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]);

dist/human-nobundle.cjs

@@ -23,12 +23,12 @@ var require_blazeface = __commonJS((exports2) => {
     anchors: [2, 6]
   };
   const NUM_LANDMARKS = 6;
-  function generateAnchors(width, height, outputSpec) {
+  function generateAnchors(anchorSize, outputSpec) {
     const anchors = [];
     for (let i = 0; i < outputSpec.strides.length; i++) {
       const stride = outputSpec.strides[i];
-      const gridRows = Math.floor((height + stride - 1) / stride);
+      const gridRows = Math.floor((anchorSize + stride - 1) / stride);
-      const gridCols = Math.floor((width + stride - 1) / stride);
+      const gridCols = Math.floor((anchorSize + stride - 1) / stride);
       const anchorsNum = outputSpec.anchors[i];
       for (let gridY = 0; gridY < gridRows; gridY++) {
         const anchorY = stride * (gridY + 0.5);
@@ -83,11 +83,11 @@ var require_blazeface = __commonJS((exports2) => {
       this.blazeFaceModel = model;
       this.width = config2.detector.inputSize;
       this.height = config2.detector.inputSize;
+      this.anchorSize = config2.detector.anchorSize;
       this.maxFaces = config2.detector.maxFaces;
-      this.anchorsData = generateAnchors(config2.detector.inputSize, config2.detector.inputSize, ANCHORS_CONFIG);
+      this.anchorsData = generateAnchors(config2.detector.anchorSize, ANCHORS_CONFIG);
       this.anchors = tf2.tensor2d(this.anchorsData);
-      this.inputSizeData = [config2.detector.inputSize, config2.detector.inputSize];
+      this.inputSize = tf2.tensor1d([this.width, this.height]);
-      this.inputSize = tf2.tensor1d([config2.detector.inputSize, config2.detector.inputSize]);
       this.iouThreshold = config2.detector.iouThreshold;
       this.scaleFaces = 0.8;
       this.scoreThreshold = config2.detector.scoreThreshold;
@@ -97,7 +97,16 @@ var require_blazeface = __commonJS((exports2) => {
         const resizedImage = inputImage.resizeBilinear([this.width, this.height]);
         const normalizedImage = tf2.mul(tf2.sub(resizedImage.div(255), 0.5), 2);
         const batchedPrediction = this.blazeFaceModel.predict(normalizedImage);
-        const prediction = batchedPrediction.squeeze();
+        let prediction;
+        if (Array.isArray(batchedPrediction)) {
+          const sorted = batchedPrediction.sort((a, b) => a.size - b.size);
+          const concat384 = tf2.concat([sorted[0], sorted[2]], 2);
+          const concat512 = tf2.concat([sorted[1], sorted[3]], 2);
+          const concat = tf2.concat([concat512, concat384], 1);
+          prediction = concat.squeeze(0);
+        } else {
+          prediction = batchedPrediction.squeeze();
+        }
         const decodedBounds = decodeBounds(prediction, this.anchors, this.inputSize);
         const logits = tf2.slice(prediction, [0, 0], [-1, 1]);
         const scoresOut = tf2.sigmoid(logits).squeeze();
@@ -130,7 +139,7 @@ var require_blazeface = __commonJS((exports2) => {
       detectedOutputs.dispose();
       return {
         boxes: annotatedBoxes,
-        scaleFactor: [inputImage.shape[2] / this.inputSizeData[0], inputImage.shape[1] / this.inputSizeData[1]]
+        scaleFactor: [inputImage.shape[2] / this.width, inputImage.shape[1] / this.height]
       };
     }
     async estimateFaces(input) {
@@ -5041,7 +5050,8 @@ var require_config = __commonJS((exports2) => {
     face: {
       enabled: true,
       detector: {
-        modelPath: "../models/blazeface/model.json",
+        modelPath: "../models/blazeface/tfhub/model.json",
+        anchorSize: 128,
         inputSize: 128,
         maxFaces: 10,
         skipFrames: 10,
@@ -5112,7 +5122,7 @@ var require_config = __commonJS((exports2) => {
 var require_package = __commonJS((exports2, module2) => {
   module2.exports = {
     name: "@vladmandic/human",
-    version: "0.3.1",
+    version: "0.3.2",
     description: "human: 3D Face Detection, Iris Tracking and Age & Gender Prediction",
     sideEffects: false,
     main: "dist/human.cjs",
@@ -5242,17 +5252,13 @@ async function detect(input, userConfig) {
     const perf = {};
     let timeStamp;
     timeStamp = performance.now();
-    let poseRes = [];
     tf.engine().startScope();
-    if (config.body.enabled)
+    const poseRes = config.body.enabled ? await models.posenet.estimatePoses(input, config.body) : [];
-      poseRes = await models.posenet.estimatePoses(input, config.body);
     tf.engine().endScope();
     perf.body = Math.trunc(performance.now() - timeStamp);
     timeStamp = performance.now();
-    let handRes = [];
     tf.engine().startScope();
-    if (config.hand.enabled)
+    const handRes = config.hand.enabled ? await models.handpose.estimateHands(input, config.hand) : [];
-      handRes = await models.handpose.estimateHands(input, config.hand);
     tf.engine().endScope();
     perf.hand = Math.trunc(performance.now() - timeStamp);
     const faceRes = [];

