| import random | |
| import cv2 | |
| import numpy as np | |
| import torch | |
| from PIL import Image | |
| def crop_4_patches(image): | |
| crop_size = int(image.size[0]/2) | |
| return (image.crop((0, 0, crop_size, crop_size)), image.crop((0, crop_size, crop_size, 2*crop_size)), | |
| image.crop((crop_size, 0, 2*crop_size, crop_size)), image.crop((crop_size, crop_size, 2*crop_size, 2*crop_size))) | |
| def pre_processing(image, transform): | |
| high_level = [] | |
| middle_level = [] | |
| low_level = [] | |
| crops_4 = crop_4_patches(image) | |
| for c_4 in crops_4: | |
| crops_8 = crop_4_patches(c_4) | |
| high_level.append(transform(crops_8[0])) | |
| high_level.append(transform(crops_8[3])) | |
| for c_8 in [crops_8[1], crops_8[2]]: | |
| crops_16 = crop_4_patches(c_8) | |
| middle_level.append(transform(crops_16[0])) | |
| middle_level.append(transform(crops_16[3])) | |
| for c_16 in [crops_16[1], crops_16[2]]: | |
| crops_32 = crop_4_patches(c_16) | |
| low_level.append(transform(crops_32[0])) | |
| low_level.append(transform(crops_32[3])) | |
| return torch.stack(high_level), torch.stack(middle_level), torch.stack(low_level) | |