//----------------- shows image's histogram on terminal-----------------------------------
void MainWindow::on_pushButton_2_clicked()
{
Histogram1D terminal;
cv::MatND histo=terminal.getHistogram(image);
// shows values of histgoram on terminal
for (int i=0; i<256; i++)
std::cout<<"value"<<i<<"="<<histo.at<float>(i)<<std::endl;
}
void case1histogram() {
cv::Mat image=cv::imread( inputImagePath4case1histogram,cv::IMREAD_GRAYSCALE );//open in b&w
Histogram1D h;
cv::MatND histo=h.getHistogram(image);
for(int i=0; i<256; i++) {
cout<<"Value "<< i << " = " << histo.at<float>( i )<<endl;
}
}
int main(int argc, char *argv[])
{
QCoreApplication a(argc, argv);
Mat image = imread("D:\\workspace\\pictures\\771413900_m.jpg",0);
Mat thresholded, result;
Histogram1D h;
MatND histo = h.getHistogram(image);
for (int i=0;i<256;i++)
cout << "Value" << i << "=" << histo.at<float>(i) << endl;
imshow("Orginal", image);
imshow("Histogram",h.getHistogramImage(image));
//threshold(image, thresholded,128,255,THRESH_BINARY);
//imshow("Threshold",thresholded);
equalizeHist(image,result);
//Mat stretched = h.stretch(image,100);
//imshow("Stretched",stretched);
imshow("Equalize",result);
imshow("After Equalize Histogram",h.getHistogramImage(result));
waitKey(50000);
return a.exec();
}
int main()
{
// Read input image
cv::Mat image= cv::imread("waves.jpg",0);
if (!image.data)
return 0;
// define image ROI
cv::Mat imageROI;
imageROI= image(cv::Rect(216,33,24,30)); // Cloud region
// Display reference patch
cv::namedWindow("Reference");
cv::imshow("Reference",imageROI);
// Find histogram of reference
Histogram1D h;
cv::Mat hist= h.getHistogram(imageROI);
cv::namedWindow("Reference Hist");
cv::imshow("Reference Hist",h.getHistogramImage(imageROI));
// Create the content finder
ContentFinder finder;
// set histogram to be back-projected
finder.setHistogram(hist);
finder.setThreshold(-1.0f);
// Get back-projection
cv::Mat result1;
result1= finder.find(image);
// Create negative image and display result
cv::Mat tmp;
result1.convertTo(tmp,CV_8U,-1.0,255.0);
cv::namedWindow("Backprojection result");
cv::imshow("Backprojection result",tmp);
// Get binary back-projection
finder.setThreshold(0.12f);
result1= finder.find(image);
// Draw a rectangle around the reference area
cv::rectangle(image, cv::Rect(216, 33, 24, 30), cv::Scalar(0, 0, 0));
// Display image
cv::namedWindow("Image");
cv::imshow("Image",image);
// Display result
cv::namedWindow("Detection Result");
cv::imshow("Detection Result",result1);
// Load color image
ColorHistogram hc;
cv::Mat color= cv::imread("waves.jpg");
// extract region of interest
imageROI= color(cv::Rect(0,0,100,45)); // blue sky area
// Get 3D colour histogram (8 bins per channel)
hc.setSize(8); // 8x8x8
cv::Mat shist= hc.getHistogram(imageROI);
// set histogram to be back-projected
finder.setHistogram(shist);
finder.setThreshold(0.05f);
// Get back-projection of color histogram
result1= finder.find(color);
cv::namedWindow("Color Detection Result");
cv::imshow("Color Detection Result",result1);
// Second color image
cv::Mat color2= cv::imread("dog.jpg");
cv::namedWindow("Second Image");
cv::imshow("Second Image",color2);
// Get back-projection of color histogram
cv::Mat result2= finder.find(color2);
cv::namedWindow("Result color (2)");
cv::imshow("Result color (2)",result2);
// Get ab color histogram
hc.setSize(256); // 256x256
cv::Mat colorhist= hc.getabHistogram(imageROI);
// display 2D histogram
colorhist.convertTo(tmp,CV_8U,-1.0,255.0);
cv::namedWindow("ab histogram");
cv::imshow("ab histogram",tmp);
