Face Detector
A computer vision app that detects faces in uploaded or camera-captured images using OpenCV.
Project Overview
An interactive face detection web app built with Python, Streamlit, OpenCV, NumPy, and Pillow. The app allows users to upload a JPG image or capture one through their browser camera, converts the image into an OpenCV-compatible format, applies a Haar Cascade face detector, counts the number of detected faces, and displays the image with green bounding boxes and corner markers around each detected face.
The Problem
Computer vision models and image processing scripts are often difficult for non-technical users to test because they usually require running code locally. For a face detection task, users need a simple interface where they can upload or capture an image and immediately see the detected face regions.
The Solution
Built a Streamlit application that accepts image input through file upload or camera capture, processes the image with OpenCV, converts it to grayscale, applies a Haar Cascade face detector, counts the detected faces, and visually annotates the image with bounding boxes.
The Impact
Created a simple and accessible computer vision demo that turns an OpenCV face detection script into an interactive web app, making it easier to showcase image processing, object detection, camera input handling, and Streamlit deployment.