DeepFace 用于提取图像的矢量信息
DeepFace for extracting vector information of an image
为了使用 Deepface 进行人脸识别，我正在尝试提取图像的矢量信息以存储在数据库中。这样下次为了匹配，我将提取新图像的矢量信息并查看 db 。如果搜索有结果，那么它是一个匹配项。我使用了 DeepFace 的验证方法，但它在 2 个图像之间进行比较并返回：
from deepface import DeepFace
import os
detected_face = DeepFace.detectFace("sly.jpg")
print (detected_face)
这是上面的输出：
result = DeepFace.verify("sly1.jpg","sly2.jpg");
为此我得到：
Using VGG-Face model backend and cosine distance.
{'verified': True, 'distance': 1.1920928955078125e-07, 'max_threshold_to_verify': 0.4, 'model': 'VGG-Face', 'similarity_metric': 'cosine'}
这是比对结果，但我只需要一张图像的信息而不用比对，因为当测试一张新面孔时，我将有很多记录要搜索（矢量信息）。任何帮助将不胜感激。
我假设它是 this 同名的 repo，并安装了 setup.py 或 pip install deepface。
我在 google colab 上对此进行了测试。要在本地使用，请使用 cv2.imshow(...) 而不是 cv2_imshow(...)。
正在下载测试图片
!wget "http://*.jpg" -O "1.jpg"
!wget "https://*.jpg" -O "2.jpg"
查看图片
import cv2
from google.colab.patches import cv2_imshow
im1 = cv2.imread("1.jpg")
#cv2.imshow("img", im1)
cv2_imshow(im1)
人脸检测
DeepFace.detectFace returns 标准化裁剪面的输出。对于 mtcnn，我得到了形状 (224, 224, 3) 的图像。您可以使用
验证和查看图像
from deepface import DeepFace
import cv2
from google.colab.patches import cv2_imshow
#backends = ['opencv', 'ssd', 'dlib', 'mtcnn']
backends = ['mtcnn']
for backend in backends:
#face detection and alignment
detected_face = DeepFace.detectFace("1.jpg", detector_backend = backend)
print(detected_face)
print(detected_face.shape)
im = cv2.cvtColor(detected_face * 255, cv2.COLOR_BGR2RGB)
#cv2.imshow("image", im)
cv2_imshow(im)
输出
[[[0.12156863 0.05882353 0.02352941]
[0.2901961 0.18039216 0.1254902 ]
[0.3137255 0.20392157 0.14901961]
...
[0.06666667 0.01176471 0.01176471]
[0.05882353 0.01176471 0.00784314]
[0.03921569 0.00784314 0.00392157]]
[[0.26666668 0.2 0.16470589]
[0.19215687 0.08235294 0.02745098]
[0.33333334 0.22352941 0.16862746]
...
[0.03921569 0.00392157 0.00392157]
[0.04313726 0.00784314 0.00784314]
[0.04313726 0. 0.00392157]]
[[0.11764706 0.05098039 0.01568628]
[0.21176471 0.10588235 0.05882353]
[0.44313726 0.3372549 0.27058825]
...
[0.02352941 0.00392157 0. ]
[0.02352941 0.00392157 0. ]
[0.02745098 0. 0. ]]
...
[[0.24313726 0.1882353 0.13725491]
[0.24313726 0.18431373 0.13725491]
[0.22745098 0.16470589 0.11372549]
...
[0.654902 0.69803923 0.78431374]
[0.62352943 0.67058825 0.7529412 ]
[0.38431373 0.4117647 0.45882353]]
[[0.23529412 0.18039216 0.12941177]
[0.22352941 0.16862746 0.11764706]
[0.22745098 0.16470589 0.11764706]
...
[0.6392157 0.69803923 0.78039217]
[0.6156863 0.6745098 0.75686276]
[0.36862746 0.40392157 0.4627451 ]]
[[0.21568628 0.16862746 0.10980392]
[0.2 0.15294118 0.09803922]
[0.20784314 0.14901961 0.10196079]
...
