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<!-- *******************************Start: What source js file to use? **************************************************** -->
<!-- the online version that these examples were made with -->
<script src="https://cdn.jsdelivr.net/npm/@tensorflow/tfjs@0.13.1"></script>
<!-- the newest version script tag is below but by using it all the examples may not work
<script src="https://cdn.jsdelivr.net/npm/@tensorflow/tfjs@latest"></script>
-->
<!-- Or you could download the newest version, save it as myNewDownloadedTensorflow.js and use the link below and work completely offline using a tag similar to
<script src="myNewDownloadedTensorflow.js"></script>
-->
<!-- Note: often nice to load the readable version of the src file. Remember to match the numbers to whatever the newest version is.
<script src="https://cdn.jsdelivr.net/npm/@tensorflow/tfjs@0.13.0/dist/tf.js"></script>
-->
<!-- *******************************Stop: What source js file to use? **************************************************** -->
<h2 align=center> As easy as I can make Loading Tensorflowjs KNN-Classifier Demo </h2>
<div style="font-size:15px; background-color:lightyellow; width:88%; border:5px solid blue; padding:5px; margin:5px;">
Online demo of the very interesting <a href="https://github.com/tensorflow/tfjs-models/tree/master/knn-classifier">KNN-Classifier </a>
Github in the <a href="https://github.com/tensorflow/tfjs-models">TFJS-models </a> repository<br><br>
KNN-Classifier takes a Deep Learning model and uses K-Nearest-Neighbor analysis to it. So you can, from a website,
live "Train" objects from the model and get results at the same time! <br>
This time we are going to use <a href="https://github.com/justadudewhohacks/face-api.js">face-api.js</a> to run a 68 point face detection
model then train the knn-classifier on people. Kind of fun to google celebrities for training then check out the rest of us. The KNN classifier
will detect the closest celebrity.<br><br>
I am looking into how to save the knn-classfier data with labels. <br><br>
Data from:
<a href="https://www.imdb.com/list/ls000760476/">https://www.imdb.com/list/ls000760476/</a>
</div><br>
<div id="myDiv123Code">
<!-- Load face-api from this github -->
<script src="face-api.min.js"></script>
<!-- Load KNN Classifier -->
<script src="https://cdn.jsdelivr.net/npm/@tensorflow-models/knn-classifier@0.2.2"></script>
<input type=button value="Load Face-api.js face_landmark_68_tiny_model"style="background-color:lime" onclick="{
myRunFace()
}"><br><br>
<select size=1 id="myCheck">
<option value='environment'>Rear Mobile Camera
<option value='user'>Front Mobile Camera
</select>
<input type=button id="myButtonActivateWebCam" value="Activate WebCam" onclick="{getVideo()}">
<input type=button id="myButtonActivateWebCam" value="Stop WebCam" onclick="{ stopVideo() }"> <br><br>
Train a few items with a group number and a text label<br>
<input type=button value="Train. Get close, no bounding box used" onclick="{myNewTrain()}">
<input type=number value=0 size=3 id="myClassNumber" onchange = "{
myTextChange()
}">
<input type=text id="myClassText" placeholder="Label for this trained class: example Cell Phone" value="Joe Smith"><br><br>
<input type=button value="Save Classifier" onclick="{myClassifierSave()}"> <br><br>
<input type=text id="myInFile" size=120 value="models/myClassifierModel01.json"> <br>
<input type=button value="Load Classifier" onclick="{myClassifierLoad()}">
<video id="myVideoElement" width="320" height="320" style="display:inline; border: 1px solid #ddd;" title="myVideoElement: WebCam-Live"></video>
<canvas id="my32x32CanvasA" width="320" height="320" style="display:inline; border: 1px solid #ddd; interpolation-mode: nearest-neighbor;" title="my32x32CanvasA: interval snapshots, presently for training"></canvas><br>
<canvas id="myFaceCanvas" width="320" height="320" style="border: 1px solid #ddd;" title="myFaceCanvas: Zoomed in face"></canvas>
<canvas id="myVideoAsCanvas" width="320" height="320" style="border: 1px solid #ddd;" title="myVideoAsCanvas: back layer"></canvas>
<canvas id="myBorderAsCanvas" style="position:relative; left:-325px" title="myBorderAsCanvas: Layer with box, in front of myVideoAsCanvas interval snapshot"></canvas><br>
<div id="myDivLoss">...</div><br>
