remove some codes

.idea/Dlib_face_recognition_from_camera.iml

@@ -4,7 +4,7 @@
     <content url="file://$MODULE_DIR$">
       <sourceFolder url="file://$MODULE_DIR$/data" isTestSource="false" />
     </content>
-    <orderEntry type="jdk" jdkName="Python 3.7" jdkType="Python SDK" />
+    <orderEntry type="jdk" jdkName="Python 3.7 (2)" jdkType="Python SDK" />
     <orderEntry type="sourceFolder" forTests="false" />
   </component>
   <component name="TestRunnerService">

.idea/misc.xml

@@ -1,4 +1,4 @@
 <?xml version="1.0" encoding="UTF-8"?>
 <project version="4">
-  <component name="ProjectRootManager" version="2" project-jdk-name="Python 3.7" project-jdk-type="Python SDK" />
+  <component name="ProjectRootManager" version="2" project-jdk-name="Python 3.7 (2)" project-jdk-type="Python SDK" />
 </project>

.idea/workspace.xml

@@ -2,11 +2,8 @@
 <project version="4">
   <component name="ChangeListManager">
     <list default="true" id="e58b655a-3a9b-4001-b4da-39e07ab46629" name="Default Changelist" comment="">
-      <change beforePath="$PROJECT_DIR$/.idea/Dlib_face_recognition_from_camera.iml" beforeDir="false" afterPath="$PROJECT_DIR$/.idea/Dlib_face_recognition_from_camera.iml" afterDir="false" />
-      <change beforePath="$PROJECT_DIR$/.idea/misc.xml" beforeDir="false" afterPath="$PROJECT_DIR$/.idea/misc.xml" afterDir="false" />
       <change beforePath="$PROJECT_DIR$/.idea/workspace.xml" beforeDir="false" afterPath="$PROJECT_DIR$/.idea/workspace.xml" afterDir="false" />
-      <change beforePath="$PROJECT_DIR$/face_reco_from_camera.py" beforeDir="false" afterPath="$PROJECT_DIR$/face_reco_from_camera.py" afterDir="false" />
-      <change beforePath="$PROJECT_DIR$/get_faces_from_camera.py" beforeDir="false" afterPath="$PROJECT_DIR$/get_faces_from_camera.py" afterDir="false" />
+      <change beforePath="$PROJECT_DIR$/features_extraction_to_mysql.py" beforeDir="false" afterPath="$PROJECT_DIR$/features_extraction_to_mysql.py" afterDir="false" />
     </list>
     <option name="EXCLUDED_CONVERTED_TO_IGNORED" value="true" />
     <option name="SHOW_DIALOG" value="false" />
@@ -33,7 +30,7 @@
   </component>
   <component name="PropertiesComponent">
     <property name="SHARE_PROJECT_CONFIGURATION_FILES" value="true" />
-    <property name="last_opened_file_path" value="$PROJECT_DIR$" />
+    <property name="last_opened_file_path" value="$PROJECT_DIR$/../Django_MySQL_Table" />
     <property name="settings.editor.selected.configurable" value="com.jetbrains.python.configuration.PyActiveSdkModuleConfigurable" />
   </component>
   <component name="RunDashboard">
@@ -48,8 +45,8 @@
       </list>
     </option>
   </component>
-  <component name="RunManager" selected="Python.face_descriptor_from_camera">
-    <configuration name="face_descriptor_compute" type="PythonConfigurationType" factoryName="Python" temporary="true" nameIsGenerated="true">
+  <component name="RunManager" selected="Python.features_extraction_to_mysql">
+    <configuration name="face_reco_from_camera_mysql" type="PythonConfigurationType" factoryName="Python" temporary="true">
       <module name="Dlib_face_recognition_from_camera" />
       <option name="INTERPRETER_OPTIONS" value="" />
       <option name="PARENT_ENVS" value="true" />
@@ -61,7 +58,7 @@
       <option name="IS_MODULE_SDK" value="true" />
       <option name="ADD_CONTENT_ROOTS" value="true" />
       <option name="ADD_SOURCE_ROOTS" value="true" />
