diff --git a/Class/collect_data/face_reco_from_camera.py b/Class/collect_data/face_reco_from_camera.py
index 57a4505..8b461ec 100644
--- a/Class/collect_data/face_reco_from_camera.py
+++ b/Class/collect_data/face_reco_from_camera.py
@@ -1,14 +1,17 @@
 # 摄像头实时人脸识别
 import threading
 
+import requests
 import dlib
 import numpy as np
 import cv2
 import pandas as pd
 import os
 import time
+
 import facenet
 from PIL import Image, ImageDraw, ImageFont
+from sklearn.neighbors import KNeighborsClassifier
 
 from Post import post
 from model import create_model
@@ -23,7 +26,7 @@ start_time = 0
 detector = cv2.dnn.readNetFromCaffe("data/data_opencv/deploy.prototxt.txt",
                                     "data/data_opencv/res10_300x300_ssd_iter_140000.caffemodel")
 
-# 2. Dlib 人脸 landmark 特征点检测器
+# 14. Dlib 人脸 landmark 特征点检测器
 # predictor = dlib.shape_predictor('data/data_dlib/shape_predictor_68_face_landmarks.dat')
 
 # 3. Dlib Resnet 人脸识别模型，提取 128D 的特征矢量
@@ -42,6 +45,7 @@ class Face_Recognizer:
         self.loaded = False
         self.name_known_cnt = 0
         self.name_known_list = []
+        self.type_known_list = []
 
         self.metadata = []
         self.embedded = []
@@ -66,24 +70,38 @@ class Face_Recognizer:
         else:
             if os.path.exists("data/data_faces_from_camera/"):
                 self.metadata = facenet.load_metadata("data/data_faces_from_camera/")
-                self.name_known_cnt = self.metadata.shape[0]
-                self.embedded = np.zeros((self.metadata.shape[0], 128))
+                self.name_known_cnt = 0
+                for i in range(0, len(self.metadata)):
+                    for j in range(0, len(self.metadata[i])):
+                        self.name_known_cnt += 1
+                self.embedded = np.zeros((self.name_known_cnt * 8, 128))
 
                 for i, m in enumerate(self.metadata):
                     for j, n in enumerate(m):
                         for k, p in enumerate(n):
-                            img = facenet.load_image(p.image_path())
+                            img = facenet.load_image(p.image_path().replace("\\", "/"))
                             # img = align_image(img)
                             img = cv2.resize(img, (96, 96))
                             # scale RGB values to interval [0,1]
                             img = (img / 255.).astype(np.float32)
                             # obtain embedding vector for image
                             self.embedded[i] = nn4_small2_pretrained.predict(np.expand_dims(img, axis=0))[0]
-                        # self.embedded[i] = self.embedded[i] / len(m)
-                        path = p.image_path().replace("\\", "/")
+                            # self.embedded[i] = self.embedded[i] / len(m)
+                            path = p.image_path().replace("\\", "/")
                         self.name_known_list.append(path.split('/')[-2])
-                        self.type_camera_list.append(path.split('/')[-3])
-                        self.loaded = True
+                        self.type_known_list.append(path.split('/')[-3])
+                # print(self.embedded.shape)
+                for i in range(len(self.name_known_list)):
+                    if self.type_known_list[i] == 'elder':
+                        type = 'old'
+                    elif self.type_known_list[i] == 'volunteer':
+                        type = 'employee'
+                    self.name_known_list[i] = requests.get("http://zhuooyu.cn:8000/api/person/" + str(type) + "/" + str(
+                        self.name_known_list[i]) + "/").text
+
+                self.loaded = True
+                # print(self.name_known_list)
+
                 return 1
             else:
                 print('##### Warning #####', '\n')
@@ -99,7 +117,7 @@ class Face_Recognizer:
     # def return_euclidean_distance(feature_1, feature_2):
     #     feature_1 = np.array(feature_1)
     #     feature_2 = np.array(feature_2)
-    #     dist = np.sqrt(np.sum((feature_1 - feature_2) ** 2))
+    #     dist = np.sqrt(np.sum((feature_1 - feature_2) ** 14))
     #     return dist
 
     # 更新 FPS
@@ -133,10 +151,10 @@ class Face_Recognizer:
     # 修改显示人名
     def modify_name_camera_list(self):
         # TODO 数据库 ID
-        # Default known name: 1, 2, person_3
+        # Default known name: 1, 14, person_3
         self.name_known_list[0] = '1'.encode('utf-8').decode()
         self.name_known_list[1] = 'Tony Blair'.encode('utf-8').decode()
-        # self.name_known_list[2] = '唐保生'.encode('utf-8').decode()
+        # self.name_known_list[14] = '唐保生'.encode('utf-8').decode()
         # self.name_known_list[3] = '1'.encode('utf-8').decode()
         # self.name_known_list[4] ='xx'.encode('utf-8').decode()
 
@@ -157,6 +175,7 @@ class Face_Recognizer:
                     self.faces_cnt = 0
                     self.pos_camera_list = []
                     self.name_camera_list = []
+                    self.type_camera_list = []
 
                     (h, w) = img_rd.shape[:2]
                     blob = cv2.dnn.blobFromImage(cv2.resize(img_rd, (300, 300)), 1.0,
@@ -164,11 +183,11 @@ class Face_Recognizer:
                     detector.setInput(blob)
                     faces = detector.forward()
 
-                    # 2. 检测到人脸
+                    # 14. 检测到人脸
                     if faces.shape[2] != 0:
                         # 3. 获取当前捕获到的图像的所有人脸的特征，存储到 self.features_camera_list
-                        # for i in range(0, faces.shape[2]):
-                        #     confidence = faces[0, 0, i, 2]
+                        # for i in range(0, faces.shape[14]):
+                        #     confidence = faces[0, 0, i, 14]
                         #
                         #     # filter out weak detections by ensuring the `confidence` is
                         #     # greater than the minimum confidence
@@ -196,6 +215,7 @@ class Face_Recognizer:
                             # 先默认所有人不认识，是 unknown
                             # Set the default names of faces with "unknown"
                             self.name_camera_list.append("unknown")
+                            self.type_camera_list.append('unknown')
 
                             # 每个捕获人脸的名字坐标
                             box = faces[0, 0, k, 3:7] * np.array([w, h, w, h])
@@ -208,6 +228,7 @@ class Face_Recognizer:
 
                             # img_blank = np.zeros((height, width, 3), np.uint8)
                             img_blank = img_rd[startY:endY, startX:endX]
+                            img_blank = img_blank[..., ::-1]
                             try:
                                 # for ii in range(height):
                                 #     for jj in range(width):
@@ -233,57 +254,57 @@ class Face_Recognizer:
                                 #         # 空数据 person_X
                                 #         e_distance_list.append(999999999)
                                 # # 6. 寻找出最小的欧式距离匹配
+                                # print(self.neigh.predict([img]))
                                 similar_person_num = e_distance_list.index(min(e_distance_list))
                                 # print("Minimum e distance with person", self.name_known_list[similar_person_num])
-                                # print(min(e_distance_list))
+                                print(min(e_distance_list))
                                 if min(e_distance_list) < 0.58:
                                     self.name_camera_list[k] = self.name_known_list[similar_person_num % 8]
-                                    if self.type_camera_list[similar_person_num % 8] == 'elder':
+                                    self.type_camera_list[k] = self.type_known_list[similar_person_num % 8]
+                                    cv2.rectangle(img_rd, tuple([startX, startY]), tuple([endX, endY]),
+                                                  (0, 255, 0), 2)
+                                    cv2.rectangle(img_rd, tuple([startX, startY - 35]), tuple([endX, startY]),
+                                                  (0, 255, 0), cv2.FILLED)
+                                    img_with_name = self.draw_name(img_rd)
+                                    if self.type_camera_list[k] == 'elder':
                                         mode = smile_detection.smile_detect(img_blank)
                                         if mode == 'happy':
-                                            cv2.imwrite('smile_detection.jpg', img_rd)
-                                            cv2.rectangle(img_rd, tuple([startX, startY - 70]),
+                                            cv2.rectangle(img_with_name, tuple([startX, startY - 70]),
                                                           tuple([endX, startY - 35]),
                                                           (0, 215, 255), cv2.FILLED)
-                                            cv2.putText(img_rd, 'happy', (startX + 5, startY - 45), cv2.FONT_ITALIC, 1,
+                                            cv2.putText(img_with_name, 'happy', (startX + 5, startY - 45),
+                                                        cv2.FONT_ITALIC, 1,
                                                         (255, 255, 255), 1, cv2.LINE_AA)
+                                            cv2.imwrite('smile_detection.jpg', img_with_name)
                                             # t = threading.Thread(target=post(elder_id=self.name_camera_list[k], event=0,
                                             #                                  imagePath='smile_detection.jpg'))
                                             # t.start()
                                     # print("May be person " + str(self.name_known_list[similar_person_num]))
                                 elif min(e_distance_list) > 0.75:
                                     self.name_camera_list[k] = '陌生人'
-                                    cv2.imwrite('stranger_detection.jpg', img_rd)
-                                    # t = threading.Thread(target=post(event=2, imagePath='stranger_detection.jpg'))
+                                    cv2.rectangle(img_rd, tuple([startX, startY]), tuple([endX, endY]),
+                                                  (0, 0, 255), 2)
+                                    cv2.rectangle(img_rd, tuple([startX, startY - 35]), tuple([endX, startY]),
+                                                  (0, 0, 255), cv2.FILLED)
+                                    img_with_name = self.draw_name(img_rd)
+                                    cv2.imwrite('stranger_detection.jpg', img_with_name)
+                                    # t = threading.Thread(target=post(event=14, imagePath='stranger_detection.jpg'))
                                     # t.start()
                                 else:
                                     pass
                                     # print("Unknown person")
 
