Update face_reco_from_camera.py

.idea/Dlib_face_recognition_from_camera.iml

@@ -1,12 +1,14 @@
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.idea/inspectionProfiles/Project_Default.xml

@@ -8,5 +8,12 @@
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.idea/workspace.xml

@@ -1,10 +1,25 @@
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+      <change beforePath="$PROJECT_DIR$/introduction/face_reco_single_person.png" beforeDir="false" afterPath="$PROJECT_DIR$/introduction/face_reco_single_person.png" afterDir="false" />
+      <change beforePath="$PROJECT_DIR$/introduction/face_reco_single_person_customize_name.png" beforeDir="false" afterPath="$PROJECT_DIR$/introduction/face_reco_single_person_customize_name.png" afterDir="false" />
+      <change beforePath="$PROJECT_DIR$/introduction/face_reco_two_people.png" beforeDir="false" afterPath="$PROJECT_DIR$/introduction/face_reco_two_people.png" afterDir="false" />
+      <change beforePath="$PROJECT_DIR$/introduction/face_reco_two_people_in_database.png" beforeDir="false" afterPath="$PROJECT_DIR$/introduction/face_reco_two_people_in_database.png" afterDir="false" />
+      <change beforePath="$PROJECT_DIR$/introduction/get_face_from_camera.png" beforeDir="false" afterPath="$PROJECT_DIR$/introduction/get_face_from_camera.png" afterDir="false" />
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@@ -77,40 +75,25 @@
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-      <find>dir_pics</find>
-      <find>current_face_dir</find>
-      <find>person_cnt</find>
-      <find>cnt_ss</find>
+      <find>path_photos_from_camera</find>
+      <find>path_csv_from_photos</find>
+      <find>facerec</find>
+      <find>img</find>
+      <find>feature_mean_list_personX</find>
+      <find>feature_list_personX</find>
+      <find>feature</find>
+      <find>features_list_personX</find>
+      <find>feature_mean_personX</find>
+      <find>data_csvs</find>
+      <find>features_known_arr</find>
+      <find>with</find>
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-      <replace>faces</replace>
-      <replace>cnt_ss</replace>
-      <replace>,</replace>
+      <replace>face_rec</replace>
       <replace>img_rd</replace>
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-      <replace>pre_work</replace>
-      <replace>path_csvs_from_photos</replace>
-      <replace>path_photos_from_camera</replace>
-      <replace>path_csv_from_photos</replace>
-      <replace>feature_mean_list</replace>
-      <replace>photos_list</replace>
+      <replace>descriptor_mean_list_personX</replace>
+      <replace>features_list_personX</replace>
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@@ -119,21 +102,22 @@
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-        <option value="$PROJECT_DIR$/how_to_use_camera.py" />
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         <option value="$PROJECT_DIR$/get_features_into_CSV.py" />
+        <option value="$PROJECT_DIR$/get_origin.py" />
         <option value="$PROJECT_DIR$/test.py" />
+        <option value="$PROJECT_DIR$/features_extraction_to_csv.py" />
         <option value="$PROJECT_DIR$/get_faces_from_camera.py" />
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@@ -161,15 +145,8 @@
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@@ -205,6 +182,27 @@
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+    <configuration name="features_extraction_to_csv" type="PythonConfigurationType" factoryName="Python" temporary="true">
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     <configuration name="get_faces_from_camera" type="PythonConfigurationType" factoryName="Python" temporary="true">
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@@ -226,7 +224,7 @@
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@@ -291,18 +268,18 @@
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         <item itemvalue="Python.get_faces_from_camera" />
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-          <caret line="96" selection-start-line="96" selection-end-line="96" />
+        <state relative-caret-position="489">
+          <caret line="160" column="23" lean-forward="true" selection-start-line="160" selection-start-column="23" selection-end-line="160" selection-end-column="23" />
           <folding>
-            <element signature="e#201#234#0" expanded="true" />
+            <element signature="e#230#264#0" expanded="true" />
           </folding>
         </state>
       </provider>

README.rst

@@ -1,5 +1,5 @@
-Face recognition from camera
-############################
+Face recognition from camera with Dlib
+######################################
 
 Introduction
 ************
@@ -14,6 +14,12 @@ Detect and recognize single/multi-faces from camera;
    .. image:: introduction/get_face_from_camera.png
       :align: center
 
