Institution: Munroe-Meyer Institute in the University of Nebraska Medical Center & Electrical and Computing Engineering Department in the University of Nebraska-Lincoln
Laboratory: Virtual Reality Laboratory & IMAGE-Signals Analysis Laboratory
Advisor: Dr. James Gehringer & Dr. Benjamin Riggan
Developer: Walker Arce
This software repository contains all scripts developed to produce the results in our paper "Detecting Aggression in Clinical Treatment Videos". Due to patient confidentiality issues, the dataset used for this study cannot be shared.
clip_export.py (180 LOC)
- Generates a dataset using cometrics coded videos
- Paths to folders and raw data must be added
cnn_transformer.py (290 LOC)
- Contains Transformer model source
- Contains recurrent model source
- Contains unused model source
convert_to_reli.py (63 LOC)
- Converts a primary session to a reliability session
dataloader_utils.py (572 LOC)
- Contains multiple routines for processing the dataset and pickling it to speed up processing
datapal_inference.py (302 LOC)
- Contains routines for inferencing using DataPal source files, is a precursor to future scripts
experiment_setup.py (655 LOC)
- Contains configuration data for experiments including the main training and evaluation routines
full_dataset.py (529 LOC)
- Generates dataset statistics
hog_testing.py (155 LOC)
- Creates HOG detector
identify_patient.py (43 LOC)
- Used to adapt an old dataset to new dataset format
keras_models.py (873 LOC)
- This script was the main engine of the experiments, sets up filepaths and initiates training and evaluation
keypoint_detection.py (71 LOC)
- Uses detectron2 to generate keypoint images
keypoint_utils.py (78 LOC)
- Utilities for processing keypoint data
model_metrics.py (136 LOC)
- Utilities for generating performance metrics on keras models
patient_dataset_generator.py (401 LOC)
- Tkinter user interface for generating annotations of patient and clinician using a stock YOLO model
patient_dataset_generator_auto.py (436 LOC)
- Tkinter user interface for automatically generating annotations of patient and clinician using a finetuned YOLO model
roc_creator.py (227 LOC)
- Creates ROC curves used in this study
table_creator.py (72 LOC)
- Creates tables used in this study
tag_lut.py (85 LOC)
- Lookup table for tags used in this study
update_paths.py (67 LOC)
- Recreates YOLO dataset for training using the annotations generated by patient_dataset_generator.py
vivit.py (129 LOC)
- Instantiates ViViT model