基于python_opencv人脸录入、识别系统（应用dlib机器学习库）
近几年应用opencv机器学习方法识别人脸的技术成为了热潮，本人根据当今的识别技术与方法，历时四个多月开发出一套基于dlib机器学习库的识别项目。希望大家能一起交流学习。
项目英文名：Face recognition from camera with Dlib
1、项目功能介绍
- Tkinter 人脸录入界面, 支持录入时设置 (中文) 姓名；
- 调用摄像头进行人脸识别, 支持多张人脸同时识别；
- 定制显示名字, 可以写中文；
2、项目运行截图
下面直接上运行截图：
GUI界面运行结果： UI界面可以录入使用者的信息，并设置保存录入图片按钮
初次录入界面：
距离摄像头太近或太远会有提示：
中文识别界面运行结果： 识别信息包括开始录入时设置的名字，以及镜头下识别的人数
支持多人识别：
3、项目流程图
项目的流程图如下：
4、项目源码结构及模块化介绍（需要源码的朋友关注并私信我）
项目源码的结构如下：
- get_faces_from_camera_tkinter.py：进行人脸信息采集录入, Tkinter GUI
- get_face_from_camera.py：进行人脸信息采集录入, OpenCV GUI
- features_extraction_to_csv.py：提取所有录入人脸数据存入
features_all.csv - face_reco_from_camera.py：调用摄像头进行实时人脸识别
- face_reco_from_camera_single_face.py：对于人脸数<=1, 调用摄像头进行实时人脸识别
- face_reco_from_camera_ot.py：利用 OT 算法, 调用摄像头进行实时人脸识别
Python详细源码模块化介绍：
人脸信息采集录入模块（get_face_from_camera.py）：
- 请注意存储人脸图片时, 矩形框不要超出摄像头范围, 要不然无法保存到本地;
- 超出会有 “out of range” 的提醒;
class Face_Register: def __init__(self): self.path_photos_from_camera = "data/data_faces_from_camera/" self.font = cv2.FONT_ITALIC 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 personX 人脸时图片计数器 / cnt for screen shots self.current_frame_faces_cnt = 0 # 录入人脸计数器 / cnt for counting faces in current frame self.save_flag = 1 # 之后用来控制是否保存图像的 flag / The flag to control if save self.press_n_flag = 0 # 之后用来检查是否先按 'n' 再按 's' / The flag to check if press 'n' before 's' # FPS self.frame_time = 0 self.frame_start_time = 0 self.fps = 0 self.fps_show = 0 self.start_time = time.time() # 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) # 删除之前存的人脸数据文件夹 / Delete old face folders def pre_work_del_old_face_folders(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") # 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_num_list.append(int(person.split('_')[-1])) 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 # 生成的 cv2 window 上面添加说明文字 / PutText on cv2 window def draw_note(self, img_rd): # 添加说明 / Add some notes cv2.putText(img_rd, "Face Register", (20, 40), self.font, 1, (255, 255, 255), 1, cv2.LINE_AA) cv2.putText(img_rd, "FPS: " + str(self.fps_show.__round__(2)), (20, 100), self.font, 0.8, (0, 255, 0), 1, cv2.LINE_AA) cv2.putText(img_rd, "Faces: " + str(self.current_frame_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) # 获取人脸 / Main process of face detection and saving def process(self, stream): # 1. 新建储存人脸图像文件目录 / Create folders to save photos self.pre_work_mkdir() # 2. 删除 "/data/data_faces_from_camera" 中已有人脸图像文件 # / Uncomment if want to delete the saved faces and start from person_1 # if os.path.isdir(self.path_photos_from_camera): # self.pre_work_del_old_face_folders() # 3. 检查 "/data/data_faces_from_camera" 中已有人脸文件 self.check_existing_faces_cnt() while stream.isOpened(): flag, img_rd = stream.read() # Get camera video stream kk = cv2.waitKey(1) faces = detector(img_rd, 0) # Use Dlib face detector # 4. 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'): self.existing_faces_cnt += 1 current_face_dir = self.path_photos_from_camera + "person_" + str(self.existing_faces_cnt) os.makedirs(current_face_dir) logging.info("\n%-40s %s", "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 / Clear the cnt of screen shots self.press_n_flag = 1 # 已经按下 'n' / Pressed 'n' already # 5. 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): # 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) # 6. 判断人脸矩形框是否超出 480x640 / If the size of ROI > 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), self.font, 0.8, (0, 0, 255), 1, cv2.LINE_AA) color_rectangle = (0, 0, 255) save_flag = 0 if kk == ord('s'): logging.warning("请调整位置 / Please adjust your position") else: color_rectangle = (255, 255, 255) save_flag = 1 cv2.rectangle(img_rd, tuple([d.left() - ww, d.top() - hh]), tuple([d.right() + ww, d.bottom() + hh]), color_rectangle, 2) # 7. 