added blazeface back and front models

README.md

@@ -218,12 +218,13 @@ human.defaults = {
   face: {
     enabled: true,          // controls if specified modul is enabled (note: module is not loaded until it is required)
     detector: {
-      modelPath: '../models/blazeface/model.json', // path to specific pre-trained model
+      modelPath: '../models/blazeface/tfhub/model.json', // can be 'tfhub', 'front' or 'back'
+      inputSize: 128,       // 128 for tfhub and front models, 256 for back
       maxFaces: 10,         // how many faces are we trying to analyze. limiting number in busy scenes will result in higher performance
       skipFrames: 10,       // how many frames to skip before re-running bounding box detection
-      minConfidence: 0.8,   // threshold for discarding a prediction
+      minConfidence: 0.5,   // threshold for discarding a prediction
       iouThreshold: 0.3,    // threshold for deciding whether boxes overlap too much in non-maximum suppression
-      scoreThreshold: 0.75, // threshold for deciding when to remove boxes based on score in non-maximum suppression
+      scoreThreshold: 0.7,  // threshold for deciding when to remove boxes based on score in non-maximum suppression
     },
     mesh: {
       enabled: true,
@@ -235,12 +236,12 @@ human.defaults = {
     },
     age: {
       enabled: true,
-      modelPath: '../models/ssrnet-imdb-age/model.json',
+      modelPath: '../models/ssrnet-age/imdb/model.json', // can be 'imdb' or 'wiki'
       skipFrames: 10,       // how many frames to skip before re-running bounding box detection
     },
     gender: {
       enabled: true,
-      modelPath: '../models/ssrnet-imdb-gender/model.json',
+      modelPath: '../models/ssrnet-gender/imdb/model.json', // can be 'imdb' or 'wiki'
     },
     emotion: {
       enabled: true,
@@ -254,15 +255,15 @@ human.defaults = {
     enabled: true,
     modelPath: '../models/posenet/model.json',
     maxDetections: 5,       // how many faces are we trying to analyze. limiting number in busy scenes will result in higher performance  
-    scoreThreshold: 0.75,   // threshold for deciding when to remove boxes based on score in non-maximum suppression
+    scoreThreshold: 0.7,    // threshold for deciding when to remove boxes based on score in non-maximum suppression
     nmsRadius: 20,          // radius for deciding points are too close in non-maximum suppression
   },
   hand: {
     enabled: true,
     skipFrames: 10,         // how many frames to skip before re-running bounding box detection
-    minConfidence: 0.8,     // threshold for discarding a prediction
+    minConfidence: 0.5,     // threshold for discarding a prediction
     iouThreshold: 0.3,      // threshold for deciding whether boxes overlap too much in non-maximum suppression
-    scoreThreshold: 0.75,   // threshold for deciding when to remove boxes based on score in non-maximum suppression
+    scoreThreshold: 0.7,    // threshold for deciding when to remove boxes based on score in non-maximum suppression
     detector: {
       anchors: '../models/handdetect/anchors.json',
       modelPath: '../models/handdetect/model.json',
@@ -361,14 +362,14 @@ For example, on a desktop with a low-end nVidia GTX1050 it can perform multiple
 Performance per module:
 
 - Enabled all: 10 FPS
-- Face Detect: 80 FPS
+- Face Detect: 80 FPS (standalone)
 - Face Geometry: 30 FPS (includes face detect)
 - Face Iris: 25 FPS (includes face detect and face geometry)
 - Age: 60 FPS (includes face detect)
 - Gender: 60 FPS (includes face detect)
 - Emotion: 60 FPS (includes face detect)
-- Hand: 40 FPS
+- Hand: 40 FPS (standalone)
-- Body: 50 FPS
+- Body: 50 FPS (standalone)
 
 Library can also be used on mobile devices  
 

demo/demo-esm-webworker.js

@@ -13,8 +13,7 @@ onmessage = async (msg) => {
   config = msg.data.config;
   let result = {};
   try {
-    // result = await human.detect(image, config);
+    result = await human.detect(image, config);
-    result = {};
   } catch (err) {
     result.error = err.message;
     log('Worker thread error:', err.message);

demo/demo-esm.js

@@ -60,7 +60,7 @@ async function drawFace(result, canvas) {
     const labelIris = face.iris ? `iris: ${face.iris}` : '';
     const labelEmotion = face.emotion && face.emotion[0] ? `emotion: ${Math.trunc(100 * face.emotion[0].score)}% ${face.emotion[0].emotion}` : '';
     ctx.fillStyle = ui.baseLabel;
-    ctx.fillText(`face ${labelAgeGender} ${labelIris} ${labelEmotion}`, face.box[0] + 2, face.box[1] + 22, face.box[2]);
+    ctx.fillText(`${Math.trunc(100 * face.confidence)}% face ${labelAgeGender} ${labelIris} ${labelEmotion}`, face.box[0] + 2, face.box[1] + 22);
     ctx.stroke();
     ctx.lineWidth = 1;
     if (face.mesh) {
@@ -238,7 +238,7 @@ function webWorker(input, image, canvas) {
     log('Creating worker thread');
     worker = new Worker('demo-esm-webworker.js', { type: 'module' });
     // after receiving message from webworker, parse&draw results and send new frame for processing
-    worker.addEventListener('message', async (msg) => drawResults(input, msg.data, canvas));
+    worker.addEventListener('message', (msg) => drawResults(input, msg.data, canvas));
   }
   // pass image data as arraybuffer to worker by reference to avoid copy
   worker.postMessage({ image: image.data.buffer, width: canvas.width, height: canvas.height, config }, [image.data.buffer]);

src/config.js

@@ -2,8 +2,9 @@ export default {
   face: {
     enabled: true, // refers to detector, but since all other face modules rely on detector, it should be a global
     detector: {
-      modelPath: '../models/blazeface/model.json',
+      modelPath: '../models/blazeface/tfhub/model.json', // can be blazeface-front or blazeface-back
-      inputSize: 128, // fixed value
+      anchorSize: 128, // fixed regardless of model
+      inputSize: 128, // fixed value: 128 for front and tfhub and 256 for back
       maxFaces: 10, // maximum number of faces detected in the input, should be set to the minimum number for performance
       skipFrames: 10, // how many frames to go without running the bounding box detector
       minConfidence: 0.5, // threshold for discarding a prediction

src/index.js

@@ -79,17 +79,15 @@ async function detect(input, userConfig) {
 
     // run posenet
     timeStamp = performance.now();
-    let poseRes = [];
     tf.engine().startScope();
-    if (config.body.enabled) poseRes = await models.posenet.estimatePoses(input, config.body);
+    const poseRes = config.body.enabled ? await models.posenet.estimatePoses(input, config.body) : [];
     tf.engine().endScope();
     perf.body = Math.trunc(performance.now() - timeStamp);
 
     // run handpose
     timeStamp = performance.now();
-    let handRes = [];
     tf.engine().startScope();
-    if (config.hand.enabled) handRes = await models.handpose.estimateHands(input, config.hand);
+    const handRes = config.hand.enabled ? await models.handpose.estimateHands(input, config.hand) : [];
     tf.engine().endScope();
     perf.hand = Math.trunc(performance.now() - timeStamp);