[FEATURE] Implement Image Recognition Using TensorFlow in .NET

[guibranco]

Overview

This repository will serve as a Proof-of-Concept (PoC) for a .NET project that demonstrates image recognition using TensorFlow. The goal is to build a simple application capable of recognizing objects in images by following the steps outlined in this tutorial.

Tasks

1. Set up TensorFlow in .NET

   • Install required NuGet packages: TensorFlow.NET and SciSharp.TensorFlow.Redist.

2. Preprocess Images

   • Implement image loading and preprocessing logic.

3. Build Model Integration

   • Load the pre-trained TensorFlow model and integrate it into the .NET project.

4. Perform Image Recognition

   • Execute image recognition on a sample image and output results.

Example Code

Below is a sample snippet in C# that shows how to load a TensorFlow model and use it for image recognition:

using System;
using Tensorflow;
using Tensorflow.NumPy;

class Program
{
    static void Main(string[] args)
    {
        // Load TensorFlow model
        var graph = new Graph().as_default();
        var modelPath = "path_to_your_model.pb";
        var session = new Session(graph);
        graph.Import(modelPath);

        // Prepare image input
        var image = LoadImage("path_to_your_image.jpg");

        // Run the model
        var output = session.run(new[] { "output_node" }, new FeedItem[] { new FeedItem("input_node", image) });

        // Process and display results
        Console.WriteLine($"Predicted: {ProcessOutput(output)}");
    }

    static NDArray LoadImage(string imagePath)
    {
        // Implement image loading and preprocessing logic here
        return new NDArray();
    }

    static string ProcessOutput(NDArray output)
    {
        // Implement output processing logic here
        return "Object name";
    }
}

This code shows how to:

1. Load a pre-trained TensorFlow model.
2. Feed an image into the model for recognition.
3. Process the output to display recognized objects.

Resources

• TensorFlow.NET Documentation
• Original Tutorial

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