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utils.py
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import numpy as np
import pandas as pd
import os
from torch.utils.data import Dataset, DataLoader
import torch
from torchvision import transforms
from PIL import Image
import random
import math
from matplotlib import pyplot as plt
from torch.utils.data import DataLoader, TensorDataset
from glob import glob
import cv2
class ImageDataset(Dataset):
def __init__(self, labels, image_path, image_size, single_channel=True, random_transform=True):
self.labels = torch.tensor(labels, dtype=torch.long)
self.image_path = image_path
self.image_size = image_size
self.single_channel = single_channel
self.random_transform = random_transform
self.normalize = transforms.Compose(
[
# todo
# transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]),
transforms.Normalize([0.5], [0.5]),
])
self.transform_with_random = transforms.Compose(
[
transforms.ToTensor(),
transforms.RandomHorizontalFlip(p=0.5),
transforms.RandomVerticalFlip(p=0.5),
transforms.Resize(size=self.image_size),
transforms.RandomCrop(size=self.image_size),
])
self.transform_without_random = transforms.Compose(
[
transforms.ToTensor(),
transforms.Resize(size=self.image_size),
transforms.CenterCrop(size=self.image_size),
])
def get_image(self, path):
image = Image.open(path)
if self.single_channel:
image = image.convert('L')
if self.random_transform:
image = self.transform_with_random(image)
else:
image = self.transform_without_random(image)
image = self.normalize(image)
return image
def __len__(self):
return len(self.image_path)
def __getitem__(self, idx):
image = self.get_image(self.image_path[idx])
label = self.labels[idx]
return image, label
def create_dataloaders(dataset_train, dataset_test, params):
temp_params = params.copy()
shuffle_train = temp_params.pop('shuffle_train')
shuffle_test = temp_params.pop('shuffle_test')
dataloader_train = DataLoader(dataset_train, shuffle=shuffle_train, **temp_params)
dataloader_test = DataLoader(dataset_test, shuffle=shuffle_test, **temp_params)
return dataloader_train, dataloader_test
def get_dataloaders_imagedata(df_train, df_test, image_size, dataloader_params, single_channel=True, random_transform_train=True, random_transform_test=False):
dataset_train = ImageDataset(df_train.label_cat.values, df_train.image_path.values, image_size, single_channel, random_transform_train)
dataset_test = ImageDataset(df_test.label_cat.values, df_test.image_path.values, image_size, single_channel, random_transform_test)
return create_dataloaders(dataset_train, dataset_test, dataloader_params)
def get_dataloaders_clf(X_train, Y_train, X_test, Y_test, dataloader_params):
dataset_train = TensorDataset(X_train, Y_train)
dataset_test = TensorDataset(X_test, Y_test)
return create_dataloaders(dataset_train, dataset_test, dataloader_params)
def get_BUSI_dataset(data_path, drop_normals=False, make_balance=False,random_state=-1, normal_vs_cancer=False):
image_path = []
labels = []
if make_balance and random_state == -1:
print('Random state has not been passed')
for label in os.listdir(data_path):
for image in os.listdir(f'{data_path}/{label}'):
image_path.append(f'{data_path}/{label}/{image}')
labels.append(label)
df = pd.DataFrame({'image_path': image_path, 'label': labels})
if normal_vs_cancer:
df['label'] = df['label'].apply(lambda x: x if x=='normal' else 'cancer')
df['label_cat'] = df['label'].astype('category').cat.codes
df = df[~df.image_path.str.contains('mask')].copy()
if make_balance:
g = df.groupby('label')
df = g.apply(lambda x: x.sample(g.size().min(), random_state=random_state).reset_index(drop=True)).reset_index(drop=True)
return df
def get_chestxray_dataset(data_path, make_balance=False,random_state=-1):
data_split = ['train','val','test']
df = {}
if make_balance and random_state == -1:
print('Random state has not been passed')
for ds in data_split:
image_path = []
labels = []
for label in os.listdir(f'{data_path}/{ds}'):
if label[0] == '.':
continue
for image in os.listdir(f'{data_path}/{ds}/{label}'):
if image[0] == '.':
continue
image_path.append(f'{data_path}/{ds}/{label}/{image}')
labels.append(label)
df[ds] = pd.DataFrame({'image_path': image_path, 'label': labels})
df[ds]['label_cat'] = df[ds]['label'].astype('category').cat.codes
if make_balance:
g = df['train'].groupby('label')
df['train'] = g.apply(lambda x: x.sample(g.size().min(), random_state=random_state).reset_index(drop=True)).reset_index(drop=True)
return df['train']
def get_MRI_dataset(data_path, make_balance=False, random_state=-1):
mask_files = glob(f'{data_path}/kaggle_3m/*/*_mask*')
image_files = [file.replace('_mask', '') for file in mask_files]
def label(mask):
value = np.max(cv2.imread(mask))
return 'abnormal' if value > 0 else 'normal'
df = pd.DataFrame({"image_path": image_files,
"mask_path": mask_files,
"label":[label(x) for x in mask_files]})
df['label_cat'] = df['label'].astype('category').cat.codes
if make_balance:
g = df.groupby('label')
df = g.apply(lambda x: x.sample(g.size().min(), random_state=random_state).reset_index(drop=True)).reset_index(drop=True)
return df
def get_figure(*imgs):
from skimage import color
imgs = [img.squeeze().detach().cpu() for img in imgs]
imgs = [color.rgb2gray(img.permute(1, 2, 0).numpy()) if img.shape[0]==3 else img.numpy() for img in imgs]
fig = plt.figure(figsize=(15, 5))
for i in range(len(imgs)):
plt.subplot(1, len(imgs), i+1)
plt.imshow(imgs[i])
return fig
def accuracy(output, labels):
preds = output.max(1)[1].type_as(labels)
correct = preds.eq(labels).double()
correct = correct.sum()
return correct / len(labels)
def dice_score(pred, targs):
non_prob = (pred>0.5).float()
dice = 2. * (non_prob*targs).sum(dim=(1,2,3)) / (non_prob+targs).sum(dim=(1,2,3))
dice[torch.isinf(dice)] = 0.
dice[torch.isnan(dice)] = 0.
return dice
def correct_list(output, labels):
preds = output.max(1)[1].type_as(labels)
correct = preds.eq(labels).double()
return correct