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customize_
| Author | SHA1 | Date | |
|---|---|---|---|
| 58466ce87b |
6
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generated
6
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<entry file="file://$PROJECT_DIR$/introduction/face_reco_single_person_custmize_name.png" />
|
||||
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|
||||
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|
||||
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|
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|
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|
||||
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|
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
</entry>
|
||||
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|
||||
<provider selected="true" editor-type-id="csv-text-editor" />
|
||||
</entry>
|
||||
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|
||||
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|
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
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|
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|
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|
||||
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|
||||
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|
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|
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
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|
||||
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|
||||
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||||
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|
||||
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|
||||
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|
||||
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|
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|
||||
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|
||||
</project>
|
||||
41
README.rst
41
README.rst
@ -14,12 +14,6 @@ Detect and recognize single/multi-faces from camera;
|
||||
.. image:: introduction/get_face_from_camera.png
|
||||
:align: center
|
||||
|
||||
请不要离摄像头过近，人脸超出摄像头范围时会有 "OUT OF RANGE" 提醒 /
|
||||
Please do not too close to the camera, or you can't save faces with "OUT OF RANGE" warning;
|
||||
|
||||
.. image:: introduction/get_face_from_camera_out_of_range.png
|
||||
:align: center
|
||||
|
||||
#. 提取特征建立人脸数据库 / Generate database from images captured
|
||||
#. 利用摄像头进行人脸识别 / Face recognizer
|
||||
|
||||
@ -29,53 +23,33 @@ Detect and recognize single/multi-faces from camera;
|
||||
:align: center
|
||||
|
||||
当多张人脸 / When multi-faces:
|
||||
|
||||
一张已录入人脸 + 未录入 unknown 人脸:
|
||||
|
||||
|
||||
.. image:: introduction/face_reco_two_people.png
|
||||
:align: center
|
||||
|
||||
同时识别多张已录入人脸:
|
||||
|
||||
.. image:: introduction/face_reco_two_people_in_database.png
|
||||
:align: center
|
||||
|
||||
** 关于精度 / About accuracy:
|
||||
|
||||
* When using a distance threshold of ``0.6``, the dlib model obtains an accuracy of ``99.38%`` on the standard LFW face recognition benchmark.
|
||||
|
||||
Overview
|
||||
********
|
||||
|
||||
此项目中人脸识别的实现流程 / The design of this repo:
|
||||
|
||||
.. image:: introduction/overview.png
|
||||
:align: center
|
||||
|
||||
Steps
|
||||
*****
|
||||
|
||||
#. 下载源码 / Download from website or via GitHub Desktop in windows, or clone repo in Ubuntu
|
||||
|
||||
.. code-block:: bash
|
||||
|
||||
git clone https://github.com/coneypo/Dlib_face_recognition_from_camera
|
||||
|
||||
#. 进行 face register / 人脸信息采集录入
|
||||
|
||||
.. code-block:: bash
|
||||
.. code-block:: python
|
||||
|
||||
python3 get_face_from_camera.py
|
||||
|
||||
#. 提取所有录入人脸数据存入 features_all.csv
|
||||
|
||||
.. code-block:: bash
|
||||
.. code-block:: python
|
||||
|
||||
python3 get_features_into_CSV.py
|
||||
|
||||
#. 调用摄像头进行实时人脸识别
|
||||
|
||||
.. code-block:: bash
|
||||
.. code-block:: python
|
||||
|
||||
python3 face_reco_from_camera.py
|
||||
|
||||
@ -109,11 +83,6 @@ Python 源码介绍如下:
|
||||
|
||||
* 将捕获到的人脸数据和之前存的人脸数据进行对比计算欧式距离, 由此判断是否是同一个人;
|
||||
|
||||
修改显示的人名 / If you want customize the names shown, please refer to this patch and modify the code: https://github.com/coneypo/Dlib_face_recognition_from_camera/commit/58466ce87bf3a42ac5ef855b791bf8c658d408df?diff=unified
|
||||
|
||||
|
||||
.. image:: introduction/face_reco_single_person_customize_name.png
|
||||
:align: center
|
||||
|
||||
More
|
||||
****
|
||||
@ -137,4 +106,4 @@ For more details, please refer to my blog (in chinese) or mail to me /
|
||||
|
||||
仅限于交流学习, 商业合作勿扰;
|
||||
|
||||
Thanks for your support.
