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datasets.py
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datasets.py
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# -*- coding: utf-8 -*-
"""
读取图像统一用PIL而非cv2
"""
import os
import random
import numpy as np
from PIL import Image
import torch
import torch.utils.data as data
from torchvision import transforms
import torchvision.transforms.functional as TF
class CT_loader(data.Dataset):
def __init__(self, data_dir, channel=1, isTraining=True):
super(CT_loader, self).__init__()
self.low_lst, self.high_lst = self.get_dataPath(data_dir, isTraining)
self.channel = channel
self.isTraining = isTraining
self.name = ""
def __getitem__(self, index):
low_path = self.low_lst[index]
self.name = low_path.split("/")[-1]
high_path = self.high_lst[index]
simple_transform = transforms.ToTensor()
low = Image.open(low_path)
high = Image.open(high_path)
if self.channel == 1:
low = low.convert("L")
high = high.convert("L")
norm_transform = transforms.Normalize((0.5,), (0.5,))
else:
low = low.convert("RGB")
high = high.convert("RGB")
norm_transform = transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
if self.isTraining:
# augumentation
rotate = 10
angel = random.randint(-rotate, rotate)
low = low.rotate(angel)
high = high.rotate(angel)
low = norm_transform(simple_transform(low))
high = norm_transform(simple_transform(high))
return low, high
def __len__(self):
"""
返回总的图像数量
:return:
"""
return len(self.low_lst)
def get_dataPath(self, data_dir, isTraining):
"""
依次读取输入图片和label的文件路径,并放到array中返回
:param data_dir: 存放的文件夹
:return:
"""
if isTraining:
low_dir = os.path.join(data_dir, "train", "1E4")
high_dir = os.path.join(data_dir, "train", "high")
else:
low_dir = os.path.join(data_dir, "test", "1E4")
high_dir = os.path.join(data_dir, "test", "high")
low_lst = sorted(list(map(lambda x: os.path.join(low_dir, x), os.listdir(low_dir))))
high_lst = sorted(list(map(lambda x: os.path.join(high_dir, x), os.listdir(high_dir))))
assert len(low_lst) == len(high_lst)
return low_lst, high_lst
def getFileName(self):
return self.name
class OCT_loader(data.Dataset):
def __init__(self, data_dir, channel=1, scale_size=512, isTraining=True):
super(OCT_loader, self).__init__()
self.low_lst, self.high_lst = self.get_dataPath(data_dir, isTraining)
self.channel = channel
self.scale_size = scale_size
self.isTraining = isTraining
self.name = ""
def __getitem__(self, index):
low_path = self.low_lst[index]
self.name = low_path.split("/")[-1]
high_path = self.high_lst[index]
simple_transform = transforms.ToTensor()
low = Image.open(low_path)
high = Image.open(high_path)
if self.channel == 1:
low = low.convert("L")
high = high.convert("L")
norm_transform = transforms.Normalize((0.5,), (0.5,))
else:
low = low.convert("RGB")
high = high.convert("RGB")
norm_transform = transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
low = transforms.Resize((self.scale_size, self.scale_size))(low)
high = transforms.Resize((self.scale_size, self.scale_size))(high)
if self.isTraining:
# augumentation
rotate = 10
angel = random.randint(-rotate, rotate)
low = low.rotate(angel)
high = high.rotate(angel)
low = norm_transform(simple_transform(low))
high = norm_transform(simple_transform(high))
return low, high
def __len__(self):
"""
返回总的图像数量
:return:
"""
return len(self.low_lst)
def get_dataPath(self, data_dir, isTraining):
"""
依次读取输入图片和label的文件路径,并放到array中返回
:param data_dir: 存放的文件夹
:return:
"""
if isTraining:
low_dir = os.path.join(data_dir, "train", "images")
high_dir = os.path.join(data_dir, "train", "labels")
else:
low_dir = os.path.join(data_dir, "test", "images")
high_dir = os.path.join(data_dir, "test", "labels")
low_lst = sorted(list(map(lambda x: os.path.join(low_dir, x), os.listdir(low_dir))))
high_lst = sorted(list(map(lambda x: os.path.join(high_dir, x), os.listdir(high_dir))))
assert len(low_lst) == len(high_lst)
return low_lst, high_lst
def getFileName(self):
return self.name
class OCTA_loader(data.Dataset):
def __init__(self, data_dir, channel=1, scale_size=512, isTraining=True):
super(OCTA_loader, self).__init__()
self.low_lst, self.high_lst = self.get_dataPath(data_dir, isTraining)
self.channel = channel
self.scale_size = scale_size
self.isTraining = isTraining
self.name = ""
def __getitem__(self, index):
low_path = self.low_lst[index]
self.name = low_path.split("/")[-1]
high_path = self.high_lst[index]
simple_transform = transforms.ToTensor()
low = Image.open(low_path)
high = Image.open(high_path)
if self.channel == 1:
low = low.convert("L")
high = high.convert("L")
norm_transform = transforms.Normalize((0.5,), (0.5,))
else:
low = low.convert("RGB")
high = high.convert("RGB")
norm_transform = transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
low = transforms.Resize((self.scale_size, self.scale_size))(low)
high = transforms.Resize((self.scale_size, self.scale_size))(high)
if self.isTraining:
# augumentation
rotate = 10
angel = random.randint(-rotate, rotate)
low = low.rotate(angel)
high = high.rotate(angel)
low = norm_transform(simple_transform(low))
high = norm_transform(simple_transform(high))
return low, high
def __len__(self):
"""
返回总的图像数量
:return:
"""
return len(self.low_lst)
def get_dataPath(self, data_dir, isTraining):
"""
依次读取输入图片和label的文件路径,并放到array中返回
:param data_dir: 存放的文件夹
:return:
"""
if isTraining:
low_dir = os.path.join(data_dir, "train", "low")
high_dir = os.path.join(data_dir, "train", "high")
else:
low_dir = os.path.join(data_dir, "test", "low")
high_dir = os.path.join(data_dir, "test", "high")
low_lst = sorted(list(map(lambda x: os.path.join(low_dir, x), os.listdir(low_dir))))
high_lst = sorted(list(map(lambda x: os.path.join(high_dir, x), os.listdir(high_dir))))
assert len(low_lst) == len(high_lst)
return low_lst, high_lst
def getFileName(self):
return self.name