Fix

faces;
2. `features_all.csv` modified to n x 129, 129D will be person_name + 128D features;

Signed-off-by: Zhengtian Xie <coneypo@gmail.com>

README.rst

@@ -8,8 +8,12 @@ Detect and recognize single/multi-faces from camera;
 
 调用摄像头进行人脸识别, 支持多张人脸同时识别;
 
+#. Tkinter 人脸录入界面, 支持录入时设置姓名 / Face register GUI with Tkinter, support setting name when registering
 
-#. 摄像头人脸录入 / Face register
+   .. image:: introduction/face_register_tkinter_GUI.png
+      :align: center
+
+#. 简单的 OpenCV 摄像头人脸录入界面 / Simple face register GUI with OpenCV
 
    .. image:: introduction/face_register.png
       :align: center
@@ -84,7 +88,13 @@ Steps
 
       git clone https://github.com/coneypo/Dlib_face_recognition_from_camera
 
-#. 进行人脸信息采集录入 / Register faces 
+#. 进行人脸信息采集录入, Tkinter GUI / Register faces with Tkinter GUI
+
+   .. code-block:: bash
+
+      python3 get_faces_from_camera_tkinter.py
+
+#. 进行人脸信息采集录入, OpenCV GUI / Register faces with OpenCV GUI
 
    .. code-block:: bash
 
@@ -122,11 +132,12 @@ Repo 的 tree / 树状图:
 ::
 
     .
-    ├── get_faces_from_camera.py        		# Step 1. Face register
+    ├── get_faces_from_camera.py        		# Step 1. Face register GUI with OpenCV
+    ├── get_faces_from_camera_tkinter.py        	# Step 1. Face register GUI with Tkinter
     ├── features_extraction_to_csv.py   		# Step 2. Feature extraction
     ├── face_reco_from_camera.py        		# Step 3. Face recognizer
-    ├── face_reco_from_camera_single_face.py    # Step 3. Face recognizer for single person
-    ├── face_reco_from_camera_ot.py             # Step 3. Face recognizer with OT
+    ├── face_reco_from_camera_single_face.py            # Step 3. Face recognizer for single person
+    ├── face_reco_from_camera_ot.py                     # Step 3. Face recognizer with OT
     ├── face_descriptor_from_camera.py  		# Face descriptor computation
     ├── how_to_use_camera.py            		# Use the default camera by opencv
     ├── data
@@ -193,7 +204,7 @@ Python 源码介绍如下:
    从上一步存下来的图像文件中, 提取人脸数据存入CSV;
   
    * 会生成一个存储所有特征人脸数据的 "features_all.csv"；
-   * size: n*128 , n means n people you registered and 128 means 128D features of the face
+   * size: n*129 , n means nx faces you registered and 129 means face name + 128D features of this face
 
 
 #. face_reco_from_camera.py: 

face_reco_from_camera.py

@@ -54,13 +54,13 @@ class Face_Recognizer:
             csv_rd = pd.read_csv(path_features_known_csv, header=None)
             for i in range(csv_rd.shape[0]):
                 features_someone_arr = []
-                for j in range(0, 128):
+                self.face_name_known_list.append(csv_rd.iloc[i][0])
+                for j in range(1, 129):
                     if csv_rd.iloc[i][j] == '':
                         features_someone_arr.append('0')
                     else:
                         features_someone_arr.append(csv_rd.iloc[i][j])
                 self.face_feature_known_list.append(features_someone_arr)
-                self.face_name_known_list.append("Person_"+str(i+1))
             logging.info("Faces in Database：%d", len(self.face_feature_known_list))
             return 1
         else:
@@ -187,7 +187,7 @@ class Face_Recognizer:
                         self.current_frame_face_cnt = len(faces)
 
                         # 7. 在这里更改显示的人名 / Modify name if needed
-                        self.show_chinese_name()
+                        # self.show_chinese_name()
 
