1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
| import net
import torch
import os
from face_alignment import align
import numpy as np
import pandas as pd
adaface_models = {'ir_101': "pretrained/adaface_ir101_webface12m.ckpt",}
def load_pretrained_model(architecture='ir_101'):
assert architecture in adaface_models.keys()
model = net.build_model(architecture)
statedict = torch.load(
adaface_models[architecture], map_location=torch.device('cpu'))['state_dict']
model_statedict = {key[6:]: val for key,
val in statedict.items() if key.startswith('model.')}
model.load_state_dict(model_statedict)
model.eval()
return model
def to_input(pil_rgb_image):
tensor = None
try:
np_img = np.array(pil_rgb_image)
brg_img = ((np_img[:, :, ::-1] / 255.) - 0.5) / 0.5
tensor = torch.tensor([brg_img.transpose(2, 0,1)]).float()
except Exception :
return tensor
return tensor
def read_vector_pkl(db_path, adaface_model_name):
"""
@Time : 2023/06/16 12:10:47
@Author : liruilonger@gmail.com
@Version : 1.0
@Desc : 读取特征向量文件
Args:
Returns:
df
"""
import pickle
file_name = f"representations_adaface_{adaface_model_name}.pkl"
file_name = file_name.replace("-", "_").lower()
with open(f"{db_path}/{file_name}", "rb") as f:
representations = pickle.load(f)
df = pd.DataFrame(representations, columns=["identity", f"{adaface_model_name}_representation"])
return df
def build_vector_pkl(db_path, adaface_model_name='adaface_model'):
"""
@Time : 2023/06/16 11:40:23
@Author : liruilonger@gmail.com
@Version : 1.0
@Desc : 构建特征向量文件
Args:
Returns:
void
"""
import time
from os import path
from tqdm import tqdm
import pickle
if os.path.isdir(db_path) is not True:
raise ValueError("Passed db_path does not exist!")
file_name = f"representations_adaface_{adaface_model_name}.pkl"
file_name = file_name.replace("-", "_").lower()
if path.exists(db_path + "/" + file_name):
pass
else:
employees = []
for r, _, f in os.walk(db_path):
for file in f:
if (
(".jpg" in file.lower())
or (".jpeg" in file.lower())
or (".png" in file.lower())
):
exact_path = r + "/" + file
employees.append(exact_path)
if len(employees) == 0:
raise ValueError(
"没有任何图像在 ",
db_path,
" 文件夹! 验证此路径中是否存在.jpg或.png文件。",
)
representations = []
pbar = tqdm(
range(0, len(employees)),
desc="生成向量特征文件中：😍😊🔬🔬🔬⚒️⚒️⚒️🎢🎢🎢🎢🎢",
)
for index in pbar:
employee = employees[index]
img_representation = get_represent(employee)
instance = []
instance.append(employee)
instance.append(img_representation)
representations.append(instance)
with open(f"{db_path}/{file_name}", "wb") as f:
pickle.dump(representations, f)
def get_represent(path):
"""
@Time : 2023/06/16 11:54:11
@Author : liruilonger@gmail.com
@Version : 1.0
@Desc : 获取脸部特征向量
Args:
Returns:
void
"""
feature = None
aligned_rgb_img = align.get_aligned_face(path)
bgr_tensor_input = to_input(aligned_rgb_img)
if bgr_tensor_input is not None:
feature, _ = model(bgr_tensor_input)
else:
print(f"无法提取脸部特征向量{path}")
return feature
def findCosineDistance(source_representation, test_representation):
"""
@Time : 2023/06/16 12:19:27
@Author : liruilonger@gmail.com
@Version : 1.0
@Desc : 计算两个向量之间的余弦相似度得分
Args:
Returns:
void
"""
import torch.nn.functional as F
return F.cosine_similarity(source_representation, test_representation)
def demo1():
model_name = "test"
build_vector_pkl(test_image_path,model_name)
df = read_vector_pkl(test_image_path, model_name)
for index, instance in df.iterrows():
source_representation = instance[f"{model_name}_representation"]
print(source_representation)
features.append(source_representation)
similarity_scores = torch.cat(features) @ torch.cat(features).T
print(similarity_scores)
def find(test_image_path,threshold=0.5):
"""
@Time : 2023/06/16 14:02:52
@Author : liruilonger@gmail.com
@Version : 1.0
@Desc : 根据图片在人脸库比对找人
Args:
Returns:
void
"""
test_representation = get_represent(test_image_path)
if test_representation is not None:
reset = {}
for index, instance in df.iterrows():
source_representation = instance[f"{model_name}_representation"]
ten = findCosineDistance(source_representation,test_representation)
reset[ten.item()]= instance["identity"]
cosine_similarity = max(reset.keys())
print(f"💝💝💝💝💝💝💝💝💝💝 {cosine_similarity} 💝💝💝💝💝{threshold}")
return cosine_similarity > threshold ,reset[cosine_similarity]
else:
return False,0
def marge(m1,m2):
from PIL import Image
import uuid
image1 = Image.open(m1)
image2 = Image.open(m2)
width1, height1 = image1.size
width2, height2 = image2.size
new_image = Image.new("RGB", (width1 + width2, max(height1, height2)))
new_image.paste(image1, (0, 0))
new_image.paste(image2, (width1, 0))
new_image.save(str(uuid.uuid4()).replace('-', '')+"new_image.jpg")
if __name__ == '__main__':
import imutils
from imutils import paths
import cv2
import uuid
model = load_pretrained_model('ir_101')
test_image_path = 'face_alignment/ser'
features = set()
model_name = "test_img"
build_vector_pkl("face_alignment/test",model_name)
df = read_vector_pkl("face_alignment/test", model_name)
for path in paths.list_images(test_image_path):
b, r = find(path,0.25)
if b:
if r not in features:
features.add(r)
marge(r,path)
else:
img = cv2.imread(path)
cv2.imwrite('__not' + str(uuid.uuid4()).replace('-', '')+".jpg", img)
|
