//! YOLOv5u nano with OpenCV backend (Ultralytics updated)
//! Model: pretrained/yolov5nu.onnx (from ultralytics/assets)
//! Output format: [1, 84, 8400] - same as YOLOv8, no objectness score

use std::time::Instant;

use od_opencv::{Model, DnnBackend, DnnTarget};

use opencv::{
    core::{Scalar, Vector},
    imgcodecs::imread,
    imgcodecs::imwrite,
    imgproc::LINE_4,
    imgproc::rectangle,
};

fn main() {
    let cv_version = opencv::core::get_version_string().unwrap();
    println!("OpenCV version: {}", cv_version);

    let classes_labels: Vec<&str> = vec!["person", "bicycle", "car", "motorbike", "aeroplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "sofa", "pottedplant", "bed", "diningtable", "toilet", "tvmonitor", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"];

    let net_width = 640;
    let net_height = 640;

    // YOLOv5u uses opencv() - same output format as YOLOv8
    let mut model = Model::opencv(
        "pretrained/yolov5nu.onnx",
        (net_width, net_height),
        DnnBackend::Cuda,
        DnnTarget::Cuda,
    ).unwrap();

    let mut frame = imread("images/dog.jpg", 1).unwrap();

    let start = Instant::now();
    let (bboxes, class_ids, confidences) = model.forward(&frame, 0.25, 0.4).unwrap();
    println!("Inference time: {:?}", start.elapsed());

    for (i, bbox) in bboxes.iter().enumerate() {
        rectangle(&mut frame, *bbox, Scalar::from((0.0, 255.0, 0.0)), 2, LINE_4, 0).unwrap();
        println!("Class: {}", classes_labels[class_ids[i]]);
        println!("\tBounding box: {:?}", bbox);
        println!("\tConfidence: {:.2}", confidences[i]);
    }

    imwrite("images/dog_yolov5u_n.jpg", &frame, &Vector::new()).unwrap();
}
