一、环境搭建

１．系统环境

<pre style="-webkit-tap-highlight-color: transparent; box-sizing: border-box; font-family: Consolas, Menlo, Courier, monospace; font-size: 16px; white-space: pre-wrap; position: relative; line-height: 1.5; color: rgb(153, 153, 153); margin: 1em 0px; padding: 12px 10px; background: rgb(244, 245, 246); border: 1px solid rgb(232, 232, 232); font-style: normal; font-variant-ligatures: normal; font-variant-caps: normal; font-weight: normal; letter-spacing: normal; orphans: 2; text-align: start; text-indent: 0px; text-transform: none; widows: 2; word-spacing: 0px; -webkit-text-stroke-width: 0px; text-decoration-style: initial; text-decoration-color: initial;">Ubuntu 17.04
Python 2.7.14
pycharm 开发工具
</pre>

2.开发环境，安装各种系统包

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• 人脸检测基于dlib，dlib依赖Boost和cmake
• 在windows中如果要使用dlib还是比较麻烦的，如果想省时间可以在anaconda中安装

conda install -c conda-forge dlib=19.4

<pre style="-webkit-tap-highlight-color: transparent; box-sizing: border-box; font-family: Consolas, Menlo, Courier, monospace; font-size: 16px; white-space: pre-wrap; position: relative; line-height: 1.5; color: rgb(153, 153, 153); margin: 1em 0px; padding: 12px 10px; background: rgb(244, 245, 246); border: 1px solid rgb(232, 232, 232); font-style: normal; font-variant-ligatures: normal; font-variant-caps: normal; font-weight: normal; letter-spacing: normal; orphans: 2; text-align: start; text-indent: 0px; text-transform: none; widows: 2; word-spacing: 0px; -webkit-text-stroke-width: 0px; text-decoration-style: initial; text-decoration-color: initial;"> sudo apt-get install libgtk-3-dev
$ sudo apt-get install libboost-all-dev
</pre>

• 其他重要的包

<pre style="-webkit-tap-highlight-color: transparent; box-sizing: border-box; font-family: Consolas, Menlo, Courier, monospace; font-size: 16px; white-space: pre-wrap; position: relative; line-height: 1.5; color: rgb(153, 153, 153); margin: 1em 0px; padding: 12px 10px; background: rgb(244, 245, 246); border: 1px solid rgb(232, 232, 232); font-style: normal; font-variant-ligatures: normal; font-variant-caps: normal; font-weight: normal; letter-spacing: normal; orphans: 2; text-align: start; text-indent: 0px; text-transform: none; widows: 2; word-spacing: 0px; -webkit-text-stroke-width: 0px; text-decoration-style: initial; text-decoration-color: initial;"> pip install scipy
pip install dlib
</pre>

• 安装 face_recognition

<pre style="-webkit-tap-highlight-color: transparent; box-sizing: border-box; font-family: Consolas, Menlo, Courier, monospace; font-size: 16px; white-space: pre-wrap; position: relative; line-height: 1.5; color: rgb(153, 153, 153); margin: 1em 0px; padding: 12px 10px; background: rgb(244, 245, 246); border: 1px solid rgb(232, 232, 232); font-style: normal; font-variant-ligatures: normal; font-variant-caps: normal; font-weight: normal; letter-spacing: normal; orphans: 2; text-align: start; text-indent: 0px; text-transform: none; widows: 2; word-spacing: 0px; -webkit-text-stroke-width: 0px; text-decoration-style: initial; text-decoration-color: initial;"># 安装 face_recognition
$ pip install face_recognition

