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https://github.com/shaqian/flutter_tflite
Flutter plugin for TensorFlow Lite
https://github.com/shaqian/flutter_tflite
dart flutter tensorflow tensorflowlite
Last synced: 6 days ago
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Flutter plugin for TensorFlow Lite
- Host: GitHub
- URL: https://github.com/shaqian/flutter_tflite
- Owner: shaqian
- License: mit
- Created: 2018-09-23T23:29:03.000Z (over 6 years ago)
- Default Branch: master
- Last Pushed: 2024-06-05T15:59:17.000Z (7 months ago)
- Last Synced: 2024-12-14T00:03:38.610Z (13 days ago)
- Topics: dart, flutter, tensorflow, tensorflowlite
- Language: Objective-C++
- Homepage: https://pub.dartlang.org/packages/tflite
- Size: 63.3 MB
- Stars: 633
- Watchers: 23
- Forks: 413
- Open Issues: 191
-
Metadata Files:
- Readme: README.md
- Changelog: CHANGELOG.md
- License: LICENSE
Awesome Lists containing this project
README
# tflite
A Flutter plugin for accessing TensorFlow Lite API. Supports image classification, object detection ([SSD](https://github.com/tensorflow/models/tree/master/research/object_detection) and [YOLO](https://pjreddie.com/darknet/yolov2/)), [Pix2Pix](https://phillipi.github.io/pix2pix/) and [Deeplab](https://github.com/tensorflow/models/tree/master/research/deeplab) and [PoseNet](https://www.tensorflow.org/lite/models/pose_estimation/overview) on both iOS and Android.
### Table of Contents
- [Installation](#Installation)
- [Usage](#Usage)
- [Image Classification](#Image-Classification)
- [Object Detection](#Object-Detection)
- [SSD MobileNet](#SSD-MobileNet)
- [YOLO](#Tiny-YOLOv2)
- [Pix2Pix](#Pix2Pix)
- [Deeplab](#Deeplab)
- [PoseNet](#PoseNet)
- [Example](#Example)
- [Prediction in Static Images](#Prediction-in-Static-Images)
- [Real-time Detection](#Real-time-Detection)
### 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](https://flutter.io/using-packages/).
### 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
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:
```dart
import 'package:tflite/tflite.dart';
```
3. Load the model and labels:
```dart
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
);
```
4. See the section for the respective model below.
5. Release resources:
```
await Tflite.close();
```
### GPU Delegate
When using GPU delegate, refer to [this step](https://www.tensorflow.org/lite/performance/gpu#step_5_release_mode) for release mode setting to get better performance.
### Image Classification
- Output format:
```
{
index: 0,
label: "person",
confidence: 0.629
}
```
- Run on image:
```dart
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:
```dart
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](https://pub.dartlang.org/packages/camera). Video format: (iOS) kCVPixelFormatType_32BGRA, (Android) YUV_420_888.
```dart
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:
```dart
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:
```dart
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](https://pub.dartlang.org/packages/camera). Video format: (iOS) kCVPixelFormatType_32BGRA, (Android) YUV_420_888.
```dart
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:
```dart
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:
```dart
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](https://pub.dartlang.org/packages/camera). Video format: (iOS) kCVPixelFormatType_32BGRA, (Android) YUV_420_888.
```dart
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](https://github.com/shaqian/flutter_tflite/pull/18) from [Green Appers](https://github.com/GreenAppers)
- 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:
```dart
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:
```dart
var result = await runPix2PixOnBinary(
binary: binary, // required
asynch: true // defaults to true
);
```
- Run on image stream (video frame):
```dart
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](https://github.com/shaqian/flutter_tflite/pull/22) from [see--](https://github.com/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:
```dart
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:
```dart
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):
```dart
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](https://stackoverflow.com/a/55288616).
- 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:
```dart
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:
```dart
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):
```dart
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](https://github.com/shaqian/flutter_tflite/tree/master/example).
### Real-time detection
Refer to [flutter_realtime_Detection](https://github.com/shaqian/flutter_realtime_detection).
## Run test cases
`flutter test test/tflite_test.dart`