using YoloDotNet;

// Instantiate a new Yolo object with your ONNX-model
using var yolo = new Yolo(@"path\to\model.onnx");

Console.WriteLine(yolo.OnnxModel.ModelType); // Output modeltype...

using YoloDotNet;
using YoloDotNet.Enums;
using YoloDotNet.Models;
using YoloDotNet.Extensions;
using SkiaSharp;

// Instantiate a new Yolo object
using var yolo = new Yolo(new YoloOptions
{
    OnnxModel = @"path\to\model.onnx",      // Your Yolov8 or Yolov10 model in onnx format
    ModelType = ModelType.ObjectDetection,  // Model type
    Cuda = false,                           // Use CPU or CUDA for GPU accelerated inference. Default = true
    GpuId = 0                               // Select Gpu by id. Default = 0
    PrimeGpu = false,                       // Pre-allocate GPU before first. Default = false
});

// Load image
using var image = SKImage.FromEncodedData(@"path\to\image.jpg");

// Run inference and get the results
var results = yolo.RunObjectDetection(image, confidence: 0.25, iou: 0.7);

// Draw results
using var resultsImage = image.Draw(results);

// Save to file
resultsImage.Save(@"save\as\new_image.jpg", SKEncodedImageFormat.Jpeg, 80);

using YoloDotNet;
using YoloDotNet.Enums;
using YoloDotNet.Models;

// Instantiate a new Yolo object
using var yolo = new Yolo(new YoloOptions
{
    OnnxModel = @"path\to\model.onnx",      // Your Yolov8 or Yolov10 model in onnx format
    ModelType = ModelType.ObjectDetection,  // Model type
    Cuda = false,                           // Use CPU or CUDA for GPU accelerated inference. Default = true
    GpuId = 0                               // Select Gpu by id. Default = 0
    PrimeGpu = false,                       // Pre-allocate GPU before first. Default = false
});

// Set video options
var options = new VideoOptions
{
    VideoFile = @"path\to\video.mp4",
    OutputDir = @"path\to\output\dir",
    //GenerateVideo = true,
    //DrawLabels = true,
    //FPS = 30,
    //Width = 640,  // Resize video...
    //Height = -2,  // -2 automatically calculate dimensions to keep proportions
    //Quality = 28,
    //DrawConfidence = true,
    //KeepAudio = true,
    //KeepFrames = false,
    //DrawSegment = DrawSegment.Default,
    //PoseOptions = MyPoseMarkerConfiguration // Your own pose marker configuration...
};

// Run inference on video
var results = yolo.RunObjectDetection(options, 0.25, 0.7);

// Do further processing with 'results'...

// Pass in a KeyPoint options parameter to the Draw() extension method. Ex:
image.Draw(poseEstimationResults, poseOptions);

using var yolo = new Yolo(@"path\to\model.onnx");

// ONNX metadata and labels resides inside yolo.OnnxModel
Console.WriteLine(yolo.OnnxModel);

// Instantiate a new object
using var yolo = new Yolo(@"path\to\model.onnx");

// Display metadata
foreach (var property in yolo.OnnxModel.GetType().GetProperties())
{
    var value = property.GetValue(yolo.OnnxModel);
    Console.WriteLine($"{property.Name,-20}{value!}");

    if (property.Name == nameof(yolo.OnnxModel.CustomMetaData))
        foreach (var data in (Dictionary<string, string>)value!)
            Console.WriteLine($"{"",-20}{data.Key,-20}{data.Value}");
}

// Get ONNX labels
var labels = yolo.OnnxModel.Labels;

Console.WriteLine();
Console.WriteLine($"Labels ({labels.Length}):");
Console.WriteLine(new string('-', 58));

// Display
for (var i = 0; i < labels.Length; i++)
    Console.WriteLine($"index: {i,-8} label: {labels[i].Name,20} color: {labels[i].Color}");

// Output:

// ModelType           ObjectDetection
// InputName           images
// OutputName          output0
// CustomMetaData      System.Collections.Generic.Dictionary`2[System.String,System.String]
//                     date                2023-11-07T13:33:33.565196
//                     description         Ultralytics YOLOv8n model trained on coco.yaml
//                     author              Ultralytics
//                     task                detect
//                     license             AGPL-3.0 https://ultralytics.com/license
//                     version             8.0.202
//                     stride              32
//                     batch               1
//                     imgsz               [640, 640]
//                     names               {0: 'person', 1: 'bicycle', 2: 'car' ... }
// ImageSize           Size [ Width=640, Height=640 ]
// Input               Input { BatchSize = 1, Channels = 3, Width = 640, Height = 640 }
// Output              ObjectDetectionShape { BatchSize = 1, Elements = 84, Channels = 8400 }
// Labels              YoloDotNet.Models.LabelModel[]
//
// Labels (80):
// ---------------------------------------------------------
// index: 0        label: person              color: #5d8aa8
// index: 1        label: bicycle             color: #f0f8ff
// index: 2        label: car                 color: #e32636
// index: 3        label: motorcycle          color: #efdecd
// ...