Class: AIServerModel

AIServerModel(options, measureTime, additionalParamsopt)

A comprehensive class for handling AI model inference using an AIServer over WebSocket. Designed to provide a streamlined interface for sending data to the server for inference, receiving processed results, and displaying or further processing these results as needed.

Features:
- WebSocket Communication: Handles the full lifecycle of a WebSocket connection for real-time data streaming.
- Preprocessing & Postprocessing: Integrates with PreProcess and PostProcess classes to prepare data for the model and visualize results.
- Queue Management: Uses AsyncQueue instances to manage inbound and outbound data flow.
- Concurrency Control: Ensures thread-safe operations through mutex usage.
- Dynamic Configuration: Allows runtime modification of model and overlay parameters.
- Callback Integration: Supports custom callback functions for handling results outside the class.

Constructor

new AIServerModel(options, measureTime, additionalParamsopt)

Do not call the constructor directly. Use the `loadModel` method of an AIServerZoo instance to create an AIServerModel.
Parameters:
Name Type Attributes Default Description
options Object Options for initializing the model.
Properties
Name Type Attributes Default Description
modelName string The name of the model to load.
serverUrl string The URL of the server.
modelParams Object The default model parameters.
max_q_len number <optional>
 10 Maximum queue length.
callback function <optional>
 null Callback function for handling results.
labels Object <optional>
 null Label dictionary for the model.
systemDeviceTypes Array.<string> Array of 'RUNTIME/DEVICE' strings supported by the AIServer.
measureTime boolean false Whether to measure inference and collect other statistics.
additionalParams Object <optional>
 Additional parameters for the model.
Source:
Example

Usage:

- Create an instance with the required model details and server URL.
let model = zoo.loadModel('some_model_name', {} );
- Use the `predict` method for inference with individual data items or `predict_batch` for multiple items.
let result = await model.predict(someImage);
for await (let result of model.predict_batch(someDataGeneratorFn)) { ... }
- Access processed results directly or set up a callback function for custom result handling.
- You can display results to a canvas to view drawn overlays.
await model.displayResultToCanvas(result, canvas);

Methods

(async) cleanup()

Cleans up resources and closes the WebSocket connection. Does so by following a destructor-like pattern which is manually called by the user. Makes sure to close the WebSocket connection, stop all inferences, remove the listeners, clear async queues, and nullify all references.
Call this whenever switching models or when the model instance is no longer needed.
Source:

(async) displayResultToCanvas(combinedResult, outputCanvasName, justResultsopt)

Overlay the result onto the image frame and display it on the canvas.
Parameters:
Name Type Attributes Default Description
combinedResult Object The result object combined with the original image frame. This is directly received from `predict` or `predict_batch`
outputCanvasName string | HTMLCanvasElement The canvas to draw the image onto. Either the canvas element or the ID of the canvas element.
justResults boolean <optional>
 false Whether to show only the result overlay without the image frame.
Source:

getTimeStats()

Returns the stats dict to the user
Source:

labelDictionary() → {Object}

Returns the label dictionary for this AIServerModel instance.
Source:
Returns:
The label dictionary.
Type
Object

modelInfo() → {Object}

Returns a read-only copy of the model parameters.
Source:
Returns:
The model parameters.
Type
Object

(async) predict(imageFile, infoopt, bypassPreprocessingopt) → {Promise.<Object>}

Predicts the result for a given image.
Parameters:
Name Type Attributes Default Description
imageFile Blob | File | string | HTMLImageElement | HTMLVideoElement | HTMLCanvasElement | ArrayBuffer | TypedArray | ImageBitmap
info string <optional>
 performance.now() Unique frame information provided by user (such as frame num). Used for matching results back to input images within callback.
bypassPreprocessing boolean <optional>
 false Whether to bypass preprocessing. Used to send Blob data directly to the socket without any preprocessing.
Source:
Returns:
The prediction result.
Type
Promise.<Object>
Examples
If callback is provided:
The WebSocket onmessage will invoke the callback directly when the result arrives.
If callback is not provided:
The function waits for the resultQ to get a result, then returns it.
let result = await model.predict(someImage);

(async, generator) predict_batch(data_source, bypassPreprocessingopt) → {Object}

Predicts results for a batch of data. Will yield results if a callback is not provided.
Parameters:
Name Type Attributes Default Description
data_source AsyncIterable An async iterable data source.
bypassPreprocessing boolean <optional>
 false Whether to bypass preprocessing.
Source:
Yields:
The prediction result.
Type
Object
Example
The function asynchronously processes results. If a callback is not provided, it will yield results.
for await (let result of model.predict_batch(data_source)) { console.log(result); }

(async) processImageFile(combinedResult) → {Promise.<Blob>}

Processes the original image and draws the results on it, return png image with overlayed results.
Parameters:
Name Type Description
combinedResult Object The result object combined with the original image frame.
Source:
Returns:
The processed image file as a Blob of a PNG image.
Type
Promise.<Blob>

resetTimeStats()

Resets the stats dict to an empty dict
Source: