Compare commits
45 Commits
customize_
...
2021_11_09
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| ecae353dde |
12
.idea/Dlib_face_recognition_from_camera.iml
generated
12
.idea/Dlib_face_recognition_from_camera.iml
generated
@ -1,12 +0,0 @@
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||||
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4
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4
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8
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6
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411
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|
||||
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21
LICENSE
Normal file
21
LICENSE
Normal file
@ -0,0 +1,21 @@
|
||||
MIT License
|
||||
|
||||
Copyright (c) 2018-2021 coneypo
|
||||
|
||||
Permission is hereby granted, free of charge, to any person obtaining a copy
|
||||
of this software and associated documentation files (the "Software"), to deal
|
||||
in the Software without restriction, including without limitation the rights
|
||||
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
|
||||
copies of the Software, and to permit persons to whom the Software is
|
||||
furnished to do so, subject to the following conditions:
|
||||
|
||||
The above copyright notice and this permission notice shall be included in all
|
||||
copies or substantial portions of the Software.
|
||||
|
||||
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
|
||||
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
|
||||
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
|
||||
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
|
||||
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
|
||||
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
|
||||
SOFTWARE.
|
||||
211
README.rst
Normal file → Executable file
211
README.rst
Normal file → Executable file
@ -1,78 +1,210 @@
|
||||
Face recognition from camera
|
||||
############################
|
||||
Face recognition from camera with Dlib
|
||||
######################################
|
||||
|
||||
Introduction
|
||||
************
|
||||
|
||||
Detect and recognize single/multi-faces from camera;
|
||||
|
||||
调用摄像头进行人脸识别，支持多张人脸同时识别;
|
||||
调用摄像头进行人脸识别, 支持多张人脸同时识别;
|
||||
|
||||
#. Tkinter 人脸录入界面, 支持录入时设置姓名 / Face register GUI with Tkinter, support setting name when registering
|
||||
|
||||
#. 摄像头人脸录入 / Face register
|
||||
|
||||
.. image:: introduction/get_face_from_camera.png
|
||||
.. image:: introduction/face_register_tkinter_GUI.png
|
||||
:align: center
|
||||
|
||||
#. 提取特征建立人脸数据库 / Generate database from images captured
|
||||
#. 简单的 OpenCV 摄像头人脸录入界面 / Simple face register GUI with OpenCV
|
||||
|
||||
.. image:: introduction/face_register.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 be too close to the camera, or you can't save faces with "OUT OF RANGE" warning;
|
||||
|
||||
.. image:: introduction/face_register_warning.png
|
||||
:align: center
|
||||
|
||||
#. 提取特征建立人脸数据库 / Generate face database from images captured
|
||||
#. 利用摄像头进行人脸识别 / Face recognizer
|
||||
|
||||
当单张人脸 / When single-face:
|
||||
face_reco_from_camera.py, 对于每一帧都做检测识别 / Do detection and recognition for every frame:
|
||||
|
||||
.. image:: introduction/face_reco_single_person.png
|
||||
.. image:: introduction/face_reco.png
|
||||
:align: center
|
||||
|
||||
当多张人脸 / When multi-faces:
|
||||
|
||||
.. image:: introduction/face_reco_two_people.png
|
||||
face_reco_from_camera_single_face.py, 对于人脸<=1, 只有新人脸出现才进行再识别来提高 FPS / Do re-reco only for new single face:
|
||||
|
||||
.. image:: introduction/face_reco_single.png
|
||||
:align: center
|
||||
|
||||
face_reco_from_camera_ot.py, 利用 OT 来实现再识别提高 FPS / Use OT to instead of re-reco for every frame to improve FPS:
|
||||
|
||||
.. image:: introduction/face_reco_ot.png
|
||||
:align: center
|
||||
|
||||
定制显示名字, 可以写中文 / Show chinese name:
|
||||
|
||||
.. image:: introduction/face_reco_chinese_name.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.
|
||||
|
||||
** 关于算法 / About algorithm
|
||||
|
||||
* 基于 Residual Neural Network / 残差网络的 CNN 模型;
|
||||
|
||||
* This model is a ResNet network with 29 conv layers. It's essentially a version of the ResNet-34 network from the paper Deep Residual Learning for Image Recognition by He, Zhang, Ren, and Sun with a few layers removed and the number of filters per layer reduced by half.
|
||||
|
||||
Overview
|
||||
********
|
||||
|
||||
此项目中人脸识别的实现流程 (no OT, 每一帧都进行检测+识别) / The design of this repo:
|
||||
|
||||
.. image:: introduction/overview.png
|
||||
:align: center
|
||||
|
||||
实现流程(with OT, 初始帧进行检测+识别, 后续帧检测+质心跟踪) / The design of this repo:
|
||||
|
||||
.. image:: introduction/overview_with_ot.png
|
||||
:align: center
|
||||
|
||||
如果利用 OT 来跟踪, 可以大大提高 FPS, 因为做识别时候需要提取特征描述子的耗时很多;
|
||||
|
||||
Steps
|
||||
*****
|
||||
|
||||
#. 进行 face register / 人脸信息采集录入
|
||||
#. 安装依赖库 / Install some python packages needed
|
||||
|
||||
.. code-block:: python
|
||||
.. code-block:: bash
|
||||
|
||||
pip install -r requirements.txt
|
||||
|
||||
#. 下载源码 / Download zip from website or via GitHub Desktop in windows, or git clone repo in Ubuntu
|
||||
|
||||
.. code-block:: bash
|
||||
|
||||
git clone https://github.com/coneypo/Dlib_face_recognition_from_camera
|
||||
|
||||
#. 进行人脸信息采集录入, Tkinter GUI / Register faces with Tkinter GUI
|
||||
|
||||
.. code-block:: bash
|
||||
|
||||
python3 get_faces_from_camera_tkinter.py
|
||||
|
||||
#. 进行人脸信息采集录入, OpenCV GUI / Register faces with OpenCV GUI
|
||||
|
||||
.. code-block:: bash
|
||||
|
||||
python3 get_face_from_camera.py
|
||||
|
||||
#. 提取所有录入人脸数据存入 features_all.csv
|
||||
#. 提取所有录入人脸数据存入 "features_all.csv" / Features extraction and save into "features_all.csv"
|
||||
|
||||
.. code-block:: python
|
||||
.. code-block:: bash
|
||||
|
||||
python3 get_features_into_CSV.py
|
||||
python3 features_extraction_to_csv.py
|
||||
|
||||
#. 调用摄像头进行实时人脸识别
|
||||
#. 调用摄像头进行实时人脸识别 / Real-time face recognition
|
||||
|
||||
.. code-block:: python
|
||||
.. code-block:: bash
|
||||
|
||||
python3 face_reco_from_camera.py
|
||||
|
||||
#. 对于人脸数<=1, 调用摄像头进行实时人脸识别 / Real-time face recognition (FPS improved)
|
||||
|
||||
.. code-block:: bash
|
||||
|
||||
python3 face_reco_from_camera_single_face.py
|
||||
|
||||
#. 利用 OT 算法, 调用摄像头进行实时人脸识别 / Real-time face recognition with OT (FPS improved)
|
||||
|
||||
.. code-block:: bash
|
||||
|
||||
python3 face_reco_from_camera_ot.py
|
||||
|
||||
About Source Code
|
||||
*****************
|
||||
|
||||
Repo 的 tree / 树状图:
|
||||
|
||||
::
|
||||
|
||||
.
|
||||
├── get_faces_from_camera.py # Step 1. Face register GUI with OpenCV
|
||||
├── get_faces_from_camera_tkinter.py # Step 1. Face register GUI with Tkinter
|
||||
├── features_extraction_to_csv.py # Step 2. Feature extraction
|
||||
├── face_reco_from_camera.py # Step 3. Face recognizer
|
||||
├── face_reco_from_camera_single_face.py # Step 3. Face recognizer for single person
|
||||
├── face_reco_from_camera_ot.py # Step 3. Face recognizer with OT
|
||||
├── face_descriptor_from_camera.py # Face descriptor computation
|
||||
├── how_to_use_camera.py # Use the default camera by opencv
|
||||
├── data
|
||||
│ ├── data_dlib # Dlib's model
|
||||
│ │ ├── dlib_face_recognition_resnet_model_v1.dat
|
||||
│ │ └── shape_predictor_68_face_landmarks.dat
|
||||
│ ├── data_faces_from_camera # Face images captured from camera (will generate after step 1)
|
||||
│ │ ├── person_1
|
||||
│ │ │ ├── img_face_1.jpg
|
||||
│ │ │ └── img_face_2.jpg
|
||||
│ │ └── person_2
|
||||
│ │ └── img_face_1.jpg
|
||||
│ │ └── img_face_2.jpg
|
||||
│ └── features_all.csv # CSV to save all the features of known faces (will generate after step 2)
|
||||
├── README.rst
|
||||
└── requirements.txt # Some python packages needed
|
||||
|
||||
用到的 Dlib 相关模型函数:
|
||||
|
||||
#. Dlib 正向人脸检测器 (based on HOG), output: <class 'dlib.dlib.rectangles'>
|
||||
|
||||
|
||||
.. code-block:: python
|
||||
|
||||
detector = dlib.get_frontal_face_detector()
|
||||
faces = detector(img_gray, 0)
|
||||
|
||||
|
||||
#. Dlib 人脸 landmark 特征点检测器, output: <class 'dlib.dlib.full_object_detection'>,
|
||||
will use shape_predictor_68_face_landmarks.dat
|
||||
|
||||
.. code-block:: python
|
||||
|
||||
# This is trained on the ibug 300-W dataset (https://ibug.doc.ic.ac.uk/resources/facial-point-annotations/)
|
||||
# Also note that this model file is designed for use with dlib's HOG face detector.
|
||||
# That is, it expects the bounding boxes from the face detector to be aligned a certain way, the way dlib's HOG face detector does it.
|
||||
# It won't work as well when used with a face detector that produces differently aligned boxes,
|
||||
# such as the CNN based mmod_human_face_detector.dat face detector.
|
||||
|
||||
predictor = dlib.shape_predictor("data/data_dlib/shape_predictor_68_face_landmarks.dat")
|
||||
shape = predictor(img_rd, faces[i])
|
||||
|
||||
|
||||
#. Dlib 特征描述子 Face recognition model, the object maps human faces into 128D vectors
|
||||
|
||||
|
||||
.. code-block:: python
|
||||
|
||||
face_rec = dlib.face_recognition_model_v1("data/data_dlib/dlib_face_recognition_resnet_model_v1.dat")
|
||||
|
||||
|
||||
Python 源码介绍如下:
|
||||
|
||||
#. get_face_from_camera.py:
|
||||
|
||||
进行 Face register / 人脸信息采集录入
|
||||
|
||||
* 请注意存储人脸图片时，矩形框不要超出摄像头范围，要不然无法保存到本地;
|
||||
* 请注意存储人脸图片时, 矩形框不要超出摄像头范围, 要不然无法保存到本地;
|
||||
* 超出会有 "out of range" 的提醒;
|
||||
|
||||
|
||||
#. get_features_into_CSV.py:
|
||||
#. features_extraction_to_csv.py:
|
||||
|
||||
从上一步存下来的图像文件中，提取人脸数据存入CSV;
|
||||
从上一步存下来的图像文件中, 提取人脸数据存入CSV;
|
||||
|
||||
* 会生成一个存储所有特征人脸数据的 "features_all.csv"；
|
||||
* size: n*128 , n means n people you registered and 128 means 128D features of the face
|
||||
* size: n*129 , n means nx faces you registered and 129 means face name + 128D features of this face
|
||||
|
||||
|
||||
#. face_reco_from_camera.py:
|
||||
@ -83,27 +215,44 @@ 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;
|
||||
|
||||
#. face_reco_from_camera_single_face.py:
|
||||
|
||||
针对于人脸数 <=1 的场景, 区别于 face_reco_from_camera.py (对每一帧都进行检测+识别), 只有人脸出现的时候进行识别;
|
||||
|
||||
#. face_reco_from_camera_ot.py:
|
||||
|
||||
只会对初始帧做检测+识别, 对后续帧做检测+质心跟踪;
|
||||
|
||||
#. (optional) face_descriptor_from_camera.py
|
||||
|
||||
调用摄像头进行实时特征描述子计算; / Real-time face descriptor computation;
|
||||
|
||||
More
|
||||
****
|
||||
|
||||
Tips:
|
||||
|
||||
1. Windows下建议不要把代码放到 ``C:\``, 可能会出现权限读取问题
|
||||
#. 如果希望详细了解 dlib 的用法, 请参考 Dlib 官方 Python api 的网站 / You can refer to this link for more information of how to use dlib: http://dlib.net/python/index.html
|
||||
|
||||
2. 代码最好不要有中文路径
|
||||
#. Modify log level to ``logging.basicConfig(level=logging.DEBUG)`` to print info for every frame if needed (Default is ``logging.INFO``)
|
||||
|
||||
3. 人脸录入的时候先建文件夹再保存图片, 先 ``N`` 再 ``S``
|
||||
#. 代码最好不要有中文路径 / No chinese characters in your code directory
|
||||
|
||||
For more details, please refer to my blog (in chinese) or mail to me /
|
||||
#. 人脸录入的时候先建文件夹再保存图片, 先 ``N`` 再 ``S`` / Press ``N`` before ``S``
|
||||
|
||||
可以访问我的博客获取本项目的更详细介绍，如有问题可以邮件联系我:
|
||||
#. 关于 `face_reco_from_camera.py` 人脸识别卡顿 FPS 低问题, 原因是特征描述子提取很费时间, 光跑 face_descriptor_from_camera.py 中
|
||||
face_reco_model.compute_face_descriptor 在我的机器上得到的平均 FPS 在 5 左右 (检测在 0.03s, 特征描述子提取在 0.158s, 和已知人脸进行遍历对比在 0.003s 左右),
|
||||
所以主要提取特征时候耗资源, 可以用 OT 去做追踪 (使用`face_reco_from_camera_ot.py`), 而不是对每一帧都做检测+识别, 识别的性能从 20 FPS -> 200 FPS
|
||||
|
||||
可以访问我的博客获取本项目的更详细介绍, 如有问题可以邮件联系我 /
|
||||
For more details, please refer to my blog (in chinese) or mail to me :
|
||||
|
||||
* Blog: https://www.cnblogs.com/AdaminXie/p/9010298.html
|
||||
|
||||
* 关于 OT 部分的更新在 Blog: https://www.cnblogs.com/AdaminXie/p/13566269.html
|
||||
|
||||
* Mail: coneypo@foxmail.com
|
||||
* Mail: coneypo@foxmail.com ( Dlib 相关 repo 问题请联系 @foxmail 而不是 @intel )
|
||||
|
||||
|
||||
仅限于交流学习, 商业合作勿扰;
|
||||
* Feel free to creatE issue or PR for this repo :)
|
||||
|
||||
Thanks for your support.
