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Description
Multiple image augmentation methods were implemented: Gaussian blur, directional blur, contrast, brightness, saturation, exposure, random rotation, and random flip. Comments have been included to try to explain the method implemented. Unique ids are generated for every new image made based on time stamps down to the microseconds because the images are generated so fast.
Tasks
Implemented and tested the creation of images for all the methods independently. I have not tested the result of the pipeline when all are included.
Test Plan
I verified random rotation visually by drawing a circle for the new calculated coordinates on the rotated image and checked for alignment. I did not run that through flip because flip is more simple than random rotation. The annotated circles are not saved on the photos; they only served for debugging purposes
A few photo examples;
#18 should be solved by this PR
To run the sample pipeline, you can run data_augmentation.py by itself. Currently it only has directional and rotational in its pipeline list.