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dataset.py
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#!/usr/bin/env python
# -*- encoding: utf-8 -*-
'''
@File :dataset.py
@Description :
@Time :2021/04/12 09:41:27
@Author :Jinkui Hao
@Version :1.0
'''
import torch.utils.data as data
import torch
import numpy as np
import os
from PIL import Image
import random
from torchvision import transforms
import torchvision.transforms.functional as TF
import cv2
from scipy import misc
import scipy.io as sio
import csv
import nibabel as nib
import torchvision.transforms.functional as TF
from torch.utils.data import DataLoader
def random_crop(data, label, crop_size):
# Random crop
i, j, h, w = transforms.RandomCrop.get_params(data, output_size=(crop_size, crop_size))
data = TF.crop(data, i, j, h, w)
label = TF.crop(label, i, j, h, w)
return data, label
def img_transforms(img, label, crop_size):
trans_pad = transforms.Pad(10)
trans_tensor = transforms.ToTensor()
img, label = trans_pad(img), trans_pad(label)
img, label = random_crop(img, label, crop_size)
img, label = trans_tensor(img), trans_tensor(label)
return img, label
class datasetCT(data.Dataset):
def __init__(self, root, isOri = False, isTraining = True, dataName = 'merged'):
#dataPath = os.path.join(root,angel,illumination,struction)
self.root = root
self.isOri = isOri
self.isTrain = isTraining
self.dataName = dataName
self.pathAndLabel = self.getPathAndLabel(root, isTraining)
self.name = ''
def __getitem__(self, index):
imgPath, label = self.pathAndLabel[index]
self.name = imgPath
oriPath = os.path.join(self.root,'2.allJPG',imgPath)
segPath = os.path.join(self.root,'3.allSeg',imgPath)
oriImage = Image.open(oriPath)
oriImage = oriImage.convert('L')
segImage = Image.open(segPath)
segImage = segImage.convert('L')
imgTransform_test = transforms.Compose([
# transforms.Pad(10),
# transforms.RandomCrop(448),
transforms.CenterCrop(448),
transforms.ToTensor()
])
if self.isTrain:
rotate = 10
angel = random.randint(-rotate, rotate)
oriImage = oriImage.rotate(angel)
segImage = segImage.rotate(angel)
# gamma_v = round(np.random.uniform(0.7,1.9),2)
# oriImage = TF.adjust_gamma(img=oriImage, gamma = gamma_v)
# segImage = TF.adjust_gamma(img=segImage, gamma = gamma_v)
oriImage, segImage = img_transforms(oriImage, segImage, 448)
else:
segImage = imgTransform_test(segImage)
oriImage = imgTransform_test(oriImage)
if self.isOri:
image = torch.stack((oriImage,segImage),dim=0)
image = torch.squeeze(image)
else:
#image = segImage
image = oriImage
return image, int(label)
def __len__(self):
return len(self.pathAndLabel)
def getPathAndLabel(self,root, isTrain):
if isTrain:
#labelPath = os.path.join(root,'train.csv')
labelPath = os.path.join(root,'label','slice',self.dataName,'train.csv')
else:
#labelPath = os.path.join(root,'test.csv')
labelPath = os.path.join(root,'label','slice',self.dataName,'test.csv')
items = []
file = open(labelPath,'r')
fileReader = csv.reader(file)
for line in fileReader:
pathRelative = os.path.join(line[0],line[1],line[2])
items.append((pathRelative,line[3]))
return items
def getFileName(self):
return self.name
class datasetCTall(data.Dataset):
def __init__(self, root, isOri = False, isTraining = True, dataName = 'merged'):
#dataPath = os.path.join(root,angel,illumination,struction)
self.root = root
self.isOri = isOri
self.isTrain = isTraining
self.dataName = dataName
self.pathAndLabel = self.getPathAndLabel(root, isTraining)
self.name = ''
def __getitem__(self, index):
imgPath, label = self.pathAndLabel[index]
self.name = imgPath
oriPath = os.path.join(self.root,'2.allJPG',imgPath)
