app.py

import streamlit as st
import cv2
import numpy as np
import time
import os
from keras.models import load_model
from PIL import Image
import tempfile

# Larger title
st.markdown("<h1 style='text-align: center;'>Emotion Detection with Face Recognition</h1>", unsafe_allow_html=True)

# Smaller subtitle
st.markdown("<h3 style='text-align: center;'>angry, fear, happy, neutral, sad, surprise</h3>", unsafe_allow_html=True)

start = time.time()

# Load the emotion model
@st.cache_resource
def load_emotion_model():
    model = load_model('CNN_Model_acc_75.h5')  # Ensure this file is in your Space
    return model

model = load_emotion_model()
print("time taken to load model: ", time.time() - start)

# Emotion labels
emotion_labels = ['angry', 'fear', 'happy', 'neutral', 'sad', 'surprise']

# Load known faces (from images in a folder)
known_faces = []
known_names = []
face_recognizer = cv2.face.LBPHFaceRecognizer_create()

def load_known_faces():
    folder_path = "known_faces"  # Place your folder with known faces here
    for image_name in os.listdir(folder_path):
        if image_name.endswith(('.jpg', '.jpeg', '.png')):
            image_path = os.path.join(folder_path, image_name)
            image = cv2.imread(image_path)
            gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
            # Detect face in the image
            faces = cv2.CascadeClassifier(cv2.data.haarcascades + 'haarcascade_frontalface_default.xml').detectMultiScale(gray, scaleFactor=1.1, minNeighbors=5, minSize=(30, 30))

            for (x, y, w, h) in faces:
                roi_gray = gray[y:y+h, x:x+w]
                # We only need the face, so we crop it and store it for training
                known_faces.append(roi_gray)
                known_names.append(image_name.split('.')[0])  # Assuming file name is the person's name

    # Train the recognizer with the known faces
    face_recognizer.train(known_faces, np.array([i for i in range(len(known_faces))]))

load_known_faces()

# Face detection using OpenCV
face_cascade = cv2.CascadeClassifier(cv2.data.haarcascades + 'haarcascade_frontalface_default.xml')
img_shape = 48

def process_frame(frame):
    gray_frame = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
    faces = face_cascade.detectMultiScale(gray_frame, scaleFactor=1.1, minNeighbors=5, minSize=(30, 30))

    result_text = ""  # Initialize the result text for display

    for (x, y, w, h) in faces:
        roi_gray = gray_frame[y:y+h, x:x+w]
        roi_color = frame[y:y+h, x:x+w]
        face_roi = cv2.resize(roi_color, (img_shape, img_shape))  # Resize to 48x48
        face_roi = cv2.cvtColor(face_roi, cv2.COLOR_BGR2RGB)  # Convert to RGB (3 channels)
        face_roi = np.expand_dims(face_roi, axis=0)  # Add batch dimension
        face_roi = face_roi / 255.0  # Normalize the image

        # Emotion detection
        predictions = model.predict(face_roi)
        emotion = emotion_labels[np.argmax(predictions[0])]

        # Face recognition using LBPH
        label, confidence = face_recognizer.predict(roi_gray)
        name = "Unknown"
        if confidence < 100:
            name = known_names[label]

        # Format the result text as "Name is feeling Emotion"
        result_text = f"person is feeling {emotion}"

        # Draw bounding box and label on the frame
        cv2.rectangle(frame, (x, y), (x + w, y + h), (0, 255, 0), 2)
        cv2.putText(frame, result_text, (x, y - 10), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 0), 2)

    return frame, result_text

# Video feed
def video_feed(video_source):
    frame_placeholder = st.empty()  # This placeholder will be used to replace frames in-place
    text_placeholder = st.empty()  # This placeholder will display the result text

    while True:
        ret, frame = video_source.read()
        if not ret:
            break

        frame, result_text = process_frame(frame)

        # Display the frame in the placeholder
        frame_placeholder.image(frame, channels="BGR", use_column_width=True)

        # Display the result text in the text placeholder
        text_placeholder.markdown(f"<h3 style='text-align: center;'>{result_text}</h3>", unsafe_allow_html=True)

# Sidebar for video or image upload
upload_choice = st.sidebar.radio("Choose input source", ["Upload Image", "Upload Video", "Camera"])

if upload_choice == "Camera":
    # Use Streamlit's built-in camera input widget for capturing images from the webcam
    image = st.camera_input("Take a picture")

    if image is not None:
        # Convert the image to a numpy array
        frame = np.array(Image.open(image))
        frame, result_text = process_frame(frame)
        st.image(frame, caption='Processed Image', use_column_width=True)
        st.markdown(f"<h3 style='text-align: center;'>{result_text}</h3>", unsafe_allow_html=True)

elif upload_choice == "Upload Image":
    uploaded_image = st.file_uploader("Upload Image", type=["png", "jpg", "jpeg", "gif"])
    if uploaded_image:
        image = Image.open(uploaded_image)
        frame = np.array(image)
        frame, result_text = process_frame(frame)
        st.image(frame, caption='Processed Image', use_column_width=True)
        st.markdown(f"<h3 style='text-align: center;'>{result_text}</h3>", unsafe_allow_html=True)

elif upload_choice == "Upload Video":
    uploaded_video = st.file_uploader("Upload Video", type=["mp4", "mov", "avi", "mkv", "webm"])
    if uploaded_video:
        # Temporarily save the video to disk
        with tempfile.NamedTemporaryFile(delete=False) as tfile:
            tfile.write(uploaded_video.read())
            video_source = cv2.VideoCapture(tfile.name)
            video_feed(video_source)

st.sidebar.write("Emotion Labels: Angry, Fear, Happy, Neutral, Sad, Surprise")