implemented movenet-multipose model

CHANGELOG.md

@@ -9,12 +9,13 @@ Repository: **<git+https://github.com/vladmandic/human.git>**
 
 ## Changelog
 
+### **HEAD -> main** 2021/08/19 mandic00@live.com
+
+
 ### **2.1.4** 2021/08/19 mandic00@live.com
 
 - add static type definitions to main class
-
+- fix interpolation overflow
-### **origin/main** 2021/08/18 mandic00@live.com
-
 - rebuild full
 - improve face box caching
 - strict type checks

TODO.md

@@ -7,10 +7,6 @@ WebGL shader optimizations for faster load and initial detection
 - Implement WebGL uniforms for shaders: <https://github.com/tensorflow/tfjs/issues/5205>
 - Fix shader packing: <https://github.com/tensorflow/tfjs/issues/5343>
 
-MoveNet MultiPose Model: <https://github.com/vladmandic/movenet>
-
-- Implementation is ready, but model is 2x size and 0.5x performance
-
 <br>
 
 ## Exploring
@@ -55,6 +51,14 @@ Hand detection using WASM backend has reduced precision due to math rounding err
 
 <br>
 
+### Body Detection
+
+MoveNet MultiPose model does not work with WASM backend due to missing F32 implementation
+
+- Backend WASM missing F32 implementation  
+  <https://github.com/tensorflow/tfjs/issues/5516>  
+  *Target: N/A*  
+
 ### Object Detection
 
 Object detection using CenterNet or NanoDet models is not working when using WASM backend due to missing kernel ops in TFJS  

demo/index.js

@@ -51,6 +51,7 @@ let userConfig = {
   gesture: { enabled: false },
   hand: { enabled: false },
   body: { enabled: false },
+  // body: { enabled: true, modelPath: 'movenet-multipose.json' },
   // body: { enabled: true, modelPath: 'posenet.json' },
   segmentation: { enabled: false },
   */

package.json

@@ -76,7 +76,7 @@
     "esbuild": "^0.12.21",
     "eslint": "^7.32.0",
     "eslint-config-airbnb-base": "^14.2.1",
-    "eslint-plugin-import": "^2.24.0",
+    "eslint-plugin-import": "^2.24.1",
     "eslint-plugin-json": "^3.1.0",
     "eslint-plugin-node": "^11.1.0",
     "eslint-plugin-promise": "^5.1.0",

src/movenet/movenet.ts

@@ -11,8 +11,9 @@ import { Config } from '../config';
 let model: GraphModel;
 
 type Keypoints = { score: number, part: string, position: [number, number], positionRaw: [number, number] };
-
 const keypoints: Array<Keypoints> = [];
+type Person = { id: number, score: number, box: [number, number, number, number], boxRaw: [number, number, number, number], keypoints: Array<Keypoints> }
+
 let box: [number, number, number, number] = [0, 0, 0, 0];
 let boxRaw: [number, number, number, number] = [0, 0, 0, 0];
 let score = 0;
@@ -29,28 +30,8 @@ export async function load(config: Config): Promise<GraphModel> {
   return model;
 }
 
