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In this project, I've to use OpenCV and Viola-Jones Algorithm to build a face detection system that works in real-time.
I help companies to leverage Machine Learning to create innovative products through an end-to-end machine learning development process that designs, builds, and manages reproducible, testable, scalable, and evolvable ML-powered software with minimal cost.
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Here I show a summary description of the projects I've been working on in the last few years. Most of the projects have the source code publicly available.
This project aimed to build a predictive model that can predict the probability that a particular claim will be approved immediately.
How AI-Driven Programming is Transforming the Tech Industry and Reshaping Developer Roles
How Automated Tests Can Transform Your Nights From Stressful to Restful
Streamlining Business Communication for Enhanced Collaboration, Customer Support, Marketing, and Decision-Making.
Machine Learning Operations is a process of automating and productionalizing machine learning applications and workflows.
In this project, I've built a Computer Vision model to find basket icons in e-commerces.
In this project, I've to use OpenCV and Viola-Jones Algorithm to build a simple face detection system that works in real-time.
I need to detect faces on images and from a webcam in real-time.
Build an algorithm or machine learning model to detect faces on images.
I've used Python, OpenCV, and the Viola-Jones Algorithm to build a solution to detect faces fast.
The solution has good accuracy and can detect faces really fast.
To detect the face the software uses the Haar-like features with the help of the haarcascade_eye.xml and haarcascade_frontalface_default.xml files.
To run this code, download Python 3 from Python.org.
After installing Python, add the required package using pip installer:
pip install -r requirements.txt
Then, clone the GitHub repository:
git clone https://github.com/cpatrickalves/cv-realtime-face-detection
To run the detection in real-time using the webcam, run:
python face_recognition.py
To perform the face detection on an image, just add the file name as an argument:
python face_recognition.py -f image.jpg
The script will create an output with a similar name. It with add the _output in the file name (ex: image_output.jpg)
Add the -s flag to perform smile detection.
You can also run this script as an Streamlit application:
streamlit run app.py
It will automatically open the browser and show the app.
The source code is available at Github.
