urban_oculus

Code used for conducting research (and possible reproduction of results) for the following publications:

• First Gradually, Then Suddenly: Understanding the Impact of Image Compression on Object Detection Using Deep Learning (article)
• "Training deep convolutional object detectors for images affected by lossy compression" (chapter)
• "Detekcja obiektów w obrazach cyfrowych z użyciem uczenia głębokiego w warunkach stratnej kompresji obrazu" (thesis)

Extra data can be obtained from data repositories:

• https://doi.org/10.7910/DVN/UPIKSF
• https://doi.org/10.7910/DVN/UHEP3C

Dependencies

• Detectron2: https://detectron2.readthedocs.io/en/latest/tutorials/install.html
• Pandas, Matplotlib, Jupyter Notebook / Lab: standard pip installation.

Changelog of the included python library

Most of the code is in standalone scripts and Jupyter notebooks, but there is also a library of utilities, installable from sources via python setup.py install or pip install ..

• 0.1.2 - view_gt improved logger configuration when -v
• 0.1.1 - save_plot(figure, name_base) function added
• 0.1.0 - made the detectron dependencies optional, now run pip install -e .[detectron]
• 0.0.9 - view_obj <gt_id> command, update detectron2 dep
• 0.0.8 - summary_table; plots by model in evaldets.postprocess
• 0.0.7 - symlink_q from postprocess (and previous changes: Summary, GrandSummary)
• 0.0.5 - added view_gt command, with --scale and --verbose
• 0.0.1 - 0.0.4: jpeg, quantization, quality identification, evaldets, uo.utils

Examples of included command usage

View image GT objects by image ID

Displays the image in matplotlib window, with JSON metadata and GT annotations.

$ view_gt 448263 -v