tflite-react-native
v0.0.5
Published
A react native library for accessing TensorFlow Lite API. Supports Classification and Object Detection on both iOS and Android.
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tflite-react-native
A React Native library for accessing TensorFlow Lite API. Supports Classification, Object Detection, Deeplab and PoseNet on both iOS and Android.
Table of Contents
Installation
$ npm install tflite-react-native --save
iOS (only)
TensorFlow Lite is installed using CocoaPods:
Initialize Pod:
cd ios pod init
Open Podfile and add:
target '[your project's name]' do pod 'TensorFlowLite', '1.12.0' end
Install:
pod install
Automatic link
$ react-native link tflite-react-native
Manual link
iOS
- In XCode, in the project navigator, right click
Libraries
➜Add Files to [your project's name]
- Go to
node_modules
➜tflite-react-native
and addTfliteReactNative.xcodeproj
- In XCode, in the project navigator, select your project. Add
libTfliteReactNative.a
to your project'sBuild Phases
➜Link Binary With Libraries
- Run your project (
Cmd+R
)<
Android
- Open up
android/app/src/main/java/[...]/MainApplication.java
- Add
import com.reactlibrary.TfliteReactNativePackage;
to the imports at the top of the file - Add
new TfliteReactNativePackage()
to the list returned by thegetPackages()
method
- Append the following lines to
android/settings.gradle
:include ':tflite-react-native' project(':tflite-react-native').projectDir = new File(rootProject.projectDir, '../node_modules/tflite-react-native/android')
- Insert the following lines inside the dependencies block in
android/app/build.gradle
:compile project(':tflite-react-native')
Add models to the project
iOS
In XCode, right click on the project folder, click Add Files to "xxx"..., select the model and label files.
Android
In Android Studio (1.0 & above), right-click on the
app
folder and go to New > Folder > Assets Folder. Click Finish to create the assets folder.Place the model and label files at
app/src/main/assets
.In
android/app/build.gradle
, add the following setting inandroid
block.
aaptOptions {
noCompress 'tflite'
}
Usage
import Tflite from 'tflite-react-native';
let tflite = new Tflite();
Load model:
tflite.loadModel({
model: 'models/mobilenet_v1_1.0_224.tflite',// required
labels: 'models/mobilenet_v1_1.0_224.txt', // required
numThreads: 1, // defaults to 1
},
(err, res) => {
if(err)
console.log(err);
else
console.log(res);
});
Image classification:
tflite.runModelOnImage({
path: imagePath, // required
imageMean: 128.0, // defaults to 127.5
imageStd: 128.0, // defaults to 127.5
numResults: 3, // defaults to 5
threshold: 0.05 // defaults to 0.1
},
(err, res) => {
if(err)
console.log(err);
else
console.log(res);
});
- Output fomart:
{
index: 0,
label: "person",
confidence: 0.629
}
Object detection:
SSD MobileNet
tflite.detectObjectOnImage({
path: imagePath,
model: 'SSDMobileNet',
imageMean: 127.5,
imageStd: 127.5,
threshold: 0.3, // defaults to 0.1
numResultsPerClass: 2,// defaults to 5
},
(err, res) => {
if(err)
console.log(err);
else
console.log(res);
});
Tiny YOLOv2
tflite.detectObjectOnImage({
path: imagePath,
model: 'YOLO',
imageMean: 0.0,
imageStd: 255.0,
threshold: 0.3, // defaults to 0.1
numResultsPerClass: 2, // defaults to 5
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
},
(err, res) => {
if(err)
console.log(err);
else
console.log(res);
});
- 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
}
}
Deeplab
tflite.runSegmentationOnImage({
path: imagePath,
imageMean: 127.5, // defaults to 127.5
imageStd: 127.5, // defaults to 127.5
labelColors: [...], // defaults to https://github.com/shaqian/tflite-react-native/blob/master/index.js#L59
outputType: "png", // defaults to "png"
},
(err, res) => {
if(err)
console.log(err);
else
console.log(res);
});
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
PoseNet
Model is from StackOverflow thread.
tflite.runPoseNetOnImage({
path: imagePath,
imageMean: 127.5, // defaults to 127.5
imageStd: 127.5, // defaults to 127.5
numResults: 3, // defaults to 5
threshold: 0.8, // defaults to 0.5
nmsRadius: 20, // defaults to 20
},
(err, res) => {
if(err)
console.log(err);
else
console.log(res);
});
- 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
}
......
}
},
......
]
Release resources:
tflite.close();
Example
Refer to the example.