-                                # 矩形框
-                                for kk, d in enumerate(faces):
-                                    # 绘制矩形框
-                                    if self.name_camera_list[k] == '陌生人':
-                                        cv2.rectangle(img_rd, tuple([startX, startY]), tuple([endX, endY]),
-                                                      (0, 0, 255), 2)
-                                        cv2.rectangle(img_rd, tuple([startX, startY - 35]), tuple([endX, startY]),
-                                                      (0, 0, 255), cv2.FILLED)
-                                    elif self.name_camera_list[k] != 'unknown':
-                                        cv2.rectangle(img_rd, tuple([startX, startY]), tuple([endX, endY]),
-                                                      (0, 255, 0), 2)
-                                        cv2.rectangle(img_rd, tuple([startX, startY - 35]), tuple([endX, startY]),
-                                                      (0, 255, 0), cv2.FILLED)
 
                             except:
                                 continue
                             # print('\n')
-                            # self.faces_cnt = faces.shape[2]
+                            # self.faces_cnt = faces.shape[14]
                             # if len(self.name_camera_list) > 0:
                             # 7. 在这里更改显示的人名
                             # self.modify_name_camera_list()
                             # 8. 写名字
                             # self.draw_name(img_rd)
-                            img_with_name = self.draw_name(img_rd)
+
                     else:
                         img_with_name = img_rd
 
diff --git a/Class/collect_data/get_faces_from_camera.py b/Class/collect_data/get_faces_from_camera.py
index 67fd81f..e58b573 100644
--- a/Class/collect_data/get_faces_from_camera.py
+++ b/Class/collect_data/get_faces_from_camera.py
@@ -17,7 +17,7 @@ action_map = {'look_ahead': '请看前方', 'blink': '请眨眼', 'open_mouth':
               'smile': '请笑一笑', 'rise_head': '请抬头',
               'bow_head': '请低头', 'look_left': '请看左边',
               'look_right': '请看右边', 'over': '录入完成'}
-people_type_dict = {'0': 'elder', '1': 'worker', '2': 'volunteer'}
+people_type_dict = {'0': 'elder', '1': 'employee', '14': 'volunteer'}
 
 # Dlib 正向人脸检测器
 # detector = dlib.get_frontal_face_detector()
@@ -131,7 +131,7 @@ class Face_Register:
         # 1. 新建储存人脸图像文件目录
         # self.pre_work_mkdir()
 
-        # 2. 删除 "/data/data_faces_from_camera" 中已有人脸图像文件
+        # 14. 删除 "/data/data_faces_from_camera" 中已有人脸图像文件
         # self.pre_work_del_old_face_folders()
 
         # 3. 检查 "/data/data_faces_from_camera" 中已有人脸文件
@@ -183,8 +183,8 @@ class Face_Register:
 
                     height = (endY - startY)
                     width = (endX - startX)
-                    # hh = int(height / 2)
-                    # ww = int(width / 2)
+                    # hh = int(height / 14)
+                    # ww = int(width / 14)
 
                     # 6. 判断人脸矩形框是否超出 480x640
                     if endX > 640 or endY > 480or startX < 0 or startY < 0:
@@ -205,7 +205,7 @@ class Face_Register:
                                   color_rectangle, 2)
 
                     # 7. 根据人脸大小生成空的图像
-                    # img_blank = np.zeros((int(height * 2), width * 2, 3), np.uint8)
+                    # img_blank = np.zeros((int(height * 14), width * 14, 3), np.uint8)
                     img_blank = np.zeros((height, width, 3), np.uint8)
 
                     if save_flag:
diff --git a/Class/detection/Calibration.py b/Class/detection/Calibration.py
index 7705ee2..e9aa505 100644
--- a/Class/detection/Calibration.py
+++ b/Class/detection/Calibration.py
@@ -181,7 +181,7 @@ class Calibration:
                     gridpoints[i][j] = 0
                 j = j + 1
             i = i + 1
-        # cv2.imshow('2', frame)
+        # cv2.imshow('14', frame)
         # cv2.waitKey(0)  # 按0退出
         return gridpoints
 
@@ -279,7 +279,7 @@ class Calibration:
         # cv2.imshow('lab', frame)
         # cv2.waitKey(0)  # 按0退出
         data = GF(1, 1, 1, 1, 1, 1)
-        # map = np.zeros((4, 4, 4, 4, 4, 4, 2))
+        # map = np.zeros((4, 4, 4, 4, 4, 4, 14))
 
         map = np.zeros((4096, 2))
 
@@ -342,7 +342,7 @@ class Calibration:
 
                     cv2.imwrite('testt.png', img_rd)
 
-                    # img_rd = cv2.resize(img_rd, (img_rd.shape[0]//2, img_rd.shape[1]//2))
+                    # img_rd = cv2.resize(img_rd, (img_rd.shape[0]//14, img_rd.shape[1]//14))
                     # 1024*1280 -1候选点，-3非候选点
                     candidate = self.get_candidate_points(frame=img_rd)
                     # 1024*1280 1角点，0非角点
@@ -364,8 +364,8 @@ class Calibration:
                     # distance = []
                     # for i in range(0, len(featurepoints_position) - 1):
                     #     distance.append(
-                    #         math.sqrt((featurepoints_position[i + 1][0] - featurepoints_position[i][0]) ** 2 +
-                    #                   (featurepoints_position[i + 1][1] - featurepoints_position[i][1]) ** 2))
+                    #         math.sqrt((featurepoints_position[i + 1][0] - featurepoints_position[i][0]) ** 14 +
+                    #                   (featurepoints_position[i + 1][1] - featurepoints_position[i][1]) ** 14))
                     # print(distance)
                     # distance = sorted(distance)
 
@@ -380,7 +380,7 @@ class Calibration:
                     # print(feature_points[index + 1][0] - feature_points[index][0],
                     #       feature_points[index + 1][1] - feature_points[index][1])
                     # print(distance)
-                    # for i in range(index - 1, index + 2):
+                    # for i in range(index - 1, index + 14):
                     #     print(distance[i])
                     # 绘制特征点
                     point_size = 1
diff --git a/Class/detection/Calibration_On_Desk.py b/Class/detection/Calibration_On_Desk.py
index aac3992..67633f8 100644
--- a/Class/detection/Calibration_On_Desk.py
+++ b/Class/detection/Calibration_On_Desk.py
@@ -180,7 +180,7 @@ class Calibration:
                     gridpoints[i][j] = 0
                 j = j + 1
             i = i + 1
-        # cv2.imshow('2', frame)
+        # cv2.imshow('14', frame)
         # cv2.waitKey(0)  # 按0退出
         return gridpoints
 
@@ -260,7 +260,7 @@ class Calibration:
                 if l[i][j] < 100:
                     color_map[i][j] = 3
                     frame[i][j] = np.array([0, 0, 0])
-                elif l[i][j] > 220:
+                elif l[i][j] > 200:
                     frame[i][j] = np.array([255, 255, 255])
                     color_map[i][j] = 255
                 else:
@@ -278,7 +278,7 @@ class Calibration:
         # cv2.imshow('lab', frame)
         # cv2.waitKey(0)  # 按0退出
         data = GF(1, 1, 1, 1, 1, 1)
-        # map = np.zeros((4, 4, 4, 4, 4, 4, 2))
+        # map = np.zeros((4, 4, 4, 4, 4, 4, 14))
 
         map = np.zeros((4096, 2))
 
@@ -321,6 +321,7 @@ class Calibration:
                         position.append([i, j])
                 except:
                     pass
+        cv2.imwrite('testt3.png',frame)
         # cv2.imshow('lab', frame)
         # cv2.waitKey(0)  # 按0退出
         return points, position
@@ -351,6 +352,7 @@ class Calibration:
 
         for point in featurepoints_position:
             cv2.circle(img_rd, (int(point[1]), int(point[0])), point_size, point_color, thickness)
+        cv2.imwrite('testt2.png', img_rd)
 
         self.draw_note(img_rd)
         self.update_fps()
diff --git a/Class/detection/Camera_Beside_Com.py b/Class/detection/Camera_Beside_Com.py
index fe8b176..952b8d5 100644
--- a/Class/detection/Camera_Beside_Com.py
+++ b/Class/detection/Camera_Beside_Com.py
@@ -1,8 +1,5 @@
 # 进行人脸录入 / face register
 # 录入多张人脸 / support multi-faces
-import datetime
-
-import dlib
 import numpy as np
 import cv2
 import os
@@ -18,7 +15,7 @@ action_map = {'look_ahead': '请看前方', 'blink': '请眨眼', 'open_mouth':
               'smile': '请笑一笑', 'rise_head': '请抬头',
               'bow_head': '请低头', 'look_left': '请看左边',
               'look_right': '请看右边', 'over': '录入完成'}
-people_type_dict = {'0': 'elder', '1': 'worker', '2': 'volunteer'}
+people_type_dict = {'0': 'elder', '1': 'employee', '2': 'volunteer'}
 
 # Dlib 正向人脸检测器
 # detector = dlib.get_frontal_face_detector()
@@ -50,7 +47,7 @@ class Face_Register:
         self.fps = 0
 
         self.people_type = people_type_dict[str(people_type)]
-        self.id = id
+        self.id = str(id)
 
     def speak(self):
         text = action_map[action_list[self.index]]
@@ -111,9 +108,9 @@ class Face_Register:
         cv2.putText(img_rd, "FPS:   " + str(self.fps.__round__(2)), (20, 100), self.font, 0.8, (0, 255, 0), 1,
                     cv2.LINE_AA)
         cv2.putText(img_rd, "Faces: " + str(self.faces_cnt), (20, 140), self.font, 0.8, (0, 255, 0), 1, cv2.LINE_AA)
-        cv2.putText(img_rd, "N: Create face folder", (20, 350), self.font, 0.8, (255, 255, 255), 1, cv2.LINE_AA)
-        cv2.putText(img_rd, "S: Save current face", (20, 400), self.font, 0.8, (255, 255, 255), 1, cv2.LINE_AA)
-        cv2.putText(img_rd, "Q: Quit", (20, 450), self.font, 0.8, (255, 255, 255), 1, cv2.LINE_AA)
+        # cv2.putText(img_rd, "N: Create face folder", (20, 350), self.font, 0.8, (255, 255, 255), 1, cv2.LINE_AA)
+        # cv2.putText(img_rd, "S: Save current face", (20, 400), self.font, 0.8, (255, 255, 255), 1, cv2.LINE_AA)
+        # cv2.putText(img_rd, "Q: Quit", (20, 450), self.font, 0.8, (255, 255, 255), 1, cv2.LINE_AA)
 
         font = ImageFont.truetype("simsun.ttc", 30, index=1)
         img_rd = Image.fromarray(cv2.cvtColor(img_rd, cv2.COLOR_BGR2RGB))
@@ -137,18 +134,68 @@ class Face_Register:
         detector.setInput(blob)
         faces = detector.forward()
 