|
||||
Thanks for your support.
|
||||
|
||||
@ -1,12 +1,11 @@
|
||||
# 摄像头实时人脸识别
|
||||
# Real-time face recognition
|
||||
|
||||
# Author: coneypo
|
||||
# Blog: http://www.cnblogs.com/AdaminXie
|
||||
# GitHub: https://github.com/coneypo/Dlib_face_recognition_from_camera
|
||||
|
||||
# Created at 2018-05-11
|
||||
# Updated at 2019-03-23
|
||||
# Updated at 2019-02-26
|
||||
|
||||
import dlib # 人脸处理的库 Dlib
|
||||
import numpy as np # 数据处理的库 numpy
|
||||
@ -15,12 +14,10 @@ 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("data/data_dlib/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)
|
||||
@ -37,12 +34,14 @@ def return_euclidean_distance(feature_1, feature_2):
|
||||
path_features_known_csv = "data/features_all.csv"
|
||||
csv_rd = pd.read_csv(path_features_known_csv, header=None)
|
||||
|
||||
# 存储的特征人脸个数
|
||||
# print(csv_rd.shape[0])
|
||||
|
||||
# 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
|
||||
# known faces
|
||||
for i in range(csv_rd.shape[0]):
|
||||
features_someone_arr = []
|
||||
for j in range(0, len(csv_rd.ix[i, :])):
|
||||
@ -51,21 +50,31 @@ for i in range(csv_rd.shape[0]):
|
||||
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('data/data_dlib/shape_predictor_68_face_landmarks.dat')
|
||||
|
||||
# 创建 cv2 摄像头对象
|
||||
# cv2.VideoCapture(0) to use the default camera of PC,
|
||||
# and you can use local video name by use cv2.VideoCapture(filename)
|
||||
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)
|
||||
|
||||
|
||||
# 返回一张图像多张人脸的 128D 特征
|
||||
def get_128d_features(img_gray):
|
||||
faces = detector(img_gray, 1)
|
||||
if len(faces) != 0:
|
||||
face_des = []
|
||||
for i in range(len(faces)):
|
||||
shape = predictor(img_gray, faces[i])
|
||||
face_des.append(facerec.compute_face_descriptor(img_gray, shape))
|
||||
else:
|
||||
face_des = []
|
||||
return face_des
|
||||
|
||||
|
||||
# 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()
|
||||
@ -77,42 +86,36 @@ while cap.isOpened():
|
||||
# 人脸数 faces
|
||||
faces = detector(img_gray, 0)
|
||||
|
||||
# 待会要写的字体 font to write later
|
||||
# 待会要写的字体
|
||||
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 q 键退出
|
||||
# press 'q' to exit
|
||||
if kk == ord('q'):
|
||||
break
|
||||
else:
|
||||
# 检测到人脸 when face detected
|
||||
# 检测到人脸
|
||||
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)):
|
||||
# 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
|
||||
# 每个捕获人脸的名字坐标
|
||||
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
|
||||
for i in range(len(features_known_arr)):
|
||||
print("with person_", str(i+1), "the ", end='')
|
||||
# 将某张人脸与存储的所有人脸数据进行比对
|
||||
@ -123,34 +126,32 @@ while cap.isOpened():
|
||||
# 可以在这里改称 Jack, Tom and others
|
||||
# Here you can modify the names shown on the camera
|
||||
if i == 0:
|
||||
name_namelist[k] = "Person 1"
|
||||
name_namelist[k] = "Jack"
|
||||
elif i == 1:
|
||||
name_namelist[k] = "Person 2"
|
||||
name_namelist[k] = "Tom"
|
||||
elif i == 2:
|
||||
name_namelist[k] = "Person 3"
|
||||
name_namelist[k] = "Tony"
|
||||
|
||||
# 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)
|
||||
|
||||
# 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")
|