根据人脸大小生成空的图像 / Create blank image according to the size of face detected img_blank = np.zeros((int(height*2), width*2, 3), np.uint8) if save_flag: # 8. 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'): # 检查有没有先按'n'新建文件夹 / Check if you have pressed 'n' if self.press_n_flag: self.ss_cnt += 1 for ii in range(height*2): for jj in range(width*2): img_blank[ii][jj] = img_rd[d.top()-hh + ii][d.left()-ww + jj] cv2.imwrite(current_face_dir + "/img_face_" + str(self.ss_cnt) + ".jpg", img_blank) logging.info("%-40s %s/img_face_%s.jpg", "写入本地 / Save into：", str(current_face_dir), str(self.ss_cnt)) else: logging.warning("请先按 'N' 来建文件夹, 按 'S' / Please press 'N' and press 'S'") self.current_frame_faces_cnt = len(faces) # 9. 生成的窗口添加说明文字 / Add note on cv2 window self.draw_note(img_rd) # 10. 按下 'q' 键退出 / Press 'q' to exit if kk == ord('q'): break # 11. Update FPS self.update_fps() cv2.namedWindow("camera", 1) cv2.imshow("camera", img_rd) def run(self): # cap = cv2.VideoCapture("video.mp4") # Get video stream from video file cap = cv2.VideoCapture(0) # Get video stream from camera self.process(cap) cap.release() cv2.destroyAllWindows() def main(): logging.basicConfig(level=logging.INFO) Face_Register_con = Face_Register() Face_Register_con.run() if __name__ == '__main__': main()
进行人脸信息采集录入 Tkinter GUI（get_faces_from_camera_tkinter.py）：
class Face_Register: def __init__(self): self.current_frame_faces_cnt = 0 # 当前帧中人脸计数器 / cnt for counting faces in current frame 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 # Tkinter GUI self.win = tk.Tk() self.win.title("Face Register @coneypo") # PLease modify window size here if needed self.win.geometry("1300x550") # GUI left part self.frame_left_camera = tk.Frame(self.win) self.label = tk.Label(self.win) self.label.pack(side=tk.LEFT) self.frame_left_camera.pack() # GUI right part self.frame_right_info = tk.Frame(self.win) self.label_cnt_face_in_database = tk.Label(self.frame_right_info, text=str(self.existing_faces_cnt)) self.label_fps_info = tk.Label(self.frame_right_info, text="") self.input_name = tk.Entry(self.frame_right_info) self.input_name_char = "" self.label_warning = tk.Label(self.frame_right_info) self.label_face_cnt = tk.Label(self.frame_right_info, text="Faces in current frame: ") self.log_all = tk.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): tk.Label(self.frame_right_info, text="Face register", font=self.font_title).grid(row=0, column=0, columnspan=3, sticky=tk.W, padx=2, pady=20) tk.Label(self.frame_right_info, text="FPS: ").grid(row=1, column=0, columnspan=2, sticky=tk.W, padx=5, pady=2) self.label_fps_info.grid(row=1, column=2, sticky=tk.W, padx=5, pady=2) tk.Label(self.frame_right_info, text="Faces in database: ").grid(row=2, column=0, columnspan=2, sticky=tk.W, padx=5, pady=2) self.label_cnt_face_in_database.grid(row=2, column=2, columnspan=3, sticky=tk.W, padx=5, pady=2) tk.Label(self.frame_right_info, text="Faces in current frame: ").grid(row=3, column=0, columnspan=2, sticky=tk.W, padx=5, pady=2) self.label_face_cnt.grid(row=3, column=2, columnspan=3, sticky=tk.W, padx=5, pady=2) self.label_warning.grid(row=4, column=0, columnspan=3, sticky=tk.W, padx=5, pady=2) # Step 1: Clear old data tk.Label(self.frame_right_info, font=self.font_step_title, text="Step 1: Clear face photos").grid(row=5, column=0, columnspan=2, sticky=tk.W, padx=5, pady=20) tk.Button(self.frame_right_info, text='Clear', command=self.GUI_clear_data).grid(row=6, column=0, columnspan=3, sticky=tk.W, padx=5, pady=2) # Step 2: Input name and create folders for face tk.Label(self.frame_right_info, font=self.font_step_title, text="Step 2: Input name").grid(row=7, column=0, columnspan=2, sticky=tk.W, padx=5, pady=20) tk.Label(self.frame_right_info, text="Name: ").grid(row=8, column=0, sticky=tk.W, padx=5, pady=0) self.input_name.grid(row=8, column=1, sticky=tk.W, padx=0, pady=2) tk.