|
||||
|
||||
0
data/data_dlib/dlib_face_recognition_resnet_model_v1.dat
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0
data/data_dlib/dlib_face_recognition_resnet_model_v1.dat
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data/data_dlib/shape_predictor_68_face_landmarks.dat
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92
face_descriptor_from_camera.py
Normal file
92
face_descriptor_from_camera.py
Normal file
@ -0,0 +1,92 @@
|
||||
# Copyright (C) 2018-2021 coneypo
|
||||
# SPDX-License-Identifier: MIT
|
||||
|
||||
# 摄像头实时人脸特征描述子计算 / Real-time face descriptor computing
|
||||
|
||||
import dlib # 人脸识别的库 Dlib
|
||||
import cv2 # 图像处理的库 OpenCV
|
||||
import time
|
||||
|
||||
# 1. Dlib 正向人脸检测器
|
||||
detector = dlib.get_frontal_face_detector()
|
||||
|
||||
# 2. Dlib 人脸 landmark 特征点检测器
|
||||
predictor = dlib.shape_predictor('data/data_dlib/shape_predictor_68_face_landmarks.dat')
|
||||
|
||||
# 3. Dlib Resnet 人脸识别模型，提取 128D 的特征矢量
|
||||
face_reco_model = dlib.face_recognition_model_v1("data/data_dlib/dlib_face_recognition_resnet_model_v1.dat")
|
||||
|
||||
|
||||
class Face_Descriptor:
|
||||
def __init__(self):
|
||||
self.frame_time = 0
|
||||
self.frame_start_time = 0
|
||||
self.fps = 0
|
||||
self.frame_cnt = 0
|
||||
|
||||
def update_fps(self):
|
||||
now = time.time()
|
||||
self.frame_time = now - self.frame_start_time
|
||||
self.fps = 1.0 / self.frame_time
|
||||
self.frame_start_time = now
|
||||
|
||||
def run(self):
|
||||
cap = cv2.VideoCapture(0)
|
||||
cap.set(3, 480)
|
||||
self.process(cap)
|
||||
cap.release()
|
||||
cv2.destroyAllWindows()
|
||||
|
||||
def process(self, stream):
|
||||
while stream.isOpened():
|
||||
flag, img_rd = stream.read()
|
||||
self.frame_cnt+=1
|
||||
k = cv2.waitKey(1)
|
||||
|
||||
print('- Frame ', self.frame_cnt, " starts:")
|
||||
|
||||
timestamp1 = time.time()
|
||||
faces = detector(img_rd, 0)
|
||||
timestamp2 = time.time()
|
||||
print("--- Time used to `detector`: %s seconds ---" % (timestamp2 - timestamp1))
|
||||
|
||||
font = cv2.FONT_HERSHEY_SIMPLEX
|
||||
|
||||
# 检测到人脸
|
||||
if len(faces) != 0:
|
||||
for face in faces:
|
||||
timestamp3 = time.time()
|
||||
face_shape = predictor(img_rd, face)
|
||||
timestamp4 = time.time()
|
||||
print("--- Time used to `predictor`: %s seconds ---" % (timestamp4 - timestamp3))
|
||||
|
||||
timestamp5 = time.time()
|
||||
face_desc = face_reco_model.compute_face_descriptor(img_rd, face_shape)
|
||||
timestamp6 = time.time()
|
||||
print("--- Time used to `compute_face_descriptor：` %s seconds ---" % (timestamp6 - timestamp5))
|
||||
|
||||
# 添加说明
|
||||
cv2.putText(img_rd, "Face descriptor", (20, 40), font, 1, (255, 255, 255), 1, cv2.LINE_AA)
|
||||
cv2.putText(img_rd, "FPS: " + str(self.fps.__round__(2)), (20, 100), font, 0.8, (0, 255, 0), 1, cv2.LINE_AA)
|
||||
cv2.putText(img_rd, "Faces: " + str(len(faces)), (20, 140), font, 0.8, (0, 255, 0), 1, cv2.LINE_AA)
|
||||
cv2.putText(img_rd, "S: Save current face", (20, 400), font, 0.8, (255, 255, 255), 1, cv2.LINE_AA)
|
||||
cv2.putText(img_rd, "Q: Quit", (20, 450), font, 0.8, (255, 255, 255), 1, cv2.LINE_AA)
|
||||
|
||||
# 按下 'q' 键退出
|
||||
if k == ord('q'):
|
||||
break
|
||||
|
||||
self.update_fps()
|
||||
|
||||
cv2.namedWindow("camera", 1)
|
||||
cv2.imshow("camera", img_rd)
|
||||
print('\n')
|
||||
|
||||
|
||||
def main():
|
||||
Face_Descriptor_con = Face_Descriptor()
|
||||
Face_Descriptor_con.run()
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
main()
|
||||
350
face_reco_from_camera.py
Normal file → Executable file
350
face_reco_from_camera.py
Normal file → Executable file
@ -1,157 +1,225 @@
|
||||
# 摄像头实时人脸识别
|
||||
# Copyright (C) 2018-2021 coneypo
|
||||
# SPDX-License-Identifier: MIT
|
||||
|
||||
# Author: coneypo
|
||||
# Blog: http://www.cnblogs.com/AdaminXie
|
||||
# GitHub: https://github.com/coneypo/Dlib_face_recognition_from_camera
|
||||
# Mail: coneypo@foxmail.com
|
||||
|
||||
# Created at 2018-05-11
|
||||
# Updated at 2019-02-26
|
||||
# 摄像头实时人脸识别 / Real-time face detection and recognition
|
||||
|
||||
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 pandas as pd # 数据处理的库 Pandas
|
||||
import dlib
|
||||
import numpy as np
|
||||
import cv2
|
||||
import pandas as pd
|
||||
import os
|
||||
import time
|
||||
import logging
|
||||
from PIL import Image, ImageDraw, ImageFont
|
||||
|
||||
# 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
|
||||
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)))
|
||||
print("e_distance: ", dist)
|
||||
|
||||
if dist > 0.4:
|
||||
return "diff"
|
||||
else:
|
||||
return "same"
|
||||
|
||||
|
||||
# 处理存放所有人脸特征的 csv
|
||||
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
|
||||
features_known_arr = []
|
||||
|
||||
# 读取已知人脸数据
|
||||
# known faces
|
||||
for i in range(csv_rd.shape[0]):
|
||||
features_someone_arr = []
|
||||
for j in range(0, len(csv_rd.ix[i, :])):
|
||||
features_someone_arr.append(csv_rd.ix[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
|
||||
# Dlib 正向人脸检测器 / Use frontal face detector of Dlib
|
||||
detector = dlib.get_frontal_face_detector()
|
||||
|
||||
# Dlib 人脸 landmark 特征点检测器 / Get face landmarks
|
||||
predictor = dlib.shape_predictor('data/data_dlib/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)
|
||||
# Dlib Resnet 人脸识别模型，提取 128D 的特征矢量 / Use Dlib resnet50 model to get 128D face descriptor
|
||||
face_reco_model = dlib.face_recognition_model_v1("data/data_dlib/dlib_face_recognition_resnet_model_v1.dat")
|
||||
|
||||
|
||||
# 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
|
||||
class Face_Recognizer:
|
||||
def __init__(self):
|
||||
self.face_feature_known_list = [] # 用来存放所有录入人脸特征的数组 / Save the features of faces in database
|
||||
self.face_name_known_list = [] # 存储录入人脸名字 / Save the name of faces in database
|
||||
|
||||
self.current_frame_face_cnt = 0 # 存储当前摄像头中捕获到的人脸数 / Counter for faces in current frame
|
||||
self.current_frame_face_feature_list = [] # 存储当前摄像头中捕获到的人脸特征 / Features of faces in current frame
|
||||
self.current_frame_face_name_list = [] # 存储当前摄像头中捕获到的所有人脸的名字 / Names of faces in current frame
|
||||
self.current_frame_face_name_position_list = [] # 存储当前摄像头中捕获到的所有人脸的名字坐标 / Positions of faces in current frame
|
||||
|
||||
# Update FPS
|
||||
self.fps = 0 # FPS of current frame
|
||||
self.fps_show = 0 # FPS per second
|
||||
self.frame_start_time = 0
|
||||
self.frame_cnt = 0
|
||||
self.start_time = time.time()
|
||||
|
||||
self.font = cv2.FONT_ITALIC
|
||||
self.font_chinese = ImageFont.truetype("simsun.ttc", 30)
|
||||
|
||||
# 从 "features_all.csv" 读取录入人脸特征 / Read known faces from "features_all.csv"
|
||||
def get_face_database(self):
|
||||
if os.path.exists("data/features_all.csv"):
|
||||
path_features_known_csv = "data/features_all.csv"
|
||||
csv_rd = pd.read_csv(path_features_known_csv, header=None)
|
||||
for i in range(csv_rd.shape[0]):
|
||||
features_someone_arr = []
|
||||
self.face_name_known_list.append(csv_rd.iloc[i][0])
|
||||
for j in range(1, 129):
|
||||
if csv_rd.iloc[i][j] == '':
|
||||
features_someone_arr.append('0')
|
||||
else:
|
||||
features_someone_arr.append(csv_rd.iloc[i][j])
|
||||
self.face_feature_known_list.append(features_someone_arr)
|
||||
logging.info("Faces in Database：%d", len(self.face_feature_known_list))
|
||||
return 1
|
||||
else:
|
||||
logging.warning("'features_all.csv' not found!")