                         # 8. 写名字 / Draw name
                         img_with_name = self.draw_name(img_rd)
@@ -206,8 +206,8 @@ class Face_Recognizer:
     # OpenCV 调用摄像头并进行 process
     def run(self):
         # cap = cv2.VideoCapture("video.mp4")  # Get video stream from video file
-        cap = cv2.VideoCapture(0)              # Get video stream from camera
-        cap.set(3, 480)                         # 640x480
+        cap = cv2.VideoCapture("0")            # Get video stream from camera
+        cap.set(3, 480)                        # 640x480
         self.process(cap)
 
         cap.release()

face_reco_from_camera_ot.py

@@ -82,13 +82,13 @@ class Face_Recognizer:
             csv_rd = pd.read_csv(path_features_known_csv, header=None)
             for i in range(csv_rd.shape[0]):
                 features_someone_arr = []
-                for j in range(0, 128):
+                self.face_name_known_list.append(csv_rd.iloc[i][0])
+                for j in range(1, 129):
                     if csv_rd.iloc[i][j] == '':
                         features_someone_arr.append('0')
                     else:
                         features_someone_arr.append(csv_rd.iloc[i][j])
                 self.face_features_known_list.append(features_someone_arr)
-                self.face_name_known_list.append("Person_" + str(i + 1))
             logging.info("Faces in Database： %d", len(self.face_features_known_list))
             return 1
         else:

face_reco_from_camera_single_face.py

@@ -80,13 +80,13 @@ class Face_Recognizer:
             csv_rd = pd.read_csv(path_features_known_csv, header=None)
             for i in range(csv_rd.shape[0]):
                 features_someone_arr = []
-                for j in range(0, 128):
+                self.face_name_known_list.append(csv_rd.iloc[i][0])
+                for j in range(1, 129):
                     if csv_rd.iloc[i][j] == '':
                         features_someone_arr.append('0')
                     else:
                         features_someone_arr.append(csv_rd.iloc[i][j])
                 self.features_known_list.append(features_someone_arr)
-                self.face_name_known_list.append("Person_" + str(i + 1))
             logging.info("Faces in Database： %d", len(self.features_known_list))
             return 1
         else:

features_extraction_to_csv.py

@@ -70,7 +70,7 @@ def return_features_mean_personX(path_face_personX):
     # 计算 128D 特征的均值 / Compute the mean
     # 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)
+        features_mean_personX = np.array(features_list_personX, dtype=object).mean(axis=0)
     else:
         features_mean_personX = np.zeros(128, dtype=int, order='C')
     return features_mean_personX
@@ -80,17 +80,23 @@ def main():
     logging.basicConfig(level=logging.INFO)
     # 获取已录入的最后一个人脸序号 / Get the order 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)
+    person_list.sort()
 
     with open("data/features_all.csv", "w", newline="") as csvfile:
         writer = csv.writer(csvfile)
-        for person in range(person_cnt):
+        for person in person_list:
             # Get the mean/average features of face/personX, it will be a list with a length of 128D
-            logging.info("%sperson_%s", path_images_from_camera, str(person + 1))
-            features_mean_personX = return_features_mean_personX(path_images_from_camera + "person_" + str(person + 1))
+            logging.info("%sperson_%s", path_images_from_camera, person)
+            features_mean_personX = return_features_mean_personX(path_images_from_camera + person)
+
+            if len(person.split('_', 2)) == 2:
+                # "person_x"
+                person_name = person
+            else:
+                # "person_x_tom"
+                person_name = person.split('_', 2)[-1]
+            features_mean_personX = np.insert(features_mean_personX, 0, person_name, axis=0)
+            # features_mean_personX will be 129D, person name + 128 features
             writer.writerow(features_mean_personX)
             logging.info('\n')
         logging.info("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_tkinter.py

