Rename hand-detection package to hand-pose-detection (#854)

Author: ahmedsabie

hand-detection/.npmignore renamed to hand-pose-detection/.npmignore

@@ -1,6 +1,6 @@
-# Hand Detection
+# Hand Pose Detection
 
-This package provides multiple models for running real-time hand detection.
+This package provides models for running real-time hand pose detection.
 
 Currently, we provide 1 model option:
 
@@ -22,8 +22,8 @@ including `MediaPipeHands`.
 For example:
 
 ```javascript
-const model = handDetection.SupportedModels.MediaPipeHands;
-const detector = await handDetection.createDetector(model);
+const model = handPoseDetection.SupportedModels.MediaPipeHands;
+const detector = await handPoseDetection.createDetector(model);
 ```
 
 Then you can use the detector to detect hands.
@@ -62,9 +62,9 @@ The name provides a label for each keypoint, such as 'wrist', 'pinky_tip', etc.
 Refer to each model's documentation for specific configurations for the model
 and their performance.
 
-[MediaPipeHands MediaPipe Documentation](https://github.com/tensorflow/tfjs-models/tree/master/hand-detection/src/mediapipe)
+[MediaPipeHands MediaPipe Documentation](https://github.com/tensorflow/tfjs-models/tree/master/hand-pose-detection/src/mediapipe)
 
-[MediaPipeHands TFJS Documentation](https://github.com/tensorflow/tfjs-models/tree/master/hand-detection/src/tfjs)
+[MediaPipeHands TFJS Documentation](https://github.com/tensorflow/tfjs-models/tree/master/hand-pose-detection/src/tfjs)
 
 -------------------------------------------------------------------------------
 

hand-detection/README.md renamed to hand-pose-detection/README.md

@@ -6,33 +6,33 @@ steps:
   entrypoint: 'yarn'
   args: ['install']
 
-# Install hand-detection dependencies.
+# Install hand-pose-detection dependencies.
 - name: 'node:10'
-  dir: 'hand-detection'
+  dir: 'hand-pose-detection'
   entrypoint: 'yarn'
   id: 'yarn'
   args: ['install']
   waitFor: ['yarn-common']
 
 # Lint.
 - name: 'node:10'
-  dir: 'hand-detection'
+  dir: 'hand-pose-detection'
   entrypoint: 'yarn'
   id: 'lint'
   args: ['lint']
   waitFor: ['yarn']
 
 # Build.
 - name: 'node:10'
-  dir: 'hand-detection'
+  dir: 'hand-pose-detection'
   entrypoint: 'yarn'
   id: 'build'
   args: ['build']
   waitFor: ['yarn']
 
 # Run browser tests.
 - name: 'node:10'
-  dir: 'hand-detection'
+  dir: 'hand-pose-detection'
   entrypoint: 'yarn'
   id: 'test'
   args: ['test-ci']

hand-detection/cloudbuild.yml renamed to hand-pose-detection/cloudbuild.yml

@@ -1,6 +1,6 @@
 # Demos
 
-Try our demos and get inspired with what you can do with hand-detection models!
+Try our demos and get inspired with what you can do with hand-pose-detection models!
 
 ## Table of Contents
 1. [Live Camera Demo](#live-camera-demo)
@@ -14,7 +14,7 @@ This demo uses your camera to get live stream and tracks your hands in real-time
 You can try out different runtimes to see the difference. It
 works on laptops, iPhones and android phones.
 
-[MediaPipeHands model entry](https://storage.googleapis.com/tfjs-models/demos/hand-detection/index.html?model=mediapipe_hands)
+[MediaPipeHands model entry](https://storage.googleapis.com/tfjs-models/demos/hand-pose-detection/index.html?model=mediapipe_hands)
 
 ## How to Run a Demo
 If you want to run any of the demos locally, follow these steps:

hand-detection/demos/README.md renamed to hand-pose-detection/demos/README.md

@@ -1,7 +1,7 @@
 {
-  "name": "handdetection_demo",
+  "name": "handposedetection_demo",
   "version": "0.0.1",
-  "description": "Demo for hand detection api",
+  "description": "Demo for hand pose detection api",
   "main": "index.js",
   "license": "Apache-2.0",
   "private": true,
@@ -10,7 +10,7 @@
   },
   "dependencies": {
     "@mediapipe/hands": "~0.3.0",
-    "@tensorflow-models/hand-detection": "file:../../dist",
+    "@tensorflow-models/hand-pose-detection": "file:../../dist",
     "@tensorflow/tfjs-backend-wasm": "^3.9.0",
     "@tensorflow/tfjs-backend-webgl": "^3.9.0",
     "@tensorflow/tfjs-converter": "^3.9.0",

hand-detection/demos/live_video/.babelrc renamed to hand-pose-detection/demos/live_video/.babelrc

