python dlib瀛︿範锛堜簲锛夛細姣斿浜鸿劯

Posted 2021-03-23

鏍囩锛?a href='http://www.mamicode.com/so/1/extract' title='extract'>extract   final   宸ヤ綔   _for   mod   create   set   ott   绋嬪簭   

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distance=(x1−x2)2+(y1−y2)2−−−−−−−−−−−−−−−−−−√
distance=(x1−x2)2+(y1−y2)2

涓夌淮鎯呭喌涓嬶細
distance=(x1−x2)2+(y1−y2)2+(z1−z2)2−−−−−−−−−−−−−−−−−−−−−−−−−−−−√
distance=(x1−x2)2+(y1−y2)2+(z1−z2)2

灏嗗叾鎵╁睍鍒?28缁寸殑鎯呭喌涓嬪嵆鍙€?br />閫氬父浣跨敤鐨勫垽鍒槇鍊兼槸0.6锛屽嵆濡傛灉涓や釜浜鸿劯鐨勫悜閲忕┖闂寸殑娆у紡璺濈瓒呰繃浜?.6锛屽嵆璁ゅ畾涓嶆槸鍚屼竴涓汉锛涘鏋滄姘忚窛绂诲皬浜?.6锛屽垯璁や负鏄悓涓€涓汉銆傝繖涓窛绂讳篃鍙互鐢辫嚜宸卞畾锛屽彧瑕佹晥鏋滆兘鏇村ソ銆?br />瀹為獙涓娇鐢ㄤ簡涓や釜妯″瀷锛?/p>

shape_predictor_68_face_landmarks.dat锛?br />http://dlib.net/files/shape_predictor_68_face_landmarks.dat.bz2

dlib_face_recognition_resnet_model_v1.dat锛?br />http://dlib.net/files/dlib_face_recognition_resnet_model_v1.dat.bz2

鏂囦欢澶圭洰褰曪細

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瀹屾暣宸ョ▼涓嬭浇閾炬帴锛?br />http://pan.baidu.com/s/1boCDZ7T

绋嬪簭1
涓嶈搴熻瘽浜嗭紝鐩存帴涓婁唬鐮併€?/p>

# -*- coding: utf-8 -*-
import sys
import dlib
import cv2
import os
import glob

current_path = os.getcwd() # 鑾峰彇褰撳墠璺緞
# 妯″瀷璺緞
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"
#娴嬭瘯鍥剧墖璺緞
faces_folder_path = current_path + "\faces\"

# 璇诲叆妯″瀷
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 璇诲彇鍥剧墖锛屽苟鏄剧ず
img = cv2.imread(img_path, cv2.IMREAD_COLOR)
# opencv鐨刡gr鏍煎紡鍥剧墖杞崲鎴恟gb鏍煎紡
b, g, r = cv2.split(img)
img2 = cv2.merge([r, g, b])

dets = detector(img, 1) # 浜鸿劯鏍囧畾
print("Number of faces detected: {}".format(len(dets)))

for index, face in enumerate(dets):
print(鈥榝ace {}; left {}; top {}; right {}; bottom {}鈥?format(index, face.left(), face.top(), face.right(), face.bottom()))

shape = shape_predictor(img2, face) # 鎻愬彇68涓壒寰佺偣
for i, pt in enumerate(shape.parts()):
#print(鈥楶art {}: {}鈥?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) # 璁＄畻浜鸿劯鐨?28缁寸殑鍚戦噺
print(face_descriptor)

k = cv2.waitKey(0)
cv2.destroyAllWindows()
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绋嬪簭1缁撴灉

閮ㄥ垎鎵撳嵃缁撴灉锛?/p>

F:Pythonmy_dlib_codesface_recognition>python my_face_recogniton.py
Processing file: F:Pythonmy_dlib_codesface_recognitionfacesjobs.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
....
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绋嬪簭2
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# -*- 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鈥榮 the same person"
else:
print "It鈥榮 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 + 鈥?npy鈥?br /> vectors = np.array([])
for i, num in enumerate(face_descriptor):
vectors = np.append(vectors, num)
# print(num)
print(鈥楽aving files to :鈥?filePath)
np.save(filePath, vectors)
return vectors

def loadPersonData(face_rec_class, personName):
if personName == None:
return
filePath = face_rec_class.dataPath + personName + 鈥?npy鈥?br /> vectors = np.load(filePath)
print(vectors)
return vectors

class face_recognition(object):
def __init__(self):
self.current_path = os.getcwd() # 鑾峰彇褰撳墠璺緞
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=鈥榩eople鈥? img_path=None):
if img_path == None:
print(鈥楴o file! 鈥?
return