安装face_recognition过程中会自动安装 numpy、scipy 等

</pre>

---

二、使用教程

1、facial_features文件夹

此demo主要展示了识别指定图片中人脸的特征数据，下面就是人脸的八个特征，我们就是要获取特征数据

<pre style="-webkit-tap-highlight-color: transparent; box-sizing: border-box; font-family: Consolas, Menlo, Courier, monospace; font-size: 16px; white-space: pre-wrap; position: relative; line-height: 1.5; color: rgb(153, 153, 153); margin: 1em 0px; padding: 12px 10px; background: rgb(244, 245, 246); border: 1px solid rgb(232, 232, 232); font-style: normal; font-variant-ligatures: normal; font-variant-caps: normal; font-weight: normal; letter-spacing: normal; orphans: 2; text-align: start; text-indent: 0px; text-transform: none; widows: 2; word-spacing: 0px; -webkit-text-stroke-width: 0px; text-decoration-style: initial; text-decoration-color: initial;"> 'chin',
'left_eyebrow',
'right_eyebrow',
'nose_bridge',
'nose_tip',
'left_eye',
'right_eye',
'top_lip',
'bottom_lip'
</pre>

运行结果：

自动识别图片中的人脸，并且识别它的特征

原图：

python基于open cv实现人脸识别技术，小白也可以做出来（代码） python基于open cv实现人脸识别技术，小白也可以做出来（代码）

特征数据，数据就是运行出来的矩阵，也就是一个二维数组

python基于open cv实现人脸识别技术，小白也可以做出来（代码）

image

代码：

<pre style="-webkit-tap-highlight-color: transparent; box-sizing: border-box; font-family: Consolas, Menlo, Courier, monospace; font-size: 16px; white-space: pre-wrap; position: relative; line-height: 1.5; color: rgb(153, 153, 153); margin: 1em 0px; padding: 12px 10px; background: rgb(244, 245, 246); border: 1px solid rgb(232, 232, 232); font-style: normal; font-variant-ligatures: normal; font-variant-caps: normal; font-weight: normal; letter-spacing: normal; orphans: 2; text-align: start; text-indent: 0px; text-transform: none; widows: 2; word-spacing: 0px; -webkit-text-stroke-width: 0px; text-decoration-style: initial; text-decoration-color: initial;"># -- coding: utf-8 --

自动识别人脸特征

filename : find_facial_features_in_picture.py

导入pil模块 ，可用命令安装 apt-get install python-Imaging

from PIL import Image, ImageDraw

导入face_recogntion模块，可用命令安装 pip install face_recognition

import face_recognition

将jpg文件加载到numpy 数组中

image = face_recognition.load_image_file("chenduling.jpg")

查找图像中所有面部的所有面部特征

face_landmarks_list = face_recognition.face_landmarks(image)
print("I found {} face(s) in this photograph.".format(len(face_landmarks_list)))
for face_landmarks in face_landmarks_list:

打印此图像中每个面部特征的位置

facial_features = [
'chin',
'left_eyebrow',
'right_eyebrow',
'nose_bridge',
'nose_tip',
'left_eye',
'right_eye',
'top_lip',
'bottom_lip'
]
for facial_feature in facial_features:
print("The {} in this face has the following points: {}".format(facial_feature, face_landmarks[facial_feature]))

让我们在图像中描绘出每个人脸特征！

pil_image = Image.fromarray(image)
d = ImageDraw.Draw(pil_image)
for facial_feature in facial_features:
d.line(face_landmarks[facial_feature], width=5)
pil_image.show()
</pre>

2、find_face文件夹

不仅能识别出来所有的人脸，而且可以将其截图挨个显示出来,打印在前台窗口

原始的图片

python基于open cv实现人脸识别技术，小白也可以做出来（代码）

这里写图片描述

识别的图片

python基于open cv实现人脸识别技术，小白也可以做出来（代码）

这里写图片描述

代码：

<pre style="-webkit-tap-highlight-color: transparent; box-sizing: border-box; font-family: Consolas, Menlo, Courier, monospace; font-size: 16px; white-space: pre-wrap; position: relative; line-height: 1.5; color: rgb(153, 153, 153); margin: 1em 0px; padding: 12px 10px; background: rgb(244, 245, 246); border: 1px solid rgb(232, 232, 232); font-style: normal; font-variant-ligatures: normal; font-variant-caps: normal; font-weight: normal; letter-spacing: normal; orphans: 2; text-align: start; text-indent: 0px; text-transform: none; widows: 2; word-spacing: 0px; -webkit-text-stroke-width: 0px; text-decoration-style: initial; text-decoration-color: initial;"># -- coding: utf-8 --

