167 lines
7.1 KiB
Python
167 lines
7.1 KiB
Python
import random
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import numpy as np
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import cv2
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import torch
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import torch.utils.data as data
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import data.util as util
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from PIL import Image, ImageOps
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from io import BytesIO
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import torchvision.transforms.functional as F
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# Reads full-quality images and pulls tiles at regular zoom intervals from them. Only usable for training purposes.
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from data.image_corruptor import ImageCorruptor
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# Selects the smallest dimension from the image and crops it randomly so the other dimension matches. The cropping
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# offset from center is chosen on a normal probability curve.
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def get_square_image(image):
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h, w, _ = image.shape
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if h == w:
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return image
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offset = max(min(np.random.normal(scale=.3), 1.0), -1.0)
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if h > w:
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diff = h - w
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center = diff // 2
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top = max(int(center + offset * (center - 2)), 0)
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return image[top:top + w, :, :]
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else:
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diff = w - h
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center = diff // 2
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left = max(int(center + offset * (center - 2)), 0)
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return image[:, left:left + h, :]
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class MultiScaleDataset(data.Dataset):
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def __init__(self, opt):
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super(MultiScaleDataset, self).__init__()
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self.opt = opt
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self.data_type = 'img'
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self.tile_size = self.opt['hq_tile_size']
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self.num_scales = self.opt['num_scales']
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self.hq_size_cap = self.tile_size * 2 ** self.num_scales
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self.scale = self.opt['scale']
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self.paths_hq, self.sizes_hq = util.find_files_of_type(self.data_type, opt['paths'], [1 for _ in opt['paths']])
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self.corruptor = ImageCorruptor(opt)
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def recursively_extract_patches(self, input_img, result_list, depth):
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if depth >= self.num_scales:
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return
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patch_size = self.hq_size_cap // (2 ** depth)
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# First pull the four sub-patches. Important: if this is changed, be sure to edit build_multiscale_patch_index_map() below.
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patches = [input_img[:patch_size, :patch_size],
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input_img[:patch_size, patch_size:],
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input_img[patch_size:, :patch_size],
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input_img[patch_size:, patch_size:]]
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result_list.extend([cv2.resize(p, (self.tile_size, self.tile_size), interpolation=cv2.INTER_AREA) for p in patches])
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for p in patches:
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self.recursively_extract_patches(p, result_list, depth+1)
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def __getitem__(self, index):
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# get full size image
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full_path = self.paths_hq[index % len(self.paths_hq)]
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loaded_img = util.read_img(None, full_path, None)
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img_full1 = util.channel_convert(loaded_img.shape[2], 'RGB', [loaded_img])[0]
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img_full2 = util.augment([img_full1], True, True)[0]
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img_full3 = get_square_image(img_full2)
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# This error crops up from time to time. I suspect an issue with util.read_img.
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if img_full3.shape[0] == 0 or img_full3.shape[1] == 0:
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print("Error with image: %s. Loaded image shape: %s" % (full_path,str(loaded_img.shape)), str(img_full1.shape), str(img_full2.shape), str(img_full3.shape))
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# Attempt to recover by just using a fixed array of zeros, which the downstream networks should be fine training against, within reason.
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img_full3 = np.zeros((1024,1024,3), dtype=np.int)
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img_full = cv2.resize(img_full3, (self.hq_size_cap, self.hq_size_cap), interpolation=cv2.INTER_AREA)
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patches_hq = [cv2.resize(img_full, (self.tile_size, self.tile_size), interpolation=cv2.INTER_AREA)]
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self.recursively_extract_patches(img_full, patches_hq, 1)
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# Image corruption is applied against the full size image for this dataset.
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img_corrupted = self.corruptor.corrupt_images([img_full])[0]
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patches_hq_corrupted = [cv2.resize(img_corrupted, (self.tile_size, self.tile_size), interpolation=cv2.INTER_AREA)]
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self.recursively_extract_patches(img_corrupted, patches_hq_corrupted, 1)
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# BGR to RGB, HWC to CHW, numpy to tensor
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if patches_hq[0].shape[2] == 3:
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patches_hq = [cv2.cvtColor(p, cv2.COLOR_BGR2RGB) for p in patches_hq]
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patches_hq_corrupted = [cv2.cvtColor(p, cv2.COLOR_BGR2RGB) for p in patches_hq_corrupted]
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patches_hq = [torch.from_numpy(np.ascontiguousarray(np.transpose(p, (2, 0, 1)))).float() for p in patches_hq]
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patches_hq = torch.stack(patches_hq, dim=0)
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patches_hq_corrupted = [torch.from_numpy(np.ascontiguousarray(np.transpose(p, (2, 0, 1)))).float() for p in patches_hq_corrupted]
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patches_lq = [torch.nn.functional.interpolate(p.unsqueeze(0), scale_factor=1/self.scale, mode='area').squeeze() for p in patches_hq_corrupted]
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patches_lq = torch.stack(patches_lq, dim=0)
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d = {'lq': patches_lq, 'hq': patches_hq, 'GT_path': full_path}
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return d
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def __len__(self):
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return len(self.paths_hq)
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class MultiscaleTreeNode:
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def __init__(self, index, parent, i):
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self.index = index
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self.parent = parent
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self.children = []
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# These represent the offset from left and top of the image for the individual patch as a proportion of the entire image.
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# Tightly tied to the implementation above for the order in which the patches are pulled from the base image.
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lefts = [0, .5, 0, .5]
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tops = [0, 0, .5, .5]
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self.left = lefts[i]
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self.top = tops[i]
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def add_child(self, child):
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self.children.append(child)
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return child
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def build_multiscale_patch_index_map(depth):
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if depth < 0:
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return
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root = MultiscaleTreeNode(0, None, 0)
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leaves = []
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_build_multiscale_patch_index_map(depth-1, 1, root, leaves)
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return leaves
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def _build_multiscale_patch_index_map(depth, ind, node, leaves):
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subnodes = [node.add_child(MultiscaleTreeNode(ind+i, node, i)) for i in range(4)]
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ind += 4
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if depth == 1:
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leaves.extend(subnodes)
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else:
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for n in subnodes:
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ind = _build_multiscale_patch_index_map(depth-1, ind, n, leaves)
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return ind
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if __name__ == '__main__':
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opt = {
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'name': 'amalgam',
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'paths': ['F:\\4k6k\\datasets\\images\\div2k\\DIV2K_train_HR'],
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'num_scales': 4,
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'scale': 2,
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'hq_tile_size': 128,
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'fixed_corruptions': ['jpeg'],
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'random_corruptions': ['gaussian_blur', 'motion-blur', 'noise-5'],
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'num_corrupts_per_image': 1,
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'corruption_blur_scale': 5
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}
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import torchvision
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ds = MultiScaleDataset(opt)
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import os
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os.makedirs("debug", exist_ok=True)
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multiscale_tree = build_multiscale_patch_index_map(4)
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for i in range(500, len(ds)):
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quadrant=2
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print(i)
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o = ds[random.randint(0, len(ds))]
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tree_ind = random.randint(0, len(multiscale_tree))
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for k, v in o.items():
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if 'path' in k:
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continue
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depth = 0
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node = multiscale_tree[tree_ind]
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#for j, img in enumerate(v):
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# torchvision.utils.save_image(img.unsqueeze(0), "debug/%i_%s_%i.png" % (i, k, j))
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while node is not None:
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torchvision.utils.save_image(v[node.index].unsqueeze(0), "debug/%i_%s_%i.png" % (i, k, depth))
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depth += 1
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node = node.parent |