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u2net_utils.py
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import numpy as np
import torch
from skimage import io, transform, color
from torch.utils.data import Dataset
class SalObjDataset(Dataset):
def __init__(self, imgs_list, lbl_name_list, transform=None):
self.imgs_list = imgs_list
self.label_name_list = lbl_name_list
self.transform = transform
def __len__(self):
return len(self.imgs_list)
def __getitem__(self, idx):
image = np.array(self.imgs_list[idx])
imidx = np.array([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 = {'imidx': imidx, 'image': image, 'label': label}
if self.transform:
sample = self.transform(sample)
return sample['image']
class RescaleT(object):
def __init__(self, output_size):
assert isinstance(output_size, (int, tuple))
self.output_size = output_size
def __call__(self, sample):
imidx, image, label = sample['imidx'], 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 {'imidx': imidx, 'image': img, 'label': lbl}
class ToTensorLab(object):
"""Convert ndarrays in sample to Tensors."""
def __init__(self, flag=0):
self.flag = flag
def __call__(self, sample):
imidx, image, label = sample['imidx'], sample['image'], sample['label']
tmpLbl = np.zeros(label.shape)
if (np.max(label) < 1e-6):
label = label
else:
label = label / np.max(label)
# change the color space
if self.flag == 2: # with rgb and Lab colors
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)
# nomalize image to range [0,1]
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 = tmpImg/(np.max(tmpImg)-np.min(tmpImg))
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: # with Lab color
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 = tmpImg/(np.max(tmpImg)-np.min(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: # with rgb color
tmpImg = np.zeros((image.shape[0], image.shape[1], 3))
image = image / np.max(image)
if image.shape[2] == 1:
tmpImg[:, :, 0] = (image[:, :, 0] - 0.485) / 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]
tmpImg = tmpImg.transpose((2, 0, 1))
tmpLbl = label.transpose((2, 0, 1))
return {'imidx': torch.from_numpy(imidx), 'image': torch.from_numpy(tmpImg), 'label': torch.from_numpy(tmpLbl)}