Update app.py

app.py

@@ -1,74 +1,181 @@
-import os
+import streamlit as st
 import cv2
 import numpy as np
-import face_recognition
-import streamlit as st
+import time
+import os
+from keras.models import load_model
 from PIL import Image
+from mtcnn import MTCNN  # MTCNN for better face detection
+from keras.preprocessing import image
+from tensorflow.keras.applications.inception_v3 import preprocess_input
+import pymongo
+from datetime import datetime
+import tempfile
+from facenet_pytorch import MTCNN, InceptionResnetV1  # FaceNet model for face recognition
+
+# MongoDB Atlas Connection String
+MONGO_URI = "mongodb+srv://test:[email protected]/?retryWrites=true&w=majority"
+
+# Connect to MongoDB
+client = pymongo.MongoClient(MONGO_URI)
+db = client.get_database("emotion_detection")
+collection = db.get_collection("face_data")
+
+# 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 for measuring performance
+start = time.time()
+
+# Load the emotion detection 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 FaceNet Model for Face Recognition
+@st.cache_resource
+def load_facenet_model():
+    # Load FaceNet model for face recognition
+    facenet_model = InceptionResnetV1(pretrained='vggface2').eval()
+    return facenet_model
+
+facenet_model = load_facenet_model()
+
+# MTCNN for face detection
+detector = MTCNN()
+
+# Process a single frame
+def process_frame(frame):
+    # Detect faces
+    faces = detector.detect_faces(frame)
+
+    result_text = ""  # Initialize result text
+
+    if len(faces) > 0:
+        for face in faces:
+            x, y, w, h = face['box']
+            roi_color = frame[y:y+h, x:x+w]
+
+            # Apply histogram equalization for better feature extraction
+            roi_gray = cv2.cvtColor(roi_color, cv2.COLOR_BGR2GRAY)
+            roi_gray = cv2.equalizeHist(roi_gray)
+
+            # Emotion detection
+            face_roi = cv2.resize(roi_color, (48, 48))
+            face_roi = cv2.cvtColor(face_roi, cv2.COLOR_BGR2RGB)
+            face_roi = np.expand_dims(face_roi, axis=0) / 255.0  # Normalize
+
+            predictions = model.predict(face_roi)
+            emotion = emotion_labels[np.argmax(predictions[0])]
+
+            # Extract embedding for face recognition using FaceNet
+            face_embedding = facenet_model.predict(preprocess_input(np.expand_dims(face['keypoints'], axis=0)))
+
+            # Compare face embeddings with known faces (replace with your own database)
+            known_faces = []  # Load known face embeddings from database
+            known_names = []  # Corresponding names
+
+            name = "Unknown"
+            min_distance = float('inf')
+
+            for i, known_face in enumerate(known_faces):
+                distance = np.linalg.norm(face_embedding - known_face)
+                if distance < min_distance:
+                    min_distance = distance
+                    name = known_names[i]
+
+            # Save data to MongoDB if face is recognized (name != Unknown)
+            if name != "Unknown":
+                timestamp = datetime.now().strftime("%Y-%m-%d %H:%M:%S")
+                document = {
+                    "name": name,
+                    "emotion": emotion,
+                    "timestamp": timestamp
+                }
+                # Insert the data into MongoDB
+                collection.insert_one(document)
+                print(f"Data inserted into MongoDB: {document}")
+
+            result_text = f"{name} is feeling {emotion}"
+
+            # Draw bounding box and label
+            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, 0.9, (0, 255, 0), 2)
+
+    return frame, result_text
+
+# Video feed display
+def video_feed(video_source):
+    frame_placeholder = st.empty()  # Placeholder for displaying video frames
+    text_placeholder = st.empty()  # Placeholder for displaying result text
+
+    while True:
+        ret, frame = video_source.read()
+        if not ret:
+            break
+
+        frame, result_text = process_frame(frame)
+
+        # Display frame and result text
+        frame_placeholder.image(frame, channels="BGR", use_column_width=True)
+        text_placeholder.markdown(f"<h3 style='text-align: center;'>{result_text}</h3>", unsafe_allow_html=True)
+
+# Sidebar for user input source selection
+upload_choice = st.sidebar.radio("Choose Input Source", ["Upload Image", "Upload Video", "Camera"])
+
+if upload_choice == "Camera":
+    image = st.camera_input("Take a picture")
+
+    if image:
+        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:
+        with tempfile.NamedTemporaryFile(delete=False) as tfile:
+            tfile.write(uploaded_video.read())
+            video_source = cv2.VideoCapture(tfile.name)
+            video_feed(video_source)
+
+# Display the records stored in MongoDB with latest records on top
+st.markdown("### MongoDB Records")
+records = collection.find().sort("timestamp", -1)  # Sort records by timestamp in descending order
 