-        # # 新建存储人脸的文件夹
-        # if self.index == 0:
-        #     # self.existing_faces_cnt += 1
-        #     current_face_dir = self.path_photos_from_camera + '/' + self.people_type + '/' + self.id
-        #     # current_face_dir = self.path_photos_from_camera + "person_" + str(self.existing_faces_cnt)
-        #     os.makedirs(current_face_dir)
-        #     # print('\n')
-        #     # print("新建的人脸文件夹 / Create folders: ", current_face_dir)
-        #
-        #     self.ss_cnt = 0  # 将人脸计数器清零
-        #     self.index = 0
-        #     self.press_n_flag = 1  # 已经按下 'n'
+        # 检测到人脸
+        if faces.shape[2] != 0:
+            # 矩形框
+            for i in range(0, faces.shape[2]):
+                # 计算矩形框大小
+                confidence = faces[0, 0, i, 2]
+
+                # filter out weak detections by ensuring the `confidence` is
+                # greater than the minimum confidence
+                if confidence < 0.5:
+                    continue
+
+                self.faces_cnt += 1
+
+                # compute the (x, y)-coordinates of the bounding box for the
+                # object
+                box = faces[0, 0, i, 3:7] * np.array([w, h, w, h])
+                (startX, startY, endX, endY) = box.astype("int")
+
+                # 判断人脸矩形框是否超出 480x640
+                if endX > 640 or endY > 480 or startX < 0 or startY < 0:
+                    # if (endX + ww) > 640 or (endY + hh > 480) or (startX - ww < 0) or (
+                    #         startY - hh < 0):
+                    cv2.putText(img_rd, "OUT OF RANGE", (20, 300), self.font, 0.8, (0, 0, 255), 1, cv2.LINE_AA)
+                    color_rectangle = (0, 0, 255)
+                    save_flag = 0
+                    print("请调整位置 / Please adjust your position")
+                else:
+                    color_rectangle = (0, 255, 0)
+                    save_flag = 1
+
+                cv2.rectangle(img_rd, tuple([startX, startY]), tuple([endX, endY]), color_rectangle, 2)
+
+        # 生成的窗口添加说明文字
+        img_rd = self.draw_note(img_rd)
+
+        self.update_fps()
+
+        if not self.init:
+            self.speak()
+            self.init = True
+
+        return img_rd
+
+    def take_photo(self, img_rd):
+        (h, w) = img_rd.shape[:2]
+        blob = cv2.dnn.blobFromImage(cv2.resize(img_rd, (300, 300)), 1.0,
+                                     (300, 300), (104.0, 177.0, 123.0))
+        detector.setInput(blob)
+        faces = detector.forward()
+        current_face_dir = self.path_photos_from_camera + self.people_type + '/' + self.id
+        # 新建存储人脸的文件夹
+        if not os.path.exists(current_face_dir):
+            # self.existing_faces_cnt += 1
+            # current_face_dir = self.path_photos_from_camera + "person_" + str(self.existing_faces_cnt)
+            os.makedirs(current_face_dir)
+            # print('\n')
+            # print("新建的人脸文件夹 / Create folders: ", current_face_dir)
+            #
+            self.ss_cnt = 0  # 将人脸计数器清零
+            # self.index = 0
+            self.press_n_flag = 1  # 已经按下 'n'
 
         # 检测到人脸
         if faces.shape[2] != 0:
@@ -169,10 +216,10 @@ class Face_Register:
                 box = faces[0, 0, i, 3:7] * np.array([w, h, w, h])
                 (startX, startY, endX, endY) = box.astype("int")
 
-                height = (endY - startY)
-                width = (endX - startX)
-                # hh = int(height / 2)
-                # ww = int(width / 2)
+                # height = (endY - startY)
+                # width = (endX - startX)
+                # # hh = int(height / 14)
+                # # ww = int(width / 14)
 
                 # 判断人脸矩形框是否超出 480x640
                 if endX > 640 or endY > 480 or startX < 0 or startY < 0:
@@ -188,29 +235,26 @@ class Face_Register:
 
                 cv2.rectangle(img_rd, tuple([startX, startY]), tuple([endX, endY]), color_rectangle, 2)
 
-                # 根据人脸大小生成空的图像
-                # img_blank = np.zeros((int(height * 2), width * 2, 3), np.uint8)
-                img_blank = np.zeros((height, width, 3), np.uint8)
+                if save_flag:
+                    # 保存摄像头中的人脸到本地
+                    # 检查有没有先按'n'新建文件夹
+                    if self.press_n_flag:
+                        self.ss_cnt += 1
 
-            #     if save_flag:
-            #         # 保存摄像头中的人脸到本地
-            #         # 检查有没有先按'n'新建文件夹
-            #         if self.press_n_flag:
-            #             self.ss_cnt += 1
-            #
-            #             if self.index <= 7:
-            #                 for ii in range(height):
-            #                     for jj in range(width):
-            #                         img_blank[ii][jj] = img_rd[startY + ii][startX + jj]
-            #                 cv2.imwrite(current_face_dir + "/img_face_" + str(self.ss_cnt) + ".jpg", img_blank)
-            #                 print("写入本地 / Save into：",
-            #                       str(current_face_dir) + "/img_face_" + str(self.ss_cnt) + ".jpg")
-            #             if self.index < len(action_list) - 1:
-            #                 self.index += 1
-            #             self.speak()
-            #         else:
-            #             print("请先按 'N' 来建文件夹, 按 'S' / Please press 'N' and press 'S'")
-            # # self.faces_cnt = len(faces)
+                        if self.index <= 7:
+                            img_blank = img_rd[startY:endY, startX:endX]
+                            cv2.imwrite(current_face_dir + "/img_face_" + str(self.ss_cnt) + ".jpg", img_blank)
+                            print("写入本地 / Save into：",
+                                  str(current_face_dir) + "/img_face_" + str(self.ss_cnt) + ".jpg")
+                        if self.index < len(action_list) - 1:
+                            self.index += 1
+                        else:
+                            self.init = False
+
+                        self.speak()
+                    else:
+                        print("请先按 'N' 来建文件夹, 按 'S' / Please press 'N' and press 'S'")
+            # self.faces_cnt = len(faces)
 
         # 生成的窗口添加说明文字
         img_rd = self.draw_note(img_rd)
@@ -218,13 +262,11 @@ class Face_Register:
         self.update_fps()
 
         if not self.init:
+            self.index = 0
             self.speak()
             self.init = True
 
         return img_rd
 
-    def take_photo(self, frame):
-        pass
-
     def run(self, frame):
         return self.process(frame)
diff --git a/Class/detection/Camera_In_Hall.py b/Class/detection/Camera_In_Hall.py
index 96151c8..54c5ffe 100644
--- a/Class/detection/Camera_In_Hall.py
+++ b/Class/detection/Camera_In_Hall.py
@@ -7,6 +7,7 @@ import os
 from sys import platform
 import argparse
 import numpy as np
+from Post import post
 
 from PIL import ImageDraw, ImageFont
 from PIL import Image
@@ -158,8 +159,9 @@ class Fall_Detection:
                               fill=(255, 0, 0))
                     img_rd = cv2.cvtColor(np.array(img_rd), cv2.COLOR_RGB2BGR)
                     cv2.imwrite('fall_detection.jpg', frame)
-                    # t = threading.Thread(target=post(event=3, imagePath='fall_detection.jpg'))
-                    # t.start()
+                    t = threading.Thread(target=post(event=3, imagePath='fall_detection.jpg'))
+                    t.setDaemon(False)
+                    t.start()
                     # status = post(event=3, imagePath='fall_detection.jpg')
                     # print("fall")
 
@@ -192,3 +194,4 @@ class Fall_Detection:
         frame = cv2.resize(img_rd, (640, 480))
         # cv2.imshow("OpenPose 1.6.0 - Tutorial Python API", img_rd)
         return frame
+# http://zhuooyu.cn:8000/api/person/old/10
\ No newline at end of file
diff --git a/Class/detection/Camera_In_Room.py b/Class/detection/Camera_In_Room.py
index 1596873..eef2ad1 100644
--- a/Class/detection/Camera_In_Room.py
+++ b/Class/detection/Camera_In_Room.py
@@ -1,37 +1,22 @@
 # 摄像头实时人脸识别
 import threading
 from datetime import datetime
-
-import dlib
 import numpy as np
 import cv2
-import pandas as pd
 import os
 import time
+
+import requests
+
 import facenet
-from model import create_model
 from PIL import Image, ImageDraw, ImageFont
-from Post import post
+from Post import post, post_person
 
 import smile_detection
 
 start_time = 0
 
-# 1. Dlib 正向人脸检测器
-# detector = dlib.get_frontal_face_detector()
-
-# OpenCV DNN face detector
-# detector = cv2.dnn.readNetFromCaffe("data/data_opencv/deploy.prototxt.txt",
-#                                     "data/data_opencv/res10_300x300_ssd_iter_140000.caffemodel")
-
-# 2. Dlib 人脸 landmark 特征点检测器
-# predictor = dlib.shape_predictor('data/data_dlib/shape_predictor_68_face_landmarks.dat')
-
-# 3. Dlib Resnet 人脸识别模型，提取 128D 的特征矢量
-# face_reco_model = dlib.face_recognition_model_v1("data/data_dlib/dlib_face_recognition_resnet_model_v1.dat")
-
-# nn4_small2 = create_model()
-# nn4_small2.load_weights('weights/nn4.small2.v1.h5')
+api_transfer = {'elder': 'old', 'employee': 'employee', 'volunteer': 'volunteer'}
 
 
 class Face_Recognizer:
@@ -40,6 +25,8 @@ class Face_Recognizer:
         self.detector = detector
         self.nn4_small2 = nn4_small2
 
