Compare commits
18 Commits
patch_cust
...
use_mysql
| Author | SHA1 | Date | |
|---|---|---|---|
| 068be82ee3 | |||
| dced9d9ef7 | |||
| fbb1dbd3f5 | |||
| e9b923a864 | |||
| 7599f86d07 | |||
| fefd44a54c | |||
| c57db57dfd | |||
| baaf84226a | |||
| 64d797efcb | |||
| 3ca94d6d96 | |||
| 262609f91f | |||
| bd5a4034f4 | |||
| a0a070ce3a | |||
| 13b6807441 | |||
| fd13a6b7ec | |||
| c80b1d19b8 | |||
| 7e85411ae3 | |||
| 08deb0d608 |
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2
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||||
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64
README.rst
64
README.rst
@ -15,7 +15,7 @@ Detect and recognize single/multi-faces from camera;
|
||||
:align: center
|
||||
|
||||
请不要离摄像头过近，人脸超出摄像头范围时会有 "OUT OF RANGE" 提醒 /
|
||||
Please do not too close to the camera, or you can't save faces with "OUT OF RANGE" warning;
|
||||
Please do not be too close to the camera, or you can't save faces with "OUT OF RANGE" warning;
|
||||
|
||||
.. image:: introduction/get_face_from_camera_out_of_range.png
|
||||
:align: center
|
||||
@ -30,20 +30,31 @@ Detect and recognize single/multi-faces from camera;
|
||||
|
||||
当多张人脸 / When multi-faces:
|
||||
|
||||
一张已录入人脸 + 未录入 unknown 人脸 / 1x known face + 1x unknown face:
|
||||
一张已录入人脸 + 未录入 unknown 人脸 / 1x known face + 2x unknown face:
|
||||
|
||||
.. image:: introduction/face_reco_two_people.png
|
||||
.. image:: introduction/face_reco_multi_people.png
|
||||
:align: center
|
||||
|
||||
同时识别多张已录入人脸 / multi-faces recognition at the same time:
|
||||
同时识别多张已录入人脸 / Multi-faces recognition at the same time:
|
||||
|
||||
.. image:: introduction/face_reco_two_people_in_database.png
|
||||
:align: center
|
||||
|
||||
实时人脸特征描述子计算 / Real-time face descriptor computation:
|
||||
|
||||
.. image:: introduction/face_descriptor_single_person.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
|
||||
********
|
||||
|
||||
@ -55,19 +66,27 @@ Overview
|
||||
Steps
|
||||
*****
|
||||
|
||||
#. 下载源码 / Download zip from website or via GitHub Desktop in windows, or git clone in Ubuntu
|
||||
#. 安装依赖库 / Install some python packages if needed
|
||||
|
||||
.. code-block:: bash
|
||||
|
||||
pip3 install opencv-python
|
||||
pip3 install scikit-image
|
||||
pip3 install dlib
|
||||
|
||||
#. 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 face register / 人脸信息采集录入
|
||||
#. 进行人脸信息采集录入 / Register faces
|
||||
|
||||
.. code-block:: bash
|
||||
|
||||
python3 get_face_from_camera.py
|
||||
|
||||
#. 提取所有录入人脸数据存入 features_all.csv / Features extraction and save into 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:: bash
|
||||
|
||||
@ -88,14 +107,14 @@ Repo 的 tree / 树状图:
|
||||
::
|
||||
|
||||
.
|
||||
├── get_faces_from_camera.py # Step1. Faces register
|
||||
├── features_extraction_to_csv.py # Step2. Features extraction
|
||||
├── face_reco_from_camera.py # Step3. Faces recognition
|
||||
├── get_faces_from_camera.py # Step1. Face register
|
||||
├── features_extraction_to_csv.py # Step2. Feature extraction
|
||||
├── face_reco_from_camera.py # Step3. Face recognizer
|
||||
├── 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_5_face_landmarks.dat
|
||||
│ │ └── shape_predictor_68_face_landmarks.dat
|
||||
│ ├── data_faces_from_camera # Face images captured from camera (will generate after step 1)
|
||||
│ │ ├── person_1
|
||||
@ -115,7 +134,7 @@ Repo 的 tree / 树状图:
|
||||
│ ├── get_face_from_camera.png
|
||||
│ └── overview.png
|
||||
├── README.rst
|
||||
└── requirements.txt # Some python packages needed
|
||||
└── requirements.txt # Some python packages needed
|
||||
|
||||
用到的 Dlib 相关模型函数:
|
||||
|
||||
@ -128,15 +147,22 @@ Repo 的 tree / 树状图:
|
||||
faces = detector(img_gray, 0)
|
||||
|
||||
|
||||
#. Dlib 人脸预测器, output: <class 'dlib.dlib.full_object_detection'>
|
||||
#. 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
|
||||
|
||||
predictor = dlib.shape_predictor("data/data_dlib/shape_predictor_5_face_landmarks.dat")
|
||||
# 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])
|
||||
|
||||
|
||||
#. 特征描述子 Face recognition model, the object maps human faces into 128D vectors
|
||||
#. Dlib 特征描述子 Face recognition model, the object maps human faces into 128D vectors
|
||||
|
||||
|
||||
.. code-block:: python
|
||||
@ -169,7 +195,10 @@ Python 源码介绍如下:
|
||||
* Compare the faces captured from camera with the faces you have registered which are saved in "features_all.csv"
|
||||
|
||||
* 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;
|
||||
|
||||
|
||||
#. (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
|
||||
****
|
||||
@ -184,6 +213,9 @@ Tips:
|
||||
|
||||
#. 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``
|
||||
|
||||
#. 关于人脸识别卡顿 FPS 低问题, 不做 compare 的时候, 光跑 face_descriptor_from_camera.py 中 face_reco_model.compute_face_descriptor
|
||||
在 CPU: i7-8700K FPS: 5~6, 所以主要提取特征时候耗资源
|
||||
|
||||
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 :
|
||||
|
||||
|
||||
BIN
data/data_faces_from_camera/person_1/img_face_1.jpg
Normal file
BIN
data/data_faces_from_camera/person_1/img_face_1.jpg
Normal file
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|
After Width: | Height: | Size: 27 KiB |
BIN
data/data_faces_from_camera/person_1/img_face_2.jpg
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BIN
data/data_faces_from_camera/person_1/img_face_2.jpg
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|
After Width: | Height: | Size: 23 KiB |
BIN
data/data_faces_from_camera/person_1/img_face_3.jpg
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BIN
data/data_faces_from_camera/person_1/img_face_3.jpg
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|
After Width: | Height: | Size: 21 KiB |
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data/data_faces_from_camera/person_1/img_face_4.jpg
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BIN
data/data_faces_from_camera/person_1/img_face_4.jpg
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|
After Width: | Height: | Size: 26 KiB |
BIN
data/data_faces_from_camera/person_1/img_face_5.jpg
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data/data_faces_from_camera/person_1/img_face_5.jpg
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|
After Width: | Height: | Size: 27 KiB |
BIN
data/data_faces_from_camera/person_1/img_face_6.jpg
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BIN
data/data_faces_from_camera/person_1/img_face_6.jpg
Normal file
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|
After Width: | Height: | Size: 27 KiB |
BIN
data/data_faces_from_camera/person_2/img_face_1.jpg
Normal file
BIN
data/data_faces_from_camera/person_2/img_face_1.jpg
Normal file
Binary file not shown.
