// Daniel Shiffman
// The Nature of Code
// http://www.shiffman.net/teaching/nature

// Using a Neural Network to recognize pixels of an input image
// Neural network code is all in the "code" folder
import processing.video.Capture;

import nn.*;
import java.io.File;

/////camera
Capture video;// regular processing libary
int threshold = 130;
int minY;
PImage backgroundImage;
boolean lookForChange = false;
int y;
int x;
boolean startGame = false;
boolean startRecord = false;
//////////



// The pixel data from each image file will be our training set
ArrayList pixelData;

// The size of our image data
int w = 0;
int h = 0;


// Here are the training images


// The Neural Network
Network nn;         

// How many training cycles have we done
int trainingCount;  

// We will train 5,000 iterations
// We will train 5,000 iterations


// A submit button
Button submit;
Button record;

// The points a user has drawn
ArrayList points;
int maxTrainingIterations = 0;
// The input image from the user
PImage inputImage;

// Some booleans to keep track of what is going on
boolean welcome = true;
boolean training = false;
boolean trainingComplete = false;
boolean guessing = false;

PFont f;
int total = 0;
PImage[] trainingImages;
// What is the neural network's guess
float guess = 0;

void setup() {
  size(640,480);
  background(255);
  points = new ArrayList();

 w = width/100;
 h = height/100;
File folder = new File(sketchPath("numbers"));
File[] filelist = folder.listFiles();
println(filelist.length);
total = filelist.length; 
 trainingImages = new PImage[total];
maxTrainingIterations = 15000*total;
  // Load all the images
  loadTrainingImages();

  // Setup network
  nn = new Network(w*h,16);
  f = createFont("Georgia",16,true);

  // Make a blank input image
  inputImage = createImage(w,h,RGB);

  // Submit button
  submit = new Button(100,440,160,25, "submit");
record = new Button(270, 440, 160, 25, "record");


  //////camera


  video = new Capture(this, 640, 480);
  backgroundImage = new PImage(video.width, video.height, RGB);

  ///////////

}

void draw() {
  background(255);
  if (video.available()) {
   // background(0);
    video.read();

    minY = height;

    loadPixels();
    for (int row = 0; row < video.height; row++) {
      for (int col = 0; col < video.width; col++) {
        int offset = row * width + col;
        int fgColor = video.pixels[offset];
        int bgColor = backgroundImage.pixels[offset];
        float fgR = red(fgColor);
        float fgG = green(fgColor);
        float fgB = blue(fgColor);

        float bgR = red(bgColor);
        float bgG = green(bgColor);
        float bgB = blue(bgColor);
        float diff = dist(fgR, fgG, fgB, bgR, bgG, bgB);
        if (diff > threshold) {
         // pixels[offset] = fgColor;
          y = offset/width;
        }
        if(minY> y){
          minY = y;
          //println(minY);
          x = offset%width;
        }
      }
    }
    //if (lookForChange) //grabBackground();
    updatePixels();
    ellipse(x,minY,10,10);
  }
  

  drawing();


}




void grabBackground(){

  backgroundImage.copy(video,0,0,video.width,video.height,0,0,video.width,video.height);
  backgroundImage.updatePixels();

}


void drawing(){
 // Welcome screen
  if (welcome) {
    welcome();
    // Training screen
  } 
  else if (training) {
    if (!trainingComplete) {
      train();
    }
    showTraining();
    // Guessing screen
  } 
  else {
    
    userDraw();
    guess();
  }


  // Draw the guessing image and training images
  //noFill();
  //stroke(0);
 // strokeWeight(1);
 // image(inputImage,0,0);
 // rect(0,0,w,h);
  //for (int i = 0; i < trainingImages.length; i++) {
    //image(trainingImages[i],(i+1)*w,0);
   // rect((i+1)*w,0,w,h);
  //}
 
}