Install numpy and you're good to go! The functions are implemented in RobustPCA.py.
# Robust PCA via the Exact ALM Method
Low_Rank_M, Sparse_M = RPCA(Data, Lambda, mu, rho)
# Robust PCA via the Inexact ALM Method
Low_Rank_M, Sparse_M = RPCA_inexact(Data, Lambda, mu, rho)If you're interested in the details of these applications, please refer to the report.
- Part 1: Video Denoising
- Extract the noise from a noisy video.
- Separate the foreground (moving objects) and background (stationary objects) from a video.
- Part 2: Anomaly Detection
- Use robust PCA to detect anomalies in a dataset of images.
- numpy
- pandas
- scikit-video
- tqdm
- Data
-- dataset
|-- Part1
|-- boat_GT.jpg 'Ground truth of boat.mp4'
|-- boat.mp4 'Boat video'
|-- flower_GT.mp4 'Ground truth of flower.mp4'
|-- flower.mp4 'Flower video'
|-- TrainStation.mp4 'Train station video'- Usage
python Part1.py --i input_video_path \
--o output_video_path \
--l lambda (optional) \
--r rho (optional) \
--save_noise (optional) \
--all (optional)
E.g., python Part1.py --i ./dataset/Part1/boat.mp4 \
--o ./boat_denoised.mp4 \
--l 0.1 \
--save_noise- Data
-- dataset
|-- Part2
|-- train_data.npy 'MNIST Images'
|-- train_label.npy 'Labels (0: normal, 1: anomaly)'- Usage
python Part2.py --i input_data_path \
--g input_label_path \
--o output_csv_path \
--l lambda (optional) \
--r rho (optional) \
--t threshold (optional) \
E.g., python Part2.py --i ./dataset/Part2/train_data.npy \
--g ./dataset/Part2/train_label.npy \
--o ./detection_result.csv \
--l 0.053 \
--t 0.999999