|
After Width: | Height: | Size: 8.7 KiB |
BIN
data/data_faces_from_camera/person_2/img_face_2.jpg
Normal file
BIN
data/data_faces_from_camera/person_2/img_face_2.jpg
Normal file
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|
After Width: | Height: | Size: 9.1 KiB |
BIN
data/data_faces_from_camera/person_2/img_face_3.jpg
Normal file
BIN
data/data_faces_from_camera/person_2/img_face_3.jpg
Normal file
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|
After Width: | Height: | Size: 9.0 KiB |
BIN
data/data_faces_from_camera/person_2/img_face_4.jpg
Normal file
BIN
data/data_faces_from_camera/person_2/img_face_4.jpg
Normal file
Binary file not shown.
|
After Width: | Height: | Size: 12 KiB |
BIN
data/data_faces_from_camera/person_2/img_face_5.jpg
Normal file
BIN
data/data_faces_from_camera/person_2/img_face_5.jpg
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|
After Width: | Height: | Size: 12 KiB |
BIN
data/data_faces_from_camera/person_2/img_face_6.jpg
Normal file
BIN
data/data_faces_from_camera/person_2/img_face_6.jpg
Normal file
Binary file not shown.
|
After Width: | Height: | Size: 8.8 KiB |
75
face_descriptor_from_camera.py
Normal file
75
face_descriptor_from_camera.py
Normal file
@ -0,0 +1,75 @@
|
||||
# 摄像头实时人脸特征描述子计算 / Real-time face descriptor compute
|
||||
|
||||
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
|
||||
|
||||
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()
|
||||
k = cv2.waitKey(1)
|
||||
|
||||
faces = detector(img_rd, 0)
|
||||
|
||||
font = cv2.FONT_HERSHEY_SIMPLEX
|
||||
|
||||
# 检测到人脸
|
||||
if len(faces) != 0:
|
||||
for face in faces:
|
||||
face_shape = predictor(img_rd, face)
|
||||
face_desc = face_reco_model.compute_face_descriptor(img_rd, face_shape)
|
||||
|
||||
# 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)
|
||||
|
||||
|
||||
def main():
|
||||
Face_Descriptor_con = Face_Descriptor()
|
||||
Face_Descriptor_con.run()
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
main()
|
||||
@ -6,156 +6,217 @@
|
||||
# GitHub: https://github.com/coneypo/Dlib_face_recognition_from_camera
|
||||
|
||||
# Created at 2018-05-11
|
||||
# Updated at 2019-04-09
|
||||
# Updated at 2020-05-29
|
||||
|
||||
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 # 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 os
|
||||
import time
|
||||
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
|
||||
# Refer this tutorial: http://dlib.net/python/index.html#dlib.face_recognition_model_v1
|
||||
facerec = dlib.face_recognition_model_v1("data/data_dlib/dlib_face_recognition_resnet_model_v1.dat")
|
||||
|
||||
|
||||
# 计算两个128D向量间的欧式距离
|
||||
# compute the e-distance between two 128D features
|
||||
def return_euclidean_distance(feature_1, feature_2):
|
||||
feature_1 = np.array(feature_1)
|
||||
feature_2 = np.array(feature_2)
|
||||
dist = np.sqrt(np.sum(np.square(feature_1 - feature_2)))
|
||||
return dist
|
||||
|
||||
|
||||
# 处理存放所有人脸特征的 csv
|
||||
path_features_known_csv = "data/features_all.csv"
|
||||
csv_rd = pd.read_csv(path_features_known_csv, header=None)
|
||||
|
||||
# 用来存放所有录入人脸特征的数组
|
||||
# the array to save the features of faces in the database
|
||||
features_known_arr = []
|
||||
|
||||
# 读取已知人脸数据
|
||||
# print known faces
|
||||
for i in range(csv_rd.shape[0]):
|
||||
features_someone_arr = []
|
||||
for j in range(0, len(csv_rd.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
|
||||
# The detector and predictor will be used
|
||||
# 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')
|
||||
|
||||
# 创建 cv2 摄像头对象
|
||||
# cv2.VideoCapture(0) to use the default camera of PC,
|
||||
# and you can use local video name by use cv2.VideoCapture(filename)
|
||||
cap = cv2.VideoCapture(0)
|
||||
# 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")
|
||||
|
||||
# cap.set(propId, value)
|
||||
# 设置视频参数，propId 设置的视频参数，value 设置的参数值
|
||||
cap.set(3, 480)
|
||||
|
||||
# cap.isOpened() 返回 true/false 检查初始化是否成功
|
||||
# when the camera is open
|
||||
while cap.isOpened():
|
||||
class Face_Recognizer:
|
||||
def __init__(self):
|
||||
# 用来存放所有录入人脸特征的数组 / Save the features of faces in the database
|
||||
self.features_known_list = []
|
||||
|
||||
flag, img_rd = cap.read()
|
||||
kk = cv2.waitKey(1)
|
||||
# 存储录入人脸名字 / Save the name of faces known
|
||||
self.name_known_cnt = 0
|
||||
self.name_known_list = []
|
||||
|
||||
# 取灰度
|
||||
img_gray = cv2.cvtColor(img_rd, cv2.COLOR_RGB2GRAY)
|
||||
# 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.pos_camera_list = []
|
||||
self.name_camera_list = []
|
||||
# 存储当前摄像头中捕获到的人脸数
|
||||
self.faces_cnt = 0
|
||||
# 存储当前摄像头中捕获到的人脸特征
|
||||
self.features_camera_list = []
|
||||
|
||||
# 人脸数 faces
|
||||
faces = detector(img_gray, 0)
|
||||
# Update FPS
|
||||
self.fps = 0
|
||||
self.frame_start_time = 0
|
||||
|
||||
# 待会要写的字体 font to write later
|
||||
font = cv2.FONT_HERSHEY_COMPLEX
|
||||
|
||||
# 存储当前摄像头中捕获到的所有人脸的坐标/名字
|
||||
# the list to save the positions and names of current faces captured
|
||||
pos_namelist = []
|
||||
name_namelist = []
|
||||
|
||||
# 按下 q 键退出
|
||||
# press 'q' to exit
|
||||
if kk == ord('q'):
|
||||
break
|
||||
else:
|
||||
# 检测到人脸 when face detected
|
||||
if len(faces) != 0:
|
||||
# 获取当前捕获到的图像的所有人脸的特征，存储到 features_cap_arr
|
||||
# get the features captured and save into features_cap_arr
|
||||
features_cap_arr = []
|
||||
for i in range(len(faces)):
|
||||
shape = predictor(img_rd, faces[i])
|
||||
features_cap_arr.append(facerec.compute_face_descriptor(img_rd, shape))
|
||||
|
||||
# 遍历捕获到的图像中所有的人脸
|
||||
# traversal all the faces in the database
|
||||
for k in range(len(faces)):
|
||||
print("##### camera person", k+1, "#####")
