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data.py
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data.py
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import torch.utils.data
import torchvision.transforms as transforms
from PIL import Image
import numpy as np
import os
import csv
import glob
import random
import cv2
class Dataset(torch.utils.data.Dataset):
def __init__(self, root, mode, crop_size=(256, 256)):
super(type(self), self).__init__()
self.blurry_img_list = []
self.clear_img_list = []
self.snow_img_list = []
self.crop_size = crop_size
self.to_tensor = transforms.ToTensor()
self.root = root
self.nameclass = {}
for name in sorted(os.listdir(os.path.join(root))):
if not os.path.isdir(os.path.join(root, name)):
continue
self.nameclass[name] = len(self.nameclass.keys())
print(self.nameclass)
self.blurry_img_list, self.clear_img_list, self.snow_img_list = self.load_csv('data.csv')
if mode == 'train':
self.blurry_img_list = self.blurry_img_list[:int(0.98 * len(self.blurry_img_list))]
self.clear_img_list = self.clear_img_list[:int(0.98 * len(self.clear_img_list))]
self.snow_img_list = self.snow_img_list[:int(0.98 * len(self.snow_img_list))]
if mode == 'val':
self.blurry_img_list = self.blurry_img_list[int(0.98 * len(self.blurry_img_list)):]
self.clear_img_list = self.clear_img_list[int(0.98 * len(self.clear_img_list)):]
self.snow_img_list = self.snow_img_list[int(0.98 * len(self.snow_img_list)):]
print(len(self.blurry_img_list))
def load_csv(self, filename):
if not os.path.exists(os.path.join(self.root, filename)):
images = []
for name in self.nameclass.keys():
# 'all\\gt\\00001.jpg
images += glob.glob(os.path.join(self.root, name, '*.tif'))
with open(os.path.join(self.root, filename), mode='w', newline='') as f:
writer = csv.writer(f)
for img in images: # 'all\\gt\\00000000.jpg'
name = img.split(os.sep)[-2]
category = self.nameclass[name]
# 'all\\gt\\00000000.png', 0
writer.writerow([img, category])
print('writen into csv file:', filename)
blurry_img, clear_img, snow_img = [], [], []
with open(os.path.join(self.root, filename)) as f:
reader = csv.reader(f)
for row in reader:
# 'all\\gt\\00000000.jpg', 0
img, category = row
category = int(category)
if category == 0:
clear_img.append(img)
elif category == 1:
snow_img.append(img)
else:
blurry_img.append(img)
assert len(clear_img) == len(snow_img) and len(snow_img) == len(blurry_img)
return blurry_img, clear_img, snow_img
def crop_resize_totensor(self, img, crop_location):
img256 = img.crop(crop_location)
img128 = img256.resize((self.crop_size[0] // 2, self.crop_size[1] // 2), resample=Image.BILINEAR)
img64 = img128.resize((self.crop_size[0] // 4, self.crop_size[1] // 4), resample=Image.BILINEAR)
return self.to_tensor(img256), self.to_tensor(img128), self.to_tensor(img64)
def __len__(self):
return len(self.clear_img_list)
def __getitem__(self, idx):
# filename processing
blurry_img_name = self.blurry_img_list[idx]
clear_img_name = self.clear_img_list[idx]
snow_mask_name = self.snow_img_list[idx]
blurry_left_img = cv2.imread(blurry_img_name, -1)
clear_img = cv2.imread(clear_img_name, -1)
snow_mask = cv2.imread(snow_mask_name, -1)
blurry_left_img = self.to_tensor(blurry_left_img)
clear_img = self.to_tensor(clear_img)
snow_mask = self.to_tensor(snow_mask)
blurry_left_img = torch.flip(blurry_left_img, dims=[0])
clear_img = torch.flip(clear_img, dims=[0])
snow_mask = torch.flip(snow_mask, dims=[0])
blurry_left_img = transforms.ToPILImage()(blurry_left_img)
clear_img = transforms.ToPILImage()(clear_img)
snow_mask = transforms.ToPILImage()(snow_mask)
assert blurry_left_img.size == clear_img.size and clear_img.size == snow_mask.size
crop_left = int(np.floor(np.random.uniform(0, blurry_left_img.size[0] - self.crop_size[0] + 1)))
crop_top = int(np.floor(np.random.uniform(0, blurry_left_img.size[1] - self.crop_size[1] + 1)))
crop_location = (crop_left, crop_top, crop_left + self.crop_size[0], crop_top + self.crop_size[1])
img256_left, img128_left, img64_left = self.crop_resize_totensor(blurry_left_img, crop_location)
