最近也是临危受命，要把3月做的人脸识别嵌入到项目的总体系统中，为了满足“客户要求”，我对当时的代码结构进行了一些改动和增删，用时两天…我太菜了
一、人脸识别的原理
dlib是python的一个库，其配合已训练好的dat模型（检测器）可以简单地实现人脸录入与检测识别
表层原理很简单，调用检测器检测人脸并将人脸的图像特征转化成128D的特征向量并保存在csv文件中
给几个图：
漂亮姐姐i了i了
另外关于人脸框的定位：如图
主体采用Resnet生成一个128D的特征向量，resnet是采用34层的（resnet34）。
但由于resnet34最后一层神经网络里实际上有1000个神经元，所以dlib在后面加了Dense（128），所以生成的是128维度的向量
二、python实现
人脸录入源码如下：
这里我们采用opencv调用电脑摄像头进行人脸录入
支持功能：范围提醒、保存图像、命名人名（到文件夹）等等吧（健忘）
# 进行人脸录入 / face register
# 录入多张人脸 / support multi-faces
# EotStxTaB
#!/usr/bin/python3
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()
# OpenCv 调用摄像头 / Use camera
cap = cv2.VideoCapture(0)
# 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 face images
current_face_dir = ""
# 保存 faces images 的路径 / The directory to save images of faces
path_photos_from_camera = "C:/Users/EotStxTaB/Documents/face/data/data_faces_from_camera/"
# 1. 新建保存人脸图像文件和数据CSV文件夹
# 1. 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 #####
# 2. 删除之前存的人脸数据文件夹
# 2. 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("C:/Users/EotStxTaB/Documents/face/data/features_all.csv"):
os.remove("C:/Users/EotStxTaB/Documents/face/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()
##################################
'''
'''
# 3. Check people order: person_cnt
# 如果有之前录入的人脸 / If the old folders existsq
# 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("C:/Users/EotStxTaB/Documents/face/data/data_faces_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("C:/Users/EotStxTaB/Documents/face/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)
# 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
#name = []
k = 0
while cap.isOpened():
flag, img_rd = cap.read()
# print(img_rd.shape)
# It should be 480 height * 640 width in Windows and Ubuntu by default
# Maybe 1280x720 in macOS
kk = cv2.waitKey(1)
img_gray = cv2.cvtColor(img_rd, cv2.COLOR_RGB2GRAY)
# 人脸 / Faces
faces = detector(img_gray, 0)
# 待会要写的字体 / Font to write
font = cv2.FONT_ITALIC
# 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'):
#person_cnt += 1
name = input("请输入：")
current_face_dir = path_photos_from_camera + name
os.makedirs(current_face_dir)
print('\n')
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 / Face detected
if len(faces) != 0:
# 矩形框 / Show the rectangle box of face
for k, d in enumerate(faces):
# 计算矩形大小
# 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
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 shape of face detected
im_blank = np.zeros((int(height*2), width*2, 3), np.uint8)
if save_flag:
# 5. 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):
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: Create 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)
# 6. 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 and destroy all windows
cap.release()
cv2.destroyAllWindows()接下来是转化图像为128向量
根据要求，我把csv每个都拆分开来存在各自文件夹
# 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
# EotStxTaB
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 = "C:/Users/EotStxTaB/Documents/face/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("C:/Users/EotStxTaB/Documents/face/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
face_rec = dlib.face_recognition_model_v1("C:/Users/EotStxTaB/Documents/face/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 + "/img_face_1.jpg"))
features_128d = return_128d_features(path_faces_personX + "/img_face_1.jpg")
