Show-Segmentation

GSoC 2021 project with Red Hen Lab. I want to improve the clustering algorithm to the in-production code and enhance the previous work. The main problem is to find the correct anchor. Currently, the most time-consuming process in the program is going frame by frame and extracting faces. The face-recognition method used in the production code processes each frame individually. I want to upgrade it to a parallelized algorithm to process multiple frames in a batch and increase the processing speed exponentially, which will also help in faster testing of hyperparameters. Batch processing can be much quicker than processing single images at a time. So I think we use batch processing for multi-threading. More information about the project statement can be found here.

Mentors: Anna Wilson, Francis Steen, Frankie Robertson

Blog detailing the research and working can be found at edoates84.github.io

Usage

1. Clone the repo to your machine

git clone https://github.com/EdOates84/Show-Segmentation-2021.git

2. Install the required python packages using either of these commands

pip install numpy pandas matplotlib opencv-python scikit-learn face_recognition wikipedia ffmpeg traceback2

3. Download the anchors-encodings pickle and place it in this location.

/Show-Segmentation-2021/final_celeb_detection/final_pickles/anchors-with-TV-encodings.pickle

4. Download the IMDB Datasets and place it in this location.

Show-Segmentation-2021/IMDB_Datasets/name.basics.tsv
Show-Segmentation-2021/IMDB_Datasets/title.basics.tsv
Show-Segmentation-2021/IMDB_Datasets/title.principals.tsv
Show-Segmentation-2021/IMDB_Datasets/name.akas.tsv
Show-Segmentation-2021/IMDB_Datasets/name.crew.tsv

5. Navigate to Show-Segmentation-2021/final_usable_code/

cd Show-Segmentation-2021/final_usable_code

6. segment_tv.py takes 3 inputs, the path to the input video, path to the output location and a flag --verbose.

python3 segment_tv.py path/to/input/video.mp4 path/to/store/output --verbose

7. Make sure that the input video's name follows RedHenLab's Tv dataset's format. Here's an example

1980-06-03_0000_US_00020088_V0_U2_M9_EG1_DB.mp4

Singularity Usage

Setup

**This is for those using the singularity image (segmentation_production.simg) on the CWRU HPC Cluster.

1. Connect to the CWR VPN.
2. Login to the cluster using your CWR ID and your credentials. Example:

ssh abc123@rider.case.edu

3. Navigate to the project's location on the cluster.

cd /mnt/rds/redhen/gallina/Singularity/Show-Segmentation-2021/final_usable_code

4. Request a computing node using

srun --mem=16gb --pty /bin/bash

5. Load singularity 2.5.1 to your environment using

module load singularity

6. I have made segment_tv.py for testing and final_script.py for final production. After setup, read the Testing section or the Production section according to the requirement.

Testing

1. segment_tv.py is made to work on a single video file. It takes 3 inputs (in this order)

• path/to/input/video.mp4
• path/to/output/directory (where the output will be stored)
• --verbose (an optional flag which will make the program print progress statements like 'done extracting faces', 'done clustering faces' etc.)

2. The main command is of the form

singularity exec -B /mnt../show-segementation-2021_latest.sif python3 segment_video.py {INPUT_VIDEO_PATH} {OUTPUT_PATH} {--verbose}

3. The Tv dataset is present at /mnt/rds/redhen/gallina/tv, we can take some video file from this as our input.
4. An example command for the file 1998-01/1998-01-01/1998-01-01_0000_US_00019495_V3_VHS50_MB20_H17_WR.mp4 is

singularity exec -B /mnt../show-segementation-2021_latest.sif python3 segment_video.py /mnt/rds/redhen/gallina/tv/1998/1998-01/1998-01-01/1998-01-01_0000_US_00019495_V3_VHS50_MB20_H17_WR.mp4 mnt/path/to/output/directory --verbose

Production

1. final_script.py is made to work recursively on all the video files present in /mnt/rds/redhen/gallina/tv/ and store the outputs in /mnt/rds/redhen/gallina/Singularity/Show-Segmentation-2021/TvSplit_2
2. --verbose flag mentioned earlier is set to False by default for production.
3. Run the script using

python3 final_script.py

Please raise an issue if you run into any errors.

Future work

• If possible, replace the current celeb detection method with Azure’s Computer Vision service.

• Currently the most time consuming process in the program is that of going frame by frame and extracting faces. This can be speed up using multi-threading or any other means possible.

• Explore the dataset that contains news shows with anchors and include it in the pipeline.

• Shows with single or a few episodes can just be dropped to improve precision at the expense of recall.