olokobayusuf

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,

olokobayusuf

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

olokobayusuf

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

namespace NatSuite.Devices.Outputs {

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

olokobayusuf

// 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}");

olokobayusuf

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.

olokobayusuf

Camera2: Hardware level: 1
06-02 10:49:07.625 21849 21865 I Unity   : Camera2: FrameSize 4032 x 3024 [2.5824869781268642]
06-02 10:49:07.625 21849 21865 I Unity   : Camera2: FrameSize 4032 x 2268 [2.2948049056750834]
06-02 10:49:07.625 21849 21865 I Unity   : Camera2: FrameSize 3984 x 2988 [2.558534596033433]
06-02 10:49:07.625 21849 21865 I Unity   : Camera2: FrameSize 3264 x 2448 [2.1598687907924505]
06-02 10:49:07.626 21849 21865 I Unity   : Camera2: FrameSize 3264 x 1836 [1.8721867183406695]
06-02 10:49:07.626 21849 21865 I Unity   : Camera2: FrameSize 3024 x 3024 [2.2948049056750834]
06-02 10:49:07.626 21849 21865 I Unity   : Camera2: FrameSize 2976 x 2976 [2.2628042229822007]
06-02 10:49:07.626 21849 21865 I Unity   : Camera2: FrameSize 2880 x 2160 [1.9095425048844386]
06-02 10:49:07.626 21849 21865 I Unity   : Camera2: FrameSize 2592 x 1944 [1.698821473568786]

olokobayusuf

  
/* 
*   NatCorder
*   Copyright (c) 2020 Yusuf Olokoba.
*/

namespace NatSuite.Recorders.Inputs {

    using UnityEngine;
    using UnityEngine.Rendering;