Update app.py

app.py

@@ -17,6 +17,42 @@ EMOTIONS = ['neutral', 'happiness', 'surprise', 'sadness', 'anger', 'disgust', '
 # face_detector_mtcnn = MTCNN()
 classifier = load_model(emotion_model_path)
 
+# def predict_emotion(frame):
+#     frame = imutils.resize(frame, width=300)
+#     gray = cv2.cvtColor(frame, cv2.COLOR_RGB2GRAY)
+#     faces = face_detection.detectMultiScale(gray, scaleFactor=1.1,
+#                                             minNeighbors=5, minSize=(30, 30),
+#                                             flags=cv2.CASCADE_SCALE_IMAGE)
+
+#     frame_clone = frame.copy()
+#     if len(faces) > 0:
+#         faces = sorted(faces, reverse=True,
+#                        key=lambda x: (x[2] - x[0]) * (x[3] - x[1]))[0]
+#         (fX, fY, fW, fH) = faces
+        
+#         # Extract the ROI of the face from the grayscale image, resize it to a fixed 28x28 pixels, and then prepare
+#         # the ROI for classification via the CNN
+#         roi = gray[fY:fY + fH, fX:fX + fW]
+#         roi = cv2.resize(roi, (48, 48))
+#         roi = roi.astype("float") / 255.0
+#         roi = img_to_array(roi)
+#         roi = np.expand_dims(roi, axis=0)
+
+#         preds = emotion_classifier.predict(roi)[0]
+#         label = EMOTIONS[preds.argmax()]
+
+#         # Overlay a box over the detected face
+#         cv2.putText(frame_clone, label, (fX, fY + 100),
+#                     cv2.FONT_HERSHEY_DUPLEX, 1, (238, 164, 64), 1)
+#         cv2.rectangle(frame_clone, (fX, fY), (fX + fW, fY + fH),
+#                       (238, 164, 64), 2)
+
+#     else:
+#         label = "Can't find your face"
+
+#     return frame_clone
+
+
 def predict_emotion(frame):
     frame = imutils.resize(frame, width=300)
     gray = cv2.cvtColor(frame, cv2.COLOR_RGB2GRAY)
@@ -24,12 +60,7 @@ def predict_emotion(frame):
                                             minNeighbors=5, minSize=(30, 30),
                                             flags=cv2.CASCADE_SCALE_IMAGE)
 
-    frame_clone = frame.copy()
-    if len(faces) > 0:
-        faces = sorted(faces, reverse=True,
-                       key=lambda x: (x[2] - x[0]) * (x[3] - x[1]))[0]
-        (fX, fY, fW, fH) = faces
-        
+    for (fX, fY, fW, fH) in faces:
         # Extract the ROI of the face from the grayscale image, resize it to a fixed 28x28 pixels, and then prepare
         # the ROI for classification via the CNN
         roi = gray[fY:fY + fH, fX:fX + fW]
@@ -42,15 +73,12 @@ def predict_emotion(frame):
         label = EMOTIONS[preds.argmax()]
 
         # Overlay a box over the detected face
-        cv2.putText(frame_clone, label, (fX, fY + 10),
+        cv2.putText(frame, label, (fX, fY + 100),
                     cv2.FONT_HERSHEY_DUPLEX, 1, (238, 164, 64), 1)
-        cv2.rectangle(frame_clone, (fX, fY), (fX + fW, fY + fH),
+        cv2.rectangle(frame, (fX, fY), (fX + fW, fY + fH),
                       (238, 164, 64), 2)
 
-    else:
-        label = "Can't find your face"
-
-    return frame_clone
+    return frame