// set histogram to be back-projected
finder.setHistogram(colorhist);
finder.setThreshold(0.05f);
// Convert to Lab space
cv::Mat lab;
cv::cvtColor(color, lab, CV_BGR2Lab);
// Get back-projection of ab histogram
int ch[2]={1,2};
result1= finder.find(lab,0,256.0f,ch);
cv::namedWindow("Result ab (1)");
cv::imshow("Result ab (1)",result1);
// Second colour image
cv::cvtColor(color2, lab, CV_BGR2Lab);
// Get back-projection of ab histogram
result2= finder.find(lab,0,256.0,ch);
cv::namedWindow("Result ab (2)");
cv::imshow("Result ab (2)",result2);
// Draw a rectangle around the reference sky area
cv::rectangle(color,cv::Rect(0,0,100,45),cv::Scalar(0,0,0));
cv::namedWindow("Color Image");
cv::imshow("Color Image",color);
// Get Hue colour histogram
hc.setSize(180); // 180 bins
colorhist= hc.getHueHistogram(imageROI);
// set histogram to be back-projected
finder.setHistogram(colorhist);
// Convert to HSV space
cv::Mat hsv;
cv::cvtColor(color, hsv, CV_BGR2HSV);
// Get back-projection of hue histogram
ch[0]=0;
result1= finder.find(hsv,0.0f,180.0f,ch);
cv::namedWindow("Result Hue (1)");
cv::imshow("Result Hue (1)",result1);
// Second colour image
color2= cv::imread("dog.jpg");
// Convert to HSV space
cv::cvtColor(color2, hsv, CV_BGR2HSV);
// Get back-projection of hue histogram
result2= finder.find(hsv,0.0f,180.0f,ch);
cv::namedWindow("Result Hue (2)");
cv::imshow("Result Hue (2)",result2);
cv::waitKey();
return 0;
}
int main()
{
VideoCapture cap(1);
cap.set(CV_CAP_PROP_FRAME_WIDTH,640);
cap.set(CV_CAP_PROP_FRAME_HEIGHT,480);
if(!cap.isOpened())
return -1;
Mat edges, dst, color_dst,gredst;
CvMemStorage* storage =NULL;
IplImage* img = NULL;
for(;;)
{
Mat frame;
cap >> frame;
edges = frame;
cvtColor(frame, gredst, CV_BGR2GRAY);
cvtColor(frame, dst, CV_BGR2GRAY);
vertx1.clear();
vertx2.clear();
vertx3.clear();
vertx4.clear();
Mat bgr[3];
split(edges, bgr);
Histogram1D h;
MatND histo = h.getHistogram(bgr[0]);
colorSegmentation(edges, dst);
colorSegmentation1(edges,gredst);
//imshow("rectanger", dst);
//Houghlinesp
//GaussianBlur(dst, dst, Size(7,7),1.5,1.5);
cvtColor( dst, color_dst, CV_GRAY2BGR );
//Canny( dst, dst, 60, 180, 3 );
//for(int i = 0; i < 1; i++)GaussianBlur(dst, dst, Size(7,7),3,3);
//double
/*vector<Vec4i> lines;
//cvShowImage("Transform",&img);
HoughLinesP( dst, lines, 1, CV_PI/180, 60, 30, 0);
for( size_t i = 0; i < lines.size(); i++ )
{
double theta = atan( fabs(lines[i][3] - lines[i][1]) / fabs(lines[i][2] - lines[i][0]));
if (fabs(theta - CV_PI / 2) < CV_PI/180 * 6 || fabs(theta) < CV_PI/180 * 6)
{
line( color_dst, Point(lines[i][0], lines[i][1]),
Point(lines[i][2], lines[i][3]), Scalar(0,0,255), 3, 8 );
}
}*/
//Canny(edges, edges,0,90,3);
imshow("init graph", edges);
//imshow("rectanger", dst);
// 转换成opencv1.x处理
//IplImage img0(dst);
pyrUp(dst,dst,Size(dst.cols*2,dst.rows*2));
pyrDown(dst,dst,Size(dst.cols/2,dst.rows/2));
pyrUp(gredst,gredst,Size((gredst.cols)*2,(gredst.rows)*2));
pyrDown(gredst,gredst,Size((gredst.cols)/2,(gredst.rows)/2));
IplImage img0 = IplImage(dst);
IplImage img1 = IplImage(gredst);
img = &img0;
//img = &IplImage(dst);
//cvShowImage("Transform",img);
storage = cvCreateMemStorage(0);
drawSquares( img, findSquares4( img, storage ) );
img = &img1;
storage = cvCreateMemStorage(0);
drawSquares1( img, findSquares4( img, storage ) );
//imshow("rectanger_line", color_dst);
//imshow("qq", dst);
char c= waitKey(33);
if(c==27) break;
}
}
int main()
{
// Read input image
cv::Mat image= cv::imread("group.jpg",0);
if (!image.data)
return 0;
// Resize by 70% for book printing
cv::resize(image, image, cv::Size(), 0.7, 0.7);
