[33097] | 1 | import org.opencv.core.*;
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| 2 | import org.opencv.core.Point;
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| 3 | import org.opencv.highgui.HighGui;
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| 4 | import org.opencv.imgcodecs.Imgcodecs;
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| 5 | import org.opencv.imgproc.Imgproc;
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| 6 | import static org.opencv.imgcodecs.Imgcodecs.imwrite;
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| 7 | import java.awt.image.BufferedImage;
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| 8 | import java.awt.image.DataBufferByte;
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| 9 | import java.io.File;
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| 10 | import javax.imageio.ImageIO;
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| 11 |
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| 12 | //REFERENCES:
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| 13 | //https://docs.opencv.org/3.4.3/d9/db0/tutorial_hough_lines.
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| 14 | //https://stackoverflow.com/questions/43443309/count-red-pixel-in-a-given-image
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| 15 | //https://www.wikihow.com/Calculate-Percentage-in-Java
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| 16 | //https://riptutorial.com/opencv/example/21963/converting-an-mat-object-to-an-bufferedimage-object
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| 17 |
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| 18 |
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| 19 |
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| 20 | //GOAL for 21st
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| 21 |
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| 22 |
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| 23 | //Classifier 01
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| 24 | //Have args so can call "java image-identification-classifier01 XX XX"
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| 25 | //args can be parameters in algorthim such as threshold or theta?
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| 26 | //Run on 5000 images.
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| 27 | //Record success rates
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| 28 | //All done with makefile
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| 29 |
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| 30 |
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| 31 | //But first understand houghline transform
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| 32 | //Know what the algorithm being used is doing.
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| 33 | //MAke constants for this classifier
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| 34 | //Make java be able to run on CMD line
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| 35 |
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| 36 | public class terminalImageClassifier {
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| 37 |
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| 38 | //GLOBAL_CONSTANTS
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| 39 | //SHOULD TURN INTO ARGS
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| 40 |
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| 41 | private static BufferedImage toBufferedImage(Mat mat){
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| 42 | //MOSTLY COPY PASTE!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
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| 43 | //MOSTLY COPY PASTE!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
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| 44 | //https://riptutorial.com/opencv/example/21963/converting-an-mat-object-to-an-bufferedimage-object
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| 45 | try{
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| 46 | int type = BufferedImage.TYPE_3BYTE_BGR;
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| 47 | int bufferSize = mat.channels() * mat.cols() * mat.rows();
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| 48 | byte[] b = new byte[bufferSize];
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| 49 | //get all the pixels
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| 50 | mat.get(0, 0, b);
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| 51 | BufferedImage image = new BufferedImage(mat.cols(), mat.rows(), type);
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| 52 | final byte[] targetPixels = ((DataBufferByte) image.getRaster().getDataBuffer()).getData();
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| 53 | System.arraycopy(b, 0, targetPixels, 0, b.length);
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| 54 | return image;
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| 55 | }
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| 56 | catch(Exception e){
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| 57 | System.err.println(e);
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| 58 | }
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| 59 | return null;
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| 60 | }
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| 61 |
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| 62 | private static boolean Classifier(BufferedImage img){
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| 63 | try {
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| 64 | //Read file
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| 65 | //BufferedImage img = ImageIO.read(new File(processedFile));
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| 66 | int x = img.getWidth();
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| 67 | int y = img.getHeight();
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| 68 | int pixelCount = 0;
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| 69 | int redCount = 0;
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| 70 | float percentage = 0;
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| 71 |
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| 72 | //Go Thru every pixel
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| 73 | for(int i=0; i < y; i++){
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| 74 | for(int j=0;j < x; j++){
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| 75 | //Get value for current pixels RGB value
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| 76 | int currPixelRGB = img.getRGB(j, i);
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| 77 | //Check if pixel is red (hex value of red)
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| 78 | if(currPixelRGB == 0xFFFF0000){
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| 79 | redCount++;
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| 80 | }
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| 81 | pixelCount++;
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| 82 | }
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| 83 | }
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| 84 | //Calculate percentage of Red in image
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| 85 | percentage = ((float)redCount/(float)pixelCount)*(float)100;
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| 86 | //If more than %10 and less than %50 then its sheet music!
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| 87 | if(percentage > 10 && percentage < 50){ //MAKE THESE CONSTANTS!!
