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
  1. Import the library:
import 'package:tflite/tflite.dart';
  1. 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
);
  1. See Image Classication and Object Detection below.

  2. Release resources:

await Tflite.close();

Image Classification

  • Output fomart:
{
  index: 0,
  label: "person",
  confidence: 0.629
}
  • Run on image:
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 on binary:
var recognitions = await Tflite.runModelOnBinary(
  binary: imageToByteListFloat32(image, 224, 127.5, 127.5),// required
  numResults: 6,    // defaults to 5
  threshold: 0.05,  // defaults to 0.1
);

Uint8List imageToByteListFloat32(
    img.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(j, i);
      buffer[pixelIndex++] = (img.getRed(pixel) - mean) / std;
      buffer[pixelIndex++] = (img.getGreen(pixel) - mean) / std;
      buffer[pixelIndex++] = (img.getBlue(pixel) - mean) / std;
    }
  }
  return convertedBytes.buffer.asUint8List();
}

Uint8List imageToByteListUint8(img.Image image, int inputSize) {
  var convertedBytes = Uint8List(1 * inputSize * inputSize * 3);
  var buffer = Uint8List.view(convertedBytes.buffer);
  int pixelIndex = 0;
  for (var i = 0; i < inputSize; i++) {
    for (var j = 0; j < inputSize; j++) {
      var pixel = image.getPixel(j, i);
      buffer[pixelIndex++] = img.getRed(pixel);
      buffer[pixelIndex++] = img.getGreen(pixel);
      buffer[pixelIndex++] = img.getBlue(pixel);
    }
  }
  return convertedBytes.buffer.asUint8List();
}
  • Run on image stream (video frame):

Works with camera plugin 4.0.0. Video format: (iOS) kCVPixelFormatType_32BGRA, (Android) YUV_420_888.

var recognitions = await Tflite.runModelOnFrame(
  bytesList: img.planes.map((plane) {return plane.bytes;}).toList(),// required
  imageHeight: img.height,
  imageWidth: img.width,
  imageMean: 127.5,   // defaults to 127.5
  imageStd: 127.5,    // defaults to 127.5
  rotation: 90,       // defaults to 90, Android only
  numResults: 2,      // defaults to 5
  threshold: 0.1,     // defaults to 0.1
);

Object Detection

  • 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
  }
}

SSD MobileNet:

  • Run on image:
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
);
  • Run on binary:
var recognitions = await Tflite.detectObjectOnBinary(
  binary: imageToByteListUint8(resizedImage, 300), // required
  model: "SSDMobileNet",  
  threshold: 0.4,                                  // defaults to 0.1
  numResultsPerClass: 2,                           // defaults to 5
);
  • Run on image stream (video frame):

Works with camera plugin 4.0.0. Video format: (iOS) kCVPixelFormatType_32BGRA, (Android) YUV_420_888.

var recognitions = await Tflite.detectObjectOnFrame(
  bytesList: img.planes.map((plane) {return plane.bytes;}).toList(),// required
  model: "SSDMobileNet",  
  imageHeight: img.height,
  imageWidth: img.width,
  imageMean: 127.5,   // defaults to 127.5
  imageStd: 127.5,    // defaults to 127.5
  rotation: 90,       // defaults to 90, Android only
  numResults: 2,      // defaults to 5
  threshold: 0.1,     // defaults to 0.1
);

Tiny YOLOv2:

  • Run on image:
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
  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
);
  • Run on binary:
var recognitions = await Tflite.detectObjectOnBinary(
  binary: imageToByteListFloat32(resizedImage, 416, 0.0, 255.0), // required
  model: "YOLO",  
  threshold: 0.3,       // defaults to 0.1
  numResultsPerClass: 2,// defaults to 5
  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
);
  • Run on image stream (video frame):

Works with camera plugin 4.0.0. Video format: (iOS) kCVPixelFormatType_32BGRA, (Android) YUV_420_888.

var recognitions = await Tflite.detectObjectOnFrame(
  bytesList: img.planes.map((plane) {return plane.bytes;}).toList(),// required
  model: "YOLO",  
  imageHeight: img.height,
  imageWidth: img.width,
  imageMean: 0,         // defaults to 127.5
  imageStd: 255.0,      // defaults to 127.5
  numResults: 2,        // defaults to 5
  threshold: 0.1,       // defaults to 0.1
  numResultsPerClass: 2,// defaults to 5
  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
);

Demo

Libraries

tflite

Dart

dart:ui
Built-in types and core primitives for a Flutter application. [...]

Core

dart:async
Support for asynchronous programming, with classes such as Future and Stream. [...]
dart:collection
Classes and utilities that supplement the collection support in dart:core. [...]
dart:convert
Encoders and decoders for converting between different data representations, including JSON and UTF-8. [...]
dart:core
Built-in types, collections, and other core functionality for every Dart program. [...]
dart:developer
Interact with developer tools such as the debugger and inspector. [...]
dart:math
Mathematical constants and functions, plus a random number generator. [...]
dart:typed_data
Lists that efficiently handle fixed sized data (for example, unsigned 8 byte integers) and SIMD numeric types. [...]

VM

dart:io
File, socket, HTTP, and other I/O support for non-web applications. [...]
dart:isolate
Concurrent programming using isolates: independent workers that are similar to threads but don't share memory, communicating only via messages. [...]