人脸任务在计算机视觉领域中十分重要，本项目主要使用了两类技术：人脸检测+人脸识别。
代码分为两部分内容：人脸注册 和 人脸识别
- 人脸注册：将人脸特征存储进数据库，这里用feature.csv代替
- 人脸识别：将人脸特征与CSV文件中人脸特征进行比较，如果成功匹配则写入考勤文件attendance.csv
文章前半部分为一步步实现流程介绍，最后会有整理过后的完整项目代码。
一、项目实现
A. 注册：
导入相关包
-
import cv2
-
import numpy
as np
-
import dlib
-
import time
-
import csv
-
# from argparse import ArgumentParser
-
from PIL
import Image, ImageDraw, ImageFont
设计注册功能
注册过程我们需要完成的事：
- 打开摄像头获取画面图片
- 在图片中检测并获取人脸位置
- 根据人脸位置获取68个关键点
- 根据68个关键点生成特征描述符
- 保存
- (优化)展示界面，加入注册时成功提示等
1、基本步骤
我们首先进行前三步：
-
# 检测人脸，获取68个关键点，获取特征描述符
-
def
faceRegister(
faceId=1, userName='default', interval=3, faceCount=3, resize_w=700, resize_h=400):
-
'''
-
faceId:人脸ID
-
userName: 人脸姓名
-
faceCount: 采集该人脸图片的数量
-
interval: 采集间隔
-
'''
-
-
cap = cv2.VideoCapture(
0)
-
# 人脸检测模型
-
hog_face_detector = dlib.get_frontal_face_detector()
-
# 关键点 检测模型
-
shape_detector = dlib.shape_predictor(
'./weights/shape_predictor_68_face_landmarks.dat')
-
# resnet模型
-
face_descriptor_extractor = dlib.face_recognition_model_v1(
'./weights/dlib_face_recognition_resnet_model_v1.dat')
-
-
while
True:
-
ret, frame = cap.read()
-
-
# 镜像
-
frame = cv2.flip(frame,
1)
-
-
# 转为灰度图
-
frame_gray = cv2.cvtColor(frame,cv2.COLOR_BGR2GRAY)
-
-
# 检测人脸
-
detections = hog_face_detector(frame,
1)
-
-
for face
in detections:
-
# 人脸框坐标 左上和右下
-
l, t, r, b = face.left(), face.top(), face.right(), face.bottom()
-
-
# 获取68个关键点
-
points = shape_detector(frame,face)
-
-
# 绘制关键点
-
for point
in points.parts():
-
cv2.circle(frame,(point.x,point.y),
2,(
0,
255,
0),
1)
-
-
# 绘制矩形框
-
cv2.rectangle(frame,(l,t),(r,b),(
0,
255,
0),
2)
-
-
cv2.imshow(
"face",frame)
-
if cv2.waitKey(
10) &
0xFF ==
ord(
'q'):
-
break
-
-
cap.release()
-
cv2.destroyAllWindows
-
-
-
faceRegister()
此时一张帅脸如下：
2、描述符的采集
之后，我们根据参数，即faceCount 和 Interval 进行描述符的生成和采集。
(这里我默认是faceCount=3，Interval=3，即每3秒采集一次，共3次）
-
def
faceRegister(
faceId=1, userName='default', interval=3, faceCount=3, resize_w=700, resize_h=400):
-
'''
-
faceId:人脸ID
-
userName: 人脸姓名
-
faceCount: 采集该人脸图片的数量
-
interval: 采集间隔
-
'''
-
-
cap = cv2.VideoCapture(
0)
-
# 人脸检测模型
-
hog_face_detector = dlib.get_frontal_face_detector()
-
# 关键点 检测模型
-
shape_detector = dlib.shape_predictor(
'./weights/shape_predictor_68_face_landmarks.dat')
-
# resnet模型
-
face_descriptor_extractor = dlib.face_recognition_model_v1(
'./weights/dlib_face_recognition_resnet_model_v1.dat')
-
-
-
# 开始时间
-
start_time = time.time()
-
# 执行次数
-
collect_times =
0
-
-
-
while
True:
-
ret, frame = cap.read()
-
-
# 镜像
-
frame = cv2.flip(frame,
1)
-
-
# 转为灰度图
-
frame_gray = cv2.cvtColor(frame,cv2.COLOR_BGR2GRAY)
-
-
# 检测人脸
-
detections = hog_face_detector(frame,
1)
-
-
for face
in detections:
-
# 人脸框坐标 左上和右下
-
l, t, r, b = face.left(), face.top(), face.right(), face.bottom()
-
-
# 获取68个关键点
-
points = shape_detector(frame,face)
-
-
# 绘制人脸关键点
-
for point
in points.parts():
-
cv2.circle(frame, (point.x, point.y),
2, (
0,
255,
0),
1)
-
# 绘制矩形框
-
cv2.rectangle(frame, (l, t), (r, b), (
0,
255,
0),
2)
-
-
# 采集：
-
if collect_times < faceCount:
-
# 获取当前时间
-
now = time.time()
-
# 时间限制
-
if now - start_time > interval:
-
# 获取特征描述符
-
face_descriptor = face_descriptor_extractor.compute_face_descriptor(frame,points)
-
# dlib格式转为数组
-
face_descriptor = [f
for f
in face_descriptor]
-
-
collect_times +=
1
-
start_time = now
-
print(
"成功采集{}次".
