app.py

import cv2
import streamlit as st
import numpy as np
from PIL import Image

# Load the pre-trained Haar Cascade face detector
face_cascade = cv2.CascadeClassifier(cv2.data.haarcascades + 'haarcascade_frontalface_default.xml')

def detect_faces(frame):
    """
    Detect faces in the frame.
    Returns the frame with bounding boxes drawn around detected faces.
    """
    # Convert the frame to grayscale (Haar Cascade works on grayscale images)
    gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)

    # Detect faces in the image
    faces = face_cascade.detectMultiScale(gray, scaleFactor=1.1, minNeighbors=5, minSize=(30, 30))

    # Draw rectangles around the faces
    for (x, y, w, h) in faces:
        cv2.rectangle(frame, (x, y), (x + w, y + h), (255, 0, 0), 2)

    return frame

# Streamlit UI for the app
st.title("Real-Time Face Detection")

# Capture the video from the webcam
camera = st.camera_input("Capture a photo")

# Process the webcam image if available
if camera:
    # Convert the camera image into a numpy array
    img = Image.open(camera)
    img_array = np.array(img)
    
    # Convert the image to a format OpenCV can process (BGR)
    img_bgr = cv2.cvtColor(img_array, cv2.COLOR_RGB2BGR)

    # Detect faces in the image
    result_frame = detect_faces(img_bgr)

    # Convert result frame back to RGB (for displaying in Streamlit)
    result_frame_rgb = cv2.cvtColor(result_frame, cv2.COLOR_BGR2RGB)

    # Display the result in Streamlit
    st.image(result_frame_rgb, caption="Detected Faces", use_column_width=True)