-      <option name="SCRIPT_NAME" value="$PROJECT_DIR$/face_descriptor_compute.py" />
+      <option name="SCRIPT_NAME" value="$PROJECT_DIR$/face_reco_from_camera_mysql.py" />
       <option name="PARAMETERS" value="" />
       <option name="SHOW_COMMAND_LINE" value="false" />
       <option name="EMULATE_TERMINAL" value="false" />
@@ -70,7 +67,7 @@
       <option name="INPUT_FILE" value="" />
       <method v="2" />
     </configuration>
-    <configuration name="face_descriptor_from_camera" type="PythonConfigurationType" factoryName="Python" temporary="true" nameIsGenerated="true">
+    <configuration name="features_extraction_to_mysql" type="PythonConfigurationType" factoryName="Python" temporary="true">
       <module name="Dlib_face_recognition_from_camera" />
       <option name="INTERPRETER_OPTIONS" value="" />
       <option name="PARENT_ENVS" value="true" />
@@ -82,49 +79,7 @@
       <option name="IS_MODULE_SDK" value="true" />
       <option name="ADD_CONTENT_ROOTS" value="true" />
       <option name="ADD_SOURCE_ROOTS" value="true" />
-      <option name="SCRIPT_NAME" value="$PROJECT_DIR$/face_descriptor_from_camera.py" />
-      <option name="PARAMETERS" value="" />
-      <option name="SHOW_COMMAND_LINE" value="false" />
-      <option name="EMULATE_TERMINAL" value="false" />
-      <option name="MODULE_MODE" value="false" />
-      <option name="REDIRECT_INPUT" value="false" />
-      <option name="INPUT_FILE" value="" />
-      <method v="2" />
-    </configuration>
-    <configuration name="face_reco_from_camera" type="PythonConfigurationType" factoryName="Python" temporary="true" nameIsGenerated="true">
-      <module name="Dlib_face_recognition_from_camera" />
-      <option name="INTERPRETER_OPTIONS" value="" />
-      <option name="PARENT_ENVS" value="true" />
-      <envs>
-        <env name="PYTHONUNBUFFERED" value="1" />
-      </envs>
-      <option name="SDK_HOME" value="" />
-      <option name="WORKING_DIRECTORY" value="$PROJECT_DIR$" />
-      <option name="IS_MODULE_SDK" value="true" />
-      <option name="ADD_CONTENT_ROOTS" value="true" />
-      <option name="ADD_SOURCE_ROOTS" value="true" />
-      <option name="SCRIPT_NAME" value="$PROJECT_DIR$/face_reco_from_camera.py" />
-      <option name="PARAMETERS" value="" />
-      <option name="SHOW_COMMAND_LINE" value="false" />
-      <option name="EMULATE_TERMINAL" value="false" />
-      <option name="MODULE_MODE" value="false" />
-      <option name="REDIRECT_INPUT" value="false" />
-      <option name="INPUT_FILE" value="" />
-      <method v="2" />
-    </configuration>
-    <configuration name="features_extraction_to_csv" type="PythonConfigurationType" factoryName="Python" temporary="true">
-      <module name="Dlib_face_recognition_from_camera" />
-      <option name="INTERPRETER_OPTIONS" value="" />
-      <option name="PARENT_ENVS" value="true" />
-      <envs>
-        <env name="PYTHONUNBUFFERED" value="1" />
-      </envs>
-      <option name="SDK_HOME" value="" />
-      <option name="WORKING_DIRECTORY" value="$PROJECT_DIR$" />
-      <option name="IS_MODULE_SDK" value="true" />
-      <option name="ADD_CONTENT_ROOTS" value="true" />
-      <option name="ADD_SOURCE_ROOTS" value="true" />
-      <option name="SCRIPT_NAME" value="$PROJECT_DIR$/features_extraction_to_csv.py" />
+      <option name="SCRIPT_NAME" value="$PROJECT_DIR$/features_extraction_to_mysql.py" />
       <option name="PARAMETERS" value="" />
       <option name="SHOW_COMMAND_LINE" value="false" />
