Each module is fully self enclosed:

• Defined in a separate folder under /src/
• Performs load and predict functions
• Uses global configuration object
• Runs prediction on global input image
• Merges results into global results object

Define module that implements load and predict async methods:

export async function load(config: Config | any) { ... }

• loads specific model using modelPath configuration
• returns tf.GraphModel

export async function predict(image: Tensor, config: Config, idx: number, count: number) { ... }

• input image is already preprocessed and passed to predict method as tensor
• takes input tensor and resizes and normalizes it as needed
• optionally implements skipFrames for caching of results
• optionally uses idx and count params to map cached objects to current objects
• uses any other optional configuration params
• returns result object

in human.ts:

• define model placeholder (module.load() stores model here) in the main constructor

in config.ts:

• define configuration types
• set configuration defaul values

in result.ts:

• define results type

in models.ts:

• enable model loading sync and async by calling module.load()

if model works on full image, execute from human.ts if model works on face image, execute from face.ts

• add results placeholder
• implement calls to module.predict() for sync and async
• merge results object

• For simple module that works on pre-detected face tensor,
  follow existing module defintion is emotion/emotion.ts
• For simple module that works on a full image tensor,
  follow existing module definition in object/centernet.ts