套件詳情v1.1.2

tflite

一個用於存取 TensorFlow Lite 的 Flutter 插件。支援 iOS 與 Android。

AndroidiOS

pub.dev API 文件原始碼

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安裝套件

flutter pub add tflite

套件介紹

根據套件公開資料產生的選型評估，僅供參考。

tflite 是一個 Flutter 插件，可實現 TensorFlow Lite 的整合，用於設備上的機器學習推論。它支援 Android 和 iOS 平台，讓開發者能在行動裝置上直接執行輕量級機器學習模型。適用於影像分類、物件偵測和即時處理等任務，提供簡單直觀的 API，以高效載入和執行模型。該插件由社群維護，並為 Flutter 應用程式提供無縫存取 TensorFlow Lite 功能的能力。

適用情境

圖像分類
目標檢測
即時推論
設備端AI處理
移動OCR

主要特色

跨平台支援（Android 與 iOS）
輕量級模型執行
模型加載的簡單API
支援各種 TensorFlow Lite 模型
高效設備端推論

適合對象

具備AI功能的行動應用程式
電腦視覺專案
離線機器學習
教育類機器學習示範
對性能敏感的應用程式

使用建議

需要將模型轉換為 TensorFlow Lite 格式
僅限推論（不包含訓練）
模型大小影響應用程式封裝大小
可能需要針對低階設備進行優化
沒有內建的模型管理工具

原始 README

以下為關聯原始碼儲存庫的 README 原文快照，最新內容請造訪原始碼儲存庫。

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.

Installation
Usage
Example
- Prediction in Static Images
- Real-time Detection

Breaking changes

Since 1.1.0:

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:

Updated to TensorFlow Lite API v1.12.0.
No longer accepts parameter inputSize and numChannels. They will be retrieved from input tensor.
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:

'vector' file not found"

Open ios/Runner.xcworkspace in Xcode, click Runner > Tagets > Runner > Build Settings, search Compile Sources As, change the value to Objective-C++
'tensorflow/lite/kernels/register.h' file not found

The plugin assumes the tensorflow header files are located in path "tensorflow/lite/kernels".

However, for early versions of tensorflow the header path is "tensorflow/contrib/lite/kernels".

Use CONTRIB_PATH to toggle the path. Uncomment //#define CONTRIB_PATH from here: https://github.com/shaqian/flutter_tflite/blob/master/ios/Classes/TflitePlugin.mm#L1

Usage

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

Import the library:

import 'package:tflite/tflite.dart';

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
);

See the section for the respective model below.
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

Output format:

{
  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
  asynch: true      // defaults to true
);

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
  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();
}

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
  asynch: true        // defaults to true
);

Object Detection

Output format:

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
  asynch: true          // defaults to true
);

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
  asynch: true                                     // defaults to true
);

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
  asynch: true        // defaults to true
);

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
  asynch: true          // defaults to true
);

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
  asynch: true          // defaults to true
);

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
  asynch: true          // defaults to true
);

Pix2Pix

Thanks to RP from Green Appers

Output format:

The output of Pix2Pix inference is Uint8List type. Depending on the outputType used, the output is:
- (if outputType is png) byte array of a png image
- (otherwise) byte array of the raw output
Run on image:

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
);

Run on binary:

var result = await runPix2PixOnBinary(
  binary: binary,       // required
  asynch: true      // defaults to true
);

Run on image stream (video frame):

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.

Output format:

The output of Deeplab inference is Uint8List type. Depending on the outputType used, the output is:
- (if outputType is png) byte array of a png image
- (otherwise) byte array of r, g, b, a values of the pixels
Run on image:

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
);

Run on binary:

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
);

Run on image stream (video frame):

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.

Output format:

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
      }
      ......
    }
  },
  ......
]

Run on image:

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
);

Run on binary:

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
);

Run on image stream (video frame):

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

所屬分類

機器學習、OCR 與電腦視覺

依賴

依賴清單 1 項

meta^1.3.0

原始碼儲存庫

shaqian/flutter_tflite

flutter_tflite

☆ 636 星標⑂ 434 分支

Objective-C++Javafluttertensorflowtensorflowlite

使用此套件的專案

sumn2u/deep-waste-app

搜尋套件、儲存庫與 APP 分類

tflite

安裝套件

套件介紹

適用情境

主要特色

適合對象

使用建議

原始 README

tflite

Table of Contents

Breaking changes

Since 1.1.0:

Since 1.0.0:

Installation

Android

iOS

Usage

GPU Delegate

Image Classification

Object Detection

SSD MobileNet:

Tiny YOLOv2:

Pix2Pix

Deeplab

PoseNet

Example

Prediction in Static Images

Real-time detection

Run test cases

所屬分類

依賴

原始碼儲存庫

flutter_tflite

使用此套件的專案

深坑

植物疾病檢測器

HelpingHand

手勢控制器