//! Original YOLOv5 nano with ORT backend
//! Model: pretrained/yolov5n.onnx (from ultralytics/yolov5 repo)
//! Output format: [1, 25200, 85] with objectness score

use std::time::Instant;

use od_opencv::{ImageBuffer, Model};

fn main() {
    ort::init().commit();

    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;

    // Original YOLOv5 uses yolov5_ort() - has objectness score
    let mut model = Model::yolov5_ort(
        "pretrained/yolov5n.onnx",
        (net_width, net_height),
    ).expect("Failed to load model");

    let img = image::open("images/dog.jpg").expect("Failed to load image");
    let img_buffer = ImageBuffer::from_dynamic_image(img);

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

    for (i, bbox) in bboxes.iter().enumerate() {
        println!("Class: {}", classes_labels[class_ids[i]]);
        println!("\tBounding box: x={}, y={}, w={}, h={}", bbox.x, bbox.y, bbox.width, bbox.height);
        println!("\tConfidence: {:.2}", confidences[i]);
    }
}
