first commit

models/emotion_model_best.h5

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+version https://git-lfs.github.com/spec/v1
+oid sha256:32bc4d63ae296293c472c3861da4af54c21f8c2432646f0a23a9a8b53b6ea255
+size 17770192

requirements.txt

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-altair
-pandas
-streamlit
+streamlit
+opencv-python
+numpy
+tensorflow

src/streamlit_app.py

@@ -1,40 +1,174 @@
-import altair as alt
-import numpy as np
-import pandas as pd
+# app.py
+
 import streamlit as st
+import cv2
+import numpy as np
+import tensorflow as tf
+import time
+import os
+
+# --- Streamlit Page Configuration (MUST BE THE FIRST STREAMLIT COMMAND) ---
+st.set_page_config(page_title="Real-time Emotion Recognition", layout="wide")
+
+# --- 1. Load Model and Face Detector (Cached for Performance) ---
+
+@st.cache_resource
+def load_emotion_model():
+    model_path = 'models/emotion_model_best.h5' # Path to your trained model
+    if not os.path.exists(model_path):
+        st.error(f"Error: Model file not found at {model_path}. Please ensure training was successful and the file exists.")
+        st.stop()
+    try:
+        model = tf.keras.models.load_model(model_path)
+        return model
+    except Exception as e:
+        st.error(f"Error loading model from {model_path}: {e}")
+        st.stop()
+
+@st.cache_resource
+def load_face_detector():
+    cascade_path = 'haarcascade_frontalface_default.xml' # Path to your Haar Cascade file
+    if not os.path.exists(cascade_path):
+        st.error(f"Error: Haar Cascade file not found at {cascade_path}.")
+        st.markdown("Please download `haarcascade_frontalface_default.xml` from:")
+        st.markdown("[https://github.com/opencv/opencv/blob/4.x/data/haarcascades/haarcascade_frontalface_default.xml](https://github.com/opencv/opencv/blob/4.x/data/haarcascades/haarcascade_frontalface_default.xml)")
+        st.markdown("And place it in a `cascades` folder next to `app.py`.")
+        st.stop()
+    face_cascade = cv2.CascadeClassifier(cascade_path)
+    if face_cascade.empty():
+        st.error(f"Error: Could not load Haar Cascade classifier from {cascade_path}. Check file integrity.")
+        st.stop()
+    return face_cascade
+
+# Load the model and face detector when the app starts
+model = load_emotion_model()
+face_detector = load_face_detector()
+
+# --- 2. Define Constants and Labels ---
+IMG_HEIGHT = 48
+IMG_WIDTH = 48
+emotion_labels = ['angry', 'disgust', 'fear', 'happy', 'neutral', 'sad', 'surprise']
+
+label_colors = {
+    'angry': (0, 0, 255),    # BGR Red
+    'disgust': (0, 165, 255), # BGR Orange
+    'fear': (0, 255, 255),   # BGR Yellow
+    'happy': (0, 255, 0),    # BGR Green
+    'neutral': (255, 255, 0), # BGR Cyan
+    'sad': (255, 0, 0),      # BGR Blue
+    'surprise': (255, 0, 255) # BGR Magenta
+}
+
+# --- 3. Streamlit App Layout ---
+st.title("Live Facial Emotion Recognition")
+
+st.markdown("""
+This application uses a deep learning model (trained on FER-2013) to detect emotions from faces in real-time.
+It requires access to your computer's webcam.
+""")
+
+stframe = st.empty()
+st_status = st.empty()
+
+col1, col2 = st.columns([1,1])
+with col1:
+    start_button = st.button("Start Camera", key="start_camera")
+with col2:
+    stop_button = st.button("Stop Camera", key="stop_camera")
+
+# Initialize session state for camera control and performance tracking
+if "camera_started" not in st.session_state:
+    st.session_state.camera_started = False
+if "cap" not in st.session_state:
+    st.session_state.cap = None
+if "last_process_time" not in st.session_state:
+    st.session_state.last_process_time = 0.0
+
+# --- Performance Configuration ---
+DESIRED_FPS = 15 # Aim for 15 frames per second for processing
+FRAME_INTERVAL_SECONDS = 1.0 / DESIRED_FPS
+FACE_DETECTION_DOWNSCALE = 0.5 # Scale factor for face detection (e.g., 0.5 means half size)
+
+# --- 4. Main Camera Loop Logic ---
+
+if start_button:
+    st.session_state.camera_started = True
+
+if stop_button:
+    st.session_state.camera_started = False
+    st_status.info("Camera stopped.")
+    if st.session_state.cap is not None and st.session_state.cap.isOpened():
+        st.session_state.cap.release()
+        st.session_session.cap = None
+    stframe.empty()
+    # Updated: use_container_width instead of use_column_width
+    stframe.image(np.zeros((480, 640, 3), dtype=np.uint8), channels="RGB", use_container_width=True)
+
+if st.session_state.camera_started:
+    st_status.info("Starting camera... Please allow camera access if prompted.")
+
+    if st.session_state.cap is None or not st.session_state.cap.isOpened():
+        st.session_state.cap = cv2.VideoCapture(0, cv2.CAP_DSHOW)
+        if not st.session_state.cap.isOpened():
+            st_status.error("Failed to open camera. Please check if it's connected and not in use.")
+            st.session_state.camera_started = False
+            st.stop()
+
+    while st.session_state.camera_started:
+        ret, frame = st.session_state.cap.read()
+        if not ret:
+            st_status.error("Failed to read frame from camera. It might be disconnected or an error occurred.")
+            st.session_state.camera_started = False
+            break
+
+        current_time = time.time()
+        if current_time - st.session_state.last_process_time >= FRAME_INTERVAL_SECONDS:
+            st.session_state.last_process_time = current_time
+
+            gray_frame = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
+
+            small_frame = cv2.resize(gray_frame, (0, 0), fx=FACE_DETECTION_DOWNSCALE, fy=FACE_DETECTION_DOWNSCALE)
+            
+            faces = face_detector.detectMultiScale(small_frame, scaleFactor=1.1, minNeighbors=5, minSize=(30, 30))
+
+            original_faces = []
+            for (x, y, w, h) in faces:
+                x_orig = int(x / FACE_DETECTION_DOWNSCALE)
+                y_orig = int(y / FACE_DETECTION_DOWNSCALE)
+                w_orig = int(w / FACE_DETECTION_DOWNSCALE)
+                h_orig = int(h / FACE_DETECTION_DOWNSCALE)
+                original_faces.append((x_orig, y_orig, w_orig, h_orig))
+
+            for (x, y, w, h) in original_faces:
+                cv2.rectangle(frame, (x, y), (x+w, y+h), (255, 0, 0), 2)
+
+                face_roi = gray_frame[max(0, y):min(gray_frame.shape[0], y+h), max(0, x):min(gray_frame.shape[1], x+w)]
+
+                if face_roi.size == 0:
+                    continue
+
+                face_roi = cv2.resize(face_roi, (IMG_WIDTH, IMG_HEIGHT))
+                face_roi = np.expand_dims(face_roi, axis=0)
+                face_roi = np.expand_dims(face_roi, axis=-1)
+                face_roi = face_roi / 255.0
+
+                predictions = model.predict(face_roi, verbose=0)[0]
+                emotion_index = np.argmax(predictions)
+                predicted_emotion = emotion_labels[emotion_index]
+                confidence = predictions[emotion_index] * 100
+
+                text_color = label_colors.get(predicted_emotion, (255, 255, 255))
+                text = f"{predicted_emotion} ({confidence:.2f}%)"
+                text_y = y - 10 if y - 10 > 10 else y + h + 20
+                cv2.putText(frame, text, (x, text_y), cv2.FONT_HERSHEY_SIMPLEX, 0.9, text_color, 2, cv2.LINE_AA)
+
+            frame_rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
+            # Updated: use_container_width instead of use_column_width
+            stframe.image(frame_rgb, channels="RGB", use_container_width=True)
+
+        time.sleep(0.001) # Small sleep to yield control, can be adjusted or removed
 
