feature_dataset.py
7.41 KB
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
'''
현재 사용하지 않음.
'''
# from torch.utils.data import Dataset
# from PIL import Image
# import os
# from glob import glob
# from torchvision import transforms
# from torch.utils.data.dataset import Dataset
# import torch
# import pdb
# import math
# import numpy as np
# import cv2
# class FeatureDataset(Dataset):
# def __init__(self, data_path, datatype, rescale_factor, valid):
# self.data_path = data_path
# self.datatype = datatype
# self.rescale_factor = rescale_factor
# if not os.path.exists(data_path):
# raise Exception(f"[!] {self.data_path} not existed")
# if (valid):
# self.hr_path = os.path.join(self.data_path, 'valid')
# self.hr_path = os.path.join(self.hr_path, self.datatype)
# else:
# self.hr_path = os.path.join(self.data_path, 'LR_2')
# self.hr_path = os.path.join(self.hr_path, self.datatype)
# print(self.hr_path)
# self.names = os.listdir(self.hr_path)
# self.hr_path = sorted(glob(os.path.join(self.hr_path, "*.*")))
# self.hr_imgs = []
# w, h = Image.open(self.hr_path[0]).size
# self.width = int(w / 16)
# self.height = int(h / 16)
# self.lwidth = int(self.width / self.rescale_factor) # rescale_factor만큼 크기를 줄인다.
# self.lheight = int(self.height / self.rescale_factor)
# print("lr: ({} {}), hr: ({} {})".format(self.lwidth, self.lheight, self.width, self.height))
# self.original_hr_imgs = [] #원본 250개
# print("crop features ...")
# for hr in self.hr_path: # 256개의 피쳐로 나눈다.
# hr_cropped_imgs = []
# hr_image = Image.open(hr) # .convert('RGB')\
# self.original_hr_imgs.append(np.array(hr_image).astype(float)) # 원본을 저장한다.
# for i in range(16):
# for j in range(16):
# (left, upper, right, lower) = (
# i * self.width, j * self.height, (i + 1) * self.width, (j + 1) * self.height)
# crop = hr_image.crop((left, upper, right, lower))
# hr_cropped_imgs.append(crop)
# self.hr_imgs.append(hr_cropped_imgs)
# self.final_results = [] # [250개]
# print("resize and concat features ...")
# for i in range(0, len(self.hr_imgs)):
# hr_img = self.hr_imgs[i]
# interpolated_images = []
# for img in hr_img:
# image = img.resize((self.lwidth, self.lheight), Image.BICUBIC)
# image = image.resize((self.width, self.height), Image.BICUBIC)
# interpolated_images.append(np.array(image).astype(float))
# self.final_results.append(concatFeatures(interpolated_images, self.names[i], self.datatype))
# print(self.original_hr_imgs)
# print(self.final_results)
# def __getitem__(self, idx):
# ground_truth = self.original_hr_imgs[idx]
# final_result = self.final_results[idx] # list
# return transforms.ToTensor()(final_result), transforms.ToTensor()(ground_truth) # hr_image를 변환한 것과, 변환하지 않은 것을 Tensor로 각각 반환
# def __len__(self):
# return len(self.hr_path)
# def concatFeatures(features, image_name, feature_type):
# features_0 = features[:16]
# features_1 = features[16:32]
# features_2 = features[32:48]
# features_3 = features[48:64]
# features_4 = features[64:80]
# features_5 = features[80:96]
# features_6 = features[96:112]
# features_7 = features[112:128]
# features_8 = features[128:144]
# features_9 = features[144:160]
# features_10 = features[160:176]
# features_11 = features[176:192]
# features_12 = features[192:208]
# features_13 = features[208:224]
# features_14 = features[224:240]
# features_15 = features[240:256]
# features_new = list()
# features_new.extend([
# concat_vertical(features_0),
# concat_vertical(features_1),
# concat_vertical(features_2),
# concat_vertical(features_3),
# concat_vertical(features_4),
# concat_vertical(features_5),
# concat_vertical(features_6),
# concat_vertical(features_7),
# concat_vertical(features_8),
# concat_vertical(features_9),
# concat_vertical(features_10),
# concat_vertical(features_11),
# concat_vertical(features_12),
# concat_vertical(features_13),
# concat_vertical(features_14),
# concat_vertical(features_15)
# ])
# final_concat_feature = concat_horizontal(features_new)
# save_path = "features/LR_2/" + feature_type + "/" + image_name
# if not os.path.exists("features/"):
# os.makedirs("features/")
# if not os.path.exists("features/LR_2/"):
# os.makedirs("features/LR_2/")
# if not os.path.exists("features/LR_2/" + feature_type):
# os.makedirs("features/LR_2/" + feature_type)
# cv2.imwrite(save_path, final_concat_feature)
# return np.array(final_concat_feature).astype(float)
# def concat_horizontal(feature):
# result = cv2.hconcat([feature[0], feature[1]])
# for i in range(2, len(feature)):
# result = cv2.hconcat([result, feature[i]])
# return result
# def concat_vertical(feature):
# result = cv2.vconcat([feature[0], feature[1]])
# for i in range(2, len(feature)):
# result = cv2.vconcat([result, feature[i]])
# return result
# def get_data_loader_test_version(data_path, feature_type, rescale_factor, batch_size, num_workers):
# full_dataset = FeatureDataset(data_path, feature_type, rescale_factor, False)
# print("dataset의 사이즈는 {}".format(len(full_dataset)))
# for f in full_dataset:
# print(type(f))
# def get_data_loader(data_path, feature_type, rescale_factor, batch_size, num_workers):
# full_dataset = FeatureDataset(data_path, feature_type, rescale_factor, False)
# train_size = int(0.9 * len(full_dataset))
# test_size = len(full_dataset) - train_size
# train_dataset, test_dataset = torch.utils.data.random_split(full_dataset, [train_size, test_size])
# torch.manual_seed(3334)
# train_loader = torch.utils.data.DataLoader(dataset=train_dataset, batch_size=batch_size, shuffle=True,
# num_workers=num_workers, pin_memory=False)
# test_loader = torch.utils.data.DataLoader(dataset=test_dataset, batch_size=batch_size, shuffle=False,
# num_workers=num_workers, pin_memory=True)
# return train_loader, test_loader
# def get_training_data_loader(data_path, feature_type, rescale_factor, batch_size, num_workers):
# full_dataset = FeatureDataset(data_path, feature_type, rescale_factor, False)
# torch.manual_seed(3334)
# train_loader = torch.utils.data.DataLoader(dataset=full_dataset, batch_size=batch_size, shuffle=True,
# num_workers=num_workers, pin_memory=False)
# return train_loader
# def get_infer_dataloader(data_path, feature_type, rescale_factor, batch_size, num_workers):
# dataset = FeatureDataset(data_path, feature_type, rescale_factor, True)
# data_loader = torch.utils.data.DataLoader(dataset=dataset, batch_size=batch_size, shuffle=False,
# num_workers=num_workers, pin_memory=False)
# return data_loader