116 lines
4.5 KiB
Python
116 lines
4.5 KiB
Python
import glob
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import os
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import numpy as np
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import torch.utils.data.dataset
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from torch.utils.data import Dataset
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from torch.utils.data import DataLoader
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from torchvision import transforms
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from PIL import Image
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import random
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import ex4
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IMG_SIZE = 100
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class ImageDataset(Dataset):
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def __init__(self, image_dir, offsetrange: (int, int), spacingrange: (int, int), transform_chain: transforms,
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precision: np.float32 or np.float64 = np.float32):
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self.image_files = sorted(glob.glob(os.path.join(image_dir, "**", "*.jpg"), recursive=True))
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self.precision = precision
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self.offsetrange = offsetrange
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self.spacingrange = spacingrange
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self.transform_chain = transform_chain
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def __getitem__(self, index):
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# Open image file, convert to numpy array and scale to [0, 1]
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target_image = Image.open(self.image_files[index])
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target_image = crop_image(target_image)
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target_image = self.transform_chain(target_image)
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target_image = preprocess(target_image, self.precision)
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# calculate image with black grid
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offset = (random.randint(*self.offsetrange), random.randint(*self.offsetrange))
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spacing = (random.randint(*self.spacingrange), random.randint(*self.spacingrange))
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doomed_image = ex4.ex4(target_image, offset, spacing)
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return doomed_image[0], doomed_image[1], np.transpose(target_image, (2, 0, 1))
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def __len__(self):
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return len(self.image_files)
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def crop_image(image: Image) -> np.array:
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resize_transforms = transforms.Compose([
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transforms.Resize(size=IMG_SIZE),
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transforms.CenterCrop(size=(IMG_SIZE, IMG_SIZE)),
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])
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return resize_transforms(image)
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def preprocess(input: np.array, precision: np.float32 or np.float64) -> np.array:
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# image = np.array(Image.open(self.image_files[index]), dtype=np.float32) / 255
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# https://www.geeksforgeeks.org/how-to-normalize-images-in-pytorch/
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# normalize image from -1 - 1
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target_image = np.array(input, dtype=precision)
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target_image = target_image / 255.0
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# Perform normalization for each channel
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# image = (image - self.norm_mean) / self.norm_std
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return target_image
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# postprecess should be the inverese function of preprocess!
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def postprocess(input: np.array) -> np.array:
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# todo clipping here correct? some values are >1 because of model
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target = np.clip(input, 0, 1)
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target_image = (target * 255.0).astype(np.uint8)
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return target_image
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def get_image_loader(path: str, precision: np.float32 or np.float64):
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# ranges due to project spec
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image_dataset = ImageDataset(path,
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offsetrange=(0, 8),
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spacingrange=(2, 6),
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transform_chain=transforms.Compose([transforms.RandomHorizontalFlip(),
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transforms.RandomVerticalFlip()]),
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precision=precision)
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#
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# image_dataset_augmented = ImageDataset(path,
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# offsetrange=(0, 8),
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# spacingrange=(2, 6),
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# transform_chain=transforms.Compose([transforms.RandomHorizontalFlip(),
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# transforms.RandomVerticalFlip(),
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# transforms.GaussianBlur(3, 4)]),
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# precision=precision)
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# merge different datasets here!
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merged_dataset = torch.utils.data.ConcatDataset([image_dataset])
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totlen = len(merged_dataset)
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test_set_size = .1
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train_split, test_split = torch.utils.data.dataset.random_split(merged_dataset,
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lengths=(totlen - int(totlen * test_set_size),
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int(totlen * test_set_size)))
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train_loader = DataLoader(
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train_split,
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shuffle=True, # shuffle the order of our samples
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batch_size=25, # stack 4 samples to a minibatch
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num_workers=4 # no background workers (see comment below)
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)
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test_loader = DataLoader(
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test_split,
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shuffle=True, # shuffle the order of our samples
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batch_size=5, # stack 4 samples to a minibatch
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num_workers=0 # no background workers (see comment below)
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)
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return train_loader, test_loader
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