react-native - React Native TensorFlow 问题

问题描述

我正在尝试构建一个对图像进行分类的应用程序。为此，我使用 TensorFlow。我从使用 TensorFlow.js 和 MobileNet 的 React Native 上的图像分类中获取了这些代码

但是当我尝试单击“点击以选择图像”按钮时，我什么也没有。这是我的代码：.................................................. ..................................................... ..................................................... .....................................................

import React from 'react'
import { StyleSheet, Text, View, StatusBar, ActivityIndicator, TouchableOpacity, Image } from 'react-native'
import * as tf from '@tensorflow/tfjs'
import { fetch } from '@tensorflow/tfjs-react-native'
import * as mobilenet from '@tensorflow-models/mobilenet'
import Constants from 'expo-constants'
import * as Permissions from 'expo-permissions'
import * as jpeg from 'jpeg-js'

class App extends React.Component {
  state = {
  isTfReady: false,
  isModelReady: false,
  predictions: null,
  image: null
}
getPermissionAsync = async () => {
  if (Constants.platform.ios) {
    const { status } = await Permissions.askAsync(Permissions.CAMERA_ROLL)
    if (status !== 'granted') {
      alert('Sorry, we need camera roll permissions to make this work!')
    }
  }
}
imageToTensor(rawImageData) {
    const TO_UINT8ARRAY = true
    const { width, height, data } = jpeg.decode(rawImageData, TO_UINT8ARRAY)
    // Drop the alpha channel info for mobilenet
    const buffer = new Uint8Array(width * height * 3)
    let offset = 0 // offset into original data
    for (let i = 0; i < buffer.length; i += 3) {
      buffer[i] = data[offset]
      buffer[i + 1] = data[offset + 1]
      buffer[i + 2] = data[offset + 2]

      offset += 4
    }

    return tf.tensor3d(buffer, [height, width, 3])
  }

classifyImage = async () => {
  try {
    const imageAssetPath = Image.resolveAssetSource(this.state.image)
    const response = await fetch(imageAssetPath.uri, {}, { isBinary: true })
    const rawImageData = await response.arrayBuffer()
    const imageTensor = this.imageToTensor(rawImageData)
    const predictions = await this.model.classify(imageTensor)
    this.setState({ predictions })
    console.log(predictions)
  } catch (error) {
    console.log(error)
  }
}

selectImage = async () => {
  try {
    let response = await ImagePicker.launchImageLibraryAsync({
      mediaTypes: ImagePicker.MediaTypeOptions.All,
      allowsEditing: true,
      aspect: [4, 3]
    })

    if (!response.cancelled) {
      const source = { uri: response.uri }
      this.setState({ image: source })
      this.classifyImage()
    }
  } catch (error) {
    console.log(error)
  }
}
  async componentDidMount() {
    await tf.ready()
    this.setState({
      isTfReady: true
    })
    this.model = await mobilenet.load()
    this.setState({ isModelReady: true })

    // add this
    this.getPermissionAsync()
  }

  render() {
    const { isTfReady, isModelReady, predictions, image } = this.state

    return (
      <View style={styles.container}>
        <StatusBar barStyle='light-content' />
        <View style={styles.loadingContainer}>
          <Text style={styles.commonTextStyles}>
            TFJS ready? {isTfReady ? <Text>✅&lt;/Text> : ''}
          </Text>

          <View style={styles.loadingModelContainer}>
            <Text style={styles.text}>Model ready? </Text>
            {isModelReady ? (
              <Text style={styles.text}>✅&lt;/Text>
            ) : (
              <ActivityIndicator size='small' />
            )}
          </View>
        </View>
        <TouchableOpacity
          style={styles.imageWrapper}
          onPress={isModelReady ? this.selectImage : undefined}>
          {image && <Image source={image} style={styles.imageContainer} />}

          {isModelReady && !image && (
            <Text style={styles.transparentText}>Tap to choose image</Text>
          )}
        </TouchableOpacity>
        <View style={styles.predictionWrapper}>
          {isModelReady && image && (
            <Text style={styles.text}>
              Predictions: {predictions ? '' : 'Predicting...'}
            </Text>
          )}
          {isModelReady &&
            predictions &&
            predictions.map(p => this.renderPrediction(p))}
        </View>
        <View style={styles.footer}>
          <Text style={styles.poweredBy}>Powered by:</Text>
          <Image source={require('./assets/tfjs.jpg')} style={styles.tfLogo} />
        </View>
      </View>
    )
  }
}




const styles = StyleSheet.create({
  container: {
    flex: 1,
    backgroundColor: '#171f24',
    alignItems: 'center'
  },
  loadingContainer: {
    marginTop: 80,
    justifyContent: 'center'
  },
  text: {
    color: '#ffffff',
    fontSize: 16
  },
  loadingModelContainer: {
    flexDirection: 'row',
    marginTop: 10
  },
  imageWrapper: {
    width: 280,
    height: 280,
    padding: 10,
    borderColor: '#cf667f',
    borderWidth: 5,
    borderStyle: 'dashed',
    marginTop: 40,
    marginBottom: 10,
    position: 'relative',
    justifyContent: 'center',
    alignItems: 'center'
  },
  imageContainer: {
    width: 250,
    height: 250,
    position: 'absolute',
    top: 10,
    left: 10,
    bottom: 10,
    right: 10
  },
  predictionWrapper: {
    height: 100,
    width: '100%',
    flexDirection: 'column',
    alignItems: 'center'
  },
  transparentText: {
    color: '#ffffff',
    opacity: 0.7
  },
  footer: {
    marginTop: 40
  },
  poweredBy: {
    fontSize: 20,
    color: '#e69e34',
    marginBottom: 6
  },
  tfLogo: {
    width: 125,
    height: 70
  }
})

export default App

标签： react-nativetensorflowvisual-studio-code

react-native - React Native TensorFlow 问题

问题描述

解决方案

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