face detection example

examples/public/commons.js

@@ -8,47 +8,134 @@ function getFaceImageUri(className, idx) {
   return `images/${className}/${className}${idx}.png`
 }
 
-async function initNet() {
+async function fetchImage(uri) {
+  return (await axios.get(uri, { responseType: 'blob' })).data
+}
+
+function round(num) {
+  return Math.floor(num * 100) / 100
+}
+
+function getElement(arg) {
+  if (typeof arg === 'string') {
+    return document.getElementById(arg)
+  }
+  return arg
+}
+
+async function initFaceDetectionNet() {
+  const res = await axios.get('face_detection_model.weights', { responseType: 'arraybuffer' })
+  const weights = new Float32Array(res.data)
+  return facerecognition.faceDetectionNet(weights)
+}
+
+async function initFaceRecognitionNet() {
   const res = await axios.get('face_recognition_model.weights', { responseType: 'arraybuffer' })
   const weights = new Float32Array(res.data)
   return facerecognition.faceRecognitionNet(weights)
 }
 
-function bufferToImgSrc(buf) {
-  return new Promise((resolve, reject) => {
-    const reader = new window.FileReader()
-    reader.onload = () => resolve(reader.result)
-    reader.onerror = reject
-    reader.readAsDataURL(buf)
-  })
+function drawImgToCanvas(canvasArg, imgArg) {
+  const canvas = getElement(canvasArg)
+  const img = getElement(imgArg)
+  canvas.width = img.width
+  canvas.height = img.height
+  const ctx = canvas.getContext('2d')
+  ctx.drawImage(img, 0, 0, img.width, img.height)
+  return ctx
 }
 
-function imgSrcToData(src) {
+function imgSrcToImageData(src) {
   return new Promise((resolve, reject) => {
-    const canvas = document.createElement('canvas')
-    canvas.width = 150
-    canvas.height = 150
-    const ctx = canvas.getContext('2d')
     const img = new Image()
     img.onload = function() {
-      ctx.drawImage(img, 0, 0)
-      resolve(ctx.getImageData(0, 0, 150, 150))
+      const ctx = drawImgToCanvas(document.createElement('canvas'), img)
+      resolve(ctx.getImageData(0, 0, img.width, img.height))
     }
     img.onerror = reject
     img.src = src
   })
 }
 
+function bufferToImgSrc(buf) {
+  return new Promise((resolve, reject) => {
+    const reader = new window.FileReader()
+    reader.onload = () => resolve(reader.result)
+    reader.onerror = reject
+    reader.readAsDataURL(buf)
+  })
+}
+
 async function bufferToImageData(buf) {
-  return imgSrcToData(await bufferToImgSrc(buf))
+  return imgSrcToImageData(await bufferToImgSrc(buf))
 }
 
-async function fetchImage(uri) {
-  return (await axios.get(uri, { responseType: 'blob' })).data
+function drawBox(canvasArg, x, y, w, h, lineWidth = 2, color = 'blue') {
+  const canvas = getElement(canvasArg)
+  const ctx = canvas.getContext('2d')
+  ctx.strokeStyle = color
+  ctx.lineWidth = lineWidth
+  ctx.strokeRect(x, y, w, h)
 }
 
-function round(num) {
-  return Math.floor(num * 100) / 100
+function drawText(canvasArg, x, y, text, fontSize = 20, fontStyle = 'Georgia', color = 'blue') {
+  const canvas = getElement(canvasArg)
+  const ctx = canvas.getContext('2d')
+  ctx.fillStyle = color
+  ctx.font = fontSize + 'px ' + fontStyle
+  ctx.fillText(text, x, y)
+}
+
+function drawDetection(canvasArg, detection, options = {}) {
+  const canvas = getElement(canvasArg)
+  const detectionArray = Array.isArray(detection)
+    ? detection
+    : [detection]
+
+  detectionArray.forEach((det) => {
+    const {
+      score,
+      box
+    } = det
+
+    const {
+      left,
+      right,
+      top,
+      bottom
+    } = box
+
+    const {
+      color,
+      lineWidth = 2,
+      fontSize = 20,
+      fontStyle,
+      withScore = true
+    } = options
+
+    const padText = 2 + lineWidth
+
+    drawBox(
+      canvas,
+      left,
+      top,
+      right - left,
+      bottom - top,
+      lineWidth,
+      color
+    )
+    if (withScore) {
+      drawText(
+        canvas,
+        left + padText,
+        top + (fontSize * 0.6) + padText,
+        round(score),
+        fontSize,
+        fontStyle,
+        color
+      )
+    }
+  })
 }
 
 function renderNavBar(navbarId, exampleUri) {

examples/public/styles.css

 .page-container {
-  position: fixed;
   left: 0;
   right: 0;
   margin: auto;
@@ -29,9 +28,4 @@
 
