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import os
import pickle
import nibabel as nib
import numpy as np
import pandas as pd
import torch
import torch.nn.functional as F
from PIL import Image
from torch.utils.data import Dataset
from utils import generate_click_prompt, random_box, random_click
class Pendal(Dataset):
def __init__(self, args, data_path , transform = None, transform_msk = None, mode = 'Training',prompt = 'click', plane = False):
self.args = args
self.data_path = data_path
self.name_list = os.listdir(os.path.join(self.data_path,'Images'))
self.mode = mode
self.prompt = prompt
self.img_size = args.image_size
self.transform = transform
self.transform_msk = transform_msk
def __len__(self):
return len(self.name_list)
def __getitem__(self, index):
# if self.mode == 'Training':
# point_label = random.randint(0, 1)
# inout = random.randint(0, 1)
# else:
# inout = 1
# point_label = 1
point_label = 1
"""Get the images"""
name = self.name_list[index]
img = Image.open(os.path.join(self.data_path, 'Images',name)).convert('RGB')
mask = Image.open(os.path.join(self.data_path, 'Segmentation1',name)).convert('L')
mask = np.array(mask)
mask[mask==mask.min()]=0
mask[mask>0] = 255
if self.prompt == 'click':
point_label, pt = random_click(np.array(mask) / 255, point_label)
if self.transform:
state = torch.get_rng_state()
img = self.transform(img)
torch.set_rng_state(state)
if self.transform_msk:
mask = Image.fromarray(mask)
mask = self.transform_msk(mask).int()
image_meta_dict = {'filename_or_obj':name}
return {
'image':img,
'label': mask,
'p_label':point_label,
'pt':pt,
'image_meta_dict':image_meta_dict,
}
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