label256, label128, label64 = self.crop_resize_totensor(clear_img, crop_location)
mask256, mask128, mask64 = self.crop_resize_totensor(snow_mask, crop_location)
batch = {'img256': img256_left, 'img128': img128_left,
'img64': img64_left, 'label256': label256, 'label128': label128,
'label64': label64, 'mask256': mask256, 'mask128': mask128, 'mask64': mask64}
for k in batch:
batch[k] = batch[k] * 2 - 1.0 # in range [-1,1]
return batch
class TestDataset(torch.utils.data.Dataset):
def __init__(self, root, crop_size=(640, 480)):
super(type(self), self).__init__()
self.blurry_img_list = []
self.clear_img_list = []
self.snow_img_list = []
self.crop_size = crop_size
self.to_tensor = transforms.ToTensor()
self.root = root
self.nameclass = {}
for name in sorted(os.listdir(os.path.join(root))):
if not os.path.isdir(os.path.join(root, name)):
continue
self.nameclass[name] = len(self.nameclass.keys())
print(self.nameclass)
self.blurry_img_list, self.clear_img_list, self.snow_img_list = self.load_csv('test_data.csv')
print(len(self.blurry_img_list))
def load_csv(self, filename):
if not os.path.exists(os.path.join(self.root, filename)):
images = []
for name in self.nameclass.keys():
# 'all\\gt\\00001.jpg
images += glob.glob(os.path.join(self.root, name, '*.tif'))
with open(os.path.join(self.root, filename), mode='w', newline='') as f:
writer = csv.writer(f)
for img in images: # 'all\\gt\\00000000.jpg'
name = img.split(os.sep)[-2]
category = self.nameclass[name]
# 'all\\gt\\00000000.png', 0
writer.writerow([img, category])
print('writen into csv file:', filename)
blurry_img, clear_img, snow_img = [], [], []
with open(os.path.join(self.root, filename)) as f:
reader = csv.reader(f)
for row in reader:
# 'all\\gt\\00000000.jpg', 0
img, category = row
category = int(category)
if category == 0:
clear_img.append(img)
elif category == 1:
snow_img.append(img)
else:
blurry_img.append(img)
assert len(clear_img) == len(snow_img) and len(snow_img) == len(blurry_img)
return blurry_img, clear_img, snow_img
def crop_resize_totensor(self, img, crop_location):
img256 = img.crop(crop_location)
img128 = img256.resize((self.crop_size[0] // 2, self.crop_size[1] // 2), resample=Image.BILINEAR)
img64 = img128.resize((self.crop_size[0] // 4, self.crop_size[1] // 4), resample=Image.BILINEAR)
return self.to_tensor(img256), self.to_tensor(img128), self.to_tensor(img64)
def __len__(self):
return len(self.clear_img_list)
def __getitem__(self, idx):
# filename processing
blurry_img_name = self.blurry_img_list[idx]
clear_img_name = self.clear_img_list[idx]
snow_mask_name = self.snow_img_list[idx]
blurry_left_img = cv2.imread(blurry_img_name, -1)
clear_img = cv2.imread(clear_img_name, -1)
snow_mask = cv2.imread(snow_mask_name, -1)
blurry_left_img = self.to_tensor(blurry_left_img)
clear_img = self.to_tensor(clear_img)
snow_mask = self.to_tensor(snow_mask)
blurry_left_img = torch.flip(blurry_left_img, dims=[0])
clear_img = torch.flip(clear_img, dims=[0])
snow_mask = torch.flip(snow_mask, dims=[0])
blurry_left_img = transforms.ToPILImage()(blurry_left_img)
clear_img = transforms.ToPILImage()(clear_img)
snow_mask = transforms.ToPILImage()(snow_mask)
assert blurry_left_img.size == clear_img.size and clear_img.size == snow_mask.size
crop_left = int(np.floor(np.random.uniform(0, blurry_left_img.size[0] - self.crop_size[0] + 1)))
crop_top = int(np.floor(np.random.uniform(0, blurry_left_img.size[1] - self.crop_size[1] + 1)))
crop_location = (crop_left, crop_top, crop_left + self.crop_size[0], crop_top + self.crop_size[1])
img256_left, img128_left, img64_left = self.crop_resize_totensor(blurry_left_img, crop_location)
label256, label128, label64 = self.crop_resize_totensor(clear_img, crop_location)
mask256, mask128, mask64 = self.crop_resize_totensor(snow_mask, crop_location)
batch = {'img256': img256_left, 'img128': img128_left,
'img64': img64_left, 'label256': label256, 'label128': label128,
'label64': label64, 'mask256': mask256, 'mask128': mask128, 'mask64': mask64}
for k in batch:
batch[k] = batch[k] * 2 - 1.0 # in range [-1,1]
return batch
if __name__ == '__main__':
print('here')