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+import cv2
+import dlib
+import numpy as np
+from imutils import face_utils
+from keras.models import load_model
+import time
+from eyecrop import crop_eye
+
+import firebase_admin
+from firebase_admin import credentials
+from firebase_admin import db
+
+
+#Firebase database 인증 및 앱 초기화
+cred = credentials.Certificate('mykey.json')
+firebase_admin.initialize_app(cred,{
+    'databaseURL' : 'https://finalproject-1404a.firebaseio.com/'
+})
+ref=db.reference()
+
+#통신
+
+
+IMG_SIZE = (34, 26)
+
+
+#얼굴 detecting saucecode(opencvDNN)
+model_path = 'sauce/opencv_face_detector_uint8.pb'
+config_path = 'sauce/opencv_face_detector.pbtxt'
+net = cv2.dnn.readNetFromTensorflow(model_path, config_path)
+conf_threshold = 0.7
+#얼굴 detecting saucecode(opencvDNN)
+
+
+
+#눈깜빡임 detecting saucecode
+detector = dlib.get_frontal_face_detector()
+predictor = dlib.shape_predictor('sauce/shape_predictor_68_face_landmarks.dat')
+model = load_model('sauce/models.h5')
+#눈깜빡임 detecting saucecode
+
+face_count=0
+eye_count=0
+fps=0
+face_control_sec=3
+eye_control_sec=3
+face_found=False
+numb=1
+numb_1=1
+
+now = time.localtime()
+
+cap = cv2.VideoCapture('sauce/my2.mp4')
+frame_count, tt = 0, 0
+
+
+while cap.isOpened():
+  #read()의 리턴값은 ret, frame.
+  # ret은 읽히면 true 아니면 false.
+  ret, img = cap.read()
+  if not ret:
+    break
+
+  frame_count += 1
+  start_time = time.time()
+
+  faces = detector(img)
+  #print(faces)
+
+  eye_found=False
+  for face in faces:
+    shapes = predictor(img, face)
+    shapes = face_utils.shape_to_np(shapes)
+    #face_util.shape_to_np는 얼굴은 포인트 숫자화 한 것
+    # left eye는 36~41
+    # right eye는 42~47
+    eye_img_l, eye_rect_l = crop_eye( img,eye_points=shapes[36:42])
+    eye_img_r, eye_rect_r = crop_eye( img,eye_points=shapes[42:48])
+
+
+
+
+    eye_img_l = cv2.resize(eye_img_l, dsize=IMG_SIZE)
+    eye_img_r = cv2.resize(eye_img_r, dsize=IMG_SIZE)
+    eye_img_r = cv2.flip(eye_img_r, flipCode=1)
+    eye_input_l = eye_img_l.copy().reshape((1, IMG_SIZE[1], IMG_SIZE[0], 1)).astype(np.float32) / 255.
+    eye_input_r = eye_img_r.copy().reshape((1, IMG_SIZE[1], IMG_SIZE[0], 1)).astype(np.float32) / 255.
+
+    pred_l = model.predict(eye_input_l)
+    pred_r = model.predict(eye_input_r)
+    # 시각화 0~1
+    state_l = 'O %.2f' if pred_l > 0.1 else '- %.2f'
+    state_r = 'O %.2f' if pred_r > 0.1 else '- %.2f'
+    state_l = state_l % pred_l
+    state_r = state_r % pred_r
+    #   '%.1f' %pred_l
+
+    cv2.rectangle(img, (eye_rect_l[0],eye_rect_l[1]), (eye_rect_l[2],eye_rect_l[3]), (255,255,255), 2)
+    cv2.rectangle(img, (eye_rect_r[0],eye_rect_r[1]), (eye_rect_r[2],eye_rect_r[3]), (255,255,255), 2)
+
+    cv2.putText(img, state_l, tuple(eye_rect_l[0:2]), cv2.FONT_HERSHEY_SIMPLEX, 0.7, (255,0,255), 2)
+    cv2.putText(img, state_r, tuple(eye_rect_r[0:2]), cv2.FONT_HERSHEY_SIMPLEX, 0.7, (255,0,255), 2)
+    #조건문
+    if (pred_r < 0.3 and pred_l < 0.3 and face_found):
+
+
+        a="Not 집중 or Sleep %d회"%(numb)
+        eye_count = eye_count + 1
+        if (eye_count > fps * eye_control_sec):
+
+            ref.update({'nob': a})
+            numb = numb + 1
+            #print("Not concentrate or Sleep")
+            eye_count = 0
+    else:
+        eye_count = 0
+
+    print("eye_count  = ",eye_count)
+
+  # prepare input
+  result_img = img.copy()
+  h, w, _ = result_img.shape
+  blob = cv2.dnn.blobFromImage(result_img, 1.0, (300, 300), [104, 117, 123], False, False)
+  # blob은 전처리 등 영상 처리
+  net.setInput(blob)
+  # inference, find faces
+  detections = net.forward()
+  face_found = False
+  # postprocessing
+  for i in range(detections.shape[2]):
+      confidence = detections[0, 0, i, 2]
+
+      if confidence > conf_threshold:
+          face_found = True
+          x1 = int(detections[0, 0, i, 3] * w)
+          y1 = int(detections[0, 0, i, 4] * h)
+          x2 = int(detections[0, 0, i, 5] * w)
+          y2 = int(detections[0, 0, i, 6] * h)
+          rect_1 = dlib.rectangle(x1, y1, x2, y2)
+
+          # draw rects
+          cv2.rectangle(result_img, (x1, y1), (x2, y2), (255, 255, 255), int(round(h / 150)), cv2.LINE_AA)
+          cv2.putText(result_img, '%.2f%%' % (confidence * 100.), (x1, y1 - 10), cv2.FONT_HERSHEY_SIMPLEX, 1,
+                      (255, 255, 255), 2, cv2.LINE_AA)
+
+  #조건문
+  if(face_found is False):
+
+    b = "Not Detected %d회"%(numb_1)
+    face_count=face_count+1
+
+    if(face_count>=fps*face_control_sec):
+      #print("not detected")
+      #print("not Detected ")
+      ref.update({'nob':b})
+      numb_1=numb_1+1
+      face_count=0
+  else:
+    face_count=0
+  print("face_count = ", face_count)
+
+
+
+  cv2.putText(result_img, 'FPS: %.2f' % (fps), (10, 30), cv2.FONT_HERSHEY_SIMPLEX, 1, (100, 100, 100), 2, cv2.LINE_AA)
+  # visualize
+
+
+
+  tt += time.time() - start_time
+  fps = frame_count / tt
+  cv2.imshow('result', result_img)
+  if cv2.waitKey(1) == ord('x'):
+    break
+
+
+
+cap.release()
+
+cv2.destroyAllWindows()
+
+
+