+        self.pre = datetime.now()
+
         # 用来存放所有录入人脸特征的数组
         self.features_known_list = []
 
@@ -47,6 +34,7 @@ class Face_Recognizer:
         self.loaded = False
         self.name_known_cnt = 0
         self.name_known_list = []
+        self.type_known_list = []
 
         self.metadata = []
         self.embedded = []
@@ -71,24 +59,34 @@ class Face_Recognizer:
         else:
             if os.path.exists("data/data_faces_from_camera/"):
                 self.metadata = facenet.load_metadata("data/data_faces_from_camera/")
-                self.name_known_cnt = self.metadata.shape[0]
-                self.embedded = np.zeros((self.metadata.shape[0], 128))
+                self.name_known_cnt = 0
+                for i in range(0, len(self.metadata)):
+                    for j in range(0, len(self.metadata[i])):
+                        self.name_known_cnt += 1
+                self.embedded = np.zeros((self.name_known_cnt * 8, 128))
 
                 for i, m in enumerate(self.metadata):
                     for j, n in enumerate(m):
                         for k, p in enumerate(n):
-                            img = facenet.load_image(p.image_path())
+                            img = facenet.load_image(p.image_path().replace("\\", "/"))
                             # img = align_image(img)
                             img = cv2.resize(img, (96, 96))
                             # scale RGB values to interval [0,1]
                             img = (img / 255.).astype(np.float32)
                             # obtain embedding vector for image
                             self.embedded[i] = self.nn4_small2.predict(np.expand_dims(img, axis=0))[0]
-                        # self.embedded[i] = self.embedded[i] / len(m)
-                        path = p.image_path().replace("\\", "/")
+                            # self.embedded[i] = self.embedded[i] / len(m)
+                            path = p.image_path().replace("\\", "/")
                         self.name_known_list.append(path.split('/')[-2])
-                        self.type_camera_list.append(path.split('/')[-3])
-                        self.loaded = True
+                        self.type_known_list.append(path.split('/')[-3])
+                for i in range(len(self.name_known_list)):
+                    if self.type_known_list[i] == 'elder':
+                        type = 'old'
+                    elif self.type_known_list[i] == 'volunteer':
+                        type = 'employee'
+                    self.name_known_list[i] = requests.get("http://zhuooyu.cn:8000/api/person/" + str(type) + "/" + str(
+                        self.name_known_list[i]) + "/").text
+                self.loaded = True
                 return 1
             else:
                 print('##### Warning #####', '\n')
@@ -110,7 +108,7 @@ class Face_Recognizer:
         font = cv2.FONT_ITALIC
 
         # cv2.putText(img_rd, "Face Recognizer", (20, 40), font, 1, (255, 255, 255), 1, cv2.LINE_AA)
-        # cv2.putText(img_rd, "FPS:   " + str(self.fps.__round__(2)), (20, 100), font, 0.8, (0, 255, 0), 1, cv2.LINE_AA)
+        # cv2.putText(img_rd, "FPS:   " + str(self.fps.__round__(14)), (20, 100), font, 0.8, (0, 255, 0), 1, cv2.LINE_AA)
         cv2.putText(img_rd, "Faces: " + str(self.faces_cnt), (20, 40), font, 0.8, (0, 255, 0), 1, cv2.LINE_AA)
         # cv2.putText(img_rd, "Q: Quit", (20, 450), font, 0.8, (255, 255, 255), 1, cv2.LINE_AA)
 
@@ -130,16 +128,23 @@ class Face_Recognizer:
     # 修改显示人名
     def modify_name_camera_list(self):
         # TODO 数据库 ID
-        # Default known name: 1, 2, person_3
+        # Default known name: 1, 14, person_3
         self.name_known_list[0] = '1'.encode('utf-8').decode()
         self.name_known_list[1] = 'Tony Blair'.encode('utf-8').decode()
-        # self.name_known_list[2] = '唐保生'.encode('utf-8').decode()
+        # self.name_known_list[14] = '唐保生'.encode('utf-8').decode()
         # self.name_known_list[3] = '1'.encode('utf-8').decode()
         # self.name_known_list[4] ='xx'.encode('utf-8').decode()
 
     # 进行人脸识别和微笑检测
     def process(self, img_rd):
         img_with_name = img_rd
+        data_type_three = {
+            'old': 0,
+            'employee': 0,
+            'volunteer': 0,
+            'stranger': 0
+        }
+
         # 读取所有人脸
         if self.get_face_database():
             cv2.putText(img_rd, "Faces: " + str(self.faces_cnt), (20, 40), cv2.FONT_ITALIC, 0.8, (0, 255, 0), 1,
@@ -150,6 +155,7 @@ class Face_Recognizer:
             self.faces_cnt = 0
             self.pos_camera_list = []
             self.name_camera_list = []
+            self.type_camera_list = []
 
             (h, w) = img_rd.shape[:2]
             blob = cv2.dnn.blobFromImage(cv2.resize(img_rd, (300, 300)), 1.0,
@@ -174,6 +180,7 @@ class Face_Recognizer:
                     # 确定人名的位置坐标
                     # 先默认所有人不认识，是 unknown
                     self.name_camera_list.append("unknown")
+                    self.type_camera_list.append('unknown')
 
                     # 每个捕获人脸的名字坐标
                     box = faces[0, 0, k, 3:7] * np.array([w, h, w, h])
@@ -185,6 +192,7 @@ class Face_Recognizer:
                     # width = (endX - startX)
 
                     img_blank = img_rd[startY:endY, startX:endX]
+                    img_blank = img_blank[..., ::-1]
                     try:
                         # for ii in range(height):
                         #     for jj in range(width):
@@ -200,59 +208,57 @@ class Face_Recognizer:
                             e_distance_list.append(facenet.distance(self.embedded[i], img))
 
                         similar_person_num = e_distance_list.index(min(e_distance_list))
-
                         # print(min(e_distance_list))
                         if min(e_distance_list) < 0.58:
                             self.name_camera_list[k] = self.name_known_list[similar_person_num % 8]
-                            if self.type_camera_list[similar_person_num % 8] == 'elder':
-                                mode = smile_detection.smile_detect(img_blank)
-                                if mode == 'happy':
-                                    cv2.imwrite('smile_detection.jpg', img_rd)
-                                    cv2.rectangle(img_rd, tuple([startX, startY - 70]),
-                                                  tuple([endX, startY - 35]),
-                                                  (0, 215, 255), cv2.FILLED)
-                                    cv2.putText(img_rd, 'happy', (startX + 5, startY - 45), cv2.FONT_ITALIC, 1,
-                                                (255, 255, 255), 1, cv2.LINE_AA)
-                                    # t = threading.Thread(target=post(elder_id=self.name_camera_list[k], event=0,
-                                    #                                  imagePath='smile_detection.jpg'))
-                                    # t.start()
-                            # print("May be person " + str(self.name_known_list[similar_person_num]))
-                        elif min(e_distance_list) > 0.75:
-                            self.name_camera_list[k] = '陌生人'
-                            cv2.imwrite('stranger_detection.jpg', img_rd)
-                            # t = threading.Thread(target=post(event=2, imagePath='stranger_detection.jpg'))
-                            # t.start()
-                        else:
-                            pass
-
-                        if self.name_camera_list[k] == '陌生人':
-                            cv2.rectangle(img_rd, tuple([startX, startY]), tuple([endX, endY]),
-                                          (0, 0, 255), 2)
-                            cv2.rectangle(img_rd, tuple([startX, startY - 35]), tuple([endX, startY]),
-                                          (0, 0, 255), cv2.FILLED)
-                        elif self.name_camera_list[k] != 'unknown':
+                            self.type_camera_list[k] = self.type_known_list[similar_person_num % 8]
+                            data_type_three[api_transfer[self.type_camera_list[k]]] += 1
                             cv2.rectangle(img_rd, tuple([startX, startY]), tuple([endX, endY]),
                                           (0, 255, 0), 2)
                             cv2.rectangle(img_rd, tuple([startX, startY - 35]), tuple([endX, startY]),
                                           (0, 255, 0), cv2.FILLED)
+                            img_with_name = self.draw_name(img_rd)
+                            if self.type_camera_list[k] == 'elder':
+                                mode = smile_detection.smile_detect(img_blank)
+                                if mode == 'happy':
+                                    cv2.rectangle(img_with_name, tuple([startX, startY - 70]),
+                                                  tuple([endX, startY - 35]),
+                                                  (0, 215, 255), cv2.FILLED)
+                                    cv2.putText(img_with_name, 'happy', (startX + 5, startY - 45), cv2.FONT_ITALIC, 1,
+                                                (255, 255, 255), 1, cv2.LINE_AA)
+                                    cv2.imwrite('smile_detection.jpg', img_with_name)
+                                    if (datetime.now() - self.pre).total_seconds() > 5:
+                                        t = threading.Thread(target=post(elder_id=self.name_camera_list[k], event=0,
+                                                                         imagePath='smile_detection.jpg'))
+                                        t.setDaemon(False)
+                                        t.start()
+                                        self.pre = datetime.now()
+                            # print("May be person " + str(self.name_known_list[similar_person_num]))
+                        elif min(e_distance_list) > 0.75:
+                            data_type_three['stranger'] += 1
+                            self.name_camera_list[k] = '陌生人'
+                            cv2.rectangle(img_rd, tuple([startX, startY]), tuple([endX, endY]),
+                                          (0, 0, 255), 2)
+                            cv2.rectangle(img_rd, tuple([startX, startY - 35]), tuple([endX, startY]),
+                                          (0, 0, 255), cv2.FILLED)
+                            img_with_name = self.draw_name(img_rd)
+                            cv2.imwrite('stranger_detection.jpg', img_with_name)
+                            if (datetime.now() - self.pre).total_seconds() > 5:
+                                t = threading.Thread(target=post(event=2, imagePath='stranger_detection.jpg'))
+                                t.setDaemon(False)
+                                t.start()
+                                self.pre = datetime.now()
+                        else:
+                            pass
 
                     except:
                         continue
-
-                    img_with_name = self.draw_name(img_rd)
             else:
                 img_with_name = img_rd
 