|
||||
# 让人名跟随在矩形框的下方
|
||||
# 确定人名的位置坐标
|
||||
# 先默认所有人不认识，是 unknown
|
||||
# set the default names of faces with "unknown"
|
||||
name_namelist.append("unknown")
|
||||
|
||||
# 每个捕获人脸的名字坐标 the positions of faces captured
|
||||
pos_namelist.append(tuple([faces[k].left(), int(faces[k].bottom() + (faces[k].bottom() - faces[k].top())/4)]))
|
||||
|
||||
# 对于某张人脸，遍历所有存储的人脸特征
|
||||
# for every faces detected, compare the faces in the database
|
||||
e_distance_list = []
|
||||
for i in range(len(features_known_arr)):
|
||||
# 如果 person_X 数据不为空
|
||||
if str(features_known_arr[i][0]) != '0.0':
|
||||
print("with person", str(i + 1), "the e distance: ", end='')
|
||||
e_distance_tmp = return_euclidean_distance(features_cap_arr[k], features_known_arr[i])
|
||||
print(e_distance_tmp)
|
||||
e_distance_list.append(e_distance_tmp)
|
||||
# 从 "features_all.csv" 读取录入人脸特征
|
||||
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)
|
||||
# 2. 读取已知人脸数据 / Print known faces
|
||||
for i in range(csv_rd.shape[0]):
|
||||
features_someone_arr = []
|
||||
for j in range(0, 128):
|
||||
if csv_rd.iloc[i][j] == '':
|
||||
features_someone_arr.append('0')
|
||||
else:
|
||||
# 空数据 person_X
|
||||
e_distance_list.append(999999999)
|
||||
# Find the one with minimum e distance
|
||||
similar_person_num = e_distance_list.index(min(e_distance_list))
|
||||
print("Minimum e distance with person", int(similar_person_num)+1)
|
||||
features_someone_arr.append(csv_rd.iloc[i][j])
|
||||
self.features_known_list.append(features_someone_arr)
|
||||
self.name_known_list.append("Person_"+str(i+1))
|
||||
self.name_known_cnt = len(self.name_known_list)
|
||||
print("Faces in Database：", len(self.features_known_list))
|
||||
return 1
|
||||
else:
|
||||
print('##### Warning #####', '\n')
|
||||
print("'features_all.csv' not found!")
|
||||
print(
|
||||
"Please run 'get_faces_from_camera.py' and 'features_extraction_to_csv.py' before 'face_reco_from_camera.py'",
|
||||
'\n')
|
||||
print('##### End Warning #####')
|
||||
return 0
|
||||
|
||||
if min(e_distance_list) < 0.4:
|
||||
# 在这里修改 person_1, person_2 ... 的名字
|
||||
# 可以在这里改称 Jack, Tom and others
|
||||
# Here you can modify the names shown on the camera
|
||||
name_namelist[k] = "Person "+str(int(similar_person_num)+1)
|
||||
print("May be person "+str(int(similar_person_num)+1))
|
||||
# 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()
|
||||
self.frame_time = now - self.frame_start_time
|
||||
self.fps = 1.0 / self.frame_time
|
||||
self.frame_start_time = now
|
||||
|
||||
def draw_note(self, img_rd):
|
||||
font = cv2.FONT_ITALIC
|
||||
|
||||
cv2.putText(img_rd, "Face Recognizer", (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(self.faces_cnt), (20, 140), font, 0.8, (0, 255, 0), 1, cv2.LINE_AA)
|
||||
cv2.putText(img_rd, "Q: Quit", (20, 450), 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
|
||||
font = ImageFont.truetype("simsun.ttc", 30)
|
||||
img = Image.fromarray(cv2.cvtColor(img_rd, cv2.COLOR_BGR2RGB))
|
||||
draw = ImageDraw.Draw(img)
|
||||
for i in range(self.faces_cnt):
|
||||
# cv2.putText(img_rd, self.name_camera_list[i], self.pos_camera_list[i], font, 0.8, (0, 255, 255), 1, cv2.LINE_AA)
|
||||
draw.text(xy=self.pos_camera_list[i], text=self.name_camera_list[i], font=font)
|
||||
img_with_name = cv2.cvtColor(np.array(img), cv2.COLOR_RGB2BGR)
|
||||
return img_with_name
|
||||
|
||||
# 修改显示人名
|
||||
def modify_name_camera_list(self):
|
||||
# Default known name: person_1, person_2, person_3
|
||||
self.name_known_list[0] ='张三'.encode('utf-8').decode()
|
||||
self.name_known_list[1] ='李四'.encode('utf-8').decode()
|
||||
# self.name_known_list[2] ='xx'.encode('utf-8').decode()
|
||||
# self.name_known_list[3] ='xx'.encode('utf-8').decode()
|
||||
# self.name_known_list[4] ='xx'.encode('utf-8').decode()
|
||||
|
||||
# 处理获取的视频流，进行人脸识别 / Input video stream and face reco process
|
||||
def process(self, stream):
|
||||
# 1. 读取存放所有人脸特征的 csv
|
||||
if self.get_face_database():
|
||||
while stream.isOpened():
|
||||
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:
|
||||
print("Unknown person")
|
||||
self.draw_note(img_rd)
|
||||
self.features_camera_list = []
|
||||
self.faces_cnt = 0
|
||||
self.pos_camera_list = []
|
||||
self.name_camera_list = []
|
||||
|
||||
# 矩形框
|
||||
# draw rectangle
|
||||
for kk, d in enumerate(faces):
|
||||
# 绘制矩形框
|
||||
cv2.rectangle(img_rd, tuple([d.left(), d.top()]), tuple([d.right(), d.bottom()]), (0, 255, 255), 2)
|
||||
print('\n')
|
||||
# 2. 检测到人脸 / when face detected
|
||||
if len(faces) != 0:
|
||||
# 3. 获取当前捕获到的图像的所有人脸的特征，存储到 self.features_camera_list
|
||||
# 3. Get the features captured and save into self.features_camera_list
|
||||
for i in range(len(faces)):
|
||||
shape = predictor(img_rd, faces[i])
|
||||
self.features_camera_list.append(face_reco_model.compute_face_descriptor(img_rd, shape))
|
||||
|
||||
# 在人脸框下面写人脸名字
|
||||
# write names under rectangle
|
||||
for i in range(len(faces)):
|
||||
cv2.putText(img_rd, name_namelist[i], pos_namelist[i], font, 0.8, (0, 255, 255), 1, cv2.LINE_AA)
|
||||
# 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)):
|
||||
print("##### camera person", k + 1, "#####")
|
||||
# 让人名跟随在矩形框的下方
|
||||
# 确定人名的位置坐标
|
||||
# 先默认所有人不认识，是 unknown
|
||||
# Set the default names of faces with "unknown"
|
||||
self.name_camera_list.append("unknown")
|
||||
|
||||
print("Faces in camera now:", name_namelist, "\n")
|
||||
# 每个捕获人脸的名字坐标 / Positions of faces captured
|
||||
self.pos_camera_list.append(tuple(
|
||||
[faces[k].left(), int(faces[k].bottom() + (faces[k].bottom() - faces[k].top()) / 4)]))
|
||||
|
||||
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)