segPath = os.path.join(self.root,'4.allSegNoDiscard',imgPath)
oriImage = Image.open(oriPath)
oriImage = oriImage.convert('L')
segImage = Image.open(segPath)
segImage = segImage.convert('L')
imgTransform_test = transforms.Compose([
# transforms.Pad(10),
# transforms.RandomCrop(448),
transforms.CenterCrop(448),
transforms.ToTensor()
])
segImage = imgTransform_test(segImage)
oriImage = imgTransform_test(oriImage)
if self.isOri:
image = torch.stack((oriImage,segImage),dim=0)
image = torch.squeeze(image)
else:
image = segImage
#image = oriImage
return image, int(label)
def __len__(self):
return len(self.pathAndLabel)
def getPathAndLabel(self,root, isTrain):
labelPath = os.path.join(root,'label','graph',self.dataName,'test.csv')
items = []
file = open(labelPath,'r')
fileReader = csv.reader(file)
for line in fileReader:
pathRelative = os.path.join(line[0],line[1],line[2])
items.append((pathRelative,line[3]))
return items
def getFileName(self):
return self.name
class datasetGraphCla(data.Dataset):
#For graph classification
def __init__(self, root, isTraining = True, imgNum = 64, dataName = 'easy'):
'''
@imgNum: number of graph nodes
'''
self.root = root
self.isTrain = isTraining
self.imgNum = imgNum
self.dataName = dataName
self.pathAndLabel = self.getPathAndLabel(root, isTraining)
self.name = ''
def __getitem__(self, index):
pathList = self.pathAndLabel[index]
label = pathList[-1]
self.name = pathList[0].split('/')[-2]
nodeFeature = np.zeros((self.imgNum+1,512),dtype=np.float32)
uncertainty_all = np.zeros(self.imgNum+1,dtype=np.float32)
for i in range(self.imgNum):
allData = np.load(pathList[i])
feat = allData['arr_0'].astype(np.float32)
max_u = np.max(feat)
min_u = np.min(feat)
feat = (feat-min_u)/(max_u-min_u)
nodeFeature[i+1,:] = feat
uncertainty_one = allData['arr_1'].astype(np.float32)
uncertainty_all[i+1] = uncertainty_one
#nodeFeature[0,:] = np.mean(nodeFeature, axis=0)
uncertainty_all[0] = 0
#normalization and diagonalization of uncertainty
max_u = np.max(uncertainty_all)
min_u = np.min(uncertainty_all)
uncertainty_all = (uncertainty_all-min_u)/(max_u-min_u)
uncertainty_all = np.diag(uncertainty_all)
featTransform = transforms.Compose([
transforms.ToTensor()
])
nodeFeature = featTransform(nodeFeature)
uncertainty_all = featTransform(uncertainty_all)
#image = oriImage
return nodeFeature, uncertainty_all, int(label)
def __len__(self):
return len(self.pathAndLabel)
def getPathAndLabel(self,root, isTrain):
#
if isTrain:
#labelPath = os.path.join(root,'train.csv')
labelPath = os.path.join(root,'label','graph',self.dataName,'train.csv')
else:
#labelPath = os.path.join(root,'test.csv')
labelPath = os.path.join(root,'label','graph',self.dataName,'test.csv')
items = []
file = open(labelPath,'r')
fileReader = csv.reader(file)
currentName = ''
for line in fileReader:
graphPath = []
if currentName == line[1]:
continue
currentName = line[1]
pathRelative = os.path.join(self.root, '5.featForGraph','merged', line[0],line[1])
featList = os.listdir(pathRelative)
featList.sort()
featNum = len(featList)
if featNum < self.imgNum:
#copy
copyNum = self.imgNum-featNum
lastName = ''
for name in featList:
graphPath.append(os.path.join(self.root, '5.featForGraph','merged', line[0],line[1],name))
lastName = name
for i in range(copyNum):
graphPath.append(os.path.join(self.root, '5.featForGraph', 'merged', line[0],line[1],lastName))
else:
startNum = int((featNum-self.imgNum)/2)
for i in range(self.imgNum):
graphPath.append(os.path.join(self.root, '5.featForGraph','merged', line[0],line[1],featList[startNum+i]))
graphPath.append(line[3])
items.append(graphPath)
return items
def getFileName(self):
return self.name