fakerlove1
commented
9 months ago 这是由于ISIC.py的数据增强出现的问题。albumentations库坑太多。等我开始写论文，准备在更新最新的算法。
This is due to a problem with data augmentation in ISIC.py. The albumentations library has too many pits. I'm going to be updating the latest algorithm when I start working on my thesis.
from typing import Any
import torch
import numpy as np
from torch.utils.data import Dataset, DataLoader
import albumentations as A
from PIL import Image
from albumentations.pytorch.transforms import ToTensorV2
# from albumentations.pytorch.transforms import
import matplotlib.pyplot as plt
from torchvision.utils import make_grid
from scipy import ndimage
from skimage import io
# import albumentations.augmentations.transforms as A
from scipy.ndimage import zoom
import random
import cv2
import random
import math
import numpy as np
from PIL import Image, ImageOps, ImageFilter
import torch
from torchvision import transforms
class Transform():
def __init__(self,mode="train",size=224) -> None:
self.mode=mode
self.size=size
def __call__(self, image:Image.Image,mask:Image.Image) -> Any:
if self.mode == 'test':
image, mask = resize(image, mask,self.size)
image, mask = normalize(image, mask)
return {
"image":image,
"mask":mask
}
image, mask = rand_resize(image, mask, (0.5, 2.0))
image, mask = crop(image, mask, self.size, 255)
image, mask = hflip(image, mask, p=0.5)
image, mask = normalize(image, mask)
return {
"image":image,
"mask":mask
}
def crop(img, mask, size, ignore_value=255):
w, h = img.size
padw = size - w if w < size else 0
padh = size - h if h < size else 0
img = ImageOps.expand(img, border=(0, 0, padw, padh), fill=0)
mask = ImageOps.expand(mask, border=(0, 0, padw, padh), fill=ignore_value)
w, h = img.size
x = random.randint(0, w - size)
y = random.randint(0, h - size)
img = img.crop((x, y, x + size, y + size))
mask = mask.crop((x, y, x + size, y + size))
return img, mask
def hflip(img, mask, p=0.5):
if random.random() < p:
img = img.transpose(Image.FLIP_LEFT_RIGHT)
mask = mask.transpose(Image.FLIP_LEFT_RIGHT)
return img, mask
def normalize(img, mask=None):
img = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]),
])(img)
if mask is not None:
mask = torch.from_numpy(np.array(mask)).long()
return img, mask
return img
def rand_resize(img, mask, ratio_range):
w, h = img.size
long_side = random.randint(int(max(h, w) * ratio_range[0]), int(max(h, w) * ratio_range[1]))
if h > w:
oh = long_side
ow = int(1.0 * w * long_side / h + 0.5)
else:
ow = long_side
oh = int(1.0 * h * long_side / w + 0.5)
img = img.resize((ow, oh), Image.BILINEAR)
mask = mask.resize((ow, oh), Image.NEAREST)
return img, mask
def resize(img, mask, size):
img = img.resize((size,size), Image.BILINEAR)
mask = mask.resize((size,size), Image.NEAREST)
return img, mask
def blur(img, p=0.5):
if random.random() < p:
sigma = np.random.uniform(0.1, 2.0)
img = img.filter(ImageFilter.GaussianBlur(radius=sigma))
return img
class ISIC(Dataset):
PALETTE = np.array([
[0, 0, 0],
[255, 255, 255],
])
def __init__(self, root=r"E:\note\ssl\data\ACDC", split="train", transform=None, index=None):
super(ISIC, self).__init__()
self.split = split
self.root = root
self.transform = transform
self.img_dir = []
self.ann_dir = []
self.load_annotations() # 加载文件路径
print("total {} samples".format(len(self.img_dir)))
def __len__(self):
return len(self.img_dir)
def __getitem__(self, idx):
image = Image.open(self.img_dir[idx]).convert("RGB")
mask = np.array(Image.open(self.ann_dir[idx]).convert("L"),dtype=np.uint8)
# image = image.astype('float32') / 255
mask[mask > 0] = 1
mask =Image.fromarray(mask)
if self.transform is not None:
result = self.transform(image=image, mask=mask)
image = result["image"]
mask = result["mask"]
return image, mask
def label_to_img(self, label):
if isinstance(label, torch.Tensor):
label = label.cpu().numpy()
if not isinstance(label, np.ndarray):
label = np.array(label)
label = label.astype(np.uint8)
label[label == 255] = 0
img = self.PALETTE[label]
if len(img.shape) == 4:
img = torch.tensor(img).permute(0, 3, 1, 2)
img = make_grid(tensor=img, nrow=8, scale_each=True)
img = img.permute(1, 2, 0).numpy()
return img.astype(np.uint8)
def load_annotations(self):
if self.split == "train":
with open(self.root + "/train.txt", "r") as f:
self.sample_list = f.readlines()
else:
with open(self.root + "/test.txt", "r") as f:
self.sample_list = f.readlines()
self.sample_list = [item.replace("\n", "") for item in self.sample_list]