|
||||
# 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
|
||||
e_distance_list = []
|
||||
for i in range(len(self.features_known_list)):
|
||||
# 如果 person_X 数据不为空
|
||||
if str(self.features_known_list[i][0]) != '0.0':
|
||||
print("with person", str(i + 1), "the e distance: ", end='')
|
||||
e_distance_tmp = self.return_euclidean_distance(self.features_camera_list[k],
|
||||
self.features_known_list[i])
|
||||
print(e_distance_tmp)
|
||||
e_distance_list.append(e_distance_tmp)
|
||||
else:
|
||||
# 空数据 person_X
|
||||
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 = e_distance_list.index(min(e_distance_list))
|
||||
print("Minimum e distance with person", self.name_known_list[similar_person_num])
|
||||
|
||||
# 窗口显示 show with opencv
|
||||
cv2.imshow("camera", img_rd)
|
||||
if min(e_distance_list) < 0.4:
|
||||
self.name_camera_list[k] = self.name_known_list[similar_person_num]
|
||||
print("May be person " + str(self.name_known_list[similar_person_num]))
|
||||
else:
|
||||
print("Unknown person")
|
||||
|
||||
# 释放摄像头 release camera
|
||||
cap.release()
|
||||
# 矩形框 / Draw rectangle
|
||||
for kk, d in enumerate(faces):
|
||||
# 绘制矩形框
|
||||
cv2.rectangle(img_rd, tuple([d.left(), d.top()]), tuple([d.right(), d.bottom()]),
|
||||
(0, 255, 255), 2)
|
||||
print('\n')
|
||||
|
||||
# 删除建立的窗口 delete all the windows
|
||||
cv2.destroyAllWindows()
|
||||
self.faces_cnt = len(faces)
|
||||
# 7. 在这里更改显示的人名 / Modify name if needed
|
||||
self.modify_name_camera_list()
|
||||
# 8. 写名字 / Draw name
|
||||
# self.draw_name(img_rd)
|
||||
img_with_name = self.draw_name(img_rd)
|
||||
else:
|
||||
img_with_name = img_rd
|
||||
|
||||
print("Faces in camera now:", self.name_camera_list, "\n")
|
||||
|
||||
cv2.imshow("camera", img_with_name)
|
||||
|
||||
# 9. 更新 FPS / Update stream FPS
|
||||
self.update_fps()
|
||||
|
||||
# OpenCV 调用摄像头并进行 process
|
||||
def run(self):
|
||||
cap = cv2.VideoCapture(0)
|
||||
cap.set(3, 480)
|
||||
self.process(cap)
|
||||
|
||||
cap.release()
|
||||
cv2.destroyAllWindows()
|
||||
|
||||
|
||||
def main():
|
||||
Face_Recognizer_con = Face_Recognizer()
|
||||
Face_Recognizer_con.run()
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
main()
|
||||
221
face_reco_from_camera_mysql.py
Executable file
221
face_reco_from_camera_mysql.py
Executable file
@ -0,0 +1,221 @@
|
||||
# 摄像头实时人脸识别
|
||||
# Real-time face recognition
|
||||
|
||||
# Author: coneypo
|
||||
# Blog: http://www.cnblogs.com/AdaminXie
|
||||
# GitHub: https://github.com/coneypo/Dlib_face_recognition_from_camera
|
||||
|
||||
# Created at 2018-05-11
|
||||
# Updated at 2020-05-29
|
||||
|
||||
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 os
|
||||
import time
|
||||
from PIL import Image, ImageDraw, ImageFont
|
||||
import pymysql
|
||||
|
||||
db = pymysql.connect("localhost", "root", "intel@123", "dlib_database")
|
||||
cursor = db.cursor()
|
||||
|
||||
# 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_Recognizer:
|
||||
def __init__(self):
|
||||
# 用来存放所有录入人脸特征的数组 / Save the features of faces in the database
|
||||
self.features_known_list = []
|
||||
|
||||
# 存储录入人脸名字 / Save the name of faces known
|
||||
self.name_known_cnt = 0
|
||||
self.name_known_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.pos_camera_list = []
|
||||
self.name_camera_list = []
|
||||
# 存储当前摄像头中捕获到的人脸数
|
||||
self.faces_cnt = 0
|
||||
# 存储当前摄像头中捕获到的人脸特征
|
||||
self.features_camera_list = []
|
||||
|
||||
# Update FPS
|
||||
self.fps = 0
|
||||
self.frame_start_time = 0
|
||||
|
||||
# 从 "features_all.csv" 读取录入人脸特征
|
||||
def get_face_database(self):
|
||||
# 1. get database face numbers
|
||||
cmd_rd = "select count(*) from dlib_face_table;"
|
||||
cursor.execute(cmd_rd)
|
||||
results = cursor.fetchall()
|
||||
person_cnt = int(results[0][0])
|
||||
|
||||
# 2. get features for person X
|
||||
for person in range(person_cnt):
|
||||
# lookup for personX
|
||||
cmd_lookup = "select * from dlib_face_table where person_x=\"person_" + str(person + 1) + "\";"
|
||||
cursor.execute(cmd_lookup)
|
||||
results = cursor.fetchall()
|
||||
results = list(results[0][1:])
|
||||
self.features_known_list.append(results)
|
||||
self.name_known_list.append("Person_" + str(person + 1))
|
||||
print(results)
|
||||
self.name_known_cnt = len(self.name_known_list)
|
||||
print("Faces in Database：", len(self.features_known_list))
|
||||
return 1
|
||||
|
||||
# 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()
|
||||
self.frame_time = now - self.frame_start_time
|
||||
self.fps = 1.0 / self.frame_time
|
||||
self.frame_start_time = now
|
||||
|
||||
def draw_note(self, img_rd):
|
||||
font = cv2.FONT_ITALIC
|
||||
|
||||
cv2.putText(img_rd, "Face Recognizer", (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(self.faces_cnt), (20, 140), font, 0.8, (0, 255, 0), 1, cv2.LINE_AA)
|
||||
cv2.putText(img_rd, "Q: Quit", (20, 450), 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
|
||||
font = ImageFont.truetype("simsun.ttc", 30)
|
||||
img = Image.fromarray(cv2.cvtColor(img_rd, cv2.COLOR_BGR2RGB))
|
||||
draw = ImageDraw.Draw(img)
|
||||
for i in range(self.faces_cnt):
|
||||
# cv2.putText(img_rd, self.name_camera_list[i], self.pos_camera_list[i], font, 0.8, (0, 255, 255), 1, cv2.LINE_AA)
|
||||
draw.text(xy=self.pos_camera_list[i], text=self.name_camera_list[i], font=font)
|
||||
img_with_name = cv2.cvtColor(np.array(img), cv2.COLOR_RGB2BGR)
|
||||
return img_with_name
|
||||
|
||||
# 修改显示人名
|
||||
def modify_name_camera_list(self):
|
||||
# Default known name: person_1, person_2, person_3
|
||||
self.name_known_list[0] ='张三'.encode('utf-8').decode()
|
||||
