从零开始学Python人脸识别技术,人工智能不过如此!(内附教程)

一、环境搭建

1.系统环境

04
Python 2.7.14
pycharm 开发工具

2.开发环境,安装各种系统包

  • 人脸检测基于dlib,dlib依赖Boost和cmake
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  • 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

$ sudo apt-get install build-essential cmake
$ sudo apt-get install libgtk-3-dev$ sudo apt-get install libboost-all-dev
  • 其他重要的包
$ pip install numpy$ pip install scipy$ pip install opencv-python$ pip install dlib
  • 安装 face_recognition
# 安装 face_recognition$ pip install face_recognition# 安装face_recognition过程中会自动安装 numpy、scipy 等

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

'chin', 'left_eyebrow', 'right_eyebrow', 'nose_bridge', 'nose_tip', 'left_eye', 'right_eye', 'top_lip', 'bottom_lip'

运行结果:

自动识别图片中的人脸,并且识别它的特征

原图:

从零开始学Python人脸识别技术,人工智能不过如此!(内附教程)

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从零开始学Python人脸识别技术,人工智能不过如此!(内附教程)

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特征数据,数据就是运行出来的矩阵,也就是一个二维数组

从零开始学Python人脸识别技术,人工智能不过如此!(内附教程)

image

代码:

# -*- 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-Imagingfrom PIL import Image, ImageDraw# 导入face_recogntion模块,可用命令安装 pip install face_recognitionimport 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()

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

从零开始学Python人脸识别技术,人工智能不过如此!(内附教程)

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识别的图片

从零开始学Python人脸识别技术,人工智能不过如此!(内附教程)

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代码:

# -*- 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-Imagingfrom PIL import Image# 导入face_recogntion模块,可用命令安装 pip install face_recognitionimport 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.pyface_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()

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

# -*- 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_recognitionimport 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))

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:

从零开始学Python人脸识别技术,人工智能不过如此!(内附教程)

image

代码:

# -*- 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 = Truewhile 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()

5、boss文件夹

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

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