+   请不要离摄像头过近，人脸超出摄像头范围时会有 "OUT OF RANGE" 提醒 /
+   Please do not too close to the camera, or you can't save faces with "OUT OF RANGE" warning;
+
+   .. image:: introduction/get_face_from_camera_out_of_range.png
+      :align: center
+
 #. 提取特征建立人脸数据库 / Generate database from images captured
 #. 利用摄像头进行人脸识别 / Face recognizer
    
@@ -23,33 +29,53 @@ Detect and recognize single/multi-faces from camera;
       :align: center
 
    当多张人脸 / When multi-faces:
-   
+
+   一张已录入人脸 + 未录入 unknown 人脸 / 1x known face + 1x unknown face:
+
    .. image:: introduction/face_reco_two_people.png
       :align: center
 
+   同时识别多张已录入人脸 / multi-faces recognition at the same time:
+
+   .. image:: introduction/face_reco_two_people_in_database.png
+      :align: center
+
 ** 关于精度 / About accuracy:
 
 * When using a distance threshold of ``0.6``, the dlib model obtains an accuracy of ``99.38%`` on the standard LFW face recognition benchmark.
 
+Overview
+********
+
+此项目中人脸识别的实现流程 / The design of this repo:
+
+.. image:: introduction/overview.png
+   :align: center
 
 Steps
 *****
 
+#. 下载源码 / Download zip from website or via GitHub Desktop in windows, or git clone in Ubuntu
+
+   .. code-block:: bash
+
+      git clone https://github.com/coneypo/Dlib_face_recognition_from_camera
+
 #. 进行 face register / 人脸信息采集录入
 
-   .. code-block:: python
+   .. code-block:: bash
 
       python3 get_face_from_camera.py
 
-#. 提取所有录入人脸数据存入 features_all.csv
+#. 提取所有录入人脸数据存入 features_all.csv / Features extraction and save into features_all.csv
 
-   .. code-block:: python
+   .. code-block:: bash
 
-      python3 get_features_into_CSV.py
+      python3 features_extraction_to_csv.py
 
-#. 调用摄像头进行实时人脸识别
+#. 调用摄像头进行实时人脸识别 / Real-time face recognition
 
-   .. code-block:: python
+   .. code-block:: bash
 
       python3 face_reco_from_camera.py
 
@@ -57,6 +83,67 @@ Steps
 About Source Code
 *****************
 
+Repo 的 tree / 树状图:
+
+::
+
+    .
+    ├── get_faces_from_camera.py        # Step1. Faces register
+    ├── features_extraction_to_csv.py   # Step2. Features extraction
+    ├── face_reco_from_camera.py        # Step3. Faces recognition
+    ├── how_to_use_camera.py            # Use the default camera by opencv
+    ├── data
+    │   ├── data_dlib                   # Dlib's model
+    │   │   ├── dlib_face_recognition_resnet_model_v1.dat
+    │   │   ├── shape_predictor_5_face_landmarks.dat
+    │   │   └── shape_predictor_68_face_landmarks.dat
+    │   ├── data_faces_from_camera      # Face images captured from camera (will generate after step 1)
+    │   │   ├── person_1
+    │   │   │   ├── img_face_1.jpg
+    │   │   │   └── img_face_2.jpg
+    │   │   └── person_2
+    │   │       └── img_face_1.jpg
+    │   │       └── img_face_2.jpg
+    │   └── features_all.csv            # CSV to save all the features of known faces (will generate after step 2)
+    ├── introduction                    # Some files for readme.rst
+    │   ├── Dlib_Face_recognition_by_coneypo.pptx
+    │   ├── face_reco_single_person_customize_name.png
+    │   ├── face_reco_single_person.png
+    │   ├── face_reco_two_people_in_database.png
+    │   ├── face_reco_two_people.png
+    │   ├── get_face_from_camera_out_of_range.png
+    │   ├── get_face_from_camera.png
+    │   └── overview.png
+    ├── README.rst
+    └── requirements.txt            # Some python packages needed
+
+用到的 Dlib 相关模型函数:
+
+#. Dlib 正向人脸检测器 (based on HOG), output: <class 'dlib.dlib.rectangles'>
+
+
+   .. code-block:: python
+
+      detector = dlib.get_frontal_face_detector()
+      faces = detector(img_gray, 0)
+
+	  
+#. Dlib 人脸预测器, output: <class 'dlib.dlib.full_object_detection'>
+
+   .. code-block:: python
+
+      predictor = dlib.shape_predictor("data/data_dlib/shape_predictor_5_face_landmarks.dat")
+      shape = predictor(img_rd, faces[i])
+
+	  
+#. 特征描述子 Face recognition model, the object maps human faces into 128D vectors
+
+
+   .. code-block:: python
+
+      face_rec = dlib.face_recognition_model_v1("data/data_dlib/dlib_face_recognition_resnet_model_v1.dat")
+
+
 Python 源码介绍如下:
 