[0.6313726 0.6901961 0.77254903]
[0.6039216 0.6627451 0.74509805]
[0.36078432 0.39607844 0.4509804 ]]]
(224, 224, 3)
人脸嵌入
因为您正在寻找嵌入向量，您可以在下面找到它。它是 verify 函数的修改版本。我保留了两张图片、距离计算、验证的选项，但你可以修改它只为一张脸生成人脸嵌入。我没有删除任何未使用的导入。
"""
Modified verify function for face embedding generation
backends = ['opencv', 'ssd', 'dlib', 'mtcnn']
"""
from keras.preprocessing import image
import warnings
warnings.filterwarnings("ignore")
import time
import os
from os import path
from pathlib import Path
import gdown
import numpy as np
import pandas as pd
from tqdm import tqdm
import json
import cv2
from keras import backend as K
import keras
import tensorflow as tf
import pickle
from deepface import DeepFace
from deepface.basemodels import VGGFace, OpenFace, Facenet, FbDeepFace, DeepID
from deepface.extendedmodels import Age, Gender, Race, Emotion
from deepface.commons import functions, realtime, distance as dst
def FaceEmbeddingAndDistance(img1_path, img2_path = '', model_name ='Facenet', distance_metric = 'cosine', model = None, enforce_detection = True, detector_backend = 'mtcnn'):
#--------------------------------
#ensemble learning disabled.
if model == None:
if model_name == 'VGG-Face':
print("Using VGG-Face model backend and", distance_metric,"distance.")
model = VGGFace.loadModel()
elif model_name == 'OpenFace':
print("Using OpenFace model backend", distance_metric,"distance.")
model = OpenFace.loadModel()
elif model_name == 'Facenet':
print("Using Facenet model backend", distance_metric,"distance.")
model = Facenet.loadModel()
elif model_name == 'DeepFace':
print("Using FB DeepFace model backend", distance_metric,"distance.")
model = FbDeepFace.loadModel()
elif model_name == 'DeepID':
print("Using DeepID2 model backend", distance_metric,"distance.")
model = DeepID.loadModel()
elif model_name == 'Dlib':
print("Using Dlib ResNet model backend", distance_metric,"distance.")
from deepface.basemodels.DlibResNet import DlibResNet #this is not a must because it is very huge.
model = DlibResNet()
else:
raise ValueError("Invalid model_name passed - ", model_name)
else: #model != None
print("Already built model is passed")
#------------------------------
#face recognition models have different size of inputs
#my environment returns (None, 224, 224, 3) but some people mentioned that they got [(None, 224, 224, 3)]. I think this is because of version issue.
if model_name == 'Dlib': #this is not a regular keras model
input_shape = (150, 150, 3)
else: #keras based models
input_shape = model.layers[0].input_shape
if type(input_shape) == list:
input_shape = input_shape[0][1:3]
else:
input_shape = input_shape[1:3]
input_shape_x = input_shape[0]
input_shape_y = input_shape[1]
#------------------------------
#tuned thresholds for model and metric pair
threshold = functions.findThreshold(model_name, distance_metric)
#------------------------------
#----------------------
#crop and align faces
img1 = functions.preprocess_face(img=img1_path, target_size=(input_shape_y, input_shape_x), enforce_detection = enforce_detection, detector_backend = detector_backend)
img2 = functions.preprocess_face(img=img2_path, target_size=(input_shape_y, input_shape_x), enforce_detection = enforce_detection, detector_backend = detector_backend)
#----------------------
#find embeddings
img1_representation = model.predict(img1)[0,:]
img2_representation = model.predict(img2)[0,:]
print("FACE 1 Embedding:")
print(img1_representation)
print("FACE 2 Embedding:")
print(img2_representation)
#----------------------
#find distances between embeddings
if distance_metric == 'cosine':
distance = dst.findCosineDistance(img1_representation, img2_representation)
elif distance_metric == 'euclidean':
distance = dst.findEuclideanDistance(img1_representation, img2_representation)
elif distance_metric == 'euclidean_l2':
distance = dst.findEuclideanDistance(dst.l2_normalize(img1_representation), dst.l2_normalize(img2_representation))
else:
raise ValueError("Invalid distance_metric passed - ", distance_metric)
print("DISTANCE")
print(distance)
