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 java.io.BufferedWriter;
import java.io.FileWriter;
import javax.imageio.ImageIO;
import java.util.logging.Logger;

//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
//https://stackoverflow.com/questions/15758685/how-to-write-logs-in-text-file-when-using-java-util-logging-logger
//https://stackoverflow.com/questions/9961292/write-to-text-file-without-overwriting-in-java


//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 javaImageClassifier{

    public static void main(String[] args) {
    	
        try {
			if (args.length != 2) {
			System.out.println("Usage: imageClassifier <inputFilename> <classifierType>");
			}
			else {
				String imageFilename = args[0];
				int classifierType = Integer.parseInt(args[1]);
				
				String result = null;
				//Execute classifierType defined from arguement
				switch(classifierType){
				case 1:
					result = classifier01(imageFilename);
					break;
				case 2:
					System.out.println("unknown");
					break;
				}
			
				//Write output to disc
				File log = new File("log.txt");
				FileWriter fileWriter = new FileWriter(log, true);
				BufferedWriter bw = new BufferedWriter(fileWriter);
				bw.write(result + classifierType + '\n');
				bw.close();

				
			}
        }
        catch(Exception e){
            System.err.println(e);
        }
    }  
    private static String classifier01(String filename){
	System.loadLibrary(Core.NATIVE_LIBRARY_NAME);
	Boolean isSheetMusic = null;
	try{
	    //Variables
	    Mat edgesDetected = new Mat();
	    Mat edgesDetectedRGB = new Mat();
	    Mat edgesDetectedRGBProb;
	    // 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);
	    // 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(edgesDetectedRGB, 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(edgesDetectedRGB);
	    //Calculate if its sheet music or not
	    isSheetMusic = Classifier(toBeClassifiedImg);  
	            
        //Save Processed Image
	    String processedFile = filename;
	    if (isSheetMusic == true) {
		processedFile = "proc_T_"+filename;
	    }else {
		processedFile = "proc_F_"+filename;
	    }
	    imwrite(processedFile, edgesDetectedRGB);
	    
	     //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);
        }
        if (isSheetMusic == true){    
    		return (1 + "\t" + "Filename: " + filename + "  Status: " + isSheetMusic  +"\t" );}
    	
    	else{
    		return (0 + "\t" + "Filename: " + filename + "  Status: " + isSheetMusic  +"\t" );}
    }
    
    private static boolean Classifier(BufferedImage img){
    System.loadLibrary(Core.NATIVE_LIBRARY_NAME);
    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;
    }

        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;
    }

 
}