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data_loader.py
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data_loader.py
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# data loader
from __future__ import print_function, division
import torch
from skimage import io, transform, color
import numpy as np
import math
#import matplotlib.pyplot as plt
from torch.utils.data import Dataset
#==========================dataset load==========================
class RescaleT(object):
def __init__(self,output_size):
assert isinstance(output_size,(int,tuple))
self.output_size = output_size
def __call__(self,sample):
image, label = sample['image'],sample['label']
h, w = image.shape[:2]
if isinstance(self.output_size,int):
if h > w:
new_h, new_w = self.output_size*h/w,self.output_size
else:
new_h, new_w = self.output_size,self.output_size*w/h
else:
new_h, new_w = self.output_size
new_h, new_w = int(new_h), int(new_w)
# #resize the image to new_h x new_w and convert image from range [0,255] to [0,1]
# img = transform.resize(image,(new_h,new_w),mode='constant')
# lbl = transform.resize(label,(new_h,new_w),mode='constant', order=0, preserve_range=True)
img = transform.resize(image,(self.output_size,self.output_size),mode='constant')
lbl = transform.resize(label,(self.output_size,self.output_size),mode='constant', order=0, preserve_range=True)
return {'image':img,'label':lbl}
class Rescale(object):
def __init__(self,output_size):
assert isinstance(output_size,(int,tuple))
self.output_size = output_size
def __call__(self,sample):
image, label = sample['image'],sample['label']
h, w = image.shape[:2]
if isinstance(self.output_size,int):
if h > w:
new_h, new_w = self.output_size*h/w,self.output_size
else:
new_h, new_w = self.output_size,self.output_size*w/h
else:
new_h, new_w = self.output_size
new_h, new_w = int(new_h), int(new_w)
# #resize the image to new_h x new_w and convert image from range [0,255] to [0,1]
img = transform.resize(image,(new_h,new_w),mode='constant')
lbl = transform.resize(label,(new_h,new_w),mode='constant', order=0, preserve_range=True)
return {'image':img,'label':lbl}
class CenterCrop(object):
def __init__(self,output_size):
assert isinstance(output_size, (int, tuple))
if isinstance(output_size, int):
self.output_size = (output_size, output_size)
else:
assert len(output_size) == 2
self.output_size = output_size
def __call__(self,sample):
image, label = sample['image'], sample['label']
h, w = image.shape[:2]
new_h, new_w = self.output_size
# print("h: %d, w: %d, new_h: %d, new_w: %d"%(h, w, new_h, new_w))
assert((h >= new_h) and (w >= new_w))
h_offset = int(math.floor((h - new_h)/2))
w_offset = int(math.floor((w - new_w)/2))
image = image[h_offset: h_offset + new_h, w_offset: w_offset + new_w]
label = label[h_offset: h_offset + new_h, w_offset: w_offset + new_w]
return {'image': image, 'label': label}
class RandomCrop(object):
def __init__(self,output_size):
assert isinstance(output_size, (int, tuple))
if isinstance(output_size, int):
self.output_size = (output_size, output_size)
else:
assert len(output_size) == 2
self.output_size = output_size
def __call__(self,sample):
image, label = sample['image'], sample['label']
h, w = image.shape[:2]
new_h, new_w = self.output_size
top = np.random.randint(0, h - new_h)
left = np.random.randint(0, w - new_w)
image = image[top: top + new_h, left: left + new_w]
label = label[top: top + new_h, left: left + new_w]
return {'image': image, 'label': label}
#csx
class ToTensor(object):
"""Convert ndarrays in sample to Tensors."""
## 不减均值除方差
def __call__(self, sample):
image, label = sample['image'], sample['label']
# image = (image / 255).transpose((2, 0, 1))
# # src = torch.from_numpy(src).float()
# # tar => float to float tensor
# label = label.transpose((2, 0, 1))
# # tar = torch.from_numpy(tar).float()
tmpImg = np.zeros((image.shape[0],image.shape[1],3))
tmpLbl = np.zeros(label.shape)
# tmpImg = (image / 255).transpose((2, 0, 1))
# tmpLbl = label.transpose((2, 0, 1))
image = image/np.max(image)
if(np.max(label)<1e-6):
label = label
else:
label = label/np.max(label)
# if image.shape[2]==1:
# tmpImg[:,:,0] = (image[:,:,0]-0.485)/0.229
# tmpImg[:,:,1] = (image[:,:,0]-0.485)/0.229
# tmpImg[:,:,2] = (image[:,:,0]-0.485)/0.229
# else:
# tmpImg[:,:,0] = (image[:,:,0]-0.485)/0.229
# tmpImg[:,:,1] = (image[:,:,1]-0.456)/0.224
# tmpImg[:,:,2] = (image[:,:,2]-0.406)/0.225
# tmpLbl[:,:,0] = label[:,:,0]
# # change the r,g,b to b,r,g from [0,255] to [0,1]
# #transforms.Normalize(mean = (0.485, 0.456, 0.406), std = (0.229, 0.224, 0.225))
tmpImg = image.transpose((2, 0, 1))
tmpLbl = label.transpose((2, 0, 1))
return {'image': torch.from_numpy(tmpImg),
'label': torch.from_numpy(tmpLbl)}
class BuildingDataset(Dataset):
def __init__(self,img_name_list,lbl_name_list,transform=None):
# self.root_dir = root_dir
# self.image_name_list = glob.glob(image_dir+'*.png')
# self.label_name_list = glob.glob(label_dir+'*.png')
self.image_name_list = img_name_list
self.label_name_list = lbl_name_list
self.transform = transform
def __len__(self):
return len(self.image_name_list)
def __getitem__(self,idx):
# image = Image.open(self.image_name_list[idx])#io.imread(self.image_name_list[idx])
# label = Image.open(self.label_name_list[idx])#io.imread(self.label_name_list[idx])
image = io.imread(self.image_name_list[idx], plugin='pil')
if(0==len(self.label_name_list)):
label_3 = np.zeros(image.shape)
else:
label_3 = io.imread(self.label_name_list[idx], plugin='pil')
#print("len of label3")
#print(len(label_3.shape))
#print(label_3.shape)
label = np.zeros(label_3.shape[0:2])
if(3==len(label_3.shape)):
label = label_3[:,:,0]
elif(2==len(label_3.shape)):
label = label_3
if(3==len(image.shape) and 2==len(label.shape)):
label = label[:,:,np.newaxis]
elif(2==len(image.shape) and 2==len(label.shape)):
image = image[:,:,np.newaxis]
label = label[:,:,np.newaxis]
# #vertical flipping
# # fliph = np.random.randn(1)
# flipv = np.random.randn(1)
#
# if flipv>0:
# image = image[::-1,:,:]
# label = label[::-1,:,:]
# #vertical flip
sample = {'image':image, 'label':label}
if self.transform:
sample = self.transform(sample)
return sample