python dlib学习(五):比对人脸
2017-10-30 10:55
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前言
在前面的博客中介绍了,如何使用dlib标定人脸(python dlib学习(一):人脸检测),提取68个特征点(python dlib学习(二):人脸特征点标定)。这次要在这两个工作的基础之上,将人脸的信息提取成一个128维的向量空间。在这个向量空间上,同一个人脸的更接近,不同人脸的距离更远。度量采用欧式距离,欧氏距离计算不算复杂。二维情况下:
distance=(x1−x2)2+(y1−y2)2−−−−−−−−−−−−−−−−−−√
三维情况下:
distance=(x1−x2)2+(y1−y2)2+(z1−z2)2−−−−−−−−−−−−−−−−−−−−−−−−−−−−√
将其扩展到128维的情况下即可。
通常使用的判别阈值是0.6,即如果两个人脸的向量空间的欧式距离超过了0.6,即认定不是同一个人;如果欧氏距离小于0.6,则认为是同一个人。这个距离也可以由自己定,只要效果能更好。
实验中使用了两个模型:
shape_predictor_68_face_landmarks.dat:
http://dlib.net/files/shape_predictor_68_face_landmarks.dat.bz2
dlib_face_recognition_resnet_model_v1.dat:
http://dlib.net/files/dlib_face_recognition_resnet_model_v1.dat.bz2
文件夹目录:
两个模型放在model文件夹中,测试图片放在faces中,图片自己随便下几张就行。
完整工程下载链接:
http://pan.baidu.com/s/1boCDZ7T
程序1
不说废话了,直接上代码。# -*- coding: utf-8 -*- import sys import dlib import cv2 import os import glob current_path = os.getcwd() # 获取当前路径 # 模型路径 predictor_path = current_path + "\\model\\shape_predictor_68_face_landmarks.dat" face_rec_model_path = current_path + "\\model\\dlib_face_recognition_resnet_model_v1.dat" #测试图片路径 faces_folder_path = current_path + "\\faces\\" # 读入模型 detector = dlib.get_frontal_face_detector() shape_predictor = dlib.shape_predictor(predictor_path) face_rec_model = dlib.face_recognition_model_v1(face_rec_model_path) for img_path in glob.glob(os.path.join(faces_folder_path, "*.jpg")): print("Processing file: {}".format(img_path)) # opencv 读取图片,并显示 img = cv2.imread(img_path, cv2.IMREAD_COLOR) # opencv的bgr格式图片转换成rgb格式 b, g, r = cv2.split(img) img2 = cv2.merge([r, g, b]) dets = detector(img, 1) # 人脸标定 print("Number of faces detected: {}".format(len(dets))) for index, face in enumerate(dets): print('face {}; left {}; top {}; right {}; bottom {}'.format(index, face.left(), face.top(), face.right(), face.bottom())) shape = shape_predictor(img2, face) # 提取68个特征点 for i, pt in enumerate(shape.parts()): #print('Part {}: {}'.format(i, pt)) pt_pos = (pt.x, pt.y) cv2.circle(img, pt_pos, 2, (255, 0, 0), 1) #print(type(pt)) #print("Part 0: {}, Part 1: {} ...".format(shape.part(0), shape.part(1))) cv2.namedWindow(img_path+str(index), cv2.WINDOW_AUTOSIZE) cv2.imshow(img_path+str(index), img) face_descriptor = face_rec_model.compute_face_descriptor(img2, shape) # 计算人脸的128维的向量 print(face_descriptor) k = cv2.waitKey(0) cv2.destroyAllWindows()
程序1结果
部分打印结果:
F:\Python\my_dlib_codes\face_recognition>python my_face_recogniton.py Processing file: F:\Python\my_dlib_codes\face_recognition\faces\jobs.jpg Number of faces detected: 1 face 0; left 184; top 64; right 339; bottom 219 -0.179784 0.15487 0.10509 -0.0973604 -0.19153 0.000418252 -0.0357536 -0.0206766 0.129741 -0.0628359 ....
