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import gradio as gr
import cv2
import numpy as np
# Load the pre-trained Haar Cascade classifier for face detection
face_cascade = cv2.CascadeClassifier(cv2.data.haarcascades + 'haarcascade_frontalface_default.xml')
def detect_faces(image, video):
# Read the video frame-by-frame
frame = video
# Convert the frame to an OpenCV-compatible format
if isinstance(frame, np.ndarray):
# Convert to grayscale for face detection
gray = cv2.cvtColor(frame, cv2.COLOR_RGB2GRAY)
# Perform face detection
faces = face_cascade.detectMultiScale(gray, scaleFactor=1.1, minNeighbors=5, minSize=(30, 30))
# Draw rectangles around detected faces
for (x, y, w, h) in faces:
cv2.rectangle(frame, (x, y), (x+w, y+h), (0, 255, 0), 2)
return [frame]
# Gradio interface setup for face detection on live video feed
demo = gr.Interface(
detect_faces,
[gr.Video(sources=["webcam"])],
["video"],
title="Live Webcam Face Detection",
description="Displays the live feed from your webcam and detects faces in real-time."
)
if __name__ == "__main__":
demo.launch()
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