#----------------------
#decision
if distance <= threshold:
identified = "true"
else:
identified = "false"
print("IDENTIFIED")
print(identified)
以上函数是通过
调用的
FaceEmbeddingAndDistance("1.jpg", "2.jpg", model_name='Facenet', detector_backend = 'mtcnn')
输出
FACE 1 Embedding:
[-0.7229302 -1.766835 -1.5399052 0.59634393 1.203212 -1.693247
-0.90845925 0.5264039 2.148173 -0.9786542 -0.00369854 -1.2710322
-1.5515596 -0.4111185 -0.36896533 -0.30051672 0.35091963 0.5073533
-1.7270111 -0.5230838 0.3376239 -1.0811361 1.5242224 -0.6137103
-1.3100258 0.80050004 -0.7087368 -0.64483845 1.0830203 2.6056807
-0.76527536 -0.83047277 -0.7335422 -0.01964059 -0.86749244 2.9645889
-2.426583 -0.11157394 -2.3535717 -0.65058017 0.30864614 -0.77746457
-0.6233895 0.44898677 2.5578005 -0.583796 0.8406945 1.1105415
-1.652044 -0.6351479 0.07651432 -1.0454555 -1.8752071 0.50948805
-1.6050931 -1.1769634 -0.02965304 1.5107706 0.83292925 -0.5382068
-1.5981512 -0.6405941 0.5521577 0.22957848 0.506649 0.24680384
-0.91464925 -0.18441322 -0.6801975 -1.0448433 0.52288735 -0.79405725
0.5974493 -0.40668172 -0.00640235 -0.742475 0.1928863 0.31236258
-0.37383577 -1.5883486 -1.5336255 -0.74254227 -0.8524561 -1.4625055
-2.718953 -0.7180952 -1.2140683 -0.5232462 1.2576898 -1.1097553
2.3971314 0.8855096 -0.16556528 -0.07307663 -1.8778017 0.8690948
-0.39043528 -0.5494097 -2.2382076 0.7101087 0.15859437 0.2959841
0.8605075 -0.2040207 0.77952844 0.04542177 0.92514265 -1.988945
0.9418363 1.6509243 -0.20324889 0.2974357 0.37681833 1.095943
1.6308782 -1.2553837 -0.10246387 -1.4697052 -0.5832107 -0.34192032
-1.1347024 1.5154309 -0.00527111 -1.165709 -0.7296148 -0.20767921
1.2530949 -0.9487353 ]
FACE 2 Embedding:
[ 0.9399996 1.3996615 -1.2931366 0.6869738 -0.03219241 0.96111965
0.7378809 -0.24804354 -0.8128112 0.19901593 0.48911542 -0.91603553
-1.1671298 0.88576627 0.25427592 1.1395477 0.45400882 -1.4845027
-0.90582514 -1.1371222 0.47669724 1.2933927 1.4533392 -0.46943524
0.10245587 -1.4916894 -2.3223586 -0.10979578 1.7803721 1.0051152
-0.09164213 -0.64848715 -1.4191641 1.811776 0.73174113 0.2582223
-0.26430857 1.7021953 -1.0571098 -1.1215096 0.3606074 1.5136883
-0.30045512 0.26225814 -0.19101554 1.269355 1.0674374 -0.2550623
-1.0582973 1.7474637 -1.7739134 -0.67914337 -0.1877765 1.1581128
-2.281225 1.3955555 -1.2690883 -0.16299461 1.337664 -0.8831901
-0.6862674 2.0526903 -0.6325836 1.333468 -0.10851342 -0.64831966
-1.0277263 1.4572504 -0.29905424 -0.33187118 -0.54727656 1.1528811
0.12454037 -1.5835186 -0.2271783 1.3911225 1.0170195 0.5741334
-1.3088373 -0.5950714 -0.6856393 -0.910367 -2.0136826 -0.73777384
0.319223 -2.1968741 0.9673934 -0.604423 -0.08049382 -1.948634
1.88159 0.20169139 0.7295723 -1.0224706 1.2995481 -0.3402595
1.1711328 -0.64862376 0.42063504 -0.01502114 -0.7048841 1.4360497
-1.2988033 0.31773448 1.534014 0.98858756 1.3450235 -0.9417385
0.26414695 -0.01988658 0.7418235 -0.04945141 -0.44838902 1.5288658
-1.1905407 0.13961646 -0.17101136 -0.18599203 -1.9648114 0.66071814
-0.07431012 1.5870664 1.5989372 -0.21751085 0.78908855 -1.5576671
0.02266342 0.20999858]
DISTANCE
0.807837575674057
IDENTIFIED
false
在 DeepFace 0.0.41 中变得更容易了。
from deepface import DeepFace
from deepface.commons import functions
models = ['VGG-Face', 'Facenet', 'OpenFace', 'DeepFace', 'DeepID', 'Dlib']
model = DeepFace.build_model(models[0])
target_size = model.layers[0].input_shape
img1_path = "img1.jpg"
img2_path = "img2.jpg"
#detect and align
img1 = functions.preprocess_face(img1_path, target_size = target_size)
img2 = functions.preprocess_face(img2_path, target_size = target_size)
#find vector embeddings
img1_embedding = model.predict(img1)
img2_embedding = model.predict(img2)
为了使用 Deepface 进行人脸识别，我正在尝试提取图像的矢量信息以存储在数据库中。这样下次为了匹配，我将提取新图像的矢量信息并查看 db 。如果搜索有结果，那么它是一个匹配项。我使用了 DeepFace 的验证方法，但它在 2 个图像之间进行比较并返回：
from deepface import DeepFace
import os
detected_face = DeepFace.detectFace("sly.jpg")
print (detected_face)
这是上面的输出：
result = DeepFace.verify("sly1.jpg","sly2.jpg");
为此我得到：
Using VGG-Face model backend and cosine distance.