 #. get_face_from_camera.py: 
@@ -67,7 +154,7 @@ Python 源码介绍如下:
    * 超出会有 "out of range" 的提醒;
 
 
-#. get_features_into_CSV.py: 
+#. features_extraction_to_csv.py:
      
    从上一步存下来的图像文件中，提取人脸数据存入CSV;
   
@@ -82,28 +169,29 @@ Python 源码介绍如下:
    * Compare the faces captured from camera with the faces you have registered which are saved in "features_all.csv"
    
    * 将捕获到的人脸数据和之前存的人脸数据进行对比计算欧式距离, 由此判断是否是同一个人;
-
+   
 
 More
 ****
 
 Tips:
 
-1. Windows下建议不要把代码放到 ``C:\``, 可能会出现权限读取问题
+#. 如果希望详细了解 dlib 的用法，请参考 Dlib 官方 Python api 的网站 / You can refer to this link for more information of how to use dlib: http://dlib.net/python/index.html
 
-2. 代码最好不要有中文路径
+#. Windows下建议不要把代码放到 ``C:\``, 可能会出现权限读取问题 / In windows, we will not recommend that running this repo in dir ``C:\``
 
-3. 人脸录入的时候先建文件夹再保存图片, 先 ``N`` 再 ``S``
+#. 代码最好不要有中文路径 / No chinese characters in your code directory
 
-For more details, please refer to my blog (in chinese) or mail to me /
+#. 人脸录入的时候先建文件夹再保存图片, 先 ``N`` 再 ``S`` / Press ``N`` before ``S``
 
-可以访问我的博客获取本项目的更详细介绍，如有问题可以邮件联系我:
+可以访问我的博客获取本项目的更详细介绍，如有问题可以邮件联系我 /
+For more details, please refer to my blog (in chinese) or mail to me :
 
 * Blog: https://www.cnblogs.com/AdaminXie/p/9010298.html
   
-* Mail: coneypo@foxmail.com
+* Mail: coneypo@foxmail.com ( Dlib 相关 repo 问题请联系 @foxmail 而不是 @intel )
 
 
 仅限于交流学习, 商业合作勿扰;
 
-Thanks for your support.
+Thanks for your support.

face_reco_from_camera.py

@@ -1,11 +1,12 @@
 # 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 2019-02-26
+# Updated at 2019-04-09
 
 import dlib          # 人脸处理的库 Dlib
 import numpy as np   # 数据处理的库 numpy
@@ -14,34 +15,29 @@ import pandas as pd  # 数据处理的库 Pandas
 
 # 人脸识别模型，提取128D的特征矢量
 # face recognition model, the object maps human faces into 128D vectors
+# Refer this tutorial: http://dlib.net/python/index.html#dlib.face_recognition_model_v1
 facerec = dlib.face_recognition_model_v1("data/data_dlib/dlib_face_recognition_resnet_model_v1.dat")
 
 
 # 计算两个128D向量间的欧式距离
+# compute the e-distance between two 128D features
 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)))
-    print("e_distance: ", dist)
-
-    if dist > 0.4:
-        return "diff"
-    else:
-        return "same"
+    return dist
 
 
 # 处理存放所有人脸特征的 csv
 path_features_known_csv = "data/features_all.csv"
 csv_rd = pd.read_csv(path_features_known_csv, header=None)
 
-# 存储的特征人脸个数
-# print(csv_rd.shape[0])
-
 # 用来存放所有录入人脸特征的数组
+# the array to save the features of faces in the database
 features_known_arr = []
 
 # 读取已知人脸数据
-# known faces
+# print known faces
 for i in range(csv_rd.shape[0]):
     features_someone_arr = []
     for j in range(0, len(csv_rd.ix[i, :])):
@@ -50,31 +46,21 @@ for i in range(csv_rd.shape[0]):
 print("Faces in Database：", len(features_known_arr))
 
