forked from mrq/DL-Art-School
784b96c059
- Allow image_folder_dataset to normalize inbound images - ExtensibleTrainer can denormalize images on the output path - Support .webp - an output from LSUN - Support logistic GAN divergence loss - Support stylegan2 TF weight extraction for discriminator - New injector that produces latent noise (with separated paths) - Modify FID evaluator to be operable with rosinality-style GANs
106 lines
3.6 KiB
Python
106 lines
3.6 KiB
Python
"""A multi-thread tool to crop large images to sub-images for faster IO."""
|
|
import os
|
|
import os.path as osp
|
|
import numpy as np
|
|
import cv2
|
|
from PIL import Image
|
|
import data.util as data_util # noqa: E402
|
|
import torch.utils.data as data
|
|
from tqdm import tqdm
|
|
import torch
|
|
|
|
|
|
def main():
|
|
split_img = False
|
|
opt = {}
|
|
opt['n_thread'] = 5
|
|
opt['compression_level'] = 95 # JPEG compression quality rating.
|
|
# CV_IMWRITE_PNG_COMPRESSION from 0 to 9. A higher value means a smaller size and longer
|
|
# compression time. If read raw images during training, use 0 for faster IO speed.
|
|
|
|
opt['dest'] = 'file'
|
|
opt['input_folder'] = ['F:\\4k6k\\datasets\\images\\lsun\\lsun\\cats']
|
|
opt['save_folder'] = 'F:\\4k6k\\datasets\\images\\lsun\\lsun\\cats\\cropped'
|
|
opt['imgsize'] = 256
|
|
opt['bottom_crop'] = 0
|
|
opt['keep_folder'] = False
|
|
|
|
save_folder = opt['save_folder']
|
|
if not osp.exists(save_folder):
|
|
os.makedirs(save_folder)
|
|
print('mkdir [{:s}] ...'.format(save_folder))
|
|
|
|
extract_single(opt)
|
|
|
|
|
|
class TiledDataset(data.Dataset):
|
|
def __init__(self, opt):
|
|
self.opt = opt
|
|
input_folder = opt['input_folder']
|
|
self.images = data_util.get_image_paths('img', input_folder)[0]
|
|
print("Found %i images" % (len(self.images),))
|
|
|
|
def __getitem__(self, index):
|
|
return self.get(index)
|
|
|
|
def get(self, index):
|
|
path = self.images[index]
|
|
basename = osp.basename(path)
|
|
img = cv2.imread(path, cv2.IMREAD_UNCHANGED)
|
|
|
|
# Greyscale not supported.
|
|
if img is None:
|
|
print("Error with ", path)
|
|
return None
|
|
if len(img.shape) == 2:
|
|
print("Skipping due to greyscale")
|
|
return None
|
|
|
|
# Perform explicit crops first. These are generally used to get rid of watermarks so we dont even want to
|
|
# consider these areas of the image.
|
|
if 'bottom_crop' in self.opt.keys() and self.opt['bottom_crop'] > 0:
|
|
bc = self.opt['bottom_crop']
|
|
if bc > 0 and bc < 1:
|
|
bc = int(bc * img.shape[0])
|
|
img = img[:-bc, :, :]
|
|
|
|
h, w, c = img.shape
|
|
# Uncomment to filter any image that doesnt meet a threshold size.
|
|
if min(h,w) < self.opt['imgsize']:
|
|
print("Skipping due to threshold")
|
|
return None
|
|
|
|
# We must convert the image into a square.
|
|
dim = min(h, w)
|
|
# Crop the image so that only the center is left, since this is often the most salient part of the image.
|
|
img = img[(h - dim) // 2:dim + (h - dim) // 2, (w - dim) // 2:dim + (w - dim) // 2, :]
|
|
img = cv2.resize(img, (self.opt['imgsize'], self.opt['imgsize']), interpolation=cv2.INTER_AREA)
|
|
output_folder = self.opt['save_folder']
|
|
if self.opt['keep_folder']:
|
|
# Attempt to find the folder name one level above opt['input_folder'] and use that.
|
|
pts = [os.path.dirname(path)]
|
|
while pts[0] != self.opt['input_folder'][0]:
|
|
pts = os.path.split(pts[0])
|
|
output_folder = osp.join(self.opt['save_folder'], pts[-1])
|
|
os.makedirs(output_folder, exist_ok=True)
|
|
cv2.imwrite(osp.join(output_folder, basename.replace('.webp', '.jpg')), img, [cv2.IMWRITE_JPEG_QUALITY, self.opt['compression_level']])
|
|
return None
|
|
|
|
def __len__(self):
|
|
return len(self.images)
|
|
|
|
|
|
def identity(x):
|
|
return x
|
|
|
|
def extract_single(opt):
|
|
dataset = TiledDataset(opt)
|
|
dataloader = data.DataLoader(dataset, num_workers=opt['n_thread'], collate_fn=identity)
|
|
tq = tqdm(dataloader)
|
|
for spl_imgs in tq:
|
|
pass
|
|
|
|
|
|
if __name__ == '__main__':
|
|
main()
|