Changeset 33304 for other-projects/is-sheet-music-encore/trunk/image-identification-development/src/Main.java

Timestamp:

2019-07-06T15:45:02+12:00 (5 years ago)

Author:

cpb16

Message:

Backup for computer crash, only lost 5 lines of code in development section. They have been rewritten.

File:

• other-projects/is-sheet-music-encore/trunk/image-identification-development/src/Main.java (modified) (9 diffs)

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

@@ -10 +10 @@
 import java.io.File;
 import java.util.ArrayList;
+import java.util.Collection;
+import java.util.Collections;
 import javax.imageio.ImageIO;
 
@@ -17 +19 @@
 //https://www.wikihow.com/Calculate-Percentage-in-Java
 //https://riptutorial.com/opencv/example/21963/converting-an-mat-object-to-an-bufferedimage-object
+//https://beginnersbook.com/2013/12/java-arraylist-of-object-sort-example-comparable-and-comparator/
+//https://www.programiz.com/java-programming/examples/standard-deviation
+//https://www.geeksforgeeks.org/how-to-remove-duplicates-from-arraylist-in-java/
+
 
 
@@ -38 +44 @@
 public class Main {
 
+    //DEPENDENT FUNCTIONS AND CLASSES
+    static class StartAndEndPoint {
+        //PRIVATES
+        private Point _p1;
+        private Point _p2;
+        //CONSTRUCTOR
+        public StartAndEndPoint(Point p1, Point p2){
+            _p1 = p1;
+            _p2 = p2;
+        }
+        //GETTERS
+        public Point getP1(){
+            return _p1;
+        }
+        public Point getP2(){
+            return  _p2;
+        }
+        //SETTERS
+        public void setP1(Point p1){
+            _p1 = p1;
+        }
+        public void setP2(Point p2){
+            _p2 = p2;
+        }
+
+        //ToString
+        public  String toString(){
+            return "Start: " + _p1 + " End: " + _p2;
+        }
+        /*
+        //CompareToOverride
+        //Compares start point y co ordinates of input PointArray
+        //With this. start point y co ordinate
+        @Override
+        public double compareTo(StartAndEndPoint comparePointArray){
+            Point comparePoint = (comparePointArray.getP1());
+            return (this.getP1().y) - (comparePoint.y);
+        }
+        */
+    }
+    public static <T> ArrayList<T> removeDuplicates(ArrayList<T> list) {
+        //DIRECTLY COPIED//DIRECTLY COPIED//DIRECTLY COPIED//DIRECTLY COPIED//DIRECTLY COPIED//DIRECTLY COPIED
+        // Function to remove duplicates from an ArrayList
+        // Create a new ArrayList
+        ArrayList<T> newList = new ArrayList();
+        // Traverse through the first list
+        for (T element : list) {
+            // If this element is not present in newList
+            // then add it
+            if (!newList.contains(element)) {
+                newList.add(element);
+            }
+        }
+        // return the new list
+        return newList;
+        //DIRECTLY COPIED//DIRECTLY COPIED//DIRECTLY COPIED//DIRECTLY COPIED//DIRECTLY COPIED//DIRECTLY COPIED
+    }
+    public static double StandardDeviation(double parseArray[])
+    {
+        double mean;
+        double sum =0;
+        double standardDeviation = 0;
+        //calculate sum of array
+        for(int i =0; i > parseArray.length; i++){
+            sum += parseArray[i];
+        }
+        //calculate mean of array
+        mean = sum/parseArray.length;
+        //calculate SD of array
+        for(int j =0; j > parseArray.length; j++){
+            standardDeviation += Math.pow(parseArray[j]-mean, 2);
+        }
+        return Math.sqrt(standardDeviation/parseArray.length);
+    }
+
     //GLOBAL_CONSTANTS
     static int CLASSIFIER_HOUGHLINESP_MIN   = 10;
@@ -43 +124 @@
     static int HOUGHLINEP_THRESHOLD         = 10;
     static int MINLINECOUNT                 = 40;
-    static double MAXLINEGAP                = 4;
+    static double MAXLINEGAP                = 1;  //4
     static double SLOPEGRADIENT             = 0.02;
     //SHOULD TURN INTO ARGS
 
