3 data preprocessing #8
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This commit includes a files to parse XML files. I am still working on
loading images using Python's PIL. There is also a model to visualize
the annotations by drawing bounding boxes around labeled images.
The boxes are in one of three colors:
- green for wearing a mask
- yellow for wearing a mask incorrectly
- red for not wearing a mask
Use python run_pipeline.py --help to see options. Also commented out pytorch code because it is too complicated for now and is causing compile-time errors.
The new dataset is formed by taking each of the original images and cropping out each face as specified by the annotations. To create this new dataset, you must have the original dataset in ./archive/images and ./archive/annotations. Then, run python crop.py, and a new directory will be created called ./archive/cropped_images. Furthermore, a file called cropped_labels.csv will be created to store the new labels for the images matching to their ids.
SimpleImage.py will make it so we can take a png file and convert it to an image array.
Ignores .idea
…/Face-Mask-Detection into 3-data-preprocessing
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Oct 21, 2020
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