use kornia_rs::image::Image;
use kornia_rs::io::functional as F;

fn main() -> Result<(), Box<dyn std::error::Error>> {
    // read the image
    let image_path = std::path::Path::new("tests/data/dog.jpeg");
    let image: Image<u8, 3> = F::read_image_any(image_path)?;

    // binarize the image as u8
    let _image_bin: Image<u8, 3> =
        kornia_rs::threshold::threshold_binary(&image.clone(), 127, 255)?;

    // normalize the image between 0 and 1
    let image_f32: Image<f32, 3> = image.cast_and_scale::<f32>(1.0 / 255.0)?;

    // convert to grayscale as floating point
    let gray_f32: Image<f32, 1> = kornia_rs::color::gray_from_rgb(&image_f32)?;

    // binarize the gray image as floating point
    let gray_bin: Image<f32, 1> = kornia_rs::threshold::threshold_binary(&gray_f32, 0.5, 1.0)?;

    // create a Rerun recording stream
    let rec = rerun::RecordingStreamBuilder::new("Kornia App").spawn()?;

    rec.log("image", &rerun::Image::try_from(image_f32.data)?)?;
    rec.log("gray", &rerun::Image::try_from(gray_f32.data)?)?;
    rec.log("gray_bin", &rerun::Image::try_from(gray_bin.data)?)?;

    Ok(())
}