Agml

Main Changes

- Introduced two new public object detection datasets from the Western Growers Global Harvest Automation Initiative (https://github.com/AxisAg/GHAIDatasets): `ghai_romaine_detection`, and `ghai_green_cabbage_detection`.
- Added the ability to generate multiple different types of annotations when generating synthetic datasets with Helios.

Other Changes
- Fixed errors with different visualization utilities as well as synthetic data annotation conversions.

**Full Changelog**: https://github.com/Project-AgML/AgML/compare/v0.4.6...v0.4.7

Main Changes

- You can now manually generate synthetic data with Helios using your own C++ generation file and CMake parameters/XML style file, using the method `agml.synthetic.manual_generate_data`. This allows for a greater deal of customizability and control over how Helios is compiled, as well as which methods are being used.
- A convenience method `agml.viz.show_sample` has been added which enables quick visualization of an image and its annotations: this works for all types of annotations, simply pass a loader as the argument.

Other Improvements

- If you want to overwrite files when generating synthetic data with the `HeliosDataGenerator`, you can now pass the argument `clear_existing_files = True` to the `generate` method.
- You can now compile Helios using the standard API in both the 'Release' and 'Debug' modes.

*[Read the Full Changelog Here.](https://github.com/Project-AgML/AgML/compare/v0.4.5...v0.4.6)*

This feature adds a couple of new features as well as bugfixes for the existing API.

Main Changes
- You can now use image classification models (`agml.models.ClassificationModel`) as image regression models by passing `regression = True` upon instantiation. This drops the final `argmax` computation and returns the softmax regression values.
- Pass a custom set of RGB values to `agml.viz.set_colormap` to use a custom colormap.
- A new preprocessing function has been added to `agml.models.preprocessing`: `agml.models.preprocessing.imagenet_preprocess`, which prepares images for input to an ImageNet-backend model (image classification, semantic segmentation).

Bugfixes
- The `MeanAveragePrecision` metric has been fixed, and no longer throws errors for empty predictions (or for early-stage training results).
- Custom object detection datasets can now be auto-loaded and classes automatically inferred without throwing an error.

[*Read the Full Changelog Here.*](https://github.com/Project-AgML/AgML/compare/v0.4.4...v0.4.5)

This release introduces a number of updates and bugfixes.

Main Changes

`agml.synthetic`
- Helios compilation has now been optimized, meaning that canopy generation speed should be increased by 3 to 5 times.
- When using `HeliosOptions.camera.generate_positions()`, camera views are no longer angled by default.

`agml.models`
- The `agml.models.ClassificationModel` is now initialized with `num_classes`, rather than a `dataset` name.
- For the `agml.models.SegmentationModel.predict()` method, a new argument, `overlay`, has been added, which allows the resulting segmentation mask to be overlaid or displayed side-by-side.

`agml.viz`
- A new method has been added, `agml.viz.convert_figure_to_image()`, which converts the in-built Matplotlib figure when using `agml.viz` methods into an image array.

[*Read the Full Changelog Here*.](https://github.com/Project-AgML/AgML/compare/v0.4.3...v0.4.4)

This release contains additional bugfixes for Helios installation and compilation.

This release fixes installation problems when installing Helios.

Bugfixes

- Helios will now auto-install when running any configuration or compilation methods, rather than throwing an installation error.
- The installation script which fetches Helios is now correctly included with the package.

*Note*: AgML v0.4.1 contains a *partial* fix of these errors, so please use this version instead. This GitHub release collapses v0.4.1 and v0.4.2 into a single release.