format(collect_times))
-
else:
-
# 时间间隔不到interval
-
print(
"等待进行下一次采集")
-
pass
-
else:
-
# 已经成功采集完3次了
-
print(
"采集完毕")
-
cap.release()
-
cv2.destroyAllWindows()
-
return
-
-
cv2.imshow(
"face",frame)
-
if cv2.waitKey(
10) &
0xFF ==
ord(
'q'):
-
break
-
-
cap.release()
-
cv2.destroyAllWindows()
-
-
-
faceRegister()
等待进行下一次采集 ... 成功采集1次 等待进行下一次采集 ... 成功采集2次 等待进行下一次采集 ... 成功采集3次 采集完毕
3、完整的注册
最后就是写入csv文件
这里加入了注册成功等的提示，且把一些变量放到了全局，因为后面人脸识别打卡时也会用到。
-
# 加载人脸检测器
-
hog_face_detector = dlib.get_frontal_face_detector()
-
cnn_detector = dlib.cnn_face_detection_model_v1(
'./weights/mmod_human_face_detector.dat')
-
haar_face_detector = cv2.CascadeClassifier(
'./weights/haarcascade_frontalface_default.xml')
-
-
# 加载关键点检测器
-
points_detector = dlib.shape_predictor(
'./weights/shape_predictor_68_face_landmarks.dat')
-
# 加载resnet模型
-
face_descriptor_extractor = dlib.face_recognition_model_v1(
'./weights/dlib_face_recognition_resnet_model_v1.dat')
-
# 绘制中文
-
def
cv2AddChineseText(
img, text, position, textColor=(0, 255, 0), textSize=30):
-
if (
isinstance(img, np.ndarray)):
# 判断是否OpenCV图片类型
-
img = Image.fromarray(cv2.cvtColor(img, cv2.COLOR_BGR2RGB))
-
# 创建一个可以在给定图像上绘图的对象
-
draw = ImageDraw.Draw(img)
-
# 字体的格式
-
fontStyle = ImageFont.truetype(
-
"./fonts/songti.ttc", textSize, encoding=
"utf-8")
-
# 绘制文本
-
draw.text(position, text, textColor, font=fontStyle)
-
# 转换回OpenCV格式
-
return cv2.cvtColor(np.asarray(img), cv2.COLOR_RGB2BGR)
-
# 绘制左侧信息
-
def
drawLeftInfo(
frame, fpsText, mode="Reg", detector='haar', person=1, count=1):
-
# 帧率
-
cv2.putText(frame,
"FPS: " +
str(
round(fpsText,
2)), (
30,
50), cv2.FONT_ITALIC,
0.8, (
0,
255,
0),
2)
-
-
# 模式：注册、识别
-
cv2.putText(frame,
"Mode: " +
str(mode), (
30,
80), cv2.FONT_ITALIC,
0.8, (
0,
255,
0),
2)
-
-
if mode ==
'Recog':
-
# 检测器
-
cv2.putText(frame,
"Detector: " + detector, (
30,
110), cv2.FONT_ITALIC,
0.8, (
0,
255,
0),
2)
-
-
# 人数
-
cv2.putText(frame,
"Person: " +
str(person), (
30,
140), cv2.FONT_ITALIC,
0.8, (
0,
255,
0),
2)
-
-
# 总人数
-
cv2.putText(frame,
"Count: " +
str(count), (
30,
170), cv2.FONT_ITALIC,
0.8, (
0,
255,
0),
2)
-
# 注册人脸
-
def
faceRegiser(
faceId=1, userName='default', interval=3, faceCount=3, resize_w=700, resize_h=400):
-
# 计数
-
count =
0
-
# 开始注册时间
-
startTime = time.time()
-
-
# 视频时间
-
frameTime = startTime
-
-
# 控制显示打卡成功的时长
-
show_time = (startTime -
10)
-
-
# 打开文件
-
f =
open(
'./data/feature.csv',
'a', newline=
'')
-
csv_writer = csv.writer(f)
-
-
cap = cv2.VideoCapture(
0)
-
-
while
True:
-
ret, frame = cap.read()
-
frame = cv2.resize(frame, (resize_w, resize_h))
-
frame = cv2.flip(frame,
1)
-
-
# 检测
-
face_detetion = hog_face_detector(frame,
1)
-
-
for face
in face_detetion:
-
# 识别68个关键点
-
points = points_detector(frame, face)
-
# 绘制人脸关键点
-
for point
in points.parts():
-
cv2.circle(frame, (point.x, point.y),