|
||||
logging.warning("Please run 'get_faces_from_camera.py' "
|
||||
"and 'features_extraction_to_csv.py' before 'face_reco_from_camera.py'")
|
||||
return 0
|
||||
|
||||
# 计算两个128D向量间的欧式距离 / Compute the e-distance between two 128D features
|
||||
@staticmethod
|
||||
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
|
||||
|
||||
# 更新 FPS / Update FPS of Video stream
|
||||
def update_fps(self):
|
||||
now = time.time()
|
||||
# 每秒刷新 fps / Refresh fps per second
|
||||
if str(self.start_time).split(".")[0] != str(now).split(".")[0]:
|
||||
self.fps_show = self.fps
|
||||
self.start_time = now
|
||||
self.frame_time = now - self.frame_start_time
|
||||
self.fps = 1.0 / self.frame_time
|
||||
self.frame_start_time = now
|
||||
|
||||
# 生成的 cv2 window 上面添加说明文字 / PutText on cv2 window
|
||||
def draw_note(self, img_rd):
|
||||
cv2.putText(img_rd, "Face Recognizer", (20, 40), self.font, 1, (255, 255, 255), 1, cv2.LINE_AA)
|
||||
cv2.putText(img_rd, "Frame: " + str(self.frame_cnt), (20, 100), self.font, 0.8, (0, 255, 0), 1,
|
||||
cv2.LINE_AA)
|
||||
cv2.putText(img_rd, "FPS: " + str(self.fps_show.__round__(2)), (20, 130), self.font, 0.8, (0, 255, 0), 1,
|
||||
cv2.LINE_AA)
|
||||
cv2.putText(img_rd, "Faces: " + str(self.current_frame_face_cnt), (20, 160), self.font, 0.8, (0, 255, 0), 1,
|
||||
cv2.LINE_AA)
|
||||
cv2.putText(img_rd, "Q: Quit", (20, 450), self.font, 0.8, (255, 255, 255), 1, cv2.LINE_AA)
|
||||
|
||||
def draw_name(self, img_rd):
|
||||
# 在人脸框下面写人脸名字 / Write names under rectangle
|
||||
img = Image.fromarray(cv2.cvtColor(img_rd, cv2.COLOR_BGR2RGB))
|
||||
draw = ImageDraw.Draw(img)
|
||||
for i in range(self.current_frame_face_cnt):
|
||||
# cv2.putText(img_rd, self.current_frame_face_name_list[i], self.current_frame_face_name_position_list[i], self.font, 0.8, (0, 255, 255), 1, cv2.LINE_AA)
|
||||
draw.text(xy=self.current_frame_face_name_position_list[i], text=self.current_frame_face_name_list[i], font=self.font_chinese,
|
||||
fill=(255, 255, 0))
|
||||
img_rd = cv2.cvtColor(np.array(img), cv2.COLOR_RGB2BGR)
|
||||
return img_rd
|
||||
|
||||
# 修改显示人名 / Show names in chinese
|
||||
def show_chinese_name(self):
|
||||
# Default known name: person_1, person_2, person_3
|
||||
if self.current_frame_face_cnt >= 1:
|
||||
# 修改录入的人脸姓名 / Modify names in face_name_known_list to chinese name
|
||||
self.face_name_known_list[0] = '张三'.encode('utf-8').decode()
|
||||
# self.face_name_known_list[1] = '张四'.encode('utf-8').decode()
|
||||
|
||||
# 处理获取的视频流，进行人脸识别 / Face detection and recognition from input video stream
|
||||
def process(self, stream):
|
||||
# 1. 读取存放所有人脸特征的 csv / Read known faces from "features.all.csv"
|
||||
if self.get_face_database():
|
||||
while stream.isOpened():
|
||||
self.frame_cnt += 1
|
||||
logging.debug("Frame %d starts", self.frame_cnt)
|
||||
flag, img_rd = stream.read()
|
||||
faces = detector(img_rd, 0)
|
||||
kk = cv2.waitKey(1)
|
||||
# 按下 q 键退出 / Press 'q' to quit
|
||||
if kk == ord('q'):
|
||||
break
|
||||
else:
|
||||
self.draw_note(img_rd)
|
||||
self.current_frame_face_feature_list = []
|
||||
self.current_frame_face_cnt = 0
|
||||
self.current_frame_face_name_position_list = []
|
||||
self.current_frame_face_name_list = []
|
||||
|
||||
# 2. 检测到人脸 / Face detected in current frame
|
||||
if len(faces) != 0:
|
||||
# 3. 获取当前捕获到的图像的所有人脸的特征 / Compute the face descriptors for faces in current frame
|
||||
for i in range(len(faces)):
|
||||
shape = predictor(img_rd, faces[i])
|
||||
self.current_frame_face_feature_list.append(face_reco_model.compute_face_descriptor(img_rd, shape))
|
||||
# 4. 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)):
|
||||
logging.debug("For face %d in camera:", k+1)
|
||||
# 先默认所有人不认识，是 unknown / Set the default names of faces with "unknown"
|
||||
self.current_frame_face_name_list.append("unknown")
|
||||
|
||||
# 每个捕获人脸的名字坐标 / Positions of faces captured
|
||||
self.current_frame_face_name_position_list.append(tuple(
|
||||
[faces[k].left(), int(faces[k].bottom() + (faces[k].bottom() - faces[k].top()) / 4)]))
|
||||
|
||||
# 5. 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
|
||||
current_frame_e_distance_list = []
|
||||
for i in range(len(self.face_feature_known_list)):
|
||||
# 如果 person_X 数据不为空
|
||||
if str(self.face_feature_known_list[i][0]) != '0.0':
|
||||
e_distance_tmp = self.return_euclidean_distance(self.current_frame_face_feature_list[k],
|
||||
self.face_feature_known_list[i])
|
||||
logging.debug(" With person %s, the e-distance is %f", str(i + 1), e_distance_tmp)
|
||||
current_frame_e_distance_list.append(e_distance_tmp)
|
||||
else:
|
||||
# 空数据 person_X
|
||||
current_frame_e_distance_list.append(999999999)
|
||||
# 6. 寻找出最小的欧式距离匹配 / Find the one with minimum e-distance
|
||||
similar_person_num = current_frame_e_distance_list.index(min(current_frame_e_distance_list))
|
||||
logging.debug("Minimum e-distance with %s: %f", self.face_name_known_list[similar_person_num], min(current_frame_e_distance_list))
|
||||
|
||||
if min(current_frame_e_distance_list) < 0.4:
|
||||
self.current_frame_face_name_list[k] = self.face_name_known_list[similar_person_num]
|
||||
logging.debug("Face recognition result: %s", self.face_name_known_list[similar_person_num])
|
||||
else:
|
||||
logging.debug("Face recognition result: Unknown person")
|
||||
logging.debug("\n")
|
||||
|
||||
# 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()]),
|
||||
(255, 255, 255), 2)
|
||||
|
||||
self.current_frame_face_cnt = len(faces)
|
||||
|
||||
# 7. 在这里更改显示的人名 / Modify name if needed
|
||||
# self.show_chinese_name()
|
||||
|
||||
# 8. 写名字 / Draw name
|
||||
img_with_name = self.draw_name(img_rd)
|
||||
|
||||
else:
|
||||
img_with_name = img_rd
|
||||
|
||||
logging.debug("Faces in camera now: %s", self.current_frame_face_name_list)
|
||||
|
||||
cv2.imshow("camera", img_with_name)
|
||||
|
||||
# 9. 更新 FPS / Update stream FPS
|
||||
self.update_fps()
|
||||
logging.debug("Frame ends\n\n")
|
||||
|
||||
# OpenCV 调用摄像头并进行 process
|
||||
def run(self):
|
||||
# cap = cv2.VideoCapture("video.mp4") # Get video stream from video file
|
||||
cap = cv2.VideoCapture("0") # Get video stream from camera
|
||||
cap.set(3, 480) # 640x480
|
||||
self.process(cap)
|
||||
|
||||
cap.release()
|
||||
cv2.destroyAllWindows()
|
||||
|
||||
|
||||
# cap.isOpened() 返回 true/false 检查初始化是否成功
|
||||
while cap.isOpened():
|
||||
def main():
|
||||
# logging.basicConfig(level=logging.DEBUG) # Set log level to 'logging.DEBUG' to print debug info of every frame
|
||||
logging.basicConfig(level=logging.INFO)
|
||||
Face_Recognizer_con = Face_Recognizer()
|
||||
Face_Recognizer_con.run()
|
||||
|
||||
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 = 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
|
||||
pos_namelist = []
|
||||
name_namelist = []
|
||||
|
||||
# 按下 q 键退出
|
||||
if kk == ord('q'):
|
||||
break
|
||||
else:
|
||||
# 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
|
||||
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
|
||||
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
|
||||
name_namelist.append("unknown")
|
||||
|
||||
# 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 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
|
||||
compare = return_euclidean_distance(features_cap_arr[k], features_known_arr[i])
|
||||
if compare == "same": # 找到了相似脸
|
||||
# 在这里修改 person_1, person_2 ... 的名字
|
||||
# 这里只写了前三个
|
||||
# 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"
|
||||
elif i == 1:
|
||||
name_namelist[k] = "Person 2"
|
||||
elif i == 2:
|
||||
name_namelist[k] = "Person 3"
|
||||
|
||||
# 矩形框
|
||||
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
|
||||
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("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
|
||||
cv2.imshow("camera", img_rd)
|
||||
|
||||
# 释放摄像头
|
||||
cap.release()
|
||||
|
||||
# 删除建立的窗口
|
||||
cv2.destroyAllWindows()
|
||||
if __name__ == '__main__':
|
||||
main()
|
||||
|
||||
306
face_reco_from_camera_ot.py
Normal file
306
face_reco_from_camera_ot.py
Normal file
@ -0,0 +1,306 @@
|
||||
# Copyright (C) 2018-2021 coneypo
|
||||
# SPDX-License-Identifier: MIT
|
||||
|
||||
# Author: coneypo
|
||||
# Blog: http://www.cnblogs.com/AdaminXie
|
||||
# GitHub: https://github.com/coneypo/Dlib_face_recognition_from_camera
|
||||
# Mail: coneypo@foxmail.com
|
||||
|
||||
# 利用 OT 人脸追踪, 进行人脸实时识别 / Real-time face detection and recognition via OT for multi faces
|
||||
# 检测 -> 识别人脸, 新人脸出现 -> 不需要识别, 而是利用质心追踪来判断识别结果 / Do detection -> recognize face, new face -> not do re-recognition
|
||||
# 人脸进行再识别需要花费大量时间, 这里用 OT 做跟踪 / Do re-recognition for multi faces will cost much time, OT will be used to instead it
|
||||
|
||||
import dlib
|
||||
import numpy as np
|
||||
import cv2
|
||||
import os
|
||||
import pandas as pd
|
||||
import time
|
||||
import logging
|
||||
|
||||
# Dlib 正向人脸检测器 / Use frontal face detector of Dlib
|
||||
detector = dlib.get_frontal_face_detector()
|
||||
|
||||
# Dlib 人脸 landmark 特征点检测器 / Get face landmarks
|
||||
predictor = dlib.shape_predictor('data/data_dlib/shape_predictor_68_face_landmarks.dat')
|
||||
|
||||
# Dlib Resnet 人脸识别模型, 提取 128D 的特征矢量 / Use Dlib resnet50 model to get 128D face descriptor
|
||||
face_reco_model = dlib.face_recognition_model_v1("data/data_dlib/dlib_face_recognition_resnet_model_v1.dat")
|
||||
|
||||
|
||||
class Face_Recognizer:
|
||||
def __init__(self):
|
||||
self.font = cv2.FONT_ITALIC
|
||||
|
||||
# FPS
|
||||
self.frame_time = 0
|
||||
self.frame_start_time = 0
|
||||
self.fps = 0
|
||||
self.fps_show = 0
|
||||
self.start_time = time.time()
|
||||
|
||||
# cnt for frame
|
||||
self.frame_cnt = 0
|
||||
|
||||
# 用来存放所有录入人脸特征的数组 / Save the features of faces in the database
|
||||
self.face_features_known_list = []
|
||||
# 存储录入人脸名字 / Save the name of faces in the database
|
||||
self.face_name_known_list = []
|
||||
|
||||
# 用来存储上一帧和当前帧 ROI 的质心坐标 / List to save centroid positions of ROI in frame N-1 and N
|
||||
self.last_frame_face_centroid_list = []
|
||||
self.current_frame_face_centroid_list = []
|
||||
|
||||
# 用来存储上一帧和当前帧检测出目标的名字 / List to save names of objects in frame N-1 and N
|
||||
self.last_frame_face_name_list = []
|
||||
self.current_frame_face_name_list = []
|
||||
|
||||
# 上一帧和当前帧中人脸数的计数器 / cnt for faces in frame N-1 and N
|
||||
self.last_frame_face_cnt = 0
|
||||
self.current_frame_face_cnt = 0
|
||||
|
||||
# 用来存放进行识别时候对比的欧氏距离 / Save the e-distance for faceX when recognizing
|
||||
self.current_frame_face_X_e_distance_list = []
|
||||
|
||||
# 存储当前摄像头中捕获到的所有人脸的坐标名字 / Save the positions and names of current faces captured
|
||||
self.current_frame_face_position_list = []
|
||||
# 存储当前摄像头中捕获到的人脸特征 / Save the features of people in current frame
|
||||
self.current_frame_face_feature_list = []
|
||||
|
||||
# e distance between centroid of ROI in last and current frame
|
||||
self.last_current_frame_centroid_e_distance = 0
|
||||
|
||||
# 控制再识别的后续帧数 / Reclassify after 'reclassify_interval' frames
|
||||
# 如果识别出 "unknown" 的脸, 将在 reclassify_interval_cnt 计数到 reclassify_interval 后, 对于人脸进行重新识别
|
||||
self.reclassify_interval_cnt = 0
|
||||
self.reclassify_interval = 10
|
||||
|
||||
# 从 "features_all.csv" 读取录入人脸特征 / Get known faces from "features_all.csv"
|
||||
def get_face_database(self):
|
||||
if os.path.exists("data/features_all.csv"):
|
||||
path_features_known_csv = "data/features_all.csv"
|
||||
csv_rd = pd.read_csv(path_features_known_csv, header=None)
|
||||
for i in range(csv_rd.shape[0]):
|
||||
features_someone_arr = []
|
||||
self.face_name_known_list.append(csv_rd.iloc[i][0])
|
||||
for j in range(1, 129):
|
||||
if csv_rd.iloc[i][j] == '':
|
||||
features_someone_arr.append('0')
|
||||
else:
|
||||
features_someone_arr.append(csv_rd.iloc[i][j])
|
||||
self.face_features_known_list.append(features_someone_arr)
|
||||
logging.info("Faces in Database： %d", len(self.face_features_known_list))
|
||||
return 1
|
||||
else:
|
||||
logging.warning("'features_all.csv' not found!")
|
||||
logging.warning("Please run 'get_faces_from_camera.py' "
|
||||
"and 'features_extraction_to_csv.py' before 'face_reco_from_camera.py'")
|
||||
return 0
|
||||
|
||||
def update_fps(self):
|
||||
now = time.time()
|
||||
# 每秒刷新 fps / Refresh fps per second
|
||||
if str(self.start_time).split(".")[0] != str(now).split(".")[0]:
|
||||
self.fps_show = self.fps
|
||||
self.start_time = now
|
||||
self.frame_time = now - self.frame_start_time
|
||||
self.fps = 1.0 / self.frame_time
|
||||
self.frame_start_time = now
|
||||
|
||||
@staticmethod
|
||||
# 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
|
||||
|
||||
# 使用质心追踪来识别人脸 / Use centroid tracker to link face_x in current frame with person_x in last frame
|
||||
def centroid_tracker(self):
|
||||
for i in range(len(self.current_frame_face_centroid_list)):
|
||||
e_distance_current_frame_person_x_list = []
|
||||
# 对于当前帧中的人脸1, 和上一帧中的 人脸1/2/3/4/.. 进行欧氏距离计算 / For object 1 in current_frame, compute e-distance with object 1/2/3/4/... in last frame
|
||||
for j in range(len(self.last_frame_face_centroid_list)):
|
||||
self.last_current_frame_centroid_e_distance = self.return_euclidean_distance(
|
||||
self.current_frame_face_centroid_list[i], self.last_frame_face_centroid_list[j])
|
||||
|
||||
e_distance_current_frame_person_x_list.append(
|
||||
self.last_current_frame_centroid_e_distance)
|
||||
|
||||
last_frame_num = e_distance_current_frame_person_x_list.index(
|
||||
min(e_distance_current_frame_person_x_list))
|
||||
self.current_frame_face_name_list[i] = self.last_frame_face_name_list[last_frame_num]
|
||||
|
||||
# 生成的 cv2 window 上面添加说明文字 / putText on cv2 window
|
||||
def draw_note(self, img_rd):
|
||||
# 添加说明 / Add some info on windows
|
||||
cv2.putText(img_rd, "Face Recognizer with OT", (20, 40), self.font, 1, (255, 255, 255), 1, cv2.LINE_AA)
|
||||
cv2.putText(img_rd, "Frame: " + str(self.frame_cnt), (20, 100), self.font, 0.8, (0, 255, 0), 1,
|
||||
cv2.LINE_AA)
|
||||
cv2.putText(img_rd, "FPS: " + str(self.fps.__round__(2)), (20, 130), self.font, 0.8, (0, 255, 0), 1,
|
||||
cv2.LINE_AA)
|
||||
cv2.putText(img_rd, "Faces: " + str(self.current_frame_face_cnt), (20, 160), self.font, 0.8, (0, 255, 0), 1,
|
||||
cv2.LINE_AA)
|
||||
cv2.putText(img_rd, "Q: Quit", (20, 450), self.font, 0.8, (255, 255, 255), 1, cv2.LINE_AA)
|
||||
|
||||
for i in range(len(self.current_frame_face_name_list)):
|
||||
img_rd = cv2.putText(img_rd, "Face_" + str(i + 1), tuple(
|
||||
[int(self.current_frame_face_centroid_list[i][0]), int(self.current_frame_face_centroid_list[i][1])]),
|
||||
self.font,
|
||||
0.8, (255, 190, 0),
|
||||
1,
|
||||
cv2.LINE_AA)
|
||||
|
||||
# 处理获取的视频流, 进行人脸识别 / Face detection and recognition wit OT from input video stream
|
||||
def process(self, stream):
|
||||
# 1. 读取存放所有人脸特征的 csv / Get faces known from "features.all.csv"
|
||||
if self.get_face_database():
|
||||
while stream.isOpened():
|
||||
self.frame_cnt += 1
|
||||
logging.debug("Frame " + str(self.frame_cnt) + " starts")
|
||||
flag, img_rd = stream.read()
|
||||
kk = cv2.waitKey(1)
|
||||
|
||||
# 2. 检测人脸 / Detect faces for frame X
|
||||
faces = detector(img_rd, 0)
|
||||
|
||||
# 3. 更新人脸计数器 / Update cnt for faces in frames
|
||||
self.last_frame_face_cnt = self.current_frame_face_cnt
|
||||
self.current_frame_face_cnt = len(faces)
|
||||
|
||||
# 4. 更新上一帧中的人脸列表 / Update the face name list in last frame
|
||||
self.last_frame_face_name_list = self.current_frame_face_name_list[:]
|
||||
|
||||
# 5. 更新上一帧和当前帧的质心列表 / update frame centroid list
|
||||
self.last_frame_face_centroid_list = self.current_frame_face_centroid_list
|
||||
self.current_frame_face_centroid_list = []
|
||||
|
||||
# 6.1 如果当前帧和上一帧人脸数没有变化 / if cnt not changes
|
||||
if (self.current_frame_face_cnt == self.last_frame_face_cnt) and (
|
||||
self.reclassify_interval_cnt != self.reclassify_interval):
|
||||
logging.debug("scene 1: 当前帧和上一帧相比没有发生人脸数变化 / No face cnt changes in this frame!!!")