 # Dlib 检测器和预测器
+# The detector and predictor will be used
 detector = dlib.get_frontal_face_detector()
 predictor = dlib.shape_predictor('data/data_dlib/shape_predictor_68_face_landmarks.dat')
 
 # 创建 cv2 摄像头对象
+# cv2.VideoCapture(0) to use the default camera of PC,
+# and you can use local video name by use cv2.VideoCapture(filename)
 cap = cv2.VideoCapture(0)
 
 # 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
 cap.set(3, 480)
 
-
-# 返回一张图像多张人脸的 128D 特征
-def get_128d_features(img_gray):
-    faces = detector(img_gray, 1)
-    if len(faces) != 0:
-        face_des = []
-        for i in range(len(faces)):
-            shape = predictor(img_gray, faces[i])
-            face_des.append(facerec.compute_face_descriptor(img_gray, shape))
-    else:
-        face_des = []
-    return face_des
-
-
 # cap.isOpened() 返回 true/false 检查初始化是否成功
+# when the camera is open
 while cap.isOpened():
 
     flag, img_rd = cap.read()
@@ -86,72 +72,95 @@ while cap.isOpened():
     # 人脸数 faces
     faces = detector(img_gray, 0)
 
-    # 待会要写的字体
+    # 待会要写的字体 font to write later
     font = cv2.FONT_HERSHEY_COMPLEX
 
     # 存储当前摄像头中捕获到的所有人脸的坐标/名字
+    # the list to save the positions and names of current faces captured
     pos_namelist = []
     name_namelist = []
 
     # 按下 q 键退出
+    # press 'q' to exit
     if kk == ord('q'):
         break
     else:
-        # 检测到人脸
+        # 检测到人脸 when face detected
         if len(faces) != 0:
             # 获取当前捕获到的图像的所有人脸的特征，存储到 features_cap_arr
+            # get the features captured and save into features_cap_arr
             features_cap_arr = []
             for i in range(len(faces)):
                 shape = predictor(img_rd, faces[i])
                 features_cap_arr.append(facerec.compute_face_descriptor(img_rd, shape))
 
             # 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"
                 name_namelist.append("unknown")
 
-                # 每个捕获人脸的名字坐标
+                # 每个捕获人脸的名字坐标 the positions of faces captured
                 pos_namelist.append(tuple([faces[k].left(), int(faces[k].bottom() + (faces[k].bottom() - faces[k].top())/4)]))
 
                 # 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(features_known_arr)):
-                    print("with person_", str(i+1), "the ", end='')
-                    # 将某张人脸与存储的所有人脸数据进行比对
-                    compare = return_euclidean_distance(features_cap_arr[k], features_known_arr[i])
-                    if compare == "same":  # 找到了相似脸
-                        # 在这里修改 person_1, person_2 ... 的名字
-                        # 这里只写了前三个
-                        # 可以在这里改称 Jack, Tom and others
-                        # Here you can modify the names shown on the camera
-                        if i == 0:
-                            name_namelist[k] = "Jack"
-                        elif i == 1:
-                            name_namelist[k] = "Tom"
-                        elif i == 2:
-                            name_namelist[k] = "Tony"
-                            
+                    # 如果 person_X 数据不为空
+                    if str(features_known_arr[i][0]) != '0.0':
+                        print("with person", str(i + 1), "the e distance: ", end='')
+                        e_distance_tmp = return_euclidean_distance(features_cap_arr[k], features_known_arr[i])
+                        print(e_distance_tmp)
+                        e_distance_list.append(e_distance_tmp)
+                    else:
+                        # 空数据 person_X
+                        e_distance_list.append(999999999)
+                # Find the one with minimum e distance
+                similar_person_num = e_distance_list.index(min(e_distance_list))
+                print("Minimum e distance with person", int(similar_person_num)+1)
+
+                if min(e_distance_list) < 0.4:
+                    # 在这里修改 person_1, person_2 ... 的名字
+                    # 可以在这里改称 Jack, Tom and others
+                    # Here you can modify the names shown on the camera
+                    name_namelist[k] = str("Person "+str(int(similar_person_num)+1))\
+                        .replace("Person 1", "Sherry")\
+                        .replace("Person 2", "Jack")\
+                        .replace("Person 3", "Ronnie")\
+                        .replace("Person 4", "Terry")\
+                        .replace("Person 5", "Wilson")
+                    # print("May be person "+str(int(similar_person_num)+1))
+                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')
 
