306 lines
14 KiB
Python
306 lines
14 KiB
Python
import functools
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import random
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import cv2
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import numpy as np
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import torch
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import torchvision
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from torch.utils.data import DataLoader
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from torchvision.transforms import Normalize
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from tqdm import tqdm
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from data import util
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# Builds a dataset created from a simple folder containing a list of training/test/validation images.
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from data.images.image_corruptor import ImageCorruptor, kornia_color_jitter_numpy
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from data.images.image_label_parser import VsNetImageLabeler
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from utils.util import opt_get
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def ndarray_center_crop(crop, img):
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y, x, c = img.shape
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startx = x // 2 - crop // 2
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starty = y // 2 - crop // 2
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return img[starty:starty + crop, startx:startx + crop, :]
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class ImageFolderDataset:
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def __init__(self, opt):
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self.opt = opt
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self.corruptor = ImageCorruptor(opt)
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if 'center_crop_hq_sz' in opt.keys():
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self.center_crop = functools.partial(ndarray_center_crop, opt['center_crop_hq_sz'])
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self.target_hq_size = opt['target_size'] if 'target_size' in opt.keys() else None
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self.multiple = opt['force_multiple'] if 'force_multiple' in opt.keys() else 1
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self.scale = opt['scale']
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self.paths = opt['paths']
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self.corrupt_before_downsize = opt['corrupt_before_downsize'] if 'corrupt_before_downsize' in opt.keys() else False
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self.fetch_alt_image = opt['fetch_alt_image'] # If specified, this dataset will attempt to find a second image
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# from the same video source. Search for 'fetch_alt_image' for more info.
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self.fetch_alt_tiled_image = opt['fetch_alt_tiled_image'] # If specified, this dataset will attempt to find anoter tile from the same source image
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# Search for 'fetch_alt_tiled_image' for more info.
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assert not (self.fetch_alt_image and self.fetch_alt_tiled_image) # These are mutually exclusive.
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self.skip_lq = opt_get(opt, ['skip_lq'], False)
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self.disable_flip = opt_get(opt, ['disable_flip'], False)
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self.rgb_n1_to_1 = opt_get(opt, ['rgb_n1_to_1'], False)
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self.force_square = opt_get(opt, ['force_square'], True)
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self.fixed_parameters = {k: torch.tensor(v) for k, v in opt_get(opt, ['fixed_parameters'], {}).items()}
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self.all_image_color_jitter = opt_get(opt, ['all_image_color_jitter'], 0)
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if 'normalize' in opt.keys():
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if opt['normalize'] == 'stylegan2_norm':
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self.normalize = Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5), inplace=True)
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elif opt['normalize'] == 'imagenet':
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self.normalize = Normalize((0.485, 0.456, 0.406), (0.229, 0.224, 0.225), inplace=True)
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else:
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raise Exception('Unsupported normalize')
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else:
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self.normalize = None
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if self.target_hq_size is not None:
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assert (self.target_hq_size // self.scale) % self.multiple == 0 # If we dont throw here, we get some really obscure errors.
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if not isinstance(self.paths, list):
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self.paths = [self.paths]
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self.weights = [1]
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else:
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self.weights = opt['weights']
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if 'labeler' in opt.keys():
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if opt['labeler']['type'] == 'patch_labels':
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self.labeler = VsNetImageLabeler(opt['labeler']['label_file'])
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assert len(self.paths) == 1 # Only a single base-path is supported for labeled images.
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self.image_paths = self.labeler.get_labeled_paths(self.paths[0])
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else:
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self.labeler = None
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# Just scan the given directory for images of standard types.
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supported_types = ['jpg', 'jpeg', 'png', 'gif']
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self.image_paths = []
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for path, weight in zip(self.paths, self.weights):
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cache_path = os.path.join(path, 'cache.pth')
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if os.path.exists(cache_path):
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imgs = torch.load(cache_path)
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else:
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print("Building image folder cache, this can take some time for large datasets..")
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imgs = util.find_files_of_type('img', path)[0]
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torch.save(imgs, cache_path)
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for w in range(weight):
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self.image_paths.extend(imgs)
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self.len = len(self.image_paths)
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def get_paths(self):
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return self.image_paths
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# Given an HQ square of arbitrary size, resizes it to specifications from opt.
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def resize_hq(self, imgs_hq):
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# Enforce size constraints
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h, w, _ = imgs_hq[0].shape
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if self.target_hq_size is not None and self.target_hq_size != h:
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hqs_adjusted = []
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for hq in imgs_hq:
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# It is assumed that the target size is a square.