@@ -24,7 +24,7 @@ tfjsWasm.setWasmPaths(
     `https://cdn.jsdelivr.net/npm/@tensorflow/tfjs-backend-wasm@${
         tfjsWasm.version_wasm}/dist/`);
 
-import * as handdetection from '@tensorflow-models/hand-detection';
+import * as handdetection from '@tensorflow-models/hand-pose-detection';
 
 import {Camera} from './camera';
 import {setupDatGui} from './option_panel';

hand-detection/demos/live_video/index.html renamed to hand-pose-detection/demos/live_video/index.html

@@ -14,7 +14,7 @@
  * limitations under the License.
  * =============================================================================
  */
-import * as handdetection from '@tensorflow-models/hand-detection';
+import * as handdetection from '@tensorflow-models/hand-pose-detection';
 import * as tf from '@tensorflow/tfjs-core';
 
 import * as params from './params';

hand-detection/demos/live_video/package.json renamed to hand-pose-detection/demos/live_video/package.json

@@ -14,7 +14,7 @@
  * limitations under the License.
  * =============================================================================
  */
-import * as handdetection from '@tensorflow-models/hand-detection';
+import * as handdetection from '@tensorflow-models/hand-pose-detection';
 
 export const DEFAULT_LINE_WIDTH = 2;
 export const DEFAULT_RADIUS = 4;

hand-detection/demos/live_video/src/camera.js renamed to hand-pose-detection/demos/live_video/src/camera.js

@@ -1,12 +1,12 @@
 {
-  "name": "@tensorflow-models/hand-detection",
+  "name": "@tensorflow-models/hand-pose-detection",
   "version": "0.0.1",
-  "description": "Pretrained hand detection model",
+  "description": "Pretrained hand pose detection model",
   "main": "dist/index.js",
-  "jsnext:main": "dist/hand-detection.esm.js",
-  "module": "dist/hand-detection.esm.js",
-  "unpkg": "dist/hand-detection.min.js",
-  "jsdelivr": "dist/hand-detection.min.js",
+  "jsnext:main": "dist/hand-pose-detection.esm.js",
+  "module": "dist/hand-pose-detection.esm.js",
+  "unpkg": "dist/hand-pose-detection.min.js",
+  "jsdelivr": "dist/hand-pose-detection.min.js",
   "types": "dist/index.d.ts",
   "repository": {
     "type": "git",

hand-detection/demos/live_video/src/index.js renamed to hand-pose-detection/demos/live_video/src/index.js

@@ -66,15 +66,15 @@ function config({ plugins = [], output = {}, tsCompilerOptions = {} }) {
   };
 }
 
-const packageName = 'handDetection';
+const packageName = 'handPoseDetection';
 export default [
-  config({ output: { format: 'umd', name: packageName, file: 'dist/hand-detection.js' } }),
+  config({ output: { format: 'umd', name: packageName, file: 'dist/hand-pose-detection.js' } }),
   config({
     plugins: [terser({output: {preamble: PREAMBLE, comments: false}})],
-    output: { format: 'umd', name: packageName, file: 'dist/hand-detection.min.js' }
+    output: { format: 'umd', name: packageName, file: 'dist/hand-pose-detection.min.js' }
   }),
   config({
     plugins: [terser({output: {preamble: PREAMBLE, comments: false}})],
-    output: { format: 'es', file: 'dist/hand-detection.esm.js' }
+    output: { format: 'es', file: 'dist/hand-pose-detection.esm.js' }
   })
 ];

hand-detection/demos/live_video/src/option_panel.js renamed to hand-pose-detection/demos/live_video/src/option_panel.js