识别图片中的所有人脸并显示出来

filename : find_faces_in_picture.py

导入pil模块 ，可用命令安装 apt-get install python-Imaging

from PIL import Image

导入face_recogntion模块，可用命令安装 pip install face_recognition

import face_recognition

将jpg文件加载到numpy 数组中

image = face_recognition.load_image_file("yiqi.jpg")

使用默认的给予HOG模型查找图像中所有人脸

这个方法已经相当准确了，但还是不如CNN模型那么准确，因为没有使用GPU加速

另请参见: find_faces_in_picture_cnn.py

face_locations = face_recognition.face_locations(image)

使用CNN模型

face_locations = face_recognition.face_locations(image, number_of_times_to_upsample=0, model="cnn")

打印：我从图片中找到了 多少 张人脸

print("I found {} face(s) in this photograph.".format(len(face_locations)))

循环找到的所有人脸

for face_location in face_locations:

打印每张脸的位置信息

top, right, bottom, left = face_location
print("A face is located at pixel location Top: {}, Left: {}, Bottom: {}, Right: {}".format(top, left, bottom, right))

指定人脸的位置信息，然后显示人脸图片

face_image = image[top:bottom, left:right]
pil_image = Image.fromarray(face_image)
pil_image.show()
</pre>

３、know_face文件夹

通过设定的人脸图片识别未知图片中的人脸

<pre style="-webkit-tap-highlight-color: transparent; box-sizing: border-box; font-family: Consolas, Menlo, Courier, monospace; font-size: 16px; white-space: pre-wrap; position: relative; line-height: 1.5; color: rgb(153, 153, 153); margin: 1em 0px; padding: 12px 10px; background: rgb(244, 245, 246); border: 1px solid rgb(232, 232, 232); font-style: normal; font-variant-ligatures: normal; font-variant-caps: normal; font-weight: normal; letter-spacing: normal; orphans: 2; text-align: start; text-indent: 0px; text-transform: none; widows: 2; word-spacing: 0px; -webkit-text-stroke-width: 0px; text-decoration-style: initial; text-decoration-color: initial;"># -- coding: utf-8 --

识别人脸鉴定是哪个人

导入face_recogntion模块，可用命令安装 pip install face_recognition

import face_recognition

将jpg文件加载到numpy数组中

chen_image = face_recognition.load_image_file("chenduling.jpg")

要识别的图片

unknown_image = face_recognition.load_image_file("sunyizheng.jpg")

获取每个图像文件中每个面部的面部编码

由于每个图像中可能有多个面，所以返回一个编码列表。

但是由于我知道每个图像只有一个脸，我只关心每个图像中的第一个编码，所以我取索引0。

chen_face_encoding = face_recognition.face_encodings(chen_image)[0]
print("chen_face_encoding:{}".format(chen_face_encoding))
unknown_face_encoding = face_recognition.face_encodings(unknown_image)[0]
print("unknown_face_encoding :{}".format(unknown_face_encoding))
known_faces = [
chen_face_encoding
]

结果是True/false的数组，未知面孔known_faces阵列中的任何人相匹配的结果

results = face_recognition.compare_faces(known_faces, unknown_face_encoding)
print("result :{}".format(results))
print("这个未知面孔是 陈都灵 吗? {}".format(results[0]))
print("这个未知面孔是 我们从未见过的新面孔吗? {}".format(not True in results))
</pre>

