This repo presents code for a deep-learning-based algorithm for detecting violence, fire and human presence in indoor or outdoor environments. The algorithm can accurately detect the following scenarios: fight, fire, car crash, and even more.
To detect other scenarios you have to add descriptive text label of a
scenario in settings.yaml file under labels key. At this moment model can
detect 16+1 scenarios, where one is default Unknown label. You can change,
add or remove labels according to your use case. The model is trained on wide
variety of data. The task for the model at training was to predict similar
vectors for image and text that describes well a scene on the image. Thus model
can generalize well on other scenarios too if you provide proper textual
information about a scene of interest.
First install requirements:
pip install -r requirements.txt
Dataset can be taken from:
https://github.com/sukhitashvili/violence-detection/tree/main/data
https://drive.google.com/open?id=1qpnajiy9wa5dZStqIhFHVgy2hE_fK4fb
To test the model you can either run:
python run.py --image-path ./data/7.jpg
Or you can test it through web app:
streamlit run app.py
Or you can see the example code in tutorial.ipynb jupyter notebook
Or incorporate this model in your project using this code:
from model import Model
import cv2
model = Model()
image = cv2.imread('./your_image.jpg')
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
label = model.predict(image=image)['label']
print('Image label is: ', label)Below are the resulting images. I used the model to make predictions
on each frame of the videos and print model's predictions on the left side of
frame of saved videos. In case of images, titles are model's predictions. You
can find code that produces that result in tutorial.ipynb jupyter notebook.
For further enhancements like: Batch processing support for speedup, return of multiple suggestions, threshold fine-tuning for specific data, ect. contact me:
My Linkedin: ![Aditi Sharma][https://www.linkedin.com/in/aditi-sharma-663709202/]