-export async function predict(image: Tensor, config: Config): Promise<Body[]> {
+async function parseSinglePose(res, config, image) {
-  if ((skipped < config.body.skipFrames) && config.skipFrame && Object.keys(keypoints).length > 0) {
-    skipped++;
-    return [{ id: 0, score, box, boxRaw, keypoints }];
-  }
-  skipped = 0;
-  return new Promise(async (resolve) => {
-    const tensor = tf.tidy(() => {
-      if (!model.inputs[0].shape) return null;
-      const resize = tf.image.resizeBilinear(image, [model.inputs[0].shape[2], model.inputs[0].shape[1]], false);
-      const cast = tf.cast(resize, 'int32');
-      return cast;
-    });
-
-    let resT;
-    if (config.body.enabled) resT = await model.predict(tensor);
-    tf.dispose(tensor);
-
-    if (resT) {
   keypoints.length = 0;
-      const res = await resT.array();
-      tf.dispose(resT);
   const kpt = res[0][0];
   for (let id = 0; id < kpt.length; id++) {
     score = kpt[id][2];
@@ -69,7 +50,6 @@ export async function predict(image: Tensor, config: Config): Promise<Body[]> {
       });
     }
   }
-    }
   score = keypoints.reduce((prev, curr) => (curr.score > prev ? curr.score : prev), 0);
   const x = keypoints.map((a) => a.position[0]);
   const y = keypoints.map((a) => a.position[1]);
@@ -87,6 +67,80 @@ export async function predict(image: Tensor, config: Config): Promise<Body[]> {
     Math.max(...xRaw) - Math.min(...xRaw),
     Math.max(...yRaw) - Math.min(...yRaw),
   ];
-    resolve([{ id: 0, score, box, boxRaw, keypoints }]);
+  const persons: Array<Person> = [];
+  persons.push({ id: 0, score, box, boxRaw, keypoints });
+  return persons;
+}
+
+async function parseMultiPose(res, config, image) {
+  const persons: Array<Person> = [];
+  for (let p = 0; p < res[0].length; p++) {
+    const kpt = res[0][p];
+    score = Math.round(100 * kpt[51 + 4]) / 100;
+    // eslint-disable-next-line no-continue
+    if (score < config.body.minConfidence) continue;
+    keypoints.length = 0;
+    for (let i = 0; i < 17; i++) {
+      const partScore = Math.round(100 * kpt[3 * i + 2]) / 100;
+      if (partScore > config.body.minConfidence) {
+        keypoints.push({
+          part: bodyParts[i],
+          score: partScore,
+          positionRaw: [
+            kpt[3 * i + 1],
+            kpt[3 * i + 0],
+          ],
+          position: [
+            Math.trunc(kpt[3 * i + 1] * (image.shape[2] || 0)),
+            Math.trunc(kpt[3 * i + 0] * (image.shape[1] || 0)),
+          ],
+        });
+      }
+    }
+    boxRaw = [kpt[51 + 1], kpt[51 + 0], kpt[51 + 3] - kpt[51 + 1], kpt[51 + 2] - kpt[51 + 0]];
+    persons.push({
+      id: p,
+      score,
+      boxRaw,
+      box: [
+        Math.trunc(boxRaw[0] * (image.shape[2] || 0)),
+        Math.trunc(boxRaw[1] * (image.shape[1] || 0)),
+        Math.trunc(boxRaw[2] * (image.shape[2] || 0)),
+        Math.trunc(boxRaw[3] * (image.shape[1] || 0)),
+      ],
+      keypoints,
+    });
+  }
+  return persons;
+}
+
+export async function predict(image: Tensor, config: Config): Promise<Body[]> {
+  if ((skipped < config.body.skipFrames) && config.skipFrame && Object.keys(keypoints).length > 0) {
+    skipped++;
+    return [{ id: 0, score, box, boxRaw, keypoints }];
+  }
+  skipped = 0;
+  return new Promise(async (resolve) => {
+    const tensor = tf.tidy(() => {
+      if (!model.inputs[0].shape) return null;
+      let inputSize = model.inputs[0].shape[2];
+      if (inputSize === -1) inputSize = 256;
+      const resize = tf.image.resizeBilinear(image, [inputSize, inputSize], false);
+      const cast = tf.cast(resize, 'int32');
+      return cast;
+    });
+
+    let resT;
+    if (config.body.enabled) resT = await model.predict(tensor);
+    tf.dispose(tensor);
+
+    if (!resT) resolve([]);
+    const res = await resT.array();
+    let persons;
+    if (resT.shape[2] === 17) persons = await parseSinglePose(res, config, image);
+    else if (resT.shape[2] === 56) persons = await parseMultiPose(res, config, image);
+    tf.dispose(resT);
+
+    resolve(persons);
   });
 }

wiki

@@ -1 +1 @@
-Subproject commit bdc4077a3df07abdf4a2d5b2d2beadf2e573e8d8
+Subproject commit c12e036ac382043f4b3a85cf71f93927af56cfe4