-# List for storing images and class names
-Images = []
-classnames = []
-
-# Directory where known faces are stored
-directory = "known_faces"  # Directory containing images of known faces
-
-# Load images and classnames from the directory
-myList = os.listdir(directory)
-
-for cls in myList:
-    if os.path.splitext(cls)[1] in [".jpg", ".jpeg"]:
-        img_path = os.path.join(directory, cls)
-        curImg = cv2.imread(img_path)
-        Images.append(curImg)
-        classnames.append(os.path.splitext(cls)[0])
-
-# Function to find face encodings
-def findEncodings(Images):
-    encodeList = []
-    for img in Images:
-        img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
-        encode = face_recognition.face_encodings(img)[0]
-        encodeList.append(encode)
-    return encodeList
-
-# Find encodings for known faces
-encodeListknown = findEncodings(Images)
-
-# Streamlit UI for capturing image using the camera
-img_file_buffer = st.camera_input("Take a picture")
-
-if img_file_buffer is not None:
-    # Open the captured image using PIL
-    test_image = Image.open(img_file_buffer)
-    image = np.asarray(test_image)
-
-    # Resize and convert image for face recognition
-    imgS = cv2.resize(image, (0, 0), None, 0.25, 0.25)  # Resize for faster processing
-    imgS = cv2.cvtColor(imgS, cv2.COLOR_BGR2RGB)
+for record in records:
+    col1, col2, col3 = st.columns([3, 3, 1])
+    with col1:
+        st.write(f"**Name**: {record['name']}")
+    with col2:
+        st.write(f"**Emotion**: {record['emotion']}")
+    with col3:
+        st.write(f"**Timestamp**: {record['timestamp']}")
     
-    # Find faces in the current frame
-    facesCurFrame = face_recognition.face_locations(imgS)
-    encodesCurFrame = face_recognition.face_encodings(imgS, facesCurFrame)
-
-    name = "Unknown"  # Default name for unknown faces
-
-    if len(encodesCurFrame) > 0:
-        for encodeFace, faceLoc in zip(encodesCurFrame, facesCurFrame):
-            # Compare the face with known faces
-            matches = face_recognition.compare_faces(encodeListknown, encodeFace)
-            faceDis = face_recognition.face_distance(encodeListknown, encodeFace)
-            matchIndex = np.argmin(faceDis)  # Find the closest match
-
-            if matches[matchIndex]:
-                name = classnames[matchIndex].upper()  # Assign name if match is found
-
-            # Draw a rectangle around the face and label it with the name
-            y1, x2, y2, x1 = faceLoc
-            y1, x2, y2, x1 = y1 * 4, x2 * 4, y2 * 4, x1 * 4  # Scale coordinates back
-            cv2.rectangle(image, (x1, y1), (x2, y2), (0, 255, 0), 2)
-            cv2.rectangle(image, (x1, y2 - 35), (x2, y2), (0, 255, 0), cv2.FILLED)
-            cv2.putText(image, name, (x1 + 6, y2 - 6), cv2.FONT_HERSHEY_COMPLEX, 1, (255, 255, 255), 2)
-
-    # Display the image with the bounding box and label
-    st.image(image, caption='Processed Image', use_column_width=True)
-    st.markdown(f"<h3 style='text-align: center;'>{name} is recognized</h3>", unsafe_allow_html=True)
+    # Delete record button
+    delete_button = st.button(f"Delete {record['_id']}", key=record['_id'])
+    if delete_button:
+        collection.delete_one({"_id": record["_id"]})
+        st.success(f"Record with ID {record['_id']} has been deleted.")