Update app.py

app.py

@@ -1,94 +1,74 @@
-import base64
-from io import BytesIO
-import numpy as np
-import cv2
-from flask import Flask, render_template, request, Response, url_for
-import tensorflow 
-from PIL import Image
-from mtcnn import MTCNN
-import time
-
-app = Flask(__name__)
-app.secret_key = 'shamstabrez'
-
-# Load Model 
-model = tensorflow.keras.models.load_model('model/RAFDB_Custom.h5')
-class_labels = ['Angry', 'Disgust', 'Fear', 'Happy', 'Neutral', 'Sad', 'Surprise']
-IMG_SIZE = (48, 48)
-detector = MTCNN()
-
-streaming = False  
-
-def detect_and_classify(frame):
-    faces = detector.detect_faces(frame)
-    top_emotions = []
-    if faces:
-        for face in faces:
-            x, y, w, h = face['box']
-            x, y = max(0, x), max(0, y)  # Prevent negative values
-            cropped_face = frame[y:y+h, x:x+w]
-
-            if cropped_face.shape[0] > 0 and cropped_face.shape[1] > 0:
-                face_rgb = cv2.resize(cropped_face, (IMG_SIZE[1], IMG_SIZE[0])) 
-                face_array = tensorflow.keras.preprocessing.image.img_to_array(face_rgb) / 255.0  # Normalize
-                face_array = np.expand_dims(face_array, axis=0)
-
-                predictions = model.predict(face_array)[0]
-                top_indices = np.argsort(predictions)[-3:][::-1]  # Get top 3 emotions sorted
-                top_emotions = [(class_labels[i], round(predictions[i] * 100, 2)) for i in top_indices]
-
-                # bounding box & text for highest emotion
-                cv2.rectangle(frame, (x, y), (x+w, y+h), (0, 255, 0), 2)
-                top_text = f"{top_emotions[0][1]}% {top_emotions[0][0]}"
-                cv2.putText(frame, top_text, (x, y-10), cv2.FONT_HERSHEY_SIMPLEX, 0.8, (0, 255, 0), 2)
-    return frame, top_emotions
-
-@app.route('/')
-def index():
-    return render_template('index.html', top_emotions=None, img_base64=None, show_upload=True, show_camera=False, initial_image=True)
-
-@app.route('/classify', methods=['POST'])
-def classify_image():
-    image = request.files['image']
-    img = Image.open(image)
-    img = np.array(img)
-    img = cv2.cvtColor(img, cv2.COLOR_RGB2BGR)
-    processed_frame, top_emotions = detect_and_classify(img)
-    _, buffer = cv2.imencode('.png', processed_frame)
-    img_base64 = base64.b64encode(buffer).decode('utf-8')
-    return render_template('index.html', top_emotions=top_emotions, img_base64=img_base64, show_upload=True, show_camera=False, initial_image=False)
-
-def video_stream():
-    global streaming
-    cap = cv2.VideoCapture(0)
-    while streaming and cap.isOpened():
-        ret, frame = cap.read()
-        if not ret:
-            break
-        frame, top_emotions = detect_and_classify(frame)
-        _, buffer = cv2.imencode('.jpg', frame)
-        frame_bytes = buffer.tobytes()
-        yield (b'--frame\r\n' b'Content-Type: image/jpeg\r\n\r\n' + frame_bytes + b'\r\n')
-    cap.release()
-
-@app.route('/start_video')
-def start_video():
-    global streaming
-    streaming = True
-    return '', 204
-
-@app.route('/video_feed')
-def video_feed():
-    if streaming:
-        return Response(video_stream(), mimetype='multipart/x-mixed-replace; boundary=frame')
-    else:
-        return '', 204
-
-@app.route('/stop_video')
-def stop_video():
-    global streaming
-    streaming = False
-    return '', 204
-
-if __name__ == '__main__':
-    app.run(debug=True)
+import base64
+import numpy as np
+import cv2
+from flask import Flask, render_template, request, jsonify
+import tensorflow
+from PIL import Image
+from io import BytesIO
+from mtcnn import MTCNN
+
+app = Flask(__name__)
+app.secret_key = 'shamstabrez'
+
+model = tensorflow.keras.models.load_model('model/RAFDB_Custom.h5')
+class_labels = ['Angry', 'Disgust', 'Fear', 'Happy', 'Neutral', 'Sad', 'Surprise']
+IMG_SIZE = (48, 48)
+detector = MTCNN()
+
+def detect_and_classify(frame):
+    faces = detector.detect_faces(frame)
+    top_emotions = [("No Face Detected", 0)] if not faces else []
+    
+    for face in faces:
+        x, y, w, h = face['box']
+        x, y = max(0, x), max(0, y)
+        cropped_face = frame[y:y+h, x:x+w]
+
+        if cropped_face.shape[0] > 0 and cropped_face.shape[1] > 0:
+            face_rgb = cv2.resize(cropped_face, IMG_SIZE)
+            face_array = tensorflow.keras.preprocessing.image.img_to_array(face_rgb) / 255.0
+            face_array = np.expand_dims(face_array, axis=0)
+
+            predictions = model.predict(face_array)[0]
+            top_indices = np.argsort(predictions)[-3:][::-1]
+            top_emotions = [(class_labels[i], round(predictions[i] * 100, 2)) for i in top_indices]
+
+    return top_emotions
+
+@app.route('/')
+def index():
+    return render_template('index.html', top_emotions=None, img_base64=None, initial_image=True)
+
+@app.route('/classify', methods=['POST'])
+def classify_image():
+    image = request.files['image']
+    img = Image.open(image)
+    img = np.array(img)
+    img = cv2.cvtColor(img, cv2.COLOR_RGB2BGR)
+    top_emotions = detect_and_classify(img)
+
+    _, buffer = cv2.imencode('.png', img)
+    img_base64 = base64.b64encode(buffer).decode('utf-8')
+
+    return render_template('index.html', top_emotions=top_emotions, img_base64=img_base64, initial_image=False)
+
+@app.route('/process_frame', methods=['POST'])
+def process_frame():
+    """Receives base64-encoded video frame, processes it, and returns emotions."""
+    data = request.json
+    frame_data = data.get('frame', '')
+
+    if not frame_data:
+        return jsonify({"error": "No frame received"}), 400
+
+    # Convert base64 to image
+    img_data = base64.b64decode(frame_data.split(',')[1])
+    img = Image.open(BytesIO(img_data))
+    img = np.array(img)
+    img = cv2.cvtColor(img, cv2.COLOR_RGB2BGR)
+
+    top_emotions = detect_and_classify(img)
+    return jsonify({"emotions": top_emotions})
+
+if __name__ == '__main__':
+    app.run(debug=True)