Python3 利用face_recognition实现人脸识别的方法
前言
之前实践了下face++在线人脸识别版本,这回做一下离线版本。github上面有关于face_recognition的相关资料,本人只是做个搬运工,对其中的一些内容进行搬运,对其中一些例子进行实现。
官方描述:
face_recognition是一个强大、简单、易上手的人脸识别开源项目,并且配备了完整的开发文档和应用案例,特别是兼容树莓派系统。本项目是世界上最简洁的人脸识别库,你可以使用Python和命令行工具提取、识别、操作人脸。本项目的人脸识别是基于业内领先的C++开源库dlib中的深度学习模型,用LabeledFacesintheWild人脸数据集进行测试,有高达99.38%的准确率。但对小孩和亚洲人脸的识别准确率尚待提升。
(关于兼容树莓派,以后有板子了再做一下)
下面两个链接划重点
https://github.com/ageitgey/face_recognition/blob/master/README_Simplified_Chinese.md
https://face-recognition.readthedocs.io/en/latest/face_recognition.html
环境配置
- ubuntu16.04(其他环境的安装可以参考第一个链接,官方有说明)
- pycharm(可忽略,怎么舒服怎么来)
- python3
- opencv(我的是4.1.2,三点几的版本应该也一样)
实际上只需要安装face_recognition,当然,没有opencv的也需要安装一下opencv
pip3installface_recognition
图片准备
由于需要做一些图片的比对,因此需要准备一些图片,本文图片取自以下链接
https://www.zhihu.com/question/314169580/answer/872770507
接下来开始操作
官方还有提供命令行的操作(这个没去做),本文不做这个,我们只要是要在python中用face_recognition,因此定位到这一块。
这个api文档地址就是上面的第二个链接。进去之后可以看到:
part1.识别图片中的人是谁
代码
#part1 #识别图片中的人是谁 importface_recognition known_image=face_recognition.load_image_file("lyf1.jpg") unknown_image=face_recognition.load_image_file("lyf2.jpg") lyf_encoding=face_recognition.face_encodings(known_image)[0] unknown_encoding=face_recognition.face_encodings(unknown_image)[0] results=face_recognition.compare_faces([lyf_encoding],unknown_encoding) #AlistofTrue/Falsevaluesindicatingwhichknown_face_encodingsmatchthefaceencodingtocheck print(type(results)) print(results) ifresults[0]==True: print("yes") else: print("no")
结果
[True]
yes
part2.从图片中找到人脸
代码
#part2 #从图片中找到人脸(定位人脸位置) importface_recognition importcv2 image=face_recognition.load_image_file("lyf1.jpg") face_locations_useCNN=face_recognition.face_locations(image,model='cnn') #model–Whichfacedetectionmodeltouse.“hog”islessaccuratebutfasteronCPUs. #“cnn”isamoreaccuratedeep-learningmodelwhichisGPU/CUDAaccelerated(ifavailable).Thedefaultis“hog”. face_locations_noCNN=face_recognition.face_locations(image) #Alistoftuplesoffoundfacelocationsincss(top,right,bottom,left)order #因为返回值的顺序是这样子的,因此在后面的for循环里面赋值要注意按这个顺序来 print("face_location_useCNN:") print(face_locations_useCNN) face_num1=len(face_locations_useCNN) print(face_num1)#Thenumberoffaces print("face_location_noCNN:") print(face_locations_noCNN) face_num2=len(face_locations_noCNN) print(face_num2)#Thenumberoffaces #到这里为止,可以观察两种情况的坐标和人脸数,一般来说,坐标会不一样,但是检测出来的人脸数应该是一样的 #也就是说face_num1=face_num2;face_locations_useCNN和face_locations_noCNN不一样 org=cv2.imread("lyf1.jpg") img=cv2.imread("lyf1.jpg") cv2.imshow("lyf1.jpg",img)#原始图片 #Gotogetthedataanddrawtherectangle #useCNN foriinrange(0,face_num1): top=face_locations_useCNN[i][0] right=face_locations_useCNN[i][1] bottom=face_locations_useCNN[i][2] left=face_locations_useCNN[i][3] start=(left,top) end=(right,bottom) color=(0,255,255) thickness=2 cv2.rectangle(img,start,end,color,thickness)#opencv里面画矩形的函数 #Showtheresult cv2.imshow("useCNN",img) #forface_locationinface_locations_noCNN: # ##Printthelocationofeachfaceinthisimage #top,right,bottom,left=face_location ##等价于下面的这种写法 foriinrange(0,face_num2): top=face_locations_noCNN[i][0] right=face_locations_noCNN[i][1] bottom=face_locations_noCNN[i][2] left=face_locations_noCNN[i][3] start=(left,top) end=(right,bottom) color=(0,255,255) thickness=2 cv2.rectangle(org,start,end,color,thickness) cv2.imshow("nocnn",org) cv2.waitKey(0) cv2.destroyAllWindows()
结果
face_location_useCNN:
[(223,470,427,266)]
1
face_location_noCNN:
[(242,489,464,266)]
1
图片效果大致是这样
part3.找到人脸并将其裁剪打印出来(使用cnn定位人脸)
代码
#part3 #找到人脸并将其裁剪打印出来(使用cnn定位人脸) fromPILimportImage importface_recognition #Loadthejpgfileintoanumpyarray image=face_recognition.load_image_file("lyf1.jpg") face_locations=face_recognition.face_locations(image,number_of_times_to_upsample=0,model="cnn") print("Ifound{}face(s)inthisphotograph.".format(len(face_locations))) forface_locationinface_locations: top,right,bottom,left=face_location print("AfaceislocatedatpixellocationTop:{},Left:{},Bottom:{},Right:{}".format(top,left,bottom,right)) face_image=image[top:bottom,left:right] pil_image=Image.fromarray(face_image) pil_image.show()
结果
Ifound1face(s)inthisphotograph.
