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:
image
image
特征数据,数据就是运行出来的矩阵,也就是一个二维数组
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
image
识别的图片
image
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:
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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