# img_name += self.faces_folder_path + img_name
self.name = name
self.img_bgr = cv2.imread(self.current_path+img_path)
# opencv鐨刡gr鏍煎紡鍥剧墖杞崲鎴恟gb鏍煎紡
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(鈥榝ace {}; left {}; top {}; right {}; bottom {}鈥?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

 

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绋嬪簭2缁撴灉
娴嬭瘯浠ｇ爜1锛?/p>

import face_rec as fc
face_rec = fc.face_recognition() # 鍒涘缓瀵硅薄
face_rec.inputPerson(name=鈥榡obs鈥? img_path=鈥榎faces\jobs.jpg鈥? # name涓啓绗竴涓汉鍚嶅瓧锛宨mg_name涓哄浘鐗囧悕瀛楋紝娉ㄦ剰瑕佹斁鍦╢aces鏂囦欢澶逛腑
vector = face_rec.create128DVectorSpace() # 鎻愬彇128缁村悜閲忥紝鏄痙lib.vector绫荤殑瀵硅薄
person_data1 = fc.savePersonData(face_rec, vector ) # 灏嗘彁鍙栧嚭鐨勬暟鎹繚瀛樺埌data鏂囦欢澶癸紝涓轰究浜庢搷浣滆繑鍥瀗umpy鏁扮粍锛屽唴瀹硅繕鏄竴鏍风殑

# 瀵煎叆绗簩寮犲浘鐗囷紝骞舵彁鍙栫壒寰佸悜閲?br />face_rec.inputPerson(name=鈥榡obs2鈥? img_path=鈥榎faces\jobs2.jpg鈥?
vector = face_rec.create128DVectorSpace() # 鎻愬彇128缁村悜閲忥紝鏄痙lib.vector绫荤殑瀵硅薄
person_data2 = fc.savePersonData(face_rec, vector )

# 璁＄畻娆у紡璺濈锛屽垽鏂槸鍚︽槸鍚屼竴涓汉
fc.comparePersonData(person_data1, person_data2)
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濡傛灉data鏂囦欢澶逛腑宸茬粡鏈変簡妯″瀷鏂囦欢锛屽彲浠ョ洿鎺ュ鍏ワ細

import face_rec as fc
face_rec = fc.face_recognition() # 鍒涘缓瀵硅薄
person_data1 = fc.loadPersonData(face_rec , 鈥榡obs鈥? # 鍒涘缓涓€涓被淇濆瓨鐩稿叧淇℃伅锛屽悗闈㈣繕瑕佽窡涓婁汉鍚嶏紝绋嬪簭浼氬湪data鏂囦欢涓煡鎵惧搴攏py鏂囦欢锛屾瘮濡傝繖閲屽氨鏄€榡obs.npy鈥?br />person_data2 = fc.loadPersonData(face_rec , 鈥榡obs2鈥? # 瀵煎叆绗簩寮犲浘鐗?br />fc.comparePersonData(person_data1, person_data2) # 璁＄畻娆у紡璺濈锛屽垽鏂槸鍚︽槸鍚屼竴涓汉
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绋嬪簭2缁撴灉
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=鈥榡obs鈥? img_path=鈥榎faces\jobs.jpg鈥?
>>> 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:Pythonmy_dlib_codesface_recognitiondatajobs.npy
>>> face_rec.inputPerson(name=鈥榡obs2鈥? img_path=鈥榎faces\jobs2.jpg鈥?
>>> 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:Pythonmy_dlib_codesface_recognitiondatajobs2.npy
>>> fc.comparePersonData(person_data1, person_data2)
0.490491048429
It鈥榮 the same person
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瀹樻柟渚嬬▼
#!/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鈥榮 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鈥榮 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: "
" ./face_recognition.py shape_predictor_68_face_landmarks.dat dlib_face_recognition_resnet_model_v1.dat ../examples/faces "
"You can download a trained facial shape predictor and recognition model from: "
" http://dlib.net/files/shape_predictor_68_face_landmarks.dat.bz2 "
" 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()
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鍘熸枃閾炬帴锛歨ttps://blog.csdn.net/hongbin_xu/article/details/78390982