opencv之dlib库人脸识别
基础知识
python知识:
import os,shutil
shutil.rmtree("C:\Users\yangwj\Desktop\test") #删除目录
os.remove("C:\Users\yangwj\Desktop\replay_pid28076.log") # 删除文件
os.path.isfile() # 判断是否为文件
os.listdir() # 列出路径下的目录
1、从摄像头获取人脸图片
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 os # 读写文件 import shutil # 读写文件 # 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 / 68 points features predictor predictor = dlib.shape_predictor('data/data_dlib/shape_predictor_68_face_landmarks.dat') # OpenCv 调用摄像头 use camera cap = cv2.VideoCapture(0) # 设置视频参数 set camera cap.set(3, 480) # 人脸截图的计数器 the counter for screen shoot cnt_ss = 0 # 存储人脸的文件夹 the folder to save faces current_face_dir = "" # 保存 faces images 的路径 the directory to save images of faces path_photos_from_camera = "data/data_faces_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 / make folders to save faces images and csv if os.path.isdir(path_photos_from_camera): pass else: os.mkdir(path_photos_from_camera) pre_work_mkdir() ##### optional/可选, 默认关闭 ##### # 删除之前存的人脸数据文件夹 # 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]) 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 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 / 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(path_photos_from_camera): # 获取已录入的最后一个人脸序号 / get the num of latest person person_list = os.listdir(path_photos_from_camera) 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 / 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(): 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) # 人脸数 faces faces = detector(img_gray, 0) # 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 / press 'n' to create the folders for saving faces if kk == ord('n'): person_cnt += 1 print("请输入名字") person_name = input() current_face_dir = path_photos_from_camera + "person_" + str(person_cnt) os.makedirs(current_face_dir) print(' ') print("新建的人脸文件夹 / Create folders: ", current_face_dir) 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 / if face detected if len(faces) != 0: # 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 / 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 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 # TODO 可以考虑不减 ,看效果---->结果是只有脸部图像 cv2.rectangle(img_rd, tuple([d.left() - ww, d.top() - hh]), tuple([d.right() + ww, d.bottom() + hh]), color_rectangle, 2) # 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 / 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 press_n_flag: cnt_ss += 1 for ii in range(height*2): for jj in range(width*2): # 将人脸图像填充到空图像中 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 / 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 / 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 / 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 / press 'q' to exit if kk == ord('q'): break # 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 / release camera cap.release() cv2.destroyAllWindows()
2、将获取的人脸图片转为csv文件
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q path_images_from_camera = "data/data_faces_from_camera/" # Dlib 正向人脸检测器 detector = dlib.get_frontal_face_detector() # Dlpredictorib 人脸预测器 predictor = dlib.shape_predictor("data/data_dlib/shape_predictor_68_face_landmarks.dat") # Dlib 人脸识别模型 # Face recognition model, the object maps human faces into 128D vectors # shape_predictor_68_face_landmarks.dat 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), ' ') # 因为有可能截下来的人脸再去,检测不出检测来人脸了 # 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) print("faces") 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: continue 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 + '/', ' ') # 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(' ') print("所有录入人脸数据存入 / Save all the features of faces registered into: data/features_all.csv")