forked from mrq/DL-Art-School
161 lines
6.6 KiB
Python
161 lines
6.6 KiB
Python
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 |