self.name_known_list[1] ='李四'.encode('utf-8').decode()
|
||||
# self.name_known_list[2] ='xx'.encode('utf-8').decode()
|
||||
# self.name_known_list[3] ='xx'.encode('utf-8').decode()
|
||||
# self.name_known_list[4] ='xx'.encode('utf-8').decode()
|
||||
|
||||
# 处理获取的视频流，进行人脸识别 / Input video stream and face reco process
|
||||
def process(self, stream):
|
||||
# 1. 读取存放所有人脸特征的 csv
|
||||
if self.get_face_database():
|
||||
while stream.isOpened():
|
||||
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.features_camera_list = []
|
||||
self.faces_cnt = 0
|
||||
self.pos_camera_list = []
|
||||
self.name_camera_list = []
|
||||
|
||||
# 2. 检测到人脸 / when face detected
|
||||
if len(faces) != 0:
|
||||
# 3. 获取当前捕获到的图像的所有人脸的特征，存储到 self.features_camera_list
|
||||
# 3. Get the features captured and save into self.features_camera_list
|
||||
for i in range(len(faces)):
|
||||
shape = predictor(img_rd, faces[i])
|
||||
self.features_camera_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)):
|
||||
print("##### camera person", k + 1, "#####")
|
||||
# 让人名跟随在矩形框的下方
|
||||
# 确定人名的位置坐标
|
||||
# 先默认所有人不认识，是 unknown
|
||||
# Set the default names of faces with "unknown"
|
||||
self.name_camera_list.append("unknown")
|
||||
|
||||
# 每个捕获人脸的名字坐标 / Positions of faces captured
|
||||
self.pos_camera_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
|
||||
e_distance_list = []
|
||||
for i in range(len(self.features_known_list)):
|
||||
# 如果 person_X 数据不为空
|
||||
if str(self.features_known_list[i][0]) != '0.0':
|
||||
print("with person", str(i + 1), "the e distance: ", end='')
|
||||
e_distance_tmp = self.return_euclidean_distance(self.features_camera_list[k],
|
||||
self.features_known_list[i])
|
||||
print(e_distance_tmp)
|
||||
e_distance_list.append(e_distance_tmp)
|
||||
else:
|
||||
# 空数据 person_X
|
||||
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 = e_distance_list.index(min(e_distance_list))
|
||||
print("Minimum e distance with person", self.name_known_list[similar_person_num])
|
||||
|
||||
if min(e_distance_list) < 0.4:
|
||||
self.name_camera_list[k] = self.name_known_list[similar_person_num]
|
||||
print("May be person " + str(self.name_known_list[similar_person_num]))
|
||||
else:
|
||||
print("Unknown person")
|
||||
|
||||
# 矩形框 / Draw rectangle
|
||||
for kk, d in enumerate(faces):
|
||||
# 绘制矩形框
|
||||
cv2.rectangle(img_rd, tuple([d.left(), d.top()]), tuple([d.right(), d.bottom()]),
|
||||
(0, 255, 255), 2)
|
||||
print('\n')
|
||||
|
||||
self.faces_cnt = len(faces)
|
||||
# 7. 在这里更改显示的人名 / Modify name if needed
|
||||
# self.modify_name_camera_list()
|
||||
# 8. 写名字 / Draw name
|
||||
# self.draw_name(img_rd)
|
||||
img_with_name = self.draw_name(img_rd)
|
||||
else:
|
||||
img_with_name = img_rd
|
||||
|
||||
print("Faces in camera now:", self.name_camera_list, "\n")
|
||||
|
||||
cv2.imshow("camera", img_with_name)
|
||||
|
||||
# 9. 更新 FPS / Update stream FPS
|
||||
self.update_fps()
|
||||
|
||||
# OpenCV 调用摄像头并进行 process
|
||||
def run(self):
|
||||
cap = cv2.VideoCapture(0)
|
||||
cap.set(3, 480)
|
||||
self.process(cap)
|
||||
|
||||
cap.release()
|
||||
cv2.destroyAllWindows()
|
||||
|
||||
|
||||
def main():
|
||||
Face_Recognizer_con = Face_Recognizer()
|
||||
Face_Recognizer_con.run()
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
main()
|
||||
@ -7,9 +7,8 @@
|
||||
# Mail: coneypo@foxmail.com
|
||||
|
||||
# Created at 2018-05-11
|
||||
# Updated at 2019-04-04
|
||||
# Updated at 2020-04-02
|
||||
|
||||
import cv2
|
||||
import os
|
||||
import dlib
|
||||
from skimage import io
|
||||
@ -19,30 +18,28 @@ import numpy as np
|
||||
# 要读取人脸图像文件的路径
|
||||
path_images_from_camera = "data/data_faces_from_camera/"
|
||||
|
||||
# Dlib 正向人脸检测器
|
||||
# 1. 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")
|
||||
# 2. Dlib 人脸 landmark 特征点检测器
|
||||
predictor = dlib.shape_predictor('data/data_dlib/shape_predictor_68_face_landmarks.dat')
|
||||
|
||||
# Dlib 人脸识别模型
|
||||
# Face recognition model, the object maps human faces into 128D vectors
|
||||
face_rec = dlib.face_recognition_model_v1("data/data_dlib/dlib_face_recognition_resnet_model_v1.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")
|
||||
|
||||
|
||||
# 返回单张图像的 128D 特征
|
||||
def return_128d_features(path_img):
|
||||
img_rd = io.imread(path_img)
|
||||
img_gray = cv2.cvtColor(img_rd, cv2.COLOR_BGR2RGB)
|
||||
faces = detector(img_gray, 1)
|
||||
faces = detector(img_rd, 1)
|
||||
|
||||
print("%-40s %-20s" % ("检测到人脸的图像 / image with faces detected:", path_img), '\n')
|
||||
print("%-40s %-20s" % ("检测到人脸的图像 / Image with faces detected:", path_img), '\n')
|
||||
|
||||
# 因为有可能截下来的人脸再去检测，检测不出来人脸了
|
||||
# 所以要确保是 检测到人脸的人脸图像 拿去算特征
|
||||
if len(faces) != 0:
|
||||
shape = predictor(img_gray, faces[0])
|
||||
face_descriptor = face_rec.compute_face_descriptor(img_gray, shape)
|
||||
shape = predictor(img_rd, faces[0])
|
||||
face_descriptor = face_reco_model.compute_face_descriptor(img_rd, shape)
|
||||
else:
|
||||
face_descriptor = 0
|
||||
print("no face")
|
||||
@ -57,7 +54,7 @@ def return_features_mean_personX(path_faces_personX):
|
||||
if photos_list:
|
||||
for i in range(len(photos_list)):
|
||||
# 调用return_128d_features()得到128d特征
|
||||
print("%-40s %-20s" % ("正在读的人脸图像 / image to read:", path_faces_personX + "/" + photos_list[i]))
|
||||
print("%-40s %-20s" % ("正在读的人脸图像 / Image to read:", 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
|
||||
@ -73,7 +70,7 @@ def return_features_mean_personX(path_faces_personX):
|
||||
if features_list_personX:
|
||||
features_mean_personX = np.array(features_list_personX).mean(axis=0)
|
||||
else:
|
||||
features_mean_personX = '0'
|
||||
