import numpy as np
from torch.utils import data
import torch
import os
from  skimage.io import imread
import augmenter


class HistoDataset(data.Dataset):
    def __init__(self, split,path_to_data,s1=0,s2=0,s3=0,s1m=0,s2m=0,s3m=0,cn='no'):
        self.split=split
        self.s1=s1
        self.s2=s2
        self.s3=s3
        self.s1m=s1m
        self.s2m=s2m
        self.s3m=s3m
        self.cn=cn
       

        self.folder_path=os.path.join(path_to_data,split)
        
        self.subfolders = [f.path for f in os.scandir(self.folder_path) if f.is_dir() ] 
        
        
        self.file_names=[]
        for root, dirs, files in os.walk(self.folder_path):
            for name in files:
                if name.endswith(".tif") and name.startswith("data_1_"):
                    self.file_names.append(os.path.join(root,name))
                    
        self.num_of_img=len(self.file_names)

    def __len__(self):
        return self.num_of_img



    def __getitem__(self, index):
        
        name=self.file_names[index]
        
        name_s1=name
        name_s2=name.replace('data_1_','data_2_')
        name_s3=name.replace('data_2_','data_3_')
        
        name_s1m=name.replace('data_1_','mask_1_')
        name_s2m=name.replace('data_2_','mask_2_')
        name_s3m=name.replace('data_3_','mask_3_')
        
        lbl=int(name[name.rfind('data_1_')+1+6:name.rfind('data_1_')+2+6])
        
        
        data_im=[]
        data_mask=[]
        if self.s1:
            data_im.append(imread(name_s1))
        if self.s2:
            data_im.append(imread(name_s2))
        if self.s3:
            data_im.append(imread(name_s3))
        if self.s1m:
            data_mask.append(imread(name_s1m)>0)
        if self.s2m:
            data_mask.append(imread(name_s2m)>0)
        if self.s3m:
            data_mask.append(imread(name_s3m)>0)
            
            

        if self.cn=='no':    
            data_tmp=[]
            for img in data_im:
                data_tmp.append(img/(255/2)-1)
            data_im=data_tmp                

        
            
        rand_rot_flip=[np.random.randint(2),np.random.randint(2),np.random.randint(4)]   
        
        data_tmp=[]
        for im in data_im:
            im=np.float32(im)
            if  (self.split == 'train'):
                im=augmenter.my_rot_flip(im,rand_rot_flip)
            im=np.transpose(im,(2, 0, 1)).copy()
            im=torch.from_numpy(im)
            
            data_tmp.append(im)
        data_im=data_tmp
        
        data_tmp=[]
        for im in data_mask:
            im=np.float32(im)
            if  (self.split == 'train'):
                im=augmenter.my_rot_flip(im,rand_rot_flip)
            im=np.expand_dims(im ,axis=2)
            im=np.transpose(im,(2, 0, 1)).copy()
            im=torch.from_numpy(im)
            
            data_tmp.append(im)
        data_mask=data_tmp
        
        return data_im,data_mask,lbl
        
        
        
        
        
        
            

            
                    
        

9f018da4a6a7b25360ccfcc74a2d08c3aa4789cd
<mask token>


class HistoDataset(data.Dataset):
    <mask token>
    <mask token>
    <mask token>

<mask token>


class HistoDataset(data.Dataset):

    def __init__(self, split, path_to_data, s1=0, s2=0, s3=0, s1m=0, s2m=0,
        s3m=0, cn='no'):
        self.split = split
        self.s1 = s1
        self.s2 = s2
        self.s3 = s3
        self.s1m = s1m
        self.s2m = s2m
        self.s3m = s3m
        self.cn = cn
        self.folder_path = os.path.join(path_to_data, split)
        self.subfolders = [f.path for f in os.scandir(self.folder_path) if
            f.is_dir()]
        self.file_names = []
        for root, dirs, files in os.walk(self.folder_path):
            for name in files:
                if name.endswith('.tif') and name.startswith('data_1_'):
                    self.file_names.append(os.path.join(root, name))
        self.num_of_img = len(self.file_names)
    <mask token>