后面的那一堆数字就是人脸在128维向量空间上的值。
程序2
前面只是测试了一下,把要用的值给求到了。这里我封装了一下,把比对功能实现了。没加多少东西,所以不做赘述了。# -*- coding: utf-8 -*- import sys import dlib import cv2 import os import glob import numpy as np def comparePersonData(data1, data2): diff = 0 # for v1, v2 in data1, data2: # diff += (v1 - v2)**2 for i in xrange(len(data1)): diff += (data1[i] - data2[i])**2 diff = np.sqrt(diff) print diff if(diff < 0.6): print "It's the same person" else: print "It's not the same person" def savePersonData(face_rec_class, face_descriptor): if face_rec_class.name == None or face_descriptor == None: return filePath = face_rec_class.dataPath + face_rec_class.name + '.npy' vectors = np.array([]) for i, num in enumerate(face_descriptor): vectors = np.append(vectors, num) # print(num) print('Saving files to :'+filePath) np.save(filePath, vectors) return vectors def loadPersonData(face_rec_class, personName): if personName == None: return filePath = face_rec_class.dataPath + personName + '.npy' vectors = np.load(filePath) print(vectors) return vectors class face_recognition(object): def __init__(self): self.current_path = os.getcwd() # 获取当前路径 self.predictor_path = self.current_path + "\\model\\shape_predictor_68_face_landmarks.dat" self.face_rec_model_path = self.current_path + "\\model\\dlib_face_recognition_resnet_model_v1.dat" self.faces_folder_path = self.current_path + "\\faces\\" self.dataPath = self.current_path + "\\data\\" self.detector = dlib.get_frontal_face_detector() self.shape_predictor = dlib.shape_predictor(self.predictor_path) self.face_rec_model = dlib.face_recognition_model_v1(self.face_rec_model_path) self.name = None self.img_bgr = None self.img_rgb = None self.detector = dlib.get_frontal_face_detector() self.shape_predictor = dlib.shape_predictor(self.predictor_path) self.face_rec_model = dlib.face_recognition_model_v1(self.face_rec_model_path) def inputPerson(self, name='people', img_path=None): if img_path == None: print('No file!\n') return # img_name += self.faces_folder_path + img_name self.name = name self.img_bgr = cv2.imread(self.current_path+img_path) # opencv的bgr格式图片转换成rgb格式 b, g, r = cv2.split(self.img_bgr) self.img_rgb = cv2.merge([r, g, b]) def create128DVectorSpace(self): dets = self.detector(self.img_rgb, 1) print("Number of faces detected: {}".format(len(dets))) for index, face in enumerate(dets): print('face {}; left {}; top {}; right {}; bottom {}'.format(index, face.left(), face.top(), face.right(), face.bottom())) shape = self.shape_predictor(self.img_rgb, face) face_descriptor = self.face_rec_model.compute_face_descriptor(self.img_rgb, shape) # print(face_descriptor) # for i, num in enumerate(face_descriptor): # print(num) # print(type(num)) return face_descriptor
程序2结果
测试代码1:import face_rec as fc face_rec = fc.face_recognition() # 创建对象 face_rec.inputPerson(name='jobs', img_path='\\faces\\jobs.jpg') # name中写第一个人名字,img_name为图片名字,注意要放在faces文件夹中 vector = face_rec.create128DVectorSpace() # 提取128维向量,是dlib.vector类的对象 person_data1 = fc.savePersonData(face_rec, vector ) # 将提取出的数据保存到data文件夹,为便于操作返回numpy数组,内容还是一样的 # 导入第二张图片,并提取特征向量 face_rec.inputPerson(name='jobs2', img_path='\\faces\\jobs2.jpg') vector = face_rec.create128DVectorSpace() # 提取128维向量,是dlib.vector类的对象 person_data2 = fc.savePersonData(face_rec, vector ) # 计算欧式距离,判断是否是同一个人 fc.comparePersonData(person_data1, person_data2)
如果data文件夹中已经有了模型文件,可以直接导入:
import face_rec as fc face_rec = fc.face_recognition() # 创建对象 person_data1 = fc.loadPersonData(face_rec , 'jobs') # 创建一个类保存相关信息,后面还要跟上人名,程序会在data文件中查找对应npy文件,比如这里就是'jobs.npy' person_data2 = fc.loadPersonData(face_rec , 'jobs2') # 导入第二张图片 fc.comparePersonData(person_data1, person_data2) # 计算欧式距离,判断是否是同一个人
程序2结果
Python 2.7.10 |Anaconda 2.3.0 (64-bit)| (default, May 28 2015, 16:44:52) [MSC v.1500 64 bit (AMD64)] on win32 Type "help", "copyright", "credits" or "license" for more information. Anaconda is brought to you by Continuum Analytics. Please check out: http://continuum.io/thanks and https://binstar.org >>> import face_rec as fc >>> face_rec = fc.face_recognition() >>> face_rec.inputPerson(name='jobs', img_path='\\faces\\jobs.jpg') >>> vector = face_rec.create128DVectorSpace() Number of faces detected: 1 face 0; left 184; top 64; right 339; bottom 219 >>> person_data1 = fc.savePersonData(face_rec, vector ) Saving files to :F:\Python\my_dlib_codes\face_recognition\data\jobs.npy >>> face_rec.inputPerson(name='jobs2', img_path='\\faces\\jobs2.jpg') >>> vector = face_rec.create128DVectorSpace() Number of faces detected: 1 face 0; left 124; top 39; right 253; bottom 168 >>> person_data2 = fc.savePersonData(face_rec, vector ) Saving files to :F:\Python\my_dlib_codes\face_recognition\data\jobs2.npy >>> fc.comparePersonData(person_data1, person_data2) 0.490491048429 It's the same person
官方例程