AfaceislocatedatpixellocationTop:205,Left:276,Bottom:440,Right:512
图片效果大致是这样
part4.识别单张图片中人脸的关键点
代码
#part4识别单张图片中人脸的关键点 fromPILimportImage,ImageDraw importface_recognition #Loadthejpgfileintoanumpyarray image=face_recognition.load_image_file("lyf1.jpg") #Findallfacialfeaturesinallthefacesintheimage face_landmarks_list=face_recognition.face_landmarks(image) #print(face_landmarks_list) print("Ifound{}face(s)inthisphotograph.".format(len(face_landmarks_list))) #CreateaPILimagedrawobjectsowecandrawonthepicture pil_image=Image.fromarray(image) d=ImageDraw.Draw(pil_image) forface_landmarksinface_landmarks_list: #Printthelocationofeachfacialfeatureinthisimage forfacial_featureinface_landmarks.keys(): print("The{}inthisfacehasthefollowingpoints:{}".format(facial_feature,face_landmarks[facial_feature])) #Let'straceouteachfacialfeatureintheimagewithaline! forfacial_featureinface_landmarks.keys(): d.line(face_landmarks[facial_feature],width=5) #Showthepicture pil_image.show()
结果
Ifound1face(s)inthisphotograph.
Theleft_eyebrowinthisfacehasthefollowingpoints:[(305,285),(321,276),(340,277),(360,281),(377,288)]
Theright_eyeinthisfacehasthefollowingpoints:[(422,313),(432,303),(446,302),(459,305),(449,312),(435,314)]
Thenose_bridgeinthisfacehasthefollowingpoints:[(394,309),(394,331),(395,354),(396,375)]
Theright_eyebrowinthisfacehasthefollowingpoints:[(407,287),(424,278),(442,273),(461,272),(478,279)]
Thebottom_lipinthisfacehasthefollowingpoints:[(429,409),(419,421),(408,428),(398,430),(389,429),(377,424),(364,412),(370,413),(389,414),(398,415),(407,413),(423,411)]
Thechininthisfacehasthefollowingpoints:[(289,295),(291,323),(296,351),(303,378),(315,403),(332,428),(353,448),(376,464),(400,467),(422,461),(441,444),(459,425),(473,403),(484,377),(490,351),(493,323),(493,296)]
Thetop_lipinthisfacehasthefollowingpoints:[(364,412),(377,407),(389,403),(397,406),(406,402),(417,405),(429,409),(423,411),(406,412),(397,414),(389,413),(370,413)]
Theleft_eyeinthisfacehasthefollowingpoints:[(327,308),(339,304),(353,306),(364,314),(352,317),(338,316)]
Thenose_tipinthisfacehasthefollowingpoints:[(375,383),(386,387),(396,390),(407,385),(416,381)]
图片效果
到此这篇关于Python3利用face_recognition实现人脸识别的方法的文章就介绍到这了,更多相关Python3人脸识别内容请搜索毛票票以前的文章或继续浏览下面的相关文章希望大家以后多多支持毛票票!
声明:本文内容来源于网络,版权归原作者所有,内容由互联网用户自发贡献自行上传,本网站不拥有所有权,未作人工编辑处理,也不承担相关法律责任。如果您发现有涉嫌版权的内容,欢迎发送邮件至:czq8825#qq.com(发邮件时,请将#更换为@)进行举报,并提供相关证据,一经查实,本站将立刻删除涉嫌侵权内容。