cpp
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‎Exercises_13_1-2-11.cpp‎

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//Exercises_9_4.cpp Exercises at end of Chapter 9
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#include <opencv2/opencv.hpp>
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#include <iostream>
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using namespace cv;
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using namespace std;
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void help(const char **argv) {
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cout << "\n\n"
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<< "This program solves the Exercise 4、5 at the end of Chapter 9 \n"
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<< "Call:\n"
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<< argv[0] << " <path/image_name1>" << " <path/image_name2>\n\n"
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<< "For example: ./" << argv[0] << " ../left.jpg "<< " ../left.jpg\n"
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<< endl;
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}
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int main( int argc, const char** argv )
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{
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help(argv);
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if(argc < 3) {
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cout << "\nERROR: You had too few parameters.\n" << endl;
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return -1;
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}
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/************************************************************************/
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/* 1.In this exercise, we learn to experiment with parameters by setting good low
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Thresh and highThresh values in cv::Canny(). Load an image with suitably
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interesting line structures. We’ll use three different high:low threshold settings of
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1.5:1, 2.75:1, and 4:1.
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a. Report what you see with a high setting of less than 50.
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b. Report what you see with high settings between 50 and 100.
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c. Report what you see with high settings between 100 and 150.
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d. Report what you see with high settings between 150 and 200.
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e. Report what you see with high settings between 200 and 250.
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f. Summarize your results and explain what happens as best you can. */
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/************************************************************************/
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Mat src = imread(argv[1],IMREAD_GRAYSCALE);
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Mat dst;
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int iHighThresh = 50;
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Canny(src,dst,static_cast<int>(iHighThresh/1.5),iHighThresh);
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imshow("iHighThresh is 50 and high:low is 1.5:1",dst);
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Canny(src,dst,static_cast<int>(iHighThresh/2.75),iHighThresh);
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imshow("iHighThresh is 50 and high:low is 2.75:1",dst);
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Canny(src,dst,static_cast<int>(iHighThresh/4),iHighThresh);
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imshow("iHighThresh is 50 and high:low is 4:1",dst);
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iHighThresh = (50+100)/2;
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Canny(src,dst,static_cast<int>(iHighThresh/1.5),iHighThresh);
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imshow("iHighThresh is (50+100)/2 and high:low is 1.5:1",dst);
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Canny(src,dst,static_cast<int>(iHighThresh/2.75),iHighThresh);
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imshow("iHighThresh is (50+100)/2 and high:low is 2.75:1",dst);
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Canny(src,dst,static_cast<int>(iHighThresh/4),iHighThresh);
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imshow("iHighThresh is (50+100)/2 and high:low is 4:1",dst);
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iHighThresh = (100+150)/2;
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Canny(src,dst,static_cast<int>(iHighThresh/1.5),iHighThresh);
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imshow("iHighThresh is (100+150)/2 and high:low is 1.5:1",dst);
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Canny(src,dst,static_cast<int>(iHighThresh/2.75),iHighThresh);
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imshow("iHighThresh is (100+150)/2 and high:low is 2.75:1",dst);
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Canny(src,dst,static_cast<int>(iHighThresh/4),iHighThresh);
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imshow("iHighThresh is (100+150)/2 and high:low is 4:1",dst);
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iHighThresh = (150+200)/2;
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Canny(src,dst,static_cast<int>(iHighThresh/1.5),iHighThresh);
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imshow("iHighThresh is (150+200)/2 and high:low is 1.5:1",dst);
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Canny(src,dst,static_cast<int>(iHighThresh/2.75),iHighThresh);
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imshow("iHighThresh is (150+200)/2 and high:low is 2.75:1",dst);
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Canny(src,dst,static_cast<int>(iHighThresh/4),iHighThresh);
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imshow("iHighThresh is (150+200)/2 and high:low is 4:1",dst);
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iHighThresh = (200+250)/2;
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Canny(src,dst,static_cast<int>(iHighThresh/1.5),iHighThresh);
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imshow("iHighThresh is (200+250)/2 and high:low is 1.5:1",dst);
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Canny(src,dst,static_cast<int>(iHighThresh/2.75),iHighThresh);
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imshow("iHighThresh is (200+250)/2 and high:low is 2.75:1",dst);
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Canny(src,dst,static_cast<int>(iHighThresh/4),iHighThresh);
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imshow("iHighThresh is (200+250)/2 and high:low is 4:1",dst);
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/************************************************************************/
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/* 2. Load an image containing clear lines and circles such as a side view of a bicycle.
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Use the Hough line and Hough circle calls and see how they respond to your
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image. */
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/************************************************************************/
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Mat src = imread(argv[1],IMREAD_GRAYSCALE);//a bike in gray
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GaussianBlur( src, src, Size(9, 9), 2, 2 );
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Mat temp;
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vector<Vec4i> linesP;
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vector<Vec3f> circles;
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//first find the canny edge
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Canny(src,temp,50,200);
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//find lines
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HoughLinesP(temp,linesP,1,CV_PI/180,80,50,10);
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//find circles
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HoughCircles( src, circles, CV_HOUGH_GRADIENT, 1, src.rows/8, 200, 100, 0, 0 );
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//draw lines and circles in the source image
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for (int i = 0;i<linesP.size();i++)
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{
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Vec4i l = linesP[i];
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line(src,Point(l[0],l[1]),Point(l[2],l[3]),Scalar(255));
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}
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for( size_t i = 0; i < circles.size(); i++ )
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{
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Point center(cvRound(circles[i][0]), cvRound(circles[i][1]));
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int radius = cvRound(circles[i][2]);
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// circle center
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circle( src, center, 3, Scalar(255,255,255), -1, 8, 0 );
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// circle outline
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circle( src, center, radius, Scalar(255,255,255), 1, 8, 0 );
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}
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/*11. Create an image with a 20 × 20 square in it. Rotate it to an arbitrary angle. Take a
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distance transform of this image. Create a 20 × 20 square shape. Use the distance
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transform image to algorithmically overlay the shape onto the rotated square in
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the image you made.*/
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// create a 20 × 20 square
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Mat matBoard = Mat(500,500,CV_8UC3,Scalar(0));
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rectangle(matBoard,Rect(250,250,20,20),Scalar(255,255,255),-1);
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// create an arbitary angle
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cv::RNG rng = cv::theRNG();
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float fDegree = rng.uniform(0,360);
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Mat rotMatS = getRotationMatrix2D(Point(250,250), fDegree, 1.0);
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warpAffine(matBoard, matBoard, rotMatS, matBoard.size(), 1, 0, Scalar(0,0,0));
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//solove the problem
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Mat matDistance;
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cvtColor(matBoard,gray,COLOR_BGR2GRAY);
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threshold(gray,gray,100,255,CV_THRESH_BINARY);
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distanceTransform(gray,matDistance,CV_DIST_L2,3);
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//find the center corners
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double minVal;
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double maxVal2;
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Point minLoc,maxLoc;
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minMaxLoc( matDistance, &minVal, &maxVal2, &minLoc, &maxLoc, matDistance>0);
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//draw the result
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circle(matBoard,maxLoc,1,Scalar(0),1);
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circle(matBoard,minLoc,2,Scalar(255),2);
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rectangle(matBoard,Rect(maxLoc.x - 10,maxLoc.y - 10,20,20),Scalar(0,0,255),-1);
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return 0;
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}
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