       <option name="EMULATE_TERMINAL" value="false" />
@@ -154,20 +109,62 @@
       <option name="INPUT_FILE" value="" />
       <method v="2" />
     </configuration>
+    <configuration name="mysql_insert" type="PythonConfigurationType" factoryName="Python" temporary="true">
+      <module name="Dlib_face_recognition_from_camera" />
+      <option name="INTERPRETER_OPTIONS" value="" />
+      <option name="PARENT_ENVS" value="true" />
+      <envs>
+        <env name="PYTHONUNBUFFERED" value="1" />
+      </envs>
+      <option name="SDK_HOME" value="" />
+      <option name="WORKING_DIRECTORY" value="$PROJECT_DIR$" />
+      <option name="IS_MODULE_SDK" value="true" />
+      <option name="ADD_CONTENT_ROOTS" value="true" />
+      <option name="ADD_SOURCE_ROOTS" value="true" />
+      <option name="SCRIPT_NAME" value="$PROJECT_DIR$/mysql_insert.py" />
+      <option name="PARAMETERS" value="" />
+      <option name="SHOW_COMMAND_LINE" value="false" />
+      <option name="EMULATE_TERMINAL" value="false" />
+      <option name="MODULE_MODE" value="false" />
+      <option name="REDIRECT_INPUT" value="false" />
+      <option name="INPUT_FILE" value="" />
+      <method v="2" />
+    </configuration>
+    <configuration name="read_mysql" type="PythonConfigurationType" factoryName="Python" temporary="true">
+      <module name="Dlib_face_recognition_from_camera" />
+      <option name="INTERPRETER_OPTIONS" value="" />
+      <option name="PARENT_ENVS" value="true" />
+      <envs>
+        <env name="PYTHONUNBUFFERED" value="1" />
+      </envs>
+      <option name="SDK_HOME" value="" />
+      <option name="WORKING_DIRECTORY" value="$PROJECT_DIR$" />
+      <option name="IS_MODULE_SDK" value="true" />
+      <option name="ADD_CONTENT_ROOTS" value="true" />
+      <option name="ADD_SOURCE_ROOTS" value="true" />
+      <option name="SCRIPT_NAME" value="$PROJECT_DIR$/read_mysql.py" />
+      <option name="PARAMETERS" value="" />
+      <option name="SHOW_COMMAND_LINE" value="false" />
+      <option name="EMULATE_TERMINAL" value="false" />
+      <option name="MODULE_MODE" value="false" />
+      <option name="REDIRECT_INPUT" value="false" />
+      <option name="INPUT_FILE" value="" />
+      <method v="2" />
+    </configuration>
     <list>
-      <item itemvalue="Python.face_descriptor_compute" />
-      <item itemvalue="Python.face_descriptor_from_camera" />
-      <item itemvalue="Python.face_reco_from_camera" />
-      <item itemvalue="Python.features_extraction_to_csv" />
+      <item itemvalue="Python.mysql_insert" />
+      <item itemvalue="Python.features_extraction_to_mysql" />
+      <item itemvalue="Python.face_reco_from_camera_mysql" />
+      <item itemvalue="Python.read_mysql" />
       <item itemvalue="Python.get_faces_from_camera" />
     </list>
     <recent_temporary>
       <list>
-        <item itemvalue="Python.face_descriptor_from_camera" />
-        <item itemvalue="Python.face_reco_from_camera" />
-        <item itemvalue="Python.features_extraction_to_csv" />
+        <item itemvalue="Python.features_extraction_to_mysql" />
+        <item itemvalue="Python.read_mysql" />
+        <item itemvalue="Python.face_reco_from_camera_mysql" />
         <item itemvalue="Python.get_faces_from_camera" />
-        <item itemvalue="Python.face_descriptor_compute" />
+        <item itemvalue="Python.mysql_insert" />
       </list>
     </recent_temporary>
   </component>