#!/usr/bin/python # The contents of this file are in the public domain. See LICENSE_FOR_EXAMPLE_PROGRAMS.txt # # This example shows how to use dlib's face recognition tool. This tool maps # an image of a human face to a 128 dimensional vector space where images of # the same person are near to each other and images from different people are # far apart. Therefore, you can perform face recognition by mapping faces to # the 128D space and then checking if their Euclidean distance is small # enough. # # When using a distance threshold of 0.6, the dlib model obtains an accuracy # of 99.38% on the standard LFW face recognition benchmark, which is # comparable to other state-of-the-art methods for face recognition as of # February 2017. This accuracy means that, when presented with a pair of face # images, the tool will correctly identify if the pair belongs to the same # person or is from different people 99.38% of the time. # # Finally, for an in-depth discussion of how dlib's tool works you should # refer to the C++ example program dnn_face_recognition_ex.cpp and the # attendant documentation referenced therein. # # # # # COMPILING/INSTALLING THE DLIB PYTHON INTERFACE # You can install dlib using the command: # pip install dlib # # Alternatively, if you want to compile dlib yourself then go into the dlib # root folder and run: # python setup.py install # or # python setup.py install --yes USE_AVX_INSTRUCTIONS # if you have a CPU that supports AVX instructions, since this makes some # things run faster. This code will also use CUDA if you have CUDA and cuDNN # installed. # # Compiling dlib should work on any operating system so long as you have # CMake and boost-python installed. On Ubuntu, this can be done easily by # running the command: # sudo apt-get install libboost-python-dev cmake # # Also note that this example requires scikit-image which can be installed # via the command: # pip install scikit-image # Or downloaded from http://scikit-image.org/download.html. import sys import os import dlib import glob from skimage import io if len(sys.argv) != 4: print( "Call this program like this:\n" " ./face_recognition.py shape_predictor_68_face_landmarks.dat dlib_face_recognition_resnet_model_v1.dat ../examples/faces\n" "You can download a trained facial shape predictor and recognition model from:\n" " http://dlib.net/files/shape_predictor_68_face_landmarks.dat.bz2\n" " http://dlib.net/files/dlib_face_recognition_resnet_model_v1.dat.bz2") exit() predictor_path = sys.argv[1] face_rec_model_path = sys.argv[2] faces_folder_path = sys.argv[3] # Load all the models we need: a detector to find the faces, a shape predictor # to find face landmarks so we can precisely localize the face, and finally the # face recognition model. detector = dlib.get_frontal_face_detector() sp = dlib.shape_predictor(predictor_path) facerec = dlib.face_recognition_model_v1(face_rec_model_path) win = dlib.image_window() # Now process all the images for f in glob.glob(os.path.join(faces_folder_path, "*.jpg")): print("Processing file: {}".format(f)) img = io.imread(f) win.clear_overlay() win.set_image(img) # Ask the detector to find the bounding boxes of each face. The 1 in the # second argument indicates that we should upsample the image 1 time. This # will make everything bigger and allow us to detect more faces. dets = detector(img, 1) print("Number of faces detected: {}".format(len(dets))) # Now process each face we found. for k, d in enumerate(dets): print("Detection {}: Left: {} Top: {} Right: {} Bottom: {}".format( k, d.left(), d.top(), d.right(), d.bottom())) # Get the landmarks/parts for the face in box d. shape = sp(img, d) # Draw the face landmarks on the screen so we can see what face is currently being processed. win.clear_overlay() win.add_overlay(d) win.add_overlay(shape) # Compute the 128D vector that describes the face in img identified by # shape. In general, if two face descriptor vectors have a Euclidean # distance between them less than 0.6 then they are from the same # person, otherwise they are from different people. Here we just print # the vector to the screen. face_descriptor = facerec.compute_face_descriptor(img, shape) print(face_descriptor) # It should also be noted that you can also call this function like this: # face_descriptor = facerec.compute_face_descriptor(img, shape, 100) # The version of the call without the 100 gets 99.13% accuracy on LFW # while the version with 100 gets 99.38%. However, the 100 makes the # call 100x slower to execute, so choose whatever version you like. To # explain a little, the 3rd argument tells the code how many times to # jitter/resample the image. When you set it to 100 it executes the # face descriptor extraction 100 times on slightly modified versions of # the face and returns the average result. You could also pick a more # middle value, such as 10, which is only 10x slower but still gets an # LFW accuracy of 99.3%. dlib.hit_enter_to_continue()
吐槽:
dlib的确很方便,不用花多少时间就能自己做到一些目标功能。官方文档讲的很详细,很容易入门。看这个文档(dlib python api)差不多就能学会用了。导师已经安排了研究生阶段的学习任务了,后面也要忙起来了。dlib的学习虽然是我10月份才开的坑,为了善始善终我也要尽快整理完这些东西。以后要回到”泡馆”生活了。
ヽ(・ω・。)ノ
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