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dataset_Loader.py
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import os
import tqdm
import numpy as np
import torch
import random
import torch.utils.data as data_utl
from PIL import Image, ImageFilter
import imgaug.augmenters as iaa
import torchvision.transforms as transforms
from torchvision.transforms import ToTensor, ToPILImage
import warnings
warnings.filterwarnings("ignore", category=FutureWarning)
class datasetLoader(data_utl.Dataset):
def __init__(self, split_file, root, method, train_test, random=True, c2i={}):
self.split_file = split_file
self.method = method
self.root = root
self.train_test = train_test
self.random = random
self.image_size = 229
# Image pre-processing
if self.train_test == 'test':
"""inception expects (299,299) sized images"""
if self.method == 'inception':
self.image_size = 229
else:
self.image_size = 224
self.transform_test = transforms.Compose([
transforms.Resize([self.image_size, self.image_size]),
transforms.ToTensor(),
transforms.Normalize(mean=[0.485], std=[0.229])
])
# Class assignment
self.class_to_id = c2i
self.id_to_class = []
self.assign_classes()
# Data loading
self.get_data()
# Class assignment
def assign_classes(self):
for i in range(len(self.class_to_id.keys())):
for k in self.class_to_id.keys():
if self.class_to_id[k] == i:
self.id_to_class.append(k)
# Data loading (Reading data from CSV file)
def get_data(self):
self.data = []
print('Reading data from CSV file...', self.train_test)
cid = 0
with open(self.split_file, 'r') as f:
for l in f.readlines():
v = l.strip().split(',')
if self.train_test == v[0]:
image_name = v[2].replace(v[2].split('.')[-1], 'png')
imagePath = self.root + image_name
c = v[1]
if c not in self.class_to_id:
self.class_to_id[c] = cid
self.id_to_class.append(c)
cid += 1
# Storing data with image path and class
if os.path.exists(imagePath):
self.data.append([imagePath, self.class_to_id[c]])
def __len__(self):
return len(self.data)
def __getitem__(self, index):
# get image path, image name, and class from data
imagePath, cls = self.data[index]
imageName = imagePath.split('/')[-1]
path = imagePath
#####################################################################################
############### Read the train data, do pre-processing and augmentation #############
#####################################################################################
if self.train_test == 'train':
# Reading the image and convert it to gray image
img = Image.fromarray(np.array(Image.open(path).convert('RGB'))[:, :, 0], 'L')
# Resize the image
if self.method == 'inception':
img = img.resize((229, 229), Image.BILINEAR)
else:
img = img.resize((224, 224), Image.BILINEAR)
###########################################
########### Data augmentation #############
###########################################
# 1) horizontal flip
if random.random() < 0.5:
img = img.transpose(Image.FLIP_LEFT_RIGHT)
# 2) Affine transformation
aug = iaa.Affine(scale=(0.8,1.25), translate_px={"x": (-15, 15), "y": (-15, 15)}, rotate=(-30, 30), mode='edge')
img_np = aug(images = np.expand_dims(np.expand_dims(np.uint8(img), axis=0), axis=-1))
img = Image.fromarray(np.uint8(img_np[0, :, :, 0]))
# 3) Sharpening, 4)blurring, 5)Gaussian noise, 6)contrast change, 7)enhance brightness
if random.random() < 0.5:
random_choice = np.random.choice([1,2,3])
if random_choice == 1:
# 3) sharpening
random_degree = np.random.choice([1,2,3])
if random_degree == 1:
img = img.filter(ImageFilter.EDGE_ENHANCE)
elif random_degree == 2:
img= img.filter(ImageFilter.EDGE_ENHANCE_MORE)
elif random_degree == 3:
