Closed Anshg07 closed 1 week ago
Our team will soon review your PR. Thanks @Anshg07 :)
I understood that dataset is not required. Can you create a sub folder as Web App and put the app.py
inside that with a README. Also add a demonstration video of the web app inside the Web App folder.
Follow this README template, https://github.com/abhisheks008/ML-Crate/blob/main/.github/web-app-readme-template.md
added Web App subfolder
Pull Request for ML-Crate π‘
Issue Title: Add Facial Landmark Detection Using Python's MediaPipe Library
Closes: #550
Describe the add-ons or changes you've made π
Added facial landmark detection capability using MediaPipe. This feature allows the application to detect and visualize facial landmarks in real-time using both photos and live video feeds.
Project Documentation:
Overview
This application utilizes MediaPipe and Streamlit to perform real-time facial landmark detection. Users can see their facial landmarks overlaid on their video feed in real-time.
How to Install and Run the Application
pip install streamlit cv2 numpy Pillow mediapipe streamlit_webrtc
markdown Copy code
app.py
file and run the following command:streamlit run app.py
less Copy code
http://localhost:8501
. The application should be running and ready to use.How It Works
Use Cases
This tool can be used for various purposes, including:
Type of change βοΈ
How Has This Been Tested? βοΈ
The functionality was tested by integrating it into the existing app framework and running various tests with different types of facial images and live video to ensure accuracy and robustness under various conditions.
Checklist: βοΈ