||||
print("Name list 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, 0), 1, cv2.LINE_AA)
|
||||
cv2.putText(img_rd, "Faces: " + str(len(faces)), (20, 100), font, 1, (0, 0, 255), 1, cv2.LINE_AA)
|
||||
|
||||
# 窗口显示 show with opencv
|
||||
# 窗口显示
|
||||
cv2.imshow("camera", img_rd)
|
||||
|
||||
# 释放摄像头 release camera
|
||||
# 释放摄像头
|
||||
cap.release()
|
||||
|
||||
# 删除建立的窗口 delete all the windows
|
||||
cv2.destroyAllWindows()
|
||||
# 删除建立的窗口
|
||||
cv2.destroyAllWindows()
|
||||
|
||||
@ -7,7 +7,7 @@
|
||||
# Mail: coneypo@foxmail.com
|
||||
|
||||
# Created at 2018-05-11
|
||||
# Updated at 2019-03-23
|
||||
# Updated at 2019-02-21
|
||||
|
||||
import dlib # 人脸处理的库 Dlib
|
||||
import numpy as np # 数据处理的库 Numpy
|
||||
@ -16,34 +16,33 @@ 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
|
||||
# Dlib 正向人脸检测器
|
||||
detector = dlib.get_frontal_face_detector()
|
||||
|
||||
# Dlib 68 点特征预测器 / 68 points features predictor
|
||||
# Dlib 68 点特征预测器
|
||||
predictor = dlib.shape_predictor('data/data_dlib/shape_predictor_68_face_landmarks.dat')
|
||||
|
||||
# OpenCv 调用摄像头 use camera
|
||||
# OpenCv 调用摄像头
|
||||
cap = cv2.VideoCapture(0)
|
||||
|
||||
# 设置视频参数 set camera
|
||||
# 设置视频参数
|
||||
cap.set(3, 480)
|
||||
|
||||
# 人脸截图的计数器 the counter for screen shoot
|
||||
# 人脸截图的计数器
|
||||
cnt_ss = 0
|
||||
|
||||
# 存储人脸的文件夹 the folder to save faces
|
||||
current_face_dir = ""
|
||||
# 存储人脸的文件夹
|
||||
current_face_dir = 0
|
||||
|
||||
# 保存 photos/csv 的路径 the directory to save photos/csv
|
||||
# 保存 photos/csv 的路径
|
||||
path_photos_from_camera = "data/data_faces_from_camera/"
|
||||
path_csv_from_photos = "data/data_csvs_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
|
||||
# 新建文件夹
|
||||
if os.path.isdir(path_photos_from_camera):
|
||||
pass
|
||||
else:
|
||||
@ -59,8 +58,7 @@ 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():
|
||||
def pre_work_deldir():
|
||||
# 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)
|
||||
@ -73,13 +71,12 @@ def pre_work_del_old_face_folders():
|
||||
|
||||
# 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
|
||||
# pre_work_del_old_face_folders()
|
||||
# pre_work_deldir()
|
||||
##################################
|
||||
|
||||
|
||||
# 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
|
||||
# if old face exists, start from person_x+1
|
||||
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/")
|
||||
@ -88,16 +85,12 @@ if os.listdir("data/data_faces_from_camera/"):
|
||||
person_cnt = person_num_latest
|
||||
|
||||
# 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
|
||||
# 之后用来控制是否保存图像的 flag
|
||||
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():
|
||||
# 480 height * 640 width
|
||||
flag, img_rd = cap.read()
|
||||
@ -108,39 +101,37 @@ while cap.isOpened():
|
||||
# 人脸数 faces
|
||||
faces = detector(img_gray, 0)
|
||||
|
||||
# 待会要写的字体 / font to write
|
||||
# 待会要写的字体
|
||||
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
|
||||
# 按下 'n' 新建存储人脸的文件夹
|
||||
if kk == ord('n'):
|
||||
person_cnt += 1
|
||||
current_face_dir = path_photos_from_camera + "person_" + str(person_cnt)
|
||||
os.makedirs(current_face_dir)
|
||||
