detect_img.py

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


st.markdown(
    """
    <style>
    /* Global styles */
    body {
        font-family: 'Arial', sans-serif;
    }

    /* Center align the title */
    .title{
        text-align: center;
        color: #6600cc; /* A nice purple color */
        font-size: 50px; /* Larger font size */
        margin-top: 10px;
        margin-bottom: 10px; /* Space below the title */
    }
    .hh{
        text-align: center;
        color: #6600cc; /* A nice purple color */
        font-size: 30px; /* Larger font size */
        margin-top: 10px;
        margin-bottom: 10px; /* Space below the title */
    }

    /* Style the sidebar */
    .css-1aumxhk {
        background-color: #f0f4f8 !important; /* Light sidebar background */
        padding: 20px;
        border-radius: 10px;
        box-shadow: 0 4px 8px rgba(0, 0, 0, 0.1);
    }

    /* Style buttons */
    .stButton > button {
        background-color: #6600cc; /* purple background for buttons */
        color: white;
        border-radius: 8px;
        padding: 10px 20px;
        border: none;
        cursor: pointer;
        transition: background-color 0.3s;
    }

    .stButton > button:hover {
        color: white;
        background-color: green; /* light pruple on hover */
        border-style: solid;
        border-color: black;
    }
    </style>
    """,
    unsafe_allow_html=True
)

st.markdown("<h1 class='title'>Face Recognition Attendance System</h1>", unsafe_allow_html=True)
st.markdown("<h3 class='hh'>Detect Faces by Uploading Images</h3>", unsafe_allow_html=True)

# Define the face detection model path
cascade_path = '/Users/mdfarazali/Documents/AI ML/Projects/Face Detection/haarcascade_frontalface_default.xml'
model = cv2.CascadeClassifier(cascade_path)

# Directory to store detected faces
faces_dir = 'detected_faces/'
os.makedirs(faces_dir, exist_ok=True)

# Function to store detected faces with a custom name
def save_face(image, face_coordinates, custom_name):
    x, y, w, h = face_coordinates
    face = image[y:y+h, x:x+w]
    
    # Ensure the custom name has a .jpg extension
    if not custom_name.endswith(".jpg"):
        custom_name += ".jpg"
        
    face_path = os.path.join(faces_dir, custom_name)
    cv2.imwrite(face_path, face)
    st.success(f"Face saved as {custom_name} in {faces_dir}")

# Detect faces in an uploaded image and then allow saving
def detect_faces_in_image(uploaded_image):
    img_array = np.array(Image.open(uploaded_image))
    gray_img = cv2.cvtColor(img_array, cv2.COLOR_BGR2GRAY)
    faces = model.detectMultiScale(gray_img, scaleFactor=1.3, minNeighbors=5, minSize=(30, 30))

    if len(faces) == 0:
        st.warning("No faces detected.")
        return None, None

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

    st.image(img_array, channels="BGR", use_column_width=True)
    return img_array, faces

# Detect faces in real-time from the webcam and then allow saving

# File uploader to detect faces in an uploaded image
uploaded_image = st.file_uploader("Upload Image to detect", type=["jpg", "png", "jpeg"], key="uploaded_image")
if uploaded_image is not None:
    # Detect face and display it first
    img_array, faces = detect_faces_in_image(uploaded_image)
    if faces is not None:
        if 'custom_name' not in st.session_state:
            st.session_state.custom_name = "face_image"
        
        # Only show input and save button once
        custom_name = st.text_input("Enter a custom name for the uploaded face image", value=st.session_state.custom_name, key="upload_custom_name")
        
        if st.button("Save Detected Face", key="save_uploaded_face"):
            for face_coords in faces:
                save_face(img_array, face_coords, custom_name)

# Button to open the camera for live face detection