<br><br>
Analyse every <input type=number id="myInterval" value="500" size=5> milliseconds<br>
<input type=button id="myButtonAuto" value="Auto" onclick="{takeAuto()}">
<input type=button value="Stop Auto" onclick="{myStopAuto()}"><br><br>
<div id="myDivTest">...</div><br>
<div id="myDiv01">...</div><br>
<div id="myDivSummary">...</div><br><br>
<input type=button value="Load Group" onclick="{
myLoadManyImages()
}">
<input type=button value="Check Training" onclick="{
myCheckGroups()
}">
<input type=button value="Google Search" onclick="{
myShowInfo()
}">
<input type=button value="Google Search and open" onclick="{
myShowInfo2()
}">
<br>
To get faces use google search but set "tools" from "Not filtered by License" to "Labelled for reuse" I also set "any Size" to "medium"<br>
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34,Nikki_Reed,https://m.media-amazon.com/images/M/MV5BMTM5NzQ2NjQwM15BMl5BanBnXkFtZTcwNzcwNzE2OQ@@._V1_UY209_CR1,0,140,209_AL_.jpg***
35,Beyonce,https://m.media-amazon.com/images/M/MV5BMjMxMzg3MDI5NV5BMl5BanBnXkFtZTcwOTAxODc0Ng@@._V1_UY209_CR21,0,140,209_AL_.jpg***
36,Emma_Watson,https://encrypted-tbn0.gstatic.com/images?q=tbn:ANd9GcTQfKfFrONouGkk-oJzokhfthHbwGokBswHpp5wsEaFMiYFe4nV8A***
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38,Cobie_Smulders,https://encrypted-tbn0.gstatic.com/images?q=tbn:ANd9GcSl0rxew1OiEYSeixuAOI7YS4zz_3kMlYoT14PK3CbFbpAzKtnd***
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43,Ashley_Benson,https://m.media-amazon.com/images/M/MV5BMTkzMDY1NDk5MV5BMl5BanBnXkFtZTgwNTMxNzIyOTE@._V1_UY209_CR34,0,140,209_AL_.jpg***
44,Hilarie_Burton,https://m.media-amazon.com/images/M/MV5BMTIxNDQzOTA0NF5BMl5BanBnXkFtZTYwMzQwNDIz._V1_UY209_CR1,0,140,209_AL_.jpg***
45,Alexis_Bledel,https://m.media-amazon.com/images/M/MV5BMTc4NTg4ODU0Ml5BMl5BanBnXkFtZTgwOTA1MjUwNDE@._V1_UY209_CR8,0,140,209_AL_.jpg***
46,Rosario_Dawson,https://m.media-amazon.com/images/M/MV5BMTk1NjQ3NTYyNF5BMl5BanBnXkFtZTcwODU4NzQ4NQ@@._V1_UX140_CR0,0,140,209_AL_.jpg***
47,Kate_Walsh,https://m.media-amazon.com/images/M/MV5BMTk2NDEzNzg3MV5BMl5BanBnXkFtZTcwNjI1Mzg2Mw@@._V1_UX140_CR0,0,140,209_AL_.jpg***
48,Claire_Danes,https://m.media-amazon.com/images/M/MV5BMTMyMzQ1Mjk3M15BMl5BanBnXkFtZTcwNzk3ODMxNw@@._V1_UY209_CR10,0,140,209_AL_.jpg***
49,Sienna_Miller,https://encrypted-tbn0.gstatic.com/images?q=tbn:ANd9GcSetnCAq3xYdbWq5MSAX5BQm3EUSXXDrdSRY-YeoexuVCOtFVkdZQ***
50,Shannyn_Sossamon,https://encrypted-tbn0.gstatic.com/images?q=tbn:ANd9GcSvKaBv7N4TaO6cT0yfmwrHqrWOPZafvNvwmOiklCna4KTv8EAM***
51,Fan_Bingbing,https://encrypted-tbn0.gstatic.com/images?q=tbn:ANd9GcTbF1dyaYAIy_rUjjaAbWRNHuK3So_ZQArFn09VMrS8pSGAhmYHXA***52,Piper_Perabo,https://m.media-amazon.com/images/M/MV5BMGEwY2E4MzEtNjVhNy00ZTUyLWE4YmUtODIxNzU2NGRmNDUzXkEyXkFqcGdeQXVyMTMxNTAxODk@._V1_UY209_CR34,0,140,209_AL_.jpg***
53,Halla_Vilhjalmsdottir,https://encrypted-tbn0.gstatic.com/images?q=tbn:ANd9GcTl3fuXP0qR0gwLBwNHhO67PSaTZra71l87fHkeLBAqXSNupBqZ***
54,Jordana_Brewster,https://m.media-amazon.com/images/M/MV5BMTc1OTMwMzM3NF5BMl5BanBnXkFtZTgwMTM5MzIyODE@._V1_UX140_CR0,0,140,209_AL_.jpg***
55,Samaire_Armstrong,https://m.media-amazon.com/images/M/MV5BMTY5NTYyMjAzNV5BMl5BanBnXkFtZTcwNTg2NjA2NQ@@._V1_UY209_CR5,0,140,209_AL_.jpg***
56,Carey_Mulligan,https://m.media-amazon.com/images/M/MV5BMTUzODM0OTY4OF5BMl5BanBnXkFtZTgwMTg3NDk0NzE@._V1_UX140_CR0,0,140,209_AL_.jpg***
57,Kate_Hudson,https://m.media-amazon.com/images/M/MV5BMTA1NTk0MjMyOTFeQTJeQWpwZ15BbWU3MDA4NzEzMTM@._V1_UY209_CR8,0,140,209_AL_.jpg***
58,Fahriye_Evcen,https://encrypted-tbn0.gstatic.com/images?q=tbn:ANd9GcSrEEmLvgVR4Q47QqjvoT2KpP3XnsfR4oPjit_sDYL3HwLxqjKQ***
59,Gemma_Arterton,https://encrypted-tbn0.gstatic.com/images?q=tbn:ANd9GcRnnEBVFcKU-m7sQpNI9XIoX87yeBwhdJH_z_op3aNT-c4nuYgt***
60,Jennifer_Lopez,https://m.media-amazon.com/images/M/MV5BMTY0OTY3ODA3OV5BMl5BanBnXkFtZTcwMzMyMzQ1NQ@@._V1_UY209_CR21,0,140,209_AL_.jpg***
61,Naya_Rivera,https://m.media-amazon.com/images/M/MV5BOTk0MzMzODQ2OV5BMl5BanBnXkFtZTcwMTAyMzY3Mg@@._V1_UY209_CR17,0,140,209_AL_.jpg***
62,Emma_Roberts,https://m.media-amazon.com/images/M/MV5BZjRlMTA0YmYtZTE1ZS00NDk3LWI0MjQtNmEyMzllNDVmODM0XkEyXkFqcGdeQXVyMTIxMTA0Ng@@._V1_UY209_CR14,0,140,209_AL_.jpg***
63,Ariana_Grande,https://encrypted-tbn0.gstatic.com/images?q=tbn:ANd9GcQoU5_2Cw9VdjP8m0vhZORly0J_YThsAEkTgTM0o37v8_Pvhwxj***