-"""
-# Welcome to Streamlit!
-
-Edit `/streamlit_app.py` to customize this app to your heart's desire :heart:.
-If you have any questions, checkout our [documentation](https://docs.streamlit.io) and [community
-forums](https://discuss.streamlit.io).
-
-In the meantime, below is an example of what you can do with just a few lines of code:
-"""
-
-num_points = st.slider("Number of points in spiral", 1, 10000, 1100)
-num_turns = st.slider("Number of turns in spiral", 1, 300, 31)
-
-indices = np.linspace(0, 1, num_points)
-theta = 2 * np.pi * num_turns * indices
-radius = indices
-
-x = radius * np.cos(theta)
-y = radius * np.sin(theta)
-
-df = pd.DataFrame({
-    "x": x,
-    "y": y,
-    "idx": indices,
-    "rand": np.random.randn(num_points),
-})
-
-st.altair_chart(alt.Chart(df, height=700, width=700)
-    .mark_point(filled=True)
-    .encode(
-        x=alt.X("x", axis=None),
-        y=alt.Y("y", axis=None),
-        color=alt.Color("idx", legend=None, scale=alt.Scale()),
-        size=alt.Size("rand", legend=None, scale=alt.Scale(range=[1, 150])),
-    ))
+    if st.session_state.cap is not None and st.session_state.cap.isOpened():
+        st.session_state.cap.release()
+        st.session_state.cap = None
+        st_status.info("Camera released.")