文章目录
一、原理
参考前面的博客基于OpenCv+Python+Dlib实现简单人脸数据采集
二、代码实现
1.提取人脸
输入需要录制的人的姓名用来创建对应文件夹来保存图片，通过摄像头捕获到的图片进行人脸检测，当检测到人脸后用矩形进行标注。按下s键进行保存，ESC键盘退出。
import cv2
import dlib
import os
import sys
import random
# 存储位置
output_dir = './output/person/test'
size = 64
# 改变图片的亮度与对比度
def relight(img, light=1, bias=0):
w = img.shape[1]
h = img.shape[0]
# image = []
for i in range(0, w):
for j in range(0, h):
for c in range(3):
tmp = int(img[j, i, c] * light + bias)
if tmp > 255:
tmp = 255
elif tmp < 0:
tmp = 0
img[j, i, c] = tmp
return img
# 使用dlib自带的frontal_face_detector作为我们的特征提取器
detector = dlib.get_frontal_face_detector()
# 打开摄像头 参数为输入流，可以为摄像头或视频文件
camera = cv2.VideoCapture(0)
name=input("请输入录入人的姓名:")
index = 1
ok = True
output_dir+= '/' +name
if not os.path.exists(output_dir):
os.makedirs(output_dir)
while ok:
# 从摄像头读取照片
# 读取摄像头中的图像，ok为是否读取成功的判断参数
ok, img = camera.read()
# 转换成灰度图像
img_gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
# 使用detector进行人脸检测
dets = detector(img_gray, 1)
for i, d in enumerate(dets):
x1 = d.top() if d.top() > 0 else 0
y1 = d.bottom() if d.bottom() > 0 else 0
x2 = d.left() if d.left() > 0 else 0
y2 = d.right() if d.right() > 0 else 0
# 截取人脸方框
face = img[x1:y1, x2:y2]
# 绘制矩形框标注人脸
cv2.rectangle(img, tuple([x2, x1]), tuple([y2, y1]), (0, 255, 255), 2)
print('Being processed picture %s' % index)
# 调整图片的对比度与亮度， 对比度与亮度值都取随机数，这样能增加样本的多样性
face = relight(face, random.uniform(0.5, 1.5), random.randint(-50, 50))
# 重设图片大小
face = cv2.resize(face,(size,size))
# 展示摄像头读取到的图片
cv2.imshow(name, img)
key = cv2.waitKey(1)
# ESC退出
if key == 27:
break
# s保存
elif key == 115:
# 保存图片
cv2.imwrite(output_dir +'/' + str(index) + '.jpg', face)
print("save success ")
index += 1
2.获取人脸的特征点
通过遍历存放人脸图片的文件夹来获取所有人的人脸数据，再对每个人的人脸数据进行提前特征值，最后把计算出每个人的特征均值保存到文件中。
# 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
# return_128d_features() 获取某张图像的128D特征
# compute_the_mean() 计算128D特征均值
from cv2 import cv2 as 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 = "./output/person/test/"
# Dlib 正向人脸检测器
detector = dlib.get_frontal_face_detector()
# Dlib 人脸预测器
predictor = dlib.shape_predictor("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("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)
# 提取人脸坐标
faces = detector(img_gray, 1)
# 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:
# 提取人脸特征
shape = predictor(img_gray, faces[0])
# 通过特征点进行人脸识别
face_descriptor = face_rec.compute_face_descriptor(img_gray, shape)
else:
face_descriptor = 0
return face_descriptor
# 将文件夹中照片特征提取出来, 写入 CSV
def return_features_mean_person(path_faces_person):
features_list_person = []
photos_list = os.listdir(path_faces_person)
if photos_list:
for i in range(len(photos_list)):
# 调用return_128d_features()得到128d特征
features_128d = return_128d_features(path_faces_person + "/" + photos_list[i])
# 遇到没有检测出人脸的图片跳过
if features_128d == 0:
i += 1
else:
# 把提取到的特征点加入列表
features_list_person.append(features_128d)
else:
print("文件夹内图像文件为空 / Warning: No images in " + path_faces_person + '/', '\n')
print("有效人脸数量:",len(features_list_person))
# 计算 128D 特征的均值
if features_list_person:
features_mean_person = np.array(features_list_person).mean(axis=0)
else:
features_mean_person = '0'
return features_mean_person
people = os.listdir(path_images_from_camera)
people.sort()
print("人名:",people)
with open("./output/features/test.csv", "w", newline="") as csvfile:
writer = csv.writer(csvfile)
for person in people:
print("正在计算" + person + "特征均值...")