64,Kate_Upton,https://encrypted-tbn0.gstatic.com/images?q=tbn:ANd9GcSHIVBZOaGGpLXwr_kaUBBOtCcQBuO1aYBkdHQFdylfj5N8OSQcFw***
65,Elle_Fanning,https://encrypted-tbn0.gstatic.com/images?q=tbn:ANd9GcTdk1TnrL28ZrV6f23aPy6U3rPe6Yf3JUt4NIKmUg0JfwLIXbQySg***
66,Reese_Witherspoon,https://encrypted-tbn0.gstatic.com/images?q=tbn:ANd9GcT0gLGkD0DXrWFkJHFUOE51WEgE-250SdHQuobCtpZU_Ysd8qB3***
67,Brittany_Snow,https://m.media-amazon.com/images/M/MV5BZDg4ZjI3YTgtYTZkZS00Nzg2LTk1Y2QtZjMyNmRjZDA2MTg2XkEyXkFqcGdeQXVyMjQ5MTkzMjc@._V1_UY209_CR3,0,140,209_AL_.jpg***
68,Nina_Dobrev,https://m.media-amazon.com/images/M/MV5BMmFkMGIxYTItNWI0My00NmJkLWFmYTItNWIxN2Y2ZmRhMWRlXkEyXkFqcGdeQXVyMTM5NDMzMjY@._V1_UX140_CR0,0,140,209_AL_.jpg***
69,Zoe_Saldana,https://m.media-amazon.com/images/M/MV5BMjA4NDk1NTA1OV5BMl5BanBnXkFtZTcwMTIzMjQ4Ng@@._V1_UY209_CR6,0,140,209_AL_.jpg***
70,Miranda_Kerr,https://encrypted-tbn0.gstatic.com/images?q=tbn:ANd9GcTyaAocDVwwDoZzn7mewkwPj2iy0C1Pg5Khz4k1-e-4g6zuf80VnQ***
71,Kelsey_Chow,https://encrypted-tbn0.gstatic.com/images?q=tbn:ANd9GcRlGJMbgRSv2M27X8ebCI_sBK6aszxQVxOC_89vUxnlxftrhlBSxA***
72,Monica_Bellucci,https://encrypted-tbn0.gstatic.com/images?q=tbn:ANd9GcQg91ycgstV30A7DiNNtaabNxgO-dWyXwMKOWgc8xtCIKUBqTS3***
73,Liza_Berggren,https://encrypted-tbn0.gstatic.com/images?q=tbn:ANd9GcTVm-ZG_rvMQIAC0zHBove4LDcZ2KPp_4fzqAXoCsWf_aBdI4QKGg***
74,Indiana_Evans,https://encrypted-tbn0.gstatic.com/images?q=tbn:ANd9GcQVkVXsV76c6WJmBJZfNvr3N-UIaBVoNulDPRkBP9M0Z-hs4Neo***
75,Amber_Heard,https://encrypted-tbn0.gstatic.com/images?q=tbn:ANd9GcTUOaWEBB-tr1GGIiIGLRZoD6g4Dz5h5_9aHhJVV2WKHJvE1dE3***
76,Sharon_Stone,https://m.media-amazon.com/images/M/MV5BMTg0MDU1ODQwNF5BMl5BanBnXkFtZTcwOTc3MjQwNA@@._V1_UY209_CR3,0,140,209_AL_.jpg***
77,Emina_Cunmulaj,https://encrypted-tbn0.gstatic.com/images?q=tbn:ANd9GcQCfb9aS7CMydjR1B6pUoB3Ungzpu80pENRDTQ9z6P5ia9PtCxH***
78,Alicia_Keys,https://m.media-amazon.com/images/M/MV5BMTcxOTU1NTA5MF5BMl5BanBnXkFtZTcwOTQ5MDkxNw@@._V1_UX140_CR0,0,140,209_AL_.jpg***
79,Claudia_Kim,https://encrypted-tbn0.gstatic.com/images?q=tbn:ANd9GcRh_OrEw5U1nSzQD6lvP0_70KWcXJlfuHk96xf1sN_-8c9ReOndrw***
80,Hailee_Steinfeld,https://encrypted-tbn0.gstatic.com/images?q=tbn:ANd9GcQMps1Gg7hanWEOXA3fPQs9r8N0lz8lY2RmsmByIYMQtbAedo5VIQ***
81,Emilie_de_Ravin,https://encrypted-tbn0.gstatic.com/images?q=tbn:ANd9GcTfgcP7NyFX5fzTlXs27MMWKev1PU8wyeIoUN6ONId2EmlxeONu2w***
82,Jennifer_Lawrence,https://m.media-amazon.com/images/M/MV5BOTU3NDE5MDQ4MV5BMl5BanBnXkFtZTgwMzE5ODQ3MDI@._V1_UX140_CR0,0,140,209_AL_.jpg***
83,Naomi_Watts,https://m.media-amazon.com/images/M/MV5BMjIzMjY1NTA4OF5BMl5BanBnXkFtZTcwNjk3MDYwOQ@@._V1_UX140_CR0,0,140,209_AL_.jpg***
84,Ornella_Muti,https://encrypted-tbn0.gstatic.com/images?q=tbn:ANd9GcRYxMWQbZLaB99hdLdFRA2FbQBvAocogTlRmQVtqHfEQJiPcB14***
85,Nelly_Furtado,https://m.media-amazon.com/images/M/MV5BMTQ5OTMxMTMyOF5BMl5BanBnXkFtZTcwMzExMjQ4Mg@@._V1_UX140_CR0,0,140,209_AL_.jpg***
86,Charlize_Theron,https://encrypted-tbn0.gstatic.com/images?q=tbn:ANd9GcQ0jWmsZ3D-MsnFTEwMZL_wvnCwLFZz7pvj3p4s2V-X1zNqrnTV***
87,Dajana_Kllogjri,https://encrypted-tbn0.gstatic.com/images?q=tbn:ANd9GcSlBVewTAjLJ9ztdcBQzXlpOoNXy8VqiQ-i-1M1DLxHo-rLo_Yw8Q***
88,Emily_Browning,https://encrypted-tbn0.gstatic.com/images?q=tbn:ANd9GcTgZfGhCh0McF3KRU5VhGxy9w1ir4D0BGeA9sW2ovYiabkskwsG***
89,Kirsten_Dunst,https://m.media-amazon.com/images/M/MV5BMTQ3NzkwNzM1MV5BMl5BanBnXkFtZTgwMzE2MTQ3MjE@._V1_UY209_CR8,0,140,209_AL_.jpg***
90,Anne_Hathaway,https://m.media-amazon.com/images/M/MV5BNjQ5MTAxMDc5OF5BMl5BanBnXkFtZTcwOTI0OTE4OA@@._V1_UY209_CR1,0,140,209_AL_.jpg***
91,Paulina_Porizkova,https://encrypted-tbn0.gstatic.com/images?q=tbn:ANd9GcSTLTAN7aJnz9-DG8D_bnZpKtSZpLrHR7AmY9a5fYqa3vPqKJrA***
92,Cameron_Diaz,https://m.media-amazon.com/images/M/MV5BMTkxNTI5NzM4MV5BMl5BanBnXkFtZTcwMTI3ODY3Mg@@._V1_UY209_CR1,0,140,209_AL_.jpg***
93,Kate_Bosworth,https://encrypted-tbn0.gstatic.com/images?q=tbn:ANd9GcTMVGkv2j-dtjzGl0orEBx3Hds53ZJx18-riu5BmCtJPFfmVfHX***
94,Monica_Hansen,https://encrypted-tbn0.gstatic.com/images?q=tbn:ANd9GcS0QzB0R_RuNWLMOuREK4udx2vYrtkH7tR6PusuRGdsVGYkHBZ1***
95,Penelope_Cruz,https://encrypted-tbn0.gstatic.com/images?q=tbn:ANd9GcRvb4ksoOTCM2-BRNAtkcvf2DrGWgtLzfF30pQqHEgQw_j9eBmB***
96,Michelle_Pfeiffer,https://m.media-amazon.com/images/M/MV5BMTUzNjI0Njc5NF5BMl5BanBnXkFtZTYwOTM2MjYz._V1_UX140_CR0,0,140,209_AL_.jpg***
