This repository is presented as a part of the Media Center for Art History's presentation at the #dhnord2020 colloquium, The Art Historical Image Collection at Columbia University: Automating Research on its Construction and Creators.
Create a folder for images prepared for classification. If using scanned slide images, scans should be cropped to isolate transparency. Images can be stored in a different directory than the script however only images should be included in working folders. For a large quantity of images, it is recommended to run the script in batches.
pip install numpy
pip install pandas
pip install xgboost==0.90
pip install tk
pip install opencv-python
pip install joblib
pip install sklearn
pip install matplotlib
pip install tqdm
pip install scipy
Ensure finalized_model_xgboost_08_19.joblib.dat and xgb_pred_Visualizations_mcah_error_catch.py are in the same folder.
Run the python file:
python3 xgb_pred_Visualizations_mcah_error_catch.py
When prompted, select the folder containing images prepared earlier.
The script will produce:
[image_folder_name]_DFT_0- This folder contains DFT images used for classification.
results_[image_folder_name].csv- This CSV presents the halftone or non-halftone classification for each image. A probability is included.
[image_folder_name]_Processed_Visualizations_0- This folder contains images of visualizations of the 'sparkles' on each DFT and a CSV with location data for each point.
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