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ImageDataset2.py
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ImageDataset2.py
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import os
import torch
import functools
import numpy as np
import pandas as pd
from PIL import Image, ImageFile
from torch.utils.data import Dataset
from tqdm import tqdm
IMG_EXTENSIONS = ['.jpg', '.jpeg', '.png', '.ppm', '.bmp', '.pgm', '.tif']
ImageFile.LOAD_TRUNCATED_IMAGES = True
def has_file_allowed_extension(filename, extensions):
"""Checks if a file is an allowed extension.
Args:
filename (string): path to a file
extensions (iterable of strings): extensions to consider (lowercase)
Returns:
bool: True if the filename ends with one of given extensions
"""
filename_lower = filename.lower()
return any(filename_lower.endswith(ext) for ext in extensions)
def image_loader(image_name):
if has_file_allowed_extension(image_name, IMG_EXTENSIONS):
I = Image.open(image_name)
return I.convert('RGB')
def get_default_img_loader():
return functools.partial(image_loader)
class ImageDataset2(Dataset):
def __init__(self, csv_file,
img_dir,
preprocess,
num_patch,
test,
get_loader=get_default_img_loader):
"""
Args:
csv_file (string): Path to the csv file with annotations.
img_dir (string): Directory of the images.
transform (callable, optional): transform to be applied on a sample.
"""
self.data = pd.read_csv(csv_file, sep='\t', header=None)
print('%d csv data successfully loaded!' % self.__len__())
self.img_dir = img_dir
self.loader = get_loader()
self.preprocess = preprocess
self.num_patch = num_patch
self.test = test
def __getitem__(self, index):
"""
Args:
index (int): Index
Returns:
samples: a Tensor that represents a video segment.
"""
image_name = os.path.join(self.img_dir, self.data.iloc[index, 0])
I = self.loader(image_name)
I = self.preprocess(I)
I = I.unsqueeze(0)
n_channels = 3
kernel_h = 224
kernel_w = 224
if (I.size(2) >= 1024) | (I.size(3) >= 1024):
step = 48
else:
step = 32
patches = I.unfold(2, kernel_h, step).unfold(3, kernel_w, step).permute(0, 2, 3, 1, 4, 5).reshape(-1,
n_channels,
kernel_h,
kernel_w)
assert patches.size(0) >= self.num_patch
#self.num_patch = np.minimum(patches.size(0), self.num_patch)
if self.test:
sel_step = patches.size(0) // self.num_patch
sel = torch.zeros(self.num_patch)
for i in range(self.num_patch):
sel[i] = sel_step * i
sel = sel.long()
else:
sel = torch.randint(low=0, high=patches.size(0), size=(self.num_patch, ))
patches = patches[sel, ...]
mos = self.data.iloc[index, 1]
dist_type = self.data.iloc[index, 2]
scene_content1 = self.data.iloc[index, 3]
scene_content2 = self.data.iloc[index, 4]
scene_content3 = self.data.iloc[index, 5]
if scene_content2 == 'invalid':
valid = 1
elif scene_content3 == 'invalid':
valid = 2
else:
valid = 3
sample = {'I': patches, 'mos': float(mos), 'dist_type': dist_type, 'scene_content1': scene_content1,
'scene_content2':scene_content2, 'scene_content3':scene_content3, 'valid':valid}
return sample
def __len__(self):
return len(self.data.index)
class ImageDataset_qonly(Dataset):
def __init__(self, csv_file,
img_dir,
preprocess,
num_patch,
set,
test,
get_loader=get_default_img_loader):
"""
Args:
csv_file (string): Path to the csv file with annotations.
img_dir (string): Directory of the images.
transform (callable, optional): transform to be applied on a sample.
"""
if csv_file[-3:] == 'txt':
data = pd.read_csv(csv_file, sep='\t', header=None)
self.data = data
else:
data = pd.read_csv(csv_file, header=0)
self.data = data[data.split==set]
print('%d csv data successfully loaded!' % self.__len__())
self.img_dir = img_dir
self.loader = get_loader()
self.preprocess = preprocess
self.num_patch = num_patch
self.test = test
def __getitem__(self, index):
"""
Args:
index (int): Index
Returns:
samples: a Tensor that represents a video segment.
"""
image_name = os.path.join(self.img_dir, self.data.iloc[index, 0])
I = self.loader(image_name)
I = self.preprocess(I)
I = I.unsqueeze(0)
n_channels = 3
kernel_h = 224
kernel_w = 224
if (I.size(2) >= 1024) | (I.size(3) >= 1024):
step = 48
else:
step = 32
patches = I.unfold(2, kernel_h, step).unfold(3, kernel_w, step).permute(0, 2, 3, 1, 4, 5).reshape(-1,
n_channels,
kernel_h,
kernel_w)
assert patches.size(0) >= self.num_patch
#self.num_patch = np.minimum(patches.size(0), self.num_patch)
if self.test:
sel_step = patches.size(0) // self.num_patch
sel = torch.zeros(self.num_patch)
for i in range(self.num_patch):
sel[i] = sel_step * i
sel = sel.long()
else:
sel = torch.randint(low=0, high=patches.size(0), size=(self.num_patch, ))
patches = patches[sel, ...]
mos = self.data.iloc[index, 1]
sample = {'I': patches, 'mos': float(mos)}
return sample
def __len__(self):
return len(self.data)
def __len__(self):
return len(self.data.index)