face_reco_from_camera_mysql.py

@@ -0,0 +1,221 @@
+# 摄像头实时人脸识别
+# Real-time face recognition
+
+# Author:   coneypo
+# Blog:     http://www.cnblogs.com/AdaminXie
+# GitHub:   https://github.com/coneypo/Dlib_face_recognition_from_camera
+
+# Created at 2018-05-11
+# Updated at 2020-05-29
+
+import dlib         # 人脸处理的库 Dlib
+import numpy as np  # 数据处理的库 Numpy
+import cv2          # 图像处理的库 OpenCV
+import pandas as pd # 数据处理的库 Pandas
+import os
+import time
+from PIL import Image, ImageDraw, ImageFont
+import pymysql
+
+db = pymysql.connect("localhost", "root", "intel@123", "dlib_database")
+cursor = db.cursor()
+
+# 1. Dlib 正向人脸检测器
+detector = dlib.get_frontal_face_detector()
+
+# 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")
+
+
+class Face_Recognizer:
+    def __init__(self):
+        # 用来存放所有录入人脸特征的数组 / Save the features of faces in the database
+        self.features_known_list = []
+
+        # 存储录入人脸名字 / Save the name of faces known
+        self.name_known_cnt = 0
+        self.name_known_list = []
+
+        # 存储当前摄像头中捕获到的所有人脸的坐标名字 / Save the positions and names of current faces captured
+        self.pos_camera_list = []
+        self.name_camera_list = []
+        # 存储当前摄像头中捕获到的人脸数
+        self.faces_cnt = 0
+        # 存储当前摄像头中捕获到的人脸特征
+        self.features_camera_list = []
+
+        # Update FPS
+        self.fps = 0
+        self.frame_start_time = 0
+
+    # 从 "features_all.csv" 读取录入人脸特征
+    def get_face_database(self):
+        # 1. get database face numbers
+        cmd_rd = "select count(*) from dlib_face_table;"
+        cursor.execute(cmd_rd)
+        results = cursor.fetchall()
+        person_cnt = int(results[0][0])
+
+        # 2. get features for person X
+        for person in range(person_cnt):
+            # lookup for personX
+            cmd_lookup = "select * from dlib_face_table where person_x=\"person_" + str(person + 1) + "\";"
+            cursor.execute(cmd_lookup)
+            results = cursor.fetchall()
+            results = list(results[0][1:])
+            self.features_known_list.append(results)
+            self.name_known_list.append("Person_" + str(person + 1))
+            print(results)
+        self.name_known_cnt = len(self.name_known_list)
+        print("Faces in Database：", len(self.features_known_list))
+        return 1
+
+    # 计算两个128D向量间的欧式距离 / Compute the e-distance between two 128D features
+    @staticmethod
+    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(np.square(feature_1 - feature_2)))
+        return dist
+
+    # 更新 FPS / Update FPS of Video stream
+    def update_fps(self):
+        now = time.time()
+        self.frame_time = now - self.frame_start_time
+        self.fps = 1.0 / self.frame_time
+        self.frame_start_time = now
+
+    def draw_note(self, img_rd):
+        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, "Faces: " + str(self.faces_cnt), (20, 140), 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)
+
+    def draw_name(self, img_rd):
+        # 在人脸框下面写人脸名字 / Write names under rectangle
+        font = ImageFont.truetype("simsun.ttc", 30)
+        img = Image.fromarray(cv2.cvtColor(img_rd, cv2.COLOR_BGR2RGB))
+        draw = ImageDraw.Draw(img)
+        for i in range(self.faces_cnt):
+            # cv2.putText(img_rd, self.name_camera_list[i], self.pos_camera_list[i], font, 0.8, (0, 255, 255), 1, cv2.LINE_AA)
+            draw.text(xy=self.pos_camera_list[i], text=self.name_camera_list[i], font=font)
+            img_with_name = cv2.cvtColor(np.array(img), cv2.COLOR_RGB2BGR)
+        return img_with_name
+
+    # 修改显示人名
+    def modify_name_camera_list(self):
+        # Default known name: person_1, person_2, person_3
+        self.name_known_list[0] ='张三'.encode('utf-8').decode()
+        self.name_known_list[1] ='李四'.encode('utf-8').decode()
+        # self.name_known_list[2] ='xx'.encode('utf-8').decode()
+        # self.name_known_list[3] ='xx'.encode('utf-8').decode()
+        # self.name_known_list[4] ='xx'.encode('utf-8').decode()
+
+    # 处理获取的视频流，进行人脸识别 / Input video stream and face reco process
+    def process(self, stream):
+        # 1. 读取存放所有人脸特征的 csv
+        if self.get_face_database():
+            while stream.isOpened():
+                flag, img_rd = stream.read()
+                faces = detector(img_rd, 0)
+                kk = cv2.waitKey(1)
+                # 按下 q 键退出 / Press 'q' to quit
+                if kk == ord('q'):
+                    break
+                else:
+                    self.draw_note(img_rd)
