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import os
import numpy as np
import pandas as pd
import torch
from PIL import Image
from torch.utils.data import Dataset
from utils import random_box, random_click
class ISIC2016(Dataset):
def __init__(self, args, data_path , transform = None, transform_msk = None, mode = 'Training',prompt = 'click', plane = False):
df = pd.read_csv(os.path.join(data_path, 'ISBI2016_ISIC_Part1_' + mode + '_GroundTruth.csv'), encoding='gbk')
self.name_list = df.iloc[:,1].tolist()
self.label_list = df.iloc[:,2].tolist()
self.data_path = data_path
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_path = os.path.join(self.data_path, name)
mask_name = self.label_list[index]
msk_path = os.path.join(self.data_path, mask_name)
img = Image.open(img_path).convert('RGB')
mask = Image.open(msk_path).convert('L')
# if self.mode == 'Training':
# label = 0 if self.label_list[index] == 'benign' else 1
# else:
# label = int(self.label_list[index])
newsize = (self.img_size, self.img_size)
mask = mask.resize(newsize)
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 = self.transform_msk(mask).int()
# if (inout == 0 and point_label == 1) or (inout == 1 and point_label == 0):
# mask = 1 - mask
name = name.split('/')[-1].split(".jpg")[0]
image_meta_dict = {'filename_or_obj':name}
return {
'image':img,
'label': mask,
'p_label':point_label,
'pt':pt,
'image_meta_dict':image_meta_dict,
} |