网站开发中网页打印,网站初期seo怎么做,wordpress 免费版,网站建设相关资讯不久乘高铁出行#xff0c;看见高铁火车站已经实现了“刷脸进站”#xff0c;而且效率很高#xff0c;很感兴趣#xff0c;今天抽时间研究一下#xff0c;其实没那么复杂。 我基本上是基于https://github.com/ageitgey/face_recognition上的资料和源码做一些尝试和试验。 … 不久乘高铁出行看见高铁火车站已经实现了“刷脸进站”而且效率很高很感兴趣今天抽时间研究一下其实没那么复杂。 我基本上是基于https://github.com/ageitgey/face_recognition上的资料和源码做一些尝试和试验。 首先需要配置我们的python环境我悬着的python27(比较稳定)具体过程不多说了。 然后需要安装这次的主角face_recognition库这个的安装花了我不少时间需要注意一下几点(按照本人的环境) 1首先安装visual studio 2015因为vs2015默认只安装c#相关组件所以需要安装c相关组件。 ps:vs2015安装c相关组件的方法在vs2015中新建c项目出现下面场景 选择第二项确定后就会自动安装。 为什么需要安装c,因为安装face_recognition时会先安装dlib,dlib是基于c的一个库。 2安装cmake(一个跨平台编译工具),然后需要将cmake的安装路径加入到系统环境变量path中去。 最后就可以直接在dos中执行安装命令了(需要切换到python目录下的Script目录下)pip install face_recognition,命令会自动帮你安装好需要的dlib库。 到此为止我们完成了face_recognition安装工作。 ---------------------------------------------------------------分割线---------------------------------------------------------------------------------- 下面给出几个实例来逐步了解“人脸识别” 1.一行代码实现“人脸识别” 在Python目录中新建两个文件夹分别表示“已知姓名的人”和“未知姓名的人”图片以额、人名命名如下 接下来我们通过“认识的人”来识别“不认识的人” 结果表明1.jpg不认识3.jpg是obama,unkown.jpg中有两个人一个是obama,另一个不认识 结果还挺准确的很给力 2.识别图片中所有的人脸并显示出来 import Image
import face_recognition
image face_recognition.load_image_file(F:/Python27/Scripts/all.jpg)
face_locations face_recognition.face_locations(image)#face_locations face_recognition.#face_locations(image,number_of_times_to_upsample0,modelcnn)
print(i found {} face(s) in this photograph..format(len(face_locations)))
for face_location in face_locations:top,right,bottom,left face_locationprint(A face is located at pixel location Top:{},Left:{},Bottom:{},Right:{}.format(top,right,bottom,left))face_image image[top:bottom,left:right]pil_imageImage.fromarray(face_image)pil_image.show() View Code 避坑指南import Image需要先安装PIL库在pycharm中安装的时候会报错因为pil没有64位的版本这时我们安装Pillow-PIL就好了。 我们的all.jpg如下 执行以下看看结果 没有错总共12个人脸都被识别出来了 3.给照片“美颜” face_recognition可以识别人像的下巴眼睛鼻子嘴唇眼球等区域包含以下这些个特征: facial_features [ chin, left_eyebrow, right_eyebrow, nose_bridge, nose_tip, left_eye, right_eye, top_lip, bottom_lip ] 利用这些特征属性可以轻松的给人像“美颜” from PIL import Image, ImageDraw
face_recognition
import face_recognitionimage face_recognition.load_image_file(F:/Python27/Scripts/known_people/obama.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)for face_landmarks in face_landmarks_list:pil_image Image.fromarray(image)d ImageDraw.Draw(pil_image, RGBA)#让眉毛变成了一场噩梦d.polygon(face_landmarks[left_eyebrow], fill(68, 54, 39, 128))d.polygon(face_landmarks[right_eyebrow], fill(68, 54, 39, 128))d.line(face_landmarks[left_eyebrow], fill(68, 54, 39, 150), width5)d.line(face_landmarks[right_eyebrow], fill(68, 54, 39, 150), width5)#光泽的嘴唇d.polygon(face_landmarks[top_lip], fill(150, 0, 0, 128))d.polygon(face_landmarks[bottom_lip], fill(150, 0, 0, 128))d.line(face_landmarks[top_lip], fill(150, 0, 0, 64), width8)d.line(face_landmarks[bottom_lip], fill(150, 0, 0, 64), width8)#闪耀眼睛d.polygon(face_landmarks[left_eye], fill(255, 255, 255, 30))d.polygon(face_landmarks[right_eye], fill(255, 255, 255, 30))#涂一些眼线d.line(face_landmarks[left_eye] [face_landmarks[left_eye][0]], fill(0, 0, 0, 110), width6)d.line(face_landmarks[right_eye] [face_landmarks[right_eye][0]], fill(0, 0, 0, 110), width6)pil_image.show() View Code 执行下看看结果 有点辣眼睛 4.利用笔记本摄像头识别人像 回到前面说的高铁站的“刷脸”其实就是基于摄像头的“人像识别”。 这里要调用电脑的摄像头而且涉及一些计算机视觉系统的计算所以我们要先安装opencv库 安装方法 pip install --upgrade setuptools