             # 更新 FPS / Update stream FPS
             # self.update_fps()
+        if (datetime.now() - self.pre).total_seconds() > 5:
+            post_person(data_type_three)
+            self.pre = datetime.now()
         return img_with_name
-
-    # OpenCV 调用摄像头并进行 process
-    def run(self, frame):
-        # cap = cv2.VideoCapture(0)
-        # cap.set(3, 480)
-        img_with_name = self.process(frame)
-        return img_with_name
-
-        # cap.release()
-        # cv2.destroyAllWindows()
diff --git a/Class/detection/Camera_In_Yard.py b/Class/detection/Camera_In_Yard.py
index aa6d2fa..4268202 100644
--- a/Class/detection/Camera_In_Yard.py
+++ b/Class/detection/Camera_In_Yard.py
@@ -1,7 +1,11 @@
+import threading
+from datetime import datetime
+
 from oldcare.track.centroidtracker import CentroidTracker
 from oldcare.track.trackableobject import TrackableObject
 from imutils.video import FPS
 import numpy as np
+from Post import post
 import imutils
 import argparse
 import time
@@ -14,11 +18,7 @@ import cv2
 # # We’ll be using a MobileNet Single Shot Detector (SSD),
 # # “Single Shot Detectors for object detection”.
 # model_file_path = 'data/data_opencv/MobileNetSSD_deploy.caffemodel'
-skip_frames = 30  # of skip frames between detections
 
-# 超参数
-# minimum probability to filter weak detections
-minimum_confidence = 0.80
 
 # 物体识别模型能识别的物体（21种）
 CLASSES = ["background", "aeroplane", "bicycle", "bird", "boat",
@@ -27,6 +27,7 @@ CLASSES = ["background", "aeroplane", "bicycle", "bird", "boat",
            "person", "pottedplant", "sheep", "sofa", "train",
            "tvmonitor"]
 
+
 # 加载物体识别模型
 # net = cv2.dnn.readNetFromCaffe(prototxt_file_path, model_file_path)
 
@@ -38,8 +39,7 @@ class Intrusion_Detection():
         # the first frame from the video)
         self.W = None
         self.H = None
-
-
+        self.pre = datetime.now()
         # instantiate our centroid tracker, then initialize a list to store
         # each of our dlib correlation trackers, followed by a dictionary to
         # map each unique object ID to a TrackableObject
@@ -56,9 +56,8 @@ class Intrusion_Detection():
         # start the frames per second throughput estimator
         self.fps = FPS().start()
 
-
     # loop over frames from the video stream
-    def process(self,frame):
+    def process(self, frame):
         # grab the next frame and handle if we are reading from either
         # VideoCapture or VideoStream
 
@@ -70,23 +69,22 @@ class Intrusion_Detection():
 
         # initialize the current status along with our list of bounding
         # box rectangles returned by either (1) our object detector or
-        # (2) the correlation trackers
+        # (14) the correlation trackers
         status = "Waiting"
         rects = []
 
         # check to see if we should run a more computationally expensive
         # object detection method to aid our tracker
-        if self.totalFrames % skip_frames == 0:
+        if self.totalFrames % 20 == 0:
             # set the status and initialize our new set of object trackers
             status = "Detecting"
-            trackers = []
+            self.trackers = []
 
             # convert the frame to a blob and pass the blob through the
             # network and obtain the detections
             blob = cv2.dnn.blobFromImage(frame, 0.007843, (self.W, self.H), 127.5)
             self.net.setInput(blob)
             detections = self.net.forward()
-
             # loop over the detections
             for i in np.arange(0, detections.shape[2]):
                 # extract the confidence (i.e., probability) associated
@@ -95,7 +93,8 @@ class Intrusion_Detection():
 
                 # filter out weak detections by requiring a minimum
                 # confidence
-                if confidence > minimum_confidence:
+                if confidence > 0.5:
+
                     # extract the index of the class label from the
                     # detections list
                     idx = int(detections[0, 0, i, 1])
@@ -118,8 +117,7 @@ class Intrusion_Detection():
 
                     # add the tracker to our list of trackers so we can
                     # utilize it during skip frames
-                    trackers.append(tracker)
-
+                    self.trackers.append(tracker)
         # otherwise, we should utilize our object *trackers* rather than
         # object *detectors* to obtain a higher frame processing throughput
         else:
@@ -149,10 +147,10 @@ class Intrusion_Detection():
         # draw a horizontal line in the center of the frame -- once an
         # object crosses this line we will determine whether they were
         # moving 'up' or 'down'
-        # cv2.line(frame, (0, H // 2), (W, H // 2), (0, 255, 255), 2)
+        # cv2.line(frame, (0, H // 14), (W, H // 14), (0, 255, 255), 14)
 
         # use the centroid tracker to associate the (1) old object
-        # centroids with (2) the newly computed object centroids
+        # centroids with (14) the newly computed object centroids
         objects = self.ct.update(rects)
 
         # loop over the tracked objects
@@ -199,6 +197,11 @@ class Intrusion_Detection():
                         print('[EVENT] %s, 院子, 有人闯入禁止区域!!!'
                               % (current_time))
                         cv2.imwrite('intrusion.jpg', frame)
+                        if (datetime.now() - self.pre).total_seconds() > 5:
+                            t = threading.Thread(target=post(event=4, imagePath='intrusion.jpg'))
+                            t.setDaemon(False)
+                            t.start()
+                            self.pre = datetime.now()
 
                         # todo insert into database
                         # command = '%s inserting.py --event_desc %s--event_type4 - -event_location % s' % \
@@ -236,3 +239,4 @@ class Intrusion_Detection():
         # increment the total number of frames processed thus far and
         # then update the FPS counter
         self.totalFrames += 1
+        return frame
diff --git a/Class/detection/Camera_On_Desk.py b/Class/detection/Camera_On_Desk.py
index ee7bb29..fb20c16 100644
--- a/Class/detection/Camera_On_Desk.py
+++ b/Class/detection/Camera_On_Desk.py
@@ -1,5 +1,6 @@
 # 摄像头实时人脸识别
 import threading
+from datetime import datetime
 
 import dlib
 import numpy as np
@@ -7,34 +8,15 @@ import cv2
 import pandas as pd
 import os
 import time
+
+import requests
+
 import facenet
 from PIL import Image, ImageDraw, ImageFont
-
-from Calibration import Calibration
-
-from Post import post
-from model import create_model
-
-import smile_detection
+from Post import post, post_person
 
 start_time = 0
-
-
-# 1. Dlib 正向人脸检测器
-# detector = dlib.get_frontal_face_detector()
-
-# OpenCV DNN face detector
-# detector = cv2.dnn.readNetFromCaffe("data/data_opencv/deploy.prototxt.txt",
-#                                     "data/data_opencv/res10_300x300_ssd_iter_140000.caffemodel")
-
-# 2. Dlib 人脸 landmark 特征点检测器
-# predictor = dlib.shape_predictor('data/data_dlib/shape_predictor_68_face_landmarks.dat')
-
-# 3. Dlib Resnet 人脸识别模型，提取 128D 的特征矢量
-# face_reco_model = dlib.face_recognition_model_v1("data/data_dlib/dlib_face_recognition_resnet_model_v1.dat")
-
-# nn4_small2 = create_model()
-# nn4_small2.load_weights('weights/nn4.small2.v1.h5')
+api_transfer = {'elder': 'old', 'employee': 'employee', 'volunteer': 'volunteer'}
 
 
 class Interaction_Detection:
@@ -42,6 +24,7 @@ class Interaction_Detection:
         # 模型
         self.detector = detector
         self.nn4_small2 = nn4_small2
+        self.pre = datetime.now()
 
         # 用来存放所有录入人脸特征的数组
         self.features_known_list = []
@@ -75,24 +58,34 @@ class Interaction_Detection:
         else:
             if os.path.exists("data/data_faces_from_camera/"):
                 self.metadata = facenet.load_metadata("data/data_faces_from_camera/")
-                self.name_known_cnt = self.metadata.shape[0]
-                self.embedded = np.zeros((self.metadata.shape[0], 128))
+                self.name_known_cnt = 0
+                for i in range(0, len(self.metadata)):
+                    for j in range(0, len(self.metadata[i])):
+                        self.name_known_cnt += 1
+                self.embedded = np.zeros((self.name_known_cnt * 8, 128))
 
                 for i, m in enumerate(self.metadata):
                     for j, n in enumerate(m):
                         for k, p in enumerate(n):
-                            img = facenet.load_image(p.image_path())
+                            img = facenet.load_image(p.image_path().replace("\\", "/"))
                             # img = align_image(img)
                             img = cv2.resize(img, (96, 96))
                             # scale RGB values to interval [0,1]
                             img = (img / 255.).astype(np.float32)
                             # obtain embedding vector for image
                             self.embedded[i] = self.nn4_small2.predict(np.expand_dims(img, axis=0))[0]
-                        # self.embedded[i] = self.embedded[i] / len(m)
-                        path = p.image_path().replace("\\", "/")
+                            # self.embedded[i] = self.embedded[i] / len(m)
+                            path = p.image_path().replace("\\", "/")
                         self.name_known_list.append(path.split('/')[-2])
                         self.type_known_list.append(path.split('/')[-3])
-                        self.loaded = True
+                for i in range(len(self.name_known_list)):
+                    if self.type_known_list[i] == 'elder':
+                        type = 'old'
+                    elif self.type_known_list[i] == 'volunteer':
+                        type = 'employee'
+                    self.name_known_list[i] = requests.get("http://zhuooyu.cn:8000/api/person/" + str(type) + "/" + str(
+                        self.name_known_list[i]) + "/").text
+                self.loaded = True
                 return 1
             else:
                 print('##### Warning #####', '\n')
@@ -114,7 +107,7 @@ class Interaction_Detection:
         font = cv2.FONT_ITALIC
 