features_mean_personX = np.zeros(128, dtype=int, order='C')
|
||||
|
||||
return features_mean_personX
|
||||
|
||||
@ -85,13 +82,13 @@ for person in person_list:
|
||||
person_num_list.append(int(person.split('_')[-1]))
|
||||
person_cnt = max(person_num_list)
|
||||
|
||||
with open("data/features_all.csv", "w", newline="") as csvfile:
|
||||
writer = csv.writer(csvfile)
|
||||
for person in range(person_cnt):
|
||||
# Get the mean/average features of face/personX, it will be a list with a length of 128D
|
||||
print(path_images_from_camera + "person_"+str(person+1))
|
||||
features_mean_personX = return_features_mean_personX(path_images_from_camera + "person_"+str(person+1))
|
||||
writer.writerow(features_mean_personX)
|
||||
print("特征均值 / The mean of features:", list(features_mean_personX))
|
||||
print('\n')
|
||||
print("所有录入人脸数据存入 / Save all the features of faces registered into: data/features_all.csv")
|
||||
for person in range(person_cnt):
|
||||
# Get the mean/average features of face/personX, it will be a list with a length of 128D
|
||||
print(path_images_from_camera + "person_" + str(person + 1))
|
||||
features_mean_personX = return_features_mean_personX(path_images_from_camera + "person_" + str(person + 1))
|
||||
|
||||
print(features_mean_personX.shape)
|
||||
print(features_mean_personX[0])
|
||||
|
||||
print("特征均值 / The mean of features:", list(features_mean_personX))
|
||||
print('\n')
|
||||
|
||||
117
features_extraction_to_mysql.py
Executable file
117
features_extraction_to_mysql.py
Executable file
@ -0,0 +1,117 @@
|
||||
# 从人脸图像文件中提取人脸特征存入 CSV
|
||||
# Features extraction from images and save into features_all.csv
|
||||
|
||||
# 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 2020-04-02
|
||||
|
||||
import os
|
||||
import dlib
|
||||
from skimage import io
|
||||
import numpy as np
|
||||
import pymysql
|
||||
|
||||
db = pymysql.connect("localhost", "root", "intel@123", "dlib_database")
|
||||
cursor = db.cursor()
|
||||
|
||||
# 要读取人脸图像文件的路径
|
||||
path_images_from_camera = "data/data_faces_from_camera/"
|
||||
|
||||
# 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")
|
||||
|
||||
|
||||
# 返回单张图像的 128D 特征
|
||||
def return_128d_features(path_img):
|
||||
img_rd = io.imread(path_img)
|
||||
faces = detector(img_rd, 1)
|
||||
|
||||
print("%-40s %-20s" % ("检测到人脸的图像 / Image with faces detected:", path_img), '\n')
|
||||
|
||||
# 因为有可能截下来的人脸再去检测，检测不出来人脸了
|
||||
# 所以要确保是 检测到人脸的人脸图像 拿去算特征
|
||||
if len(faces) != 0:
|
||||
shape = predictor(img_rd, faces[0])
|
||||
face_descriptor = face_reco_model.compute_face_descriptor(img_rd, shape)
|
||||
else:
|
||||
face_descriptor = 0
|
||||
print("no face")
|
||||
|
||||
return face_descriptor
|
||||
|
||||
|
||||
# 将文件夹中照片特征提取出来, 写入 CSV
|
||||
def return_features_mean_personX(path_faces_personX):
|
||||
features_list_personX = []
|
||||
photos_list = os.listdir(path_faces_personX)
|
||||
if photos_list:
|
||||
for i in range(len(photos_list)):
|
||||
# 调用return_128d_features()得到128d特征
|
||||
print("%-40s %-20s" % ("正在读的人脸图像 / Image to read:", path_faces_personX + "/" + 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:
|
||||
features_list_personX.append(features_128d)
|
||||
else:
|
||||
print("文件夹内图像文件为空 / Warning: No images in " + path_faces_personX + '/', '\n')
|
||||
|
||||
# 计算 128D 特征的均值
|
||||
# personX 的 N 张图像 x 128D -> 1 x 128D
|
||||
if features_list_personX:
|
||||
features_mean_personX = np.array(features_list_personX).mean(axis=0)
|
||||
else:
|
||||
features_mean_personX = np.zeros(128, dtype=int, order='C')
|
||||
|
||||
return features_mean_personX
|
||||
|
||||
|
||||
# 获取已录入的最后一个人脸序号 / get the num 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]))
|
||||
person_cnt = max(person_num_list)
|
||||
|
||||
# 0. clear table in mysql
|
||||
# cursor.execute("truncate dlib_face_table;")
|
||||
|
||||
# 1. check existing people in mysql
|
||||
cursor.execute("select count(*) from dlib_face_table;")
|
||||
person_start = int(cursor.fetchall()[0][0])
|
||||
|
||||
for person in range(person_start, person_cnt):
|
||||
# Get the mean/average features of face/personX, it will be a list with a length of 128D
|
||||
print(path_images_from_camera + "person_" + str(person + 1))
|
||||
features_mean_personX = return_features_mean_personX(path_images_from_camera + "person_" + str(person + 1))
|
||||
|
||||
print("特征均值 / The mean of features:", list(features_mean_personX))
|
||||
print('\n')
|
||||
|
||||
|
||||
|
||||
# 2. Insert person 1 to person X
|
||||
cursor.execute("insert into dlib_face_table(person_x) values(\"person_"+str(person+1)+"\");")
|
||||
|
||||
# 3. Insert features for person X
|
||||
for i in range(128):
|
||||
# update issue_info set github_status='Open', github_type='bug' where github_id='2222';
|
||||
print("update dlib_face_table set feature_" + str(i + 1) + '=\"' + str(
|
||||
features_mean_personX[i]) + "\" where person_x=\"person_" + str(person + 1) + "\";")
|
||||
cursor.execute("update dlib_face_table set feature_" + str(i + 1) + '=\"' + str(
|
||||
features_mean_personX[i]) + "\" where person_x=\"person_" + str(person + 1) + "\";")
|
||||
|
||||
|
||||
db.commit()
|
||||
@ -7,190 +7,183 @@
|
||||
# Mail: coneypo@foxmail.com
|
||||
|
||||
# Created at 2018-05-11
|
||||
# Updated at 2019-04-12
|
||||
# Updated at 2020-04-19
|
||||
|
||||
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 cv2 # 图像处理的库 OpenCV
|
||||
import os # 读写文件
|
||||
import shutil # 读写文件
|
||||
import time
|
||||
|
||||
# Dlib 正向人脸检测器 / frontal face detector
|
||||
# Dlib 正向人脸检测器
|
||||
detector = dlib.get_frontal_face_detector()
|
||||
|
||||
# Dlib 68 点特征预测器 / 68 points features predictor
|
||||