+    //CLASSIFYING FUNCTIONS
     private static  BufferedImage toBufferedImage(Mat mat){
         //MOSTLY COPY PASTE!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
@@ -100 +182 @@
         return false;
     }
-    private static boolean Classifier(int lineCount){
+    private static boolean ClassifierLineCount(int lineCount){
 
            if(lineCount>MINLINECOUNT){
@@ -109 +191 @@
             }
     }
+    private static ArrayList ClassifierLineClusterOLD(BufferedImage img){
+        ArrayList returnArray = new ArrayList();
+        try {
+
+            //IF THIS WORKS THEN IMPLEMENT A VERSION THAT USES POINTS from the draw line code.
+            //ALSO CHECK OUT K NEAREST NEIGHBOR?
+            //0xFFFF0000 = RED
+
+            //go thru every pixel until find red pixel
+            //get y pos of red pixel
+            //continue with loop until find another red pixel
+            //get y pos of red pixel
+            //compare y pos (if close together then continue loop) else break
+
+            int x = img.getWidth();
+            int y = img.getHeight();
+            int closeLineCount = 0;
+            ArrayList<Integer> redPixelYpos = new ArrayList<Integer>();
+
+
+
+            //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) {
+
+                        //Store y pos of red pixel if there is no duplicate
+                        if(!redPixelYpos.contains(i)){
+                            redPixelYpos.add(i);
+                            //System.out.println(i );
+                        }
+                    }
+                }
+            }
+            //Check if any of the lines found are close together and that there has been more than one line found
+            if(redPixelYpos.size()>1){
+                //go through list and compare every value
+                for(int i =0; i< redPixelYpos.size(); i++){
+                    //System.out.println("i: " +redPixelYpos.get(i));
+                    for(int j=0; j< redPixelYpos.size(); j++){
+                        //System.out.println("j: "+redPixelYpos.get(j));
+                        //Check if difference is less than 4 and the values are not duplicates.
+                        if(Math.abs(redPixelYpos.get(i) - redPixelYpos.get(j)) < 4 && !redPixelYpos.get(j).equals(redPixelYpos.get(i))){
+                            closeLineCount++;
+                        }
+                    }
+                }
+            }
+            int clusterCount = closeLineCount/4;
+
+            if(closeLineCount >= 4){
+                returnArray.add(true);
+                returnArray.add(closeLineCount);
+                returnArray.add(clusterCount);
+            }
+            else{
+                returnArray.add(false);
+                returnArray.add(closeLineCount);
+                returnArray.add(clusterCount);
+            }
+        }
+        catch (Exception e) {
+            System.err.println(e);
+        }
+    return returnArray;
+    }
+
+    private static ArrayList ClassifierLineCluster(ArrayList<StartAndEndPoint> linePointsArray){
+        ArrayList returnArray = new ArrayList();
+        ArrayList<Double> closeLineYPos = new ArrayList();
+        ArrayList<double[]> clusterArray = new ArrayList();
+        int clusterCount = 0;
+        try {
+            if(linePointsArray.size()> 1) {
+
+                //Display input array TESTING PURPOSES
+                for (int i = 0; i < linePointsArray.size(); i++) {
+                    System.out.println(linePointsArray.get(i).toString());
+                }
+
+
+                //Check if y points are close together
+                //go thru list and compare values against each other
+                for (int i = 0; i < linePointsArray.size(); i++){
+                    //System.out.println("i: "+ linePointsArray.get(i).getP1().y);