|
||||
|
||||
self.current_frame_face_position_list = []
|
||||
|
||||
if "unknown" in self.current_frame_face_name_list:
|
||||
logging.debug(" 有未知人脸, 开始进行 reclassify_interval_cnt 计数")
|
||||
self.reclassify_interval_cnt += 1
|
||||
|
||||
if self.current_frame_face_cnt != 0:
|
||||
for k, d in enumerate(faces):
|
||||
self.current_frame_face_position_list.append(tuple(
|
||||
[faces[k].left(), int(faces[k].bottom() + (faces[k].bottom() - faces[k].top()) / 4)]))
|
||||
self.current_frame_face_centroid_list.append(
|
||||
[int(faces[k].left() + faces[k].right()) / 2,
|
||||
int(faces[k].top() + faces[k].bottom()) / 2])
|
||||
|
||||
img_rd = cv2.rectangle(img_rd,
|
||||
tuple([d.left(), d.top()]),
|
||||
tuple([d.right(), d.bottom()]),
|
||||
(255, 255, 255), 2)
|
||||
|
||||
# 如果当前帧中有多个人脸, 使用质心追踪 / Multi-faces in current frame, use centroid-tracker to track
|
||||
if self.current_frame_face_cnt != 1:
|
||||
self.centroid_tracker()
|
||||
|
||||
for i in range(self.current_frame_face_cnt):
|
||||
# 6.2 Write names under ROI
|
||||
img_rd = cv2.putText(img_rd, self.current_frame_face_name_list[i],
|
||||
self.current_frame_face_position_list[i], self.font, 0.8, (0, 255, 255), 1,
|
||||
cv2.LINE_AA)
|
||||
self.draw_note(img_rd)
|
||||
|
||||
# 6.2 如果当前帧和上一帧人脸数发生变化 / If cnt of faces changes, 0->1 or 1->0 or ...
|
||||
else:
|
||||
logging.debug("scene 2: 当前帧和上一帧相比人脸数发生变化 / Faces cnt changes in this frame")
|
||||
self.current_frame_face_position_list = []
|
||||
self.current_frame_face_X_e_distance_list = []
|
||||
self.current_frame_face_feature_list = []
|
||||
self.reclassify_interval_cnt = 0
|
||||
|
||||
# 6.2.1 人脸数减少 / Face cnt decreases: 1->0, 2->1, ...
|
||||
if self.current_frame_face_cnt == 0:
|
||||
logging.debug(" scene 2.1 人脸消失, 当前帧中没有人脸 / No faces in this frame!!!")
|
||||
# clear list of names and features
|
||||
self.current_frame_face_name_list = []
|
||||
# 6.2.2 人脸数增加 / Face cnt increase: 0->1, 0->2, ..., 1->2, ...
|
||||
else:
|
||||
logging.debug(" scene 2.2 出现人脸, 进行人脸识别 / Get faces in this frame and do face recognition")
|
||||
self.current_frame_face_name_list = []
|
||||
for i in range(len(faces)):
|
||||
shape = predictor(img_rd, faces[i])
|
||||
self.current_frame_face_feature_list.append(
|
||||
face_reco_model.compute_face_descriptor(img_rd, shape))
|
||||
self.current_frame_face_name_list.append("unknown")
|
||||
|
||||
# 6.2.2.1 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)):
|
||||
logging.debug(" For face %d in current frame:", k + 1)
|
||||
self.current_frame_face_centroid_list.append(
|
||||
[int(faces[k].left() + faces[k].right()) / 2,
|
||||
int(faces[k].top() + faces[k].bottom()) / 2])
|
||||
|
||||
self.current_frame_face_X_e_distance_list = []
|
||||
|
||||
# 6.2.2.2 每个捕获人脸的名字坐标 / Positions of faces captured
|
||||
self.current_frame_face_position_list.append(tuple(
|
||||
[faces[k].left(), int(faces[k].bottom() + (faces[k].bottom() - faces[k].top()) / 4)]))
|
||||
|
||||
# 6.2.2.3 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(self.face_features_known_list)):
|
||||
# 如果 q 数据不为空
|
||||
if str(self.face_features_known_list[i][0]) != '0.0':
|
||||
e_distance_tmp = self.return_euclidean_distance(
|
||||
self.current_frame_face_feature_list[k],
|
||||
self.face_features_known_list[i])
|
||||
logging.debug(" with person %d, the e-distance: %f", i + 1, e_distance_tmp)
|
||||
self.current_frame_face_X_e_distance_list.append(e_distance_tmp)
|
||||
else:
|
||||
# 空数据 person_X
|
||||
self.current_frame_face_X_e_distance_list.append(999999999)
|
||||
|
||||
# 6.2.2.4 寻找出最小的欧式距离匹配 / Find the one with minimum e distance
|
||||
similar_person_num = self.current_frame_face_X_e_distance_list.index(
|
||||
min(self.current_frame_face_X_e_distance_list))
|
||||
|
||||
if min(self.current_frame_face_X_e_distance_list) < 0.4:
|
||||
self.current_frame_face_name_list[k] = self.face_name_known_list[similar_person_num]
|
||||
logging.debug(" Face recognition result: %s",
|
||||
self.face_name_known_list[similar_person_num])
|
||||
else:
|
||||
logging.debug(" Face recognition result: Unknown person")
|
||||
|
||||
# 7. 生成的窗口添加说明文字 / Add note on cv2 window
|
||||
self.draw_note(img_rd)
|
||||
|
||||
# cv2.imwrite("debug/debug_" + str(self.frame_cnt) + ".png", img_rd) # Dump current frame image if needed
|
||||
|
||||
# 8. 按下 'q' 键退出 / Press 'q' to exit
|
||||
if kk == ord('q'):
|
||||
break
|
||||
|
||||
self.update_fps()
|
||||
cv2.namedWindow("camera", 1)
|
||||
cv2.imshow("camera", img_rd)
|
||||
|
||||
logging.debug("Frame ends\n\n")
|
||||
|
||||
def run(self):
|
||||
# cap = cv2.VideoCapture("video.mp4") # Get video stream from video file
|
||||
cap = cv2.VideoCapture(0) # Get video stream from camera
|
||||
self.process(cap)
|
||||
|
||||
cap.release()
|
||||
cv2.destroyAllWindows()
|
||||
|
||||
|
||||
def main():
|
||||
# logging.basicConfig(level=logging.DEBUG) # Set log level to 'logging.DEBUG' to print debug info of every frame
|
||||
logging.basicConfig(level=logging.INFO)
|
||||
Face_Recognizer_con = Face_Recognizer()
|
||||
Face_Recognizer_con.run()
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
main()
|
||||
329
face_reco_from_camera_single_face.py
Normal file
329
face_reco_from_camera_single_face.py
Normal file
@ -0,0 +1,329 @@
|
||||
# Copyright (C) 2018-2021 coneypo
|
||||
# SPDX-License-Identifier: MIT
|
||||
|
||||
# Author: coneypo
|
||||
# Blog: http://www.cnblogs.com/AdaminXie
|
||||
# GitHub: https://github.com/coneypo/Dlib_face_recognition_from_camera
|
||||
# Mail: coneypo@foxmail.com
|
||||
|
||||
# 单张人脸实时识别 / Real-time face detection and recognition for single face
|
||||
# 检测 -> 识别人脸, 新人脸出现 -> 再识别, 不会对于每一帧都进行识别 / Do detection -> recognize face, new face -> do re-recognition
|
||||
# 其实对于单张人脸, 不需要 OT 进行跟踪, 对于新出现的人脸, 再识别一次就好了 / No OT here, OT will be used only for multi faces
|
||||
|
||||
import dlib
|
||||
import numpy as np
|
||||
import cv2
|
||||
import os
|
||||
import pandas as pd
|
||||
import time
|
||||
from PIL import Image, ImageDraw, ImageFont
|
||||
import logging
|
||||
|
||||
# Dlib 正向人脸检测器 / Use frontal face detector of Dlib
|
||||
detector = dlib.get_frontal_face_detector()
|
||||
|
||||
# Dlib 人脸 landmark 特征点检测器 / Get face landmarks
|
||||
predictor = dlib.shape_predictor('data/data_dlib/shape_predictor_68_face_landmarks.dat')
|
||||
|
||||
# Dlib Resnet 人脸识别模型, 提取 128D 的特征矢量 / Use Dlib resnet50 model to get 128D face descriptor
|
||||
face_reco_model = dlib.face_recognition_model_v1("data/data_dlib/dlib_face_recognition_resnet_model_v1.dat")
|
||||
|
||||
|
||||
class Face_Recognizer:
|
||||
def __init__(self):
|
||||
self.font = cv2.FONT_ITALIC
|
||||
self.font_chinese = ImageFont.truetype("simsun.ttc", 30)
|
||||
|
||||
# 统计 FPS / For FPS
|
||||
self.frame_time = 0
|
||||
self.frame_start_time = 0
|
||||
self.fps = 0
|
||||
self.fps_show = 0
|
||||
self.start_time = time.time()
|
||||
|
||||
# 统计帧数 / cnt for frame
|
||||
self.frame_cnt = 0
|
||||
|
||||
# 用来存储所有录入人脸特征的数组 / Save the features of faces in the database
|
||||
self.features_known_list = []
|
||||
# 用来存储录入人脸名字 / Save the name of faces in the database
|
||||
self.face_name_known_list = []
|
||||
|
||||
# 用来存储上一帧和当前帧 ROI 的质心坐标 / List to save centroid positions of ROI in frame N-1 and N
|
||||
self.last_frame_centroid_list = []
|
||||
self.current_frame_centroid_list = []
|
||||
|
||||
# 用来存储当前帧检测出目标的名字 / List to save names of objects in current frame
|
||||
self.current_frame_name_list = []
|
||||
|
||||
# 上一帧和当前帧中人脸数的计数器 / cnt for faces in frame N-1 and N
|
||||
self.last_frame_faces_cnt = 0
|
||||
self.current_frame_face_cnt = 0
|
||||
|
||||
# 用来存放进行识别时候对比的欧氏距离 / Save the e-distance for faceX when recognizing
|
||||
self.current_frame_face_X_e_distance_list = []
|
||||
|
||||
# 存储当前摄像头中捕获到的所有人脸的坐标名字 / Save the positions and names of current faces captured
|
||||
self.current_frame_face_position_list = []
|
||||
# 存储当前摄像头中捕获到的人脸特征 / Save the features of people in current frame
|
||||
self.current_frame_face_feature_list = []
|
||||
|
||||
# 控制再识别的后续帧数 / Reclassify after 'reclassify_interval' frames
|
||||
# 如果识别出 "unknown" 的脸, 将在 reclassify_interval_cnt 计数到 reclassify_interval 后, 对于人脸进行重新识别
|
||||
self.reclassify_interval_cnt = 0
|
||||
self.reclassify_interval = 10
|
||||
|
||||
# 从 "features_all.csv" 读取录入人脸特征 / Get known faces from "features_all.csv"
|
||||
def get_face_database(self):
|
||||
if os.path.exists("data/features_all.csv"):
|
||||
path_features_known_csv = "data/features_all.csv"
|
||||
csv_rd = pd.read_csv(path_features_known_csv, header=None)
|
||||
for i in range(csv_rd.shape[0]):
|
||||
features_someone_arr = []
|
||||
self.face_name_known_list.append(csv_rd.iloc[i][0])
|
||||
for j in range(1, 129):
|
||||
if csv_rd.iloc[i][j] == '':
|
||||
features_someone_arr.append('0')
|
||||
else:
|
||||
features_someone_arr.append(csv_rd.iloc[i][j])
|
||||
self.features_known_list.append(features_someone_arr)
|
||||
logging.info("Faces in Database： %d", len(self.features_known_list))
|
||||
return 1
|
||||
else:
|
||||
logging.warning("'features_all.csv' not found!")