97,Alexandra_Daddario,https://encrypted-tbn0.gstatic.com/images?q=tbn:ANd9GcS0hf53QzX9niW4Ev6grnA2ZLsO7Af_wxZQCM-s_cUG7VCgEKS7***
98,Halle_Berry,https://m.media-amazon.com/images/M/MV5BMjIxNzc5MDAzOV5BMl5BanBnXkFtZTcwMDUxMjMxMw@@._V1_UY209_CR7,0,140,209_AL_.jpg***
99,Angela_Martini,https://encrypted-tbn0.gstatic.com/images?q=tbn:ANd9GcQ9u9_rFPBmpnCuU1DPM_j46zb2IltwDw5wjeYcxOSoDSbK0q50qg</textarea>
<!-- ************* Next we define the Javascript inside a web element so that the page can be refreshed dynamically **************-->
<!-- ************* The entire next line can be replaced with the <script> tag for a more conventional approch. ****************** -->
<style id="myButton124" onload="{document.getElementById('myButton124').click()}" onclick="{
///////////////////////////////////// Global Variables ////////////////////////////////
//position:relative; left:-650px;
let myGroups = []
let newArray = []
let newArray2 = []
let myFaceToClose = 0 // pixels to make bounding box bigger
let myIncomingClassifier = []
let myIncomingImages = []
///////////////////////////////////// Canvas and video elements ////////////////////////////////
//////////////////////////////////////// knn classifier stuff///////////////////////////
myCheckGroups = async function() {
document.getElementById('myDivLoss').innerHTML = '' // clear it
for (let x=0; x < myGroups.length; x++){
if (myGroups[x] == undefined) {
document.getElementById('myDivLoss').innerHTML += '<font color=red>#: '+ x + ', label: </font><br>'
} else {
document.getElementById('myDivLoss').innerHTML += '#: '+ x + ', label: '+ myGroups[x] + '<br>'
}
}
}
myShowInfo = async function() {
myIn = document.getElementById('myArray01').value.split('***') // made it global
document.getElementById('myDivLoss').innerHTML = '' // clear it
for (myIteration = 0; myIteration < myIn.length; myIteration++){
myIncomingImages[myIteration] = [] // make new part of 2D array
myIncomingImages[myIteration] = myIn[myIteration].split(',') // [0] = groups, [1] = labels, [2] = Image URL
document.getElementById('myDiv123').innerHTML += `<a href='https://www.google.com/search?&tbm=isch&q=`+myIncomingImages[myIteration][1]+`' target='_blank'>`+myIteration+`: `+myIncomingImages[myIteration][1]+ `</a>, `
document.getElementById('myDiv123').innerHTML += `<a href='${myIncomingImages[myIteration][2]}'>Image Only</a><br>`
}
}
myShowInfo2 = async function() {
myIn = document.getElementById('myArray01').value.split('***') // made it global
document.getElementById('myDivLoss').innerHTML = '' // clear it
for (myIteration = 0; myIteration < myIn.length; myIteration++){
myIncomingImages[myIteration] = [] // make new part of 2D array
myIncomingImages[myIteration] = myIn[myIteration].split(',') // [0] = groups, [1] = labels, [2] = Image URL
document.getElementById('myDiv123').innerHTML += `<a href='https://www.google.com/search?&tbm=isch&q=`+myIncomingImages[myIteration][1]+`' target='_blank'>`+myIteration+`: `+myIncomingImages[myIteration][1]+ `</a>, `
document.getElementById('myDiv123').innerHTML += `<a href='${myIncomingImages[myIteration][2]}'>Image Only</a><br>`
}
for (myIteration2 = 0; myIteration2 < myIn.length; myIteration2++){
( function(myIteration2) {
console.log(myIteration2)
window.open(myIncomingImages[myIteration2][2], '_blank');
} )(myIteration2)
}
}
myLoadManyImages = async function() {
document.getElementById('myDiv01').innerHTML =''
const myIn = document.getElementById('myArray01').value.split('***')
document.getElementById('myDivLoss').innerHTML = '' // clear it
for (myIteration = 0; myIteration < myIn.length; myIteration++){
myIncomingImages[myIteration] = [] // make new part of 2D array
myIncomingImages[myIteration] = myIn[myIteration].split(',') // [0] = groups, [1] = labels, [2] = Image URL
let myI = parseInt(myIncomingImages[myIteration][0])
let myFileName2 = myIncomingImages[myIteration][2] // the URL
if (myFileName2 != null) {
let img = new Image()
img.src = myFileName2
img.crossOrigin = 'Anonymous'
let can = document.getElementById('my32x32CanvasA');
let ctx = can.getContext('2d');
ctx.clearRect( 0, 0, can.width, can.height)
//const getIngredients = async () => {}
tf.nextFrame()
img.onload = await async function() {
