forked from uw-loci/demo_wsi_superres
/
data_loader.py
194 lines (153 loc) · 6.85 KB
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data_loader.py
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from __future__ import print_function, division
import os, glob, random
import torch
import pandas as pd
from skimage import io, transform, img_as_float, color, img_as_ubyte, exposure
import numpy as np
import matplotlib.pyplot as plt
from torch.utils.data import Dataset, DataLoader
from torchvision import transforms, utils
import torchvision.transforms.functional as F
from PIL import Image, ImageFilter
from utils import find
plt.ion() # interactive mode
class Compress_Dataset(Dataset):
def __init__(self, csv_file, transform=None):
self.files_list = pd.read_csv(csv_file)
self.transform = transform
def __len__(self):
return len(self.files_list)
def __getitem__(self, idx):
img_name = self.files_list.iloc[idx, 0] # image path
img = Image.open(img_name)
if self.transform:
sample = self.transform(img)
return sample
class Compose(object):
def __init__(self, transforms):
self.transforms = transforms
def __call__(self, img):
for t in self.transforms:
img = t(img)
return img
def __repr__(self):
format_string = self.__class__.__name__ + '('
for t in self.transforms:
format_string += '\n'
format_string += ' {0}'.format(t)
format_string += '\n)'
return format_string
class Rescale(object):
def __init__(self, output_size, up_factor=5, stc=False):
assert isinstance(output_size, (int, tuple))
self.output_size = output_size
self.up_factor = up_factor
self.stc = stc
def __call__(self, img):
img_high = img
if self.stc == True:
factor = max(1, np.random.normal(self.up_factor, 0.5))
else:
factor = self.up_factor
img_low = img_high.resize((int(img.size[1] / factor), int(img.size[0] / factor)))
img_low = img_low.resize(self.output_size, Image.BILINEAR)
img_low = img_low.filter(ImageFilter.GaussianBlur(radius=((factor - 1) / 2)))
return {'input': img_low, 'output': img_high}
class ToHSV(object):
def __call__(self, sample):
img_low, img_high = sample['input'], sample['output']
img_low = img_low.convert('HSV')
img_high = img_high.convert('HSV')
return {'input': img_low, 'output': img_high}
class ToTensor(object):
def __call__(self, sample):
img_low, img_high = sample['input'], sample['output']
return {'input': transforms.functional.to_tensor(img_low), 'output': transforms.functional.to_tensor(img_high)}
def show_patch(dataloader, index=0, is_hsv=False):
for i_batch, sample_batched in enumerate(dataloader):
if i_batch == index:
input_batch, output_batch = sample_batched['input'], sample_batched['output']
if is_hsv:
input_img = input_batch.numpy().transpose((0, 2, 3, 1))
output_img = output_batch.numpy().transpose((0, 2, 3, 1))
for i in range(0, input_batch.shape[0]):
input_img[i] = color.hsv2rgb(input_img[i])
output_img[i] = color.hsv2rgb(output_img[i])
input_batch = torch.from_numpy(input_img.transpose(((0, 3, 1, 2))))
output_batch = torch.from_numpy(output_img.transpose(((0, 3, 1, 2))))
batch_size = len(input_batch)
im_size = input_batch.size(2)
plt.figure(figsize=(20, 10))
grid = utils.make_grid(input_batch)
plt.imshow(grid.numpy().transpose((1, 2, 0)), interpolation='bicubic')
plt.axis('off')
plt.figure(figsize=(20, 10))
grid = utils.make_grid(output_batch)
plt.imshow(grid.numpy().transpose((1, 2, 0)), interpolation='bicubic')
plt.axis('off')
break
# def generate_compress_csv():
# train_imgs = glob.glob('dataset/TMA/20x-cores-training/*.jpg')
# random.shuffle(train_imgs)
# train_df = pd.DataFrame(train_imgs[0:int(0.8 * len(train_imgs))])
# valid_df = pd.DataFrame(train_imgs[int(0.8 * len(train_imgs)):int(0.9 * len(train_imgs))])
# test_df = pd.DataFrame(train_imgs[int(0.9 * len(train_imgs)):])
# train_df.to_csv('dataset/TMA/train-compress.csv', index=False)
# valid_df.to_csv('dataset/TMA/valid-compress.csv', index=False)
# test_df.to_csv('dataset/TMA/test-compress.csv', index=False)
# def compress_csv_path(csv='train'):
# if csv == 'train':
# return 'dataset/TMA/train-compress.csv'
# if csv == 'test':
# return 'dataset/TMA/test-compress.csv'
# if csv == 'valid':
# return 'dataset/TMA/valid-compress.csv'
def compress_csv_path(csv='train'):
if csv == 'train':
return 'dataset/train-compress.csv'
if csv == 'test':
return 'dataset/test-compress.csv'
if csv == 'valid':
return 'dataset/valid-compress.csv'
def write_split_csv(save_dir, csv_dir, train_percent=0.8, test_percent=0.1):
os.makedirs(save_dir, exist_ok=True)
slides = find('*.csv', csv_dir)
random.shuffle(slides)
test_num = min(1, int(test_percent * len(slides)))
test_slides = slides[:test_num]
train_valid_slides = slides[test_num:]
test_set = pd.DataFrame()
for s in test_slides:
test_set = test_set.append(pd.read_csv(s))
train_valid_set = pd.DataFrame()
for s in train_valid_slides:
train_valid_set = train_valid_set.append(pd.read_csv(s))
train_valid_shuffled = train_valid_set.sample(frac=1)
# Calculate index for split 80:20 ratio
train_valid_index = round(len(train_valid_shuffled) * train_percent)
# Split into training and test sets
train_set = train_valid_shuffled[:train_valid_index].reset_index(drop=True)
valid_set = train_valid_shuffled[train_valid_index:].reset_index(drop=True)
train_set.to_csv(os.path.join(save_dir, 'train-compress.csv'), index=False)
test_set.to_csv(os.path.join(save_dir, 'test-compress.csv'), index=False)
valid_set.to_csv(os.path.join(save_dir, 'valid-compress.csv'), index=False)
def move_dataset(csv='train'):
from shutil import copyfile
save_dir = '/media/jahanifar/Data/SuperRes'
compress_csv_path(csv)
train_paths = pd.read_csv(compress_csv_path(csv))
if not os.path.exists(save_dir):
raise NotADirectoryError(save_dir + ' not found')
os.makedirs(os.path.join(save_dir, csv))
new_train_paths = []
for p in train_paths['path']:
p_split = p.split('/')
save_path = os.path.join(os.path.join(save_dir, csv), p_split[-3] + '_' + p_split[-2] + '_' + p_split[-1])
new_train_paths.append(save_path)
copyfile(p, save_path)
df = pd.DataFrame({'path': new_train_paths})
df.to_csv('dataset/' + csv + '-compress_ssd.csv', index=False)
if __name__ == '__main__':
# write_split_csv('dataset', '/home/jahanifar/PycharmProjects/DataReader/image_path_csv')
move_dataset(csv='train')
print('end')