// save grayscale image
cv::imwrite("groupBW.jpg", image);
// Display the image
cv::namedWindow("Image");
cv::imshow("Image",image);
// The histogram object
Histogram1D h;
// Compute the histogram
cv::Mat histo= h.getHistogram(image);
// Loop over each bin
for (int i=0; i<256; i++)
cout << "Value " << i << " = " << histo.at<float>(i) << endl;
// Display a histogram as an image
cv::namedWindow("Histogram");
cv::imshow("Histogram",h.getHistogramImage(image));
// creating a binary image by thresholding at the valley
cv::Mat thresholded; // output binary image
cv::threshold(image,thresholded,
60, // threshold value
255, // value assigned to pixels over threshold value
cv::THRESH_BINARY); // thresholding type
// Display the thresholded image
cv::namedWindow("Binary Image");
cv::imshow("Binary Image",thresholded);
thresholded = 255 - thresholded;
cv::imwrite("binary.bmp",thresholded);
// Equalize the image
cv::Mat eq= h.equalize(image);
// Show the result
cv::namedWindow("Equalized Image");
cv::imshow("Equalized Image",eq);
// Show the new histogram
cv::namedWindow("Equalized Histogram");
cv::imshow("Equalized Histogram",h.getHistogramImage(eq));
// Stretch the image, setting the 1% of pixels at black and 1% at white
cv::Mat str= h.stretch(image,0.01f);
// Show the result
cv::namedWindow("Stretched Image");
cv::imshow("Stretched Image",str);
// Show the new histogram
cv::namedWindow("Stretched Histogram");
cv::imshow("Stretched Histogram",h.getHistogramImage(str));
// Create an image inversion table
int dim(256);
cv::Mat lut(1, // 1 dimension
&dim, // 256 entries
CV_8U); // uchar
// or cv::Mat lut(256,1,CV_8U);
for (int i=0; i<256; i++) {
lut.at<uchar>(i)= 255-i;
}
// Apply lookup and display negative image
cv::namedWindow("Negative image");
cv::imshow("Negative image",h.applyLookUp(image,lut));
cv::waitKey();
return 0;
}
int main_his()
{
// Read input image
cv::Mat image= cv::imread("Koala.jpg",0);
if (!image.data)
return 0;
// Display the image
cv::namedWindow("Image");
cv::imshow("Image",image);
// The histogram object
Histogram1D h;
// Compute the histogram
cv::MatND histo= h.getHistogram(image);
// Loop over each bin
for (int i=0; i<256; i++)
cout << "Value " << i << " = " << histo.at<float>(i) << endl;
// Display a histogram as an image
cv::namedWindow("Histogram");
cv::imshow("Histogram",h.getHistogramImage(image));
// creating a binary image by thresholding at the valley
cv::Mat thresholded;
cv::threshold(image,thresholded,60,255,cv::THRESH_BINARY);
// Display the thresholded image
cv::namedWindow("Binary Image");
cv::imshow("Binary Image",thresholded);
cv::imwrite("binary.bmp",thresholded);
// Equalize the image
cv::Mat eq= h.equalize(image);
// Show the result
cv::namedWindow("Equalized Image");
cv::imshow("Equalized Image",eq);
// Show the new histogram
cv::namedWindow("Equalized Histogram");
cv::imshow("Equalized Histogram",h.getHistogramImage(eq));
// Stretch the image ignoring bins with less than 5 pixels
cv::Mat str= h.stretch(image,5);
// Show the result
cv::namedWindow("Stretched Image");
cv::imshow("Stretched Image",str);
// Show the new histogram
cv::namedWindow("Stretched Histogram");
cv::imshow("Stretched Histogram",h.getHistogramImage(str));
// Create an image inversion table
//uchar lookup[256];
// Create lookup table
int dims[1]={256};
cv::MatND lookup(1,dims,CV_8U);
for (int i=0; i<256; i++) {
lookup.at<uchar>(i)=255-i;
}
// Apply lookup and display negative image
cv::namedWindow("Negative image");
cv::imshow("Negative image",h.applyLookUp(image,lookup));
cv::waitKey();
return 0;
}
