var fs = require("fs"),

path = require("path"),

Canvas = require("canvas"),

utils = require("../utils")

features = require("./features");

exports.collectData = collectData;

exports.extractSamples = extractSamples;

/*

* Collect the canvas representations of the images in the positive and

* negative directories and return

* an array of objects that look like:

* {

* input: <Array of floats> from image features

* output: [0,1] (depending if it's a cat or not)

* file: 'test.jpg'

* }

*/

function collectData(pos, neg, samples, posLimit, negLimit, params) {

// number of samples to extract from each negative, 0 for whole image

samples = samples || 0;

params = params || {};

var data = [];

for (var i = 0; i < pos.length; i++) {

data = data.concat(getDir(pos[i], true, 0, posLimit, params));

}

for (var i = 0; i < neg.length; i++) {

data = data.concat(getDir(neg[i], false, samples, negLimit, params));

}

// randomize so neural network doesn't get biased toward one set

data.sort(function() {

return 1 - 2 * Math.round(Math.random());

});

return data;

}

function getDir(dir, isCat, samples, limit, params) {

var files = fs.readdirSync(dir);

var images = files.filter(function(file) {

return (path.extname(file) == ".png"

|| path.extname(file) == ".jpg");

});

images = images.slice(0, limit);

var data = [];

for (var i = 0; i < images.length; i++) {

var file = dir + "/" + images[i];

try {

var canvas = utils.drawImgToCanvasSync(file);

}

catch(e) {

console.log(e, file);

continue;

}

var canvases = extractSamples(canvas, samples);

for (var j = 0; j < canvases.length; j++) {

var fts;

try {

fts = features.extractFeatures(canvases[j], params.HOG);

} catch(e) {

console.log("error extracting features", e, file);

continue;

}

data.push({

input: new Float64Array(fts),

output: [isCat ? 1 : 0],

file: file,

});

}

}

return data;

}

function extractSamples(canvas, num) {

if (num == 0) {

// 0 means "don't sample"

return [canvas];

}

var min = 48;

var max = Math.min(canvas.width, canvas.height);

var canvases = [];

for (var i = 0; i < num; i++) {

var length = Math.max(min, Math.ceil(Math.random() * max));

var x = Math.floor(Math.random() * (max - length));

var y = Math.floor(Math.random() * (max - length));

canvases.push(cropCanvas(canvas, x, y, length, length));

}

return canvases;

}

function cropCanvas(canvas, x, y, width, height) {

var cropCanvas = new Canvas(width, height);

var context = cropCanvas.getContext("2d");

context.drawImage(canvas, x, y, width, height, 0, 0, width, height);

return cropCanvas;

}