predictor = dlib.shape_predictor('data/data_dlib/shape_predictor_68_face_landmarks.dat')
|
||||
|
||||
# OpenCv 调用摄像头 use camera
|
||||
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 set camera
|
||||
cap.set(3, 480)
|
||||
self.existing_faces_cnt = 0 # 已录入的人脸计数器
|
||||
self.ss_cnt = 0 # 录入 personX 人脸时图片计数器
|
||||
self.faces_cnt = 0 # 录入人脸计数器
|
||||
|
||||
# 人脸截图的计数器 the counter for screen shoot
|
||||
cnt_ss = 0
|
||||
# 之后用来控制是否保存图像的 flag / The flag to control if save
|
||||
self.save_flag = 1
|
||||
# 之后用来检查是否先按 'n' 再按 's' / The flag to check if press 'n' before 's'
|
||||
self.press_n_flag = 0
|
||||
|
||||
# 存储人脸的文件夹 the folder to save faces
|
||||
current_face_dir = ""
|
||||
self.frame_time = 0
|
||||
self.frame_start_time = 0
|
||||
self.fps = 0
|
||||
|
||||
# 保存 faces images 的路径 the directory to save images of faces
|
||||
path_photos_from_camera = "data/data_faces_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 / make folders to save faces 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 the old data of faces
|
||||
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
|
||||
# mkdir for saving photos and csv
|
||||
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 /
|
||||
# If the old folders exists, 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 num 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 / make folders to save faces images and csv
|
||||
if os.path.isdir(path_photos_from_camera):
|
||||
pass
|
||||
else:
|
||||
os.mkdir(path_photos_from_camera)
|
||||
# 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
|
||||
|
||||
# 获取处理之后 stream 的帧数 / Get the fps of video stream
|
||||
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
|
||||
|
||||
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 statements
|
||||
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.__round__(2)), (20, 100), self.font, 0.8, (0, 255, 0), 1,
|
||||
cv2.LINE_AA)
|
||||
cv2.putText(img_rd, "Faces: " + str(self.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
|
||||
def process(self, stream):
|
||||
# 1. 新建储存人脸图像文件目录 / Uncomment if you need mkdir
|
||||
# self.pre_work_mkdir()
|
||||
|
||||
##### optional/可选, 默认关闭 #####
|
||||
# 删除之前存的人脸数据文件夹
|
||||
# delete the old data of faces
|
||||
def pre_work_del_old_face_folders():
|
||||
# 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 old faces
|
||||
self.pre_work_del_old_face_folders()
|
||||
|
||||
if os.path.isfile("data/features_all.csv"):
|
||||
os.remove("data/features_all.csv")
|
||||
# 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 person_1/,person_2/,person_3/...
|
||||
# If enable this function, it will delete all the old data in dir person_1/,person_2/,/person_3/...
|
||||
# pre_work_del_old_face_folders()
|
||||
##################################
|
||||
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)
|
||||
print('\n')
|
||||
print("新建的人脸文件夹 / Create folders: ", current_face_dir)
|
||||
|
||||
# 如果有之前录入的人脸 / if the old folders exists
|
||||
# 在之前 person_x 的序号按照 person_x+1 开始录入 / start from person_x+1
|
||||
if os.listdir("data/data_faces_from_camera/"):
|
||||
# 获取已录入的最后一个人脸序号 / get the num of latest person
|
||||
person_list = os.listdir("data/data_faces_from_camera/")
|
||||
person_num_list = []
|
||||
for person in person_list:
|
||||
person_num_list.append(int(person.split('_')[-1]))
|
||||
person_cnt = max(person_num_list)
|
||||
self.ss_cnt = 0 # 将人脸计数器清零 / clear the cnt of faces
|
||||
self.press_n_flag = 1 # 已经按下 'n' / have pressed 'n'
|
||||
|
||||
# 如果第一次存储或者没有之前录入的人脸, 按照 person_1 开始录入
|
||||
# start from person_1
|
||||
else:
|
||||
person_cnt = 0
|
||||
# 5. 检测到人脸 / Face detected
|
||||
if len(faces) != 0:
|
||||
# 矩形框 / Show the HOG 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 / the flag to control if save
|
||||
save_flag = 1
|
||||
|
||||
# 之后用来检查是否先按 'n' 再按 's' / the flag to check if press 'n' before 's'
|
||||
press_n_flag = 0
|
||||
|
||||
while cap.isOpened():
|
||||
flag, img_rd = cap.read()
|
||||
# print(img_rd.shape)
|
||||
# It should be 480 height * 640 width
|
||||
|
||||
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 to write
|
||||
font = cv2.FONT_HERSHEY_COMPLEX
|
||||
|
||||
# 按下 'n' 新建存储人脸的文件夹 / press 'n' to create the folders for saving faces
|
||||
if kk == ord('n'):
|
||||
person_cnt += 1
|
||||
current_face_dir = path_photos_from_camera + "person_" + str(person_cnt)
|
||||
os.makedirs(current_face_dir)
|
||||
print('\n')
|
||||
print("新建的人脸文件夹 / Create folders: ", current_face_dir)
|
||||
|
||||
cnt_ss = 0 # 将人脸计数器清零 / clear the cnt of faces
|
||||
press_n_flag = 1 # 已经按下 'n' / have pressed 'n'
|
||||
|
||||
# 检测到人脸 / if face detected
|
||||
if len(faces) != 0:
|
||||
# 矩形框 / show the rectangle box
|
||||
for k, d in enumerate(faces):
|
||||
# 计算矩形大小
|
||||
# we need to compute the width and height of the box
|
||||
# (x,y), (宽度width, 高度height)
|
||||
pos_start = tuple([d.left(), d.top()])
|
||||
pos_end = tuple([d.right(), d.bottom()])
|
||||
|
||||
# 计算矩形框大小 / compute the size of rectangle box
|
||||
height = (d.bottom() - d.top())
|
||||
width = (d.right() - d.left())
|
||||
|
||||
hh = int(height/2)
|
||||
ww = int(width/2)
|
||||
|
||||
# 设置颜色 / the color of rectangle of faces detected
|
||||
color_rectangle = (255, 255, 255)
|
||||
|
||||
# 判断人脸矩形框是否超出 480x640
|
||||
if (d.right()+ww) > 640 or (d.bottom()+hh > 480) or (d.left()-ww < 0) or (d.top()-hh < 0):
|
||||
cv2.putText(img_rd, "OUT OF RANGE", (20, 300), font, 0.8, (0, 0, 255), 1, cv2.LINE_AA)
|
||||
color_rectangle = (0, 0, 255)
|
||||
save_flag = 0
|
||||
if kk == ord('s'):
|
||||
print("请调整位置 / Please adjust your position")
|
||||
else:
|
||||
color_rectangle = (255, 255, 255)
|
||||
