Update app/apps/emotion_recognition/emotion_recognition.py

app/apps/emotion_recognition/emotion_recognition.py

@@ -28,7 +28,7 @@ classes = list(class_labels.values())
 face_classifier = cv2.CascadeClassifier(os.path.join(
 			BASE_DIR,'model/haarcascade_frontalface.xml'))
 
-camera = cv2.VideoCapture(0)
+# camera = cv2.VideoCapture(0)
 
 
 def text_on_detected_boxes(text, text_x, text_y, image, font_scale=1,
@@ -127,16 +127,16 @@ def emotionImageFromArray(img_array):
 
     return image, precentages, label
 
-def face_detector_video(img):
-    gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
-    faces = face_classifier.detectMultiScale(gray, 1.3, 5)
-    if faces is ():
-        return (0, 0, 0, 0), np.zeros((48, 48), np.uint8), img
-    for (x, y, w, h) in faces:
-        cv2.rectangle(img, (x, y), (x + w, y + h), (0, 255, 0), thickness=2)
-        roi_gray = gray[y:y + h, x:x + w]
-    roi_gray = cv2.resize(roi_gray, (48, 48), interpolation=cv2.INTER_AREA)
-    return (x, w, y, h), roi_gray, img
+# def face_detector_video(img):
+#     gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
+#     faces = face_classifier.detectMultiScale(gray, 1.3, 5)
+#     if faces is ():
+#         return (0, 0, 0, 0), np.zeros((48, 48), np.uint8), img
+#     for (x, y, w, h) in faces:
+#         cv2.rectangle(img, (x, y), (x + w, y + h), (0, 255, 0), thickness=2)
+#         roi_gray = gray[y:y + h, x:x + w]
+#     roi_gray = cv2.resize(roi_gray, (48, 48), interpolation=cv2.INTER_AREA)
+#     return (x, w, y, h), roi_gray, img
 
 
 # def emotionVideo():