|
||||
logging.warning("Please run 'get_faces_from_camera.py' "
|
||||
"and 'features_extraction_to_csv.py' before 'face_reco_from_camera.py'")
|
||||
return 0
|
||||
|
||||
# 获取处理之后 stream 的帧数 / Update FPS of video stream
|
||||
def update_fps(self):
|
||||
now = time.time()
|
||||
# 每秒刷新 fps / Refresh fps per second
|
||||
if str(self.start_time).split(".")[0] != str(now).split(".")[0]:
|
||||
self.fps_show = self.fps
|
||||
self.start_time = now
|
||||
self.frame_time = now - self.frame_start_time
|
||||
self.fps = 1.0 / self.frame_time
|
||||
self.frame_start_time = now
|
||||
|
||||
# 计算两个128D向量间的欧式距离 / Compute the e-distance between two 128D features
|
||||
@staticmethod
|
||||
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
|
||||
|
||||
# 生成的 cv2 window 上面添加说明文字 / putText on cv2 window
|
||||
def draw_note(self, img_rd):
|
||||
# 添加说明 (Add some statements
|
||||
cv2.putText(img_rd, "Face Recognizer for single face", (20, 40), self.font, 1, (255, 255, 255), 1,
|
||||
cv2.LINE_AA)
|
||||
cv2.putText(img_rd, "Frame: " + str(self.frame_cnt), (20, 100), self.font, 0.8, (0, 255, 0), 1,
|
||||
cv2.LINE_AA)
|
||||
cv2.putText(img_rd, "FPS: " + str(self.fps_show.__round__(2)), (20, 130), self.font, 0.8, (0, 255, 0), 1,
|
||||
cv2.LINE_AA)
|
||||
cv2.putText(img_rd, "Faces: " + str(self.current_frame_face_cnt), (20, 160), self.font, 0.8, (0, 255, 0), 1,
|
||||
cv2.LINE_AA)
|
||||
cv2.putText(img_rd, "Q: Quit", (20, 450), self.font, 0.8, (255, 255, 255), 1, cv2.LINE_AA)
|
||||
|
||||
def draw_name(self, img_rd):
|
||||
# 在人脸框下面写人脸名字 / Write names under ROI
|
||||
logging.debug(self.current_frame_name_list)
|
||||
img = Image.fromarray(cv2.cvtColor(img_rd, cv2.COLOR_BGR2RGB))
|
||||
draw = ImageDraw.Draw(img)
|
||||
draw.text(xy=self.current_frame_face_position_list[0], text=self.current_frame_name_list[0], font=self.font_chinese,
|
||||
fill=(255, 255, 0))
|
||||
img_rd = cv2.cvtColor(np.array(img), cv2.COLOR_RGB2BGR)
|
||||
return img_rd
|
||||
|
||||
def show_chinese_name(self):
|
||||
if self.current_frame_face_cnt >= 1:
|
||||
logging.debug(self.face_name_known_list)
|
||||
# 修改录入的人脸姓名 / Modify names in face_name_known_list to chinese name
|
||||
self.face_name_known_list[0] = '张三'.encode('utf-8').decode()
|
||||
# self.face_name_known_list[1] = '张四'.encode('utf-8').decode()
|
||||
|
||||
# 处理获取的视频流, 进行人脸识别 / Face detection and recognition wit OT from input video stream
|
||||
def process(self, stream):
|
||||
# 1. 读取存放所有人脸特征的 csv / Get faces known from "features.all.csv"
|
||||
if self.get_face_database():
|
||||
while stream.isOpened():
|
||||
self.frame_cnt += 1
|
||||
logging.debug("Frame " + str(self.frame_cnt) + " starts")
|
||||
flag, img_rd = stream.read()
|
||||
kk = cv2.waitKey(1)
|
||||
|
||||
# 2. 检测人脸 / Detect faces for frame X
|
||||
faces = detector(img_rd, 0)
|
||||
|
||||
# 3. 更新帧中的人脸数 / Update cnt for faces in frames
|
||||
self.last_frame_faces_cnt = self.current_frame_face_cnt
|
||||
self.current_frame_face_cnt = len(faces)
|
||||
|
||||
# 4.1 当前帧和上一帧相比没有发生人脸数变化 / If cnt not changes, 1->1 or 0->0
|
||||
if self.current_frame_face_cnt == self.last_frame_faces_cnt:
|
||||
logging.debug("scene 1: 当前帧和上一帧相比没有发生人脸数变化 / No face cnt changes in this frame!!!")
|
||||
|
||||
if "unknown" in self.current_frame_name_list:
|
||||
logging.debug(" >>> 有未知人脸, 开始进行 reclassify_interval_cnt 计数")
|
||||
self.reclassify_interval_cnt += 1
|
||||
|
||||
# 4.1.1 当前帧一张人脸 / One face in this frame
|
||||
if self.current_frame_face_cnt == 1:
|
||||
if self.reclassify_interval_cnt == self.reclassify_interval:
|
||||
logging.debug(" scene 1.1 需要对于当前帧重新进行人脸识别 / Re-classify for current frame")
|
||||
|
||||
self.reclassify_interval_cnt = 0
|
||||
self.current_frame_face_feature_list = []
|
||||
self.current_frame_face_X_e_distance_list = []
|
||||
self.current_frame_name_list = []
|
||||
|
||||
for i in range(len(faces)):
|
||||
shape = predictor(img_rd, faces[i])
|
||||
self.current_frame_face_feature_list.append(
|
||||
face_reco_model.compute_face_descriptor(img_rd, shape))
|
||||
|
||||
# a. 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)):
|
||||
self.current_frame_name_list.append("unknown")
|
||||
|
||||
# b. 每个捕获人脸的名字坐标 / Positions of faces captured
|
||||
self.current_frame_face_position_list.append(tuple(
|
||||
[faces[k].left(),
|
||||
int(faces[k].bottom() + (faces[k].bottom() - faces[k].top()) / 4)]))
|
||||
|
||||
# c. 对于某张人脸, 遍历所有存储的人脸特征 / For every face detected, compare it with all the faces in the database
|
||||
for i in range(len(self.features_known_list)):
|
||||
# 如果 person_X 数据不为空 / If the data of person_X is not empty
|
||||
if str(self.features_known_list[i][0]) != '0.0':
|
||||
e_distance_tmp = self.return_euclidean_distance(
|
||||
self.current_frame_face_feature_list[k],
|
||||
self.features_known_list[i])
|
||||
logging.debug(" with person %d, the e-distance: %f", i + 1, e_distance_tmp)
|
||||
self.current_frame_face_X_e_distance_list.append(e_distance_tmp)
|
||||
else:
|
||||
# 空数据 person_X / For empty data
|
||||
self.current_frame_face_X_e_distance_list.append(999999999)
|
||||
|
||||
# d. 寻找出最小的欧式距离匹配 / Find the one with minimum e distance
|
||||
similar_person_num = self.current_frame_face_X_e_distance_list.index(
|
||||
min(self.current_frame_face_X_e_distance_list))
|
||||
|
||||
if min(self.current_frame_face_X_e_distance_list) < 0.4:
|
||||
# 在这里更改显示的人名 / Modify name if needed
|
||||
self.show_chinese_name()
|
||||
self.current_frame_name_list[k] = self.face_name_known_list[similar_person_num]
|
||||
logging.debug(" recognition result for face %d: %s", k + 1,
|
||||
self.face_name_known_list[similar_person_num])
|
||||
else:
|
||||
logging.debug(" recognition result for face %d: %s", k + 1, "unknown")
|
||||
else:
|
||||
logging.debug(
|
||||
" scene 1.2 不需要对于当前帧重新进行人脸识别 / No re-classification needed for current frame")
|
||||
# 获取特征框坐标 / Get ROI positions
|
||||
for k, d in enumerate(faces):
|
||||
cv2.rectangle(img_rd,
|
||||
tuple([d.left(), d.top()]),
|
||||
tuple([d.right(), d.bottom()]),
|
||||
(255, 255, 255), 2)
|
||||
|
||||
self.current_frame_face_position_list[k] = tuple(
|
||||
[faces[k].left(),
|
||||
int(faces[k].bottom() + (faces[k].bottom() - faces[k].top()) / 4)])
|
||||
|
||||
img_rd = self.draw_name(img_rd)
|
||||
|
||||
# 4.2 当前帧和上一帧相比发生人脸数变化 / If face cnt changes, 1->0 or 0->1
|
||||
else:
|
||||
logging.debug("scene 2: 当前帧和上一帧相比人脸数发生变化 / Faces cnt changes in this frame")
|
||||
self.current_frame_face_position_list = []
|
||||
self.current_frame_face_X_e_distance_list = []
|
||||
self.current_frame_face_feature_list = []
|
||||
|
||||
# 4.2.1 人脸数从 0->1 / Face cnt 0->1
|
||||
if self.current_frame_face_cnt == 1:
|
||||
logging.debug(" scene 2.1 出现人脸, 进行人脸识别 / Get faces in this frame and do face recognition")
|
||||
self.current_frame_name_list = []
|
||||
|
||||
for i in range(len(faces)):
|
||||
shape = predictor(img_rd, faces[i])
|
||||
self.current_frame_face_feature_list.append(
|
||||
face_reco_model.compute_face_descriptor(img_rd, shape))
|
||||
|
||||
# a. 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)):
|
||||
self.current_frame_name_list.append("unknown")
|
||||
|
||||
# b. 每个捕获人脸的名字坐标 / Positions of faces captured
|
||||
self.current_frame_face_position_list.append(tuple(
|
||||
[faces[k].left(), int(faces[k].bottom() + (faces[k].bottom() - faces[k].top()) / 4)]))
|
||||
|
||||
# c. 对于某张人脸, 遍历所有存储的人脸特征 / For every face detected, compare it with all the faces in database
|
||||
for i in range(len(self.features_known_list)):
|
||||
# 如果 person_X 数据不为空 / If data of person_X is not empty
|
||||
if str(self.features_known_list[i][0]) != '0.0':
|
||||
e_distance_tmp = self.return_euclidean_distance(
|
||||
self.current_frame_face_feature_list[k],
|
||||
self.features_known_list[i])
|
||||
logging.debug(" with person %d, the e-distance: %f", i + 1, e_distance_tmp)
|
||||
self.current_frame_face_X_e_distance_list.append(e_distance_tmp)
|
||||
else:
|
||||
# 空数据 person_X / Empty data for person_X
|
||||
self.current_frame_face_X_e_distance_list.append(999999999)
|
||||
|
||||
# d. 寻找出最小的欧式距离匹配 / Find the one with minimum e distance
|
||||
similar_person_num = self.current_frame_face_X_e_distance_list.index(
|
||||
min(self.current_frame_face_X_e_distance_list))
|
||||
|
||||
if min(self.current_frame_face_X_e_distance_list) < 0.4:
|
||||
# 在这里更改显示的人名 / Modify name if needed
|
||||
self.show_chinese_name()
|
||||
self.current_frame_name_list[k] = self.face_name_known_list[similar_person_num]
|
||||
logging.debug(" recognition result for face %d: %s", k + 1,
|
||||
self.face_name_known_list[similar_person_num])
|
||||
else:
|
||||
logging.debug(" recognition result for face %d: %s", k + 1, "unknown")
|
||||
|
||||
if "unknown" in self.current_frame_name_list:
|
||||
self.reclassify_interval_cnt += 1
|
||||
|
||||
# 4.2.1 人脸数从 1->0 / Face cnt 1->0
|
||||
elif self.current_frame_face_cnt == 0:
|
||||
logging.debug(" scene 2.2 人脸消失, 当前帧中没有人脸 / No face in this frame!!!")
