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dataset.py
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dataset.py
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from torch.utils.data import Dataset
import os
import cv2
import torch
import numpy as np
from onehot import onehot
from torchvision import transforms
transform = transforms.Compose([transforms.ToTensor(),transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])])
class CustomDataset(Dataset):
def __init__(self,df,img_dir,mask_dir,transform=transform):
self.transform = transform
self.fname = df['0'].values.tolist()
self.fname1 = df['1'].values.tolist()
self.mask_dir = mask_dir
self.img_dir = img_dir
def __len__(self):
return len(self.fname)
def __getitem__(self,idx):
img_path = self.fname[idx]
img = cv2.imread(img_path)
img = cv2.resize(img,(160,160))
mask_path = self.fname1[idx]
mask = cv2.imread(mask_path,0)
mask = cv2.resize(mask,(160,160))
mask = mask/38
mask = mask.astype(np.uint8)
mask = onehot(mask,2)
mask = mask.swapaxes(0,2).swapaxes(1,2)
mask = torch.FloatTensor(mask)
if self.transform:
img = self.transform(img)
item = {"img":img,'mask':mask}
return item