detection with tenserflow

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 import React, { Component } from 'react';

import { View, StyleSheet } from 'react-native';

import Video from 'react-native-video';

import * as tf from '@tensorflow/tfjs';

import '@tensorflow/tfjs-react-native';


// Constants for object detection

const MODEL_URL = 'https://tfhub.dev/tensorflow/tfjs-model/ssd_mobilenet_v1/1/default/1/model.json';

const INPUT_SIZE = 224; // Input size required by the model

const OBJECT_THRESHOLD = 0.5; // Minimum confidence threshold for detected objects


class VideoScreen extends Component {

  constructor(props) {

    super(props);


    this.state = {

      isModelReady: false,

      objects: [],

    };


    // Load the TensorFlow.js model

    this.loadModel();

  }


  async loadModel() {

    try {

      await tf.ready();

      const model = await tf.loadGraphModel(MODEL_URL);

      this.setState({ isModelReady: true, model });

      this.processVideoFrames();

    } catch (error) {

      console.error('Error loading the model:', error);

    }

  }


  processVideoFrames() {

    if (!this.state.isModelReady) return;


    const videoStream = { uri: 'http://192.168.1.16/' }; // Replace with the video stream URL


    this.videoRef.seek(0);


    const loop = async () => {

      const frame = await this.videoRef.getCurrentFrame();

      if (!frame) return;


      const { width, height } = frame;

      const imageData = frame.data;

      const imageTensor = tf.browser.fromPixels({ data: new Uint8Array(imageData), width, height });

      const resizedTensor = tf.image.resizeBilinear(imageTensor, [INPUT_SIZE, INPUT_SIZE]);

      const batchedTensor = resizedTensor.expandDims(0);

      const scaledTensor = batchedTensor.div(255);


      const predictions = await this.state.model.executeAsync(scaledTensor);

      const objects = this.parseObjectDetection(predictions);


      this.setState({ objects });


      requestAnimationFrame(loop);

    };


    this.videoRef.setOnPlaybackStatusUpdate(loop);

    this.videoRef.ref.setNativeProps({ source: videoStream });

  }


  parseObjectDetection(predictions) {

    const [boxes, scores, classes, numDetections] = predictions;


    const objects = [];


    for (let i = 0; i < numDetections; i++) {

      const score = scores[i];

      const classIndex = classes[i];

      const boundingBox = boxes[i];


      if (score > OBJECT_THRESHOLD) {

        const object = {

          class: classIndex,

          score: score,

          box: boundingBox,

        };


        objects.push(object);

      }

    }


    return objects;

  }


  render() {

    return (

      <View style={styles.container}>

        <Video

          ref={(ref) => (this.videoRef = ref)}

          style={styles.videoPlayer}

          resizeMode="cover"

          repeat={true}

          paused={false}

        />


        {/* Render the detected objects as overlays */}

        {this.state.objects.map((object, index) => (

          <View

            key={index}

            style={[

              styles.boundingBox,

              {

                left: object.box.originX,

                top: object.box.originY,

                width: object.box.width,

                height: object.box.height,

              },

            ]}

          />

        ))}

      </View>

    );

  }

}


const styles = StyleSheet.create({

  container: {

    flex: 1,

    backgroundColor: '#000',

    justifyContent: 'center',

    alignItems: 'center',

  },

  videoPlayer: {

    position: 'absolute',

    top: 0,

    left: 0,

    width: '100%',

    height: '100%',

  },

  boundingBox: {

    position: 'absolute',

    borderColor: 'red',

    borderWidth: 2,

  },

});


export default VideoScreen;


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