mod cli;
mod errors;
use bounding_box::roi::MultiRoi;
use detector::{facedet, facedet::FaceDetectionConfig, faceembed};
use errors::*;
use fast_image_resize::ResizeOptions;
use ndarray::*;
use ndarray_image::*;
use ndarray_resize::NdFir;
const RETINAFACE_MODEL_MNN: &[u8] = include_bytes!("../models/retinaface.mnn");
const FACENET_MODEL_MNN: &[u8] = include_bytes!("../models/facenet.mnn");
const RETINAFACE_MODEL_ONNX: &[u8] = include_bytes!("../models/retinaface.onnx");
const FACENET_MODEL_ONNX: &[u8] = include_bytes!("../models/facenet.onnx");
pub fn main() -> Result<()> {
    tracing_subscriber::fmt()
        .with_env_filter("error")
        .with_thread_ids(true)
        .with_thread_names(true)
        .with_target(false)
        .init();
    let args = <cli::Cli as clap::Parser>::parse();
    match args.cmd {
        cli::SubCommand::Detect(detect) => {
            // Choose backend based on executor type (defaulting to MNN for backward compatibility)

            let executor = detect
                .mnn_forward_type
                .map(|f| cli::Executor::Mnn(f))
                .or_else(|| {
                    if detect.ort_execution_provider.is_empty() {
                        None
                    } else {
                        Some(cli::Executor::Ort(detect.ort_execution_provider.clone()))
                    }
                })
                .unwrap_or(cli::Executor::Mnn(mnn::ForwardType::CPU));

            match executor {
                cli::Executor::Mnn(forward) => {
                    let retinaface =
                        facedet::retinaface::mnn::FaceDetection::builder(RETINAFACE_MODEL_MNN)
                            .change_context(Error)?
                            .with_forward_type(forward)
                            .build()
                            .change_context(errors::Error)
                            .attach_printable("Failed to create face detection model")?;
                    let facenet =
                        faceembed::facenet::mnn::EmbeddingGenerator::builder(FACENET_MODEL_MNN)
                            .change_context(Error)?
                            .with_forward_type(forward)
                            .build()
                            .change_context(errors::Error)
                            .attach_printable("Failed to create face embedding model")?;

                    run_detection(detect, retinaface, facenet)?;
                }
                cli::Executor::Ort(ep) => {
                    let retinaface =
                        facedet::retinaface::ort::FaceDetection::builder(RETINAFACE_MODEL_ONNX)
                            .change_context(Error)?
                            .with_execution_providers(&ep)
                            .build()
                            .change_context(errors::Error)
                            .attach_printable("Failed to create face detection model")?;
                    let facenet =
                        faceembed::facenet::ort::EmbeddingGenerator::builder(FACENET_MODEL_ONNX)
                            .change_context(Error)?
                            .with_execution_providers(ep)
                            .build()
                            .change_context(errors::Error)
                            .attach_printable("Failed to create face embedding model")?;

                    run_detection(detect, retinaface, facenet)?;
                }
            }
        }
        cli::SubCommand::List(list) => {
            println!("List: {:?}", list);
        }
        cli::SubCommand::Completions { shell } => {
            cli::Cli::completions(shell);
        }
    }
    Ok(())
}

fn run_detection<D, E>(detect: cli::Detect, mut retinaface: D, mut facenet: E) -> Result<()>
where
    D: facedet::FaceDetector,
    E: faceembed::FaceEmbedder,
{
    let image = image::open(&detect.image)
        .change_context(Error)
        .attach_printable(detect.image.to_string_lossy().to_string())?;
    let image = image.into_rgb8();
    let mut array = image
        .into_ndarray()
        .change_context(errors::Error)
        .attach_printable("Failed to convert image to ndarray")?;
    let output = retinaface
        .detect_faces(
            array.view(),
            FaceDetectionConfig::default()
                .with_threshold(detect.threshold)
                .with_nms_threshold(detect.nms_threshold),
        )
        .change_context(errors::Error)
        .attach_printable("Failed to detect faces")?;
    for bbox in &output.bbox {
        tracing::info!("Detected face: {:?}", bbox);
        use bounding_box::draw::*;
        array.draw(bbox, color::palette::css::GREEN_YELLOW.to_rgba8(), 1);
    }
    let face_rois = array
        .view()
        .multi_roi(&output.bbox)
        .change_context(Error)?
        .into_iter()
        // .inspect(|f| {
        //     tracing::info!("Face ROI shape before resize: {:?}", f.dim());
        // })
        .map(|roi| {
            roi.as_standard_layout()
                .fast_resize(160, 160, &ResizeOptions::default())
                .change_context(Error)
        })
        // .inspect(|f| {
        //     f.as_ref().inspect(|f| {
        //         tracing::info!("Face ROI shape after resize: {:?}", f.dim());
        //     });
        // })
        .collect::<Result<Vec<_>>>()?;
    let face_roi_views = face_rois.iter().map(|roi| roi.view()).collect::<Vec<_>>();

    let chunk_size = detect.batch_size;
    let embeddings = face_roi_views
        .chunks(chunk_size)
        .map(|chunk| {
            tracing::info!("Processing chunk of size: {}", chunk.len());

            if chunk.len() < chunk_size {
                tracing::warn!("Chunk size is less than 8, padding with zeros");
                let zeros = Array3::zeros((160, 160, 3));
                let zero_array = core::iter::repeat(zeros.view())
                    .take(chunk_size)
                    .collect::<Vec<_>>();
                let face_rois: Array4<u8> = ndarray::stack(Axis(0), zero_array.as_slice())
                    .change_context(errors::Error)
                    .attach_printable("Failed to stack rois together")?;
                let output = facenet.run_models(face_rois.view()).change_context(Error)?;
                Ok(output)
            } else {
                let face_rois: Array4<u8> = ndarray::stack(Axis(0), chunk)
                    .change_context(errors::Error)
                    .attach_printable("Failed to stack rois together")?;
                let output = facenet.run_models(face_rois.view()).change_context(Error)?;
                Ok(output)
            }
        })
        .collect::<Result<Vec<Array2<f32>>>>()?;

    let v = array.view();
    if let Some(output) = detect.output {
        let image: image::RgbImage = v
            .to_image()
            .change_context(errors::Error)
            .attach_printable("Failed to convert ndarray to image")?;
        image
            .save(output)
            .change_context(errors::Error)
            .attach_printable("Failed to save output image")?;
    }

    Ok(())
}