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Release Notes

Version 0.1.3 (1/8/2025)

New Features and Modifications

  1. New drawing parameters for autoScaleDrawing in the model classes
  2. Added two optional parameters, targetDisplayWidth and targetDisplayHeight, to specify a custom reference resolution when autoScaleDrawing is enabled. (previously, the reference resolution was fixed at 1920x1080)
  3. Defaults to 1920x1080 if no values are provided.
  4. Ensures consistent scaling of overlays (e.g., bounding boxes, labels, keypoints) across varying input image dimensions.

Bug Fixes

  1. Fixed a bug where backend errors were thrown asynchronously from predict and predict_batch functions. Now, the user can catch these errors and handle them gracefully.

Version 0.1.2 (1/7/2025)

New Features and Modifications

  1. Lightweight listModels() function: Now, querying the list of models from the cloud (for CloudZoo classes) only fetches the names of the models. The parameters can now be fetched with a new function: getModelInfo(modelName).

  2. Updated autoScaleDrawing parameter for model classes displayResultToCanvas() function: Now, the parameter is made to scale all results to optimal viewing for 1080p resolution. autoScaleDrawing saves you from guesswork about how to size overlays for various input image dimensions by comparing the actual canvas size to a reference (e.g., 1080p) and scaling accordingly.

Version 0.1.1 (12/31/2024)

New Features and Modifications

  1. Asynchronous dg.connect(...)
    The dg.connect(...) method is now asynchronous. You should use await dg.connect(...) to properly wait for initialization.
    This improvement ensures the AI Server or Cloud connections (and their respective zoo classes) are fully ready before returning objects.

let dg = new dg_sdk();
// Old:
// let zoo = dg.connect('ws://localhost:8779');
// New:
let zoo = await dg.connect('ws://localhost:8779');
2. ReadableStream Support in predict_batch
Both AIServerModel and CloudServerModel now accept a ReadableStream in addition to an async iterable for the predict_batch(...) method.
This makes it easier to stream frames or data chunks directly from sources like the new WebCodecs API or other stream-based pipelines.

  1. predict() and predict_batch() Accept VideoFrame
    These methods now also allow VideoFrame objects as valid inputs.

  2. OffscreenCanvas Support in displayResultToCanvas()
    You can now draw inference results onto an OffscreenCanvas as well as a standard <canvas> element.

  3. Brighter Overlay Colors
    Default generated overlay colors have been adjusted to be more visible on dark backgrounds.

  4. Support for SegmentationYoloV8 Postprocessing
    Added the ability to draw results from models that use the SegmentationYoloV8 postprocessor.

Bug Fixes

  1. Proper Overlay Color for Age Classification
    Overlay colors for per-person text in age classification models are now correctly set.

  2. Postprocessing Improvements
    Various fixes and optimizations have been implemented in the postprocessing code.

Version 0.1.0 (10/4/2024)

New Features and Modifications

  1. Optimized Cloud inference connection handling, now resources are used only when needed and released properly.
  2. New default color generation logic creates a more visually appealing set of colors for different types of models when viewing inference results.

Version 0.0.9 (9/17/2024)

New Features and Modifications

  1. Optimized Mask Drawing in displayResultToCanvas() for results from Detection models with masks per detected object.

Bug Fixes

  1. Postprocessing for Detection models that return masks now handles inputPadMethod options properly.