@@ -30,7 +30,7 @@ process.on('unhandledRejection', e => {
 });
 
 jasmine_util.setTestEnvs(
-    [{name: 'test-hand-detection', backendName: 'cpu', flags: {}}]);
+    [{name: 'test-hand-pose-detection', backendName: 'cpu', flags: {}}]);
 
 const unitTests = 'src/**/*_test.ts';
 

hand-detection/demos/live_video/src/params.js renamed to hand-pose-detection/demos/live_video/src/params.js

@@ -19,31 +19,31 @@ Via script tags:
 
 ```html
 <script src="https://cdn.jsdelivr.net/npm/@mediapipe/hands"></script>
-<script src="https://cdn.jsdelivr.net/npm/@tensorflow-models/hand-detection"></script>
+<script src="https://cdn.jsdelivr.net/npm/@tensorflow-models/hand-pose-detection"></script>
 ```
 
 Via npm:
 ```sh
-yarn add @tensorflow-models/hand-detection
+yarn add @tensorflow-models/hand-pose-detection
 yarn add @mediapipe/hands
 ```
 
 -----------------------------------------------------------------------
 ## Usage
 
-If you are using the hand-detection API via npm, you need to import the libraries first.
+If you are using the hand-pose-detection API via npm, you need to import the libraries first.
 
 ### Import the libraries
 
 ```javascript
-import * as handDetection from '@tensorflow-models/hand-detection';
+import * as handPoseDetection from '@tensorflow-models/hand-pose-detection';
 import '@mediapipe/hands';
 ```
 
 ### Create a detector
 
-Pass in `handDetection.SupportedModels.MediaPipeHands` from the
-`handDetection.SupportedModel` enum list along with a `detectorConfig` to the
+Pass in `handPoseDetection.SupportedModels.MediaPipeHands` from the
+`handPoseDetection.SupportedModel` enum list along with a `detectorConfig` to the
 `createDetector` method to load and initialize the model.
 
 `detectorConfig` is an object that defines MediaPipeHands specific configurations for `MediaPipeHandsMediaPipeModelConfig`:
@@ -58,12 +58,12 @@ Pass in `handDetection.SupportedModels.MediaPipeHands` from the
 *   *solutionPath*: The path to where the wasm binary and model files are located.
 
 ```javascript
-const model = handDetection.SupportedModels.MediaPipeHands;
+const model = handPoseDetection.SupportedModels.MediaPipeHands;
 const detectorConfig = {
   runtime: 'mediapipe',
   solutionPath: 'base/node_modules/@mediapipe/hands'
 };
-detector = await handDetection.createDetector(model, detectorConfig);
+detector = await handPoseDetection.createDetector(model, detectorConfig);
 ```
 
 ### Run inference

hand-detection/demos/live_video/src/stats_panel.js renamed to hand-pose-detection/demos/live_video/src/stats_panel.js

@@ -20,7 +20,7 @@ import {BROWSER_ENVS, describeWithFlags} from '@tensorflow/tfjs-core/dist/jasmin
 // tslint:disable-next-line: no-imports-from-dist
 import {expectArraysClose} from '@tensorflow/tfjs-core/dist/test_util';
 
-import * as handDetection from '../index';
+import * as handPoseDetection from '../index';
 import {loadImage} from '../shared/test_util';
 
 import {MediaPipeHandsMediaPipeModelConfig} from './types';
@@ -50,7 +50,7 @@ const EXPECTED_HAND_KEYPOINTS_PREDICTION = [
 ];
 
 describeWithFlags('MediaPipe Hands multi hands ', BROWSER_ENVS, () => {
-  let detector: handDetection.HandDetector;
+  let detector: handPoseDetection.HandDetector;
   let image: HTMLImageElement;
   let timeout: number;
 
@@ -66,9 +66,9 @@ describeWithFlags('MediaPipe Hands multi hands ', BROWSER_ENVS, () => {
 
   it('test.', async () => {
     // Note: this makes a network request for model assets.
-    const model = handDetection.SupportedModels.MediaPipeHands;
+    const model = handPoseDetection.SupportedModels.MediaPipeHands;
     detector =
-        await handDetection.createDetector(model, MEDIAPIPE_MODEL_CONFIG);
+        await handPoseDetection.createDetector(model, MEDIAPIPE_MODEL_CONFIG);
 
     const result = await detector.estimateHands(image, {staticImageMode: true});
     const keypoints = result.map(

hand-detection/demos/live_video/src/util.js renamed to hand-pose-detection/demos/live_video/src/util.js

@@ -14,25 +14,25 @@ MediaPipeHands-TFJS uses TF.js runtime to execute the model, the preprocessing a
 To use MediaPipeHands, you need to first select a runtime (TensorFlow.js or MediaPipe).
 This guide is for TensorFlow.js
 runtime. The guide for MediaPipe runtime can be found
-[here](https://github.com/tensorflow/tfjs-models/tree/master/hand-detection/src/mediapipe).
+[here](https://github.com/tensorflow/tfjs-models/tree/master/hand-pose-detection/src/mediapipe).
 
