The frame processor plugin to image labeling using Google ML Kit library for react-native-vision-camera with high performance.

🚨 Required Modules

react-native-vision-camera => 4.0.0

react-native-worklets-core = 1.1.1

💻 Installation

``sh
npm install react-native-vision-camera-v3-image-labeling
yarn add react-native-vision-camera-v3-image-labeling
`


👷Features

Easy To Use.
Works Just Writing few lines of Code.
Works With React Native Vision Camera.
Works for Both Cameras.
Works Fast.
Works With Android 🤖 and IOS.📱
Writen With Kotlin and Objective-C.

💡 Usage

`js
import React, { useState } from 'react'
import { useCameraDevice } from 'react-native-vision-camera'
import { Camera } from 'react-native-vision-camera-v3-image-labeling';

function App (){
const [data,setData] = useState(null)
const device = useCameraDevice('back');
console.log(data)
return(
<>
{!!device && (
style={StyleSheet.absoluteFill}
device={device}
isActive
// optional
options={{
minConfidence: 0.1
}}
callback={(d) => setData(d)}
/>
)}

)
}

`


$3

`js
import React from 'react';
import { StyleSheet } from "react-native";
import {
Camera,
useCameraDevice,
useFrameProcessor,
} from "react-native-vision-camera";
import { useImageLabeler } from "react-native-vision-camera-v3-image-labeling";

function App() {
const device = useCameraDevice('back');
const options = {minConfidence : 0.1}
const {scanImage} = useImageLabeler(options)
const frameProcessor = useFrameProcessor((frame) => {
'worklet'
const data = scanImage(frame)
console.log(data, 'data')
}, [])
return (
<>
{!!device && (
style={StyleSheet.absoluteFill}
device={device}
isActive
frameProcessor={frameProcessor}
/>
)}

);
}
export default App;
``

---

⚙️ Options

| Name | Type | Values | Default |
| :---: | :---: |:---------:| :---: |
| minConfidence | Number | 0.1 - 1.0 | 0.1 |