ctx.drawImage(img, 0, 0, img.width, img.height, 0, 0, can.width, can.height);
} // img
tf.nextFrame()
const img2 = await tf.fromPixels(my32x32CanvasA); // now lets classify the image
const myDetectMarks = await net.detectLandmarks(img2)
tf.nextFrame()
for (let j=0; j < myDetectMarks._faceLandmarks.length; j++ ){
newArray[j] = []
newArray[j][0] = myDetectMarks._faceLandmarks[j].x
newArray[j][1] = myDetectMarks._faceLandmarks[j].y
}
tf.nextFrame()
document.getElementById('myDivLoss').innerHTML += 'Checking ' + myI + '<br>'
const forwardParams = {inputSize: 'sm', scoreThreshold:0.5} // 'xs' (224 x 224) | 'sm' (320 x 320) | 'md' (416 x 416) | 'lg' (608 x 608) //0, 1, 2, 3
result2 = await faceapi.tinyYolov2(can, forwardParams)
console.log(myI)
console.log(result2)
if (result2.length > 0){
myGroups[myI] = myIncomingImages[myIteration][1] // add the label to the myGroups array
/*
classifier.addExample(tf.tensor2d(newArray, shape=[68,2]), myI);
const myAddCount = classifier.getClassExampleCount()[myI]
document.getElementById('myDivLoss').innerHTML += myAddCount + ' times, Example Added: to group #: '+ myI +'<br>'
document.getElementById('myDiv01').innerHTML += myI+': '+ Math.round(newArray[0][0])+', '+ Math.round(newArray[0][1])+'<br>'
*/
/////////////////////////////// prove we got a face ///////////////////////////////
let { width, height } = faceapi.getMediaDimensions(can)
let canvas = document.getElementById('myBorderAsCanvas')
canvas.width = width
canvas.height = height
const forwardParams = {inputSize: 'sm', scoreThreshold: 0.5}
result2 = await faceapi.tinyYolov2(can, forwardParams)
faceapi.drawDetection('myBorderAsCanvas', result2.map(det => det.forSize(width, height)))
tf.nextFrame()
let myCanvasElement4 = document.getElementById('myVideoAsCanvas');
let myCTX4 = myCanvasElement4.getContext('2d');
myCTX4.drawImage(can, 0, 0, myCanvasElement4.width, myCanvasElement4.height);
tf.nextFrame()
let myCanvasElement3 = document.getElementById('myFaceCanvas');
let myCTX3 = myCanvasElement3.getContext('2d');
myCTX3.drawImage(myCanvasElement4, result2[0]._box._x-myFaceToClose, result2[0]._box._y-myFaceToClose, result2[0]._box._width+(2*myFaceToClose), result2[0]._box._height+(2*myFaceToClose), 0, 0, myCanvasElement3.width, myCanvasElement3.height);
tf.nextFrame()
let img3 = tf.fromPixels(myFaceCanvas); // the bounded canvas
const myLandmarks = await net.detectLandmarks(img3)
for (let j=0; j < myLandmarks._faceLandmarks.length; j++ ){
newArray2[j] = []
newArray2[j][0] = myLandmarks._faceLandmarks[j].x
newArray2[j][1] = myLandmarks._faceLandmarks[j].y
}
tf.nextFrame()
// myCTX3.drawImage(can, 0, 0, myCanvasElement3.width, myCanvasElement3.height);
// const result = await classifier.predictClass(tf.tensor2d(newArray2, shape=[68,2]), 3); // number of groups
const wow = await faceapi.drawLandmarks(myCanvasElement3, myLandmarks, { lineWidth: 2, color: 'red' }) // may remove these eventually
tf.nextFrame()
classifier.addExample(tf.tensor2d(newArray2, shape=[68,2]), myI);
const myAddCount = classifier.getClassExampleCount()[myI]
document.getElementById('myDivLoss').innerHTML += myAddCount + ' times, Example Added: to group #: '+ myI +'<br>'
document.getElementById('myDiv01').innerHTML += myI+', '+myIncomingImages[myIteration][1]+': '+ Math.round(newArray2[0][0])+', '+ Math.round(newArray2[0][1])+'<br>'
tf.nextFrame()
alert(myI+', '+myIncomingImages[myIteration][1])
// document.getElementById('myDiv01').innerHTML += myI+', double check: '+ Math.round(myLandmarks._faceLandmarks[0].x)+', '+ Math.round(myLandmarks._faceLandmarks[0].y)+'<br>'
tf.nextFrame()
///////////////////////////////// end proof ///////////////////////////////////
}
} // if
} // for
// console.log(myIncomingImages)
// console.log(myGroups)
}
myRunFace = async function() {
classifier = knnClassifier.create();
console.log('knn-classigfier loaded')
net = new faceapi.FaceLandmark68TinyNet() // made it global
await net.load('https://hpssjellis.github.io/beginner-tensorflowjs-examples-in-javascript/advanced-keras/face/models/face_landmark_68_tiny_model-weights_manifest.json')
console.log('face_landmark_68_tiny_model loaded')
await faceapi.loadTinyYolov2Model('https://hpssjellis.github.io/beginner-tensorflowjs-examples-in-javascript/advanced-keras/face/muehler/models')
console.log('loadTinyYolov2Model loaded')
}
myTextChange = async function(){
