Update app.py

app.py

@@ -1,97 +1,138 @@
-import streamlit as st
+import face_recognition
+import os
 import cv2
 import numpy as np
 import time
-# Larger title
-st.markdown("<h1 style='text-align: center;'>Emotion Detection</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()
 from keras.models import load_model
-import tempfile
 from PIL import Image
+import streamlit as st
 
+# Streamlit UI Setup
+st.markdown("<h1 style='text-align: center;'>Emotion & Face Recognition</h1>", unsafe_allow_html=True)
+st.markdown("<h3 style='text-align: center;'>angry, fear, happy, neutral, sad, surprise</h3>", unsafe_allow_html=True)
+
+# Known faces folder path
+KNOWN_FACES_DIR = "known_faces"
+
+# Load emotion detection model
 @st.cache_resource
 def load_emotion_model():
-    model = load_model('CNN_Model_acc_75.h5')
-    return model
+    return load_model("CNN_Model_acc_75.h5")
 
-# Load the model
-model = load_emotion_model()
-print("time taken to load model : " , time.time() - start)
-img_shape = 48
-emotion_labels = ['angry', 'fear', 'happy', 'neutral', 'sad', 'surprise']
-face_cascade = cv2.CascadeClassifier(cv2.data.haarcascades + 'haarcascade_frontalface_default.xml')
+emotion_model = load_emotion_model()
 
+# Face detection model
+face_cascade = cv2.CascadeClassifier(cv2.data.haarcascades + "haarcascade_frontalface_default.xml")
+emotion_labels = ['angry', 'fear', 'happy', 'neutral', 'sad', 'surprise']
+img_shape = 48
 
-def process_frame(frame):
+# Known faces dictionary
+known_faces = {"names": [], "encodings": []}
+
+def load_faces_from_folder(folder_path):
+    """
+    Load known faces from a folder, using filenames as names.
+    """
+    for filename in os.listdir(folder_path):
+        if filename.endswith(('.jpg', '.jpeg', '.png')):
+            name = os.path.splitext(filename)[0]
+            image_path = os.path.join(folder_path, filename)
+            
+            # Load and encode the image
+            image = face_recognition.load_image_file(image_path)
+            face_encodings = face_recognition.face_encodings(image)
+            
+            if face_encodings:  # Ensure a face is found
+                known_faces["names"].append(name)
+                known_faces["encodings"].append(face_encodings[0])
+                print(f"Loaded face for {name}")
+            else:
+                print(f"No face detected in {filename}")
+
+# Load known faces
+load_faces_from_folder(KNOWN_FACES_DIR)
+
+def recognize_face(unknown_face_encoding):
+    """
+    Compare an unknown face with the known faces and return the closest match.
+    """
+    matches = face_recognition.compare_faces(known_faces["encodings"], unknown_face_encoding, tolerance=0.6)
+    if True in matches:
+        match_index = matches.index(True)
+        return known_faces["names"][match_index]
+    return "Unknown"
+
+def detect_emotion(face_image):
+    """
+    Predict the emotion of a face using the emotion detection model.
+    """
+    face_resized = cv2.resize(face_image, (img_shape, img_shape))
+    face_resized = np.expand_dims(face_resized, axis=0)
+    face_resized = face_resized / 255.0  # Normalize the image
+    predictions = emotion_model.predict(face_resized)
+    return emotion_labels[np.argmax(predictions)]
+
+def process_frame_with_recognition_and_emotion(frame):
+    """
+    Detect faces, recognize names, and detect emotions in the frame.
+    """
     gray_frame = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
+    rgb_frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
     faces = face_cascade.detectMultiScale(gray_frame, scaleFactor=1.1, minNeighbors=5, minSize=(30, 30))
 
     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]
+        # Get the face area
+        face_image = rgb_frame[y:y+h, x:x+w]
+        face_encodings = face_recognition.face_encodings(face_image)
 
-        face_roi = cv2.resize(roi_color, (img_shape, img_shape))
-        face_roi = np.expand_dims(face_roi, axis=0)
-        face_roi = face_roi / float(img_shape)
-        predictions = model.predict(face_roi)
-        emotion = emotion_labels[np.argmax(predictions[0])]
+        if face_encodings:
+            name = recognize_face(face_encodings[0])  # Recognize the face
+        else:
+            name = "Unknown"
 
+        # Predict emotion
+        emotion = detect_emotion(frame[y:y+h, x:x+w])
+
+        # Display name and emotion
+        display_text = f"{name} is Feeling {emotion}"
         cv2.rectangle(frame, (x, y), (x + w, y + h), (0, 255, 0), 2)
-        cv2.putText(frame, emotion, (x, y+h), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 0), 2)
+        cv2.putText(frame, display_text, (x, y - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.8, (0, 255, 0), 2)
 
     return frame
 
-# def video_feed(video_source):
-#     # Read and process video frames
-#     while True:
-#         ret, frame = video_source.read()
-#         if not ret:
-#             break
-#         frame = process_frame(frame)
-#         st.image(frame, channels="BGR")
-
 def video_feed(video_source):
-    # Create a placeholder to display the frames
-    frame_placeholder = st.empty()  # This placeholder will be used to replace frames in-place
+    """
+    Display video feed with face recognition and emotion detection.
+    """
+    frame_placeholder = st.empty()  # Placeholder for updating frames
 
     while True:
         ret, frame = video_source.read()
         if not ret:
             break
 
-        frame = process_frame(frame)
-
-        # Display the frame in the placeholder
+        frame = process_frame_with_recognition_and_emotion(frame)
         frame_placeholder.image(frame, channels="BGR", use_column_width=True)
 
-
-
-# Sidebar for video or image upload
-upload_choice = st.sidebar.radio("Choose input source", [ "Upload Video", "Upload Image" ,"Camera"])
+# Sidebar options
+upload_choice = st.sidebar.radio("Choose input source", ["Upload Image", "Upload Video", "Camera"])
 
 if upload_choice == "Camera":
-    # Access camera
-    video_source = cv2.VideoCapture(0)
+    video_source = cv2.VideoCapture(0)  # Access webcam
     video_feed(video_source)
 
 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)
 
 elif upload_choice == "Upload Image":
-    uploaded_image = st.file_uploader("Upload Image", type=["png", "jpg", "jpeg", "gif"])
+    uploaded_image = st.file_uploader("Upload Image", type=["png", "jpg", "jpeg"])
     if uploaded_image:
         image = Image.open(uploaded_image)
         frame = np.array(image)
-        frame = process_frame(frame)
-        st.image(frame, caption='Processed Image', use_column_width=True)
-
-st.sidebar.write("Emotion Labels: Angry, Fear, Happy, Neutral, Sad, Surprise")
+        frame = process_frame_with_recognition_and_emotion(frame)
+        st.image(frame, caption="Processed Image", use_column_width=True)