4、video文件夹

通过调用电脑摄像头动态获取视频内的人脸，将其和我们指定的图片集进行匹配，可以告知我们视频内的人脸是否是我们设定好的

实现：

python基于open cv实现人脸识别技术，小白也可以做出来（代码）

image

代码：

<pre style="-webkit-tap-highlight-color: transparent; box-sizing: border-box; font-family: Consolas, Menlo, Courier, monospace; font-size: 16px; white-space: pre-wrap; position: relative; line-height: 1.5; color: rgb(153, 153, 153); margin: 1em 0px; padding: 12px 10px; background: rgb(244, 245, 246); border: 1px solid rgb(232, 232, 232); font-style: normal; font-variant-ligatures: normal; font-variant-caps: normal; font-weight: normal; letter-spacing: normal; orphans: 2; text-align: start; text-indent: 0px; text-transform: none; widows: 2; word-spacing: 0px; -webkit-text-stroke-width: 0px; text-decoration-style: initial; text-decoration-color: initial;"># -- coding: utf-8 --

摄像头头像识别

import face_recognition
import cv2
video_capture = cv2.VideoCapture(0)

本地图像

chenduling_image = face_recognition.load_image_file("chenduling.jpg")
chenduling_face_encoding = face_recognition.face_encodings(chenduling_image)[0]

本地图像二

sunyizheng_image = face_recognition.load_image_file("sunyizheng.jpg")
sunyizheng_face_encoding = face_recognition.face_encodings(sunyizheng_image)[0]

本地图片三

zhangzetian_image = face_recognition.load_image_file("zhangzetian.jpg")
zhangzetian_face_encoding = face_recognition.face_encodings(zhangzetian_image)[0]

Create arrays of known face encodings and their names

脸部特征数据的集合

known_face_encodings = [
chenduling_face_encoding,
sunyizheng_face_encoding,
zhangzetian_face_encoding
]

人物名称的集合

known_face_names = [
"michong",
"sunyizheng",
"chenduling"
]
face_locations = []
face_encodings = []
face_names = []
process_this_frame = True
while True:

读取摄像头画面

ret, frame = video_capture.read()

改变摄像头图像的大小，图像小，所做的计算就少

small_frame = cv2.resize(frame, (0, 0), fx=0.25, fy=0.25)

opencv的图像是BGR格式的，而我们需要是的RGB格式的，因此需要进行一个转换。

rgb_small_frame = small_frame[:, :, ::-1]

Only process every other frame of video to save time

if process_this_frame:

根据encoding来判断是不是同一个人，是就输出true，不是为flase

face_locations = face_recognition.face_locations(rgb_small_frame)
face_encodings = face_recognition.face_encodings(rgb_small_frame, face_locations)
face_names = []
for face_encoding in face_encodings:

默认为unknown

matches = face_recognition.compare_faces(known_face_encodings, face_encoding)
name = "Unknown"

if match[0]:

name = "michong"

If a match was found in known_face_encodings, just use the first one.

if True in matches:
first_match_index = matches.index(True)
name = known_face_names[first_match_index]
face_names.append(name)
process_this_frame = not process_this_frame

将捕捉到的人脸显示出来

for (top, right, bottom, left), name in zip(face_locations, face_names):

Scale back up face locations since the frame we detected in was scaled to 1/4 size

top *= 4
right *= 4
bottom *= 4
left *= 4

矩形框

cv2.rectangle(frame, (left, top), (right, bottom), (0, 0, 255), 2)

加上标签

cv2.rectangle(frame, (left, bottom - 35), (right, bottom), (0, 0, 255), cv2.FILLED)
font = cv2.FONT_HERSHEY_DUPLEX
cv2.putText(frame, name, (left + 6, bottom - 6), font, 1.0, (255, 255, 255), 1)

Display

cv2.imshow('monitor', frame)

按Q退出

if cv2.waitKey(1) & 0xFF == ord('q'):
break
video_capture.release()
cv2.destroyAllWindows()
</pre>

5、boss文件夹

本开源项目,主要是结合摄像头程序+极光推送，实现识别摄像头中的人脸。并且通过极光推送平台给移动端发送消息！