Java and time with Processing

Instead of using the Processing millis() function or the Java Timer class, we can also make use of the relatively new Instant and Duration classes in Java 8. Here is one simple example for demonstration.

The program uses 2 Instant variables: start, end. It computes the time duration between them using the Duration.between() function.

import java.time.Instant;
import java.time.Duration;
 
Instant start;
PFont font;
 
void setup() {
  size(640, 480);
  start = Instant.now();
  frameRate(30);
  font = loadFont("AmericanTypewriter-24.vlw");
  textFont(font, 24);
}
 
void draw() {
  background(0);
  Instant end = Instant.now();
  Duration elapsed = Duration.between(start, end);
  long ns = elapsed.toNanos();
  text(Long.toString(ns), 230, 230);
}

 

OpenCV 3.3 Java Build

The new release of OpenCV 3.3 is out now. I again prepare the Java build for the CVImage Processing library use. It also includes the optflow extra module for motion history applications. Here is the list of the 3 OpenCV releases.

The book Pro Processing for Images and Computer Vision with OpenCV will be released soon. It will include the detailed build instructions in multiple platforms.

TensorFlow in Processing

The Java binding for the Google Deep Learning library, TensorFlow is now available. The binary library files for version 1.1.0-rc1  are also available for download here. Below is the code for the Hello World program included in the distribution that I modified for Processing.
 

import org.tensorflow.Graph;
import org.tensorflow.Session;
import org.tensorflow.Tensor;
import org.tensorflow.TensorFlow;
 
Graph g1;
Output o1;
Output o2;
Output o3;
PFont font;
String res;
 
void setup() {
  size(640, 480);
  noLoop();
}
 
void draw() {
  background(0);
  Graph g = new Graph();
  String value = "Hello from " + TensorFlow.version();
  Tensor t = null;
  try {
    t = Tensor.create(value.getBytes("UTF-8"));
  } 
  catch (Exception e) {
    println(e.getMessage());
  }
  g.opBuilder("Const", "MyConst")
    .setAttr("dtype", t.dataType())
    .setAttr("value", t)
    .build();
  Session s = new Session(g);
  Tensor output = null;
  try {
    output = s.runner()
      .fetch("MyConst")
      .run()
      .get(0);
    println(new String(output.bytesValue(), "UTF-8"));
  } 
  catch (Exception e) {
    println(e.getMessage());
  }
}

Screen capture in Processing

This sketch demonstrates the use of the Robot class in Java to perform screen capture in Processing. It will create Jodi like effect with feedback in computer screen. Have fun with it.

Here are the codes. It makes use of the Robot class.

 
import java.awt.Robot;
import java.awt.image.BufferedImage;
import java.awt.Rectangle;
 
Robot robot;
 
void setup() {
  size(640, 480);
  try {
    robot = new Robot();
  } 
  catch (Exception e) {
    println(e.getMessage());
  }
}
 
void draw() {
  background(0);
  Rectangle r = new Rectangle(mouseX, mouseY, width, height);
  BufferedImage img1 = robot.createScreenCapture(r);
  PImage img2 = new PImage(img1);
  image(img2, 0, 0);
}

Save Processing screen as video with jCodec – new

It may not be easy for readers to get the old jcodec-0.1.5.jar for what I have done in the last post. I tried to work out for a newer solution but found that the latest version did change quite a lot. The latest jcodec source is 0.2.0. I built the latest two files for the Processing test

  • jcodec-0.2.0.jar
  • jcodec-javase-0.2.0.jar

You can download a compressed file of the code folder where you can drop and extract inside the Processing sketch folder. The Processing codes also change to reflect the class structure. Here it is.
 

// Save video file
import processing.video.*;
import org.jcodec.api.awt.AWTSequenceEncoder8Bit;
 
import java.awt.image.BufferedImage;
import java.io.File;
 
import org.slf4j.Logger;
import org.slf4j.LoggerFactory;
import org.apache.log4j.BasicConfigurator;
 
static Logger log;
Capture cap;
AWTSequenceEncoder8Bit enc;
String videoName;
String audioName;
boolean recording;
 
void setup() {
  size(640, 480);
  background(0);
  log = LoggerFactory.getLogger(this.getClass());
  BasicConfigurator.configure();
  cap = new Capture(this, width, height);
  videoName = "bear.mp4";
  recording = false;
  int fRate = 25;
  frameRate(fRate);
  cap.start();
  try {
    enc = AWTSequenceEncoder8Bit.createSequenceEncoder8Bit(new File(dataPath(videoName)), fRate);
  } 
  catch (IOException e) {
    e.printStackTrace();
  }
}
 
void draw() {
  image(cap, 0, 0);
  if (recording) {
    BufferedImage bi = (BufferedImage) cap.getNative();
    try {
      enc.encodeImage(bi);
    } 
    catch (IOException e) {
      e.printStackTrace();
    }
  }
}
 
void captureEvent(Capture c) {
  c.read();
}
 
void mousePressed() {
  recording = !recording;
  log.info("Recording : " + recording);
}
 
void keyPressed() {
  if (keyCode == 32) {
    try {
      enc.finish();
    } 
    catch (IOException e) {
      e.printStackTrace();
    }
  }
}

