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bourdakos1 / Tensorflow for Poets.ipynb
Created March 5, 2018 02:01
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@bourdakos1
bourdakos1 / webcam.js
Last active July 27, 2020 08:46
const video = document.getElementById("video")
navigator.mediaDevices
.getUserMedia({
audio: false,
video: {
facingMode: "user",
width: 600,
height: 500
}
@bourdakos1
bourdakos1 / flows.json
Created September 17, 2019 21:51
[{"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
@bourdakos1
bourdakos1 / detect-objects.js
Last active July 25, 2019 11:44
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))
@bourdakos1
bourdakos1 / real-time-detect.js
Last active December 8, 2018 01:33
function detectFrame() {
model.detect(video).then(predictions => {
renderOurPredictions(predictions)
requestAnimationFrame(detectFrame)
})
}
@bourdakos1
bourdakos1 / draw-box.js
Created December 8, 2018 01:09
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);
@bourdakos1
bourdakos1 / update.swift
Created April 16, 2018 23:51
let classifierID = "your-classifier-id"
let failure = { (error: Error) in print(error) }
visualRecognition.updateLocalModel(classifierID: classifierID, failure: failure) {
print("model updated")
}
@bourdakos1
bourdakos1 / classify.swift
Last active April 16, 2018 23:49
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)
}
@bourdakos1
bourdakos1 / designer.html
Created July 28, 2014 17:20
designer
<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;
@bourdakos1
bourdakos1 / CameraViewController.swift
Created June 24, 2017 19:05
// 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