# 进行人脸录入 / face register # 录入多张人脸 / support multi-faces import numpy as np import cv2 import os import shutil # 读写文件 import time import win32com.client from PIL import ImageDraw, ImageFont from PIL import Image action_list = ['look_ahead', 'look_left', 'look_right', 'rise_head', 'bow_head', 'blink', 'open_mouth', 'smile', 'over'] action_map = {'look_ahead': '请看前方', 'blink': '请眨眼', 'open_mouth': '请张嘴', 'smile': '请笑一笑', 'rise_head': '请抬头', 'bow_head': '请低头', 'look_left': '请看左边', 'look_right': '请看右边', 'over': '录入完成'} people_type_dict = {'0': 'elder', '1': 'employee', '2': 'volunteer'} # Dlib 正向人脸检测器 # detector = dlib.get_frontal_face_detector() # OpenCV DNN face detector detector = cv2.dnn.readNetFromCaffe("data/data_opencv/deploy.prototxt.txt", "data/data_opencv/res10_300x300_ssd_iter_140000.caffemodel") class Face_Register: def __init__(self, people_type, id): self.init = False 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 self.ss_cnt = 0 # 录入 personX 人脸时图片计数器 self.faces_cnt = 0 # 录入人脸计数器 # 之后用来控制是否保存图像的 flag self.save_flag = 1 # 之后用来检查是否先按 'n' 再按 's'，即先新建文件夹再保存 self.press_n_flag = 0 # 之后用来提示动作的计数器 self.index = 0 self.frame_time = 0 self.frame_start_time = 0 self.fps = 0 self.people_type = people_type_dict[str(people_type)] self.id = str(id) def speak(self): text = action_map[action_list[self.index]] speak = win32com.client.Dispatch('Sapi.SpVoice') # speak.Voice = speak.GetVoices('Microsoft Zira') speak.Volume = 100 speak.Rate = 2 speak.Speak(text) # 新建保存人脸图像文件 def pre_work_mkdir(self): if os.path.isdir(self.path_photos_from_camera): if os.path.isdir(self.path_photos_from_camera + '/' + self.people_type): pass else: os.mkdir(self.path_photos_from_camera + '/' + self.people_type) else: os.mkdir(self.path_photos_from_camera) # 删除之前存的人脸数据文件夹 def pre_work_del_old_face_folders(self): # 删除之前存的人脸数据文件夹 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 (v1) # 根据传入的人员类型和 id 录入 (v2) 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 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 1 开始录入 # Start from 1 else: self.existing_faces_cnt = 0 # 获取处理之后 stream 的帧数 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 # 生成的 cv2 window 上面添加说明文字 def draw_note(self, img_rd): # 添加说明 # cv2.putText(img_rd, action_map[action_list[index]].encode('utf-8').decode(), (20, 250), self.font, 1, # (0, 255, 0), 1, cv2.LINE_AA) 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.__round__(2)), (20, 100), self.font, 0.8, (0, 255, 0), 1, cv2.LINE_AA) cv2.putText(img_rd, "Faces: " + str(self.faces_cnt), (20, 140), self.font, 0.8, (0, 255, 0), 1, cv2.LINE_AA) # cv2.putText(img_rd, "N: Create face folder", (20, 350), self.font, 0.8, (255, 255, 255), 1, cv2.LINE_AA) # cv2.putText(img_rd, "S: Save current face", (20, 400), self.font, 0.8, (255, 255, 255), 1, cv2.LINE_AA) # cv2.putText(img_rd, "Q: Quit", (20, 450), self.font, 0.8, (255, 255, 255), 1, cv2.LINE_AA) font = ImageFont.truetype("simsun.ttc", 30, index=1) img_rd = Image.fromarray(cv2.cvtColor(img_rd, cv2.COLOR_BGR2RGB)) draw = ImageDraw.Draw(img_rd) if self.index <= 7: draw.text((20, 230), text=action_map[action_list[self.index]].encode('utf-8').decode(), font=font, fill=(0, 255, 0)) else: draw.text((20, 230), text=action_map[action_list[8]].encode('utf-8').decode(), font=font, fill=(0, 255, 0)) img_rd = cv2.cvtColor(np.array(img_rd), cv2.COLOR_RGB2BGR) return img_rd # 获取人脸 def process(self, img_rd): self.faces_cnt = 0 (h, w) = img_rd.shape[:2] blob = cv2.dnn.blobFromImage(cv2.resize(img_rd, (300, 300)), 1.0, (300, 