lint markdown

Caching.md

@@ -17,26 +17,28 @@ If difference is higher than `config.cacheSensitivity` (expressed in range 0..1)
 Setting `config.cacheSensitivity=80` disables caching  
 
 Caching can be monitored via `human.performance`:
+
 - `totalFrames`: total number of processed frames
 - `cachedFrames`: number of frames considered for caching
 
 ### Per-module results caching
 
 Each module implements its logic that interprets values of `config.<module>`:
+
 - `skipFrames`: maximum number of frames before cache is invalidated
 - `skipTime`: maximum time (in ms) before cache is invalidated
 
 Values are interpreted as **or**, meaning whichever threshold is reached first  
 Note that per-module caching logic is only active if input is considered sufficiently similar  
 
-**Single-stage Modules Caching**: 
+**Single-stage Modules Caching**:
 
 - Includes: **Body, Emotion, Description, Object, AntiSpoof**
 - Module will return last known good value for a specific object  
   For example, there is no need to re-run *age/gender* analysis on video input on each frame  
   since it probably did not change if input itself is sufficiently similar
 
-**Two-stage Modules Caching**: 
+**Two-stage Modules Caching**:
 
 - Includes: **Face, Hand**
 - Module will run analysis on the last known position of the object but will skip detecting new objects  

Diag.md

@@ -1,22 +1,25 @@
 # Diagnostics
 
 ## Get human version
+
 ```js
 console.log(human.version);
 ```
-```
+
-2.2.0
+> 2.2.0
-```
 
 ## Enable console debug output
+
 ```js
 const human = new Human({ debug: true });
 ```
 
 ## Get current configuration
+
 ```js
 console.log(human.config);
 ```
+
 ```json
 {
   "backend": "tensorflow",
@@ -27,9 +30,11 @@ console.log(human.config);
 ```
 
 ## Get current environment details
+
 ```js
 console.log(human.env);
 ```
+
 ```json
 {
   "browser": true,
@@ -47,10 +52,12 @@ console.log(human.env);
 ```
 
 ## Get list of all models
+
 ```js
 const models = human.models.list();
 console.log(models);
 ```
+
 ```js
 models = [
   { name: 'face', loaded: true },
@@ -71,6 +78,7 @@ models = [
 ```
 
 ## Get memory usage information
+
 ```js
 console.log(human.tf.engine().memory());
 ```
@@ -80,9 +88,11 @@ memory = { numTensors: 1053, numDataBuffers: 1053, numBytes: 42736024 };
 ```
 
 ## Get current TensorFlow flags
+
 ```js
 console.log(human.tf.ENV.flags);
 ```
+
 ```js
 flags = { DEBUG: false, PROD: true, CPU_HANDOFF_SIZE_THRESHOLD: 128 };
 ```
@@ -93,6 +103,7 @@ flags = { DEBUG: false, PROD: true, CPU_HANDOFF_SIZE_THRESHOLD: 128 };
 const result = await human.detect(input);
 console.log(result.performance);
 ```
+
 ```js
 performance = {
   backend: 1, load: 283, image: 1, frames: 1, cached: 0, changed: 1, total: 947, draw: 0, face: 390, emotion: 15, embedding: 97, body: 97, hand: 142, object: 312, gesture: 0,

Docker.md

@@ -9,6 +9,7 @@ This guide covers multiple scenarios:
 <br>
 
 ## Install Docker
+
 For details see [Docker Docs: Installation Guide](https://docs.docker.com/engine/install/)
 
 Example: Install Docker using official convenience script:
@@ -87,7 +88,7 @@ USER node
 
 > sudo docker build . --file myapp.docker --tag myapp
 
-### Run container 
+### Run container
 
 - Maps `models` from host to a docker container so there is no need to copy it into each container
 - Modify path as needed
@@ -128,7 +129,7 @@ USER node
 
 > sudo docker build . --file human-web.docker --tag human-web
 
-### Run container 
+### Run container
 
 - Maps `models` from host to a docker container so there is no need to copy it into each container
 - Maps human internal web server to external port 8001 so app can be accessed externally

Draw.md

@@ -17,7 +17,8 @@
 
 ## Labels
 
-If `options.drawLabels` is enabled (default)
+If `options.drawLabels` is enabled (default):
+
 - Labels for each feature are parsed using templates  
 - Label templates can use built-in values in `[]` or be provided as any string literal
 - Labels for each feature are set relative to the top-left of the detection box of that feature (face, hand, body, object, etc.)  
@@ -50,6 +51,7 @@ drawOptions = {
 ## Example
 
 Example of custom labels:
+
 ```js
 const drawOptions = {
   bodyLabels: `person confidence is [score]% and has ${human.result?.body?.[0]?.keypoints.length || 'no'} keypoints`,

Embedding.md

@@ -53,6 +53,7 @@ for (let i = 0; i < currentResult.face.length; i++) {
 ```
 
 However, note that default configuration only detects first face in the frame, so increase maximum number of detected faces as well:
+
 ```js
 const myConfig = {
   face: {
@@ -118,11 +119,14 @@ a permanent database of faces that can be expanded over time to cover any number
 For example, see `/demo/facematch/facematch.js` and example database `/demo/facematch/faces.json`:
 