if (parseInt(document.getElementById('myClassNumber').value) < 0 ){
document.getElementById('myClassNumber').value = 0
}
if (myGroups[document.getElementById('myClassNumber').value] == undefined){
document.getElementById('myClassText').value = '' // clear the box
} else {
document.getElementById('myClassText').value = myGroups[document.getElementById('myClassNumber').value]
}
}
myNewTrain = async function(){
let myCanvasElement = document.getElementById('my32x32CanvasA');
let myCTX = myCanvasElement.getContext('2d');
myCTX.drawImage(myVideoStream, 0, 0, myCanvasElement.width, myCanvasElement.height);
const img1 = tf.fromPixels(my32x32CanvasA);
const myDetectMarks = await net.detectLandmarks(img1)
for (let j=0; j < myDetectMarks._faceLandmarks.length; j++ ){
newArray[j] = []
newArray[j][0] = myDetectMarks._faceLandmarks[j].x
newArray[j][1] = myDetectMarks._faceLandmarks[j].y
}
classifier.addExample(tf.tensor2d(newArray, shape=[68,2]), parseInt(document.getElementById('myClassNumber').value));
myGroups[document.getElementById('myClassNumber').value] = document.getElementById('myClassText').value
const myAddCount = classifier.getClassExampleCount()[document.getElementById('myClassNumber').value]
const myGroupNumber = document.getElementById('myClassNumber').value
document.getElementById('myDivLoss').innerHTML = myAddCount + ' times, Example Added: to group #: '+ myGroupNumber
}
myPredict = async function(){
let scoreThreshold = 0.5 //0.5
let sizeType = 'sm' // 'xs' (224 x 224) | 'sm' (320 x 320) | 'md' (416 x 416) | 'lg' (608 x 608) //0, 1, 2, 3
let modelLoaded = false
let forwardTimes = []
let myCanvasElement = document.getElementById('my32x32CanvasA');
let myCTX = myCanvasElement.getContext('2d');
myCTX.drawImage(myVideoStream, 0, 0, myCanvasElement.width, myCanvasElement.height);
if (classifier.getNumClasses() >= 1){ // have groups to rate
let { width, height } = faceapi.getMediaDimensions(myCanvasElement)
let canvas = document.getElementById('myBorderAsCanvas')
canvas.width = width
canvas.height = height
const forwardParams = {inputSize: sizeType, scoreThreshold}
result2 = await faceapi.tinyYolov2(myCanvasElement, forwardParams)
if (result2.length >= 1){ // box drawing working
faceapi.drawDetection('myBorderAsCanvas', result2.map(det => det.forSize(width, height)))
let myCanvasElement4 = document.getElementById('myVideoAsCanvas');
let myCTX4 = myCanvasElement4.getContext('2d');
myCTX4.drawImage(myVideoStream, 0, 0, myCanvasElement4.width, myCanvasElement4.height);
let myCanvasElement3 = document.getElementById('myFaceCanvas');
let myCTX3 = myCanvasElement3.getContext('2d');
myCTX3.drawImage(myCanvasElement4, result2[0]._box._x-myFaceToClose, result2[0]._box._y-myFaceToClose, result2[0]._box._width+(2*myFaceToClose), result2[0]._box._height+(2*myFaceToClose), 0, 0, myCanvasElement3.width, myCanvasElement3.height);
let img3 = tf.fromPixels(myFaceCanvas); // the bounded canvas
const myLandmarks = await net.detectLandmarks(img3)
for (let j=0; j < myLandmarks._faceLandmarks.length; j++ ){
newArray2[j] = []
newArray2[j][0] = myLandmarks._faceLandmarks[j].x
newArray2[j][1] = myLandmarks._faceLandmarks[j].y
}
myCTX.drawImage(myVideoStream, 0, 0, myCanvasElement.width, myCanvasElement.height);
const result = await classifier.predictClass(tf.tensor2d(newArray2, shape=[68,2]), 3); // number of groups
faceapi.drawLandmarks(myCanvasElement3, myLandmarks, { lineWidth: 2, color: 'red' }) // may remove these eventually
document.getElementById('myDivTest').innerHTML = 'Group: ' + result.classIndex + ', '+
Math.round(result.confidences[result.classIndex]*100)+ '% ' + myGroups[result.classIndex]+ '<br>'
} // end box drawing OK
} else {(console.log('Need to train groups'))}
}
/////////////////////////////////////////// NEW SAVING and LOADING functions ///////////////////////////////////////////
myDefineClassifierModel = async function(myPassedClassifier){
let myLayerList = []
myLayerList[0] = [] // for the input layer name as a string
myLayerList[1] = [] // for the input layer
myLayerList[2] = [] // for the concatenate layer name as a string
myLayerList[3] = [] // for the concatenate layer
let myMaxClasses = myPassedClassifier.getNumClasses()
//console.log('myPassedClassifier.getNumClasses()')
//console.log(myMaxClasses)