             # 在人脸框下面写人脸名字
+            # write names under rectangle
             for i in range(len(faces)):
                 cv2.putText(img_rd, name_namelist[i], pos_namelist[i], font, 0.8, (0, 255, 255), 1, cv2.LINE_AA)
 
-    print("Name list now:", name_namelist, "\n")
+    print("Faces in camera now:", name_namelist, "\n")
 
     cv2.putText(img_rd, "Press 'q': Quit", (20, 450), font, 0.8, (84, 255, 159), 1, cv2.LINE_AA)
     cv2.putText(img_rd, "Face Recognition", (20, 40), font, 1, (0, 0, 0), 1, cv2.LINE_AA)
     cv2.putText(img_rd, "Faces: " + str(len(faces)), (20, 100), font, 1, (0, 0, 255), 1, cv2.LINE_AA)
 
-    # 窗口显示
+    # 窗口显示 show with opencv
     cv2.imshow("camera", img_rd)
 
-# 释放摄像头
+# 释放摄像头 release camera
 cap.release()
 
-# 删除建立的窗口
+# 删除建立的窗口 delete all the windows
 cv2.destroyAllWindows()

features_extraction_to_csv.py

@@ -0,0 +1,97 @@
+# 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 2019-04-04
+
+import cv2
+import os
+import dlib
+from skimage import io
+import csv
+import numpy as np
+
+# 要读取人脸图像文件的路径
+path_images_from_camera = "data/data_faces_from_camera/"
+
+# Dlib 正向人脸检测器
+detector = dlib.get_frontal_face_detector()
+
+# Dlib 人脸预测器
+predictor = dlib.shape_predictor("data/data_dlib/shape_predictor_5_face_landmarks.dat")
+
+# Dlib 人脸识别模型
+# Face recognition model, the object maps human faces into 128D vectors
+face_rec = 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)
+    img_gray = cv2.cvtColor(img_rd, cv2.COLOR_BGR2RGB)
+    faces = detector(img_gray, 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_gray, faces[0])
+        face_descriptor = face_rec.compute_face_descriptor(img_gray, 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 = '0'
+
+    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)
+
+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")

get_faces_from_camera.py

@@ -7,7 +7,7 @@
 # Mail:     coneypo@foxmail.com
 
 # Created at 2018-05-11
-# Updated at 2019-02-21
+# Updated at 2019-04-12
 
 import dlib         # 人脸处理的库 Dlib
 import numpy as np  # 数据处理的库 Numpy
@@ -16,41 +16,37 @@ import cv2          # 图像处理的库 OpenCv
 import os           # 读写文件
 import shutil       # 读写文件
 
-# Dlib 正向人脸检测器
+# Dlib 正向人脸检测器 / frontal face detector
 detector = dlib.get_frontal_face_detector()
 
-# Dlib 68 点特征预测器
+# Dlib 68 点特征预测器 / 68 points features predictor
 predictor = dlib.shape_predictor('data/data_dlib/shape_predictor_68_face_landmarks.dat')
 
-# OpenCv 调用摄像头
+# OpenCv 调用摄像头 use camera
 cap = cv2.VideoCapture(0)
 
-# 设置视频参数
+# 设置视频参数 set camera
 cap.set(3, 480)
 
-# 人脸截图的计数器
+# 人脸截图的计数器 the counter for screen shoot
 cnt_ss = 0
 
-# 存储人脸的文件夹
-current_face_dir = 0
+# 存储人脸的文件夹 the folder to save faces
+current_face_dir = ""
 
-# 保存 photos/csv 的路径
+# 保存 faces images 的路径 the directory to save images of faces
 path_photos_from_camera = "data/data_faces_from_camera/"
-path_csv_from_photos = "data/data_csvs_from_camera/"
 
