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dataset.py
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dataset.py
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# =============================================================================
# Dataset loader for the training process
# =============================================================================
from torch.utils.data import Dataset
from torchvision import models, transforms
import pickle
import torch
import numpy as np
import cv2
import glob
from PIL import Image, ImageOps
import PIL
import os
from utils_files.automatic_brightness_and_contrast import automatic_brightness_and_contrast
class Dataset(Dataset):
def __init__(self, type, transform, dataset, Z, fold):
self.X, self.Y = pickle.load(open(f'dataset\\data_{dataset}.pickle', 'rb'))[fold][type]
self.transform = transform
self.Z = Z
def __getitem__(self, i):
X0 = self.transform(self.X[i][0])
X1 = self.transform(self.X[i][1])
X2 = self.transform(self.X[i][2])
if self.Z==5:
X3 = self.transform(self.X[i][3])
X4 = self.transform(self.X[i][4])
elif self.Z==7:
X3 = self.transform(self.X[i][3])
X4 = self.transform(self.X[i][4])
X5 = self.transform(self.X[i][5])
X6 = self.transform(self.X[i][6])
elif self.Z==9:
X3 = self.transform(self.X[i][3])
X4 = self.transform(self.X[i][4])
X5 = self.transform(self.X[i][5])
X6 = self.transform(self.X[i][6])
X5 = self.transform(self.X[i][5])
X6 = self.transform(self.X[i][6])
X7 = self.transform(self.X[i][7])
X8 = self.transform(self.X[i][8])
Y = self.transform(self.Y[i])
if self.Z==3:
X_all=[X0, X1, X2]
elif self.Z==5:
X_all=[X0, X1, X2, X3, X4]
elif self.Z==7:
X_all=[X0, X1, X2, X3, X4, X5, X6]
elif self.Z==9:
X_all=[X0, X1, X2, X3, X4, X5, X6, X7, X8]
r=torch.randperm(self.Z)
#return X_all, Y
return [X_all[u] for u in r],Y
def __len__(self):
return len(self.X)
class Dataset_folder(Dataset):
def __init__(self, transform, path, Z, img_size):
self.path = path
X_STACKS_per_folder=[]
for idxx,image_name in enumerate(glob.glob(os.path.join(self.path, "*.jpg"))):
im = cv2.imread(image_name)
imnew=cv2.resize(im,(img_size,img_size))
X_STACKS_per_folder.append(imnew)
self.X = np.array(X_STACKS_per_folder)
self.X = np.expand_dims(self.X, axis=0)
self.transform = transform
self.Z = Z
def __getitem__(self, i):
X0 = self.transform(self.X[i][0])
X1 = self.transform(self.X[i][1])
X2 = self.transform(self.X[i][2])
if self.Z==5:
X3 = self.transform(self.X[i][3])
X4 = self.transform(self.X[i][4])
elif self.Z==7:
X3 = self.transform(self.X[i][3])
X4 = self.transform(self.X[i][4])
X5 = self.transform(self.X[i][5])
X6 = self.transform(self.X[i][6])
elif self.Z==9:
X5 = self.transform(self.X[i][5])
X6 = self.transform(self.X[i][6])
X7 = self.transform(self.X[i][7])
X8 = self.transform(self.X[i][8])
# Y = self.transform(self.Y[i])
if self.Z==3:
X_all=[X0, X1, X2]
elif self.Z==5:
X_all=[X0, X1, X2, X3, X4]
elif self.Z==7:
X_all=[X0, X1, X2, X3, X4, X5, X6]
elif self.Z==9:
X_all=[X0, X1, X2, X3, X4, X5, X6, X7, X8]
r=torch.randperm(self.Z)
#return X_all, Y
return [X_all[u] for u in r]
def __len__(self):
return len(self.X)
aug_transforms = transforms.Compose([
transforms.ToPILImage(),
transforms.Resize((512, 512)),
# automatic_brightness_and_contrast(clip_hist_perc=0.5),
transforms.Grayscale(num_output_channels=1),
transforms.ToTensor(),
# vgg normalization
# transforms.Normalize((0.485, 0.456, 0.406), (0.229, 0.224, 0.225))
])
val_transforms = transforms.Compose([
transforms.ToPILImage(),
transforms.Resize((512, 512)),
# automatic_brightness_and_contrast(clip_hist_perc=0.5),
transforms.Grayscale(num_output_channels=1),
transforms.ToTensor(), # vgg normalization
# transforms.Normalize((0.485, 0.456, 0.406), (0.229, 0.224, 0.225))
])
aug_transforms_rgb = transforms.Compose([
transforms.ToPILImage(),
transforms.Resize((512, 512)),
# automatic_brightness_and_contrast(clip_hist_perc=0.5),
transforms.ToTensor(),
# vgg normalization
# transforms.Normalize((0.485, 0.456, 0.406), (0.229, 0.224, 0.225))
])
val_transforms_rgb = transforms.Compose([
transforms.ToPILImage(),
# transforms.Resize((512, 512)),
# automatic_brightness_and_contrast(clip_hist_perc=0.5),
transforms.ToTensor(), # vgg normalization
# transforms.Normalize((0.485, 0.456, 0.406), (0.229, 0.224, 0.225))
])
# if __name__ == '__main__':
# ds = Dataset('test', aug_transforms, 'cervix93', 7, 0)
# X0, X1, X2, X3, X4 = ds[0]
# dsss=ds[1][0]