@@ -0,0 +1,291 @@
+from tkinter import *
+from tkinter import font as tkFont
+from PIL import Image, ImageTk
+import dlib
+import numpy as np
+import cv2
+import os
+import shutil
+import time
+import logging
+
+# Dlib 正向人脸检测器 / Use frontal face detector of Dlib
+detector = dlib.get_frontal_face_detector()
+
+
+class Face_Register:
+    def __init__(self):
+
+        self.existing_faces_cnt = 0         # 已录入的人脸计数器 / cnt for counting saved faces
+        self.ss_cnt = 0                     # 录入 person_n 人脸时图片计数器 / cnt for screen shots
+        self.current_frame_faces_cnt = 0    # 当前帧中人脸计数器 / cnt for counting faces in current frame
+
+        # Tkinter GUI
+        self.win = Tk()
+        self.win.title("Face Register @coneypo")
+        self.win.geometry("1300x550")
+
+        # GUI left part
+        self.frame_left_camera = Frame(self.win)
+        self.label = Label(self.win)
+        self.label.pack(side=LEFT)
+        self.frame_left_camera.pack()
+
+        # GUI right part
+        self.frame_right_info = Frame(self.win)
+        self.label_cnt_face_in_database = Label(self.frame_right_info, text=str(self.existing_faces_cnt))
+        self.label_fps_info = Label(self.frame_right_info, text="")
+        self.input_name = Entry(self.frame_right_info)
+        self.input_name_char = ""
+        self.label_warning = Label(self.frame_right_info)
+        self.label_face_cnt = Label(self.frame_right_info, text="Faces in current frame: ")
+        self.log_all = Label(self.frame_right_info)
+
+        self.font_title = tkFont.Font(family='Helvetica', size=20, weight='bold')
+        self.font_step_title = tkFont.Font(family='Helvetica', size=15, weight='bold')
+        self.font_warning = tkFont.Font(family='Helvetica', size=15, weight='bold')
+
+        self.path_photos_from_camera = "data/data_faces_from_camera/"
+        self.current_face_dir = ""
+        self.font = cv2.FONT_ITALIC
+
+        # Current frame and face ROI position
+        self.current_frame = np.ndarray
+        self.face_ROI_image = np.ndarray
+        self.face_ROI_width_start = 0
+        self.face_ROI_height_start = 0
+        self.face_ROI_width = 0
+        self.face_ROI_height = 0
+        self.ww = 0
+        self.hh = 0
+
+        self.out_of_range_flag = FALSE
+        self.face_folder_created_flag = FALSE
+
+        # FPS
+        self.frame_time = 0
+        self.frame_start_time = 0
+        self.fps = 0
+        self.fps_show = 0
+        self.start_time = time.time()
+
+        self.cap = cv2.VideoCapture(0)  # Get video stream from camera
+        # self.cap = cv2.VideoCapture("test.mp4")   # Input local video
+
+    # 删除之前存的人脸数据文件夹 / Delete old face folders
+    def GUI_clear_data(self):
+        # 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(self.path_photos_from_camera)
+        for i in range(len(folders_rd)):
+            shutil.rmtree(self.path_photos_from_camera + folders_rd[i])
+        if os.path.isfile("data/features_all.csv"):
+            os.remove("data/features_all.csv")
+        self.label_cnt_face_in_database['text'] = "0"
+        self.existing_faces_cnt = 0
+        self.log_all["text"] = "Face images and `features_all.csv` removed!"
+
+    def GUI_get_input_name(self):
+        self.input_name_char = self.input_name.get()
+        self.create_face_folder()
+        self.label_cnt_face_in_database['text'] = str(self.existing_faces_cnt)
+
+    def GUI_info(self):
+        Label(self.frame_right_info,
+              text="Face register",
+              font=self.font_title).grid(row=0, column=0, columnspan=3, sticky=W, padx=2, pady=20)
+
+        Label(self.frame_right_info,
+              text="FPS: ").grid(row=1, column=0, columnspan=2, sticky=W, padx=5, pady=2)
+        self.label_fps_info.grid(row=1, column=2, sticky=W, padx=5, pady=2)
+
+        Label(self.frame_right_info,
+              text="Faces in database: ").grid(row=2, column=0, columnspan=2, sticky=W, padx=5, pady=2)
+        self.label_cnt_face_in_database.grid(row=2, column=2, columnspan=3, sticky=W, padx=5, pady=2)
+
+        Label(self.frame_right_info,
+              text="Faces in current frame: ").grid(row=3, column=0, columnspan=2, sticky=W, padx=5, pady=2)
