import cv2
import gradio as gr
import numpy as np
# Load OpenCV's built-in face detector
face_cascade = cv2.CascadeClassifier(
cv2.data.haarcascades + "haarcascade_frontalface_default.xml"
)
def recognize_faces(image):
if image is None:
return None
# Gradio gives us an RGB image
image = np.array(image)
# Convert RGB to grayscale
gray = cv2.cvtColor(image, cv2.COLOR_RGB2GRAY)
# Detect faces
faces = face_cascade.detectMultiScale(
gray,
scaleFactor=1.1,
minNeighbors=5,
minSize=(50, 50)
)
# Draw a box around each detected face
for (x, y, w, h) in faces:
cv2.rectangle(
image,
(x, y),
(x + w, y + h),
(0, 255, 0),
3
)
cv2.putText(
image,
"Face Detected",
(x, y - 10),
cv2.FONT_HERSHEY_SIMPLEX,
0.8,
(0, 255, 0),
2
)
return image
app = gr.Interface(
fn=recognize_faces,
inputs=gr.Image(
sources=["upload", "webcam"],
type="numpy",
label="Upload or Take a Photo"
),
outputs=gr.Image(
type="numpy",
label="Face Detection Result"
),
title="😊 Face Detection App",
description="Upload a photo or use your webcam to detect faces."
)
if __name__ == "__main__":
app.launch()