features_mean_person = return_features_mean_person(path_images_from_camera + person)
writer.writerow(features_mean_person)
print(person + "特征均值计算完毕")
print("dlib特征均值 :", list(features_mean_person))
print()
print("所有录入人脸数据存入 / Save all the features of faces registered into: /output/features/test.csv")
人脸识别
通过摄像头捕获到的图片来检测人脸，把检测到人脸数据取特征值和已有的数据集进行对比，找到误差范围内的人。如果找不到则显示unknown，否则显示对应人的名字。
# 摄像头实时人脸识别
import os
import winsound # 系统音效
from playsound import playsound # 音频播放
import dlib # 人脸处理的库 Dlib
import csv # 存入表格
import time
import sys
import numpy as np # 数据处理的库 numpy
from cv2 import cv2 as cv2 # 图像处理的库 OpenCv
import pandas as pd # 数据处理的库 Pandas
# 人脸识别模型，提取128D的特征矢量
# face recognition model, the object maps human faces into 128D vectors
# Refer this tutorial: http://dlib.net/python/index.html#dlib.face_recognition_model_v1
facerec = dlib.face_recognition_model_v1(
"dlib_face_recognition_resnet_model_v1.dat")
# 计算两个128D向量间的欧式距离
# compute the e-distance between two 128D features
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
# 处理存放所有人脸特征的 csv
path_features_known_csv = "./output/features/test.csv"
csv_rd = pd.read_csv(path_features_known_csv, header=None)
# 用来存放所有录入人脸特征的数组
# the array to save the features of faces in the database
features_known_arr = []
# 读取已知人脸数据
# print known faces
for i in range(csv_rd.shape[0]):
features_someone_arr = []
for j in range(0, len(csv_rd.iloc[i, :])):
features_someone_arr.append(csv_rd.iloc[i, :][j])
features_known_arr.append(features_someone_arr)
print("Faces in Database：", len(features_known_arr))
# Dlib 检测器和预测器
# The detector and predictor will be used
detector = dlib.get_frontal_face_detector()
predictor = dlib.shape_predictor('shape_predictor_68_face_landmarks.dat')
# 创建 cv2 摄像头对象
cap = cv2.VideoCapture(0)
# cap.set(propId, value)
# 设置视频参数，propId 设置的视频参数，value 设置的参数值
cap.set(3, 480)
# cap.isOpened() 返回 true/false 检查初始化是否成功
# when the camera is open
while cap.isOpened():
flag, img_rd = cap.read()
kk = cv2.waitKey(1)
# 取灰度
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 later
font = cv2.FONT_HERSHEY_COMPLEX
# 存储当前摄像头中捕获到的所有人脸的坐标/名字
# the list to save the positions and names of current faces captured
pos_namelist = []
name_namelist = []
# 按下 ESC 键退出
if kk == 27:
break
else:
# 检测到人脸 when face detected
if len(faces) != 0:
# 获取当前捕获到的图像的所有人脸的特征，存储到 features_cap_arr
# get the features captured and save into features_cap_arr
features_cap_arr = []
for i in range(len(faces)):
shape = predictor(img_rd, faces[i])
features_cap_arr.append(facerec.compute_face_descriptor(img_rd, shape))
# 遍历捕获到的图像中所有的人脸
# traversal all the faces in the database
for k in range(len(faces)):
print("##### camera person", k + 1, "#####")
# 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
# set the default names of faces with "unknown"
name_namelist.append("unknown")
# 每个捕获人脸的名字坐标 the positions of faces captured
pos_namelist.append(
tuple([faces[k].left(), int(faces[k].bottom() + (faces[k].bottom() - faces[k].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
e_distance_list = []
for i in range(len(features_known_arr)):
# 如果 person_X 数据不为空
if str(features_known_arr[i][0]) != '0.0':
print("with person", str(i + 1), "the e distance: ", end='')
e_distance_tmp = return_euclidean_distance(features_cap_arr[k], features_known_arr[i])
print(e_distance_tmp)
e_distance_list.append(e_distance_tmp)
else:
# 空数据 person_X
e_distance_list.append(999999999)
# 找出最接近的一个人脸数据是第几个
# Find the one with minimum e distance
similar_person_num = e_distance_list.index(min(e_distance_list))
print("Minimum e distance with person", int(similar_person_num) + 1)
# 计算人脸识别特征与数据集特征的欧氏距离
# 距离小于0.4则标出为可识别人物
if min(e_distance_list) < 0.4:
# 这里可以修改摄像头中标出的人名
# Here you can modify the names shown on the camera
# 1、遍历文件夹目录
folder_name = './output/person/test'
# 最接近的人脸
sum = similar_person_num + 1
key_id = 1 # 从第一个人脸数据文件夹进行对比
# 获取文件夹中的文件名:1wang、2zhou、3...
file_names = os.listdir(folder_name)
for name in file_names:
# print(name+'->'+str(key_id))
if sum == key_id:
# winsound.Beep(300,500)# 响铃：300频率，500持续时间
name_namelist[k] = name[0:] # 人名删去第一个数字（用于视频输出标识）
key_id += 1
else:
print("Unknown person")
# 矩形框
# draw rectangle
for kk, d in enumerate(faces):
# 绘制矩形框
cv2.rectangle(img_rd, tuple([d.left(), d.top()]), tuple([d.right(), d.bottom()]), (0, 255, 255), 2)
print('\n')
# 在人脸框下面写人脸名字
# write names under rectangle
for i in range(len(faces)):
cv2.putText(img_rd, name_namelist[i], pos_namelist[i], font, 0.8, (0, 255, 255), 1, cv2.LINE_AA)
print("Faces in camera now:", name_namelist, "\n")
# cv2.putText(img_rd, "Press 'q': Quit", (20, 450), font, 0.8, (84, 255, 159), 1, cv2.LINE_AA)
cv2.putText(img_rd, "Face Recognition", (20, 40), font, 1, (0, 0, 255), 1, cv2.LINE_AA)
cv2.putText(img_rd, "Visitors: " + str(len(faces)), (20, 100), font, 1, (0, 0, 255), 1, cv2.LINE_AA)
# 窗口显示 show with opencv
cv2.imshow("camera", img_rd)
# 释放摄像头 release camera
cap.release()
# 删除建立的窗口 delete all the windows
cv2.destroyAllWindows()
三、结果
1.提取人脸效果
提取人脸过程，按下s键进行保存
提取到的图片
2.提取特征值
3.人脸预测
四、总结
通过建立人脸数据集，再提取检测到的人脸特征值，对两者进行比对，误差在一定范围内就认为是同一个人。