+        self.label_face_cnt.grid(row=3, column=2, columnspan=3, sticky=W, padx=5, pady=2)
+
+        self.label_warning.grid(row=4, column=0, columnspan=3, sticky=W, padx=5, pady=2)
+
+        # Step 1: Clear old data
+        Label(self.frame_right_info,
+              font=self.font_step_title,
+              text="Step 1: Clear face photos").grid(row=5, column=0, columnspan=2, sticky=W, padx=5, pady=20)
+        Button(self.frame_right_info,
+               text='Clear',
+               command=self.GUI_clear_data).grid(row=6, column=0, columnspan=3, sticky=W, padx=5, pady=2)
+
+        # Step 2: Input name and create folders for face
+        Label(self.frame_right_info,
+              font=self.font_step_title,
+              text="Step 2: Input name").grid(row=7, column=0, columnspan=2, sticky=W, padx=5, pady=20)
+
+        Label(self.frame_right_info, text="Name: ").grid(row=8, column=0, sticky=W, padx=5, pady=0)
+        self.input_name.grid(row=8, column=1, sticky=W, padx=0, pady=2)
+
+        Button(self.frame_right_info,
+               text='Input',
+               command=self.GUI_get_input_name).grid(row=8, column=2, padx=5)
+
+        # Step 3: Save current face in frame
+        Label(self.frame_right_info,
+              font=self.font_step_title,
+              text="Step 3: Save face image").grid(row=9, column=0, columnspan=2, sticky=W, padx=5, pady=20)
+
+        Button(self.frame_right_info,
+               text='Save current face',
+               command=self.save_current_face).grid(row=10, column=0, columnspan=3, sticky=W)
+
+        # Log
+        self.log_all.grid(row=11, column=0, columnspan=20, sticky=W, padx=5, pady=20)
+
+        self.frame_right_info.pack()
+
+    # 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(self):
+        # 新建文件夹 / Create folders to save face images and csv
+        if os.path.isdir(self.path_photos_from_camera):
+            pass
+        else:
+            os.mkdir(self.path_photos_from_camera)
+
+    # 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 / Start from person_x+1
+    def check_existing_faces_cnt(self):
+        if os.listdir("data/data_faces_from_camera/"):
+            # 获取已录入的最后一个人脸序号 / Get the order of latest person
+            person_list = os.listdir("data/data_faces_from_camera/")
+            person_num_list = []
+            for person in person_list:
+                person_order = person.split('_')[1].split('_')[0]
+                person_num_list.append(int(person_order))
+            self.existing_faces_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:
+            self.existing_faces_cnt = 0
+
+    # 更新 FPS / Update FPS of Video stream
+    def update_fps(self):
+        now = time.time()
+        # 每秒刷新 fps / Refresh fps per second
+        if str(self.start_time).split(".")[0] != str(now).split(".")[0]:
+            self.fps_show = self.fps
+        self.start_time = now
+        self.frame_time = now - self.frame_start_time
+        self.fps = 1.0 / self.frame_time
+        self.frame_start_time = now
+
+        self.label_fps_info["text"] = str(self.fps.__round__(2))
+
+    def create_face_folder(self):
+        # # 4. 新建存储人脸的文件夹 / Create the folders for saving faces
+        self.existing_faces_cnt += 1
+        if self.input_name_char:
+            self.current_face_dir = self.path_photos_from_camera + \
+                                    "person_" + str(self.existing_faces_cnt) + "_" + \
+                                    self.input_name_char
+        else:
+            self.current_face_dir = self.path_photos_from_camera + \
+                                    "person_" + str(self.existing_faces_cnt)
+        os.makedirs(self.current_face_dir)
+        self.log_all["text"] = "\"" + self.current_face_dir + "/\" created!"
+        logging.info("\n%-40s %s", "新建的人脸文件夹 / Create folders:", self.current_face_dir)
+
+        self.ss_cnt = 0  # 将人脸计数器清零 / Clear the cnt of screen shots
+        self.face_folder_created_flag = 1  # 已经按下 'n' / Pressed 'n' already
+
+    def save_current_face(self):
+        if self.face_folder_created_flag:
+            if self.current_frame_faces_cnt == 1:
+                if not self.out_of_range_flag:
+                    self.ss_cnt += 1
+                    # 根据人脸大小生成空的图像 / Create blank image according to the size of face detected