print('\n')
|
||||
print("新建的人脸文件夹 / Create folders: ", current_face_dir)
|
||||
print("新建的人脸文件夹: ", 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'
|
||||
# 将人脸计数器清零
|
||||
cnt_ss = 0
|
||||
|
||||
# 检测到人脸 / if face detected
|
||||
# 检测到人脸
|
||||
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
|
||||
# 计算矩形框大小
|
||||
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
|
||||
# 设置颜色 / The color of rectangle of faces detected
|
||||
color_rectangle = (255, 255, 255)
|
||||
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)
|
||||
@ -155,43 +146,41 @@ while cap.isOpened():
|
||||
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
|
||||
# 根据人脸大小生成空的图像
|
||||
im_blank = np.zeros((int(height*2), width*2, 3), np.uint8)
|
||||
|
||||
save_flag = 1;
|
||||
|
||||
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
|
||||
# 按下 's' 保存摄像头中的人脸到本地
|
||||
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:
|
||||
if os.path.isdir(current_face_dir):
|
||||
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")
|
||||
print("写入本地：", 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
|
||||
# 显示人脸数
|
||||
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
|
||||
# 添加说明
|
||||
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
|
||||
# 按下 'q' 键退出
|
||||
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)
|
||||
|
||||
# 窗口显示
|
||||
# cv2.namedWindow("camera", 0) # 如果需要摄像头窗口大小可调
|
||||
cv2.imshow("camera", img_rd)
|
||||
|
||||
# 释放摄像头 / release camera
|
||||
# 释放摄像头
|
||||
cap.release()
|
||||
|
||||
# 删除建立的窗口
|
||||
cv2.destroyAllWindows()
|
||||
|
||||
@ -45,7 +45,7 @@ def return_128d_features(path_img):
|
||||
img_gray = cv2.cvtColor(img, 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')
|
||||
print("检测到人脸的图像：", 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
|
||||
@ -71,7 +71,7 @@ def write_into_csv(path_faces_personX, path_csv_from_photos):
|
||||
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]))
|
||||
print("正在读的人脸图像：", 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
|
||||
@ -80,7 +80,7 @@ def write_into_csv(path_faces_personX, path_csv_from_photos):
|
||||
else:
|
||||
writer.writerow(features_128d)
|
||||
else:
|
||||
print("文件夹内图像文件为空 / Warning: Empty photos in " + path_faces_personX + '/', '\n')
|
||||
print("Warning: Empty photos in "+path_faces_personX+'/')
|
||||
writer.writerow("")
|
||||
|
||||
|
||||
@ -134,4 +134,4 @@ with open(path_csv_from_photos_feature_all, "w", newline="") as csvfile:
|
||||
for i in range(len(csv_rd)):
|
||||
feature_mean_list = compute_the_mean(path_csv_from_photos + csv_rd[i])
|
||||
print(path_csv_from_photos + csv_rd[i])
|
||||
writer.writerow(feature_mean_list)
|
||||
writer.writerow(feature_mean_list)
|
||||
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introduction/face_reco_from_camera_two_faces.png
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introduction/face_reco_from_camera_two_faces.png
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@ -1,5 +0,0 @@
|
||||
dlib==19.17.0
|
||||
numpy==1.15.1
|
||||
opencv-python==4.0.0.21
|
||||
pandas==0.23.4
|
||||
scikit-image==0.14.0
|
||||
Reference in New Issue
Block a user