source: other-projects/is-sheet-music-encore/trunk/image-identification-development/src/Main.java@ 33097

import org.opencv.core.*;
import org.opencv.core.Point;
import org.opencv.highgui.HighGui;
import org.opencv.imgcodecs.Imgcodecs;
import org.opencv.imgproc.Imgproc;
import static org.opencv.imgcodecs.Imgcodecs.imwrite;
import java.awt.image.BufferedImage;
import java.awt.image.DataBufferByte;
import java.io.File;
import javax.imageio.ImageIO;

//REFERENCES:
//https://docs.opencv.org/3.4.3/d9/db0/tutorial_hough_lines.
//https://stackoverflow.com/questions/43443309/count-red-pixel-in-a-given-image
//https://www.wikihow.com/Calculate-Percentage-in-Java
//https://riptutorial.com/opencv/example/21963/converting-an-mat-object-to-an-bufferedimage-object



//GOAL for 21st


//Classifier 01
//Have args so can call "java image-identification-classifier01 XX XX"
//args can be parameters in algorthim such as threshold or theta?
//Run on 5000 images.
//Record success rates
//All done with makefile


//But first understand houghline transform
//Know what the algorithm being used is doing.
//MAke constants for this classifier
//Make java be able to run on CMD line

public class Main {

    //GLOBAL_CONSTANTS
    //SHOULD TURN INTO ARGS

    private static  BufferedImage toBufferedImage(Mat mat){
        //MOSTLY COPY PASTE!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
        //MOSTLY COPY PASTE!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
        //https://riptutorial.com/opencv/example/21963/converting-an-mat-object-to-an-bufferedimage-object
        try{
            int type = BufferedImage.TYPE_3BYTE_BGR;
            int bufferSize = mat.channels() * mat.cols() * mat.rows();
            byte[] b = new byte[bufferSize];
            //get all the pixels
            mat.get(0, 0, b);
            BufferedImage image = new BufferedImage(mat.cols(), mat.rows(), type);
            final byte[] targetPixels = ((DataBufferByte) image.getRaster().getDataBuffer()).getData();
            System.arraycopy(b, 0, targetPixels, 0, b.length);
            return image;
        }
        catch(Exception e){
            System.err.println(e);
        }
        return null;
    }

    private static boolean Classifier(BufferedImage img){
        try {
            //Read file
            //BufferedImage img = ImageIO.read(new File(processedFile));
            int x = img.getWidth();
            int y = img.getHeight();
            int pixelCount  = 0;
            int redCount    = 0;
            float percentage = 0;

            //Go Thru every pixel
            for(int i=0; i < y; i++){
                for(int j=0;j < x; j++){
                    //Get value for current pixels RGB value
                    int currPixelRGB = img.getRGB(j, i);
                    //Check if pixel is red (hex value of red)
                    if(currPixelRGB == 0xFFFF0000){
                        redCount++;
                    }
                    pixelCount++;
                }
            }
            //Calculate percentage of Red in image
            percentage = ((float)redCount/(float)pixelCount)*(float)100;
            //If more than %10 and less than %50 then its sheet music!
            if(percentage > 10 && percentage < 50){ //MAKE THESE CONSTANTS!!
                return true;}
        }
        catch (Exception e) {
            System.err.println(e);
        }
        return false;
    }

    public static void main(String[] args) {
        System.loadLibrary(Core.NATIVE_LIBRARY_NAME);
        try {
            //Variables
            Mat edgesDetected = new Mat();
            Mat edgesDetectedRGB = new Mat();
            Mat edgesDetectedRGBProb;
            Mat justEdges; //TESTING
            String default_file = "Test02.png";
            String filename = ((args.length > 0) ? args[0] : default_file);

            // Load an image
            Mat original = Imgcodecs.imread(filename, Imgcodecs.IMREAD_GRAYSCALE);
            // Edge detection
            Imgproc.Canny(original, edgesDetected, 50, 200, 3, false);
            //Copy edges to the images that will display the results in BGR
            Imgproc.cvtColor(edgesDetected, edgesDetectedRGB, Imgproc.COLOR_GRAY2BGR);
            edgesDetectedRGBProb = edgesDetectedRGB.clone();

            justEdges = edgesDetectedRGBProb.clone();//TESTING

            // Standard Hough Line Transform
            Mat lines = new Mat(); // will hold the results of the detection
            //(edgeDetectedImage, outputOfDetection(r,Ξ), resolution of r, resolution of Ξ, threshold (minimum num of intersections)
            Imgproc.HoughLines(edgesDetected, lines, 1.4, Math.PI / 180, 500); // runs the actual detection

            // Draw the lines
            //LOOK OVER THIS AGAIN THE 1000 might be image height?
            //THRESHOLD should be changed, based on img dimensions?

            for (int x = 0; x < lines.rows(); x++) {
                double rho = lines.get(x, 0)[0],
                        theta = lines.get(x, 0)[1];
                //CONVERT to Cartisean coord
                double a = Math.cos(theta), b = Math.sin(theta);
                double x0 = a * rho, y0 = b * rho;

                Point pt1 = new Point(Math.round(x0 + 1000 * (-b)), Math.round(y0 + 1000 * (a)));
                Point pt2 = new Point(Math.round(x0 - 1000 * (-b)), Math.round(y0 - 1000 * (a)));
                Imgproc.line(edgesDetectedRGB, pt1, pt2, new Scalar(0, 0, 255), 3, Imgproc.LINE_AA, 0);
            }

            // Probabilistic Line Transform
            Mat linesP = new Mat(); // will hold the results of the detection
            Imgproc.HoughLinesP(edgesDetected, linesP, 1, Math.PI / 180, 50, 50, 10); // runs the actual detection
            // Draw the lines
            for (int x = 0; x < linesP.rows(); x++) {
                double[] l = linesP.get(x, 0);
                Imgproc.line(edgesDetectedRGBProb, new Point(l[0], l[1]), new Point(l[2], l[3]), new Scalar(0, 0, 255), 3, Imgproc.LINE_AA, 0);
            }

            //Convert MAT into a BufferedImage
            BufferedImage toBeClassifiedImg = toBufferedImage(edgesDetectedRGBProb);

            //Calculate if its sheet music or not
            Boolean isSheetMusic = Classifier(toBeClassifiedImg);
            System.out.println("Filename: " + filename + "  Status: " + isSheetMusic);

            //Save Processed Image
            String processedFile = null;
            if (isSheetMusic == true) {
                //NEED FIGURE OUT HOW RUN IN SCRIPT. THEN USE ARGS as filename + "HoughLineP + "png"
                processedFile = "SheetMusic/Test_HoughLineP.png";
            }else {
                processedFile = "Test_HoughLineP.png";
            }
            imwrite(processedFile, edgesDetectedRGBProb);

            //Display Results
            //HighGui.imshow("Source", original);
            HighGui.imshow("Just Edges", justEdges); //TESTING
            HighGui.imshow("Detected Lines (in red) - Standard Hough Line Transform", edgesDetectedRGB);
            //HighGui.imshow("Detected Lines (in red) - Probabilistic Line Transform", edgesDetectedRGBProb);

            // Wait and Exit
            HighGui.waitKey();
            System.exit(0);
        }
        catch(Exception e){
            System.err.println(e);
        }
    }
}