2, (
0,
255,
0),
1)
-
# 绘制框框
-
l, t, r, b = face.left(), face.top(), face.right(), face.bottom()
-
cv2.rectangle(frame, (l, t), (r, b), (
0,
255,
0),
2)
-
-
now = time.time()
-
-
if (now - show_time) <
0.5:
-
frame = cv2AddChineseText(frame,
-
"注册成功 {count}/{faceCount}".
format(count=(count +
1), faceCount=faceCount),
-
(l, b +
30), textColor=(
255,
0,
255), textSize=
30)
-
-
# 检查次数
-
-
if count < faceCount:
-
-
# 检查时间
-
if now - startTime > interval:
-
# 特征描述符
-
face_descriptor = face_descriptor_extractor.compute_face_descriptor(frame, points)
-
-
face_descriptor = [f
for f
in face_descriptor]
-
-
# 描述符增加进data文件
-
line = [faceId, userName, face_descriptor]
-
# 写入
-
csv_writer.writerow(line)
-
-
# 保存照片样本
-
-
print(
'人脸注册成功 {count}/{faceCount}，faceId:{faceId}，userName:{userName}'.
format(count=(count +
1),
-
faceCount=faceCount,
-
faceId=faceId,
-
userName=userName))
-
-
frame = cv2AddChineseText(frame,
-
"注册成功 {count}/{faceCount}".
format(count=(count +
1), faceCount=faceCount),
-
(l, b +
30), textColor=(
255,
0,
255), textSize=
30)
-
show_time = time.time()
-
-
# 时间重置
-
startTime = now
-
# 次数加一
-
count +=
1
-
-
-
else:
-
print(
'人脸注册完毕')
-
f.close()
-
cap.release()
-
cv2.destroyAllWindows()
-
return
-
-
now = time.time()
-
fpsText =
1 / (now - frameTime)
-
frameTime = now
-
# 绘制
-
drawLeftInfo(frame, fpsText,
'Register')
-
-
cv2.imshow(
'Face Attendance Demo: Register', frame)
-
if cv2.waitKey(
10) &
0xFF ==
ord(
'q'):
-
break
-
f.close()
-
cap.release()
-
cv2.destroyAllWindows()
此时执行：
faceRegiser(3,"用户B")
人脸注册成功 1/3，faceId:3，userName:用户B 人脸注册成功 2/3，faceId:3，userName:用户B 人脸注册成功 3/3，faceId:3，userName:用户B 人脸注册完毕
其features文件：
B. 识别、打卡
识别步骤如下：
- 打开摄像头获取画面
- 根据画面中的图片获取里面的人脸特征描述符
- 根据特征描述符将其与feature.csv文件里特征做距离判断
- 获取ID、NAME
- 考勤记录写入attendance.csv里
这里与上面流程相似，不过是加了一个对比功能，距离小于阈值，则表示匹配成功。就加快速度不一步步来了，代码如下：
-
# 刷新右侧考勤信息
-
def
updateRightInfo(
frame, face_info_list, face_img_list):
-
# 重新绘制逻辑：从列表中每隔3个取一批显示，新增人脸放在最前面
-
-
# 如果有更新，重新绘制
-
-
# 如果没有，定时往后移动
-
-
left_x =
30
-
left_y =
20
-
resize_w =
80
-
-
offset_y =
120
-
index =
0
-
-
frame_h = frame.shape[
0]
-
frame_w = frame.shape[
1]
-
-
for face
in face_info_list[:
3]:
-
name = face[
0]
-
time = face[
1]
-
face_img = face_img_list[index]
-
-
# print(face_img.shape)
-
-
face_img = cv2.resize(face_img, (resize_w, resize_w))
-
-
offset_y_value = offset_y * index
-
frame[(left_y + offset_y_value):(left_y + resize_w + offset_y_value), -(left_x + resize_w):-left_x] = face_img
-
-
cv2.putText(frame, name, ((frame_w - (left_x + resize_w)), (left_y + resize_w) +
15 + offset_y_value),
-
cv2.FONT_ITALIC,
0.5, (
0,
255,
0),
1)
-
cv2.putText(frame, time, ((frame_w - (left_x + resize_w)), (left_y + resize_w) +
30 + offset_y_value),
-
cv2.FONT_ITALIC,
0.5, (
0,
255,
0),
1)
-
-
index +=
1
-
-
return frame
-
# 返回DLIB格式的face
-
def
