File size: 6,104 Bytes
07e1105 |
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 |
import torch.utils.data as data
import torch
from PIL import Image
import os
import scipy.io
import numpy as np
import csv
from openpyxl import load_workbook
import cv2
class LIVEC(data.Dataset):
def __init__(self, root, index, transform):
imgpath = scipy.io.loadmat(os.path.join(root, 'Data', 'AllImages_release.mat'))
imgpath = imgpath['AllImages_release']
imgpath = imgpath[7:1169]
mos = scipy.io.loadmat(os.path.join(root, 'Data', 'AllMOS_release.mat'))
labels = mos['AllMOS_release'].astype(np.float32)
labels = labels[0][7:1169]
sample, gt = [], []
for i, item in enumerate(index):
sample.append(os.path.join(root, 'Images', imgpath[item][0][0]))
gt.append(labels[item])
gt = normalization(gt)
self.samples, self.gt = sample, gt
self.transform = transform
def __getitem__(self, index):
"""
Args:
index (int): Index
Returns:
tuple: (sample, target) where target is class_index of the target class.
"""
img_tensor, gt_tensor = get_item(self.samples, self.gt, index, self.transform)
return img_tensor, gt_tensor
def __len__(self):
length = len(self.samples)
return length
class Koniq10k(data.Dataset):
def __init__(self, root, index, transform):
imgname = []
mos_all = []
csv_file = os.path.join(root, 'koniq10k_distributions_sets.csv')
with open(csv_file) as f:
reader = csv.DictReader(f)
for row in reader:
imgname.append(row['image_name'])
mos = np.array(float(row['MOS'])).astype(np.float32)
mos_all.append(mos)
sample, gt = [], []
for i, item in enumerate(index):
sample.append(os.path.join(root, '1024x768', imgname[item]))
gt.append(mos_all[item])
gt = normalization(gt)
self.samples, self.gt = sample, gt
self.transform = transform
def __getitem__(self, index):
"""
Args:
index (int): Index
Returns:
tuple: (sample, target) where target is class_index of the target class.
"""
img_tensor, gt_tensor = get_item(self.samples, self.gt, index, self.transform)
return img_tensor, gt_tensor
def __len__(self):
length = len(self.samples)
return length
class SPAQ(data.Dataset):
def __init__(self, root, index, transform):
imgname = []
mos_all = []
csv_file = os.path.join(root, 'koniq10k_scores_and_distributions.csv')
with open(csv_file) as f:
reader = csv.DictReader(f)
for row in reader:
imgname.append(row['image_name'])
mos = np.array(float(row['MOS_zscore'])).astype(np.float32)
mos_all.append(mos)
sample, gt = [], []
for i, item in enumerate(index):
sample.append(os.path.join(root, '1024x768', imgname[item]))
gt.append(labels[item])
gt = norm_target(gt)
self.samples, self.gt = sample, gt
self.samples, self.gt = sample, gt
self.transform = transform
def __getitem__(self, index):
"""
Args:
index (int): Index
Returns:
tuple: (sample, target) where target is class_index of the target class.
"""
path, target = self.samples[index], self.gt[index]
sample = pil_loader(path)
sample = self.transform(sample)
return sample, target
def __len__(self):
length = len(self.samples)
return length
class BID(data.Dataset):
def __init__(self, root, index, transform):
imgname = []
mos_all = []
xls_file = os.path.join(root, 'DatabaseGrades.xlsx')
workbook = load_workbook(xls_file)
booksheet = workbook.active
rows = booksheet.rows
count = 1
for row in rows:
count += 1
img_num = booksheet.cell(row=count, column=1).value
img_name = "DatabaseImage%04d.JPG" % (img_num)
imgname.append(img_name)
mos = booksheet.cell(row=count, column=2).value
mos = np.array(mos)
mos = mos.astype(np.float32)
mos_all.append(mos)
if count == 587:
break
sample, gt = [], []
for i, item in enumerate(index):
sample.append(os.path.join(root, imgname[item]))
gt.append(mos_all[item])
gt = normalization(gt)
self.samples, self.gt = sample, gt
self.transform = transform
def __getitem__(self, index):
"""
Args:
index (int): Index
Returns:
tuple: (sample, target) where target is class_index of the target class.
"""
img_tensor, gt_tensor = get_item(self.samples, self.gt, index, self.transform)
return img_tensor, gt_tensor
def __len__(self):
length = len(self.samples)
return length
def get_item(samples, gt, index, transform):
path, target = samples[index], gt[index]
sample = load_image(path)
samples = {'img': sample, 'gt': target }
samples = transform(samples)
return samples['img'], samples['gt'].type(torch.FloatTensor)
def getFileName(path, suffix):
filename = []
f_list = os.listdir(path)
for i in f_list:
if os.path.splitext(i)[1] == suffix:
filename.append(i)
return filename
def load_image(img_path):
d_img = cv2.imread(img_path, cv2.IMREAD_COLOR)
d_img = cv2.resize(d_img, (224, 224), interpolation=cv2.INTER_CUBIC)
d_img = cv2.cvtColor(d_img, cv2.COLOR_BGR2RGB)
d_img = np.array(d_img).astype('float32') / 255
d_img = np.transpose(d_img, (2, 0, 1))
return d_img
def normalization(data):
data = np.array(data)
range = np.max(data) - np.min(data)
data = (data - np.min(data)) / range
data = list(data.astype('float').reshape(-1, 1))
return data |