[Sweep Rules] Refactor large function in data_augmentation.py - Githubissues

yihong1120 / Construction-Hazard-Detection

Enhances construction site safety using YOLO for object detection, identifying hazards like workers without helmets or safety vests, and proximity to machinery or vehicles. HDBSCAN clusters safety cone coordinates to create monitored zones. Post-processing algorithms improve detection accuracy.

https://yihong-server.mooo.com/label/koahsiung

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[Sweep Rules] Refactor large function in data_augmentation.py #4

Closed sweep-ai[bot] closed 9 months ago

sweep-ai[bot] commented 9 months ago

PR Feedback (click)

[ ] 👍 Sweep Did Well
[ ] 👎 Sweep Needs Improvement
I created this PR to address this rule:

"Refactor large functions to be more modular."

Description

This PR refactors the augment_image function in the data_augmentation.py file to improve its readability and maintainability. The function has been broken down into smaller, more focused functions to adhere to the Single Responsibility Principle.

Summary of Changes

Extracted the code block from lines 55 to 85 in the augment_image function into a new function called process_image.
Moved the process_image function to a new file called image_processing.py.
Updated the imports in the data_augmentation.py file to import the process_image function from the image_processing.py file.
Updated the augment_image function to call the process_image function with the required arguments.
Updated the class docstring for DataAugmentation to reflect the changes made to the augment_image function.

Please review and merge this PR to improve the maintainability of the codebase.

sweep-ai[bot] commented 9 months ago

Rollback Files For Sweep

[ ] Rollback changes to data_augmentation.py
[ ] Rollback changes to data_augmentation.py
[ ] Rollback changes to image_processing.py
[ ] Rollback changes to image_processing.py
[ ] Rollback changes to data_augmentation.py
[ ] Rollback changes to data_augmentation.py