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data_loader.py
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import torch
from skimage import io, transform, color
import numpy as np
from torch.utils.data import Dataset
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']
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 ToTensor(object):
def __init__(self, flag=0):
self.flag = flag
def __call__(self, sample):
image, label = sample['image'], sample['label']
tmpLbl = np.zeros(label.shape)
if np.max(label) < 1e-6:
label = label
else:
label = label/np.max(label)
if self.flag == 2:
tmpImg = np.zeros((image.shape[0], image.shape[1], 6))
tmpImgt = np.zeros((image.shape[0], image.shape[1], 3))
if image.shape[2] == 1:
tmpImgt[:, :, 0] = image[:, :, 0]
tmpImgt[:, :, 1] = image[:, :, 0]
tmpImgt[:, :, 2] = image[:, :, 0]
else:
tmpImgt = image
tmpImgtl = color.rgb2lab(tmpImgt)
tmpImg[:, :, 0] = (tmpImgt[:, :, 0]-np.min(tmpImgt[:, :, 0]))/(np.max(tmpImgt[:, :, 0])-np.min(tmpImgt[:, :, 0]))
tmpImg[:, :, 1] = (tmpImgt[:, :, 1]-np.min(tmpImgt[:, :, 1]))/(np.max(tmpImgt[:, :, 1])-np.min(tmpImgt[:, :, 1]))
tmpImg[:, :, 2] = (tmpImgt[:, :, 2]-np.min(tmpImgt[:, :, 2]))/(np.max(tmpImgt[:, :, 2])-np.min(tmpImgt[:, :, 2]))
tmpImg[:, :, 3] = (tmpImgtl[:, :, 0]-np.min(tmpImgtl[:, :, 0]))/(np.max(tmpImgtl[:, :, 0])-np.min(tmpImgtl[:, :, 0]))
tmpImg[:, :, 4] = (tmpImgtl[:, :, 1]-np.min(tmpImgtl[:, :, 1]))/(np.max(tmpImgtl[:, :, 1])-np.min(tmpImgtl[:, :, 1]))
tmpImg[:, :, 5] = (tmpImgtl[:, :, 2]-np.min(tmpImgtl[:, :, 2]))/(np.max(tmpImgtl[:, :, 2])-np.min(tmpImgtl[:, :, 2]))
tmpImg[:, :, 0] = (tmpImg[:, :, 0]-np.mean(tmpImg[:, :, 0]))/np.std(tmpImg[:, :, 0])
tmpImg[:, :, 1] = (tmpImg[:, :, 1]-np.mean(tmpImg[:, :, 1]))/np.std(tmpImg[:, :, 1])
tmpImg[:, :, 2] = (tmpImg[:, :, 2]-np.mean(tmpImg[:, :, 2]))/np.std(tmpImg[:, :, 2])
tmpImg[:, :, 3] = (tmpImg[:, :, 3]-np.mean(tmpImg[:, :, 3]))/np.std(tmpImg[:, :, 3])
tmpImg[:, :, 4] = (tmpImg[:, :, 4]-np.mean(tmpImg[:, :, 4]))/np.std(tmpImg[:, :, 4])
tmpImg[:, :, 5] = (tmpImg[:, :, 5]-np.mean(tmpImg[:, :, 5]))/np.std(tmpImg[:, :, 5])
elif self.flag == 1:
tmpImg = np.zeros((image.shape[0], image.shape[1], 3))
if image.shape[2] == 1:
tmpImg[:, :, 0] = image[:, :, 0]
tmpImg[:, :, 1] = image[:, :, 0]
tmpImg[:, :, 2] = image[:, :, 0]
else:
tmpImg = image
tmpImg = color.rgb2lab(tmpImg)
tmpImg[:, :, 0] = (tmpImg[:, :, 0]-np.min(tmpImg[:, :, 0]))/(np.max(tmpImg[:, :, 0])-np.min(tmpImg[:, :, 0]))
tmpImg[:, :, 1] = (tmpImg[:, :, 1]-np.min(tmpImg[:, :, 1]))/(np.max(tmpImg[:, :, 1])-np.min(tmpImg[:, :, 1]))
tmpImg[:, :, 2] = (tmpImg[:, :, 2]-np.min(tmpImg[:, :, 2]))/(np.max(tmpImg[:, :, 2])-np.min(tmpImg[:, :, 2]))
tmpImg[:, :, 0] = (tmpImg[:, :, 0]-np.mean(tmpImg[:, :, 0]))/np.std(tmpImg[:, :, 0])
tmpImg[:, :, 1] = (tmpImg[:, :, 1]-np.mean(tmpImg[:, :, 1]))/np.std(tmpImg[:, :, 1])
tmpImg[:, :, 2] = (tmpImg[:, :, 2]-np.mean(tmpImg[:, :, 2]))/np.std(tmpImg[:, :, 2])
else:
tmpImg = np.zeros((image.shape[0], image.shape[1], 3))
image = image/np.max(image)
if image.shape[2] == 1:
tmpImg[:, :, 0] = (image[:, :, 0]-0.4669)/0.2437
tmpImg[:, :, 1] = (image[:, :, 0]-0.4669)/0.2437
tmpImg[:, :, 2] = (image[:, :, 0]-0.4669)/0.2437
else:
tmpImg[:, :, 0] = (image[:, :, 0]-0.4669)/0.2437
tmpImg[:, :, 1] = (image[:, :, 1]-0.4669)/0.2437
tmpImg[:, :, 2] = (image[:, :, 2]-0.4669)/0.2437
tmpLbl[:, :, 0] = label[:, :, 0]
tmpImg = tmpImg.transpose((2, 0, 1))
tmpLbl = label.transpose((2, 0, 1))
return {'image': torch.from_numpy(tmpImg), 'label': torch.from_numpy(tmpLbl)}
class SalObjDataset(Dataset):
def __init__(self, img_name_list, lbl_name_list, transform=None):
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 = io.imread(self.image_name_list[idx])
if 0 == len(self.label_name_list):
label_3 = np.zeros(image.shape)
else:
label_3 = io.imread(self.label_name_list[idx])
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]
sample = {'image': image, 'label': label}
if self.transform:
sample = self.transform(sample)
return sample