new vlc media player

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

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

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

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

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

const ObjectDetection = () => {

  const vlcPlayerRef = useRef(null);

  const modelRef = useRef(null);

  useEffect(() => {

    const loadModel = async () => {

      try {

        await tf.ready();

        const modelPath = 'path/to/your/model.json';

        modelRef.current = await tf.loadLayersModel(modelPath);

      } catch (error) {

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

      }

    };

    loadModel();

  }, []);

  const processFrame = async () => {

    try {

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

      const imageTensor = tf.browser.fromPixels({ uri: `data:image/jpeg;base64,${snapshot}` });

      

      // Preprocess the image for object detection

      const preprocessedTensor = preprocessImage(imageTensor);

      // Perform object detection using the loaded model

      const predictions = modelRef.current.predict(preprocessedTensor);

      // Process the predictions and perform necessary actions

      processPredictions(predictions);

      imageTensor.dispose();

      preprocessedTensor.dispose();

      tf.dispose(predictions);

    } catch (error) {

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

    }

    requestAnimationFrame(processFrame);

  };

  const preprocessImage = (imageTensor) => {

    // Resize the image to the desired input size

    const resizedTensor = tf.image.resizeBilinear(imageTensor, [224, 224]);

    // Normalize the pixel values to the range [0, 1]

    const normalizedTensor = resizedTensor.div(255);

    // Expand the dimensions to match the expected input shape of the model

    const preprocessedTensor = normalizedTensor.expandDims();

    return preprocessedTensor;

  };

  const processPredictions = (predictions) => {

    // Process the predictions and perform necessary actions

    // You can iterate over the predictions and extract relevant information

    // For example, you can filter predictions based on a certain threshold,

    // draw bounding boxes on the image, or trigger other events based on detected objects.

    // Here's an example of logging the top prediction:

    const topPrediction = predictions.argMax().dataSync()[0];

    console.log('Top prediction:', topPrediction);

  };

  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'],

        }}

      />

      {/* Add any UI components or overlays as needed */}

      {/* Example: Display the processed frame */}

      <View style={styles.overlay}>

        <Image source={{ uri: 'data:image/jpeg;base64,${snapshot}' }} style={styles.processedImage} />

      </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;