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| 88 | return true;}
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| 89 | }
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| 90 | catch (Exception e) {
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| 91 | System.err.println(e);
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| 92 | }
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| 93 | return false;
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| 94 | }
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| 95 |
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| 96 | public static void main(String[] args) {
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| 97 | System.loadLibrary(Core.NATIVE_LIBRARY_NAME);
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| 98 | try {
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| 99 | //Variables
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| 100 | Mat edgesDetected = new Mat();
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| 101 | Mat edgesDetectedRGB = new Mat();
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| 102 | Mat edgesDetectedRGBProb;
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| 103 | Mat justEdges; //TESTING
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| 104 | String default_file = "Test02.png";
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| 105 | String filename = ((args.length > 0) ? args[0] : default_file);
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| 106 |
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| 107 | // Load an image
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| 108 | Mat original = Imgcodecs.imread(filename, Imgcodecs.IMREAD_GRAYSCALE);
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| 109 | // Edge detection
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| 110 | Imgproc.Canny(original, edgesDetected, 50, 200, 3, false);
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| 111 | //Copy edges to the images that will display the results in BGR
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| 112 | Imgproc.cvtColor(edgesDetected, edgesDetectedRGB, Imgproc.COLOR_GRAY2BGR);
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| 113 | edgesDetectedRGBProb = edgesDetectedRGB.clone();
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| 114 |
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| 115 | justEdges = edgesDetectedRGBProb.clone();//TESTING
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| 116 |
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| 117 | // Standard Hough Line Transform
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| 118 | Mat lines = new Mat(); // will hold the results of the detection
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| 119 | //(edgeDetectedImage, outputOfDetection(r,Ξ), resolution of r, resolution of Ξ, threshold (minimum num of intersections)
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| 120 | Imgproc.HoughLines(edgesDetected, lines, 1.4, Math.PI / 180, 500); // runs the actual detection
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| 121 |
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| 122 | // Draw the lines
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| 123 | //LOOK OVER THIS AGAIN THE 1000 might be image height?
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| 124 | //THRESHOLD should be changed, based on img dimensions?
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| 125 |
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| 126 | for (int x = 0; x < lines.rows(); x++) {
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| 127 | double rho = lines.get(x, 0)[0],
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| 128 | theta = lines.get(x, 0)[1];
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| 129 | //CONVERT to Cartisean coord
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| 130 | double a = Math.cos(theta), b = Math.sin(theta);
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| 131 | double x0 = a * rho, y0 = b * rho;
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| 132 |
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| 133 | Point pt1 = new Point(Math.round(x0 + 1000 * (-b)), Math.round(y0 + 1000 * (a)));
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| 134 | Point pt2 = new Point(Math.round(x0 - 1000 * (-b)), Math.round(y0 - 1000 * (a)));
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| 135 | Imgproc.line(edgesDetectedRGB, pt1, pt2, new Scalar(0, 0, 255), 3, Imgproc.LINE_AA, 0);
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| 136 | }
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| 137 |
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| 138 | // Probabilistic Line Transform
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| 139 | Mat linesP = new Mat(); // will hold the results of the detection
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| 140 | Imgproc.HoughLinesP(edgesDetected, linesP, 1, Math.PI / 180, 50, 50, 10); // runs the actual detection
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| 141 | // Draw the lines
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| 142 | for (int x = 0; x < linesP.rows(); x++) {
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| 143 | double[] l = linesP.get(x, 0);
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| 144 | 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);
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| 145 | }
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| 146 |
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| 147 | //Convert MAT into a BufferedImage
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| 148 | BufferedImage toBeClassifiedImg = toBufferedImage(edgesDetectedRGBProb);
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| 149 |
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| 150 | //Calculate if its sheet music or not
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| 151 | Boolean isSheetMusic = Classifier(toBeClassifiedImg);
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| 152 | System.out.println("Filename: " + filename + " Status: " + isSheetMusic);
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| 153 |
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| 154 | //Save Processed Image
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| 155 | String processedFile = null;
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| 156 | if (isSheetMusic == true) {
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| 157 | //NEED FIGURE OUT HOW RUN IN SCRIPT. THEN USE ARGS as filename + "HoughLineP + "png"
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| 158 | processedFile = "SheetMusic/Test_HoughLineP.png";
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| 159 | }else {
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| 160 | processedFile = "Test_HoughLineP.png";
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| 161 | }
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| 162 | imwrite(processedFile, edgesDetectedRGBProb);
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| 163 |
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| 164 | //Display Results
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| 165 | //HighGui.imshow("Source", original);
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| 166 | HighGui.imshow("Just Edges", justEdges); //TESTING
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| 167 | HighGui.imshow("Detected Lines (in red) - Standard Hough Line Transform", edgesDetectedRGB);
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| 168 | //HighGui.imshow("Detected Lines (in red) - Probabilistic Line Transform", edgesDetectedRGBProb);
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| 169 |
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| 170 | // Wait and Exit
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| 171 | HighGui.waitKey();
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| 172 | System.exit(0);
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| 173 | }
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| 174 | catch(Exception e){
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| 175 | System.err.println(e);
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| 176 | }
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| 177 | }
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| 178 | }
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