app.py

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

# Define the face detection function
def detect_faces(image):
    image_np = np.array(image)
    
    # Convert the image to grayscale for face detection
    gray_image = cv2.cvtColor(image_np, cv2.COLOR_RGB2GRAY)
    
    # Load the Haar Cascade Classifier
    face_cascade = cv2.CascadeClassifier(cv2.data.haarcascades + "haarcascade_frontalface_default.xml")
    
    # Detect faces
    faces = face_cascade.detectMultiScale(gray_image, scaleFactor=1.1, minNeighbors=5, minSize=(30, 30))
    
    # Draw rectangles around detected faces
    for (x, y, w, h) in faces:
        cv2.rectangle(image_np, (x, y), (x + w, y + h), (255, 0, 0), 5)
    
    return image_np

# Streamlit app setup
st.title("Face Detection using Haar Cascade Classifier in streamlit")
st.write("Upload an image, and the model will detect faces and draw bounding boxes around them.")

# File uploader to upload an image
uploaded_file = st.file_uploader("Choose an image...", type=["jpg", "jpeg", "png"])

if uploaded_file is not None:
    # Open the image file and convert it into a format that OpenCV can process
    image = Image.open(uploaded_file)
    
    # Detect faces in the image
    result_image = detect_faces(image)
    
    # Convert the result image from OpenCV format back to PIL format for display
    result_image_pil = Image.fromarray(result_image)
    
    # Display the image with detected faces
    st.image(result_image_pil, caption="Detected Faces", use_column_width=True)


else:
    print("no img")