aug = iaa.Sharpen(alpha=(0.0, 0.3), lightness=(0.6, 1.0))
img_np = aug(images = np.array(img))
img = Image.fromarray(img_np)
elif random_choice == 2:
# 4) blurring
random_degree = np.random.choice([1,2,3,4])
if random_degree == 1:
aug = iaa.GaussianBlur(sigma=(2.0,5.0))#(0.1, 1.0))
img_np = aug(images = np.array(img))
img = Image.fromarray(img_np)
elif random_degree == 2:
aug = iaa.imgcorruptlike.MotionBlur(severity=2)#1)
img_np = aug(images = np.array(img))
img = Image.fromarray(img_np)
elif random_degree == 3:
aug = iaa.imgcorruptlike.GlassBlur(severity=2)#1)
img_np = aug(images = np.array(img))
img = Image.fromarray(img_np)
elif random_degree == 4:
aug = iaa.imgcorruptlike.DefocusBlur(severity=3)#1)
img_np = aug(images = np.array(img))
img = Image.fromarray(img_np)
elif random_choice == 3:
# 5) AdditiveLaplaceNoise
if random.random() < 0.5:
aug = iaa.AdditiveLaplaceNoise(scale=(0, 3))
img_np = aug(images = np.array(img))
img = Image.fromarray(img_np)
if random.random() < 0.5:
# 6) contrast change
random_degree = np.random.choice([1,2,3,4,5])
if random_degree == 1:
aug = iaa.GammaContrast((1.2, 1.8))
img_np = aug(images = np.array(img))
img = Image.fromarray(img_np)
elif random_degree == 2:
aug = iaa.LinearContrast((0.4, 1.75))
img_np = aug(images = np.array(img))
img = Image.fromarray(img_np)
elif random_degree == 3:
aug = iaa.SigmoidContrast(gain=(10, 12), cutoff=(0.5, 0.75))
img_np = aug(images = np.array(img))
img = Image.fromarray(img_np)
elif random_degree == 4:
aug = iaa.LogContrast(gain=(0.5, 1.4))
img_np = aug(images = np.array(img))
img = Image.fromarray(img_np)
else:
# 7) Enhance Brightness
aug = iaa.pillike.EnhanceBrightness(factor=(0.8, 1.9))
img_np = aug(images = np.array(img))
img = Image.fromarray(img_np)
if random.random() < 0.5:
# 8) sequence of randomness
random_degree = np.random.choice([1,2,3,4,5])
if random_degree == 1:
aug = iaa.Sequential([
iaa.GammaContrast((0.5, 2.0)),
iaa.pillike.EnhanceBrightness(),
iaa.GaussianBlur(sigma=(0.1,1.0))
])
img_np = aug(images = np.array(img))
img = Image.fromarray(img_np)
elif random_degree == 2:
aug = iaa.Sequential([
iaa.LinearContrast((0.4, 1.6)),
iaa.pillike.EnhanceBrightness(),
iaa.imgcorruptlike.MotionBlur(severity=1)
])
img_np = aug(images = np.array(img))
img = Image.fromarray(img_np)
elif random_degree == 3:
aug = iaa.Sequential([
iaa.LogContrast(gain=(0.6, 1.4)),
iaa.pillike.EnhanceBrightness(),
iaa.imgcorruptlike.GlassBlur(severity=1)
])
img_np = aug(images = np.array(img))
img = Image.fromarray(img_np)
elif random_degree == 4:
aug = iaa.Sequential([
iaa.SigmoidContrast(gain=(3, 10), cutoff=(0.4, 0.6)),
iaa.pillike.EnhanceBrightness(),
iaa.GaussianBlur(sigma=(0.1,2.0))
])
img_np = aug(images = np.array(img))
img = Image.fromarray(img_np)
else:
aug = iaa.Sequential([
iaa.pillike.EnhanceBrightness(factor=(0.1, 1.9)),
iaa.imgcorruptlike.DefocusBlur(severity=1)
])
img_np = aug(images = np.array(img))
img = Image.fromarray(img_np)
# convert PIL to tensor
pil_to_torch = ToTensor()
torch_img = pil_to_torch(img)
# Normalize the tensor
tranform_img = transforms.Normalize(mean=[0.485],std=[0.229])(torch_img)
elif self.train_test == 'test':
# Reading of the image and apply transformation
img = Image.open(path)
tranform_img = self.transform_test(img)
img.close()
# Repeat NIR single channel thrice before feeding into the network
tranform_img= tranform_img.repeat(3,1,1)
return tranform_img[0:3,:,:], cls, imageName
#if __name__ == '__main__':
# dataseta = datasetLoader('../TempData/Iris_OCT_Splits_Val/test_train_split.csv', 'PathToDatasetFolder', train_test='train')
# for i in range(len(dataseta)):
# print(len(dataseta.data))