I have a tflite model that takes image as a input and predict its class. I want it to use in my unity Project. When I used the code given by chatgpt, following error occurs.can anyone help, i dont know much about unity and c#
Assets\Samples\Detection\Scripts\PythonBridge.cs(72,9): error CS0246: The type or namespace name ‘Tensor’ could not be found (are you missing a using directive or an assembly reference?)
Assets\Samples\Detection\Scripts\PythonBridge.cs(72,43): error CS0122: ‘Interpreter.GetOutputTensor(int)’ is inaccessible due to its protection level
using UnityEngine;
using TensorFlowLite;
using System.IO;
using System.Collections.Generic;
public class ObjectDetection : MonoBehaviour
{
[SerializeField]
[FilePopup("*.tflite")]
public string modelPath = "model.tflite";
[SerializeField]
private TextAsset labelFile;
[SerializeField]
private Texture2D inputImage;
private Interpreter interpreter;
private List<string> labels;
private const int IMAGE_SIZE = 224;
private const int CHANNELS = 3;
private void Start()
{
LoadModel();
LoadLabels();
PreprocessImage();
RunInference();
}
private void LoadModel()
{
interpreter = new Interpreter(File.ReadAllBytes(modelPath));
}
private void LoadLabels()
{
labels = new List<string>();
using (StringReader reader = new StringReader(labelFile.text))
{
string line;
while ((line = reader.ReadLine()) != null)
{
labels.Add(line.Trim());
}
}
}
private void PreprocessImage()
{
Texture2D resizedImage = ResizeImage(inputImage, IMAGE_SIZE, IMAGE_SIZE);
Color32[] pixels = resizedImage.GetPixels32();
float[] imgArray = new float[IMAGE_SIZE * IMAGE_SIZE * CHANNELS];
for (int i = 0; i < pixels.Length; i++)
{
imgArray[i * 3] = pixels[i].r / 255.0f;
imgArray[i * 3 + 1] = pixels[i].g / 255.0f;
imgArray[i * 3 + 2] = pixels[i].b / 255.0f;
}
interpreter.SetInputTensorData(0, imgArray);
}
private void RunInference()
{
interpreter.Invoke();
// Retrieve output and process predictions
Tensor outputTensor = interpreter.GetOutputTensor(0);
float[] results = outputTensor.Data<float>();
// Find class with highest probability
int maxIndex = 0;
float maxProbability = 0f;
for (int i = 0; i < results.Length; i++)
{
if (results[i] > maxProbability)
{
maxProbability = results[i];
maxIndex = i;
}
}
string predictedLabel = labels[maxIndex];
Debug.Log("Predicted object: " + predictedLabel);
}
private Texture2D ResizeImage(Texture2D source, int width, int height)
{
RenderTexture rt = RenderTexture.GetTemporary(width, height, 24);
RenderTexture.active = rt;
Graphics.Blit(source, rt);
Texture2D result = new Texture2D(width, height);
result.ReadPixels(new Rect(0, 0, width, height), 0, 0);
result.Apply();
RenderTexture.active = null;
RenderTexture.ReleaseTemporary(rt);
return result;
}
}