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tflite

A Flutter plugin for accessing TensorFlow Lite API. Supports image classification, object detection (SSD and YOLO), Pix2Pix and Deeplab and PoseNet on both iOS and Android.

Table of Contents

Breaking changes

Since 1.1.0:

  1. iOS TensorFlow Lite library is upgraded from TensorFlowLite 1.x to TensorFlowLiteObjC 2.x. Changes to native code are denoted with TFLITE2.

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'
        noCompress 'lite'
    }

iOS

Solutions to build errors on iOS:

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
  isAsset: true, // defaults to true, set to false to load resources outside assets
  useGpuDelegate: false // defaults to false, set to true to use GPU delegate
);
  1. See the section for the respective model below.

  2. Release resources:

await Tflite.close();

GPU Delegate

When using GPU delegate, refer to this step for release mode setting to get better performance.

Image Classification

{
  index: 0,
  label: "person",
  confidence: 0.629
}
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
  asynch: true      // defaults to true
);
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
  asynch: true      // defaults to true
);

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();
}

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
  asynch: true        // defaults to true
);

Object Detection

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:

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
  asynch: true          // defaults to true
);
var recognitions = await Tflite.detectObjectOnBinary(
  binary: imageToByteListUint8(resizedImage, 300), // required
  model: "SSDMobileNet",  
  threshold: 0.4,                                  // defaults to 0.1
  numResultsPerClass: 2,                           // defaults to 5
  asynch: true                                     // defaults to true
);

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
  asynch: true        // defaults to true
);

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
  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
  asynch: true          // defaults to true
);
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
  asynch: true          // defaults to true
);

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
  asynch: true          // defaults to true
);

Pix2Pix

Thanks to RP from Green Appers

var result = await runPix2PixOnImage(
  path: filepath,       // required
  imageMean: 0.0,       // defaults to 0.0
  imageStd: 255.0,      // defaults to 255.0
  asynch: true      // defaults to true
);
var result = await runPix2PixOnBinary(
  binary: binary,       // required
  asynch: true      // defaults to true
);
var result = await runPix2PixOnFrame(
  bytesList: img.planes.map((plane) {return plane.bytes;}).toList(),// required
  imageHeight: img.height, // defaults to 1280
  imageWidth: img.width,   // defaults to 720
  imageMean: 127.5,   // defaults to 0.0
  imageStd: 127.5,    // defaults to 255.0
  rotation: 90,       // defaults to 90, Android only
  asynch: true        // defaults to true
);

Deeplab

Thanks to RP from see-- for Android implementation.

var result = await runSegmentationOnImage(
  path: filepath,     // required
  imageMean: 0.0,     // defaults to 0.0
  imageStd: 255.0,    // defaults to 255.0
  labelColors: [...], // defaults to https://github.com/shaqian/flutter_tflite/blob/master/lib/tflite.dart#L219
  outputType: "png",  // defaults to "png"
  asynch: true        // defaults to true
);
var result = await runSegmentationOnBinary(
  binary: binary,     // required
  labelColors: [...], // defaults to https://github.com/shaqian/flutter_tflite/blob/master/lib/tflite.dart#L219
  outputType: "png",  // defaults to "png"
  asynch: true        // defaults to true
);
var result = await runSegmentationOnFrame(
  bytesList: img.planes.map((plane) {return plane.bytes;}).toList(),// required
  imageHeight: img.height, // defaults to 1280
  imageWidth: img.width,   // defaults to 720
  imageMean: 127.5,        // defaults to 0.0
  imageStd: 127.5,         // defaults to 255.0
  rotation: 90,            // defaults to 90, Android only
  labelColors: [...],      // defaults to https://github.com/shaqian/flutter_tflite/blob/master/lib/tflite.dart#L219
  outputType: "png",       // defaults to "png"
  asynch: true             // defaults to true
);

PoseNet

Model is from StackOverflow thread.

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

[ // array of poses/persons
  { // pose #1
    score: 0.6324902,
    keypoints: {
      0: {
        x: 0.250,
        y: 0.125,
        part: nose,
        score: 0.9971070
      },
      1: {
        x: 0.230,
        y: 0.105,
        part: leftEye,
        score: 0.9978438
      }
      ......
    }
  },
  { // pose #2
    score: 0.32534285,
    keypoints: {
      0: {
        x: 0.402,
        y: 0.538,
        part: nose,
        score: 0.8798978
      },
      1: {
        x: 0.380,
        y: 0.513,
        part: leftEye,
        score: 0.7090239
      }
      ......
    }
  },
  ......
]
var result = await runPoseNetOnImage(
  path: filepath,     // required
  imageMean: 125.0,   // defaults to 125.0
  imageStd: 125.0,    // defaults to 125.0
  numResults: 2,      // defaults to 5
  threshold: 0.7,     // defaults to 0.5
  nmsRadius: 10,      // defaults to 20
  asynch: true        // defaults to true
);
var result = await runPoseNetOnBinary(
  binary: binary,     // required
  numResults: 2,      // defaults to 5
  threshold: 0.7,     // defaults to 0.5
  nmsRadius: 10,      // defaults to 20
  asynch: true        // defaults to true
);
var result = await runPoseNetOnFrame(
  bytesList: img.planes.map((plane) {return plane.bytes;}).toList(),// required
  imageHeight: img.height, // defaults to 1280
  imageWidth: img.width,   // defaults to 720
  imageMean: 125.0,        // defaults to 125.0
  imageStd: 125.0,         // defaults to 125.0
  rotation: 90,            // defaults to 90, Android only
  numResults: 2,           // defaults to 5
  threshold: 0.7,          // defaults to 0.5
  nmsRadius: 10,           // defaults to 20
  asynch: true             // defaults to true
);

Example

Prediction in Static Images

Refer to the example.

Real-time detection

Refer to flutter_realtime_Detection.

Run test cases

flutter test test/tflite_test.dart