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 tk.Label(self.frame_right_info, font=self.font_step_title, text="Step 3: Save face image").grid(row=9, column=0, columnspan=2, sticky=tk.W, padx=5, pady=20) tk.Button(self.frame_right_info, text='Save current face', command=self.save_current_face).grid(row=10, column=0, columnspan=3, sticky=tk.W) # Show log in GUI self.log_all.grid(row=11, column=0, columnspan=20, sticky=tk.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): # 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 = True # Face folder already created 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) # Convert PIL.Image.Image to PIL.Image.PhotoImage img_Image = Image.fromarray(self.current_frame) img_PhotoImage = ImageTk.PhotoImage(image=img_Image) self.label.img_tk = img_PhotoImage self.label.configure(image=img_PhotoImage) # Refresh frame 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()
提取人脸数据存入 CSV（features_extraction_to_csv.py）：
- 会生成一个存储所有特征人脸数据的
features_all.csv - Size:
n*129
# 要读取人脸图像文件的路径 / Path of cropped faces path_images_from_camera = "data/data_faces_from_camera/" # Dlib 正向人脸检测器 / Use frontal face detector of Dlib detector = dlib.get_frontal_face_detector() # Dlib 人脸 landmark 特征点检测器 / Get face landmarks predictor = dlib.shape_predictor('data/data_dlib/shape_predictor_68_face_landmarks.dat') # Dlib Resnet 人脸识别模型，提取 128D 的特征矢量 / Use Dlib resnet50 model to get 128D face descriptor 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 / Return 128D features for single image # Input: path_img <class 'str'> # Output: face_descriptor <class 'dlib.vector'> def return_128d_features(path_img): img_rd = cv2.imread(path_img) faces = detector(img_rd, 1) logging.info("%-40s %-20s", "检测到人脸的图像 / Image with faces detected:", path_img) # 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 # For photos of faces saved, we need to make sure that we can detect faces from the cropped images 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 logging.warning("no face") return face_descriptor # 返回 personX 的 128D 特征均值 / Return the mean value of 128D face descriptor for person X # Input: path_face_personX <class 'str'> # Output: features_mean_personX <class 'numpy.ndarray'> def return_features_mean_personX(path_face_personX): features_list_personX = [] photos_list = os.listdir(path_face_personX) if photos_list: for i in range(len(photos_list)): # 调用 return_128d_features() 得到 128D 特征 / Get 128D features for single image of personX logging.info("%-40s %-20s", "正在读的人脸图像 / Reading image:", path_face_personX + "/" + photos_list[i]) features_128d = return_128d_features(path_face_personX + "/" + photos_list[i]) # 遇到没有检测出人脸的图片跳过 / Jump if no face detected from image if features_128d == 0: i += 1 else: features_list_personX.append(features_128d) else: logging.warning("文件夹内图像文件为空 / Warning: No images in%s/", 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, dtype=object).mean(axis=0) else: features_mean_personX = np.zeros(128, dtype=object, order='C') return features_mean_personX 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_list.sort() with open("data/features_all.csv", "w", newline="") as csvfile: writer = csv.writer(csvfile) 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, 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") if __name__ == '__main__': main()
基于Python_opencv人脸录入、识别系统（应用dlib机器学习库）（下）+
