Problem:

I am working on a Flutter project where I need to run a TensorFlow Lite (TFLite) object detection model. The model has specific input and output shape requirements, and I am encountering difficulties preprocessing the input image to match the required input format.

I have the following model details:

• Input shape: [1, 640, 640, 3] (a single image with 640×640 resolution and 3 color channels)
• Output shape: [1, 25200, 7] (model will output a list of detection results)

The main challenge is correctly preprocessing the input image. I need to resize the image to 640×640 pixels and convert it into the correct data format (float32) that the model expects.

I have tried multiple approaches, but none of them seem to work as expected. The image is not being processed correctly, leading to errors and incorrect predictions.

Specific Issues:

• The image preprocessing pipeline (resizing, normalization, and format conversion) is not working as expected.
• I’ve used the tflite_flutter_helper package, but it depends on outdated dependencies, which causes compatibility issues.
• The input format needs to be a float32 array, but I am unsure how to correctly convert the image and feed it into the model.

What I Tried:

I tried multiple approaches to preprocess the input image for my TensorFlow Lite model. Here’s what I did:

1. First Attempt:
   I used the following code to load the image and convert it to float32:

import 'dart:io';

import 'package:camera/camera.dart';
import 'package:get/get.dart';
import 'package:permission_handler/permission_handler.dart';
import 'package:tflite/tflite.dart';
import 'dart:developer' as developer;
import 'package:flutter/material.dart';
import 'package:tflite_flutter/tflite_flutter.dart';


class ScanController extends GetxController {

  @override
  void onInit() {
    super.onInit();
    loadModel();
  }

  void loadModel() async{
    final interpreter = await Interpreter.fromAsset('assets/model.tflite');

    var image = File('assets/test.jpg');
    var imageBytes = await image.readAsBytes();
    // conver the image to float32
    final input = imageBytes.buffer.asFloat32List();

    // output size is 1 x 25200 x 7
    final output = List.filled(1 * 25200 * 7, 0.0).reshape([1, 25200, 7]);

    interpreter.run(input, output);

    developer.log(output.toString());

  }

}

but this didn’t work

i also tried to use TensorImage and ImageProcessorBuilder from tflite_flutter_helper in this code

import 'package:get/get.dart';
import 'package:get/get_connect/http/src/utils/utils.dart';
import 'package:permission_handler/permission_handler.dart';
import 'package:tflite_flutter/tflite_flutter.dart' as tflite;
import 'dart:developer' as developer;
import 'dart:io';
import 'package:image/image.dart';
import 'package:image/image.dart' as img;
import 'package:tflite_flutter_helper/tflite_flutter_helper.dart';
import 'package:tflite_flutter/tflite_flutter.dart';
import 'package:tflite_flutter_helper/tflite_flutter_helper.dart';



class ScanController extends GetxController {

  @override
  void onInit() {
    super.onInit();
    loadModel();
  }



void loadModel() async {
  try {
    final interpreter = await tflite.Interpreter.fromAsset('assets/model.tflite');
    developer.log('Model loaded successfully.', name: 'scan_controller.dart');
    
    developer.log('input info:');
    var inputShape = interpreter.getInputTensor(0).shape;
    developer.log('shape: $inputShape');
    var inputType = interpreter.getInputTensor(0).type;
    developer.log('type: $inputType');
    var outputShape = interpreter.getOutputTensor(0).shape;
    developer.log('output shape: $outputShape');
    var outputType = interpreter.getOutputTensor(0).type;
    developer.log('output type: $outputType');

    var file = File('assets/test.jpg');

    // Input shape: [1, 640, 640, 3]
    ImageProcessor imageProcessor = ImageProcessorBuilder()
        .add(ResizeOp(640, 640, ResizeMethod.BILINEAR))
        .add(NormalizeOp(0, 255))
        .build();

    TensorImage tensorImage = TensorImage.fromFile(file);
    tensorImage = imageProcessor.process(tensorImage);

    // Output shape: [1, 25200, 7]
    TensorBuffer outputBuffer = TensorBuffer.createFixedSize(outputShape, tflite.TfLiteType.float32);

    // Run inference
    interpreter.run(tensorImage.buffer, outputBuffer.buffer);

    // print the result
    var output = outputBuffer.getDoubleList();
    developer.log('output: $output');
    

  } catch (e) {
    developer.log('Failed to load model: $e', name: 'scan_controller.dart');
  }
}


    
  }
  

but it also didn’t work

Can someone please guide me on how to properly preprocess the input image to match the input shape [1, 640, 640, 3] and convert it to the required float32 format so that it can be passed to the model?