300), (104.0, 177.0, 123.0)) detector.setInput(blob) faces = detector.forward() # 检测到人脸 if faces.shape[2] != 0: # 矩形框 for i in range(0, faces.shape[2]): # 计算矩形框大小 confidence = faces[0, 0, i, 2] # filter out weak detections by ensuring the `confidence` is # greater than the minimum confidence if confidence < 0.5: continue self.faces_cnt += 1 # compute the (x, y)-coordinates of the bounding box for the # object box = faces[0, 0, i, 3:7] * np.array([w, h, w, h]) (startX, startY, endX, endY) = box.astype("int") # 判断人脸矩形框是否超出 480x640 if endX > 640 or endY > 480 or startX < 0 or startY < 0: # if (endX + ww) > 640 or (endY + hh > 480) or (startX - ww < 0) or ( # startY - hh < 0): cv2.putText(img_rd, "OUT OF RANGE", (20, 300), self.font, 0.8, (0, 0, 255), 1, cv2.LINE_AA) color_rectangle = (0, 0, 255) save_flag = 0 print("请调整位置 / Please adjust your position") else: color_rectangle = (0, 255, 0) save_flag = 1 cv2.rectangle(img_rd, tuple([startX, startY]), tuple([endX, endY]), color_rectangle, 2) # 生成的窗口添加说明文字 img_rd = self.draw_note(img_rd) self.update_fps() if not self.init: self.speak() self.init = True return img_rd def take_photo(self, img_rd): (h, w) = img_rd.shape[:2] blob = cv2.dnn.blobFromImage(cv2.resize(img_rd, (300, 300)), 1.0, (300, 300), (104.0, 177.0, 123.0)) detector.setInput(blob) faces = detector.forward() current_face_dir = self.path_photos_from_camera + self.people_type + '/' + self.id # 新建存储人脸的文件夹 if not os.path.exists(current_face_dir): # self.existing_faces_cnt += 1 # current_face_dir = self.path_photos_from_camera + "person_" + str(self.existing_faces_cnt) os.makedirs(current_face_dir) # print('\n') # print("新建的人脸文件夹 / Create folders: ", current_face_dir) # self.ss_cnt = 0 # 将人脸计数器清零 # self.index = 0 self.press_n_flag = 1 # 已经按下 'n' # 检测到人脸 if faces.shape[2] != 0: # 矩形框 for i in range(0, faces.shape[2]): # 计算矩形框大小 confidence = faces[0, 0, i, 2] # filter out weak detections by ensuring the `confidence` is # greater than the minimum confidence if confidence < 0.5: continue self.faces_cnt += 1 # compute the (x, y)-coordinates of the bounding box for the # object box = faces[0, 0, i, 3:7] * np.array([w, h, w, h]) (startX, startY, endX, endY) = box.astype("int") # height = (endY - startY) # width = (endX - startX) # # hh = int(height / 14) # # ww = int(width / 14) # 判断人脸矩形框是否超出 480x640 if endX > 640 or endY > 480 or startX < 0 or startY < 0: # if (endX + ww) > 640 or (endY + hh > 480) or (startX - ww < 0) or ( # startY - hh < 0): cv2.putText(img_rd, "OUT OF RANGE", (20, 300), self.font, 0.8, (0, 0, 255), 1, cv2.LINE_AA) color_rectangle = (0, 0, 255) save_flag = 0 print("请调整位置 / Please adjust your position") else: color_rectangle = (0, 255, 0) save_flag = 1 cv2.rectangle(img_rd, tuple([startX, startY]), tuple([endX, endY]), color_rectangle, 2) if save_flag: # 保存摄像头中的人脸到本地 # 检查有没有先按'n'新建文件夹 if self.press_n_flag: self.ss_cnt += 1 if self.index <= 7: img_blank = img_rd[startY:endY, startX:endX] cv2.imwrite(current_face_dir + "/img_face_" + str(self.ss_cnt) + ".jpg", img_blank) print("写入本地 / Save into：", str(current_face_dir) + "/img_face_" + str(self.ss_cnt) + ".jpg") if self.index < len(action_list) - 1: self.index += 1 else: self.init = False self.speak() else: print("请先按 'N' 来建文件夹, 按 'S' / Please press 'N' and press 'S'") # self.faces_cnt = len(faces) # 生成的窗口添加说明文字 img_rd = self.draw_note(img_rd) self.update_fps() if not self.init: self.index = 0 self.speak() self.init = True return img_rd def run(self, frame): return self.process(frame)