+                    for (int j = 0; j < linePointsArray.size(); j++) {
+                        //System.out.println("j: "+ linePointsArray.get(j).getP1().y);
+                        //Check if difference is less than 4 and the values are not duplicates.
+                        if(Math.abs(linePointsArray.get(j).getP1().y - linePointsArray.get(i).getP1().y) < 5){
+                            if(linePointsArray.get(j).getP1().y != linePointsArray.get(i).getP1().y){
+                                closeLineYPos.add(linePointsArray.get(j).getP1().y);
+                            }
+                        }
+                    }
+                }
+
+                System.out.println(" ");
+
+                //Have all y coordinates that close to each other.
+                //Now check if there are four of these are close to each other.
+                if(closeLineYPos.size() >= 4) {
+                    //Sort array and remove duplicates
+                    Collections.sort(closeLineYPos);
+                    closeLineYPos = removeDuplicates(closeLineYPos);
+
+
+                   /*for (double num : closeLineYPos){
+                      System.out.println(num);
+                   } */
+
+
+                    //Check first four items, traverse down a step {0,1,2,3} -> {1,2,3,4} -> {2,3,4,5}
+                    for(int i= 0; i< closeLineYPos.size(); i++){
+                        //If last comparator is within the array bounds.
+                        if(i + 3 == closeLineYPos.size()){
+                            break;
+                        }
+                        else{
+                            double[] tempArray = new double[4];
+                            tempArray[0] = closeLineYPos.get(i + 0);
+                            tempArray[1] = closeLineYPos.get(i + 1);
+                            tempArray[2] = closeLineYPos.get(i + 2);
+                            tempArray[3] = closeLineYPos.get(i + 3);
+                            System.out.println(tempArray[0] + " , " + tempArray[1] + " , " + tempArray[2] + " , " + tempArray[3]);
+                            //Check standard deviation
+                            if(StandardDeviation(tempArray) < 5){
+                                //Store array
+                                clusterArray.add(tempArray);
+                                //Check if more than one item in array
+                                if(clusterArray.size() > 1){
+                                    //check for duplicate yPos in stored arrays (tempArray)
+
+                                }
+
+
+                            }
+                        }
+                    }
+
+                    //for (double num : closeLineYPos){
+                    //    System.out.println(num);
+                    //}
+                }
+
+                //PROBLEM. Definition of cluster. Need to check if cluster.
+                //check if four lines are close to each other.(four for loops)
+                //  then store these four items in an array and add one to the counter.
+                //  (will need to check if found 5th item. - DONT NEED TO? Value gained from finding the 5th line? The staffline height?)
+                //
+
+
+
+                //SETUP RETURN ARRAY
+                if(closeLineYPos.size() >= 4){
+                    returnArray.add(true);
+                    returnArray.add(closeLineYPos.size());
+                    returnArray.add(clusterCount);
+                }
+                else{
+                    returnArray.add(false);
+                    returnArray.add(closeLineYPos.size());
+                    returnArray.add(clusterCount);
+                }
+            }
+        }
+        catch (Exception e) {
+            System.err.println(e);
+        }
+        return returnArray;
+    }
+
 
