-
Notifications
You must be signed in to change notification settings - Fork 0
/
Copy pathcreate_dataset.py
77 lines (70 loc) · 3.05 KB
/
create_dataset.py
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
# -*-coding: utf-8 -*-
"""
@Project: faceRecognition
@File : create_dataset.py
@Author : panjq
@E-mail : [email protected]
@Date : 2018-12-07 11:31:09
"""
import numpy as np
from utils import image_processing , file_processing
import face_recognition
import cv2
import os
resize_width = 160
resize_height = 160
def create_face(images_dir, out_face_dir):
'''
生成人脸数据图库，保存在out_face_dir中，这些数据库将用于生成embedding数据库
:param images_dir:
:param out_face_dir:
:return:
'''
# image_list=file_processing.get_files_list(images_dir, postfix='jpg')
image_list,names_list=file_processing.gen_files_labels(images_dir,postfix='jpg')
face_detect=face_recognition.Facedetection()
for image_path ,name in zip(image_list,names_list):
image=image_processing.read_image(image_path, resize_height=0, resize_width=0, normalization=False)
# 获取 判断标识 bounding_box crop_image
bounding_box, points = face_detect.detect_face(image)
bounding_box = bounding_box[:,0:4].astype(int)
bounding_box=bounding_box[0,:]
print("face box:{}".format(bounding_box))
face_image = image_processing.crop_image(image,bounding_box)
# image_processing.show_image("face", face_image)
# image_processing.show_image_box("face",image,bounding_box)
out_path=os.path.join(out_face_dir,name)
face_image=image_processing.resize_image(face_image, resize_height, resize_width)
if not os.path.exists(out_path):
os.mkdir(out_path)
basename=os.path.basename(image_path)
out_path=os.path.join(out_path,basename)
image_processing.save_image(out_path,face_image)
# cv2.waitKey(0)
def create_embedding(model_path, emb_face_dir, out_emb_path, out_filename):
'''
产生embedding数据库，保存在out_data_path中，这些embedding其实就是人脸的特征
:param model_path:
:param emb_face_dir:
:param out_emb_path:
:param out_filename:
:return:
'''
face_net = face_recognition.facenetEmbedding(model_path)
# image_list=file_processing.get_files_list(emb_face_dir,postfix='jpg')
image_list,names_list=file_processing.gen_files_labels(emb_face_dir,postfix='jpg')
images= image_processing.get_images(image_list,resize_height,resize_width,whiten=True)
compare_emb = face_net.get_embedding(images)
np.save(out_emb_path, compare_emb)
# 可以选择保存image_list或者names_list作为人脸的标签
# 测试时建议保存image_list，这样方便知道被检测人脸与哪一张图片相似
file_processing.write_data(out_filename, image_list, model='w')
if __name__ == '__main__':
images_dir='dataset/images'
out_face_dir='dataset/emb_face'
create_face(images_dir,out_face_dir)
model_path = 'models/20180408-102900.pb'
emb_face_dir = './dataset/emb_face'
out_emb_path = 'dataset/emb/faceEmbedding.npy'
out_filename = 'dataset/emb/name.txt'
create_embedding(model_path, emb_face_dir, out_emb_path, out_filename)