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)?;

    // cast the image to floating point
    let image_f32: Image<f32, 3> = image.clone().cast_and_scale::<f32>(1.0 / 255.0)?;

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

    // normalize the image each channel
    let _image_norm: Image<f32, 3> =
        kornia_rs::normalize::normalize_mean_std(&image_f32, &[0.5, 0.5, 0.5], &[0.5, 0.5, 0.5])?;

    // normalize the grayscale image
    let _gray_norm: Image<f32, 1> =
        kornia_rs::normalize::normalize_mean_std(&gray_f32, &[0.5], &[0.5])?;

    // alternative way to normalize the image between 0 and 255
    // let _gray_norm_min_max = kornia_rs::normalize::normalize_min_max(
    //     &gray, 0.0, 255.0)?;

    let (min, max) = kornia_rs::normalize::find_min_max(&gray_f32)?;
    println!("min: {:?}, max: {:?}", min, max);

    Ok(())
}