 
 # 新建保存人脸图像文件和数据CSV文件夹
+# mkdir for saving photos and csv
 def pre_work_mkdir():
 
-    # 新建文件夹
+    # 新建文件夹 / make folders to save faces images and csv
     if os.path.isdir(path_photos_from_camera):
         pass
     else:
         os.mkdir(path_photos_from_camera)
-    if os.path.isdir(path_csv_from_photos):
-        pass
-    else:
-        os.mkdir(path_csv_from_photos)
 
 
 pre_work_mkdir()
@@ -58,42 +54,50 @@ pre_work_mkdir()
 
 ##### optional/可选, 默认关闭 #####
 # 删除之前存的人脸数据文件夹
-def pre_work_deldir():
+# delete the old data of faces
+def pre_work_del_old_face_folders():
     # 删除之前存的人脸数据文件夹
     # 删除 "/data_faces_from_camera/person_x/"...
     folders_rd = os.listdir(path_photos_from_camera)
     for i in range(len(folders_rd)):
         shutil.rmtree(path_photos_from_camera+folders_rd[i])
 
-    csv_rd = os.listdir(path_csv_from_photos)
-    for i in range(len(csv_rd)):
-        os.remove(path_csv_from_photos+csv_rd[i])
+    if os.path.isfile("data/features_all.csv"):
+        os.remove("data/features_all.csv")
 
 # 这里在每次程序录入之前, 删掉之前存的人脸数据
-# 如果这里打开，每次进行人脸录入的时候都会删掉之前的人脸图像文件夹
-# pre_work_deldir()
+# 如果这里打开，每次进行人脸录入的时候都会删掉之前的人脸图像文件夹 person_1/,person_2/,person_3/...
+# If enable this function, it will delete all the old data in dir person_1/,person_2/,/person_3/...
+# pre_work_del_old_face_folders()
 ##################################
 
 
-# 如果有之前录入的人脸
-# 在之前 person_x 的序号按照 person_x+1 开始录入
+# 如果有之前录入的人脸 / if the old folders exists
+# 在之前 person_x 的序号按照 person_x+1 开始录入 / start from person_x+1
 if os.listdir("data/data_faces_from_camera/"):
-    # 获取已录入的最后一个人脸序号
+    # 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_list.sort()
-    person_num_latest = int(str(person_list[-1]).split("_")[-1])
-    person_cnt = person_num_latest
+    person_num_list = []
+    for person in person_list:
+        person_num_list.append(int(person.split('_')[-1]))
+    person_cnt = max(person_num_list)
 
 # 如果第一次存储或者没有之前录入的人脸, 按照 person_1 开始录入
+# start from person_1
 else:
     person_cnt = 0
 
-# 之后用来控制是否保存图像的 flag
+# 之后用来控制是否保存图像的 flag / the flag to control if save
 save_flag = 1
 
+# 之后用来检查是否先按 'n' 再按 's' / the flag to check if press 'n' before 's'
+press_n_flag = 0
+
 while cap.isOpened():
-    # 480 height * 640 width
     flag, img_rd = cap.read()
+    # print(img_rd.shape)
+    # It should be 480 height * 640 width
+
     kk = cv2.waitKey(1)
 
     img_gray = cv2.cvtColor(img_rd, cv2.COLOR_RGB2GRAY)
@@ -101,42 +105,47 @@ while cap.isOpened():
     # 人脸数 faces
     faces = detector(img_gray, 0)
 
-    # 待会要写的字体
+    # 待会要写的字体 / font to write
     font = cv2.FONT_HERSHEY_COMPLEX
 
-    # 按下 'n' 新建存储人脸的文件夹
+    # 按下 'n' 新建存储人脸的文件夹 / press 'n' to create the folders for saving faces
     if kk == ord('n'):
         person_cnt += 1
         current_face_dir = path_photos_from_camera + "person_" + str(person_cnt)
         os.makedirs(current_face_dir)
         print('\n')
-        print("新建的人脸文件夹: ", current_face_dir)
+        print("新建的人脸文件夹 / Create folders: ", current_face_dir)
 
-        # 将人脸计数器清零
-        cnt_ss = 0
+        cnt_ss = 0              # 将人脸计数器清零 / clear the cnt of faces
+        press_n_flag = 1        # 已经按下 'n' / have pressed 'n'
 
-    # 检测到人脸
+    # 检测到人脸 / if face detected
     if len(faces) != 0:
-        # 矩形框
+        # 矩形框 / show the rectangle box
         for k, d in enumerate(faces):
             # 计算矩形大小
+            # we need to compute the width and height of the box
             # (x,y), (宽度width, 高度height)
             pos_start = tuple([d.left(), d.top()])
             pos_end = tuple([d.right(), d.bottom()])
 