         # cv2.putText(img_rd, "Face Recognizer", (20, 40), font, 1, (255, 255, 255), 1, cv2.LINE_AA)
-        # cv2.putText(img_rd, "FPS:   " + str(self.fps.__round__(2)), (20, 100), font, 0.8, (0, 255, 0), 1, cv2.LINE_AA)
+        # cv2.putText(img_rd, "FPS:   " + str(self.fps.__round__(14)), (20, 100), font, 0.8, (0, 255, 0), 1, cv2.LINE_AA)
         cv2.putText(img_rd, "Faces: " + str(self.faces_cnt), (20, 40), font, 0.8, (0, 255, 0), 1, cv2.LINE_AA)
         # cv2.putText(img_rd, "Q: Quit", (20, 450), font, 0.8, (255, 255, 255), 1, cv2.LINE_AA)
 
@@ -134,16 +127,22 @@ class Interaction_Detection:
     # 修改显示人名
     def modify_name_camera_list(self):
         # TODO 数据库 ID
-        # Default known name: 1, 2, person_3
+        # Default known name: 1, 14, person_3
         self.name_known_list[0] = '1'.encode('utf-8').decode()
         self.name_known_list[1] = 'Tony Blair'.encode('utf-8').decode()
-        # self.name_known_list[2] = '唐保生'.encode('utf-8').decode()
+        # self.name_known_list[14] = '唐保生'.encode('utf-8').decode()
         # self.name_known_list[3] = '1'.encode('utf-8').decode()
         # self.name_known_list[4] ='xx'.encode('utf-8').decode()
 
-    # 进行人脸识别和微笑检测
+    # 进行人脸识别
     def process(self, img_rd, scale):
         img_with_name = img_rd
+        data_type_three = {
+            'old': 0,
+            'employee': 0,
+            'volunteer': 0,
+            'stranger': 0
+        }
         # 读取所有人脸
         if self.get_face_database():
             self.draw_note(img_rd)
@@ -176,6 +175,7 @@ class Interaction_Detection:
                     # 确定人名的位置坐标
                     # 先默认所有人不认识，是 unknown
                     self.name_camera_list.append("unknown")
+                    self.type_camera_list.append("unknown")
 
                     # 每个捕获人脸的名字坐标
                     box = faces[0, 0, k, 3:7] * np.array([w, h, w, h])
@@ -184,6 +184,7 @@ class Interaction_Detection:
                         [int(startX + 5), int(startY - 30)]))
 
                     img_blank = img_rd[startY:endY, startX:endX]
+                    img_blank = img_blank[..., ::-1]
                     try:
                         # for ii in range(height):
                         #     for jj in range(width):
@@ -204,6 +205,7 @@ class Interaction_Detection:
                         if min(e_distance_list) < 0.58:
                             self.name_camera_list[k] = self.name_known_list[similar_person_num % 8]
                             self.type_camera_list[k] = self.type_known_list[similar_person_num % 8]
+                            data_type_three[api_transfer[self.type_camera_list[k]]] += 1
 
                         # 绘制矩形框
                         if self.name_camera_list[k] != 'unknown':
@@ -216,14 +218,19 @@ class Interaction_Detection:
                         continue
 
                     img_with_name = self.draw_name(img_rd)
-                    if 'volunteer' in self.type_camera_list:
+                    if 'volunteer' in self.type_camera_list and len(self.type_camera_list) > 1:
                         index = self.type_camera_list.index('volunteer')
-                        pos_vol = self.pos_camera_list[index]
+                        pos_vol = np.array(self.pos_camera_list[index])
                         for i in range(0, len(self.type_camera_list)):
-                            if i != index:
-                                d = scale * np.sqrt(facenet.distance(pos_vol, self.type_camera_list[i]))
+                            if self.type_camera_list[i] == "elder":
+                                d = scale * np.sqrt(facenet.distance(pos_vol, np.array(self.pos_camera_list[i])))
                                 if d < 50:
-                                    pass
+                                    if (datetime.now() - self.pre).total_seconds() > 5:
+                                        cv2.imwrite("interaction.jpg", img_with_name)
+                                        t = threading.Thread(target=post(event=1, imagePath='interaction.jpg'))
+                                        t.setDaemon(False)
+                                        t.start()
+                                        self.pre = datetime.now()
             else:
                 img_with_name = img_rd
 
@@ -232,7 +239,9 @@ class Interaction_Detection:
             # 更新 FPS / Update stream FPS
             self.update_fps()
             # 距离检测
-
+        if (datetime.now() - self.pre).total_seconds() > 5:
+            post_person(data_type_three)
+            self.pre = datetime.now()
         return img_with_name
 
     # OpenCV 调用摄像头并进行 process
diff --git a/Class/detection/Live_Tool.py b/Class/detection/Live_Tool.py
index 5fc39f1..28bacc2 100644
--- a/Class/detection/Live_Tool.py
+++ b/Class/detection/Live_Tool.py
@@ -1,6 +1,8 @@
 import os
 import queue
 import threading
+import time
+from numba import cuda
 import cv2
 import sys
 import platform
@@ -16,27 +18,6 @@ from Camera_In_Hall import Fall_Detection
 from Camera_On_Desk import Interaction_Detection
 from Camera_In_Yard import Intrusion_Detection
 
-# Import Openpose (Windows/Ubuntu/OSX)
-# dir_path = os.path.dirname(os.path.realpath(__file__))
-dir_path = 'D:\\BJTU\\Python\\openpose-master\\build-2017'
-try:
-    # Windows Import
-    if platform == "win32":
-        # Change these variables to point to the correct folder (Release/x64 etc.)
-        sys.path.append(dir_path + '\\python\\openpose\\Release')
-        os.environ['PATH'] = os.environ['PATH'] + ';' + dir_path + '\\x64\\Release;' + dir_path + '\\bin;'
-        import pyopenpose as op
-    else:
-        # Change these variables to point to the correct folder (Release/x64 etc.)
-        sys.path.append(dir_path + '\\python\\openpose\\Release')
-        # If you run `make install` (default path is `/usr/local/python` for Ubuntu), you can also access the OpenPose/python module from there. This will install OpenPose and the python library at your desired installation path. Ensure that this is in your python path in order to use it.
-        # sys.path.append('/usr/local/python')
-        from openpose import pyopenpose as op
-except ImportError as e:
-    print(
-        'Error: OpenPose library could not be found. Did you enable `BUILD_PYTHON` in CMake and have this Python script in the right folder?')
-    raise e
-
 
 class Live(object):
     def __init__(self):
@@ -71,7 +52,8 @@ class Live(object):
 
         # 摔倒检测
         self.transfer_flag = False
-        self.Fall_Detection_on = Fall_Detection()
+        self.trigger = False
+        self.Fall_Detection_on = None
 
         # 入侵检测
         self.net = cv2.dnn.readNetFromCaffe('data/data_opencv/MobileNetSSD_deploy.prototxt',
@@ -83,7 +65,9 @@ class Live(object):
         self.out = cv2.VideoWriter('output.avi', self.fourcc, 20, (640, 480))
 
         # Get video information
-        self.fps = 20  # 设置帧速率
+        self.fps = 15  # 设置帧速率
+        width = 640  # 宽
+        height = 480  # 高
 
         # ffmpeg command
         self.command = ['ffmpeg',
@@ -91,7 +75,7 @@ class Live(object):
                         '-f', 'rawvideo',
                         '-vcodec', 'rawvideo',
                         '-pix_fmt', 'bgr24',
-                        '-s', "{}x{}".format(480, 640),
+                        '-s', "{}x{}".format(width, height),
                         '-r', str(self.fps),
                         '-i', '-',
                         '-c:v', 'libx264',
@@ -99,19 +83,24 @@ class Live(object):
                         '-preset', 'slow',
                         '-f', 'flv',
                         self.rtmpUrl]
-        self.recieve = None
-        self.pretodo = ''
+        self.recieve = {"todo": "reboot"}
+        # self.recieve = {"todo": "change", 'data': {'fuc': '1'}}
 
         self.transfer_flag = False
+        self.take = False
+
+        # if trigger:
+        #     cuda.select_device(0)  # 选择GPU设备
+        #     cuda.close()  # 释放GPU资源
 
     def read_frame(self):
         # 根据不同的操作系统，设定读取哪个摄像头
-        if platform.system() == 'Linux':  # 如果是Linux系统
+        if platform == 'Linux':  # 如果是Linux系统
             cap = cv2.VideoCapture(10)  # 绑定编号为10的摄像头
             cap.set(cv2.CAP_PROP_FOURCC, cv2.VideoWriter_fourcc('M', 'J', 'P', 'G'))
             cap.set(3, 640)  # 设置摄像头画面的宽
             cap.set(4, 480)  # 设置摄像头画面的高
-        elif platform.system() == 'Darwin':  # 如果是苹果的OS X系统
+        elif platform == 'Darwin':  # 如果是苹果的OS X系统
             cap = cv2.VideoCapture(0)  # 绑定编号为0的摄像头
             cap.set(cv2.CAP_PROP_FOURCC, cv2.VideoWriter_fourcc('M', 'J', 'P', 'G'))
             cap.set(3, 640)
@@ -119,8 +108,8 @@ class Live(object):
         else:  # windows系统
             cap = cv2.VideoCapture(0)  # 绑定编号为0的摄像头
             cap.set(cv2.CAP_PROP_FOURCC, cv2.VideoWriter_fourcc('M', 'J', 'P', 'G'))
-            cap.set(3, 640)
-            cap.set(4, 480)
+            # cap.set(3, 640)
+            # cap.set(4, 480)
 