    def __getitem__(self, index):
        name = self.file_names[index]
        name_s1 = name
        name_s2 = name.replace('data_1_', 'data_2_')
        name_s3 = name.replace('data_2_', 'data_3_')
        name_s1m = name.replace('data_1_', 'mask_1_')
        name_s2m = name.replace('data_2_', 'mask_2_')
        name_s3m = name.replace('data_3_', 'mask_3_')
        lbl = int(name[name.rfind('data_1_') + 1 + 6:name.rfind('data_1_') +
            2 + 6])
        data_im = []
        data_mask = []
        if self.s1:
            data_im.append(imread(name_s1))
        if self.s2:
            data_im.append(imread(name_s2))
        if self.s3:
            data_im.append(imread(name_s3))
        if self.s1m:
            data_mask.append(imread(name_s1m) > 0)
        if self.s2m:
            data_mask.append(imread(name_s2m) > 0)
        if self.s3m:
            data_mask.append(imread(name_s3m) > 0)
        if self.cn == 'no':
            data_tmp = []
            for img in data_im:
                data_tmp.append(img / (255 / 2) - 1)
            data_im = data_tmp
        rand_rot_flip = [np.random.randint(2), np.random.randint(2), np.
            random.randint(4)]
        data_tmp = []
        for im in data_im:
            im = np.float32(im)
            if self.split == 'train':
                im = augmenter.my_rot_flip(im, rand_rot_flip)
            im = np.transpose(im, (2, 0, 1)).copy()
            im = torch.from_numpy(im)
            data_tmp.append(im)
        data_im = data_tmp
        data_tmp = []
        for im in data_mask:
            im = np.float32(im)
            if self.split == 'train':
                im = augmenter.my_rot_flip(im, rand_rot_flip)
            im = np.expand_dims(im, axis=2)
            im = np.transpose(im, (2, 0, 1)).copy()
            im = torch.from_numpy(im)
            data_tmp.append(im)
        data_mask = data_tmp
        return data_im, data_mask, lbl

<mask token>


class HistoDataset(data.Dataset):

    def __init__(self, split, path_to_data, s1=0, s2=0, s3=0, s1m=0, s2m=0,
        s3m=0, cn='no'):
        self.split = split
        self.s1 = s1
        self.s2 = s2
        self.s3 = s3
        self.s1m = s1m
        self.s2m = s2m
        self.s3m = s3m
        self.cn = cn
        self.folder_path = os.path.join(path_to_data, split)
        self.subfolders = [f.path for f in os.scandir(self.folder_path) if
            f.is_dir()]
        self.file_names = []
        for root, dirs, files in os.walk(self.folder_path):
            for name in files:
                if name.endswith('.tif') and name.startswith('data_1_'):
                    self.file_names.append(os.path.join(root, name))
        self.num_of_img = len(self.file_names)

    def __len__(self):
        return self.num_of_img

    def __getitem__(self, index):
        name = self.file_names[index]
        name_s1 = name
        name_s2 = name.replace('data_1_', 'data_2_')
        name_s3 = name.replace('data_2_', 'data_3_')
        name_s1m = name.replace('data_1_', 'mask_1_')
        name_s2m = name.replace('data_2_', 'mask_2_')
        name_s3m = name.replace('data_3_', 'mask_3_')
        lbl = int(name[name.rfind('data_1_') + 1 + 6:name.rfind('data_1_') +
            2 + 6])
        data_im = []
        data_mask = []
        if self.s1:
            data_im.append(imread(name_s1))
        if self.s2:
            data_im.append(imread(name_s2))
        if self.s3:
            data_im.append(imread(name_s3))
        if self.s1m:
            data_mask.append(imread(name_s1m) > 0)
        if self.s2m:
            data_mask.append(imread(name_s2m) > 0)
        if self.s3m:
            data_mask.append(imread(name_s3m) > 0)
        if self.cn == 'no':
            data_tmp = []
            for img in data_im:
                data_tmp.append(img / (255 / 2) - 1)
            data_im = data_tmp
        rand_rot_flip = [np.random.randint(2), np.random.randint(2), np.
            random.randint(4)]
        data_tmp = []
        for im in data_im:
            im = np.float32(im)
            if self.split == 'train':
                im = augmenter.my_rot_flip(im, rand_rot_flip)
            im = np.transpose(im, (2, 0, 1)).copy()
            im = torch.from_numpy(im)
            data_tmp.append(im)
        data_im = data_tmp
        data_tmp = []
        for im in data_mask:
            im = np.float32(im)
            if self.split == 'train':
                im = augmenter.my_rot_flip(im, rand_rot_flip)
            im = np.expand_dims(im, axis=2)
            im = np.transpose(im, (2, 0, 1)).copy()
            im = torch.from_numpy(im)
            data_tmp.append(im)
        data_mask = data_tmp
        return data_im, data_mask, lbl

import numpy as np
from torch.utils import data
import torch
import os
from skimage.io import imread
import augmenter


class HistoDataset(data.Dataset):

    def __init__(self, split, path_to_data, s1=0, s2=0, s3=0, s1m=0, s2m=0,
        s3m=0, cn='no'):
        self.split = split
        self.s1 = s1
        self.s2 = s2
        self.s3 = s3
        self.s1m = s1m
        self.s2m = s2m
        self.s3m = s3m
        self.cn = cn
        self.folder_path = os.path.join(path_to_data, split)
        self.subfolders = [f.path for f in os.scandir(self.folder_path) if
            f.is_dir()]
        self.file_names = []
        for root, dirs, files in os.walk(self.folder_path):
            for name in files:
                if name.endswith('.tif') and name.startswith('data_1_'):
                    self.file_names.append(os.path.join(root, name))
        self.num_of_img = len(self.file_names)