 Via script tags:
 
 ```html
-<!-- Require the peer dependencies of hand-detection. -->
+<!-- Require the peer dependencies of hand-pose-detection. -->
 <script src="https://cdn.jsdelivr.net/npm/@tensorflow/tfjs-core"></script>
 <script src="https://cdn.jsdelivr.net/npm/@tensorflow/tfjs-converter"></script>
 
 <!-- You must explicitly require a TF.js backend if you're not using the TF.js union bundle. -->
 <script src="https://cdn.jsdelivr.net/npm/@tensorflow/tfjs-backend-webgl"></script>
 
-<script src="https://cdn.jsdelivr.net/npm/@tensorflow-models/hand-detection"></script>
+<script src="https://cdn.jsdelivr.net/npm/@tensorflow-models/hand-pose-detection"></script>
 ```
 
 Via npm:
 
 ```sh
-yarn add @tensorflow-models/hand-detection
+yarn add @tensorflow-models/hand-pose-detection
 yarn add @tensorflow/tfjs-core, @tensorflow/tfjs-converter
 yarn add @tensorflow/tfjs-backend-webgl
 ```
@@ -45,15 +45,15 @@ If you are using the Hands API via npm, you need to import the libraries first.
 ### Import the libraries
 
 ```javascript
-import * as handDetection from '@tensorflow-models/hand-detection';
+import * as handPoseDetection from '@tensorflow-models/hand-pose-detection';
 import * as tf from '@tensorflow/tfjs-core';
 // Register WebGL backend.
 import '@tensorflow/tfjs-backend-webgl';
 ```
 ### Create a detector
 
-Pass in `handDetection.SupportedModels.MediaPipeHands` from the
-`handDetection.SupportedModel` enum list along with a `detectorConfig` to the
+Pass in `handPoseDetection.SupportedModels.MediaPipeHands` from the
+`handPoseDetection.SupportedModel` enum list along with a `detectorConfig` to the
 `createDetector` method to load and initialize the model.
 
 `detectorConfig` is an object that defines MediaPipeHands specific configurations for `MediaPipeHandsTfjseModelConfig`:
@@ -71,11 +71,11 @@ the detector model. This is useful for area/countries that don't have access to
 the landmark model. This is useful for area/countries that don't have access to the model hosted on tf.hub.
 
 ```javascript
-const model = handDetection.SupportedModels.MediaPipeHands;
+const model = handPoseDetection.SupportedModels.MediaPipeHands;
 const detectorConfig = {
   runtime: 'tfjs',
 };
-detector = await handDetection.createDetector(model, detectorConfig);
+detector = await handPoseDetection.createDetector(model, detectorConfig);
 ```
 
 ### Run inference
@@ -89,7 +89,7 @@ options, you can pass in a second `estimationConfig` parameter.
 
 *   *flipHorizontal*: Optional. Defaults to false. When image data comes from camera, the result has to flip horizontally.
 
-*   *staticImageMode*: Optional. Defaults to false. If set to true, hand detection
+*   *staticImageMode*: Optional. Defaults to false. If set to true, hand pose detection
 will run on every input image, otherwise if set to false then detection runs
 once and then the model simply tracks those landmarks without invoking
 another detection until it loses track of any of the hands (ideal for videos).
@@ -102,5 +102,5 @@ const hands = await detector.estimateHands(image, estimationConfig);
 ```
 
 Please refer to the Hands API
-[README](https://github.com/tensorflow/tfjs-models/blob/master/hand-detection/README.md#how-to-run-it)
+[README](https://github.com/tensorflow/tfjs-models/blob/master/hand-pose-detection/README.md#how-to-run-it)
 about the structure of the returned `hands`.