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target_size = (self.target_hq_size, self.target_hq_size)
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hqs_adjusted.append(cv2.resize(hq, target_size, interpolation=cv2.INTER_AREA))
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h, w = self.target_hq_size, self.target_hq_size
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else:
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hqs_adjusted = imgs_hq
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hq_multiple = self.multiple * self.scale # Multiple must apply to LQ image.
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if h % hq_multiple != 0 or w % hq_multiple != 0:
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hqs_conformed = []
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for hq in hqs_adjusted:
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h, w = (h - h % hq_multiple), (w - w % hq_multiple)
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hqs_conformed.append(hq[:h, :w, :])
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return hqs_conformed
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return hqs_adjusted
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def synthesize_lq(self, hs):
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h, w, _ = hs[0].shape
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ls = []
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local_scale = self.scale
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if self.corrupt_before_downsize:
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# You can downsize to a specified scale, then corrupt, then continue the downsize further using this option.
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if 'corrupt_before_downsize_factor' in self.opt.keys():
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special_factor = self.opt['corrupt_before_downsize_factor']
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hs = [cv2.resize(h_, (h // special_factor, w // special_factor), interpolation=cv2.INTER_AREA) for h_ in hs]
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local_scale = local_scale // special_factor
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else:
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hs = [h.copy() for h in hs]
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hs, ent = self.corruptor.corrupt_images(hs, return_entropy=True)
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for hq in hs:
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h, w, _ = hq.shape
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ls.append(cv2.resize(hq, (h // local_scale, w // local_scale), interpolation=cv2.INTER_AREA))
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# Corrupt the LQ image (only in eval mode)
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if not self.corrupt_before_downsize:
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ls, ent = self.corruptor.corrupt_images(ls, return_entropy=True)
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return ls, ent
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def reset_random(self):
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self.corruptor.reset_random()
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def __len__(self):
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return self.len
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def __getitem__(self, item):
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hq = util.read_img(None, self.image_paths[item], rgb=True)
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if hasattr(self, 'center_crop'):
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hq = self.center_crop(hq)
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if not self.disable_flip and random.random() < .5:
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hq = hq[:, ::-1, :]
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if self.force_square:
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h, w, _ = hq.shape
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dim = min(h, w)
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hq = hq[(h - dim) // 2:dim + (h - dim) // 2, (w - dim) // 2:dim + (w - dim) // 2, :]
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# Perform color jittering on the HQ image if specified. The given value should be between [0,1].
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if self.all_image_color_jitter > 0:
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hq = kornia_color_jitter_numpy(hq, self.all_image_color_jitter)
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if self.labeler:
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assert hq.shape[0] == hq.shape[1] # This just has not been accomodated yet.
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dim = hq.shape[0]
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hs = self.resize_hq([hq])
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if not self.skip_lq:
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for_lq = [hs[0]]
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# Convert to torch tensor
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hq = torch.from_numpy(np.ascontiguousarray(np.transpose(hs[0], (2, 0, 1)))).float()
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out_dict = {'hq': hq, 'LQ_path': self.image_paths[item], 'HQ_path': self.image_paths[item], 'has_alt': False}
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if self.fetch_alt_image:
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# This works by assuming a specific filename structure as would produced by ffmpeg. ex:
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# 'Candied Walnutsxjktqhr_SYc.webm_00000478.jpg` and
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# 'Candied Walnutsxjktqhr_SYc.webm_00000479.jpg` and
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# 'Candied Walnutsxjktqhr_SYc.webm_00000480.jpg`
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# The basic format is `<anything>%08d.<extension>`. This logic parses off that 8 digit number. If it is
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# not found, the 'alt_image' returned is just the current image. If it is found, the algorithm searches for
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# an image one number higher. If it is found - it is returned in the 'alt_hq' and 'alt_lq' keys, else the
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# current image is put in those keys.
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imname_parts = self.image_paths[item]
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while '.jpg.jpg' in imname_parts:
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imname_parts = imname_parts.replace(".jpg.jpg", ".jpg") # Hack workaround to my own bug.
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imname_parts = imname_parts.split('.')
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if len(imname_parts) >= 2 and len(imname_parts[-2]) > 8:
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try:
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imnumber = int(imname_parts[-2][-8:])
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# When we're dealing with images in the 1M range, it's straight up faster to attempt to just open
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# the file rather than searching the path list. Let the exception handler below do its work.