    def __len__(self):
        return self.num_of_img

    def __getitem__(self, index):
        name = self.file_names[index]
        name_s1 = name
        name_s2 = name.replace('data_1_', 'data_2_')
        name_s3 = name.replace('data_2_', 'data_3_')
        name_s1m = name.replace('data_1_', 'mask_1_')
        name_s2m = name.replace('data_2_', 'mask_2_')
        name_s3m = name.replace('data_3_', 'mask_3_')
        lbl = int(name[name.rfind('data_1_') + 1 + 6:name.rfind('data_1_') +
            2 + 6])
        data_im = []
        data_mask = []
        if self.s1:
            data_im.append(imread(name_s1))
        if self.s2:
            data_im.append(imread(name_s2))
        if self.s3:
            data_im.append(imread(name_s3))
        if self.s1m:
            data_mask.append(imread(name_s1m) > 0)
        if self.s2m:
            data_mask.append(imread(name_s2m) > 0)
        if self.s3m:
            data_mask.append(imread(name_s3m) > 0)
        if self.cn == 'no':
            data_tmp = []
            for img in data_im:
                data_tmp.append(img / (255 / 2) - 1)
            data_im = data_tmp
        rand_rot_flip = [np.random.randint(2), np.random.randint(2), np.
            random.randint(4)]
        data_tmp = []
        for im in data_im:
            im = np.float32(im)
            if self.split == 'train':
                im = augmenter.my_rot_flip(im, rand_rot_flip)
            im = np.transpose(im, (2, 0, 1)).copy()
            im = torch.from_numpy(im)
            data_tmp.append(im)
        data_im = data_tmp
        data_tmp = []
        for im in data_mask:
            im = np.float32(im)
            if self.split == 'train':
                im = augmenter.my_rot_flip(im, rand_rot_flip)
            im = np.expand_dims(im, axis=2)
            im = np.transpose(im, (2, 0, 1)).copy()
            im = torch.from_numpy(im)
            data_tmp.append(im)
        data_mask = data_tmp
        return data_im, data_mask, lbl

import numpy as np
from torch.utils import data
import torch
import os
from  skimage.io import imread
import augmenter


class HistoDataset(data.Dataset):
    def __init__(self, split,path_to_data,s1=0,s2=0,s3=0,s1m=0,s2m=0,s3m=0,cn='no'):
        self.split=split
        self.s1=s1
        self.s2=s2
        self.s3=s3
        self.s1m=s1m
        self.s2m=s2m
        self.s3m=s3m
        self.cn=cn
       

        self.folder_path=os.path.join(path_to_data,split)
        
        self.subfolders = [f.path for f in os.scandir(self.folder_path) if f.is_dir() ] 
        
        
        self.file_names=[]
        for root, dirs, files in os.walk(self.folder_path):
            for name in files:
                if name.endswith(".tif") and name.startswith("data_1_"):
                    self.file_names.append(os.path.join(root,name))
                    
        self.num_of_img=len(self.file_names)

    def __len__(self):
        return self.num_of_img



    def __getitem__(self, index):
        
        name=self.file_names[index]
        
        name_s1=name
        name_s2=name.replace('data_1_','data_2_')
        name_s3=name.replace('data_2_','data_3_')
        
        name_s1m=name.replace('data_1_','mask_1_')
        name_s2m=name.replace('data_2_','mask_2_')
        name_s3m=name.replace('data_3_','mask_3_')
        
        lbl=int(name[name.rfind('data_1_')+1+6:name.rfind('data_1_')+2+6])
        
        
        data_im=[]
        data_mask=[]
        if self.s1:
            data_im.append(imread(name_s1))
        if self.s2:
            data_im.append(imread(name_s2))
        if self.s3:
            data_im.append(imread(name_s3))
        if self.s1m:
            data_mask.append(imread(name_s1m)>0)
        if self.s2m:
            data_mask.append(imread(name_s2m)>0)
        if self.s3m:
            data_mask.append(imread(name_s3m)>0)
            
            

        if self.cn=='no':    
            data_tmp=[]
            for img in data_im:
                data_tmp.append(img/(255/2)-1)
            data_im=data_tmp                

        
            
        rand_rot_flip=[np.random.randint(2),np.random.randint(2),np.random.randint(4)]   
        
        data_tmp=[]
        for im in data_im:
            im=np.float32(im)
            if  (self.split == 'train'):
                im=augmenter.my_rot_flip(im,rand_rot_flip)
            im=np.transpose(im,(2, 0, 1)).copy()
            im=torch.from_numpy(im)
            
            data_tmp.append(im)
        data_im=data_tmp
        
        data_tmp=[]
        for im in data_mask:
            im=np.float32(im)
            if  (self.split == 'train'):
                im=augmenter.my_rot_flip(im,rand_rot_flip)
            im=np.expand_dims(im ,axis=2)
            im=np.transpose(im,(2, 0, 1)).copy()
            im=torch.from_numpy(im)
            
            data_tmp.append(im)
        data_mask=data_tmp
        
        return data_im,data_mask,lbl
        
        
        
        
        
        
            

            
                    
        
