last gpt4 live camera

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 import React, { useEffect, useRef, useState } from 'react';

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

import VLCPlayer from 'react-native-vlc-player';

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

import { fetch } from '@tensorflow/tfjs-react-native';

import { decodeJpeg } from '@tensorflow/tfjs-react-native';


const ObjectDetection = () => {

  const vlcPlayerRef = useRef(null);

  const modelRef = useRef(null);

  const streamingRef = useRef(false);

  const [overlayImage, setOverlayImage] = useState(null);


  useEffect(() => {

    const loadModel = async () => {

      try {

        await tf.ready();

        modelRef.current = await tf.loadGraphModel(

          fetch(

            'https://tfhub.dev/tensorflow/ssd_mobilenet_v2/2/default/1',

            { method: 'GET' }

          )

        );

        streamingRef.current = true;

      } catch (error) {

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

      }

    };


    loadModel();

  }, []);


  const processFrame = async () => {

    if (!streamingRef.current) {

      return;

    }


    try {

      const snapshot = await vlcPlayerRef.current.takeSnapshot(true);

      const imageTensor = await decodeJpeg(new Uint8Array(snapshot));

      const preprocessedTensor = imageTensor.resizeBilinear([300, 300]).expandDims();

      const predictions = await modelRef.current.executeAsync(preprocessedTensor);


      const processedImage = processPredictions(predictions, imageTensor);

      setOverlayImage(processedImage);


      imageTensor.dispose();

      preprocessedTensor.dispose();

      tf.dispose(predictions);

    } catch (error) {

      console.error('Error processing frame:', error);

    }


    requestAnimationFrame(processFrame);

  };


  const processPredictions = (predictions, imageTensor) => {

    // Process the predictions and draw bounding boxes on the image

    // You can filter predictions based on a certain threshold or other criteria


    const canvas = document.createElement('canvas');

    const ctx = canvas.getContext('2d');

    canvas.width = imageTensor.shape[1];

    canvas.height = imageTensor.shape[0];


    // Draw the original image on the canvas

    const imageData = new ImageData(new Uint8ClampedArray(imageTensor.dataSync()), canvas.width, canvas.height);

    ctx.putImageData(imageData, 0, 0);


    // Draw bounding boxes for detected objects

    const boxes = predictions[0].dataSync();

    const scores = predictions[1].dataSync();

    const classes = predictions[2].dataSync();


    for (let i = 0; i < scores.length; i++) {

      if (scores[i] > 0.5) {

        const [y1, x1, y2, x2] = boxes.slice(i * 4, i * 4 + 4);

        ctx.strokeStyle = 'red';

        ctx.lineWidth = 2;

        ctx.strokeRect(x1 * canvas.width, y1 * canvas.height, (x2 - x1) * canvas.width, (y2 - y1) * canvas.height);

      }

    }


    // Convert the canvas to a base64-encoded image

    const processedImage = canvas.toDataURL('image/jpeg');

    return processedImage;

  };


  useEffect(() => {

    processFrame();


    return () => {

      streamingRef.current = false;

    };

  }, []);


  return (

    <View style={styles.container}>

      <VLCPlayer

        ref={vlcPlayerRef}

        style={styles.videoPlayer}

        videoAspectRatio="16:9"

        source={{

          uri: 'rtsp://admin:admin@192.168.184.64:1935',

          autoplay: true,

          initOptions: ['--rtsp-tcp'],

        }}

      />

      <View style={styles.overlay}>

        <Image style={styles.processedImage} source={{ uri: overlayImage }} />

      </View>

    </View>

  );

};


const styles = StyleSheet.create({

  container: {

    flex: 1,

  },

  videoPlayer: {

    flex: 1,

    width: Dimensions.get('window').width,

    height: Dimensions.get('window').height,

  },

  overlay: {

    position: 'absolute',

    top: 0,

    left: 0,

    width: Dimensions.get('window').width,

    height: Dimensions.get('window').height,

    justifyContent: 'center',

    alignItems: 'center',

  },

  processedImage: {

    width: Dimensions.get('window').width,

    height: Dimensions.get('window').height,

    resizeMode: 'cover',

  },

});


export default ObjectDetection;


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