Save video in Processing with JCodec

As a side product of current research, I manage to save a Processing screen in an MP4 video file with the use of the JCodec library. Download the former jcodec-0.1.5.jar into the code folder of your Processing sketch. The simplest way is to use the SequenceEncoder class to add a BufferedImage to the MP4 video. Remember to finish the video file before ending.

The following example captures the live video stream from a webcam and outputs to an external MP4 file in the data folder. Use the mouse click to control the recording.

Here is the source code.

import processing.video.*;
import org.jcodec.api.SequenceEncoder;
import java.awt.image.BufferedImage;
import java.io.File;
 
Capture cap;
SequenceEncoder enc;
String videoName;
boolean recording;
 
void setup() {
  size(640, 480);
  background(0);
  cap = new Capture(this, width, height);
  videoName = "bear.mp4";
  recording = false;
  frameRate(25);
  smooth();
  noStroke();
  fill(255);
  cap.start();
  try {
    enc = new SequenceEncoder(new File(dataPath(videoName)));
  } 
  catch (IOException e) {
    e.printStackTrace();
  }
}
 
void draw() {
  image(cap, 0, 0);
  String fStr = nf(round(frameRate));
  text(fStr, 10, 20);
  if (recording) {
    BufferedImage bi = (BufferedImage) this.getGraphics().getImage();
    try {
      enc.encodeImage(bi);
    } 
    catch (IOException e) {
      e.printStackTrace();
    }
  }
}
 
void captureEvent(Capture c) {
  c.read();
}
 
void mousePressed() {
  recording = !recording;
  println("Recording : " + recording);
}
 
void keyPressed() {
  if (keyCode == 32) {
    try {
      enc.finish();
    } 
    catch (IOException e) {
      e.printStackTrace();
    }
  }
}

The program also uses the undocumented functions, getGraphics() and getImage() to obtain the raw image of the Processing sketch window.

Searching in Weka with Processing

Further to the last Weka example, I used the same CSV data file for neighbourhood search. By pressing the mouse button, it generated a random sequence of numbers between 1 to 4. The program used the sequence as an instance to match against the database from the CSV data file. The closet match will be shown together with the distance between the test case (random) and the closet match from the database.

A sample screenshot

 
Source codes

import weka.core.converters.CSVLoader;
import weka.core.Instances;
import weka.core.DenseInstance;
import weka.core.Instance;
import weka.core.neighboursearch.LinearNNSearch;
import java.util.Enumeration;
import java.io.File;
 
Instances data;
String csv;
LinearNNSearch lnn;
boolean search;
int idx;
float dist;
String testCase;
String matchCase;
String distance;
 
void setup() {
  size(500, 500);
  csv = "Testing.csv";
  try {
    loadData();
    buildModel();
  } 
  catch (Exception e) {
    e.printStackTrace();
  }
  search = false;
  idx = -1;
  dist = 0.0;
  testCase = "";
  matchCase = "";
  distance = "";
  fill(255);
}
 
void draw() {
  background(0);
  if (search) {
    text(testCase, 100, 100);
    text(matchCase, 100, 150);
    text(distance, 100, 200);
  }
}
 
void loadData() throws Exception {
  // load external CSV data file, without header row.
  CSVLoader loader = new CSVLoader();
  loader.setNoHeaderRowPresent(true);
  loader.setSource(new File(dataPath(csv)));
  data = loader.getDataSet();
  data.setClassIndex(0);
 
  println("Attributes : " + data.numAttributes());
  println("Instances : " + data.numInstances());
  println("Name : " + data.classAttribute().toString());
 