+                    self.features_camera_list = []
+                    self.faces_cnt = 0
+                    self.pos_camera_list = []
+                    self.name_camera_list = []
+
+                    # 2. 检测到人脸 / when face detected
+                    if len(faces) != 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
+                        # 3. Get the features captured and save into self.features_camera_list
+                        for i in range(len(faces)):
+                            shape = predictor(img_rd, faces[i])
+                            self.features_camera_list.append(face_reco_model.compute_face_descriptor(img_rd, shape))
+
+                        # 4. 遍历捕获到的图像中所有的人脸 / Traversal all the faces in the database
+                        for k in range(len(faces)):
+                            print("##### camera person", k + 1, "#####")
+                            # 让人名跟随在矩形框的下方
+                            # 确定人名的位置坐标
+                            # 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")
+
+                            # 每个捕获人脸的名字坐标 / Positions of faces captured
+                            self.pos_camera_list.append(tuple(
+                                [faces[k].left(), int(faces[k].bottom() + (faces[k].bottom() - faces[k].top()) / 4)]))
+
+                            # 5. 对于某张人脸，遍历所有存储的人脸特征
+                            # For every faces detected, compare the faces in the database
+                            e_distance_list = []
+                            for i in range(len(self.features_known_list)):
+                                # 如果 person_X 数据不为空
+                                if str(self.features_known_list[i][0]) != '0.0':
+                                    print("with person", str(i + 1), "the e distance: ", end='')
+                                    e_distance_tmp = self.return_euclidean_distance(self.features_camera_list[k],
+                                                                                    self.features_known_list[i])
+                                    print(e_distance_tmp)
+                                    e_distance_list.append(e_distance_tmp)
+                                else:
+                                    # 空数据 person_X
+                                    e_distance_list.append(999999999)
+                            # 6. 寻找出最小的欧式距离匹配 / Find the one with minimum e distance
+                            similar_person_num = e_distance_list.index(min(e_distance_list))
+                            print("Minimum e distance with person", self.name_known_list[similar_person_num])
+
+                            if min(e_distance_list) < 0.4:
+                                self.name_camera_list[k] = self.name_known_list[similar_person_num]
+                                print("May be person " + str(self.name_known_list[similar_person_num]))
+                            else:
+                                print("Unknown person")
+
+                            # 矩形框 / Draw rectangle
+                            for kk, d in enumerate(faces):
+                                # 绘制矩形框
+                                cv2.rectangle(img_rd, tuple([d.left(), d.top()]), tuple([d.right(), d.bottom()]),
+                                              (0, 255, 255), 2)
+                            print('\n')
+
+                        self.faces_cnt = len(faces)
+                        # 7. 在这里更改显示的人名 / Modify name if needed
+                        # self.modify_name_camera_list()
+                        # 8. 写名字 / Draw name
+                        # self.draw_name(img_rd)
+                        img_with_name = self.draw_name(img_rd)
+                    else:
+                        img_with_name = img_rd
+
+                print("Faces in camera now:", self.name_camera_list, "\n")
+
+                cv2.imshow("camera", img_with_name)
+
+                # 9. 更新 FPS / Update stream FPS
+                self.update_fps()
+
+    # OpenCV 调用摄像头并进行 process
+    def run(self):
+        cap = cv2.VideoCapture(0)
+        cap.set(3, 480)
+        self.process(cap)
+
+        cap.release()
+        cv2.destroyAllWindows()
+
+
+def main():
+    Face_Recognizer_con = Face_Recognizer()
+    Face_Recognizer_con.run()
+
+
+if __name__ == '__main__':
+    main()

features_extraction_to_csv.py

@@ -82,13 +82,13 @@ for person in person_list:
     person_num_list.append(int(person.split('_')[-1]))
 person_cnt = max(person_num_list)
 