for (let myClassifierLoop = 0; myClassifierLoop < myMaxClasses; myClassifierLoop++ ){ // need number of classifiers
//console.log(myPassedClassifier.getClassifierDataset()[myClassifierLoop])
//console.log('shape first layer =')
//console.log(myPassedClassifier.getClassifierDataset()[myClassifierLoop].shape[0])
myLayerList[0][myClassifierLoop] = 'myInput' + myClassifierLoop // input name as a string
console.log('define input for'+myClassifierLoop)
myLayerList[1][myClassifierLoop] = tf.input({shape: myPassedClassifier.getClassifierDataset()[myClassifierLoop].shape[0], name: myLayerList[1][myClassifierLoop]}); // Define input layer
console.log('define dense for: '+myClassifierLoop)
myLayerList[2][myClassifierLoop] = 'myInput'+myClassifierLoop+'Dense1' // concatenate as a string
myLayerList[3][myClassifierLoop] = tf.layers.dense({units: 136, name: myGroups[myClassifierLoop]}).apply(myLayerList[1][myClassifierLoop]); //Define concatenate layer
}
// what the layers used to look like before the loop
//const myInput2 = tf.input({shape: [1], name: 'myInput2'});
//const myInput2Dense1 = tf.layers.dense({units: 20, name: 'myInput2Dense1'}).apply(myInput2);
console.log('Concatenate Paths')
const myConcatenate1 = tf.layers.concatenate({axis : 1, name: 'myConcatenate1'}).apply(myLayerList[3]); // send the entire list of dense
const myConcatenate1Dense4 = tf.layers.dense({units: 1, name: 'myConcatenate1Dense4'}).apply(myConcatenate1)
console.log('Define Model')
const myClassifierModel = tf.model({inputs: myLayerList[1], outputs: myConcatenate1Dense4}); // This would be a global model. With list of inputs as an array
myClassifierModel.summary()
console.log('myClassifierModel.layers[myMaxClasses]')
console.log(myClassifierModel.layers[myMaxClasses])
myPassedClassifier.getClassifierDataset()[0].print(true)
for (let myClassifierLoop = 0; myClassifierLoop < myMaxClasses; myClassifierLoop++ ){ // since the first layers are inputs must add maxClasses
const myInWeight = await myPassedClassifier.getClassifierDataset()[myClassifierLoop]
myClassifierModel.layers[myClassifierLoop + myMaxClasses].setWeights([myInWeight, tf.ones([136])]); //model.layers[0].setWeights([tf.ones([10, 2]), tf.ones([2])]);
}
return myClassifierModel
}
///////////////////////////////////////////////////////////////////////////////
myClassifierSave = async function(){
const myClassifierModel2 = await myDefineClassifierModel(classifier) // pass global classifier
myClassifierModel2.save('downloads://myClassifierModel01')
myClassifierModel2.summary(null,null,x => {document.getElementById('myDivSummary').innerHTML += x + '<br>'});
}
/////////////////////////////////////////////
mySetClassiferModelWeights = async function(){
}
//////////////////////////////////////////////////////////////////////////////
myClassifierLoad = async function(){
// note global variable called myIncomingClassifier
const myLoadedModel = await tf.loadModel(document.getElementById('myInFile').value)
console.log('myLoadedModel.layers.length')
console.log(myLoadedModel.layers.length)
// console.log('myLoadedModel.layers[0].batchInputShape[1]')
// console.log(myLoadedModel.layers[0].batchInputShape[1] )
const myMaxLayers = myLoadedModel.layers.length
const myDenseEnd = myMaxLayers - 2
const myDenseStart = myDenseEnd/2 // assume 0 = first layer: if 6 layers 0-1 input, 2-3 dense, 4 concatenate, 5 dense output
for (let myWeightLoop = myDenseStart; myWeightLoop < myDenseEnd; myWeightLoop++ ){ // need number of classifiers
// console.log('myLoadedModel.layers['+myWeightLoop+']')
// console.log(myLoadedModel.layers[myWeightLoop])
console.log('myLoadedModel.layers['+myWeightLoop+'].getWeights()[0].print(true)')
// myLoadedModel.layers[myWeightLoop].getWeights()[0].print(true)
myIncomingClassifier[myWeightLoop - myDenseStart] = myLoadedModel.layers[myWeightLoop].getWeights()[0]
myGroups[myWeightLoop - myDenseStart] = myLoadedModel.layers[myWeightLoop].name // hopefully the name is the group name
}
console.log('Printing all the incoming classifiers')
for (x=0; x < myIncomingClassifier.length ; x++){
myIncomingClassifier[x].print(true)
}
console.log('Activating Classifier')
classifier.dispose() // clear old classifier
classifier.setClassifierDataset(myIncomingClassifier)
console.log('Classifier loaded')
}
///////////////////////////////////// End KNN- Classifier stuff /////////////////////////////////////