 .margin {
   margin: 20px;
-}
-
-img {
-  width: 150px;
-  height: 150px;
 }
\ No newline at end of file

examples/views/faceDetection.html

@@ -12,21 +12,70 @@
 <body>
   <div class="center-content page-container">
     <div id="navbar"></div>
-    <div class="center-content">
-      <img id="img" src="" class="margin"/>
-      <div id="selectList" class="input-field"></div>
+    <div class="progress" id="loader">
+      <div class="indeterminate"></div>
+    </div>
+    <div style="position: relative" class="margin">
+      <img id="img" src="" />
+      <canvas id="overlay" style="position: absolute; top: 0; left: 0;" />
+    </div>
+    <div class="row side-by-side">
+      <div id="selectList"></div>
+      <div class="row">
+        <label for="minConfidence">Min Confidence:</label>
+        <input disabled value="0.7" id="minConfidence" type="text" class="bold">
+      </div>
+      <button
+        class="waves-effect waves-light btn"
+        onclick="onDecreaseThreshold()"
+      >
+        <i class="material-icons left">-</i>
+      </button>
+      <button
+        class="waves-effect waves-light btn"
+        onclick="onIncreaseThreshold()"
+      >
+        <i class="material-icons left">+</i>
+      </button>
     </div>
   </div>
 
   <script>
+    let minConfidence = 0.7
+    let net, result
+
+    function onIncreaseThreshold() {
+      minConfidence = Math.min(round(minConfidence + 0.1), 1.0)
+      $('#minConfidence').val(minConfidence)
+      updateResults()
+    }
+
+    function onDecreaseThreshold() {
+      minConfidence = Math.max(round(minConfidence - 0.1), 0.1)
+      $('#minConfidence').val(minConfidence)
+      updateResults()
+    }
+
+    async function updateResults() {
+      result = await net.locateFaces(await imgSrcToImageData($(`#img`).get(0).src), minConfidence)
+      drawImgToCanvas('overlay', 'img')
+      drawDetection('overlay', result)
+    }
+
     async function onSelectionChanged(uri) {
       const imgBuf = await fetchImage(uri)
-      const imgEl = $(`#img`).get(0)
-      imgEl.src = await bufferToImgSrc(imgBuf)
+      $(`#img`).get(0).src = await bufferToImgSrc(imgBuf)
+      updateResults()
+    }
+
+    async function run() {
+      net = await initFaceDetectionNet()
+      $('#loader').hide()
+      onSelectionChanged($('#selectList select').val())
     }
 
     $(document).ready(function() {
-      renderNavBar('#navbar')
+      renderNavBar('#navbar', 'face_detection')
       renderImageSelectList(
         '#selectList',
         async (uri) => {
@@ -34,7 +83,7 @@
         },
         1
       )
-      onSelectionChanged($('#selectList select').val())
+      run()
     })
   </script>
 </body>

examples/views/faceRecognition.html

@@ -47,7 +47,7 @@
           class="waves-effect waves-light btn"
           onclick="onSlower()"
         >
-        <i class="material-icons left">-</i> Slower
+          <i class="material-icons left">-</i> Slower
         </button>
         <button
           class="waves-effect waves-light btn"
@@ -71,17 +71,17 @@
 
     let isStop = false
     let trainDescriptorsByClass = []
-    let net = {}
+    let net
     let currImageIdx = 2, currClassIdx = 0
     let to = null
 
     function onSlower() {
-      interval = Math.max(interval + 100, 0)
+      interval = Math.min(interval + 100, 2000)
       $('#interval').val(interval)
     }
 
     function onFaster() {
-      interval = Math.min(interval - 100, 2000)
+      interval = Math.max(interval - 100, 0)
       $('#interval').val(interval)
     }
 
@@ -136,7 +136,7 @@
         const imgEl = $('#face').get(0)
         imgEl.src = await bufferToImgSrc(imgBuf)
 
-        const imageData = await imgSrcToData(imgEl.src)
+        const imageData = await imgSrcToImageData(imgEl.src)
 
         const ts = Date.now()
         const result = await net.forward(imageData)
@@ -161,7 +161,7 @@
       try {
         setStatusText('loading model file...')
 
-        net = await initNet()
+        net = await initFaceRecognitionNet()
         setStatusText('computing initial descriptors...')
 
         const trainImgDatas = await loadTrainingData()

examples/views/faceSimilarity.html

@@ -20,11 +20,11 @@
       <div class="row side-by-side">
         <div class="center-content">
           <img id="face1" src="" class="margin"/>
-          <div id="selectList1" class="input-field"></div>
+          <div id="selectList1"></div>
         </div>
         <div class="center-content">
           <img id="face2" src="" class="margin"/>
-          <div id="selectList2" class="input-field"></div>
+          <div id="selectList2"></div>
         </div>
       </div>
       <div class="row">
@@ -52,7 +52,7 @@
     }
 
     async function computeDescriptorFromSrc(imgEl) {
-      return net.computeFaceDescriptor(await imgSrcToData(imgEl.src))
+      return net.computeFaceDescriptor(await imgSrcToImageData(imgEl.src))
     }
 
     async function onSelectionChanged(which, uri) {
@@ -63,7 +63,7 @@
     }
 
     async function run() {
-      net = await initNet()
+      net = await initFaceRecognitionNet()
       $('#loader').hide()
       await onSelectionChanged(1, $('#selectList1 select').val())
       await onSelectionChanged(2, $('#selectList2 select').val())