self.img_dir = [self.root + "/image/{}.jpg".format(item) for item in self.sample_list]
self.ann_dir = [self.root + "/gt/{}_segmentation.png".format(item) for item in self.sample_list]
self.img_dir = np.array(self.img_dir)
self.ann_dir = np.array(self.ann_dir)
def get_isic_loader(root=r'/home/ubuntu/data/ISIC2018_224', batch_size=2, train_crop_size=(224, 224)):
"""
:param root:
:param batch_size: 批次大小
:param label: 有标签的数量
:return:
"""
train_transform = A.Compose([
# A.R
# A.RandomCrop(train_crop_size[0], train_crop_size[1],interpolation=cv2.INTER_LINEAR,always_apply=True,p=1),
# A.RandomResizedCrop(height=train_crop_size[0], width=train_crop_size[1], scale=(0.75, 1.5),interpolation=cv2.INTER_CUBIC),
A.HorizontalFlip(p=0.5),
A.ShiftScaleRotate(p=0.6),
A.ColorJitter(0.4, 0.4, 0.4, p=0.5),
ToTensorV2()
])
test_transform = A.Compose([
# A.Resize(train_crop_size[0], trin_crop_size[1]),
ToTensorV2()
])
train_dataset = ISIC(root=root, split="train", transform=train_transform)
test_dataset = ISIC(root=root, split="test", transform=test_transform)
train_dataloader = DataLoader(train_dataset, batch_size=batch_size, num_workers=4, shuffle=True, drop_last=True)
test_dataloader = DataLoader(test_dataset, batch_size=batch_size, num_workers=4, shuffle=False)
return train_dataloader, test_dataloader
def get_ssl_isic_loader(root=r'/home/ubuntu/data/ISIC2018_224',
batch_size=8,
unlabel_batch_size=24,
train_crop_size=(224, 224),
label_num=0.2):
"""
:param root: 数据集路径
:param batch_size: 有标注数据批次大小
:param unlabel_batch_size: 无标注数据的batch大小
:param label_num: 有标签的数量
:return:
"""
# train_transform = A.Compose([
# A.RandomResizedCrop(height=train_crop_size[0], width=train_crop_size[1], scale=(0.5, 2.0),interpolation=cv2.INTER_LINEAR,always_apply=True,p=1),
# A.HorizontalFlip(p=0.5),
# A.ShiftScaleRotate(p=0.6),
# A.RandomBrightnessContrast(p=0.2),
# # A.ColorJitter(0.4, 0.4, 0.4, p=0.5),
# ToTensorV2()
# ])
# test_transform = A.Compose([
# # A.Resize(height=train_crop_size[0], width=train_crop_size[1]),
# ToTensorV2()
# ])
# from .augmentation import ToTensor,Normalize,RandResize,RandomHorizontalFlip,RandRotate,Compose,Resize,Crop,RandomGaussianBlur
# # import .augmentation as A
# train_transform = Compose([
# ToTensor(),
# Normalize([0.5,0.5,0.5],[0.5,0.5,0.5]),
# RandResize((0.5,2.0)),
# Crop(train_crop_size,crop_type='rand'),
# RandomHorizontalFlip(),
# ])
train_transform = Transform(mode="train",size= train_crop_size[0])
test_transform =Transform(mode="test",size= train_crop_size[0])
# test_transform = Compose([
# ToTensor(),
# Normalize([0.5,0.5,0.5],[0.5,0.5,0.5]),
# Resize(train_crop_size),
# ])
# from .utils import RandomGenerator,TwoStreamBatchSampler,patients_to_slices
# train_transform=RandomGenerator(train_crop_size)
train_dataset = ISIC(root=root, split="train", transform=train_transform)
label_length = int(len(train_dataset) * label_num)
train_label, train_unlabel = torch.utils.data.random_split(dataset=train_dataset,
lengths=[label_length, len(train_dataset) - label_length])
test_dataset = ISIC(root=root, split="test", transform=test_transform)
label_loader = DataLoader(train_label, batch_size=batch_size, num_workers=4, shuffle=True, drop_last=True)
unlabel_loader = DataLoader(train_unlabel, batch_size=unlabel_batch_size, num_workers=4, shuffle=True, drop_last=True)
test_loader = DataLoader(test_dataset, batch_size=batch_size, num_workers=4, shuffle=False)
return label_loader, unlabel_loader, test_loader
def show(im):
im = im.permute(1, 2, 0).numpy()
# image=Image.fromarray(im).convert('RGB')
# image.save("result.jpg")
fig = plt.figure()
plt.imshow(im)
plt.show()
fig.savefig("result.jpg")
def show_label(mask, path="label.jpg"):
plt.figure()
plt.imshow(mask)
plt.show()
Image.fromarray(mask).save(path)
if __name__ == '__main__':
train_dataloader, test_dataloader = get_isic_loader()
print(len(train_dataloader.dataset))
print(len(test_dataloader.dataset))
for image, label in train_dataloader:
print(image.shape)
print(label.shape)
print(np.max(image.numpy()))
print(np.min(image.numpy()))
print(np.unique(label.numpy()))
show(image[0])
show_label(train_dataloader.dataset.label_to_img(label))
break
for sample in test_dataloader:
image, label = sample
print(image.shape)
print(label.shape)
print(np.max(image.numpy()))
print(np.min(image.numpy()))
print(np.unique(label.numpy()))
# show(image[0])
# show_label(label[0].numpy())
break
When I was running ISIC.py, the following error occurred. How can I solve it?