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public static async Task<MediaAsset> CreateGIF (Texture2D[] textures) {
    // Make sure `textures` is not empty
    // Make sure every texture has the same size (width and height)
    // Make sure every texture has the `TextureFormat.RGBA32` format
    // Create recorder
    var width = textures[0].width;
    var height = textures[0].height;
    var frameRate = 5;
    var recorder = await MediaRecorder.Create(
        MediaFormat.GIF,

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void Detect (RenderTexture renderTexture) {
    // Create the YOLOX model
    var model = await MLEdgeModel.Create("@natsuite/yolox");
    // Create the YOLOX predictor
    var predictor = new YOLOXPredictor(model);
    // Readback the render texture
    AsyncGPUReadback.Request(renderTexture, request => {
        // Create an image feature
        var feature = new MLImageFeature(request.GetData<byte>(), request.width, request.height);
        // Predict

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/* 
*   NatDevice
*   Copyright (c) 2022 NatML Inc. All Rights Reserved.
*/

namespace NatSuite.Devices.Outputs {

    using System;
    using OpenCVForUnity.CoreModule;
    using OpenCVForUnity.UtilsModule;

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// Create a classification predictor
var labels = new [] { "cat", "dog", ... };
var predictor = new MobileNetv2Predictor(model, labels);

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// Create a classifier module
var labels = new [] { "goldfish", "tiger shark", ... };
var classifier = new MLClassifier("/path/to/classifier.onnx", labels);
// Classify an image
Texture2D image = ...;
var (label, confidence) = classifier.Predict(image);
Debug.Log($"Image contains {label} with confidence: {confidence}");

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// If you have some feature
Texture2D image = ...;
// You can predict easily
var (label, confidence) = predictor.Predict(image);
Debug.Log($"Image contains {label} with confidence {confidence}");

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// Load the ML model data
var modelData = MLModelData.FromFile("/path/to/model.onnx");
// Deserialize the model
var model = modelData.Deserialize();
// Print a model metadata value
Debug.Log("The model was created by: " + model["Author"]);
// Check the expected input size
var inputType = model.inputs[0] as MLImageType;
Debug.Log($"Model expects image with size: {inputType.width}x{inputType.height}");

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using NatSuite.Recorders.Inputs;

/// <summary>
/// Recorder input for recording video frames from textures.
/// </summary>
public class ThreadedAsyncTextureInput : ITextureInput {
    
    #region --Client API--
    /// <summary>
    /// Create a texture input which performs asynchronous readbacks.

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async void StartPreview () {
    // Start the camera preview
    ICameraDevice device = ...;
    Texture2D previewTexture = await device.StartRunning();
    // Display the preview texture on a UI panel
    rawImage.texture = previewTexture;
}

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/// <summary>
/// Camera device which provides pixel buffers.
/// </summary>
public interface ICameraDevice : IMediaDevice {

    /// <summary>
    /// Is the camera front facing?
    /// </summary>
    bool frontFacing { get; }