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AgML 0.5.0
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@amogh7joshi amogh7joshi released this 23 May 16:50
· 139 commits to main since this release
v0.5.0
f09cabd

This release introduces synthetic LiDAR data generation through Helios, a rework of the agml.viz module with better functionality, and a number of new available datasets, amongst other features.

Major Changes

  • The agml.io module has been added, with a few convenience functions for working with file and directory structures.
    • Currently available functions include get_file_list and get_dir_list which also work with nested structures, as well as recursive_dirname, parent_path, and random_file.

agml.data

  • Three new datasets have been introduced:
    • Object Detection: ghai_iceberg_lettuce_detection, ghai_broccoli_detection
    • Image Classification: riseholme_strawberry_classification_2021
  • The agml.data.ImageLoader has been added, which is essentially a simple loader designed only for images.
    • Enables loading images from a nested directory structure.
    • Enables easy resizing and transforms of the loaded images.
  • The AgMLDataLoader now has a new method show_sample which can be used to visualize samples from the dataset directly from the loader.

agml.synthetic

  • LiDAR Data Generation: You can generate LiDAR-based synthetic data now, using opt.simulation_type = agml.synthetic.SimulationType.LiDAR.
  • You can recompile Helios with LiDAR enabled, as well as in parallel (for Linux and MacOS systems) using recompile_helios(lidar_enabled = True) and recompile_helios(parallel = True) [note that parallel compilation is enabled by default].
  • A new loader agml.synthetic.LiDARLoader has been added, which can be used to load point clouds from a generated directory in the same format as ImageLoader, and can be used to get point clouds and visualize them.

agml.viz

  • The entire agml.viz module has been reworked with new methods and a functional visualization backend which enables both cv2 and matplotlib displays, depending on what the user desires.
  • The following is a mapping of the old functions to new functions:
    • visualize_images_and_labels -> show_images_and_labels
    • output_to_mask -> convert_mask_to_colored_image
    • overlay_segmentation_masks -> annotate_semantic_segmentation
    • visualize_image_and_mask -> show_image_and_mask
    • visualize_overlaid_masks -> show_image_and_overlaid_mask
    • visualize_image_mask_and_predicted -> show_semantic_segmentation_truth_and_prediction
    • annotate_bboxes_on_image -> annotate_object_detection
    • visualize_image_and_boxes -> show_image_and_boxes
    • visualize_real_and_predicted_bboxes -> show_object_detection_truth_and_prediction
    • visualize_images -> show_images
  • To swap between viz backends, use get_viz_backend and set_viz_backend.
  • In order to simply display an image, you can use display_image.
  • To visualize a point cloud (either in Open3D if installed, or matplotlib), use show_point_cloud.

Other Changes

  • Major Change for New Users: torch, torchvision, and other related modeling packages for the agml.models package are no longer distributed with the AgML requirements -- you must install these on your own if you want to use them.

Minor/Functional Changes + Bugfixes

  • Fixed backend swapping between TensorFlow and PyTorch when using clashing transforms.
  • Added the ability to run prediction with a classification and a segmentation model without normalizing the input image using model.predict(..., normalize = False).
  • Images no longer auto-show as matplotlib figures when using agml.viz.show_* methods, instead they are returned as image arrays and can be displayed in a desired format.
  • Improved access and setting of information in the backend config.json file, so information is not accidentally overwritten.

Full Changelog: v0.4.7...v0.5.0

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