|
||||
|
||||
self.reclassify_interval_cnt = 0
|
||||
self.current_frame_name_list = []
|
||||
self.current_frame_face_feature_list = []
|
||||
|
||||
# 5. 生成的窗口添加说明文字 / Add note on cv2 window
|
||||
self.draw_note(img_rd)
|
||||
|
||||
if kk == ord('q'):
|
||||
break
|
||||
|
||||
self.update_fps()
|
||||
|
||||
cv2.namedWindow("camera", 1)
|
||||
cv2.imshow("camera", img_rd)
|
||||
|
||||
logging.debug("Frame ends\n\n")
|
||||
|
||||
def run(self):
|
||||
# cap = cv2.VideoCapture("video.mp4") # Get video stream from video file
|
||||
cap = cv2.VideoCapture(0) # Get video stream from camera
|
||||
self.process(cap)
|
||||
|
||||
cap.release()
|
||||
cv2.destroyAllWindows()
|
||||
|
||||
|
||||
def main():
|
||||
# logging.basicConfig(level=logging.DEBUG) # Set log level to 'logging.DEBUG' to print debug info of every frame
|
||||
logging.basicConfig(level=logging.INFO)
|
||||
Face_Recognizer_con = Face_Recognizer()
|
||||
Face_Recognizer_con.run()
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
main()
|
||||
106
features_extraction_to_csv.py
Executable file
106
features_extraction_to_csv.py
Executable file
@ -0,0 +1,106 @@
|
||||
# Copyright (C) 2018-2021 coneypo
|
||||
# SPDX-License-Identifier: MIT
|
||||
|
||||
# Author: coneypo
|
||||
# Blog: http://www.cnblogs.com/AdaminXie
|
||||
# GitHub: https://github.com/coneypo/Dlib_face_recognition_from_camera
|
||||
# Mail: coneypo@foxmail.com
|
||||
|
||||
# 从人脸图像文件中提取人脸特征存入 "features_all.csv" / Extract features from images and save into "features_all.csv"
|
||||
|
||||
import os
|
||||
import dlib
|
||||
import csv
|
||||
import numpy as np
|
||||
import logging
|
||||
import cv2
|
||||
|
||||
# 要读取人脸图像文件的路径 / Path of cropped faces
|
||||
path_images_from_camera = "data/data_faces_from_camera/"
|
||||
|
||||
# Dlib 正向人脸检测器 / Use frontal face detector of Dlib
|
||||
detector = dlib.get_frontal_face_detector()
|
||||
|
||||
# Dlib 人脸 landmark 特征点检测器 / Get face landmarks
|
||||
predictor = dlib.shape_predictor('data/data_dlib/shape_predictor_68_face_landmarks.dat')
|
||||
|
||||
# Dlib Resnet 人脸识别模型，提取 128D 的特征矢量 / Use Dlib resnet50 model to get 128D face descriptor
|
||||
face_reco_model = 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 / Return 128D features for single image
|
||||
# Input: path_img <class 'str'>
|
||||
# Output: face_descriptor <class 'dlib.vector'>
|
||||
def return_128d_features(path_img):
|
||||
img_rd = cv2.imread(path_img)
|
||||
faces = detector(img_rd, 1)
|
||||
|
||||
logging.info("%-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，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
|
||||
# For photos of faces saved, we need to make sure that we can detect faces from the cropped images
|
||||
if len(faces) != 0:
|
||||
shape = predictor(img_rd, faces[0])
|
||||
face_descriptor = face_reco_model.compute_face_descriptor(img_rd, shape)
|
||||
else:
|
||||
face_descriptor = 0
|
||||
logging.warning("no face")
|
||||
return face_descriptor
|
||||
|
||||
|
||||
# 返回 personX 的 128D 特征均值 / Return the mean value of 128D face descriptor for person X
|
||||
# Input: path_face_personX <class 'str'>
|
||||
# Output: features_mean_personX <class 'numpy.ndarray'>
|
||||
def return_features_mean_personX(path_face_personX):
|
||||
features_list_personX = []
|
||||
photos_list = os.listdir(path_face_personX)
|
||||
if photos_list:
|
||||
for i in range(len(photos_list)):
|
||||
# 调用 return_128d_features() 得到 128D 特征 / Get 128D features for single image of personX
|
||||
logging.info("%-40s %-20s", "正在读的人脸图像 / Reading image:", path_face_personX + "/" + photos_list[i])
|
||||
features_128d = return_128d_features(path_face_personX + "/" + photos_list[i])
|
||||
# 遇到没有检测出人脸的图片跳过 / Jump if no face detected from image
|
||||
if features_128d == 0:
|
||||
i += 1
|
||||
else:
|
||||
features_list_personX.append(features_128d)
|
||||
else:
|
||||
logging.warning("文件夹内图像文件为空 / Warning: No images in%s/", path_face_personX)
|
||||
|
||||
# 计算 128D 特征的均值 / Compute the mean
|
||||
# personX 的 N 张图像 x 128D -> 1 x 128D
|
||||
if features_list_personX:
|
||||
features_mean_personX = np.array(features_list_personX, dtype=object).mean(axis=0)
|
||||
else:
|
||||
features_mean_personX = np.zeros(128, dtype=int, order='C')
|
||||
return features_mean_personX
|
||||
|
||||
|
||||
def main():
|
||||
logging.basicConfig(level=logging.INFO)
|
||||
# 获取已录入的最后一个人脸序号 / Get the order of latest person
|
||||
person_list = os.listdir("data/data_faces_from_camera/")
|
||||
person_list.sort()
|
||||
|
||||
with open("data/features_all.csv", "w", newline="") as csvfile:
|
||||
writer = csv.writer(csvfile)
|
||||
for person in person_list:
|
||||
# Get the mean/average features of face/personX, it will be a list with a length of 128D
|
||||
logging.info("%sperson_%s", path_images_from_camera, person)
|
||||
features_mean_personX = return_features_mean_personX(path_images_from_camera + person)
|
||||
|
||||
if len(person.split('_', 2)) == 2:
|
||||
# "person_x"
|
||||
person_name = person
|
||||
else:
|
||||
# "person_x_tom"
|
||||
person_name = person.split('_', 2)[-1]
|
||||
features_mean_personX = np.insert(features_mean_personX, 0, person_name, axis=0)
|
||||
# features_mean_personX will be 129D, person name + 128 features
|
||||
writer.writerow(features_mean_personX)
|
||||
logging.info('\n')
|
||||
logging.info("所有录入人脸数据存入 / Save all the features of faces registered into: data/features_all.csv")
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
main()
|
||||
314
get_faces_from_camera.py
Normal file → Executable file
314
get_faces_from_camera.py
Normal file → Executable file
@ -1,186 +1,198 @@
|
||||
# 进行人脸录入 / face register
|
||||
# 录入多张人脸 / support multi-faces
|
||||
# Copyright (C) 2018-2021 coneypo
|
||||
# SPDX-License-Identifier: MIT
|
||||
|
||||
# Author: coneypo
|
||||
# Blog: http://www.cnblogs.com/AdaminXie
|
||||
# GitHub: https://github.com/coneypo/Dlib_face_recognition_from_camera
|
||||
# Mail: coneypo@foxmail.com
|
||||
|
||||
# Created at 2018-05-11
|
||||
# Updated at 2019-02-21
|
||||
# 进行人脸录入 / Face register
|
||||
|
||||
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 dlib
|
||||
import numpy as np
|
||||
import cv2
|
||||
import os
|
||||
import shutil
|
||||
import time
|
||||
import logging
|
||||
|
||||
import os # 读写文件
|
||||
import shutil # 读写文件
|
||||
|
||||
# Dlib 正向人脸检测器
|
||||
# Dlib 正向人脸检测器 / Use frontal face detector of Dlib
|
||||
detector = dlib.get_frontal_face_detector()
|
||||
|
||||
# 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
|
||||
cap = cv2.VideoCapture(0)
|
||||
class Face_Register:
|
||||
def __init__(self):
|
||||
self.path_photos_from_camera = "data/data_faces_from_camera/"
|
||||
self.font = cv2.FONT_ITALIC
|
||||
|
||||
# 设置视频参数
|
||||
cap.set(3, 480)
|
||||
self.existing_faces_cnt = 0 # 已录入的人脸计数器 / cnt for counting saved faces
|
||||
self.ss_cnt = 0 # 录入 personX 人脸时图片计数器 / cnt for screen shots
|
||||
self.current_frame_faces_cnt = 0 # 录入人脸计数器 / cnt for counting faces in current frame
|
||||
|
||||
# 人脸截图的计数器
|
||||
cnt_ss = 0
|
||||
self.save_flag = 1 # 之后用来控制是否保存图像的 flag / The flag to control if save
|
||||
self.press_n_flag = 0 # 之后用来检查是否先按 'n' 再按 's' / The flag to check if press 'n' before 's'
|
||||
|
||||
# 存储人脸的文件夹
|
||||
current_face_dir = 0
|
||||
# FPS
|
||||
self.frame_time = 0
|
||||
self.frame_start_time = 0
|
||||
self.fps = 0
|
||||
self.fps_show = 0
|
||||
self.start_time = time.time()
|
||||
|
||||
# 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(self):
|
||||
# 新建文件夹 / Create folders to save face images and csv
|
||||
if os.path.isdir(self.path_photos_from_camera):
|
||||
pass
|
||||
else:
|
||||
os.mkdir(self.path_photos_from_camera)
|
||||
|
||||
# 删除之前存的人脸数据文件夹 / Delete old face folders
|
||||
def pre_work_del_old_face_folders(self):
|
||||
# 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(self.path_photos_from_camera)
|
||||
for i in range(len(folders_rd)):
|
||||
shutil.rmtree(self.path_photos_from_camera+folders_rd[i])
|
||||
if os.path.isfile("data/features_all.csv"):
|
||||
os.remove("data/features_all.csv")
|
||||
|
||||
# 新建保存人脸图像文件和数据CSV文件夹
|
||||
def pre_work_mkdir():
|
||||
# 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 / Start from person_x+1
|
||||
def check_existing_faces_cnt(self):
|
||||
if os.listdir("data/data_faces_from_camera/"):
|
||||
# 获取已录入的最后一个人脸序号 / Get the order 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]))
|
||||
self.existing_faces_cnt = max(person_num_list)
|
||||
|
||||
# 新建文件夹
|
||||
if os.path.isdir(path_photos_from_camera):
|
||||
pass
|
||||
else:
|
||||
os.mkdir(path_photos_from_camera)
|
||||
if os.path.isdir(path_csv_from_photos):
|
||||
pass
|
||||
else:
|
||||
os.mkdir(path_csv_from_photos)
|
||||
# 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:
|
||||
self.existing_faces_cnt = 0
|
||||
|
||||
# 更新 FPS / Update FPS of Video stream
|
||||
def update_fps(self):
|
||||
now = time.time()
|
||||
# 每秒刷新 fps / Refresh fps per second
|
||||
if str(self.start_time).split(".")[0] != str(now).split(".")[0]:
|
||||
self.fps_show = self.fps
|
||||
self.start_time = now
|
||||
self.frame_time = now - self.frame_start_time
|
||||
self.fps = 1.0 / self.frame_time
|
||||
self.frame_start_time = now
|
||||
|
||||
pre_work_mkdir()
|
||||
# 生成的 cv2 window 上面添加说明文字 / PutText on cv2 window
|
||||
def draw_note(self, img_rd):
|
||||
# 添加说明 / Add some notes
|
||||
cv2.putText(img_rd, "Face Register", (20, 40), self.font, 1, (255, 255, 255), 1, cv2.LINE_AA)
|
||||
cv2.putText(img_rd, "FPS: " + str(self.fps_show.__round__(2)), (20, 100), self.font, 0.8, (0, 255, 0), 1,
|
||||
cv2.LINE_AA)
|
||||
cv2.putText(img_rd, "Faces: " + str(self.current_frame_faces_cnt), (20, 140), self.font, 0.8, (0, 255, 0), 1, cv2.LINE_AA)
|
||||
cv2.putText(img_rd, "N: Create face folder", (20, 350), self.font, 0.8, (255, 255, 255), 1, cv2.LINE_AA)
|
||||
cv2.putText(img_rd, "S: Save current face", (20, 400), self.font, 0.8, (255, 255, 255), 1, cv2.LINE_AA)
|
||||
cv2.putText(img_rd, "Q: Quit", (20, 450), self.font, 0.8, (255, 255, 255), 1, cv2.LINE_AA)
|
||||
|
||||
# 获取人脸 / Main process of face detection and saving
|
||||
def process(self, stream):
|
||||
# 1. 新建储存人脸图像文件目录 / Create folders to save photos
|
||||
self.pre_work_mkdir()
|
||||
|
||||
##### optional/可选, 默认关闭 #####
|
||||
# 删除之前存的人脸数据文件夹
|
||||
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)
|
||||
for i in range(len(folders_rd)):
|
||||
shutil.rmtree(path_photos_from_camera+folders_rd[i])
|
||||
# 2. 删除 "/data/data_faces_from_camera" 中已有人脸图像文件
|
||||
# / Uncomment if want to delete the saved faces and start from person_1
|
||||
# if os.path.isdir(self.path_photos_from_camera):
|
||||
# self.pre_work_del_old_face_folders()
|
||||
|
||||
csv_rd = os.listdir(path_csv_from_photos)
|
||||
for i in range(len(csv_rd)):
|
||||
os.remove(path_csv_from_photos+csv_rd[i])
|
||||
# 3. 检查 "/data/data_faces_from_camera" 中已有人脸文件
|
||||
self.check_existing_faces_cnt()
|
||||
|
||||
# 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_deldir()
|
||||
##################################
|
||||
while stream.isOpened():
|
||||
flag, img_rd = stream.read() # Get camera video stream
|
||||
kk = cv2.waitKey(1)
|
||||
faces = detector(img_rd, 0) # Use Dlib face detector
|
||||
|
||||
# 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'):
|
||||
self.existing_faces_cnt += 1
|
||||
current_face_dir = self.path_photos_from_camera + "person_" + str(self.existing_faces_cnt)
|
||||
os.makedirs(current_face_dir)
|
||||
logging.info("\n%-40s %s", "新建的人脸文件夹 / Create folders:", current_face_dir)
|
||||
|
||||
# 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 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/")
|
||||
person_list.sort()
|
||||
person_num_latest = int(str(person_list[-1]).split("_")[-1])
|
||||
person_cnt = person_num_latest
|
||||
self.ss_cnt = 0 # 将人脸计数器清零 / Clear the cnt of screen shots
|
||||
self.press_n_flag = 1 # 已经按下 'n' / Pressed 'n' already
|
||||
|
||||
# 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
|
||||
else:
|
||||
person_cnt = 0
|
||||
# 5. 检测到人脸 / Face detected
|
||||
if len(faces) != 0:
|
||||
# 矩形框 / Show the ROI of faces
|
||||
for k, d in enumerate(faces):
|
||||
# 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)
|
||||
|
||||
# 之后用来控制是否保存图像的 flag
|
||||
save_flag = 1
|
||||
|
||||
while cap.isOpened():
|
||||
# 480 height * 640 width
|
||||
flag, img_rd = cap.read()
|
||||
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 = cv2.FONT_HERSHEY_COMPLEX
|
||||
|
||||
# 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: ", current_face_dir)
|
||||
|
||||
# 将人脸计数器清零
|
||||
cnt_ss = 0