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next_img = self.image_paths[item].replace(str(imnumber), str(imnumber+1))
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alt_hq = util.read_img(None, next_img, rgb=True)
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alt_hs = self.resize_hq([alt_hq])
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alt_hq = torch.from_numpy(np.ascontiguousarray(np.transpose(alt_hs[0], (2, 0, 1)))).float()
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out_dict['has_alt'] = True
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if not self.skip_lq:
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for_lq.append(alt_hs[0])
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except:
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alt_hq = hq
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if not self.skip_lq:
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for_lq.append(hs[0])
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else:
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alt_hq = hq
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if not self.skip_lq:
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for_lq.append(hs[0])
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out_dict['alt_hq'] = alt_hq
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if self.fetch_alt_tiled_image:
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# This assumes the output format generated by the tiled image generation scripts included with DLAS. Specifically,
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# all image read by this dataset are assumed to be in subfolders with other tiles from the same source image. When
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# this option is set, another random image from the same folder is selected and returned as the alt image.
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sel_path = os.path.dirname(self.image_paths[item])
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other_images = os.listdir(sel_path)
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# Assume that the directory contains at least <image>, <ref.jpg>, <centers.pt>
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try:
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if len(other_images) <= 3:
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alt_hq = hq # This is a fallback in case an alt image can't be found.
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else:
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random.shuffle(other_images)
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for oi in other_images:
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if oi == os.path.basename(self.image_paths[item]) or 'ref.' in oi or 'centers.pt' in oi:
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continue
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alt_hq = util.read_img(None, os.path.join(sel_path, oi), rgb=True)
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alt_hs = self.resize_hq([alt_hq])
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alt_hq = torch.from_numpy(np.ascontiguousarray(np.transpose(alt_hs[0], (2, 0, 1)))).float()
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except:
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alt_hq = hq
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print(f"Error with {self.image_paths[item]}")
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out_dict['has_alt'] = True
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out_dict['alt_hq'] = alt_hq
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if not self.skip_lq:
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lqs, ent = self.synthesize_lq(for_lq)
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ls = lqs[0]
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out_dict['lq'] = torch.from_numpy(np.ascontiguousarray(np.transpose(ls, (2, 0, 1)))).float()
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out_dict['corruption_entropy'] = torch.tensor(ent)
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if len(lqs) > 1:
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alt_lq = lqs[1]
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out_dict['alt_lq'] = torch.from_numpy(np.ascontiguousarray(np.transpose(alt_lq, (2, 0, 1)))).float()
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if self.labeler:
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base_file = self.image_paths[item].replace(self.paths[0], "")
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while base_file.startswith("\\"):
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base_file = base_file[1:]
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assert dim % hq.shape[1] == 0
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lbls, lbl_masks, lblstrings = self.labeler.get_labels_as_tensor(hq, base_file, dim // hq.shape[1])
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out_dict['labels'] = lbls
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out_dict['labels_mask'] = lbl_masks
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out_dict['label_strings'] = lblstrings
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for k, v in out_dict.items():
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if isinstance(v, torch.Tensor) and len(v.shape) == 3:
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if self.normalize:
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v = self.normalize(v)
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if self.rgb_n1_to_1:
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v = v * 2 - 1
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out_dict[k] = v
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out_dict.update(self.fixed_parameters)
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return out_dict
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if __name__ == '__main__':
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opt = {
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'name': 'amalgam',
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'paths': ['E:\\4k6k\\datasets\\ns_images\\imagesets\\256_only_humans_masked'],
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'weights': [1],
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'target_size': 256,
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'scale': 1,
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'corrupt_before_downsize': True,
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'fetch_alt_image': False,
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'fetch_alt_tiled_image': True,
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'disable_flip': True,
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'fixed_corruptions': ['lq_resampling', 'jpeg-medium', 'gaussian_blur', 'noise', 'color_jitter'],
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'num_corrupts_per_image': 0,
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'corruption_blur_scale': 1,
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'all_image_color_jitter': .1,
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}
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ds = DataLoader(ImageFolderDataset(opt), shuffle=True, num_workers=0, batch_size=64)
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import os
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output_path = 'F:\\tmp'
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os.makedirs(output_path, exist_ok=True)
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res = []
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for i, d in tqdm(enumerate(ds)):
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'''
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x = d['hq']
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b,c,h,w = x.shape
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x_c = x.view(c*b, h, w)
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x_c = torch.view_as_real(torch.fft.rfft(x_c))
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# Log-normalize spectrogram
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x_c = (x_c.abs() ** 2).clip(min=1e-8, max=1e16)
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x_c = torch.log(x_c)
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res.append(x_c)
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if i % 100 == 99:
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stacked = torch.cat(res, dim=0)
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print(stacked.mean(dim=[0,1,2]), stacked.std(dim=[0,1,2]))
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'''
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for k, v in d.items():
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if isinstance(v, torch.Tensor) and len(v.shape) >= 3:
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os.makedirs(f'{output_path}\\{k}', exist_ok=True)
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torchvision.utils.save_image(v, f'{output_path}\\{k}\\{i}.png')
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if i >= 200000:
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break
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