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LoadData.py
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import os
import numpy as np
from glob import glob
from PIL import Image
import torch
from torchvision.transforms import Compose, CenterCrop, Normalize, ToTensor
from transform import ReLabel, ToLabel, Scale, HorizontalFlip, VerticalFlip, ColorJitter
import random
def makedirs(path):
if not os.path.exists(path):
os.makedirs(path)
class Dataset(torch.utils.data.Dataset):
def __init__(self, root):
self.size = (180,135)
self.root = root
if not os.path.exists(self.root):
raise Exception("[!] {} not exists.".format(root))
self.img_resize = Compose([
Scale(self.size, Image.BILINEAR),
# We can do some colorjitter augmentation here
# ColorJitter(brightness=0, contrast=0, saturation=0, hue=0),
])
self.label_resize = Compose([
Scale(self.size, Image.NEAREST),
])
self.img_transform = Compose([
ToTensor(),
Normalize(mean=[0.485, 0.456, 0.406],std=[0.229, 0.224, 0.225]),
])
self.hsv_transform = Compose([
ToTensor(),
])
self.label_transform = Compose([
ToLabel(),
ReLabel(255, 1),
])
#sort file names
self.input_paths = sorted(glob(os.path.join(self.root, '{}/*.jpg'.format("ISIC-2017_Training_Data"))))
self.label_paths = sorted(glob(os.path.join(self.root, '{}/*.png'.format("ISIC-2017_Training_Part1_GroundTruth"))))
self.name = os.path.basename(root)
if len(self.input_paths) == 0 or len(self.label_paths) == 0:
raise Exception("No images/labels are found in {}".format(self.root))
def __getitem__(self, index):
image = Image.open(self.input_paths[index]).convert('RGB')
# image_hsv = Image.open(self.input_paths[index]).convert('HSV')
label = Image.open(self.label_paths[index]).convert('P')
image = self.img_resize(image)
# image_hsv = self.img_resize(image_hsv)
label = self.label_resize(label)
# brightness_factor = 1 + random.uniform(-0.4,0.4)
# contrast_factor = 1 + random.uniform(-0.4,0.4)
# saturation_factor = 1 + random.uniform(-0.4,0.4)
# hue_factor = random.uniform(-0.1,0.1)
# gamma = 1 + random.uniform(-0.1,0.1)
#randomly flip images
if random.random() > 0.5:
image = HorizontalFlip()(image)
# image_hsv = HorizontalFlip()(image_hsv)
label = HorizontalFlip()(label)
if random.random() > 0.5:
image = VerticalFlip()(image)
# image_hsv = VerticalFlip()(image_hsv)
label = VerticalFlip()(label)
#randomly crop image to size 128*128
w, h = image.size
th, tw = (128,128)
x1 = random.randint(0, w - tw)
y1 = random.randint(0, h - th)
if w == tw and h == th:
image = image
# image_hsv = image_hsv
label = label
else:
if random.random() > 0.5:
image = image.resize((128,128),Image.BILINEAR)
# image_hsv = image_hsv.resize((128,128),Image.BILINEAR)
label = label.resize((128,128),Image.NEAREST)
else:
image = image.crop((x1, y1, x1 + tw, y1 + th))
# image_hsv = image_hsv.crop((x1, y1, x1 + tw, y1 + th))
label = label.crop((x1, y1, x1 + tw, y1 + th))
# angle = random.randint(-20, 20)
# image = image.rotate(angle, resample=Image.BILINEAR)
# image_hsv = image_hsv.rotate(angle, resample=Image.BILINEAR)
# label = label.rotate(angle, resample=Image.NEAREST)
image = self.img_transform(image)
# image_hsv = self.hsv_transform(image_hsv)
# image = torch.cat([image,image_hsv],0)
label = self.label_transform(label)
return image, label
def __len__(self):
return len(self.input_paths)
class Dataset_val(torch.utils.data.Dataset):
def __init__(self, root):
size = (128,128)
self.root = root
if not os.path.exists(self.root):
raise Exception("[!] {} not exists.".format(root))
self.img_transform = Compose([
Scale(size, Image.BILINEAR),
ToTensor(),
Normalize(mean=[0.485, 0.456, 0.406],std=[0.229, 0.224, 0.225]),
])
self.hsv_transform = Compose([
Scale(size, Image.BILINEAR),
ToTensor(),
])
self.label_transform = Compose([
Scale(size, Image.NEAREST),
ToLabel(),
ReLabel(255, 1),
])
#sort file names
self.input_paths = sorted(glob(os.path.join(self.root, '{}/*.jpg'.format("ISIC-2017_Test_v2_Data"))))
self.label_paths = sorted(glob(os.path.join(self.root, '{}/*.png'.format("ISIC-2017_Test_v2_Part1_GroundTruth"))))
self.name = os.path.basename(root)
if len(self.input_paths) == 0 or len(self.label_paths) == 0:
raise Exception("No images/labels are found in {}".format(self.root))
def __getitem__(self, index):
image = Image.open(self.input_paths[index]).convert('RGB')
# image_hsv = Image.open(self.input_paths[index]).convert('HSV')
label = Image.open(self.label_paths[index]).convert('P')
if self.img_transform is not None:
image = self.img_transform(image)
# image_hsv = self.hsv_transform(image_hsv)
else:
image = image
# image_hsv = image_hsv
if self.label_transform is not None:
label = self.label_transform(label)
else:
label = label
# image = torch.cat([image,image_hsv],0)
return image, label
def __len__(self):
return len(self.input_paths)
def loader(dataset, batch_size, num_workers=8, shuffle=True):
input_images = dataset
input_loader = torch.utils.data.DataLoader(dataset=input_images,
batch_size=batch_size,
shuffle=shuffle,
num_workers=num_workers)
return input_loader