 
     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);
-
+            ArrayList<StartAndEndPoint> pointArrayList = new ArrayList<>();
 
             //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 = directory+"SheetMusic/coo.31924062612282-9.png";
             //String default_file ="TestImages/NotNot/mdp.39015080972303-3.png";
 
@@ -152 +400 @@
             Mat original = Imgcodecs.imread(filename, Imgcodecs.IMREAD_GRAYSCALE);
             // Edge detection
-            //01 CANNY
-            //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, 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);
-            //04 NO PROC
-            //edgesDetected = original.clone();
-            //05 OTSU THRESHOLD
-            //Imgproc.threshold(original, edgesDetected,0,255,Imgproc.THRESH_BINARY_INV+Imgproc.THRESH_OTSU);
-
-
-
-
+            Imgproc.adaptiveThreshold(original, edgesDetected,255, Imgproc.ADAPTIVE_THRESH_GAUSSIAN_C,Imgproc.THRESH_BINARY_INV, 15, 4);
 
             //Convert to RGB for future use
             Imgproc.cvtColor(edgesDetected, edgesDetectedRGB, Imgproc.COLOR_GRAY2BGR);
-            justEdges = edgesDetectedRGB.clone();//TESTING
-            edgesDetectedRGBProb = edgesDetectedRGB.clone();
-            edgeDoesntMakeSense = edgesDetectedRGB.clone();
 
             Mat linesP = new Mat(); // will hold the results of the detection
@@ -184 +411 @@
 
             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);
+            System.out.println("Before Gradient Filtering num lines: " + linesP.rows());
+
             // Draw the lines
             for (int x = 0; x < linesP.rows(); x++) {
                 double[] l = linesP.get(x, 0);
-
-                //Find angle that line is at
-                //double rho = linesP.get(x, 0)[0];
-                //double theta = linesP.get(x, 0)[1];
-                //double cosTheta = Math.cos(theta);
-                //double sinTheta = Math.sin(theta);
-                //double x0 = cosTheta * rho;
-                //double y0 = sinTheta * rho;
-                //double xpt1 = x0 + 1000 * (-sinTheta);
-                //double ypt1 = y0 + 1000 * (cosTheta);
-                //double xpt2 = x0 - 1000 * (-sinTheta);
-                //double ypt2 = y0 - 1000 * (cosTheta);
-                //double angle =  Math.atan2((float)ypt2 - (float)ypt1, (float)xpt2 - (float)xpt1)*(Math.PI);
-                //double testAngle = (ypt2 - ypt1)/(xpt2 - xpt1);
-
-                //New angles
                 Point p1 = new Point(l[0], l[1]);
                 Point p2 = new Point(l[2], l[3]);
                 double m = Math.abs(p2.y - p1.y)/(p2.x - p1.x);
-                //System.out.println(l[0]);
-                //System.out.println(l[1]);
-                //System.out.println(l[2]);
-                //System.out.println(l[3]);
-
 
                 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), 1, Imgproc.LINE_AA, 0);
+                    Imgproc.line(edgesDetectedRGB, p1, p2, new Scalar(0, 0, 255), 1, Imgproc.LINE_4, 0);
                     horizontalLineCount++;
+                    pointArrayList.add(new StartAndEndPoint(p1, p2));
                 }
 
@@ -226 +430 @@
             //Prove by finding number of points from one end to other:
             //Get width of image.
-            System.out.println("every matrix widths: "+edgesDetectedRGB.size().width);
             File filenameTest = new File("TestImages/NotSheetMusic02.png");
             BufferedImage i = ImageIO.read(filenameTest);
-            System.out.println("input image width: "+ i.getWidth());
-
             BufferedImage toBeClassifiedImg = toBufferedImage(edgesDetectedRGB);
-            System.out.println("Result: " + Classifier(horizontalLineCount));
-
-
-            System.out.println();
+
+            System.out.println("LINE COUNT RESULT:   " +  ClassifierLineCount(horizontalLineCount) + '\t' +"LineCount: " + horizontalLineCount); //COUNT OF LINES CLASSIFICATION
+            System.out.println("LINE CLUSTER RESULT: " +  ClassifierLineClusterOLD(toBeClassifiedImg).get(0) + '\t' + "LinesFound: " + ClassifierLineClusterOLD(toBeClassifiedImg).get(1) + '\t' + "ClustersFound: " + ClassifierLineClusterOLD(toBeClassifiedImg).get(2));
+            //System.out.println("NEW CLUSTER RESULTS: " +  ClassifierLineCluster(pointArrayList).get(0) + '\t' + "LinesFound: " + ClassifierLineCluster(pointArrayList).get(1) + '\t' + "ClustersFound: " + ClassifierLineCluster(pointArrayList).get(2));
+            System.out.println(ClassifierLineCluster(pointArrayList));
+
             //Display Results
-            HighGui.imshow("Source", original);
+            //HighGui.imshow("Source", original);
             //HighGui.imshow("Just Edges", justEdges); //TESTING
             HighGui.imshow("Detected Lines (in red) - positive", edgesDetectedRGB);