-            # 计算矩形框大小
+            # 计算矩形框大小 / compute the size of rectangle box
             height = (d.bottom() - d.top())
             width = (d.right() - d.left())
 
             hh = int(height/2)
             ww = int(width/2)
 
-            # 设置颜色 / The color of rectangle of faces detected
+            # 设置颜色 / the color of rectangle of faces detected
             color_rectangle = (255, 255, 255)
+
+            # 判断人脸矩形框是否超出 480x640
             if (d.right()+ww) > 640 or (d.bottom()+hh > 480) or (d.left()-ww < 0) or (d.top()-hh < 0):
                 cv2.putText(img_rd, "OUT OF RANGE", (20, 300), font, 0.8, (0, 0, 255), 1, cv2.LINE_AA)
                 color_rectangle = (0, 0, 255)
                 save_flag = 0
+                if kk == ord('s'):
+                    print("请调整位置 / Please adjust your position")
             else:
                 color_rectangle = (255, 255, 255)
                 save_flag = 1
@@ -146,41 +155,42 @@ while cap.isOpened():
                           tuple([d.right() + ww, d.bottom() + hh]),
                           color_rectangle, 2)
 
-            # 根据人脸大小生成空的图像
+            # 根据人脸大小生成空的图像 / create blank image according to the size of face detected
             im_blank = np.zeros((int(height*2), width*2, 3), np.uint8)
 
             if save_flag:
-                # 按下 's' 保存摄像头中的人脸到本地
+                # 按下 's' 保存摄像头中的人脸到本地 / press 's' to save faces into local images
                 if kk == ord('s'):
-                    if os.path.isdir(current_face_dir):
+                    # 检查有没有先按'n'新建文件夹 / check if you have pressed 'n'
+                    if press_n_flag:
                         cnt_ss += 1
                         for ii in range(height*2):
                             for jj in range(width*2):
                                 im_blank[ii][jj] = img_rd[d.top()-hh + ii][d.left()-ww + jj]
                         cv2.imwrite(current_face_dir + "/img_face_" + str(cnt_ss) + ".jpg", im_blank)
-                        print("写入本地：", str(current_face_dir) + "/img_face_" + str(cnt_ss) + ".jpg")
+                        print("写入本地 / Save into：", str(current_face_dir) + "/img_face_" + str(cnt_ss) + ".jpg")
                     else:
                         print("请在按 'S' 之前先按 'N' 来建文件夹 / Please press 'N' before 'S'")
 
-    # 显示人脸数
+    # 显示人脸数 / show the numbers of faces detected
     cv2.putText(img_rd, "Faces: " + str(len(faces)), (20, 100), font, 0.8, (0, 255, 0), 1, cv2.LINE_AA)
 
-    # 添加说明
+    # 添加说明 / add some statements
     cv2.putText(img_rd, "Face Register", (20, 40), font, 1, (0, 0, 0), 1, cv2.LINE_AA)
     cv2.putText(img_rd, "N: New face folder", (20, 350), font, 0.8, (0, 0, 0), 1, cv2.LINE_AA)
     cv2.putText(img_rd, "S: Save current face", (20, 400), font, 0.8, (0, 0, 0), 1, cv2.LINE_AA)
     cv2.putText(img_rd, "Q: Quit", (20, 450), font, 0.8, (0, 0, 0), 1, cv2.LINE_AA)
 
-    # 按下 'q' 键退出
+    # 按下 'q' 键退出 / press 'q' to exit
     if kk == ord('q'):
         break
 
-    # 窗口显示
-    # cv2.namedWindow("camera", 0) # 如果需要摄像头窗口大小可调
+    # 如果需要摄像头窗口大小可调 / uncomment this line if you want the camera window is resizeable
+    # cv2.namedWindow("camera", 0)
+
     cv2.imshow("camera", img_rd)
 
-# 释放摄像头
+# 释放摄像头 / release camera
 cap.release()
 
