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import os
import cv2
import numpy as np
import streamlit as st
from datetime import datetime
from tensorflow.keras.models import load_model
# Directories
KNOWN_FACES_DIR = "known_faces"
EMOTION_MODEL_PATH = "CNN_Model_acc_75.h5"
CASCADE_PATH = "haarcascade_frontalface_default.xml"
# Constants
IMG_SIZE = (200, 200)
# Load models
emotion_model = load_model(EMOTION_MODEL_PATH)
face_cascade = cv2.CascadeClassifier(CASCADE_PATH)
face_recognizer = cv2.face.LBPHFaceRecognizer_create()
# Helper Functions
def load_emotion_labels():
return ["Angry", "Disgust", "Fear", "Happy", "Sad", "Surprise", "Neutral"]
def train_recognizer():
faces = []
labels = []
label_map = {}
for idx, person_name in enumerate(os.listdir(KNOWN_FACES_DIR)):
person_path = os.path.join(KNOWN_FACES_DIR, person_name)
if not os.path.isdir(person_path):
continue
label_map[idx] = person_name
for filename in os.listdir(person_path):
filepath = os.path.join(person_path, filename)
if filepath.lower().endswith(('.jpg', '.jpeg', '.png')):
img = cv2.imread(filepath, cv2.IMREAD_GRAYSCALE)
if img is not None:
faces.append(img)
labels.append(idx)
if len(faces) == 0:
st.warning("No valid training data found. Add faces first.")
return {}
face_recognizer.train(faces, np.array(labels))
return {v: k for k, v in label_map.items()}
def detect_faces(image):
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
faces = face_cascade.detectMultiScale(gray, scaleFactor=1.1, minNeighbors=5)
return gray, faces
def detect_emotions(face_img):
resized_face = cv2.resize(face_img, (48, 48))
normalized_face = resized_face / 255.0
reshaped_face = np.expand_dims(normalized_face, axis=(0, -1))
emotion_probabilities = emotion_model.predict(reshaped_face)
emotion_idx = np.argmax(emotion_probabilities)
return load_emotion_labels()[emotion_idx]
# Streamlit App
st.title("Face Recognition and Emotion Detection")
st.sidebar.title("Options")
option = st.sidebar.selectbox("Choose an action", ["Home", "Register New Face", "Recognize Faces"])
# Train the recognizer initially
if option != "Register New Face":
label_map = train_recognizer()
if option == "Home":
st.write("Use the sidebar to register new faces or recognize them.")
elif option == "Register New Face":
person_name = st.text_input("Enter the person's name")
capture_mode = st.radio("Select input method", ["Use Camera", "Upload Image(s)"])
if person_name and st.button("Register Face"):
person_dir = os.path.join(KNOWN_FACES_DIR, person_name)
os.makedirs(person_dir, exist_ok=True)
if capture_mode == "Use Camera":
st.warning("Ensure you are running this locally to access the camera.")
# Camera logic (only available locally)
cap = cv2.VideoCapture(0)
if not cap.isOpened():
st.error("Could not access the camera. Make sure it's connected and permissions are granted.")
else:
# Capture a frame from the camera
ret, frame = cap.read()
if ret:
st.image(frame, channels="BGR")
cap.release()
elif capture_mode == "Upload Image(s)":
uploaded_files = st.file_uploader("Upload images", type=["jpg", "jpeg", "png"], accept_multiple_files=True)
if uploaded_files:
for uploaded_file in uploaded_files:
img = cv2.imdecode(np.frombuffer(uploaded_file.read(), np.uint8), cv2.IMREAD_COLOR)
gray, faces = detect_faces(img)
for (x, y, w, h) in faces:
face_img = gray[y:y+h, x:x+w]
resized_img = cv2.resize(face_img, IMG_SIZE)
timestamp = datetime.now().strftime("%Y%m%d%H%M%S")
filepath = os.path.join(person_dir, f"{timestamp}.jpg")
cv2.imwrite(filepath, resized_img)
st.success(f"Faces registered successfully for {person_name}!")
label_map = train_recognizer()
elif option == "Recognize Faces":
uploaded_file = st.file_uploader("Upload an image", type=["jpg", "jpeg", "png"])
if uploaded_file:
img = cv2.imdecode(np.frombuffer(uploaded_file.read(), np.uint8), cv2.IMREAD_COLOR)
gray, faces = detect_faces(img)
for (x, y, w, h) in faces:
face_img = gray[y:y+h, x:x+w]
resized_img = cv2.resize(face_img, IMG_SIZE)
label, confidence = face_recognizer.predict(resized_img)
name = label_map.get(label, "Unknown")
emotion = detect_emotions(face_img)
cv2.rectangle(img, (x, y), (x+w, y+h), (0, 255, 0), 2)
cv2.putText(img, f"{name}, {emotion}", (x, y-10), cv2.FONT_HERSHEY_SIMPLEX, 0.9, (0, 255, 0), 2)
st.image(img, channels="BGR")
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