save_flag = 1
|
||||
|
||||
cv2.rectangle(img_rd,
|
||||
tuple([d.left() - ww, d.top() - hh]),
|
||||
tuple([d.right() + ww, d.bottom() + hh]),
|
||||
color_rectangle, 2)
|
||||
|
||||
# 根据人脸大小生成空的图像 / create blank image according to the size of face detected
|
||||
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 / press 's' to save faces into local images
|
||||
if kk == ord('s'):
|
||||
# 检查有没有先按'n'新建文件夹 / check if you have pressed 'n'
|
||||
if press_n_flag:
|
||||
cnt_ss += 1
|
||||
for ii in range(height*2):
|
||||
for jj in range(width*2):
|
||||
im_blank[ii][jj] = img_rd[d.top()-hh + ii][d.left()-ww + jj]
|
||||
cv2.imwrite(current_face_dir + "/img_face_" + str(cnt_ss) + ".jpg", im_blank)
|
||||
print("写入本地 / Save into：", str(current_face_dir) + "/img_face_" + str(cnt_ss) + ".jpg")
|
||||
# 6. 判断人脸矩形框是否超出 480x640
|
||||
if (d.right()+ww) > 640 or (d.bottom()+hh > 480) or (d.left()-ww < 0) or (d.top()-hh < 0):
|
||||
cv2.putText(img_rd, "OUT OF RANGE", (20, 300), self.font, 0.8, (0, 0, 255), 1, cv2.LINE_AA)
|
||||
color_rectangle = (0, 0, 255)
|
||||
save_flag = 0
|
||||
if kk == ord('s'):
|
||||
print("请调整位置 / Please adjust your position")
|
||||
else:
|
||||
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 / show the numbers of faces detected
|
||||
cv2.putText(img_rd, "Faces: " + str(len(faces)), (20, 100), font, 0.8, (0, 255, 0), 1, cv2.LINE_AA)
|
||||
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 / add some statements
|
||||
cv2.putText(img_rd, "Face Register", (20, 40), font, 1, (0, 0, 0), 1, cv2.LINE_AA)
|
||||
cv2.putText(img_rd, "N: 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 shape of face detected
|
||||
img_blank = np.zeros((int(height*2), width*2, 3), np.uint8)
|
||||
|
||||
# 按下 'q' 键退出 / press 'q' to exit
|
||||
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)
|
||||
print("写入本地 / Save into：", str(current_face_dir) + "/img_face_" + str(self.ss_cnt) + ".jpg")
|
||||
else:
|
||||
print("请先按 'N' 来建文件夹, 按 'S' / Please press 'N' and press 'S'")
|
||||
self.faces_cnt = len(faces)
|
||||
|
||||
# 如果需要摄像头窗口大小可调 / uncomment this line if you want the camera window is resizeable
|
||||
# cv2.namedWindow("camera", 0)
|
||||
# 9. 生成的窗口添加说明文字 / Add note on cv2 window
|
||||
self.draw_note(img_rd)
|
||||
|
||||
cv2.imshow("camera", img_rd)
|
||||
# 10. 按下 'q' 键退出 / Press 'q' to exit
|
||||
if kk == ord('q'):
|
||||
break
|
||||
|
||||
# 释放摄像头 / release camera
|
||||
cap.release()
|
||||
self.update_fps()
|
||||
cv2.namedWindow("camera", 1)
|
||||
cv2.imshow("camera", img_rd)
|
||||
|
||||
cv2.destroyAllWindows()
|
||||
def run(self):
|
||||
cap = cv2.VideoCapture(0)
|
||||
cap.set(3, 640)
|
||||
self.process(cap)
|
||||
|
||||
cap.release()
|
||||
cv2.destroyAllWindows()
|
||||
|
||||
|
||||
def main():
|
||||
Face_Register_con = Face_Register()
|
||||
Face_Register_con.run()
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
main()
|
||||
@ -1,4 +1,4 @@
|
||||
# OpenCv 调用摄像头
|
||||
# OpenCV 调用摄像头
|
||||
# 默认调用笔记本摄像头
|
||||
|
||||
# Author: coneypo
|
||||
@ -10,9 +10,35 @@ import cv2
|
||||
|
||||
cap = cv2.VideoCapture(0)
|
||||
|
||||
# cap.set(3, 480)
|
||||
# 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).
|
||||
print 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 shape of camera will be 640x480 in Windows or Ubuntu
|
||||
# So we will not set "cap.set" here, it doesn't work
|
||||
# print(cv2.CAP_PROP_FRAME_WIDTH)
|
||||
# print(cv2.CAP_PROP_FRAME_HEIGHT)
|
||||
cap.set(3, 640)
|
||||
|
||||
# cap.isOpened() 返回 true/false, 检查摄像头初始化是否成功
|
||||
print(cap.isOpened())
|
||||
@ -38,6 +64,11 @@ 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
|
||||
|
||||
BIN
introduction/face_descriptor_single_person.png
Normal file
BIN
introduction/face_descriptor_single_person.png
Normal file
Binary file not shown.
|
After Width: | Height: | Size: 1.3 MiB |
BIN
introduction/face_reco_multi_people.png
Normal file
BIN
introduction/face_reco_multi_people.png
Normal file
Binary file not shown.
|
After Width: | Height: | Size: 1.5 MiB |
BIN
introduction/face_reco_single_person.png
Executable file → Normal file
BIN
introduction/face_reco_single_person.png
Executable file → Normal file
Binary file not shown.
|
Before Width: | Height: | Size: 428 KiB After Width: | Height: | Size: 1.1 MiB |
Binary file not shown.
|
Before Width: | Height: | Size: 457 KiB |
BIN
introduction/face_reco_single_person_with_name.png
Normal file
BIN
introduction/face_reco_single_person_with_name.png
Normal file
Binary file not shown.
|
After Width: | Height: | Size: 1.3 MiB |
Binary file not shown.
|
Before Width: | Height: | Size: 499 KiB |
BIN
introduction/face_reco_two_people_in_database.png
Executable file → Normal file
BIN
introduction/face_reco_two_people_in_database.png
Executable file → Normal file
Binary file not shown.
|
Before Width: | Height: | Size: 425 KiB After Width: | Height: | Size: 1.5 MiB |
BIN
introduction/get_face_from_camera.png
Executable file → Normal file
BIN
introduction/get_face_from_camera.png
Executable file → Normal file
Binary file not shown.
|
Before Width: | Height: | Size: 416 KiB After Width: | Height: | Size: 1.3 MiB |
BIN
introduction/get_face_from_camera_out_of_range.png
Executable file → Normal file
BIN
introduction/get_face_from_camera_out_of_range.png
Executable file → Normal file
Binary file not shown.
|
Before Width: | Height: | Size: 433 KiB After Width: | Height: | Size: 1.4 MiB |
BIN
data/data_dlib/shape_predictor_5_face_landmarks.dat → simsun.ttc
Executable file → Normal file
BIN
data/data_dlib/shape_predictor_5_face_landmarks.dat → simsun.ttc
Executable file → Normal file
Binary file not shown.
Reference in New Issue
Block a user