Nick Bourdakos bourdakos1

## flows.json
[{"id":"5b87f2d0.425eac","type":"tab","label":"Flow 1","disabled":false,"info":""},{"id":"a633f27d.72cc3","type":"http in","z":"5b87f2d0.425eac","name":"","url":"/dashboard","method":"get","upload":false,"swaggerDoc":"","x":140,"y":260,"wires":[["83927661.17c478"]]},{"id":"156284e1.bdaf6b","type":"template","z":"5b87f2d0.425eac","name":"","field":"payload","fieldType":"msg","format":"handlebars","syntax":"mustache","template":"<!DOCTYPE html>\n<html>\n  <meta charset=\"utf-8\" />\n  <body>\n      <h1>{{payload.websocketUrl}}</h1>\n      <canvas id=\"video-canvas\"></canvas>\n\n    <script>\nconst init = () => {\n  const videoCanvas = document.getElementById('video-canvas')\n  const url = `{{{payload.websocketUrl}}}`\n  new JSMpeg.Player(url, { canvas: videoCanvas })\n}\n\nif (document.readyState === 'loading') {\n  document.addEventListener('DOMContentLoaded', init)\n} else {\n  setTimeout(init, 500)\n}\n</script>\n    <script>\n       var JSMpeg={Player:null,VideoElement:null,BitBuffer:null,Source:{},Demuxer

## real-time-detect.js
function detectFrame() {
  model.detect(video).then(predictions => {
    renderOurPredictions(predictions)
    requestAnimationFrame(detectFrame)
  })
}

## webcam.js
const video = document.getElementById("video")

navigator.mediaDevices
  .getUserMedia({
    audio: false,
    video: {
      facingMode: "user",
      width: 600,
      height: 500
    }

## draw-box.js
const x = prediction.bbox[0];
const y = prediction.bbox[1];
const width = prediction.bbox[2];
const height = prediction.bbox[3];

const canvas = document.getElementById("canvas");
const ctx = canvas.getContext("2d");

ctx.strokeRect(x, y, width, height);

## detect-objects.js
import * as cocoSsd from "@tensorflow-models/coco-ssd";

const image = document.getElementById("image")

cocoSsd.load()
  .then(model => model.detect(image))
  .then(predictions => console.log(predictions))

## update.swift
let classifierID = "your-classifier-id"
let failure = { (error: Error) in print(error) }
visualRecognition.updateLocalModel(classifierID: classifierID, failure: failure) {
    print("model updated")
}

## classify.swift
let classifierID = "your-classifier-id"
let failure = { (error: Error) in print(error) }
let image = UIImage(named: "your-image-filename")
visualRecognition.classifyWithLocalModel(image: image, classifierIDs: [classifierID], failure: failure) {
    classifiedImages in print(classifiedImages)
}

## Tensorflow for Poets.ipynb

      
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                bourdakos1
                / Tensorflow for Poets.ipynb
            
            
              Created
              March 5, 2018 02:01
            
          
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## CameraViewController.swift
// Classification method.
func classify(_ image: CGImage, completion: @escaping ([VNClassificationObservation]) -> Void) {
    DispatchQueue.global(qos: .background).async {
        // Initialize the coreML vision model, you can also use VGG16().model, or any other model that takes an image.
        guard let vnCoreModel = try? VNCoreMLModel(for: Inceptionv3().model) else { return }

        // Build the coreML vision request.
        let request = VNCoreMLRequest(model: vnCoreModel) { (request, error) in
            // We get get an array of VNClassificationObservations back
            // This has the fields "confidence", which is the score

## designer.html
<link rel="import" href="../core-icon-button/core-icon-button.html">
<link rel="import" href="../core-toolbar/core-toolbar.html">
<link rel="import" href="../core-header-panel/core-header-panel.html">

<polymer-element name="my-element">

  <template>
    <style>
      :host {
        position: absolute;
	function detectFrame() {
	model.detect(video).then(predictions => {
	renderOurPredictions(predictions)
	requestAnimationFrame(detectFrame)
	})
	}
	const video = document.getElementById("video")

	navigator.mediaDevices
	.getUserMedia({
	audio: false,
	video: {
	facingMode: "user",
	width: 600,
	height: 500
	}
	const x = prediction.bbox[0];
	const y = prediction.bbox[1];
	const width = prediction.bbox[2];
	const height = prediction.bbox[3];

	const canvas = document.getElementById("canvas");
	const ctx = canvas.getContext("2d");

	ctx.strokeRect(x, y, width, height);
	import * as cocoSsd from "@tensorflow-models/coco-ssd";

	const image = document.getElementById("image")

	cocoSsd.load()
	.then(model => model.detect(image))
	.then(predictions => console.log(predictions))
	let classifierID = "your-classifier-id"
	let failure = { (error: Error) in print(error) }
	visualRecognition.updateLocalModel(classifierID: classifierID, failure: failure) {
	print("model updated")
	}
	let classifierID = "your-classifier-id"
	let failure = { (error: Error) in print(error) }
	let image = UIImage(named: "your-image-filename")
	visualRecognition.classifyWithLocalModel(image: image, classifierIDs: [classifierID], failure: failure) {
	classifiedImages in print(classifiedImages)
	}
	// Classification method.
	func classify(_ image: CGImage, completion: @escaping ([VNClassificationObservation]) -> Void) {
	DispatchQueue.global(qos: .background).async {
	// Initialize the coreML vision model, you can also use VGG16().model, or any other model that takes an image.
	guard let vnCoreModel = try? VNCoreMLModel(for: Inceptionv3().model) else { return }

	// Build the coreML vision request.
	let request = VNCoreMLRequest(model: vnCoreModel) { (request, error) in
	// We get get an array of VNClassificationObservations back
	// This has the fields "confidence", which is the score
	<link rel="import" href="../core-icon-button/core-icon-button.html">
	<link rel="import" href="../core-toolbar/core-toolbar.html">
	<link rel="import" href="../core-header-panel/core-header-panel.html">

	<polymer-element name="my-element">

	<template>
	<style>
	:host {
	position: absolute;