|
||||
|
||||
# 检测到人脸
|
||||
if len(faces) != 0:
|
||||
# 矩形框
|
||||
for k, d in enumerate(faces):
|
||||
# 计算矩形大小
|
||||
# (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
|
||||
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)
|
||||
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
|
||||
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
|
||||
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
|
||||
if kk == ord('s'):
|
||||
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：", str(current_face_dir) + "/img_face_" + str(cnt_ss) + ".jpg")
|
||||
# 6. 判断人脸矩形框是否超出 480x640 / If the size of ROI > 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), self.font, 0.8, (0, 0, 255), 1, cv2.LINE_AA)
|
||||
color_rectangle = (0, 0, 255)
|
||||
save_flag = 0
|
||||
if kk == ord('s'):
|
||||
logging.warning("请调整位置 / Please adjust your position")
|
||||
else:
|
||||
print("请在按 'S' 之前先按 'N' 来建文件夹 / Please press 'N' before 'S'")
|
||||
color_rectangle = (255, 255, 255)
|
||||
save_flag = 1
|
||||
|
||||
# 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)
|
||||
cv2.rectangle(img_rd,
|
||||
tuple([d.left() - ww, d.top() - hh]),
|
||||
tuple([d.right() + ww, d.bottom() + hh]),
|
||||
color_rectangle, 2)
|
||||
|
||||
# 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)
|
||||
# 7. 根据人脸大小生成空的图像 / Create blank image according to the size of face detected
|
||||
img_blank = np.zeros((int(height*2), width*2, 3), np.uint8)
|
||||
|
||||
# 按下 'q' 键退出
|
||||
if kk == ord('q'):
|
||||
break
|
||||
if save_flag:
|
||||
# 8. 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 self.press_n_flag:
|
||||
self.ss_cnt += 1
|
||||
for ii in range(height*2):
|
||||
for jj in range(width*2):
|
||||
img_blank[ii][jj] = img_rd[d.top()-hh + ii][d.left()-ww + jj]
|
||||
cv2.imwrite(current_face_dir + "/img_face_" + str(self.ss_cnt) + ".jpg", img_blank)
|
||||
logging.info("%-40s %s/img_face_%s.jpg", "写入本地 / Save into：",
|
||||
str(current_face_dir), str(self.ss_cnt))
|
||||
else:
|
||||
logging.warning("请先按 'N' 来建文件夹, 按 'S' / Please press 'N' and press 'S'")
|
||||
|
||||
# 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)
|
||||
self.current_frame_faces_cnt = len(faces)
|
||||
|
||||
# 释放摄像头
|
||||
cap.release()
|
||||
# 9. 生成的窗口添加说明文字 / Add note on cv2 window
|
||||
self.draw_note(img_rd)
|
||||
|
||||
# 删除建立的窗口
|
||||
cv2.destroyAllWindows()
|
||||
# 10. 按下 'q' 键退出 / Press 'q' to exit
|
||||
if kk == ord('q'):
|
||||
break
|
||||
|
||||
# 11. Update FPS
|
||||
self.update_fps()
|
||||
|
||||
cv2.namedWindow("camera", 1)
|
||||
cv2.imshow("camera", img_rd)
|
||||
|
||||
def run(self):
|
||||
# cap = cv2.VideoCapture("video.mp4") # Get video stream from video file
|
||||
cap = cv2.VideoCapture(0) # Get video stream from camera
|
||||
self.process(cap)
|
||||
|
||||
cap.release()
|
||||
cv2.destroyAllWindows()
|
||||
|
||||
|
||||
def main():
|
||||
logging.basicConfig(level=logging.INFO)
|
||||
Face_Register_con = Face_Register()
|
||||
Face_Register_con.run()
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
main()
|
||||
291
get_faces_from_camera_tkinter.py
Normal file
291
get_faces_from_camera_tkinter.py
Normal file
@ -0,0 +1,291 @@
|
||||
from tkinter import *
|
||||
from tkinter import font as tkFont
|
||||
from PIL import Image, ImageTk
|
||||
import dlib
|
||||
import numpy as np
|
||||
import cv2
|
||||
import os
|
||||
import shutil
|
||||
import time
|
||||
import logging
|
||||
|
||||
# Dlib 正向人脸检测器 / Use frontal face detector of Dlib
|
||||
detector = dlib.get_frontal_face_detector()
|
||||
|
||||
|
||||
class Face_Register:
|
||||
def __init__(self):
|
||||
|
||||
self.existing_faces_cnt = 0 # 已录入的人脸计数器 / cnt for counting saved faces
|
||||
self.ss_cnt = 0 # 录入 person_n 人脸时图片计数器 / cnt for screen shots
|
||||
self.current_frame_faces_cnt = 0 # 当前帧中人脸计数器 / cnt for counting faces in current frame
|
||||
|
||||
# Tkinter GUI
|
||||
self.win = Tk()
|
||||
self.win.title("Face Register @coneypo")
|
||||
self.win.geometry("1300x550")
|
||||
|
||||
# GUI left part
|
||||
self.frame_left_camera = Frame(self.win)
|
||||
self.label = Label(self.win)
|
||||
self.label.pack(side=LEFT)
|
||||
self.frame_left_camera.pack()
|
||||
|
||||
# GUI right part
|
||||
self.frame_right_info = Frame(self.win)
|
||||
self.label_cnt_face_in_database = Label(self.frame_right_info, text=str(self.existing_faces_cnt))
|
||||
self.label_fps_info = Label(self.frame_right_info, text="")
|
||||
self.input_name = Entry(self.frame_right_info)
|
||||
self.input_name_char = ""
|
||||
self.label_warning = Label(self.frame_right_info)
|
||||
self.label_face_cnt = Label(self.frame_right_info, text="Faces in current frame: ")
|
||||
self.log_all = Label(self.frame_right_info)
|
||||
|
||||
self.font_title = tkFont.Font(family='Helvetica', size=20, weight='bold')
|
||||
self.font_step_title = tkFont.Font(family='Helvetica', size=15, weight='bold')
|
||||
self.font_warning = tkFont.Font(family='Helvetica', size=15, weight='bold')
|
||||
|
||||
self.path_photos_from_camera = "data/data_faces_from_camera/"
|
||||
self.current_face_dir = ""
|
||||
self.font = cv2.FONT_ITALIC
|
||||
|
||||
# Current frame and face ROI position
|
||||
self.current_frame = np.ndarray
|
||||
self.face_ROI_image = np.ndarray
|
||||
self.face_ROI_width_start = 0
|
||||
self.face_ROI_height_start = 0
|
||||
self.face_ROI_width = 0
|
||||
self.face_ROI_height = 0
|
||||
self.ww = 0
|
||||
self.hh = 0
|
||||
|
||||
self.out_of_range_flag = FALSE
|
||||
self.face_folder_created_flag = FALSE
|
||||
|
||||
# FPS
|
||||
self.frame_time = 0
|
||||
self.frame_start_time = 0
|
||||
self.fps = 0
|
||||
self.fps_show = 0
|
||||
self.start_time = time.time()
|
||||
|
||||
self.cap = cv2.VideoCapture(0) # Get video stream from camera
|
||||
# self.cap = cv2.VideoCapture("test.mp4") # Input local video
|
||||
|
||||
# 删除之前存的人脸数据文件夹 / Delete old face folders
|
||||
def GUI_clear_data(self):
|
||||
# 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(self.path_photos_from_camera)
|
||||
for i in range(len(folders_rd)):
|
||||
shutil.rmtree(self.path_photos_from_camera + folders_rd[i])
|
||||
if os.path.isfile("data/features_all.csv"):
|
||||
os.remove("data/features_all.csv")
|
||||
self.label_cnt_face_in_database['text'] = "0"
|
||||
self.existing_faces_cnt = 0
|
||||
self.log_all["text"] = "Face images and `features_all.csv` removed!"
|
||||
|
||||
def GUI_get_input_name(self):
|
||||
self.input_name_char = self.input_name.get()
|
||||
self.create_face_folder()
|
||||
self.label_cnt_face_in_database['text'] = str(self.existing_faces_cnt)
|
||||
|
||||
def GUI_info(self):
|
||||
Label(self.frame_right_info,
|
||||
text="Face register",
|
||||
font=self.font_title).grid(row=0, column=0, columnspan=3, sticky=W, padx=2, pady=20)
|
||||
|
||||
Label(self.frame_right_info,
|
||||
text="FPS: ").grid(row=1, column=0, columnspan=2, sticky=W, padx=5, pady=2)
|
||||
self.label_fps_info.grid(row=1, column=2, sticky=W, padx=5, pady=2)
|
||||
|
||||
Label(self.frame_right_info,
|
||||
text="Faces in database: ").grid(row=2, column=0, columnspan=2, sticky=W, padx=5, pady=2)
|
||||
self.label_cnt_face_in_database.grid(row=2, column=2, columnspan=3, sticky=W, padx=5, pady=2)
|
||||
|
||||
Label(self.frame_right_info,
|
||||
text="Faces in current frame: ").grid(row=3, column=0, columnspan=2, sticky=W, padx=5, pady=2)
|
||||
self.label_face_cnt.grid(row=3, column=2, columnspan=3, sticky=W, padx=5, pady=2)
|
||||
|
||||
self.label_warning.grid(row=4, column=0, columnspan=3, sticky=W, padx=5, pady=2)
|
||||
|
||||
# Step 1: Clear old data
|
||||
Label(self.frame_right_info,
|
||||
font=self.font_step_title,
|
||||
text="Step 1: Clear face photos").grid(row=5, column=0, columnspan=2, sticky=W, padx=5, pady=20)
|
||||
Button(self.frame_right_info,
|
||||
text='Clear',
|
||||
command=self.GUI_clear_data).grid(row=6, column=0, columnspan=3, sticky=W, padx=5, pady=2)
|
||||
|
||||
# Step 2: Input name and create folders for face
|
||||
Label(self.frame_right_info,
|
||||
font=self.font_step_title,
|
||||
text="Step 2: Input name").grid(row=7, column=0, columnspan=2, sticky=W, padx=5, pady=20)
|
||||
|
||||
Label(self.frame_right_info, text="Name: ").grid(row=8, column=0, sticky=W, padx=5, pady=0)
|
||||
self.input_name.grid(row=8, column=1, sticky=W, padx=0, pady=2)
|
||||
|
||||
Button(self.frame_right_info,
|
||||
text='Input',
|
||||
command=self.GUI_get_input_name).grid(row=8, column=2, padx=5)
|
||||
|
||||
# Step 3: Save current face in frame
|
||||
Label(self.frame_right_info,
|
||||
font=self.font_step_title,
|
||||
text="Step 3: Save face image").grid(row=9, column=0, columnspan=2, sticky=W, padx=5, pady=20)
|
||||
|
||||
Button(self.frame_right_info,
|
||||
text='Save current face',
|
||||
command=self.save_current_face).grid(row=10, column=0, columnspan=3, sticky=W)
|
||||
|
||||
# Log
|
||||
self.log_all.grid(row=11, column=0, columnspan=20, sticky=W, padx=5, pady=20)
|
||||
|
||||
self.frame_right_info.pack()
|
||||
|
||||
# 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(self):
|
||||
# 新建文件夹 / Create folders to save face images and csv
|
||||
if os.path.isdir(self.path_photos_from_camera):
|
||||
pass
|
||||
else:
|
||||
os.mkdir(self.path_photos_from_camera)
|
||||
|
||||
# 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 / Start from person_x+1
|
||||
def check_existing_faces_cnt(self):
|
||||
if os.listdir("data/data_faces_from_camera/"):
|
||||
# 获取已录入的最后一个人脸序号 / Get the order of latest person
|
||||
person_list = os.listdir("data/data_faces_from_camera/")
|
||||
person_num_list = []
|
||||
for person in person_list:
|
||||
person_order = person.split('_')[1].split('_')[0]
|
||||
person_num_list.append(int(person_order))
|
||||
self.existing_faces_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:
|
||||
self.existing_faces_cnt = 0
|
||||
|
||||
# 更新 FPS / Update FPS of Video stream
|
||||
def update_fps(self):
|
||||
now = time.time()
|
||||
# 每秒刷新 fps / Refresh fps per second
|
||||
if str(self.start_time).split(".")[0] != str(now).split(".")[0]:
|
||||
self.fps_show = self.fps
|
||||
self.start_time = now
|
||||
self.frame_time = now - self.frame_start_time
|
||||
self.fps = 1.0 / self.frame_time
|
||||
self.frame_start_time = now
|
||||
|
||||
self.label_fps_info["text"] = str(self.fps.__round__(2))
|
||||
|
||||
def create_face_folder(self):
|
||||
# # 4. 新建存储人脸的文件夹 / Create the folders for saving faces
|
||||
self.existing_faces_cnt += 1
|
||||
if self.input_name_char:
|
||||
self.current_face_dir = self.path_photos_from_camera + \
|
||||
"person_" + str(self.existing_faces_cnt) + "_" + \
|
||||
self.input_name_char
|
||||
else:
|
||||
self.current_face_dir = self.path_photos_from_camera + \
|
||||
"person_" + str(self.existing_faces_cnt)
|
||||
os.makedirs(self.current_face_dir)
|
||||
self.log_all["text"] = "\"" + self.current_face_dir + "/\" created!"
|
||||
logging.info("\n%-40s %s", "新建的人脸文件夹 / Create folders:", self.current_face_dir)
|
||||
|
||||
self.ss_cnt = 0 # 将人脸计数器清零 / Clear the cnt of screen shots
|
||||
self.face_folder_created_flag = 1 # 已经按下 'n' / Pressed 'n' already
|
||||
|
||||
def save_current_face(self):
|
||||
if self.face_folder_created_flag:
|
||||
if self.current_frame_faces_cnt == 1:
|
||||
if not self.out_of_range_flag:
|
||||
self.ss_cnt += 1
|
||||
# 根据人脸大小生成空的图像 / Create blank image according to the size of face detected
|
||||
self.face_ROI_image = np.zeros((int(self.face_ROI_height * 2), self.face_ROI_width * 2, 3),
|
||||
np.uint8)
|
||||
for ii in range(self.face_ROI_height * 2):
|
||||
for jj in range(self.face_ROI_width * 2):
|
||||
self.face_ROI_image[ii][jj] = self.current_frame[self.face_ROI_height_start - self.hh + ii][
|
||||
self.face_ROI_width_start - self.ww + jj]
|
||||
self.log_all["text"] = "\"" + self.current_face_dir + "/img_face_" + str(
|
||||
self.ss_cnt) + ".jpg\"" + " saved!"
|
||||
self.face_ROI_image = cv2.cvtColor(self.face_ROI_image, cv2.COLOR_BGR2RGB)
|
||||
|
||||
cv2.imwrite(self.current_face_dir + "/img_face_" + str(self.ss_cnt) + ".jpg", self.face_ROI_image)
|
||||
logging.info("%-40s %s/img_face_%s.jpg", "写入本地 / Save into：",
|
||||
str(self.current_face_dir), str(self.ss_cnt) + ".jpg")
|
||||
else:
|
||||
self.log_all["text"] = "Please do not out of range!"
|
||||
else:
|
||||
self.log_all["text"] = "No face in current frame!"
|
||||
else:
|
||||
self.log_all["text"] = "Please run step 2!"
|
||||
|
||||
def get_frame(self):
|
||||
try:
|
||||
if self.cap.isOpened():
|
||||
ret, frame = self.cap.read()
|
||||
return ret, cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
|
||||
except:
|
||||
print("Error: No video input!!!")