  Enumeration all = data.enumerateInstances();
  while (all.hasMoreElements()) {
    Instance single = (Instance) all.nextElement();
    println("Instance : " + (int) single.classValue() + ": " + single.toString());
  }
}
 
void buildModel() throws Exception {
  // Build linear search model.
  lnn = new LinearNNSearch(data);
  println("Model built ...");
}
 
void test() throws Exception {
  // Construct a test case and do a linear searching.
  double [] val = new double[data.numAttributes()];
  val[0] = 0;
  testCase  = "Test case:  ";
  matchCase = "Match case: ";
  distance  = "Distance:   ";
  for (int i=1; i<val.length; i++) {
    val[i] = floor(random(4))+1;
    testCase += (nf((float)val[i]) + ",");
  }
  testCase = testCase.substring(0, testCase.length()-1);
  DenseInstance x = new DenseInstance(1.0, val);
  x.setDataset(data);
  Instance c = lnn.nearestNeighbour(x);
  double [] tmp = lnn.getDistances();
  dist = (float) tmp[0];
  idx = (int) c.classValue();
  matchCase += data.instance(idx).toString();
  distance += nf(dist);
  saveFrame("weka####.png");
}
 
void mousePressed() {
  try {
    test();
  } 
  catch (Exception e) {
    e.printStackTrace();
  }
  search = true;
}

First trial of Weka in Processing

Instead of using the machine learning module (ML) of OpenCV, I also investigated another popular machine learning library for Java, Weka, from the University of Waikato. The first trial was to load an external CSV file into the proper data structure of the Weka library. The content of the CSV file is as follows. The first column will be the index of the records.

A,1,2,3,4
B,2,3,4,1
C,3,4,1,2
D,4,1,2,3
E,4,3,2,1

The first thing to do was to download the latest Weka distribution, currently 3.8 and placed the weka.jar file into the code folder of the Processing sketch.

The complete codes

import weka.core.converters.CSVLoader;
import weka.core.Instances;
import weka.core.Instance;
import java.util.Enumeration;
import java.io.File;
 
Instances data;
// Name of the CSV data file
String csv;
 
void setup() {
  size(600, 600);
  csv = "Testing.csv";
  try {
    loadData();
  } 
  catch (Exception e) {
    e.printStackTrace();
  }
  noLoop();
}
 
void draw() {
  background(0);
}
 
void loadData() throws Exception {
  CSVLoader loader = new CSVLoader();
  loader.setNoHeaderRowPresent(true);
  loader.setSource(new File(dataPath(csv)));
  data = loader.getDataSet();
  data.setClassIndex(0);
 
  println("Attributes : " + data.numAttributes());
  println("Instances : " + data.numInstances());
  println("Name : " + data.classAttribute().toString());
  // To scan through all the records of the CSV file
  Enumeration all = data.enumerateInstances();
  while (all.hasMoreElements()) {
    Instance rec = (Instance) all.nextElement();
    println("Instance : " + rec.classValue() + ": " + rec.toString());
  }
}

The console output

Attributes : 5
Instances : 5
Name : @attribute att1 {A,B,C,D,E}
Instance : 0.0: A,1,2,3,4
Instance : 1.0: B,2,3,4,1
Instance : 2.0: C,3,4,1,2
Instance : 3.0: D,4,1,2,3
Instance : 4.0: E,4,3,2,1

Artificial Neural Network in OpenCV with Processing

This is the first trial of the Machine Learning module, artificial neural network in OpenCV with Processing. I used the same OpenCV 3.1.0 Java built files. The program took the live stream video (PImage) from webcam and down-sampled to a grid of just 8 x 6 pixels of greyscale. It started by default in the training mode such that I could click on the left hand side of the screen for an image without a hat and on the right hand side for an image of myself wearing a hat. By pressing the SPACE key, it switched to the predict mode where by clicking the video would send the image to the neural network to see if I was wearing a hat or not. I used around 20 images for positive response and 20 images for negative response.

Here are the source codes.
 
The main program

import processing.video.*;
 
Capture cap;
boolean training;
ANN ann;
int w, h;
 
void setup() {
  size(640, 480);
  System.loadLibrary(Core.NATIVE_LIBRARY_NAME);
  println(Core.VERSION);
  cap = new Capture(this, width, height);
  cap.start();
  background(0);
  training = true;
  w = 8;
  h = 6;
  ann = new ANN(w*h);
}
 