+                    self.face_ROI_image = np.zeros((int(self.face_ROI_height * 2), self.face_ROI_width * 2, 3),
+                                                   np.uint8)
+                    for ii in range(self.face_ROI_height * 2):
+                        for jj in range(self.face_ROI_width * 2):
+                            self.face_ROI_image[ii][jj] = self.current_frame[self.face_ROI_height_start - self.hh + ii][
+                                self.face_ROI_width_start - self.ww + jj]
+                    self.log_all["text"] = "\"" + self.current_face_dir + "/img_face_" + str(
+                        self.ss_cnt) + ".jpg\"" + " saved!"
+                    self.face_ROI_image = cv2.cvtColor(self.face_ROI_image, cv2.COLOR_BGR2RGB)
+
+                    cv2.imwrite(self.current_face_dir + "/img_face_" + str(self.ss_cnt) + ".jpg", self.face_ROI_image)
+                    logging.info("%-40s %s/img_face_%s.jpg", "写入本地 / Save into：",
+                                 str(self.current_face_dir), str(self.ss_cnt) + ".jpg")
+                else:
+                    self.log_all["text"] = "Please do not out of range!"
+            else:
+                self.log_all["text"] = "No face in current frame!"
+        else:
+            self.log_all["text"] = "Please run step 2!"
+
+    def get_frame(self):
+        try:
+            if self.cap.isOpened():
+                ret, frame = self.cap.read()
+                return ret, cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
+        except:
+            print("Error: No video input!!!")
+
+    # 获取人脸 / Main process of face detection and saving
+    def process(self):
+        ret, self.current_frame = self.get_frame()
+        faces = detector(self.current_frame, 0)
+        # Get frame
+        if ret:
+            self.update_fps()
+            self.label_face_cnt["text"] = str(len(faces))
+            # 检测到人脸 / Face detected
+            if len(faces) != 0:
+                # 矩形框 / Show the ROI of faces
+                for k, d in enumerate(faces):
+                    self.face_ROI_width_start = d.left()
+                    self.face_ROI_height_start = d.top()
+                    # 计算矩形框大小 / Compute the size of rectangle box
+                    self.face_ROI_height = (d.bottom() - d.top())
+                    self.face_ROI_width = (d.right() - d.left())
+                    self.hh = int(self.face_ROI_height / 2)
+                    self.ww = int(self.face_ROI_width / 2)
+
+                    # 判断人脸矩形框是否超出 480x640 / If the size of ROI > 480x640
+                    if (d.right() + self.ww) > 640 or (d.bottom() + self.hh > 480) or (d.left() - self.ww < 0) or (
+                            d.top() - self.hh < 0):
+                        self.label_warning["text"] = "OUT OF RANGE"
+                        self.label_warning['fg'] = 'red'
+                        self.out_of_range_flag = TRUE
+                        color_rectangle = (255, 0, 0)
+                    else:
+                        self.out_of_range_flag = FALSE
+                        self.label_warning["text"] = ""
+                        color_rectangle = (255, 255, 255)
+                    self.current_frame = cv2.rectangle(self.current_frame,
+                                                       tuple([d.left() - self.ww, d.top() - self.hh]),
+                                                       tuple([d.right() + self.ww, d.bottom() + self.hh]),
+                                                       color_rectangle, 2)
+            self.current_frame_faces_cnt = len(faces)
+            img = Image.fromarray(self.current_frame)
+            # Convert image to PhotoImage
+            imgtk = ImageTk.PhotoImage(image=img)
+            self.label.imgtk = imgtk
+            self.label.configure(image=imgtk)
+
+        self.win.after(20, self.process)
+
+    def run(self):
+        self.pre_work_mkdir()
+        self.check_existing_faces_cnt()
+        self.GUI_info()
+        self.process()
+        self.win.mainloop()
+
+
+def main():
+    logging.basicConfig(level=logging.INFO)
+    Face_Register_con = Face_Register()
+    Face_Register_con.run()
+
+
+if __name__ == '__main__':
+    main()