import random
import numpy as np
import cv2
import torch
import torch.utils.data as data
import data.util as util
from PIL import Image, ImageOps
from io import BytesIO
import torchvision.transforms.functional as F
# Reads full-quality images and pulls tiles at regular zoom intervals from them. Only usable for training purposes.
from data.image_corruptor import ImageCorruptor
class MultiScaleDataset(data.Dataset):
def __init__(self, opt):
super(MultiScaleDataset, self).__init__()
self.opt = opt
self.data_type = 'img'
self.tile_size = self.opt['hq_tile_size']
self.num_scales = self.opt['num_scales']
self.hq_size_cap = self.tile_size * 2 ** self.num_scales
self.scale = self.opt['scale']
self.paths_hq, self.sizes_hq = util.get_image_paths(self.data_type, opt['paths'], [1])
self.corruptor = ImageCorruptor(opt)
# Selects the smallest dimension from the image and crops it randomly so the other dimension matches. The cropping
# offset from center is chosen on a normal probability curve.
def get_square_image(self, image):
h, w, _ = image.shape
if h == w:
return image
offset = max(min(np.random.normal(scale=.3), 1.0), -1.0)
if h > w:
diff = h - w
center = diff // 2
top = int(center + offset * (center - 2))
return image[top:top+w, :, :]
else:
diff = w - h
center = diff // 2
left = int(center + offset * (center - 2))
return image[:, left:left+h, :]
def recursively_extract_patches(self, input_img, result_list, depth):
if depth >= self.num_scales:
return
patch_size = self.hq_size_cap // (2 ** depth)
# First pull the four sub-patches. Important: if this is changed, be sure to edit build_multiscale_patch_index_map() below.
patches = [input_img[:patch_size, :patch_size],
input_img[:patch_size, patch_size:],
input_img[patch_size:, :patch_size],
input_img[patch_size:, patch_size:]]
result_list.extend([cv2.resize(p, (self.tile_size, self.tile_size), interpolation=cv2.INTER_AREA) for p in patches])
for p in patches:
self.recursively_extract_patches(p, result_list, depth+1)
def __getitem__(self, index):
# get full size image
full_path = self.paths_hq[index % len(self.paths_hq)]
img_full = util.read_img(None, full_path, None)
img_full = util.channel_convert(img_full.shape[2], 'RGB', [img_full])[0]
img_full = util.augment([img_full], True, True)[0]
img_full = self.get_square_image(img_full)
img_full = cv2.resize(img_full, (self.hq_size_cap, self.hq_size_cap), interpolation=cv2.INTER_AREA)
patches_hq = [cv2.resize(img_full, (self.tile_size, self.tile_size), interpolation=cv2.INTER_AREA)]
self.recursively_extract_patches(img_full, patches_hq, 1)
# Image corruption is applied against the full size image for this dataset.
img_corrupted = self.corruptor.corrupt_images([img_full])[0]
patches_hq_corrupted = [cv2.resize(img_corrupted, (self.tile_size, self.tile_size), interpolation=cv2.INTER_AREA)]
self.recursively_extract_patches(img_corrupted, patches_hq_corrupted, 1)
# BGR to RGB, HWC to CHW, numpy to tensor
if patches_hq[0].shape[2] == 3:
patches_hq = [cv2.cvtColor(p, cv2.COLOR_BGR2RGB) for p in patches_hq]
patches_hq_corrupted = [cv2.cvtColor(p, cv2.COLOR_BGR2RGB) for p in patches_hq_corrupted]
patches_hq = [torch.from_numpy(np.ascontiguousarray(np.transpose(p, (2, 0, 1)))).float() for p in patches_hq]
patches_hq = torch.stack(patches_hq, dim=0)
patches_hq_corrupted = [torch.from_numpy(np.ascontiguousarray(np.transpose(p, (2, 0, 1)))).float() for p in patches_hq_corrupted]
patches_lq = [torch.nn.functional.interpolate(p.unsqueeze(0), scale_factor=1/self.scale, mode='area').squeeze() for p in patches_hq_corrupted]
patches_lq = torch.stack(patches_lq, dim=0)
d = {'LQ': patches_lq, 'GT': patches_hq, 'GT_path': full_path}
return d
def __len__(self):
return len(self.paths_hq)
class MultiscaleTreeNode:
def __init__(self, index, parent, i):
self.index = index
self.parent = parent
self.children = []
# These represent the offset from left and top of the image for the individual patch as a proportion of the entire image.
# Tightly tied to the implementation above for the order in which the patches are pulled from the base image.
lefts = [0, .5, 0, .5]
tops = [0, 0, .5, .5]
self.left = lefts[i]
self.top = tops[i]
def add_child(self, child):
self.children.append(child)
return child
def build_multiscale_patch_index_map(depth):
if depth < 0:
return
root = MultiscaleTreeNode(0, None, 0)
leaves = []
_build_multiscale_patch_index_map(depth-1, 1, root, leaves)
return leaves
def _build_multiscale_patch_index_map(depth, ind, node, leaves):
subnodes = [node.add_child(MultiscaleTreeNode(ind+i, node, i)) for i in range(4)]
ind += 4
if depth == 1:
leaves.extend(subnodes)
else:
for n in subnodes:
ind = _build_multiscale_patch_index_map(depth-1, ind, n, leaves)
return ind
if __name__ == '__main__':
opt = {
'name': 'amalgam',
'paths': ['F:\\4k6k\\datasets\\images\\div2k\\DIV2K_train_HR'],
'num_scales': 4,
'scale': 2,
'hq_tile_size': 128,
'fixed_corruptions': ['jpeg'],
'random_corruptions': ['gaussian_blur', 'motion-blur', 'noise-5'],
'num_corrupts_per_image': 1,
'corruption_blur_scale': 5
}
import torchvision
ds = MultiScaleDataset(opt)
import os
os.makedirs("debug", exist_ok=True)
multiscale_tree = build_multiscale_patch_index_map(4)
for i in range(500, len(ds)):
quadrant=2
print(i)
o = ds[random.randint(0, len(ds))]
tree_ind = random.randint(0, len(multiscale_tree))
for k, v in o.items():
if 'path' in k:
continue
depth = 0
node = multiscale_tree[tree_ind]
#for j, img in enumerate(v):
# torchvision.utils.save_image(img.unsqueeze(0), "debug/%i_%s_%i.png" % (i, k, j))
while node is not None:
torchvision.utils.save_image(v[node.index].unsqueeze(0), "debug/%i_%s_%i.png" % (i, k, depth))
depth += 1
node = node.parent