         # read webcamera
         counter = 0
@@ -134,13 +123,60 @@ class Live(object):
                 self.cap = cv2.VideoCapture(0)
                 ret, frame = self.cap.read()
                 self.transfer_flag = False
-            recieve = self.ws.recv()
-            self.recieve = eval(recieve)
             # put frame into queue
             now = datetime.now()
-            if (now - pre).total_seconds() > 0.15:
-                self.frame_queue.put(frame)
-                pre = now
+            self.frame_queue.put(frame)
+            # if self.recieve["todo"] == "change":
+            #     if self.recieve["data"]["fuc"] == "1":
+            #         # Get video information
+            #         self.fps = 5  # 设置帧速率
+            #         width = 640  # 宽
+            #         height = 480  # 高
+            #
+            #         # ffmpeg command
+            #         self.command = ['ffmpeg',
+            #                         '-y',
+            #                         '-f', 'rawvideo',
+            #                         '-vcodec', 'rawvideo',
+            #                         '-pix_fmt', 'bgr24',
+            #                         '-s', "{}x{}".format(width, height),
+            #                         '-r', str(self.fps),
+            #                         '-i', '-',
+            #                         '-c:v', 'libx264',
+            #                         '-pix_fmt', 'yuv420p',
+            #                         '-preset', 'slow',
+            #                         '-f', 'flv',
+            #                         self.rtmpUrl]
+            #
+            #         if (now - pre).total_seconds() > 0.14:
+            #             self.frame_queue.put(frame)
+            #             pre = now
+            #     else:
+            #         # Get video information
+            #         self.fps = 20  # 设置帧速率
+            #         width = 640  # 宽
+            #         height = 480  # 高
+            #
+            #         # ffmpeg command
+            #         self.command = ['ffmpeg',
+            #                         '-y',
+            #                         '-f', 'rawvideo',
+            #                         '-vcodec', 'rawvideo',
+            #                         '-pix_fmt', 'bgr24',
+            #                         '-s', "{}x{}".format(width, height),
+            #                         '-r', str(self.fps),
+            #                         '-i', '-',
+            #                         '-c:v', 'libx264',
+            #                         '-pix_fmt', 'yuv420p',
+            #                         '-preset', 'slow',
+            #                         '-f', 'flv',
+            #                         self.rtmpUrl]
+            #
+            #         self.frame_queue.put(frame)
+            #         pre = now
+            # else:
+            #     self.frame_queue.put(frame)
+            #     pre = now
 
     def push_frame(self):
         # 防止多线程时 command 未被设置
@@ -152,47 +188,71 @@ class Live(object):
 
         while True:
             if not self.frame_queue.empty():
+                if self.frame_queue.qsize() > 100:
+                    self.frame_queue.queue.clear()
+                    continue
                 frame = self.frame_queue.get()
-                if self.recieve.todo == 'reboot':
+                if self.recieve["todo"] == 'reboot':
                     pass
-                elif self.recieve.todo == 'entering':
-                    data = self.recieve.data
-                    people_type = data.type  # 0代表老人,1代表员工,2代表义工
-                    id = data.id
+                elif self.recieve["todo"] == 'entering':
+                    people_type = self.recieve["data"]["type"]  # 0代表老人,1代表员工,2代表义工
+                    id = self.recieve["data"]["id"]
                     self.pretodo = 'entering'
-                    self.Face_Register_on = Face_Register(people_type, id)
-                elif self.recieve.todo == 'takePhoto':
-                    if self.pretodo == 'entering':
-                        frame = self.Face_Register_on.take_photo(frame)
-                    elif self.pretodo == '2':
-                        self.scale = self.Calibration_on.run(frame)
-                elif self.recieve.todo == 'change':
-                    fuc = self.recieve.data.fuc  # 更改的功能 0:无 1微笑检测 2交互检测 3摔倒检测 4禁区入侵
-                    if fuc == 0:
-                        self.out.write(frame)
-                    elif fuc == 1:
-                        frame = self.Face_Recognizer_on.run(frame)
-                    elif fuc == 2:
-                        self.pretodo = '2'
-                        if self.scale == -1:
-                            frame = self.Calibration_on.run(frame)
+                    if not self.take:
+                        self.Face_Register_on = Face_Register(people_type, id)
+                        self.take = True
+                    frame = self.Face_Register_on.process(frame)
+                elif self.recieve["todo"] == 'takePhoto':
+                    if self.recieve["data"]["fuc"] == 'shutter':
+                        if self.take:
+                            frame = self.Face_Register_on.take_photo(frame)
+                            self.take = False
                         else:
-                            self.Interaction_on.process(frame)
-                    elif fuc == 3:
+                            frame = self.Face_Register_on.process(frame)
+                    elif self.recieve["data"]["fuc"] == 'standard':
+                        self.scale = self.Calibration_on.run(frame)
+                        self.recieve = {"todo": "change", 'data': {'fuc': '14'}}
+                elif self.recieve["todo"] == 'change':
+                    fuc = self.recieve["data"]["fuc"]  # 更改的功能 0:无 1微笑检测 2交互检测 3摔倒检测 4禁区入侵
+                    if fuc == '0':
+                        self.out.write(frame)
+                    elif fuc == '1':
+                        frame = self.Face_Recognizer_on.process(frame)
+                    elif fuc == '14':
+                        if self.scale == -1:
+                            pass
+                        else:
+                            frame = self.Interaction_on.process(frame, self.scale)
+                    elif fuc == '3' and self.trigger:
                         if not self.transfer_flag:
                             self.cap = cv2.VideoCapture("test4.mp4")
+                            ret, frame = self.cap.read()
                             self.transfer_flag = True
-                        self.Fall_Detection_on.re_init()
+                            self.Fall_Detection_on = Fall_Detection()
+                            # self.Fall_Detection_on.re_init()
                         frame = self.Fall_Detection_on.run(frame)
-                    elif fuc == 4:
+                    elif fuc == '4':
                         frame = self.Intrusion_Detection_on.process(frame)
-                # frame = Fall_Detection_on.run(frame=frame)
                 p.stdin.write(frame.tobytes())
 
+    def get_result(self):
+        self.ws.send("Hello, World")
+        while True:
+            recieve = self.ws.recv()
+            print(eval(recieve))
+            self.recieve = eval(recieve)
+            if self.recieve['todo'] == 'takePhoto' and self.recieve['data']['fuc'] == 'shutter':
+                self.take = True
+
+    def release_gpu(self):
+        cuda.select_device(0)  # 选择GPU设备
+        cuda.close()  # 释放GPU资源
+
     def run(self):
         threads = [
             threading.Thread(target=Live.read_frame, args=(self,)),
-            threading.Thread(target=Live.push_frame, args=(self,))
+            threading.Thread(target=Live.push_frame, args=(self,)),
+            threading.Thread(target=Live.get_result, args=(self,))
         ]
         [thread.setDaemon(False) for thread in threads]
         [thread.start() for thread in threads]
@@ -200,4 +260,4 @@ class Live(object):
 
 if __name__ == "__main__":
     live = Live()
-    live.run()
+    live.run()
\ No newline at end of file
diff --git a/Class/detection/Post.py b/Class/detection/Post.py
index d395c56..a64997c 100644
--- a/Class/detection/Post.py
+++ b/Class/detection/Post.py
@@ -3,6 +3,8 @@ import datetime
 
 url = "http://192.144.229.49:8000/api/event/list"
 
+api = "http://zhuooyu.cn:8000/api/websocket/total"
+
 data_type_one = {
     "oldperson_id": 1,
     "event_type": 3,  # 0代表情感检测，1代表义工交互检测，2代表陌生人检测，3代表摔倒检测，4代表禁止区域入侵检测
@@ -19,6 +21,13 @@ data_type_two = {
     "event_desc": ""  # 必填,事件描述
 }
 
+data_type_three = {
+    'old': 0,
+    'employee': 0,
+    'volunteer': 0,
+    'stranger': 0
+}
+
 
 def post(elder_id='None', event=-1, imagePath='None', volunteer='None'):
     flag = 0
@@ -61,4 +70,7 @@ def post(elder_id='None', event=-1, imagePath='None', volunteer='None'):
         status = requests.post(url, files=imageFile, data=data_type_one)
     else:
         status = requests.post(url, files=imageFile, data=data_type_two)
-    print(status)
+
+
+def post_person(data_type_three):
+    status = requests.post(api, data=data_type_three)
diff --git a/Class/detection/algorithm_fall.py b/Class/detection/algorithm_fall.py
index b7b3cc7..ac864f8 100644
--- a/Class/detection/algorithm_fall.py
+++ b/Class/detection/algorithm_fall.py
@@ -1,16 +1,15 @@
-from imutils.video import VideoStream
-import imutils
-import time
+import datetime
+import threading
 import cv2
 import numpy as np
 import statistics
-import queue
 import math
+from Post import post
 from PIL import ImageDraw, ImageFont
 from PIL import Image
 
 
-def fall_detect(cnts, defined_min_area, frame, prevX, prevY, xList, yList, centerV, alert):
+def fall_detect(cnts, defined_min_area, frame, prevX, prevY, xList, yList, centerV, alert, pre):
     for c in cnts:
         # exclusion
         if cv2.contourArea(c) < defined_min_area:
@@ -85,16 +84,16 @@ def fall_detect(cnts, defined_min_area, frame, prevX, prevY, xList, yList, cente
         # print("P_FALL2: ", P_FALL)
 
         # status display
-        # cv2.putText(frame, "Status : ", (10, 20), cv2.FONT_HERSHEY_SIMPLEX, 0.6, (0, 0, 255), 2)
+        # cv2.putText(frame, "Status : ", (10, 20), cv2.FONT_HERSHEY_SIMPLEX, 0.6, (0, 0, 255), 14)
         # cv2.putText(frame, "Fall Confidence: {0:.2f} ".format(P_FALL), (10, 50), cv2.FONT_HERSHEY_SIMPLEX, 0.5,
-        #             (0, 128, 255), 2)
-        # cv2.putText(frame, "Angle: {0:.2f}".format(angle), (10, 220),cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 0), 2)
-        # cv2.putText(frame, "AR: {0:.2f}".format(AR), (10, 237),cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 0), 2)
-        # cv2.putText(frame, "Center Speed: {0:.2f}".format(centerV), (10, 256),cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 0), 2)
+        #             (0, 128, 255), 14)
+        # cv2.putText(frame, "Angle: {0:.2f}".format(angle), (10, 220),cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 0), 14)
+        # cv2.putText(frame, "AR: {0:.2f}".format(AR), (10, 237),cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 0), 14)
+        # cv2.putText(frame, "Center Speed: {0:.2f}".format(centerV), (10, 256),cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 0), 14)
 
