visualhaar/src/main.rs

#[macro_use] extern crate log;
#[macro_use(s)] extern crate ndarray;

use simplelog::*;
use nannou::prelude::*;
use v4l::{Buffer, CaptureDevice, MappedBufferStream};

mod visualhaar;

// use std::fs::File;

static mut CAMERA: Option<CaptureDevice> = None;

fn main() {
    CombinedLogger::init(
        vec![
            TermLogger::new(LevelFilter::Info, Config::default(), TerminalMode::Mixed),
            // WriteLogger::new(LevelFilter::Info, Config::default(), File::create("my_rust_binary.log").unwrap()),
        ]
    ).unwrap();

    warn!("test");

    unsafe{
        CAMERA = Some(CaptureDevice::new(2)
            .expect("Failed to open device")
            // .format(640, 480, b"RGB3")
            .format(424, 240, b"RGB3")
            // .format(320, 240, b"RGB3")
            .expect("Failed to set format")
            .fps(30)
            .expect("Failed to set frame interval"));
    }
        
    nannou::app(model)
        .event(event)
        .update(update)
        .view(view)
        .run();
}

struct Model<'a> {
    stream: MappedBufferStream<'a>,
    _window: window::Id,
    image: Option<nannou::image::DynamicImage>,
    haar: visualhaar::HaarClassifier,
    haar_outcome: Option<visualhaar::Outcome>,
}

fn model<'a>(app: &App) -> Model<'a> {
    // Create a new capture device with a few extra parameters
    unsafe{
        // because our code is slower than the camera, set buffer to 1, then it will capture the latest frame
        let stream = MappedBufferStream::with_buffers(CAMERA.as_mut().unwrap(), 1)
            .expect("Failed to create buffer stream");

        let _window = app.new_window()
            .size(720, 720)
            // .event(window_event)
            // .raw_event(raw_window_event)
            // .key_pressed(key_pressed)
            // .key_released(key_released)
            // .mouse_moved(mouse_moved)
            // .mouse_pressed(mouse_pressed)
            // .mouse_released(mouse_released)
            // .mouse_wheel(mouse_wheel)
            // .mouse_entered(mouse_entered)
            // .mouse_exited(mouse_exited)
            // .touch(touch)
            // .touchpad_pressure(touchpad_pressure)
            // .moved(window_moved)
            // .resized(window_resized)
            // .hovered_file(hovered_file)
            // .hovered_file_cancelled(hovered_file_cancelled)
            // .dropped_file(dropped_file)
            // .focused(window_focused)
            // .unfocused(window_unfocused)
            // .closed(window_closed)
            .build()
            .unwrap();
        
        let haar = visualhaar::HaarClassifier::from_xml("haarcascade_frontalface_alt2.xml").unwrap();
        
        println!("Haar: {:?}", haar);


        Model {
            stream: stream,
            _window: _window,
            image: None,
            haar: haar,
            haar_outcome: None,
        }
    }
}

fn event(_app: &App, _model: &mut Model, event: Event) {
    match event {
        Event::WindowEvent {
            id: _window_id,
            //raw: _,
            simple: _simple,
        } => { 
            match _simple {
                None => {println!("Unkown window event")}
                // Some(nannou::event::WindowEvent:Moved(_)) => {println!("moved! {:?}", _simple.unwrap())}
                Some(_ev) => { println!("Any other window event! {:?}", _ev) }
            }
        }
        Event::DeviceEvent(_device_id, _event) => {}
        Event::Update(_dt) => {}
        Event::Suspended => {}
        Event::Resumed => {}
    }
}

/// renders each frame (called through nannou)
fn update(_app: &App, _model: &mut Model, _update: Update) {
    
    // get frame from camera stream
    let frame = _model.stream.next().unwrap();
    // let vec: Vec<u8> = frame.data().to_vec();
    // Convert to Nannou ImageBuffer
    let img_buffer: Option<nannou::image::ImageBuffer<nannou::image::Rgb<u8>, Vec<u8>>> = nannou::image::ImageBuffer::from_raw(424,240, frame.data().to_vec());

    match img_buffer {
        None => {
            // no imagebuffer for this update. set haar outcomes to empy
            _model.haar_outcome = None;
        }
        Some(ib) => {
            // let
            // ib.map( nannou::image::DynamicImage::ImageRgb8);
            // let ib_bw = nannou::image::imageops::grayscale(&ib);
            // _model.image = Some(nannou::image::DynamicImage::ImageLuma8(ib_bw));
            let outcome = _model.haar.scan_image(ib).unwrap();
            _model.haar_outcome = Some(outcome);
            // _model.image = Some(nannou::image::DynamicImage::ImageRgb8(ib));

        }
    }
    
    // println!("Fetch frame nr {:?} {:?} {:?}", frame.meta().seq, frame.meta().timestamp, frame.data())
    // println!("Fetch frame nr {:?} {:?} {:?} {:?}", frame.meta().seq, frame.meta().timestamp, frame.meta().flags, frame.len())
}


fn view(_app: &App, _model: &Model, frame: Frame){
    let draw = _app.draw();
    draw.background().color(PLUM);
    let sine = (_app.time / 1.0).sin();
    let slowersine = (_app.time / 3.0).sin();
    let rotation = _app.time % (2. * PI);
    let boundary = _app.window_rect();
    let x = map_range(sine, -1.0, 1.0, boundary.left(), boundary.right());
    let y = map_range(slowersine, -1.0, 1.0, boundary.bottom(), boundary.top());
    
    // let texture = wgpu::Texture::load_from_
    // let assets = _app.assets_path().unwrap();
    // let img_path = assets.join("test1.png");
    // let texture = wgpu::Texture::from_path(_app, img_path).unwrap();

    // let image = nannou::image::DynamicImage::new_rgb8(640, 480);
    match &_model.haar_outcome {
        Some(outcome) => {
            // let i = outcome.dyn(/);
            let texture = wgpu::Texture::from_image(_app, &outcome.dynamic_img);
            draw.texture(&texture);
        }
        _ => {}
    }

    draw.rect().color(STEELBLUE).rotate(rotation).x_y(x,y);
    draw.to_frame(_app, &frame).unwrap();
}