|
||||
|
||||
# 获取人脸 / Main process of face detection and saving
|
||||
def process(self):
|
||||
ret, self.current_frame = self.get_frame()
|
||||
faces = detector(self.current_frame, 0)
|
||||
# Get frame
|
||||
if ret:
|
||||
self.update_fps()
|
||||
self.label_face_cnt["text"] = str(len(faces))
|
||||
# 检测到人脸 / Face detected
|
||||
if len(faces) != 0:
|
||||
# 矩形框 / Show the ROI of faces
|
||||
for k, d in enumerate(faces):
|
||||
self.face_ROI_width_start = d.left()
|
||||
self.face_ROI_height_start = d.top()
|
||||
# 计算矩形框大小 / Compute the size of rectangle box
|
||||
self.face_ROI_height = (d.bottom() - d.top())
|
||||
self.face_ROI_width = (d.right() - d.left())
|
||||
self.hh = int(self.face_ROI_height / 2)
|
||||
self.ww = int(self.face_ROI_width / 2)
|
||||
|
||||
# 判断人脸矩形框是否超出 480x640 / If the size of ROI > 480x640
|
||||
if (d.right() + self.ww) > 640 or (d.bottom() + self.hh > 480) or (d.left() - self.ww < 0) or (
|
||||
d.top() - self.hh < 0):
|
||||
self.label_warning["text"] = "OUT OF RANGE"
|
||||
self.label_warning['fg'] = 'red'
|
||||
self.out_of_range_flag = TRUE
|
||||
color_rectangle = (255, 0, 0)
|
||||
else:
|
||||
self.out_of_range_flag = FALSE
|
||||
self.label_warning["text"] = ""
|
||||
color_rectangle = (255, 255, 255)
|
||||
self.current_frame = cv2.rectangle(self.current_frame,
|
||||
tuple([d.left() - self.ww, d.top() - self.hh]),
|
||||
tuple([d.right() + self.ww, d.bottom() + self.hh]),
|
||||
color_rectangle, 2)
|
||||
self.current_frame_faces_cnt = len(faces)
|
||||
img = Image.fromarray(self.current_frame)
|
||||
# Convert image to PhotoImage
|
||||
imgtk = ImageTk.PhotoImage(image=img)
|
||||
self.label.imgtk = imgtk
|
||||
self.label.configure(image=imgtk)
|
||||
|
||||
self.win.after(20, self.process)
|
||||
|
||||
def run(self):
|
||||
self.pre_work_mkdir()
|
||||
self.check_existing_faces_cnt()
|
||||
self.GUI_info()
|
||||
self.process()
|
||||
self.win.mainloop()
|
||||
|
||||
|
||||
def main():
|
||||
logging.basicConfig(level=logging.INFO)
|
||||
Face_Register_con = Face_Register()
|
||||
Face_Register_con.run()
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
main()
|
||||
@ -1,137 +0,0 @@
|
||||
# 从人脸图像文件中提取人脸特征存入 CSV
|
||||
# Get features from images and save into features_all.csv
|
||||
|
||||
# Author: coneypo
|
||||
# Blog: http://www.cnblogs.com/AdaminXie
|
||||
# GitHub: https://github.com/coneypo/Dlib_face_recognition_from_camera
|
||||
# Mail: coneypo@foxmail.com
|
||||
|
||||
# Created at 2018-05-11
|
||||
# Updated at 2019-02-25
|
||||
|
||||
# 增加录入多张人脸到 CSV 的功能
|
||||
|
||||
# return_128d_features() 获取某张图像的 128D 特征
|
||||
# write_into_csv() 获取某个路径下所有图像的特征，并写入 CSV
|
||||
# compute_the_mean() 从 CSV 中读取 128D 特征，并计算特征均值
|
||||
|
||||
import cv2
|
||||
import os
|
||||
import dlib
|
||||
from skimage import io
|
||||
import csv
|
||||
import numpy as np
|
||||
import pandas as pd
|
||||
|
||||
# 要读取人脸图像文件的路径
|
||||
path_photos_from_camera = "data/data_faces_from_camera/"
|
||||
# 储存人脸特征 csv 的路径
|
||||
path_csv_from_photos = "data/data_csvs_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("data/data_dlib/shape_predictor_5_face_landmarks.dat")
|
||||
|
||||
# Dlib 人脸识别模型
|
||||
# Face recognition model, the object maps human faces into 128D vectors
|
||||
facerec = 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 = io.imread(path_img)
|
||||
img_gray = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
|
||||
faces = detector(img_gray, 1)
|
||||
|
||||
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
|
||||
if len(faces) != 0:
|
||||
shape = predictor(img_gray, faces[0])
|
||||
face_descriptor = facerec.compute_face_descriptor(img_gray, shape)
|
||||
else:
|
||||
face_descriptor = 0
|
||||
print("no face")
|
||||
|
||||
# print(face_descriptor)
|
||||
return face_descriptor
|
||||
|
||||
|
||||
# 将文件夹中照片特征提取出来, 写入 CSV
|
||||
# path_faces_personX: 图像文件夹的路径
|
||||
# path_csv_from_photos: 要生成的 CSV 路径
|
||||
|
||||
def write_into_csv(path_faces_personX, path_csv_from_photos):
|
||||
photos_list = os.listdir(path_faces_personX)
|
||||
with open(path_csv_from_photos, "w", newline="") as csvfile:
|
||||
writer = csv.writer(csvfile)
|
||||
if photos_list:
|
||||
for i in range(len(photos_list)):
|
||||
# 调用return_128d_features()得到128d特征
|
||||
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
|
||||
if features_128d == 0:
|
||||
i += 1
|
||||
else:
|
||||
writer.writerow(features_128d)
|
||||
else:
|
||||
print("Warning: Empty photos in "+path_faces_personX+'/')
|
||||
writer.writerow("")
|
||||
|
||||
|
||||
# 读取某人所有的人脸图像的数据，写入 person_X.csv
|
||||
faces = os.listdir(path_photos_from_camera)
|
||||
faces.sort()
|
||||
for person in faces:
|
||||
print("##### " + person + " #####")
|
||||
print(path_csv_from_photos + person + ".csv")
|
||||
write_into_csv(path_photos_from_camera + person, path_csv_from_photos + person + ".csv")
|
||||
print('\n')
|
||||
|
||||
|
||||
# 从 CSV 中读取数据，计算 128D 特征的均值
|
||||
def compute_the_mean(path_csv_from_photos):
|
||||
column_names = []
|
||||
|
||||
# 128D 特征
|
||||
for feature_num in range(128):
|
||||
column_names.append("features_" + str(feature_num + 1))
|
||||
|
||||
# 利用 pandas 读取 csv
|
||||
rd = pd.read_csv(path_csv_from_photos, names=column_names)
|
||||
|
||||
if rd.size != 0:
|
||||
# 存放 128D 特征的均值
|
||||
feature_mean_list = []
|
||||
|
||||
for feature_num in range(128):
|
||||
tmp_arr = rd["features_" + str(feature_num + 1)]
|
||||
tmp_arr = np.array(tmp_arr)
|
||||
# 计算某一个特征的均值
|
||||
tmp_mean = np.mean(tmp_arr)
|
||||
feature_mean_list.append(tmp_mean)
|
||||
else:
|
||||
feature_mean_list = []
|
||||
return feature_mean_list
|
||||
|
||||
|
||||
# 存放所有特征均值的 CSV 的路径
|
||||
path_csv_from_photos_feature_all = "data/features_all.csv"
|
||||
|
||||
# 存放人脸特征的 CSV 的路径
|
||||
path_csv_from_photos = "data/data_csvs_from_camera/"
|
||||
|
||||
with open(path_csv_from_photos_feature_all, "w", newline="") as csvfile:
|
||||
writer = csv.writer(csvfile)
|
||||
csv_rd = os.listdir(path_csv_from_photos)
|
||||
csv_rd.sort()
|
||||
print("##### 得到的特征均值 / The generated average values of features stored in: #####")
|
||||
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)
|
||||
36
how_to_use_camera.py
Normal file → Executable file
36
how_to_use_camera.py
Normal file → Executable file
@ -1,6 +1,3 @@
|
||||
# OpenCv 调用摄像头
|
||||
# 默认调用笔记本摄像头
|
||||
|
||||
# Author: coneypo
|
||||
# Blog: http://www.cnblogs.com/AdaminXie
|
||||
# GitHub: https://github.com/coneypo/Dlib_face_recognition_from_camera
|
||||
@ -12,7 +9,31 @@ 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)
|
||||
"""
|
||||
0. cv2.CAP_PROP_POS_MSEC Current position of the video file in milliseconds.
|
||||
1. cv2.CAP_PROP_POS_FRAMES 0-based index of the frame to be decoded/captured next.
|
||||
2. cv2.CAP_PROP_POS_AVI_RATIO Relative position of the video file
|
||||
3. cv2.CAP_PROP_FRAME_WIDTH Width of the frames in the video stream.
|
||||
4. cv2.CAP_PROP_FRAME_HEIGHT Height of the frames in the video stream.
|
||||
5. cv2.CAP_PROP_FPS Frame rate.
|
||||
6. cv2.CAP_PROP_FOURCC 4-character code of codec.
|
||||
7. cv2.CAP_PROP_FRAME_COUNT Number of frames in the video file.
|
||||
8. cv2.CAP_PROP_FORMAT Format of the Mat objects returned by retrieve() .
|
||||
9. cv2.CAP_PROP_MODE Backend-specific value indicating the current capture mode.
|
||||
10. cv2.CAP_PROP_BRIGHTNESS Brightness of the image (only for cameras).
|
||||
11. cv2.CAP_PROP_CONTRAST Contrast of the image (only for cameras).
|
||||
12. cv2.CAP_PROP_SATURATION Saturation of the image (only for cameras).
|
||||
13. cv2.CAP_PROP_HUE Hue of the image (only for cameras).
|
||||
14. cv2.CAP_PROP_GAIN Gain of the image (only for cameras).
|
||||
15. cv2.CAP_PROP_EXPOSURE Exposure (only for cameras).
|
||||
16. cv2.CAP_PROP_CONVERT_RGB Boolean flags indicating whether images should be converted to RGB.
|
||||
17. cv2.CAP_PROP_WHITE_BALANCE Currently unsupported
|
||||
18. cv2.CAP_PROP_RECTIFICATION Rectification flag for stereo cameras (note: only supported by DC1394 v 2.x backend currently)
|
||||
"""
|
||||
|
||||
# The default size of frame from camera will be 640x480 in Windows or Ubuntu
|
||||
# So we will not set "cap.set" here, it doesn't work
|
||||
# cap.set(propId=cv2.CAP_PROP_FRAME_WIDTH, value=cap.get(cv2.CAP_PROP_FRAME_WIDTH))
|
||||
|
||||
# cap.isOpened() 返回 true/false, 检查摄像头初始化是否成功
|
||||
print(cap.isOpened())
|
||||
@ -38,6 +59,11 @@ print(cap.isOpened())
|
||||
|
||||
while cap.isOpened():
|
||||
ret_flag, img_camera = cap.read()
|
||||
|
||||
print("height: ", img_camera.shape[0])
|
||||
print("width: ", img_camera.shape[1])
|
||||
print('\n')
|
||||
|
||||
cv2.imshow("camera", img_camera)
|
||||
|
||||
# 每帧数据延时 1ms, 延时为0, 读取的是静态帧
|
||||
@ -55,4 +81,4 @@ while cap.isOpened():
|
||||
cap.release()
|
||||
|
||||
# 删除建立的所有窗口
|
||||
cv2.destroyAllWindows()
|
||||
cv2.destroyAllWindows()
|
||||
|
||||
BIN
introduction/Dlib_Face_recognition_by_coneypo.pptx
Normal file → Executable file
BIN
introduction/Dlib_Face_recognition_by_coneypo.pptx
Normal file → Executable file
Binary file not shown.
BIN
introduction/face_reco.png
Normal file
BIN
introduction/face_reco.png
Normal file
Binary file not shown.
|
After Width: | Height: | Size: 206 KiB |
BIN
introduction/face_reco_chinese_name.png
Normal file
BIN
introduction/face_reco_chinese_name.png
Normal file
Binary file not shown.
|
After Width: | Height: | Size: 148 KiB |
Binary file not shown.
|
Before Width: | Height: | Size: 853 KiB |
BIN
introduction/face_reco_ot.png
Normal file
BIN
introduction/face_reco_ot.png
Normal file
Binary file not shown.
|
After Width: | Height: | Size: 216 KiB |
BIN
introduction/face_reco_single.png
Normal file
BIN
introduction/face_reco_single.png
Normal file
Binary file not shown.
|
After Width: | Height: | Size: 358 KiB |
Binary file not shown.
|
Before Width: | Height: | Size: 357 KiB |
Binary file not shown.
|
Before Width: | Height: | Size: 397 KiB |
BIN
introduction/face_register.png
Normal file
BIN
introduction/face_register.png
Normal file
Binary file not shown.
|
After Width: | Height: | Size: 345 KiB |
BIN
introduction/face_register_tkinter_GUI.png
Normal file
BIN
introduction/face_register_tkinter_GUI.png
Normal file
Binary file not shown.
|
After Width: | Height: | Size: 184 KiB |
BIN
introduction/face_register_warning.png
Normal file
BIN
introduction/face_register_warning.png
Normal file
Binary file not shown.
|
After Width: | Height: | Size: 332 KiB |
Binary file not shown.
|
Before Width: | Height: | Size: 396 KiB |
BIN
introduction/overview.png
Executable file
BIN
introduction/overview.png
Executable file
Binary file not shown.
|
After Width: | Height: | Size: 267 KiB |
BIN
introduction/overview_with_ot.png
Normal file
BIN
introduction/overview_with_ot.png
Normal file
Binary file not shown.
|
After Width: | Height: | Size: 74 KiB |
5
requirements.txt
Executable file
5
requirements.txt
Executable file
@ -0,0 +1,5 @@
|
||||
dlib==19.17.0
|
||||
numpy==1.21.3
|
||||
scikit-image==0.18.3
|
||||
pandas==1.3.4
|
||||
opencv-python==4.5.4.58
|
||||
Binary file not shown.
0
test_tkinter.py
Normal file
0
test_tkinter.py
Normal file
Reference in New Issue
Block a user