getDlibRect(
detector='hog', face=None):
-
l, t, r, b =
None,
None,
None,
None
-
-
if detector ==
'hog':
-
l, t, r, b = face.left(), face.top(), face.right(), face.bottom()
-
-
if detector ==
'cnn':
-
l = face.rect.left()
-
t = face.rect.top()
-
r = face.rect.right()
-
b = face.rect.bottom()
-
-
if detector ==
'haar':
-
l = face[
0]
-
t = face[
1]
-
r = face[
0] + face[
2]
-
b = face[
1] + face[
3]
-
-
nonnegative =
lambda x: x
if x >=
0
else
0
-
return
map(nonnegative, (l, t, r, b))
-
# 获取CSV中信息
-
def
getFeatList():
-
print(
'加载注册的人脸特征')
-
-
feature_list =
None
-
label_list = []
-
name_list = []
-
-
# 加载保存的特征样本
-
with
open(
'./data/feature.csv',
'r')
as f:
-
csv_reader = csv.reader(f)
-
for line
in csv_reader:
-
# 重新加载数据
-
faceId = line[
0]
-
userName = line[
1]
-
face_descriptor =
eval(line[
2])
-
-
label_list.append(faceId)
-
name_list.append(userName)
-
-
# 转为numpy格式
-
face_descriptor = np.asarray(face_descriptor, dtype=np.float64)
-
# 转为二维矩阵，拼接
-
face_descriptor = np.reshape(face_descriptor, (
1, -
1))
-
# 初始化
-
if feature_list
is
None:
-
feature_list = face_descriptor
-
else:
-
# 拼接
-
feature_list = np.concatenate((feature_list, face_descriptor), axis=
0)
-
print(
"特征加载完毕")
-
return feature_list, label_list, name_list
-
# 人脸识别
-
def
faceRecognize(
detector='haar', threshold=0.5, write_video=False, resize_w=700, resize_h=400):
-
# 视频时间
-
frameTime = time.time()
-
-
# 加载特征
-
feature_list, label_list, name_list = getFeatList()
-
-
face_time_dict = {}
-
# 保存name,time人脸信息
-
face_info_list = []
-
# numpy格式人脸图像数据
-
face_img_list = []
-
-
# 侦测人数
-
person_detect =
0
-
-
# 统计人脸数
-
face_count =
0
-
-
# 控制显示打卡成功的时长
-
show_time = (frameTime -
10)
-
-
# 考勤记录
-
f =
open(
'./data/attendance.csv',
'a')
-
csv_writer = csv.writer(f)
-
-
cap = cv2.VideoCapture(
0)
-
# resize_w = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))//2
-
# resize_h = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT)) //2
-
-
videoWriter = cv2.VideoWriter(
'./record_video/out' +
str(time.time()) +
'.mp4', cv2.VideoWriter_fourcc(*
'MP4V'),
15,
-
(resize_w, resize_h))
-
-
while
True:
-
ret, frame = cap.read()
-
frame = cv2.resize(frame, (resize_w, resize_h))
-
frame = cv2.flip(frame,
1)
-
-
# 切换人脸检测器
-
if detector ==
'hog':
-
face_detetion = hog_face_detector(frame,
1)
-
if detector ==
'cnn':
-
face_detetion = cnn_detector(frame,
1)
-
if detector ==
'haar':
-
frame_gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
-
face_detetion = haar_face_detector.detectMultiScale(frame_gray, minNeighbors=
7, minSize=(
100,
100))
-
-
person_detect =
len(face_detetion)
-
-
for face
in face_detetion:
-
-
l, t, r, b = getDlibRect(detector, face)
-
-
face = dlib.rectangle(l, t, r, b)
-
-
# 识别68个关键点
-
points = points_detector(frame, face)
-
-
cv2.rectangle(frame, (l, t), (r, b), (
0,
255,
0),
2)
-
-