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/facemesh/blazeface.js

@@ -6,12 +6,12 @@ const ANCHORS_CONFIG = {
 };
 
 const NUM_LANDMARKS = 6;
-function generateAnchors(width, height, outputSpec) {
+function generateAnchors(anchorSize, outputSpec) {
   const anchors = [];
   for (let i = 0; i < outputSpec.strides.length; i++) {
     const stride = outputSpec.strides[i];
-    const gridRows = Math.floor((height + stride - 1) / stride);
+    const gridRows = Math.floor((anchorSize + stride - 1) / stride);
-    const gridCols = Math.floor((width + stride - 1) / stride);
+    const gridCols = Math.floor((anchorSize + stride - 1) / stride);
     const anchorsNum = outputSpec.anchors[i];
     for (let gridY = 0; gridY < gridRows; gridY++) {
       const anchorY = stride * (gridY + 0.5);
@@ -72,11 +72,11 @@ class BlazeFaceModel {
     this.blazeFaceModel = model;
     this.width = config.detector.inputSize;
     this.height = config.detector.inputSize;
+    this.anchorSize = config.detector.anchorSize;
     this.maxFaces = config.detector.maxFaces;
-    this.anchorsData = generateAnchors(config.detector.inputSize, config.detector.inputSize, ANCHORS_CONFIG);
+    this.anchorsData = generateAnchors(config.detector.anchorSize, ANCHORS_CONFIG);
     this.anchors = tf.tensor2d(this.anchorsData);
-    this.inputSizeData = [config.detector.inputSize, config.detector.inputSize];
+    this.inputSize = tf.tensor1d([this.width, this.height]);
-    this.inputSize = tf.tensor1d([config.detector.inputSize, config.detector.inputSize]);
     this.iouThreshold = config.detector.iouThreshold;
     this.scaleFaces = 0.8;
     this.scoreThreshold = config.detector.scoreThreshold;
@@ -87,11 +87,21 @@ class BlazeFaceModel {
       const resizedImage = inputImage.resizeBilinear([this.width, this.height]);
       const normalizedImage = tf.mul(tf.sub(resizedImage.div(255), 0.5), 2);
       const batchedPrediction = this.blazeFaceModel.predict(normalizedImage);
-      // todo: add handler for blazeface-front and blazeface-back
+      let prediction;
-      const prediction = batchedPrediction.squeeze();
+      // are we using tfhub or pinto converted model?
+      if (Array.isArray(batchedPrediction)) {
+        const sorted = batchedPrediction.sort((a, b) => a.size - b.size);
+        const concat384 = tf.concat([sorted[0], sorted[2]], 2); // dim: 384, 1 + 16
+        const concat512 = tf.concat([sorted[1], sorted[3]], 2); // dim: 512, 1 + 16
+        const concat = tf.concat([concat512, concat384], 1);
+        prediction = concat.squeeze(0);
+      } else {
+        prediction = batchedPrediction.squeeze(); // when using tfhub model
+      }
       const decodedBounds = decodeBounds(prediction, this.anchors, this.inputSize);
       const logits = tf.slice(prediction, [0, 0], [-1, 1]);
       const scoresOut = tf.sigmoid(logits).squeeze();
+      // console.log(prediction, decodedBounds, logits, scoresOut);
       return [prediction, decodedBounds, scoresOut];
     });
 
@@ -125,7 +135,7 @@ class BlazeFaceModel {
     detectedOutputs.dispose();
     return {
       boxes: annotatedBoxes,
-      scaleFactor: [inputImage.shape[2] / this.inputSizeData[0], inputImage.shape[1] / this.inputSizeData[1]],
+      scaleFactor: [inputImage.shape[2] / this.width, inputImage.shape[1] / this.height],
     };
   }
 

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);