# 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
dirct = 'C:/Users/EotStxTaB/Documents/face/data/data_faces_from_camera'
dirList=[]
n = 0
files=os.listdir(dirct) #文件夹下所有目录的列表
#print('files:',files)
for f in files:
if os.path.isdir(dirct + '/'+f): #这里是绝对路径，该句判断目录是否是文件夹
dirList.append(f)
n=n+1
elif os.path.isfile(dirct + '/'+f):#这里是绝对路径，该句判断目录是否是文件
continue
for i in range(0,n):
with open ("C:/Users/EotStxTaB/Documents/face/data/data_faces_from_camera/"+dirList[i]+"/features.csv", "w", newline='') as csvfile:
writer = csv.writer(csvfile)
features_mean_personX = return_features_mean_personX(path_images_from_camera + dirList[i])
writer.writerow(features_mean_personX)
接下来是大改的地方，由于莫名的计算延时很高，我把识别多脸改成了图像里最大的脸（hhhh）
同时改成了输出输入是单张图片，为了兼容项目系统中的另外一个摄像头
# EotStxTaB
import dlib # 人脸处理的库 Dlib
from dlib import dlib #vscode的专属错误解决方式，cv2与此相同
import numpy as np # 数据处理的库 numpy
import cv2 # 图像处理的库 OpenCv
from cv2 import cv2
import pandas as pd # 数据处理的库 Pandas
import matplotlib.pyplot as plt
import os
import csv
# 人脸识别模型，提取128D的特征矢量
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
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)))
return dist
detector = dlib.get_frontal_face_detector()
predictor = dlib.shape_predictor('./data/data_dlib/shape_predictor_68_face_landmarks.dat')
img_rd = cv2.imread('./data/input.jpg',cv2.IMREAD_UNCHANGED)
img_gray = cv2.cvtColor(img_rd,cv2.COLOR_RGB2GRAY)
faces = detector(img_gray, 0)
face=max(faces, key=lambda rect: rect.width() * rect.height())
#[x1,x2,y1,y2]=[face.left(),face.right(),face.top(),face.bottom()]
n=0
dirct = './data/data_faces_from_camera'
dirList=[]
files=os.listdir(dirct) #文件夹下所有目录的列表
for f in files:
if os.path.isdir(dirct + '/'+f): #这里是绝对路径，该句判断目录是否是文件夹
dirList.append(f)
n=n+1
elif os.path.isfile(dirct + '/'+f): #这里是绝对路径，该句判断目录是否是文件
continue
# 待会要写的字体 font to write later
font = cv2.FONT_ITALIC
#font = cv2.FONT_HERSHEY_SCRIPT_SIMPLEX#手写花体
shape = predictor(img_rd, face)
features_cap_arr=(facerec.compute_face_descriptor(img_rd, shape))
#print(features_cap_arr)
for m in range(0,n):
cs_rd = []
path_features_known_csv = "./data/data_faces_from_camera/"+dirList[m]+"/features.csv"
df = pd.read_csv(path_features_known_csv,engine='python')#据说也是vscode专属错误
file=open(path_features_known_csv,'r') #打开文件
df=csv.reader(file) #读取文件内容
for stu in df: #一行是一个数组
for i in range (0,128):
cs_rd.append(float(stu[i])) #取每个数组的第一个元素
#print(cs_rd)
# 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
name = "unknown"
# 每个捕获人脸的名字坐标 the positions of faces captured
pos_name = (tuple([face.left(), int(face.bottom() + (face.bottom() - face.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
# 如果 person_X 数据不为空
if str(cs_rd) != '0.0':
e_distance_tmp = return_euclidean_distance(features_cap_arr, cs_rd)
print(e_distance_tmp)
else:
# 空数据 person_X
e_distance_tmp = 999999999
if e_distance_tmp < 0.4:
name = dirList[m]
print("May be person ")
break
else:
print("Unknown person")
continue
# 矩形框
# draw rectangle
#下一行注释掉就可以没有矩形框
cv2.rectangle(img_rd, tuple([face.left(), face.top()]), tuple([face.right(), face.bottom()]), (0, 255, 255), 2)
#print('\n')
# 写名字
#下面（0，255，255）左边是字大小，右边是粗细
img = cv2.putText(img_rd, name, pos_name, font, 5, (0, 255, 255), 4, cv2.LINE_AA)
plt.imshow(img)
cv2.imwrite('./data/output.jpg',img)
最开始的是全程调用opencv摄像头，源码这应该还有，想要的话吱一声。
