Dev #110
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Dev #110
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Add exclusive checkboxes for 'All Images' and 'Filtered Images' to the ExportDataset groupbox and update the table based on the selected checkbox. * **QtBase.py** - Import QButtonGroup and QRadioButton. - Add `setup_options_layout` method to create a groupbox with exclusive checkboxes for 'All Images' and 'Filtered Images'. - Connect the checkboxes to the `update_image_selection` method. - Implement `update_image_selection` method to update the table based on the selected checkbox. * **QtClassify.py, QtDetect.py, QtSegment.py** - Minor formatting changes to ensure consistency.
Update `Base` class in `coralnet_toolbox/MachineLearning/ExportDataset/QtBase.py` to use annotations from all images or only from filtered images based on checkbox selection. * **Filter Annotations**: Add logic to `filter_annotations` method to filter annotations based on the selected image option (all images or filtered images). * **Update Image Selection**: Modify `update_image_selection` method to filter annotations based on the selected image option before calling `update_summary_statistics`. --- For more details, open the [Copilot Workspace session](https://copilot-workspace.githubnext.com/Jordan-Pierce/CoralNet-Toolbox/tree/export-dataset?shareId=XXXX-XXXX-XXXX-XXXX).
Update annotation filtering based on selected image option
Add exclusive checkboxes to ExportDataset
…etection, and segmentation modules
Add `change_ext.py` script to convert image file extensions using OpenCV. * **Import necessary modules**: Import `cv2`, `argparse`, `os`, `Path`, and `multiprocessing`. * **Define `change_extension` function**: Convert image format using OpenCV. * **Define `process_single_image` function**: Process and convert a single image to the specified format. * **Define `process_path` function**: Handle directory or single image processing. * **Add `argparse` code**: Handle command-line arguments and call `process_path`. * **Add docstrings and comments**: Provide documentation for each method.
Add change_ext.py script for image format conversion
…class names consistently
Add functionality to import TagLab labels from JSON files. * Create a new class `ImportTagLabLabels` in `coralnet_toolbox/IO/ImportTagLabLabels.py` to handle importing TagLab labels. * Add a method `import_taglab_labels` in `ImportTagLabLabels` to read and parse TagLab JSON files. * Use `QFileDialog` to select the JSON file and `QMessageBox` to show success or error messages in `ImportTagLabLabels`. * Add a new action `import_taglab_labels_action` in the `import_labels_menu` in `coralnet_toolbox/QtMainWindow.py` to import "TagLab Labels (.JSON)". * Connect the new action to the `import_taglab_labels` method in the `ImportTagLabLabels` class in `coralnet_toolbox/QtMainWindow.py`.
Add import TagLab Labels (.JSON) feature
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