 > download db with known faces using http/https  
+
 ```js
 const res = await fetch('/demo/facematch/faces.json');
 db = (res && res.ok) ? await res.json() : [];
 ```
+
 > download db with known faces from a local file
+
 ```js
 const fs = require('fs');
 const buffer = fs.readFileSync('/demo/facematch/faces.json');

Home.md

@@ -15,8 +15,10 @@
 <br>
 
 ## Releases
+
 - [Release Notes](https://github.com/vladmandic/human/releases)
 - [NPM Link](https://www.npmjs.com/package/@vladmandic/human)
+
 ## Demos
 
 *Check out [**Simple Live Demo**](https://vladmandic.github.io/human/demo/typescript/index.html) fully annotated app as a good start starting point ([html](https://github.com/vladmandic/human/blob/main/demo/typescript/index.html))([code](https://github.com/vladmandic/human/blob/main/demo/typescript/index.ts))*  

Image.md

@@ -1,6 +1,7 @@
 # Input Processing
 
 `Human` includes optional input pre-processing via `config.filter` configuration:
+
 - using `Canvas` features
 - using `WebGL` accelerated filters
 - using `TFJS` accelerated enhancements
@@ -41,4 +42,4 @@ Individual filters that can be set are:
 
 If set, any input will be processed via histogram equalization to maximize color dynamic range to full spectrum
 
-- `equalization`: boolean
+- `equalization`: boolean

Inputs.md

@@ -23,7 +23,7 @@ type ExternalCanvas = typeof env.Canvas | typeof globalThis.Canvas;
 
 ## Examples of Input processing in NodeJS
 
-### 1. Using decode functionality from `tfjs-node`:
+### 1. Using decode functionality from `tfjs-node`
 
 All primary functionality of `Human` is available, but `human.draw` methods cannot be used as `canvas` implementation is not present
 
@@ -36,10 +36,12 @@ human.tf.dispose(tensor); // dispose input data, required when working with tens
 ```
 
 *Note:* For all processing, correct input tensor **shape** `[1, height, width, 3]` and **dtype** `float32`
+
 - 1 means batch number and is a fixed value
 - 3 means number of channels so 3 is used for RGB format  
 
 However `Human` will automatically convert input tensor to a correct shape
+
 - if batch number is omitted
 - if input image is 4-channels such as in **RGBA** images with alpha channel
 - if input tensor is in different data type such as `int32`

Models.md

@@ -23,10 +23,12 @@ Default models in Human library are:
 `Human` includes default models but supports number of additional models and model variations of existing models  
 
 Additional models can be accessed via:
- - [GitHub repository](https://github.com/vladmandic/human-models)
+
- - [NPMjs package](https://www.npmjs.com/package/@vladmandic/human-models)
+- [GitHub repository](https://github.com/vladmandic/human-models)
+- [NPMjs package](https://www.npmjs.com/package/@vladmandic/human-models)
 
 To use alternative models from local host:
+
 - download them either from *github* or *npmjs* and either
 - set human configuration value `modelPath` for each model or
 - set global configuration value `baseModelPath` to location of downloaded models

Result.md

@@ -10,7 +10,8 @@ Result of `humand.detect()` method is a single object that includes data for all
 
 <br>
 
-Full documentation: 
+Full documentation:
+
 - [**Result Interface Specification**](https://vladmandic.github.io/human/typedoc/interfaces/Result.html)  
 - [**Result Interface Definition**](https://github.com/vladmandic/human/blob/main/src/result.ts)
 

Usage.md

@@ -18,7 +18,7 @@ There is only *ONE* method you need:
   const result = await human.detect(input, config?)   // run single detection
 ```
 
-or 
+or
 
 <!-- eslint-skip -->
 ```js
@@ -28,6 +28,7 @@ or
 ```
 
 Notes:
+
 - [**Valid Inputs**](https://github.com/vladmandic/human/wiki/Inputs)
 - [**Configuration Details**](https://github.com/vladmandic/human/wiki/Config)
 
@@ -81,6 +82,7 @@ For details, see [segmentation documentation](https://vladmandic.github.io/human
 
 Additiona helper namespaces that can be used to reduce amount of manual code that needs to be written, but do not have to be used  
 For details, see:
+
 - [Draw methods documentation](https://github.com/vladmandic/human/wiki/Draw) | [Draw options](https://vladmandic.github.io/human/typedoc/interfaces/DrawOptions.html)
 - [WebCam API specs](https://vladmandic.github.io/human/typedoc/classes/WebCam.html)
 
@@ -151,7 +153,7 @@ human.tf;                                              // instance of tfjs used
 ## Results Caching and Smoothing
 
 - By default, `Human` uses frame change detection for results caching  
-- For on-screen display best results, it is recommended to use results smoothing 
+- For on-screen display best results, it is recommended to use results smoothing
 
 For details, see <https://github.com/vladmandic/human/wiki/Caching
 

_Footer.md

@@ -1,2 +1,2 @@
 **Human Library Wiki Pages**  
-3D Face Detection, Body Pose, Hand & Finger Tracking, Iris Tracking, Age & Gender Prediction, Emotion Prediction & Gesture Recognition  
+3D Face Detection, Body Pose, Hand & Finger Tracking, Iris Tracking, Age & Gender Prediction, Emotion Prediction & Gesture Recognition