Computer Vision Violence Classification

Description

Computer Vision classification for violence using a VGG16 Architecture with pictures and videos.

VGG16 Architecture: Three Convolution2D layers with a FC dense layer as the output
Libraries: TensorFlow Keras and Sklearn for the model. CV2 and OS for data preprocessing.
Image Prediction: Predicts violence for multiple images. Graphs their predictions with the original images using MatPlotLib.
Video Prediction: Predicts violence for each frame of a video.
Video Blurring: Capable of blurring violence detected frames of videos then writing the altered version to a new .mp4v

To install and run the model on your Windows machine, follow these steps:

Clone the Repository: Download the codebase to your local machine.
https://github.com/EthanStanks/ViolenceClassificationCV.git
Install Dependencies: Ensure you have Python installed, then use pip to install the required libraries.
pip install -r requirements.txt
Download Dataset: Download the dataset from Kaggle then place the images inside:.
data/train_images/
Train Model: Set TRAIN_MODEL boolean to True. Navigate to the src directory and execute the main.py script.
python main.py
Predict Images Add images you'd like to predict inside data/validate_images/. Set PREDICT_IMAGES boolean to True. Add image paths to list on line 95. Execute the main.py script.
PREDICT_IMAGES = True
Predict Videos Add the video you'd like to predict inside data/video_input/. Set PREDICT_VIDEO boolean to True. Add the file name to the function call on line 88. Execute the main.py script.
PREDICT_VIDEO = True
Blur Videos Follow step 6. Ensure True is passed as second parameter on line 88. Execute the main.py script.
PREDICT_VIDEO = True

Data for this project is sourced from Kaggle's "Violence vs. Non-Violence: 11K Images Dataset" dataset, available here.

Note: This project is currently tailored for Windows operating systems.