{'verified': True, 'distance': 1.1920928955078125e-07, 'max_threshold_to_verify': 0.4, 'model': 'VGG-Face', 'similarity_metric': 'cosine'}
这是比对结果，但我只需要一张图像的信息而不用比对，因为当测试一张新面孔时，我将有很多记录要搜索（矢量信息）。任何帮助将不胜感激。
我假设它是 this 同名的 repo，并安装了 setup.py 或 pip install deepface。
我在 google colab 上对此进行了测试。要在本地使用，请使用 cv2.imshow(...) 而不是 cv2_imshow(...)。
正在下载测试图片
!wget "http://*.jpg" -O "1.jpg"
!wget "https://*.jpg" -O "2.jpg"
查看图片
import cv2
from google.colab.patches import cv2_imshow
im1 = cv2.imread("1.jpg")
#cv2.imshow("img", im1)
cv2_imshow(im1)
人脸检测
DeepFace.detectFace returns 标准化裁剪面的输出。对于 mtcnn，我得到了形状 (224, 224, 3) 的图像。您可以使用
from deepface import DeepFace
import cv2
from google.colab.patches import cv2_imshow
#backends = ['opencv', 'ssd', 'dlib', 'mtcnn']
backends = ['mtcnn']
for backend in backends:
#face detection and alignment
detected_face = DeepFace.detectFace("1.jpg", detector_backend = backend)
print(detected_face)
print(detected_face.shape)
im = cv2.cvtColor(detected_face * 255, cv2.COLOR_BGR2RGB)
#cv2.imshow("image", im)
cv2_imshow(im)
输出
[[[0.12156863 0.05882353 0.02352941]
[0.2901961 0.18039216 0.1254902 ]
[0.3137255 0.20392157 0.14901961]
...
[0.06666667 0.01176471 0.01176471]
[0.05882353 0.01176471 0.00784314]
[0.03921569 0.00784314 0.00392157]]
[[0.26666668 0.2 0.16470589]
[0.19215687 0.08235294 0.02745098]
[0.33333334 0.22352941 0.16862746]
...
[0.03921569 0.00392157 0.00392157]
[0.04313726 0.00784314 0.00784314]
[0.04313726 0. 0.00392157]]
[[0.11764706 0.05098039 0.01568628]
[0.21176471 0.10588235 0.05882353]
[0.44313726 0.3372549 0.27058825]
...
[0.02352941 0.00392157 0. ]
[0.02352941 0.00392157 0. ]
[0.02745098 0. 0. ]]
...
[[0.24313726 0.1882353 0.13725491]
[0.24313726 0.18431373 0.13725491]
[0.22745098 0.16470589 0.11372549]
...
[0.654902 0.69803923 0.78431374]
[0.62352943 0.67058825 0.7529412 ]
[0.38431373 0.4117647 0.45882353]]
[[0.23529412 0.18039216 0.12941177]
[0.22352941 0.16862746 0.11764706]
[0.22745098 0.16470589 0.11764706]
...
[0.6392157 0.69803923 0.78039217]
[0.6156863 0.6745098 0.75686276]
[0.36862746 0.40392157 0.4627451 ]]
[[0.21568628 0.16862746 0.10980392]
[0.2 0.15294118 0.09803922]
[0.20784314 0.14901961 0.10196079]
...
