Update app.py

app.py

@@ -1,138 +1,119 @@
-import face_recognition
-import os
+import streamlit as st
 import cv2
 import numpy as np
 import time
+import os
 from keras.models import load_model
+import face_recognition
 from PIL import Image
-import streamlit as st
+import tempfile
 
-# Streamlit UI Setup
-st.markdown("<h1 style='text-align: center;'>Emotion & Face Recognition</h1>", unsafe_allow_html=True)
+# 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)
 
-# Known faces folder path
-KNOWN_FACES_DIR = "known_faces"
+start = time.time()
 
-# Load emotion detection model
+# Load the emotion model
 @st.cache_resource
 def load_emotion_model():
-    return load_model("CNN_Model_acc_75.h5")
+    model = load_model('CNN_Model_acc_75.h5')  # Ensure this file is in your Space
+    return model
 
-emotion_model = load_emotion_model()
+model = load_emotion_model()
+print("time taken to load model: ", time.time() - start)
 
-# Face detection model
-face_cascade = cv2.CascadeClassifier(cv2.data.haarcascades + "haarcascade_frontalface_default.xml")
+# Emotion labels
 emotion_labels = ['angry', 'fear', 'happy', 'neutral', 'sad', 'surprise']
-img_shape = 48
 
-# 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
+# Load known faces (from images in a folder)
+known_faces = []
+known_names = []
+
+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 = 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.
-    """
+            encoding = face_recognition.face_encodings(image)
+            if encoding:
+                known_faces.append(encoding[0])
+                known_names.append(image_name.split('.')[0])  # Assuming file name is the person's name
+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)
-    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:
-        # Get the face area
-        face_image = rgb_frame[y:y+h, x:x+w]
-        face_encodings = face_recognition.face_encodings(face_image)
-
-        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}"
+        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))
+        face_roi = np.expand_dims(face_roi, axis=0)
+        face_roi = face_roi / float(img_shape)
+
+        # Emotion detection
+        predictions = model.predict(face_roi)
+        emotion = emotion_labels[np.argmax(predictions[0])]
+
+        # Face recognition
+        face_encoding = face_recognition.face_encodings(frame, [(y, x+w, y+h, x)])[0]  # Get face encoding
+        matches = face_recognition.compare_faces(known_faces, face_encoding)
+        name = "Unknown"
+        if True in matches:
+            first_match_index = matches.index(True)
+            name = known_names[first_match_index]
+
+        # Draw bounding box and label on the frame
         cv2.rectangle(frame, (x, y), (x + w, y + h), (0, 255, 0), 2)
-        cv2.putText(frame, display_text, (x, y - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.8, (0, 255, 0), 2)
+        cv2.putText(frame, f"{name} - {emotion}", (x, y - 10), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 0), 2)
 
     return frame
 
+# Video feed
 def video_feed(video_source):
-    """
-    Display video feed with face recognition and emotion detection.
-    """
-    frame_placeholder = st.empty()  # Placeholder for updating frames
+    frame_placeholder = st.empty()  # This placeholder will be used to replace frames in-place
 
     while True:
         ret, frame = video_source.read()
         if not ret:
             break
 
-        frame = process_frame_with_recognition_and_emotion(frame)
+        frame = process_frame(frame)
+
+        # Display the frame in the placeholder
         frame_placeholder.image(frame, channels="BGR", use_column_width=True)
 
-# Sidebar options
-upload_choice = st.sidebar.radio("Choose input source", ["Upload Image", "Upload Video", "Camera"])
+# Sidebar for video or image upload
+upload_choice = st.sidebar.radio("Choose input source", ["Upload Video", "Upload Image", "Camera"])
 
 if upload_choice == "Camera":
-    video_source = cv2.VideoCapture(0)  # Access webcam
+    # Access camera
+    video_source = cv2.VideoCapture(0)
     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"])
+    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 = process_frame_with_recognition_and_emotion(frame)
-        st.image(frame, caption="Processed Image", use_column_width=True)
+        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")