void draw() {
  image(cap, 0, 0);
}
 
void captureEvent(Capture c) {
  c.read();
}
 
void mousePressed() {
  PImage img = new PImage(w, h, ARGB);
  img.copy(cap, 0, 0, width, height, 0, 0, w, h);
  img.updatePixels();
  img.filter(GRAY);
  String fName = "";
  float [] grey = getGrey(img);
  if (training) {
    float label = 0.0;
    if (mouseX < width/2) {
      label = 0.0;
    } else {
      label = 1.0;
    }
    ann.addData(grey, label);
    fName = (label == 0.0) ? "Negative" : "Positive";
    fName += nf(ann.getCount(), 4) + ".png";
    img.save(dataPath("") + "/" + fName);
  } else {
    float val = ann.predict(grey);
    float [] res = ann.getResult();
    val = res[0];
    float diff0 = abs(val);
    float diff1 = abs(val - 1);
    if (diff0 < diff1) {
      println("Without hat");
    } else {
      println("With hat");
    }
  }
}
 
float [] getGrey(PImage m) {
  float [] g = new float[w*h];
  if (m.width != w || m.height != h) 
    return g;
  for (int i=0; i<m.pixels.length; i++) {
    color c = m.pixels[i];
    g[i] = red(c) / 256.0;
  }
  return g;
}
 
void keyPressed() {
  if (keyCode == 32) {
    training = !training;
    if (!training) 
      ann.train();
  }
  println("Training status is " + training);
}

The Artificial Neural Network class

import org.opencv.core.Core;
import org.opencv.core.CvType;
import org.opencv.core.MatOfInt;
import org.opencv.core.MatOfFloat;
import org.opencv.ml.ANN_MLP;
 
public class ANN {
  final int MAX_DATA = 1000;
  ANN_MLP mlp;
  int input;
  int output;
  ArrayList<float []>train;
  ArrayList<Float>label;
  MatOfFloat result;
  String model;
 
  public ANN(int i) {
    input = i;
    output = 1;
    mlp = ANN_MLP.create();
    MatOfInt m1 = new MatOfInt(input, input/2, output);
    mlp.setLayerSizes(m1);
    mlp.setActivationFunction(ANN_MLP.SIGMOID_SYM);
    mlp.setTrainMethod(ANN_MLP.RPROP);
    result = new MatOfFloat();
    train = new ArrayList<float[]>();
    label = new ArrayList<Float>();
    model = dataPath("trainModel.xml");
  }
 
  void addData(float [] t, float l) {
    if (t.length != input) 
      return;
    if (train.size() >= MAX_DATA) 
      return;
    train.add(t);
    label.add(l);
  }
 
  int getCount() {
    return train.size();
  }
 
  void train() {
    float [][] tr = new float[train.size()][input];
    for (int i=0; i<train.size(); i++) {
      for (int j=0; j<train.get(i).length; j++) {
        tr[i][j] = train.get(i)[j];
      }
    }
    MatOfFloat response = new MatOfFloat();
    response.fromList(label);
    float [] trf = flatten(tr);
    Mat trainData = new Mat(train.size(), input, CvType.CV_32FC1);
    trainData.put(0, 0, trf);
    mlp.train(trainData, Ml.ROW_SAMPLE, response);
    trainData.release();
    response.release();
    train.clear();
    label.clear();
  }
 
  float predict(float [] i) {
    if (i.length != input) 
      return -1;
    Mat test = new Mat(1, input, CvType.CV_32FC1);
    test.put(0, 0, i);
    float val = mlp.predict(test, result, 0);
    return val;
  }
 
  float [] getResult() {
    float [] r = result.toArray();
    return r;
  }
 
  float [] flatten(float [][] a) {
    if (a.length == 0) 
      return new float[]{};
    int rCnt = a.length;
    int cCnt = a[0].length;
    float [] res = new float[rCnt*cCnt];
    int idx = 0;
    for (int r=0; r<rCnt; r++) {
      for (int c=0; c<cCnt; c++) {
        res[idx] = a[r][c];
        idx++;
      }
    }
    return res;
  }
}

Enumerate all files in the data folder of Processing

There are lots of ways to enumerate all the files inside the data folder of Processing sketch. Here are 2 of them. The first one uses the Java DirectoryStream class. The second one uses the static function walkFileTree from the Files class.
 
Example with DirectoryStream

try {
    DirectoryStream<Path> stream = Files.newDirectoryStream(Paths.get(dataPath(""))); 
    for (Path file : stream) {
      println(file.getFileName());
    }
  } 
  catch (IOException e) {
    e.printStackTrace();
}

Example with Files.walkFileTree

try {
    Files.walkFileTree(Paths.get(dataPath("")), new SimpleFileVisitor<Path>() {
      @Override
        public FileVisitResult visitFile(Path file, BasicFileAttributes attrs) throws IOException {
        println(file.getFileName());
        return FileVisitResult.CONTINUE;
      }
    }
    );
  } 
  catch (IOException e) {
    e.printStackTrace();
}