[0.6313726 0.6901961 0.77254903]
[0.6039216 0.6627451 0.74509805]
[0.36078432 0.39607844 0.4509804 ]]]
(224, 224, 3)
人脸嵌入
因为您正在寻找嵌入向量，您可以在下面找到它。它是 verify 函数的修改版本。我保留了两张图片、距离计算、验证的选项，但你可以修改它只为一张脸生成人脸嵌入。我没有删除任何未使用的导入。
"""
Modified verify function for face embedding generation
backends = ['opencv', 'ssd', 'dlib', 'mtcnn']
"""
from keras.preprocessing import image
import warnings
warnings.filterwarnings("ignore")
import time
import os
from os import path
from pathlib import Path
import gdown
import numpy as np
import pandas as pd
from tqdm import tqdm
import json
import cv2
from keras import backend as K
import keras
import tensorflow as tf
import pickle
from deepface import DeepFace
from deepface.basemodels import VGGFace, OpenFace, Facenet, FbDeepFace, DeepID
from deepface.extendedmodels import Age, Gender, Race, Emotion
from deepface.commons import functions, realtime, distance as dst
def FaceEmbeddingAndDistance(img1_path, img2_path = '', model_name ='Facenet', distance_metric = 'cosine', model = None, enforce_detection = True, detector_backend = 'mtcnn'):
#--------------------------------
#ensemble learning disabled.
if model == None:
if model_name == 'VGG-Face':
print("Using VGG-Face model backend and", distance_metric,"distance.")
model = VGGFace.loadModel()
elif model_name == 'OpenFace':
print("Using OpenFace model backend", distance_metric,"distance.")
model = OpenFace.loadModel()
elif model_name == 'Facenet':
print("Using Facenet model backend", distance_metric,"distance.")
model = Facenet.loadModel()
elif model_name == 'DeepFace':
print("Using FB DeepFace model backend", distance_metric,"distance.")
model = FbDeepFace.loadModel()
elif model_name == 'DeepID':
print("Using DeepID2 model backend", distance_metric,"distance.")
model = DeepID.loadModel()
elif model_name == 'Dlib':
print("Using Dlib ResNet model backend", distance_metric,"distance.")
from deepface.basemodels.DlibResNet import DlibResNet #this is not a must because it is very huge.
model = DlibResNet()
else:
raise ValueError("Invalid model_name passed - ", model_name)
else: #model != None
print("Already built model is passed")
#------------------------------
#face recognition models have different size of inputs
#my environment returns (None, 224, 224, 3) but some people mentioned that they got [(None, 224, 224, 3)]. I think this is because of version issue.
if model_name == 'Dlib': #this is not a regular keras model
input_shape = (150, 150, 3)
else: #keras based models
input_shape = model.layers[0].input_shape
if type(input_shape) == list:
input_shape = input_shape[0][1:3]
else:
input_shape = input_shape[1:3]
input_shape_x = input_shape[0]
input_shape_y = input_shape[1]
#------------------------------
#tuned thresholds for model and metric pair
threshold = functions.findThreshold(model_name, distance_metric)
#------------------------------
#----------------------
#crop and align faces
img1 = functions.preprocess_face(img=img1_path, target_size=(input_shape_y, input_shape_x), enforce_detection = enforce_detection, detector_backend = detector_backend)
img2 = functions.preprocess_face(img=img2_path, target_size=(input_shape_y, input_shape_x), enforce_detection = enforce_detection, detector_backend = detector_backend)
#----------------------
#find embeddings
img1_representation = model.predict(img1)[0,:]
img2_representation = model.predict(img2)[0,:]
print("FACE 1 Embedding:")
print(img1_representation)
print("FACE 2 Embedding:")
print(img2_representation)
#----------------------
#find distances between embeddings
if distance_metric == 'cosine':
distance = dst.findCosineDistance(img1_representation, img2_representation)
elif distance_metric == 'euclidean':
distance = dst.findEuclideanDistance(img1_representation, img2_representation)
elif distance_metric == 'euclidean_l2':
