var update_memory;

var cascadeurl;

let initialized = false;

let canvas;

let ctx;

let mic;

​

function setup() {

    canvas = createCanvas(640, 480);

    pixelDensity(1);

    ctx = canvas.drawingContext;

    capture = createCapture({

        video: {

            mandatory: {

                maxWidth: 640,

                maxHeight: 480

            },

            optional: [{

                maxFrameRate: 120

            }]

        },

        audio: false

    }, function() {

        capture.hide();

    });

​

    mic = new p5.AudioIn();

    mic.start();

​

    pico_setup();

}

​

function pico_setup() {

    update_memory = pico.instantiate_detection_memory(5); // we will use the detecions of the last 5 frames

    cascadeurl = 'https://raw.githubusercontent.com/nenadmarkus/pico/c2e81f9d23cc11d1a612fd21e4f9de0921a5d0d9/rnt/cascades/facefinder';

    fetch(cascadeurl).then(function(response) {

        response.arrayBuffer().then(function(buffer) {

            var bytes = new Int8Array(buffer);

            facefinder_classify_region = pico.unpack_cascade(bytes);

            // console.log('* cascade loaded');

        })

    })

}

​

function draw() {

    pico_draw();

}

​

function pico_draw() {

    pico_process(capture.elt);

}

​

function rgba_to_grayscale(rgba, nrows, ncols) {

    var gray = new Uint8Array(nrows * ncols);

    for (var r = 0; r < nrows; ++r)

        for (var c = 0; c < ncols; ++c)

            // gray = 0.2*red + 0.7*green + 0.1*blue

            gray[r * ncols + c] = (2 * rgba[r * 4 * ncols + 4 * c + 0] + 7 * rgba[r * 4 * ncols + 4 * c + 1] + 1 * rgba[r * 4 * ncols + 4 * c + 2]) / 10;

    return gray;

}

​

function pico_process(video) {

    // render the video frame to the canvas element and extract RGBA pixel data

    ctx.drawImage(video, 0, 0);

    var rgba = ctx.getImageData(0, 0, 640, 480).data;

    // prepare input to `run_cascade`

    img = {

        "pixels": rgba_to_grayscale(rgba, 480, 640),

        "nrows": 480,

        "ncols": 640,

        "ldim": 640

    }

    params = {

        "shiftfactor": 0.1,

        "minsize": 50,

        "maxsize": 500,

        "scalefactor": 1.1

    }

    dets = pico.run_cascade(img, facefinder_classify_region, params);

    dets = update_memory(dets);

    dets = pico.cluster_detections(dets, 0.2);

    if (dets.length > 0) {

        for (i = 0; i < 1; ++i) {

            let x = dets[i][1];

            let y = dets[i][0];

            let d = dets[i][2];

            let img = get(x - d / 2, y - d / 2, d, d);

            level = mic.getLevel();

            let res = round(map(level, 0, 0.3, 5, d));

            img.resize(res, res);

            push();

            translate(x, y);

            imageMode(CENTER);

            noSmooth();

            image(img, 0, 0, d, d);

            smooth();

            pop();

​

        }

    }

}

​

var facefinder_classify_region = function(r, c, s, pixels, ldim) {

    return -1.0;

};