pip install numpy Matplotlib
pip install opencv-python ps:如果报错EnvironmentError: [Errno 13] Permission denied: 在install后加上--user即可 小技巧可以在python命令行中用 import site; site.getsitepackages()来确定当前的python环境的site-packages目录的位置 目的这里我们需要用摄像头识别自己那么首先需要有一张自己的照片我将我的照片命名为mike.jpg,然后使用摄像头来识别我自己。 看看代码 import face_recognition
import cv2# This is a demo of running face recognition on live video from your webcam. Its a little more complicated than the
# other example, but it includes some basic performance tweaks to make things run a lot faster:
# 1. Process each video frame at 1/4 resolution (though still display it at full resolution)
# 2. Only detect faces in every other frame of video.# PLEASE NOTE: This example requires OpenCV (the cv2 library) to be installed only to read from your webcam.
# OpenCV is *not* required to use the face_recognition library. Its only required if you want to run this
# specific demo. If you have trouble installing it, try any of the other demos that dont require it instead.# Get a reference to webcam #0 (the default one)
video_capture cv2.VideoCapture(0)# Load a sample picture and learn how to recognize it.
obama_image face_recognition.load_image_file(F:/Python27/Scripts/known_people/obama.jpg)
obama_face_encoding face_recognition.face_encodings(obama_image)[0]# Load a second sample picture and learn how to recognize it.
biden_image face_recognition.load_image_file(F:/Python27/Scripts/known_people/mike.jpg)
biden_face_encoding face_recognition.face_encodings(biden_image)[0]# Create arrays of known face encodings and their names
known_face_encodings [obama_face_encoding,biden_face_encoding
]
known_face_names [Barack Obama,mike
]# Initialize some variables
face_locations []
face_encodings []
face_names []
process_this_frame Truewhile True:# Grab a single frame of videoret, frame video_capture.read()# Resize frame of video to 1/4 size for faster face recognition processingsmall_frame cv2.resize(frame, (0, 0), fx0.25, fy0.25)# Convert the image from BGR color (which OpenCV uses) to RGB color (which face_recognition uses)rgb_small_frame small_frame[:, :, ::-1]# Only process every other frame of video to save timeif process_this_frame:# Find all the faces and face encodings in the current frame of videoface_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:# See if the face is a match for the known face(s)matches face_recognition.compare_faces(known_face_encodings, face_encoding)name Unknown# 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# Display the resultsfor (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 sizetop * 4right * 4bottom * 4left * 4# Draw a box around the facecv2.rectangle(frame, (left, top), (right, bottom), (0, 0, 255), 2)# Draw a label with a name below the facecv2.rectangle(frame, (left, bottom - 35), (right, bottom), (0, 0, 255), cv2.FILLED)font cv2.FONT_HERSHEY_DUPLEXcv2.putText(frame, name, (left 6, bottom - 6), font, 1.0, (255, 255, 255), 1)# Display the resulting imagecv2.imshow(Video, frame)# Hit q on the keyboard to quit!if cv2.waitKey(1) 0xFF ord(q):break# Release handle to the webcam
video_capture.release()
cv2.destroyAllWindows() View Code 只想看看结果 看来我被识别成功了。看起来有点小激动呢。 通过上面四个小例子基本了解face_recognition的用法这只是小试牛刀具体在现实中的应用要复杂很多 我们需要大量的人脸数据会涉及到机器学习和数学算法等等而且根据应用场景的不同也会出现很多不同的要求。 这里只是一起学习分享期待后续关于人工智能的内容。