-with open("data/features_all.csv", "w", newline="") as csvfile:
-    writer = csv.writer(csvfile)
-    for person in range(person_cnt):
-        # Get the mean/average features of face/personX, it will be a list with a length of 128D
-        print(path_images_from_camera + "person_" + str(person + 1))
-        features_mean_personX = return_features_mean_personX(path_images_from_camera + "person_" + str(person + 1))
-        writer.writerow(features_mean_personX)
-        print("特征均值 / The mean of features:", list(features_mean_personX))
-        print('\n')
-    print("所有录入人脸数据存入 / Save all the features of faces registered into: data/features_all.csv")
+for person in range(person_cnt):
+    # Get the mean/average features of face/personX, it will be a list with a length of 128D
+    print(path_images_from_camera + "person_" + str(person + 1))
+    features_mean_personX = return_features_mean_personX(path_images_from_camera + "person_" + str(person + 1))
+
+    print(features_mean_personX.shape)
+    print(features_mean_personX[0])
+
+    print("特征均值 / The mean of features:", list(features_mean_personX))
+    print('\n')

features_extraction_to_mysql.py

@@ -0,0 +1,117 @@
+# 从人脸图像文件中提取人脸特征存入 CSV
+# Features extraction from images and save into features_all.csv
+
+# Author:   coneypo
+# Blog:     http://www.cnblogs.com/AdaminXie
+# GitHub:   https://github.com/coneypo/Dlib_face_recognition_from_camera
+# Mail:     coneypo@foxmail.com
+
+# Created at 2018-05-11
+# Updated at 2020-04-02
+
+import os
+import dlib
+from skimage import io
+import numpy as np
+import pymysql
+
+db = pymysql.connect("localhost", "root", "intel@123", "dlib_database")
+cursor = db.cursor()
+
+# 要读取人脸图像文件的路径
+path_images_from_camera = "data/data_faces_from_camera/"
+
+# 1. Dlib 正向人脸检测器
+detector = dlib.get_frontal_face_detector()
+
+# 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")
+
+
+# 返回单张图像的 128D 特征
+def return_128d_features(path_img):
+    img_rd = io.imread(path_img)
+    faces = detector(img_rd, 1)
+
+    print("%-40s %-20s" % ("检测到人脸的图像 / Image with faces detected:", path_img), '\n')
+
+    # 因为有可能截下来的人脸再去检测，检测不出来人脸了
+    # 所以要确保是 检测到人脸的人脸图像 拿去算特征
+    if len(faces) != 0:
+        shape = predictor(img_rd, faces[0])
+        face_descriptor = face_reco_model.compute_face_descriptor(img_rd, shape)
+    else:
+        face_descriptor = 0
+        print("no face")
+
+    return face_descriptor
+
+
+# 将文件夹中照片特征提取出来, 写入 CSV
+def return_features_mean_personX(path_faces_personX):
+    features_list_personX = []
+    photos_list = os.listdir(path_faces_personX)
+    if photos_list:
+        for i in range(len(photos_list)):
+            # 调用return_128d_features()得到128d特征
+            print("%-40s %-20s" % ("正在读的人脸图像 / Image to read:", path_faces_personX + "/" + photos_list[i]))
+            features_128d = return_128d_features(path_faces_personX + "/" + photos_list[i])
+            #  print(features_128d)
+            # 遇到没有检测出人脸的图片跳过
+            if features_128d == 0:
+                i += 1
+            else:
+                features_list_personX.append(features_128d)
+    else:
+        print("文件夹内图像文件为空 / Warning: No images in " + path_faces_personX + '/', '\n')
+
+    # 计算 128D 特征的均值
+    # personX 的 N 张图像 x 128D -> 1 x 128D
+    if features_list_personX:
+        features_mean_personX = np.array(features_list_personX).mean(axis=0)
+    else:
+        features_mean_personX = np.zeros(128, dtype=int, order='C')
+
+    return features_mean_personX
+
+
+# 获取已录入的最后一个人脸序号 / get the num of latest person
+person_list = os.listdir("data/data_faces_from_camera/")
+person_num_list = []
+for person in person_list:
+    person_num_list.append(int(person.split('_')[-1]))
+person_cnt = max(person_num_list)
+
+# 0. clear table in mysql
+# cursor.execute("truncate dlib_face_table;")
+
+# 1. check existing people in mysql
+cursor.execute("select count(*) from dlib_face_table;")
+person_start = int(cursor.fetchall()[0][0])
+
+for person in range(person_start, person_cnt):
+    # Get the mean/average features of face/personX, it will be a list with a length of 128D
+    print(path_images_from_camera + "person_" + str(person + 1))
+    features_mean_personX = return_features_mean_personX(path_images_from_camera + "person_" + str(person + 1))
+
+    print("特征均值 / The mean of features:", list(features_mean_personX))
+    print('\n')
+
+
+
+    # 2. Insert person 1 to person X
+    cursor.execute("insert into dlib_face_table(person_x) values(\"person_"+str(person+1)+"\");")
+
+    # 3. Insert features for person X
+    for i in range(128):
+        # update issue_info set github_status='Open', github_type='bug' where github_id='2222';
+        print("update dlib_face_table set feature_" + str(i + 1) + '=\"' + str(
+            features_mean_personX[i]) + "\" where person_x=\"person_" + str(person + 1) + "\";")
+        cursor.execute("update dlib_face_table set feature_" + str(i + 1) + '=\"' + str(
+            features_mean_personX[i]) + "\" where person_x=\"person_" + str(person + 1) + "\";")
+
+
+db.commit()

