opencv入门笔记之三 简单图像识别,识别线,点,圆,轮廓

Edge detection

int main(){

IplImage* girl = cvLoadImage(“road.jpg”, 0);

// convert color to grag

Mat boy;

//[b]*************start******************************[/b]///

Mat cow = Mat(girl);

//Mat() convert IplImage to Mat

imshow(“cow”, cow);

Canny(cow, boy, 125, 350);

//125< |pixel - pixel| <350 (hysteresis thresholding)

imshow(“boy”, boy);

Mat Inv;

threshold(boy, Inv, 128, 255, THRESH_BINARY_INV);

// invert color

//[b]*****************end**************************[/b]///

duration = static_cast(getTickCount()) - duration;

duration /= getTickFrequency();

cout << duration;

//////////////////////////////////////////////////////////////////////////

imshow(“Inv”, Inv);

waitKey(0);

}

2/5/2015 10:12 AM - Screen Clipping

Lines detection

Mat boundless = Mat(cvLoadImage(“road.jpg”, 0));

//convert color to grey

Mat bound;

Canny(boundless, bound, 125, 350);

vector lines;

HoughLines(bound, lines, 1, M_PI / 180, 80);

// if u wanna use PI there must be a #define _USE_MATH_DEFINES before math.h

vector::const_iterator it = lines.begin();

while (it != lines.end()){

float rho = (*it)[0];

float theta = (*it)[1];

if (theta < M_PI_4||theta>3*M_PI_4)

{

Point pt1(rho / cos(theta), 0);

Point pt2(rho - bound.rows*sin(theta) / cos(theta), bound.rows);

line(boundless,pt1, pt2, Scalar(255), 1);

}

else

{

Point pt1(0, rho / sin(theta));

Point pt2(bound.cols, rho - bound.cols*cos(theta) / sin(theta));

line(boundless, pt1, pt2, Scalar(255), 1);

}

++it;

}

imshow(“new”, boundless);

Probabilistic Hough transform

class findline

{

public:

findline() :deltarho(1), deltatheta(M_PI / 180), minvote(10), minlength(0.), maxGap(0.){}

// ~findline();

void setAccResolution(double rho=1, double theta=M_PI/180){

deltatheta = theta;

deltarho = rho;

}// smaller deltatheta and deltarho means more time and more precise

void setMinVote(int minv){

minvote = minv;

}// more votes, less lines

void setLineLengthAndGap(double length, double gap){

minlength = length;

maxGap = gap;

}// gap between lines and length of lines

vector findLines(Mat& binary){

lines.clear();

HoughLinesP(binary, lines, deltarho, deltatheta, minvote, minlength, maxGap);

return lines;

} //use probabilistic hough transform

void drawDetectedLines(Mat &image, Scalar color=Scalar(255,255,255)){

vector::const_iterator it2 = lines.begin();

while(it2 != lines.end()){

Point pt1((*it2)[0], (*it2)[1]);

Point pt2((*it2)[2], (*it2)[3]);

line(image, pt1, pt2, color);

++it2;

}

}// use const_iterator to draw lines

private:

Mat image;

vector lines;

double deltarho, deltatheta;

int minvote;

double minlength;

double maxGap;

};

void m(){

findline finder;

finder.setMinVote(80);

finder.setLineLengthAndGap(100, 20);

Mat image = imread(“ground.jpg”);

Mat coutours = Mat(cvLoadImage(“ground.jpg”, 0));

Canny(coutours, coutours, 125, 350);

// imshow(“hen”, coutours);

vector lines = finder.findLines(coutours);

finder.drawDetectedLines(image,Scalar(0,0,0));

imshow(“Hough”, image);

imwrite(“ok.jpg”, image);

}

vector