///////////////////////////////////// webcam stuff /////////////////////////////////////
var myVideoStream = document.getElementById('myVideoElement') // make it a global variable
var myStoredInterval = 0
stopVideo = async function() {
clearInterval(myStoredInterval) // god idea to stop the auto snapshot taking
myVideoStream.srcObject.getTracks().forEach(track => track.stop())
}
getVideo = async function() {
const myCamera = await document.getElementById('myCheck').value
navigator.getMedia = navigator.getUserMedia || navigator.webkitGetUserMedia || navigator.mozGetUserMedia || navigator.msGetUserMedia;
navigator.getMedia({video: { facingMode: myCamera }, audio: false},
function(stream) {
myVideoStream.srcObject = stream
myVideoStream.play();
},
function(error) {
alert('webcam not working');
});
}
takeAuto = async function(){
myStoredInterval = setInterval(async function(){
await myPredict()
}, document.getElementById('myInterval').value);
}
myStopAuto = async function(){
clearInterval(myStoredInterval)
}
///////////////////////////////////////////// Done Webcam functions ////////////////////////////////////////
myLoadUrl = async function(){
//alert('The test function will need to be changed if other models are loaded')
document.getElementById('myDivTest').innerHTML = 'Expect major code changes if you load a different model than what is expected'
const myFileName = document.getElementById('myInFile').value
if (myFileName != null){
model = await tf.loadModel(myFileName); // should make the model a global variable
document.getElementById('myDivSummary').innerHTML = ''
await model.summary(null,null,x => {document.getElementById('myDivSummary').innerHTML += x + '<br>'});
// await myPredict()
}
}
/////////////////////////////////// END ALL FUNCTIONS ///////////////////////////////////////
////////////////////////////////// WEIRD STYLE TAG THAT IS ACTUALLY A DYNAMIC SCRIPT TAG ///////////////////
}"></style>
<!-- If you replaced the <style> tag with a <script> tag don't forget to change the above line to just </script> -->
</div>
<div id='myDiv123'>...</div><br>
<input id="myUpdate123" type=button value="Update and Run" style="visibility:hidden;" onclick="{
// first remove first and last line since they are injected
myFred = document.getElementById('myTextarea123').value.split('\n')
myFred.pop()
myFred.push('')
myFred.shift()
myFred.shift()
myJoe = myFred.join('\n')
document.getElementById('myDiv123Code').innerHTML = myJoe
document.getElementById('myButton123').click()
}"><br>
<textarea id="myTextarea123" wrap="off" style= "font-size:15px; color:white; background-color:black; width:90%;" rows=2 onclick="{
if (myOnce123){
myTextGrow('myTextarea123', 'myDiv123Code')
document.getElementById('myUpdate123').style.visibility = 'visible'
myOnce123 = false
}
}">
Click here to see the working HTML code.
</textarea><br>
This <a href="https://github.com/hpssjellis/beginner-tensorflowjs-examples-in-javascript">Github</a>, ...
this <a href="https://hpssjellis.github.io/beginner-tensorflowjs-examples-in-javascript/">Github Website Version</a>, ...
this <a href="http://rocksetta.com/tensorflowjs/">Hosted Website Version</a>, ...
<a href="https://js.tensorflow.org/">Tensorflowjs</a> <br> <br>
By Jeremy Ellis <br>
Twitter<a href="https://twitter.com/@rocksetta">@rocksetta</a><br>
Website <a href="http://rocksetta.com">http://rocksetta.com</a><br>
Use at your own risk!
<!-- Following is a helper functions to grow the textareas -->
<script>
myOnce123 = true // so textareas are only clicked once
function myTextGrow(myT, myB){
var myCursorStart = document.getElementById(myT).selectionStart
var myCursorEnd = document.getElementById(myT).selectionEnd
myDivName = myB.replace('Code','')
document.getElementById(myT).value = '\x3Cscript src="https://cdn.jsdelivr.net/npm/@tensorflow/tfjs@0.13.1"> \x3C/script> \n\n' + document.getElementById(myB).innerHTML
document.getElementById(myT).value += '<div id=\''+myDivName+'\'>...</div><br>'
setTimeout(function() {
while ( document.getElementById(myT).clientHeight < document.getElementById(myT).scrollHeight){
document.getElementById(myT).rows += 3;
}
}, 100)
document.getElementById(myT).selectionStart = myCursorStart
document.getElementById(myT).selectionEnd = myCursorEnd
}
</script>
</body>