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datasets.py
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datasets.py
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from __future__ import print_function, division
import os
from PIL import Image
import torch
import torch.utils.data
import torchvision
from skimage import io
from torch.utils.data import Dataset
import matplotlib.pyplot as plt
import numpy as np
class Images_Dataset(Dataset):
"""Class for getting data as a Dict
Args:
images_dir = path of input images
labels_dir = path of labeled images
transformI = Input Images transformation (default: None)
transformM = Input Labels transformation (default: None)
Output:
sample : Dict of images and labels"""
def __init__(self, images_dir, labels_dir, transformI = None, transformM = None):
self.labels_dir = labels_dir
self.images_dir = images_dir
self.transformI = transformI
self.transformM = transformM
def __len__(self):
return len(self.images_dir)
def __getitem__(self, idx):
for i in range(len(self.images_dir)):
image = Image.open(self.images_dir[i]).convert('RGB')
# #改为单通道图片进行训练
# r,g,b = image.split()
# image = r
label = Image.open(self.labels_dir[i])
imgs = self.transforms(image)
label = self.transforms(label)
sample = {'images': imgs, 'labels': label}
return sample
class Images_Dataset_folder(torch.utils.data.Dataset):
"""Class for getting individual transformations and data
Args:
images_dir = path of input images
labels_dir = path of labeled images
transformI = Input Images transformation (default: None)
transformM = Input Labels transformation (default: None)
Output:
tx = Transformed images
lx = Transformed labels"""
def __init__(self, images_dir, labels_dir,transformI = None,transformM = None):
self.images = sorted(os.listdir(images_dir))
self.labels = sorted(os.listdir(labels_dir))
self.images_dir = images_dir
self.labels_dir = labels_dir
self.transformI = transformI
self.transformM = transformM
if self.transformI:
self.tx = self.transformI
else:
self.tx = torchvision.transforms.Compose([
torchvision.transforms.Resize((240,240)),
torchvision.transforms.ToTensor(),
])
if self.transformM:
self.lx = self.transformM
else:
pass
def __len__(self):
return len(self.images)
def __getitem__(self, i):
i1 = Image.open(self.images_dir + self.images[i]).convert('RGB')
r, g, b = i1.split()
# i1 = r
l1 = Image.open(self.labels_dir + self.labels[i])
l1 = l1.resize((240, 240))
img1 = np.asarray(l1)
new_label = np.zeros(img1.shape,dtype=np.int64)
new_label[img1 > 128 ] = 0
new_label[img1 == 128 ] = 1
new_label[img1 < 128 ] = 2
l1 = new_label
self.lx = torch.from_numpy(np.array(l1)).long().unsqueeze(0)
return self.tx(i1), self.lx
##read dataset for pre processing
class Images_Dataset_folder_pre(torch.utils.data.Dataset):
"""Class for getting individual transformations and data
Args:
images_dir = path of input images
labels_dir = path of labeled images
transformI = Input Images transformation (default: None)
transformM = Input Labels transformation (default: None)
Output:
tx = Transformed images
lx = Transformed labels"""
def __init__(self, images_dir, labels_dir,transformI = None,transformM = None):
self.images = sorted(os.listdir(images_dir))
self.labels = sorted(os.listdir(labels_dir))
self.images_dir = images_dir
self.labels_dir = labels_dir
self.transformI = transformI
self.transformM = transformM
if self.transformI:
self.tx = self.transformI
else:
self.tx = torchvision.transforms.Compose([
torchvision.transforms.Resize((400,400)),
torchvision.transforms.ToTensor(),
])
if self.transformM:
self.lx = self.transformM
else:
self.lx = torchvision.transforms.Compose([
torchvision.transforms.Resize((400,400)),
torchvision.transforms.ToTensor(),
])
def __len__(self):
return len(self.images)
def __getitem__(self, i):
i1 = Image.open(self.images_dir + self.images[i]).convert('RGB')
l1 = Image.open(self.labels_dir + self.labels[i])
return self.tx(i1), self.lx(l1)