# 人脸区域
-
face_crop = frame[t:b, l:r]
-
-
# 特征
-
face_descriptor = face_descriptor_extractor.compute_face_descriptor(frame, points)
-
face_descriptor = [f
for f
in face_descriptor]
-
face_descriptor = np.asarray(face_descriptor, dtype=np.float64)
-
-
# 计算距离
-
distance = np.linalg.norm((face_descriptor - feature_list), axis=
1)
-
# 最小距离索引
-
min_index = np.argmin(distance)
-
# 最小距离
-
min_distance = distance[min_index]
-
-
predict_name =
"Not recog"
-
-
if min_distance < threshold:
-
# 距离小于阈值，表示匹配
-
predict_id = label_list[min_index]
-
predict_name = name_list[min_index]
-
-
# 判断是否新增记录：如果一个人距上次检测时间>3秒，或者换了一个人，将这条记录插入
-
need_insert =
False
-
now = time.time()
-
if predict_name
in face_time_dict:
-
if (now - face_time_dict[predict_name]) >
3:
-
# 刷新时间
-
face_time_dict[predict_name] = now
-
need_insert =
True
-
else:
-
# 还是上次人脸
-
need_insert =
False
-
-
else:
-
# 新增数据记录
-
face_time_dict[predict_name] = now
-
need_insert =
True
-
-
if (now - show_time) <
1:
-
frame = cv2AddChineseText(frame,
"打卡成功", (l, b +
30), textColor=(
0,
255,
0), textSize=
40)
-
-
if need_insert:
-
# 连续显示打卡成功1s
-
frame = cv2AddChineseText(frame,
"打卡成功", (l, b +
30), textColor=(
0,
255,
0), textSize=
40)
-
show_time = time.time()
-
-
time_local = time.localtime(face_time_dict[predict_name])
-
# 转换成新的时间格式(2016-05-05 20:28:54)
-
face_time = time.strftime(
"%H:%M:%S", time_local)
-
face_time_full = time.strftime(
"%Y-%m-%d %H:%M:%S", time_local)
-
-
# 开始位置增加
-
-
face_info_list.insert(
0, [predict_name, face_time])
-
face_img_list.insert(
0, face_crop)
-
-
# 写入考勤表
-
line = [predict_id, predict_name, min_distance, face_time_full]
-
csv_writer.writerow(line)
-
-
face_count +=
1
-
-
# 绘制人脸点
-
cv2.putText(frame, predict_name +
" " +
str(
round(min_distance,
2)), (l, b +
30), cv2.FONT_ITALIC,
0.8,
-
(
0,
255,
0),
2)
-
-
# 处理下一张脸
-
-
now = time.time()
-
fpsText =
1 / (now - frameTime)
-
frameTime = now
-
# 绘制
-
drawLeftInfo(frame, fpsText,
'Recog', detector=detector, person=person_detect, count=face_count)
-
-
# 舍弃face_img_list、face_info_list后部分，节约内存
-
if
len(face_info_list) >
10:
-
face_info_list = face_info_list[:
9]
-
face_img_list = face_img_list[:
9]
-
-
frame = updateRightInfo(frame, face_info_list, face_img_list)
-
-
if write_video:
-
videoWriter.write(frame)
-
-
cv2.imshow(
'Face Attendance Demo: Recognition', frame)
-
if cv2.waitKey(
10) &
0xFF ==
ord(
'q'):
-
break
-
-
f.close()
-
videoWriter.release()
-
cap.release()
-
cv2.destroyAllWindows()
然后效果就和我们宿舍楼下差不多了~
我年轻的时候，我大概比现在帅个几百倍吧，哎。
二、总代码
上文其实把登录和注册最后一部分代码放在一起就是了，这里就不再复制粘贴了，相关权重文件下载链接：opencv/data at master · opencv/opencv · GitHub
懒得下载或者懒得找也可以私信我发你，看见或有时间回。
当然本项目还有很多需要优化的地方，比如设置用户不能重复、考勤打卡每天只能一次、把csv改为链接成数据库等等，后续代码优化完成后就可以部署然后和室友**了。
转载：https://blog.csdn.net/suic009/article/details/127382811
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