distance = dst.findEuclideanDistance(dst.l2_normalize(img1_representation), dst.l2_normalize(img2_representation))
else:
raise ValueError("Invalid distance_metric passed - ", distance_metric)
print("DISTANCE")
print(distance)
#----------------------
#decision
if distance <= threshold:
identified = "true"
else:
identified = "false"
print("IDENTIFIED")
print(identified)
以上函数是通过
调用的FaceEmbeddingAndDistance("1.jpg", "2.jpg", model_name='Facenet', detector_backend = 'mtcnn')
输出
FACE 1 Embedding:
[-0.7229302 -1.766835 -1.5399052 0.59634393 1.203212 -1.693247
-0.90845925 0.5264039 2.148173 -0.9786542 -0.00369854 -1.2710322
-1.5515596 -0.4111185 -0.36896533 -0.30051672 0.35091963 0.5073533
-1.7270111 -0.5230838 0.3376239 -1.0811361 1.5242224 -0.6137103
-1.3100258 0.80050004 -0.7087368 -0.64483845 1.0830203 2.6056807
-0.76527536 -0.83047277 -0.7335422 -0.01964059 -0.86749244 2.9645889
-2.426583 -0.11157394 -2.3535717 -0.65058017 0.30864614 -0.77746457
-0.6233895 0.44898677 2.5578005 -0.583796 0.8406945 1.1105415
-1.652044 -0.6351479 0.07651432 -1.0454555 -1.8752071 0.50948805
-1.6050931 -1.1769634 -0.02965304 1.5107706 0.83292925 -0.5382068
-1.5981512 -0.6405941 0.5521577 0.22957848 0.506649 0.24680384
-0.91464925 -0.18441322 -0.6801975 -1.0448433 0.52288735 -0.79405725
0.5974493 -0.40668172 -0.00640235 -0.742475 0.1928863 0.31236258
-0.37383577 -1.5883486 -1.5336255 -0.74254227 -0.8524561 -1.4625055
-2.718953 -0.7180952 -1.2140683 -0.5232462 1.2576898 -1.1097553
2.3971314 0.8855096 -0.16556528 -0.07307663 -1.8778017 0.8690948
-0.39043528 -0.5494097 -2.2382076 0.7101087 0.15859437 0.2959841
0.8605075 -0.2040207 0.77952844 0.04542177 0.92514265 -1.988945
0.9418363 1.6509243 -0.20324889 0.2974357 0.37681833 1.095943
1.6308782 -1.2553837 -0.10246387 -1.4697052 -0.5832107 -0.34192032
-1.1347024 1.5154309 -0.00527111 -1.165709 -0.7296148 -0.20767921
1.2530949 -0.9487353 ]
FACE 2 Embedding:
[ 0.9399996 1.3996615 -1.2931366 0.6869738 -0.03219241 0.96111965
0.7378809 -0.24804354 -0.8128112 0.19901593 0.48911542 -0.91603553
-1.1671298 0.88576627 0.25427592 1.1395477 0.45400882 -1.4845027
-0.90582514 -1.1371222 0.47669724 1.2933927 1.4533392 -0.46943524
0.10245587 -1.4916894 -2.3223586 -0.10979578 1.7803721 1.0051152
-0.09164213 -0.64848715 -1.4191641 1.811776 0.73174113 0.2582223
-0.26430857 1.7021953 -1.0571098 -1.1215096 0.3606074 1.5136883
-0.30045512 0.26225814 -0.19101554 1.269355 1.0674374 -0.2550623
-1.0582973 1.7474637 -1.7739134 -0.67914337 -0.1877765 1.1581128
-2.281225 1.3955555 -1.2690883 -0.16299461 1.337664 -0.8831901
-0.6862674 2.0526903 -0.6325836 1.333468 -0.10851342 -0.64831966
-1.0277263 1.4572504 -0.29905424 -0.33187118 -0.54727656 1.1528811
0.12454037 -1.5835186 -0.2271783 1.3911225 1.0170195 0.5741334
-1.3088373 -0.5950714 -0.6856393 -0.910367 -2.0136826 -0.73777384
0.319223 -2.1968741 0.9673934 -0.604423 -0.08049382 -1.948634
1.88159 0.20169139 0.7295723 -1.0224706 1.2995481 -0.3402595
1.1711328 -0.64862376 0.42063504 -0.01502114 -0.7048841 1.4360497
-1.2988033 0.31773448 1.534014 0.98858756 1.3450235 -0.9417385
0.26414695 -0.01988658 0.7418235 -0.04945141 -0.44838902 1.5288658
-1.1905407 0.13961646 -0.17101136 -0.18599203 -1.9648114 0.66071814
-0.07431012 1.5870664 1.5989372 -0.21751085 0.78908855 -1.5576671
0.02266342 0.20999858]
DISTANCE
0.807837575674057
IDENTIFIED
false
在 DeepFace 0.0.41 中变得更容易了。
from deepface import DeepFace
from deepface.commons import functions
models = ['VGG-Face', 'Facenet', 'OpenFace', 'DeepFace', 'DeepID', 'Dlib']
model = DeepFace.build_model(models[0])
target_size = model.layers[0].input_shape
img1_path = "img1.jpg"
img2_path = "img2.jpg"
#detect and align
img1 = functions.preprocess_face(img1_path, target_size = target_size)
img2 = functions.preprocess_face(img2_path, target_size = target_size)
#find vector embeddings
img1_embedding = model.predict(img1)
img2_embedding = model.predict(img2)