DL-Art-School/codes/scripts/extract_square_images.py

118 lines
3.8 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['dest'] = 'file'
opt['input_folder'] = ['E:\\4k6k\datasets\\ns_images\\imagesets\\imageset_256_masked']
opt['save_folder'] = 'E:\\4k6k\datasets\\ns_images\\imagesets\\imageset_128_masked'
opt['imgsize'] = (128,128)
opt['bottom_crop'] = 0
opt['keep_folder'] = False
#opt['format'] = 'jpg'
#opt['cv2_write_options'] = [cv2.IMWRITE_JPEG_QUALITY, 95]
opt['format'] = 'png'
opt['cv2_write_options'] = [cv2.IMWRITE_PNG_COMPRESSION, 9]
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.find_files_of_type('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.
imgsz_w, imgsz_h = self.opt['imgsize']
if w < imgsz_w or h < imgsz_h:
print("Skipping due to threshold")
return None
# We must first center-crop the image to the proper aspect ratio
aspect_ratio = imgsz_h / imgsz_w
if h < w * aspect_ratio:
hdim = h
wdim = int(h / aspect_ratio)
elif w * aspect_ratio < h:
hdim = int(w * aspect_ratio)
wdim = w
else:
hdim = h
wdim = w
img = img[(h - hdim) // 2:hdim + (h - hdim) // 2, (w - wdim) // 2:wdim + (w - wdim) // 2, :]
img = cv2.resize(img, (imgsz_w, imgsz_h), 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', self.opt['format'])), img, self.opt['cv2_write_options'])
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()