-
Notifications
You must be signed in to change notification settings - Fork 1
/
Copy pathGenEmbeddingCVImage.py
147 lines (140 loc) · 5.78 KB
/
GenEmbeddingCVImage.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
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
import argparse
import fnmatch
import os
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
import pickle
import time
import warnings
warnings.filterwarnings("ignore", category=UserWarning)
import sys
import numpy as np
# import onnxruntime as ort
# import onnx
import torch
# from onnx_tf.backend import prepare
# import tensorflow as tf
from torchvision.transforms import functional as F
# from torch.utils.model_zoo import load_url
# from torch.utils.tensorboard import SummaryWriter
# from torchvision import transforms
import pickle
from tqdm import tqdm
import cv2
sys.path.append('/media/anlab/data-2tb/ANLAB_THUY/ImageSearcher/SOLAR/')
from solar_global.networks.imageretrievalnet import init_network, extract_vectors
from solar_global.datasets.testdataset import configdataset
from solar_global.utils.download import download_test
from solar_global.utils.evaluate import compute_map_and_print
from solar_global.utils.general import get_data_root, htime
from solar_global.utils.networks import load_network
from solar_global.utils.plots import plot_ranks, plot_embeddings
from torchvision import transforms
import time
# from onnx_coreml import convert
# import coremltools
# import coremltools as ct
# from cirtorch.datasets.datahelpers import im_resize
import torch.nn as nn
# class Resize_ratio():
# def __init__(self, imsize):
# self.imsize = imsize
# def __call__(self, image):
# image = im_resize(image, self.imsize)
# return image
import math
from numpy import dot
from numpy.linalg import norm
def cosine_similarity(list1, list2):
return dot(list1, list2) / (norm(list1) * norm(list2))
def cosine_distance(list1, list2):
return 1 - cosine_similarity(list1, list2)
return math.sqrt(sum([(a - b) ** 2 for a, b in zip(list1, list2)]))
class Network(nn.Module):
def __init__(self, model):
super().__init__()
self.model = model.cpu()
self.mean = torch.tensor([0.485, 0.456, 0.406]).view(3, 1, 1)
self.std = torch.tensor([0.229, 0.224, 0.225]).view(3, 1, 1)
def forward(self,x):
x1 = F.resize(x, (480, 480))
out1 = self.model(x1)
reshaped_tensor1 = out1.view(1, 2048)
# x2 = F.resize(x, (300, 300))
# out2 = self.model(x2)
# reshaped_tensor2 = out2.view(1, 2048)
return reshaped_tensor1
def calculate_resized_dimensions(image, length_ratio):
""" Calculate the new dimensions of the image based on the length ratio. """
height, width = image.shape[:2]
if width > height:
new_width = int(length_ratio)
new_height = int((length_ratio / width) * height)
else:
new_height = int(length_ratio)
new_width = int((length_ratio / height) * width)
return new_width, new_height
def square_images(images, image_size=500):
h, w = images.shape[:2]
max_wh = max(h,w)
if max_wh != image_size:
if h > w:
images = cv2.resize(images, (int(w * image_size / h), image_size))
else:
images = cv2.resize(images, (image_size,int(h*image_size / w)))
tensors = np.zeros(( image_size, image_size,3))
h, w, c = images.shape
pad_top = int((image_size - h)/2)
pad_left = int((image_size - w)/2)
tensors[ pad_top:pad_top + h, pad_left: pad_left + w,:] = images
return tensors
def get_embedding(imgpath,model):
img = cv2.imread(imgpath)
img = cv2.cvtColor(img,cv2.COLOR_BGR2RGB)
# newsize = calculate_resized_dimensions(img,500)
# x = cv2.resize(img, (newsize[0], newsize[1]))
# tensor_img = torch.from_numpy(x).float()
# scale = 1/(0.226*255.0)
# bias = [- 0.485/(0.229) , - 0.456/(0.224), - 0.406/(0.225)]
img = square_images(img,500)
tensor_img = torch.from_numpy(img).float()
tensor_img = tensor_img.unsqueeze(0)
tensor_img = tensor_img.permute(0, 3, 1, 2)
tensor_img = tensor_img/255
# bias_tensor = torch.tensor(bias).view(1, 3, 1, 1)
# normalized_tensor = tensor_img * scale + bias_tensor
torch_out = model(tensor_img)
return torch_out.cpu().squeeze().detach().numpy()
state = torch.load(os.path.join(get_data_root(), 'networks/model_best.pth.tar'))
net_params = {}
net_params['architecture'] = state['meta']['architecture']
net_params['pooling'] = state['meta']['pooling']
net_params['local_whitening'] = state['meta'].get('local_whitening', False)
net_params['regional'] = state['meta'].get('regional', False)
net_params['whitening'] = state['meta'].get('whitening', True)
net_params['mean'] = state['meta']['mean']
net_params['std'] = state['meta']['std']
net_params['pretrained'] = False
net = load_network('model_best.pth.tar')
net.load_state_dict(state['state_dict'])
net.cuda()
net.eval()
test_model = Network(net)
test_model.eval()
dictResult = []
listEmb = []
listLabel = []
listFilename = []
id = 0
rootfolder = '/media/anlab/data-2tb/ANLAB_THUY/CVSurvey/FAKE_REAL_SNAKER_SURVEY/Data/FakeSneakers(Soda)/CharacterData/AugmentData/Augment1/Val/'
for foldername in tqdm(os.listdir(rootfolder)):
for filename in tqdm(os.listdir(rootfolder+foldername)):
listFilename.append(filename)
if foldername == 'Auth':
listLabel.append(1)
else:
listLabel.append(0)
emb = get_embedding(os.path.join(rootfolder,foldername,filename),test_model)
listEmb.append(emb)
pickle.dump(np.array(listEmb), open('/media/anlab/data-2tb/ANLAB_THUY/CVSurvey/FAKE_REAL_SNAKER_SURVEY/Data/FakeSneakers(Soda)/CharacterData/AugmentData/Augment1/ValData.pickle', 'wb'))
pickle.dump(np.array(listLabel), open('/media/anlab/data-2tb/ANLAB_THUY/CVSurvey/FAKE_REAL_SNAKER_SURVEY/Data/FakeSneakers(Soda)/CharacterData/AugmentData/Augment1/ValLabel.pickle', 'wb'))
pickle.dump(np.array(listFilename), open('/media/anlab/data-2tb/ANLAB_THUY/CVSurvey/FAKE_REAL_SNAKER_SURVEY/Data/FakeSneakers(Soda)/CharacterData/AugmentData/Augment1/ValFilename.pickle', 'wb'))