README.md

tflite

A Flutter plugin for accessing TensorFlow Lite API. Supports Classification and Object Detection on both iOS and Android.

Breaking changes since 1.0.0:

1. Updated to TensorFlow Lite API v1.12.0.
2. No longer accepts parameter inputSize and numChannels. They will be retrieved from input tensor.
3. numThreads is moved to Tflite.loadModel.

Installation

Add tflite as a dependency in your pubspec.yaml file.

Android

In android/app/build.gradle, add the following setting in android block.

    aaptOptions {
        noCompress 'tflite'
    }

iOS

If you get error like "'vector' file not found", please open ios/Runner.xcworkspace in Xcode, click Runner > Tagets > Runner > Build Settings, search Compile Sources As, change the value to Objective-C++;

Usage

1. Create a assets folder and place your label file and model file in it. In pubspec.yaml add:

  assets:
   - assets/labels.txt
   - assets/mobilenet_v1_1.0_224.tflite

2. Import the library:

import 'package:tflite/tflite.dart';

3. Load the model and labels:

String res = await Tflite.loadModel(
  model: "assets/mobilenet_v1_1.0_224.tflite",
  labels: "assets/labels.txt",
  numThreads: 1 // defaults to 1
);

4. See Image Classication and Object Detection below.

5. Release resources:

await Tflite.close();

Image Classification

• Run the model on image file:

var recognitions = await Tflite.runModelOnImage(
  path: filepath,   // required
  imageMean: 0.0,   // defaults to 117.0
  imageStd: 255.0,  // defaults to 1.0
  numResults: 2,    // defaults to 5
  threshold: 0.2    // defaults to 0.1
);

• Run the model on byte list:

var recognitions = await Tflite.runModelOnBinary(
  binary: imageToByteList(image, 224, 127.5, 127.5),// required
  numResults: 6,    // defaults to 5
  threshold: 0.05,  // defaults to 0.1
);

Uint8List imageToByteList(Image image, int inputSize, double mean, double std) {
  var convertedBytes = Float32List(1 * inputSize * inputSize * 3);
  var buffer = Float32List.view(convertedBytes.buffer);
  int pixelIndex = 0;
  for (var i = 0; i < inputSize; i++) {
    for (var j = 0; j < inputSize; j++) {
      var pixel = image.getPixel(i, j);
      buffer[pixelIndex++] = (((pixel >> 16) & 0xFF) - mean) / std;
      buffer[pixelIndex++] = (((pixel >> 8) & 0xFF) - mean) / std;
      buffer[pixelIndex++] = (((pixel) & 0xFF) - mean) / std;
    }
  }
  return convertedBytes.buffer.asUint8List();
}

• Output fomart:

{
  index: 0,
  label: "person",
  confidence: 0.629
}

Object Detection

• SSD MobileNet:

var recognitions = await Tflite.detectObjectOnImage(
  path: filepath,       // required
  model: "SSDMobileNet",
  imageMean: 127.5,     
  imageStd: 127.5,      
  threshold: 0.4,       // defaults to 0.1
  numResultsPerClass: 2,// defaults to 5
);

• Tiny YOLOv2:

var recognitions = await Tflite.detectObjectOnImage(
  path: filepath,       // required
  model: "YOLO",      
  imageMean: 0.0,       
  imageStd: 255.0,      
  threshold: 0.3,       // defaults to 0.1
  numResultsPerClass: 2,// defaults to 5
  List anchors: anchors,// defaults to [0.57273,0.677385,1.87446,2.06253,3.33843,5.47434,7.88282,3.52778,9.77052,9.16828]
  blockSize: 32,        // defaults to 32
  numBoxesPerBlock: 5   // defaults to 5
);

• Output fomart:

x, y, w, h are between [0, 1]. You can scale x, w by the width and y, h by the height of the image.

{
  detectedClass: "hot dog",
  confidenceInClass: 0.123,
  rect: {
    x: 0.15,
    y: 0.33,
    w: 0.80,
    h: 0.27
  }
}

Demo

Refer to the example.