-# 删除建立的窗口
 cv2.destroyAllWindows()

get_features_into_CSV.py

@@ -1,137 +0,0 @@
-# 从人脸图像文件中提取人脸特征存入 CSV
-# Get features 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 2019-02-25
-
-# 增加录入多张人脸到 CSV 的功能
-
-# return_128d_features()          获取某张图像的 128D 特征
-# write_into_csv()                获取某个路径下所有图像的特征，并写入 CSV
-# compute_the_mean()              从 CSV 中读取 128D 特征，并计算特征均值
-
-import cv2
-import os
-import dlib
-from skimage import io
-import csv
-import numpy as np
-import pandas as pd
-
-# 要读取人脸图像文件的路径
-path_photos_from_camera = "data/data_faces_from_camera/"
-# 储存人脸特征 csv 的路径
-path_csv_from_photos = "data/data_csvs_from_camera/"
-
-# Dlib 正向人脸检测器
-detector = dlib.get_frontal_face_detector()
-
-# Dlib 人脸预测器
-predictor = dlib.shape_predictor("data/data_dlib/shape_predictor_5_face_landmarks.dat")
-
-# Dlib 人脸识别模型
-# Face recognition model, the object maps human faces into 128D vectors
-facerec = 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 = io.imread(path_img)
-    img_gray = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
-    faces = detector(img_gray, 1)
-
-    print("检测到人脸的图像：", 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_gray, faces[0])
-        face_descriptor = facerec.compute_face_descriptor(img_gray, shape)
-    else:
-        face_descriptor = 0
-        print("no face")
-
-    # print(face_descriptor)
-    return face_descriptor
-
-
-# 将文件夹中照片特征提取出来, 写入 CSV
-#   path_faces_personX:     图像文件夹的路径
-#   path_csv_from_photos:   要生成的 CSV 路径
-
-def write_into_csv(path_faces_personX, path_csv_from_photos):
-    photos_list = os.listdir(path_faces_personX)
-    with open(path_csv_from_photos, "w", newline="") as csvfile:
-        writer = csv.writer(csvfile)
-        if photos_list:
-            for i in range(len(photos_list)):
-                # 调用return_128d_features()得到128d特征
-                print("正在读的人脸图像：", 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:
-                    writer.writerow(features_128d)
-        else:
-            print("Warning: Empty photos in "+path_faces_personX+'/')
-            writer.writerow("")
-
-
-# 读取某人所有的人脸图像的数据，写入 person_X.csv
-faces = os.listdir(path_photos_from_camera)
-faces.sort()
-for person in faces:
-    print("##### " + person + " #####")
-    print(path_csv_from_photos + person + ".csv")
-    write_into_csv(path_photos_from_camera + person, path_csv_from_photos + person + ".csv")
-print('\n')
-
-
-# 从 CSV 中读取数据，计算 128D 特征的均值
-def compute_the_mean(path_csv_from_photos):
-    column_names = []
-
-    # 128D 特征
-    for feature_num in range(128):
-        column_names.append("features_" + str(feature_num + 1))
-
-    # 利用 pandas 读取 csv
-    rd = pd.read_csv(path_csv_from_photos, names=column_names)
-
-    if rd.size != 0:
-        # 存放 128D 特征的均值
-        feature_mean_list = []
-
-        for feature_num in range(128):
-            tmp_arr = rd["features_" + str(feature_num + 1)]
-            tmp_arr = np.array(tmp_arr)
-            # 计算某一个特征的均值
-            tmp_mean = np.mean(tmp_arr)
-            feature_mean_list.append(tmp_mean)
-    else:
-        feature_mean_list = []
-    return feature_mean_list
-
-
-# 存放所有特征均值的 CSV 的路径
-path_csv_from_photos_feature_all = "data/features_all.csv"
-
-# 存放人脸特征的 CSV 的路径
-path_csv_from_photos = "data/data_csvs_from_camera/"
-
-with open(path_csv_from_photos_feature_all, "w", newline="") as csvfile:
-    writer = csv.writer(csvfile)
-    csv_rd = os.listdir(path_csv_from_photos)
-    csv_rd.sort()
-    print("##### 得到的特征均值 / The generated average values of features stored in: #####")
-    for i in range(len(csv_rd)):
-        feature_mean_list = compute_the_mean(path_csv_from_photos + csv_rd[i])
-        print(path_csv_from_photos + csv_rd[i])
-        writer.writerow(feature_mean_list)

requirements.txt

@@ -0,0 +1,3 @@
+dlib==19.17.0
+numpy==1.15.1
+scikit-image==0.14.0