Changeset 33243 for other-projects/is-sheet-music-encore/trunk/image-identification-development/src/Main.java

Timestamp:

2019-06-27T15:19:39+12:00 (5 years ago)

Author:

cpb16

Message:

Had break through with the refined houghlinesP algorithm overall accurarcy rate of 93%

File:

• other-projects/is-sheet-music-encore/trunk/image-identification-development/src/Main.java (modified) (10 diffs)

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

@@ -4 +4 @@
 import org.opencv.imgcodecs.Imgcodecs;
 import org.opencv.imgproc.Imgproc;
+import org.opencv.photo.Photo;
 import static org.opencv.imgcodecs.Imgcodecs.imwrite;
 import java.awt.image.BufferedImage;
 import java.awt.image.DataBufferByte;
 import java.io.File;
+import java.util.ArrayList;
 import javax.imageio.ImageIO;
 
@@ -37 +39 @@
 
     //GLOBAL_CONSTANTS
+    static int CLASSIFIER_HOUGHLINESP_MIN   = 10;
+    static int CLASSIFIER_HOUGHLINESP_MAX   = 65;
+    static int HOUGHLINEP_THRESHOLD         = 10;
+    static int MINLINECOUNT                 = 40;
+    static double MAXLINEGAP                = 4;
+    static double SLOPEGRADIENT             = 0.02;
     //SHOULD TURN INTO ARGS
 
@@ -59 +67 @@
         return null;
     }
-
-    private static boolean Classifier(BufferedImage img){
+    private static boolean ClassifierPixelCount(BufferedImage img){
         try {
             //Read file
@@ -85 +92 @@
             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!!
+            if(percentage > CLASSIFIER_HOUGHLINESP_MIN && percentage < CLASSIFIER_HOUGHLINESP_MAX){ //MAKE THESE CONSTANTS!!
                 return true;}
         }
@@ -93 +100 @@
         return false;
     }
+    private static boolean Classifier(int lineCount){
+
+           if(lineCount>MINLINECOUNT){
+                return true;
+            }
+            else{
+                return false;
+            }
+    }
+
 
     public static void main(String[] args) {
         System.loadLibrary(Core.NATIVE_LIBRARY_NAME);
         try {
+            //temp array for terminalversion
+
+            ArrayList returnArray = new ArrayList();
+            returnArray.add(true);
+            returnArray.add(10);
+
+
             //Variables
             Mat edgesDetected = new Mat();
             Mat edgesDetectedRGB = new Mat();
+            Mat edgesExtra = new Mat();
             Mat edgesDetectedRGBProb;
             Mat edgeDoesntMakeSense;
             Mat justEdges; //TESTING
+
+            String directory = "/Scratch/cpb16/is-sheet-music-encore/download-images/MU/";
+            //!!!!!!!!!!!!!!!!!!!!!!!!!!!NOT!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
+            //mdp.39015097852365-2.png 176 lines    Contents page.
+            //mdp.39015097852555-3.png 76  lines
+            String default_file = directory+"SheetMusic/mdp.39015080972303-3.png";
+            //String default_file ="TestImages/NotNot/mdp.39015080972303-3.png";
+
+
+            //System.out.println(default_file);
             //String default_file = "TestImages/NotSheetMusic01.png";
-            String default_file = "TestImages/NotSheetMusic02.png";
+            //String default_file = "TestImages/NotSheetMusic02.png";
             //String default_file = "TestImages/SheetMusic01.png";
             //String default_file = "TestImages/SheetMusic02.png";
             //String default_file = "TestImages/vLine.png";
             String filename = ((args.length > 0) ? args[0] : default_file);
+            File file = new File(filename);
+            if(!file.exists()){System.err.println("Image not found: "+ filename);}
+
+            int horizontalLineCount =0;
 
             // Load an image
@@ -114 +153 @@
             // Edge detection
             //01 CANNY
-            Imgproc.Canny(original, edgesDetected, 50, 200, 3, false);
+            //Imgproc.Canny(original, edgesDetected, 50, 200, 3, false);
+            //Imgproc.Canny(original, edgesDetected,0, 100, 3, false );
             //Imgproc.Canny(original, edgesDetected,80, 120);
             //02 BINARYINV
-            //Imgproc.adaptiveThreshold(original, edgesDetected,255, Imgproc.ADAPTIVE_THRESH_GAUSSIAN_C,Imgproc.THRESH_BINARY_INV,15, 2);
+            Imgproc.adaptiveThreshold(original, edgesDetected,255, Imgproc.ADAPTIVE_THRESH_GAUSSIAN_C,Imgproc.THRESH_BINARY_INV,15, 2);
+
+            //Imgproc.adaptiveThreshold(original, edgesExtra,255, Imgproc.ADAPTIVE_THRESH_GAUSSIAN_C,Imgproc.THRESH_BINARY_INV,15, 2);
+            //Imgproc.medianBlur(edgesExtra, edgesDetected, 3);
             //03 BINARY
             //Imgproc.adaptiveThreshold(original, edgesDetected,255, Imgproc.ADAPTIVE_THRESH_GAUSSIAN_C,Imgproc.THRESH_BINARY,15, 2);
@@ -127 +170 @@
 
 
+
+
             //Convert to RGB for future use
             Imgproc.cvtColor(edgesDetected, edgesDetectedRGB, Imgproc.COLOR_GRAY2BGR);
@@ -136 +181 @@
             //(edgeDetectedImage, outputOfDetection(r,Ξ), resolution of rho, resolution of theta, threshold (minimum num of intersections)
 
-            double minLineLength = edgesDetectedRGB.size().width/4;
-            Imgproc.HoughLinesP(edgesDetected, linesP, 1, Math.PI / 180, 50, minLineLength, 10); // runs the actual detection
-
-
+            double minLineLength = edgesDetectedRGB.size().width/8;
+
+            Imgproc.HoughLinesP(edgesDetected, linesP, 1, Math.PI / 720, HOUGHLINEP_THRESHOLD, minLineLength,MAXLINEGAP); // runs the actual detection
+            //Imgproc.HoughLinesP(edgesDetected, linesP, 1, Math.PI / 180, HOUGHLINEP_THRESHOLD, minLineLength,MAXLINEGAP); // runs the actual detection
+            System.out.println("Before Graident Filtering num lines: " + linesP.rows());
 
             //Imgproc.HoughLinesP(edgesDetected,linesP,1,Math.PI/2, 50, 80, 5);
@@ -170 +216 @@
 
 
-
-
-
-
-                if(m<=0.1) {
+                if(m<=SLOPEGRADIENT) {
                     //System.out.println("m: " + m);
-                    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);
+                    Imgproc.line(edgesDetectedRGB, new Point(l[0], l[1]), new Point(l[2], l[3]), new Scalar(0, 0, 255), 1, Imgproc.LINE_AA, 0);
+                    horizontalLineCount++;
                 }
 
@@ -189 +232 @@
 
             BufferedImage toBeClassifiedImg = toBufferedImage(edgesDetectedRGB);
-            System.out.println("Result: " + Classifier(toBeClassifiedImg));
-
+            System.out.println("Result: " + Classifier(horizontalLineCount));
+
+
+            System.out.println();
             //Display Results
-            //HighGui.imshow("Source", original);
+            HighGui.imshow("Source", original);
             //HighGui.imshow("Just Edges", justEdges); //TESTING
             HighGui.imshow("Detected Lines (in red) - positive", edgesDetectedRGB);