         # fall
         if P_FALL > 0.88:
-            if alert >3:
+            if alert > 3:
                 # print("fall")
                 font = ImageFont.truetype("simsun.ttc", 30, index=1)
                 img_rd = Image.fromarray(cv2.cvtColor(frame, cv2.COLOR_BGR2RGB))
@@ -102,10 +101,16 @@ def fall_detect(cnts, defined_min_area, frame, prevX, prevY, xList, yList, cente
                 draw.text((10, 10), text="Fall Detected", font=font,
                           fill=(255, 0, 0))
                 frame = cv2.cvtColor(np.array(img_rd), cv2.COLOR_RGB2BGR)
+                cv2.imwrite('fall_detection.jpg', frame)
+                if (datetime.datetime.now() - pre).total_seconds() > 5:
+                    t = threading.Thread(target=post(event=3, imagePath='fall_detection.jpg'))
+                    t.setDaemon(False)
+                    t.start()
+                    pre = datetime.datetime.now()
                 # cv2.imwrite("report.jpg", frame)
                 # send_alert.SendMail("report.jpg")
                 alert = alert + 1
             else:
                 alert = alert + 1
 
-    return frame,alert
+    return frame, alert, pre
diff --git a/Class/detection/checkingfence.py b/Class/detection/checkingfence.py
index 34005b5..0980ccb 100644
--- a/Class/detection/checkingfence.py
+++ b/Class/detection/checkingfence.py
@@ -51,7 +51,7 @@ width = 400
 
 # 超参数
 # minimum probability to filter weak detections
-minimum_confidence = 0.80
+minimum_confidence = 0.50
 
 # 物体识别模型能识别的物体（21种）
 CLASSES = ["background", "aeroplane", "bicycle", "bird", "boat",
@@ -120,7 +120,7 @@ while True:
 
     # initialize the current status along with our list of bounding
     # box rectangles returned by either (1) our object detector or
-    # (2) the correlation trackers
+    # (14) the correlation trackers
     status = "Waiting"
     rects = []
 
@@ -199,10 +199,10 @@ while True:
     # draw a horizontal line in the center of the frame -- once an
     # object crosses this line we will determine whether they were
     # moving 'up' or 'down'
-    # cv2.line(frame, (0, H // 2), (W, H // 2), (0, 255, 255), 2)
+    # cv2.line(frame, (0, H // 14), (W, H // 14), (0, 255, 255), 14)
 
     # use the centroid tracker to associate the (1) old object
-    # centroids with (2) the newly computed object centroids
+    # centroids with (14) the newly computed object centroids
     objects = ct.update(rects)
 
     # loop over the tracked objects
diff --git a/Class/detection/facenet.py b/Class/detection/facenet.py
index 644bffd..969338a 100644
--- a/Class/detection/facenet.py
+++ b/Class/detection/facenet.py
@@ -57,7 +57,7 @@ def load_metadata(path):
 
 
 def load_image(path):
-    img = cv2.imread(path, 1)
+    img = cv2.imread(path)
     # OpenCV loads images with color channels
     # in BGR order. So we need to reverse them
     return img[..., ::-1]
diff --git a/Class/detection/facenet_utils.py b/Class/detection/facenet_utils.py
index 586ade1..068637f 100644
--- a/Class/detection/facenet_utils.py
+++ b/Class/detection/facenet_utils.py
@@ -55,8 +55,8 @@ def conv2d_bn(
   tensor = ZeroPadding2D(padding=padding)(tensor)
   if cv2_out == None:
     return tensor
-  tensor = Conv2D(cv2_out, cv2_filter, strides=cv2_strides, name=layer+'_conv'+'2')(tensor)
-  tensor = BatchNormalization(axis=3, epsilon=0.00001, name=layer+'_bn'+'2')(tensor)
+  tensor = Conv2D(cv2_out, cv2_filter, strides=cv2_strides, name=layer+'_conv'+'14')(tensor)
+  tensor = BatchNormalization(axis=3, epsilon=0.00001, name=layer+'_bn'+'14')(tensor)
   tensor = Activation('relu')(tensor)
   return tensor
 
diff --git a/Class/detection/fall_detection.py b/Class/detection/fall_detection.py
index 2c8caea..a526c03 100644
--- a/Class/detection/fall_detection.py
+++ b/Class/detection/fall_detection.py
@@ -22,6 +22,7 @@ import queue
 import frame_process
 import algorithm_fall
 
+pre = datetime.datetime.now()
 try:
     # Import Openpose (Windows/Ubuntu/OSX)
     # dir_path = os.path.dirname(os.path.realpath(__file__))
@@ -153,7 +154,9 @@ try:
                     # print(v, abs(a))
                     if (abs(a) > 0.2) and \
                             (np.subtract(np.array(width), np.array(height)) > np.subtract(np.array(width0),
-                             np.array(height0)) and np.subtract(np.array(width), np.array(height)) > 0):
+                                                                                          np.array(
+                                                                                              height0)) and np.subtract(
+                                np.array(width), np.array(height)) > 0):
                         couter += 1
                         # print(np.subtract(np.array(width), np.array(height)))
                         # print("alarm by v and a")
@@ -175,10 +178,13 @@ try:
                                   fill=(255, 0, 0))
                         img_rd = cv2.cvtColor(np.array(img_rd), cv2.COLOR_RGB2BGR)
                         cv2.imwrite('fall_detection.jpg', frame)
-                        # t = threading.Thread(target=post(event=3, imagePath='fall_detection.jpg'))
-                        # t.start()
-                        # status = post(event=3, imagePath='fall_detection.jpg')
-                        # print("fall")
+                        if (datetime.datetime.now() - pre).total_seconds() > 5:
+                            t = threading.Thread(target=post(event=3, imagePath='fall_detection.jpg'))
+                            t.start()
+                            status = post(event=3, imagePath='fall_detection.jpg')
+                            # print("fall")
+                            pre = datetime.datetime.now()
+                            # print(pre)
 
                     # update variables
                     frame_start_time = now
@@ -203,8 +209,9 @@ try:
                 cnts = frame_process.get_contours(firstFrame, gray)
 
                 defined_min_area = 3000
-                frame, alert = algorithm_fall.fall_detect(cnts, defined_min_area, frame, prevX, prevY, xList, yList,
-                                                          centerV, alert)
+                frame, alert, pre = algorithm_fall.fall_detect(cnts, defined_min_area, frame, prevX, prevY, xList,
+                                                               yList,
+                                                               centerV, alert, pre)
 
                 # cv2.putText(frame, datetime.datetime.now().strftime("%A %d %B %Y %I:%M:%S%p"),
                 #             (10, frame.shape[0] - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.35, (255, 255, 255), 1)
diff --git a/Class/detection/model.py b/Class/detection/model.py
index ad29956..575a946 100644
--- a/Class/detection/model.py
+++ b/Class/detection/model.py
@@ -1,7 +1,3 @@
-# -----------------------------------------------------------------------------------------
-# Code taken from https://github.com/iwantooxxoox/Keras-OpenFace (with minor modifications)
-# -----------------------------------------------------------------------------------------
-
 from keras.layers import Conv2D, ZeroPadding2D, Activation, Input, concatenate
 from keras.layers.core import Lambda, Flatten, Dense
 from keras.layers.normalization import BatchNormalization
diff --git a/Class/detection/smile_detection.py b/Class/detection/smile_detection.py
index ebe725f..eabd408 100644
--- a/Class/detection/smile_detection.py
+++ b/Class/detection/smile_detection.py
@@ -5,9 +5,6 @@ from keras.models import load_model
 import numpy as np
 
 from utils.datasets import get_labels
-from utils.inference import detect_faces
-from utils.inference import draw_text
-from utils.inference import draw_bounding_box
 from utils.inference import load_detection_model
 from utils.preprocessor import preprocess_input
 
diff --git a/Class/detection/video_emotion_color_demo.py b/Class/detection/video_emotion_color_demo.py
index aa115a6..9abc6ab 100644
--- a/Class/detection/video_emotion_color_demo.py
+++ b/Class/detection/video_emotion_color_demo.py
@@ -32,7 +32,8 @@ emotion_window = []
 
 # starting video streaming
 cv2.namedWindow('window_frame')
-video_capture = cv2.VideoCapture(0)
+video_capture = cv2.VideoCapture("testdemo.mp4")
+counter = 0
 while True:
     bgr_image = video_capture.read()[1]
     gray_image = cv2.cvtColor(bgr_image, cv2.COLOR_BGR2GRAY)
@@ -54,7 +55,7 @@ while True:
         y1 = startY - y_off
         y2 = endY + y_off
 
-        face_coordinates=np.array([startX, startY, endX-startX, endY-startY])
+        face_coordinates = np.array([startX, startY, endX - startX, endY - startY])
 
         # x1, x2, y1, y2 = apply_offsets(face_coordinates, emotion_offsets)
         gray_face = gray_image[y1:y2, x1:x2]
@@ -80,13 +81,13 @@ while True:
             continue
 
         if emotion_text == 'angry':
-            color = emotion_probability * np.asarray((255, 0, 0))
+            color = emotion_probability * np.asarray((0, 255, 0))
         elif emotion_text == 'sad':
-            color = emotion_probability * np.asarray((0, 0, 255))
+            color = emotion_probability * np.asarray((0, 255, 0))
         elif emotion_text == 'happy':
-            color = emotion_probability * np.asarray((255, 255, 0))
+            color = emotion_probability * np.asarray((0, 255, 0))
         elif emotion_text == 'surprise':
-            color = emotion_probability * np.asarray((0, 255, 255))
+            color = emotion_probability * np.asarray((0, 255, 0))
         else:
             color = emotion_probability * np.asarray((0, 255, 0))
 
@@ -96,9 +97,12 @@ while True:
         draw_bounding_box(face_coordinates, rgb_image, color)
         draw_text(face_coordinates, rgb_image, emotion_mode,
                   color, 0, -45, 1, 1)
+        print(emotion_text)
 
     bgr_image = cv2.cvtColor(rgb_image, cv2.COLOR_RGB2BGR)
     cv2.imshow('window_frame', bgr_image)
+    cv2.imwrite('smile/mine/' + str(counter) + '.png', bgr_image)
+    counter += 1
     if cv2.waitKey(1) & 0xFF == ord('q'):
         break
 video_capture.release()
diff --git a/Data/weights/nn4.small2.v1.h5 b/Data/weights/nn4.small2.v1.h5
new file mode 100644
index 0000000..54d27d4
Binary files /dev/null and b/Data/weights/nn4.small2.v1.h5 differ