get_faces_from_camera.py

@@ -173,6 +173,7 @@ class Face_Register:
 
     def run(self):
         cap = cv2.VideoCapture(0)
+        cap.set(3, 640)
         self.process(cap)
 
         cap.release()

how_to_use_camera.py

@@ -10,6 +10,7 @@ import cv2
 
 cap = cv2.VideoCapture(0)
 
+# cap.set(3, 480)
 # cap.set(propId, value)
 # 设置视频参数: propId - 设置的视频参数, value - 设置的参数值
 """
@@ -26,8 +27,7 @@ cap = cv2.VideoCapture(0)
 10. cv2.CAP_PROP_BRIGHTNESS Brightness of the image (only for cameras).
 11. cv2.CAP_PROP_CONTRAST Contrast of the image (only for cameras).
 12. cv2.CAP_PROP_SATURATION Saturation of the image (only for cameras).
-13. cv2.CAP_PROP_HUE Hue of the image (only for cameras).
-14. cv2.CAP_PROP_GAIN Gain of the image (only for cameras).
+print  14. cv2.CAP_PROP_GAIN Gain of the image (only for cameras).
 15. cv2.CAP_PROP_EXPOSURE Exposure (only for cameras).
 16. cv2.CAP_PROP_CONVERT_RGB Boolean flags indicating whether images should be converted to RGB.
 17. cv2.CAP_PROP_WHITE_BALANCE Currently unsupported
@@ -36,7 +36,9 @@ cap = cv2.VideoCapture(0)
 
 # The default shape of camera will be 640x480 in Windows or Ubuntu
 # So we will not set "cap.set" here, it doesn't work
-# cap.set(propId=cv2.CAP_PROP_FRAME_WIDTH, value=cap.get(cv2.CAP_PROP_FRAME_WIDTH))
+# print(cv2.CAP_PROP_FRAME_WIDTH)
+# print(cv2.CAP_PROP_FRAME_HEIGHT)
+cap.set(3, 640)
 
 # cap.isOpened() 返回 true/false, 检查摄像头初始化是否成功
 print(cap.isOpened())