forked from mrq/DL-Art-School
237 lines
9.5 KiB
Python
237 lines
9.5 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 lmdb
|
|
import pyarrow
|
|
import torch.utils.data as data
|
|
from tqdm import tqdm
|
|
|
|
|
|
def main():
|
|
mode = 'single' # single (one input folder) | pair (extract corresponding GT and LR pairs)
|
|
split_img = False
|
|
opt = {}
|
|
opt['n_thread'] = 12
|
|
opt['compression_level'] = 90 # 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.
|
|
if mode == 'single':
|
|
opt['input_folder'] = 'F:\\4k6k\\datasets\\ns_images\\imagesets\\images'
|
|
opt['save_folder'] = 'F:\\4k6k\\datasets\\ns_images\\imagesets\\lmdb_with_ref'
|
|
opt['crop_sz'] = 512 # the size of each sub-image
|
|
opt['step'] = 128 # step of the sliding crop window
|
|
opt['thres_sz'] = 128 # size threshold
|
|
opt['resize_final_img'] = .5
|
|
opt['only_resize'] = False
|
|
extract_single(opt, split_img)
|
|
elif mode == 'pair':
|
|
GT_folder = '../../datasets/div2k/DIV2K_train_HR'
|
|
LR_folder = '../../datasets/div2k/DIV2K_train_LR_bicubic/X4'
|
|
save_GT_folder = '../../datasets/div2k/DIV2K800_sub'
|
|
save_LR_folder = '../../datasets/div2k/DIV2K800_sub_bicLRx4'
|
|
scale_ratio = 4
|
|
crop_sz = 480 # the size of each sub-image (GT)
|
|
step = 240 # step of the sliding crop window (GT)
|
|
thres_sz = 48 # size threshold
|
|
########################################################################
|
|
# check that all the GT and LR images have correct scale ratio
|
|
img_GT_list = data_util._get_paths_from_images(GT_folder)
|
|
img_LR_list = data_util._get_paths_from_images(LR_folder)
|
|
assert len(img_GT_list) == len(img_LR_list), 'different length of GT_folder and LR_folder.'
|
|
for path_GT, path_LR in zip(img_GT_list, img_LR_list):
|
|
img_GT = Image.open(path_GT)
|
|
img_LR = Image.open(path_LR)
|
|
w_GT, h_GT = img_GT.size
|
|
w_LR, h_LR = img_LR.size
|
|
assert w_GT / w_LR == scale_ratio, 'GT width [{:d}] is not {:d}X as LR weight [{:d}] for {:s}.'.format( # noqa: E501
|
|
w_GT, scale_ratio, w_LR, path_GT)
|
|
assert w_GT / w_LR == scale_ratio, 'GT width [{:d}] is not {:d}X as LR weight [{:d}] for {:s}.'.format( # noqa: E501
|
|
w_GT, scale_ratio, w_LR, path_GT)
|
|
# check crop size, step and threshold size
|
|
assert crop_sz % scale_ratio == 0, 'crop size is not {:d}X multiplication.'.format(
|
|
scale_ratio)
|
|
assert step % scale_ratio == 0, 'step is not {:d}X multiplication.'.format(scale_ratio)
|
|
assert thres_sz % scale_ratio == 0, 'thres_sz is not {:d}X multiplication.'.format(
|
|
scale_ratio)
|
|
print('process GT...')
|
|
opt['input_folder'] = GT_folder
|
|
opt['save_folder'] = save_GT_folder
|
|
opt['crop_sz'] = crop_sz
|
|
opt['step'] = step
|
|
opt['thres_sz'] = thres_sz
|
|
extract_single(opt)
|
|
print('process LR...')
|
|
opt['input_folder'] = LR_folder
|
|
opt['save_folder'] = save_LR_folder
|
|
opt['crop_sz'] = crop_sz // scale_ratio
|
|
opt['step'] = step // scale_ratio
|
|
opt['thres_sz'] = thres_sz // scale_ratio
|
|
extract_single(opt)
|
|
assert len(data_util._get_paths_from_images(save_GT_folder)) == len(
|
|
data_util._get_paths_from_images(
|
|
save_LR_folder)), 'different length of save_GT_folder and save_LR_folder.'
|
|
else:
|
|
raise ValueError('Wrong mode.')
|
|
|
|
|
|
class LmdbWriter:
|
|
def __init__(self, lmdb_path, max_mem_size=30*1024*1024*1024, write_freq=5000):
|
|
self.db = lmdb.open(lmdb_path, subdir=True,
|
|
map_size=max_mem_size, readonly=False,
|
|
meminit=False, map_async=True)
|
|
self.txn = self.db.begin(write=True)
|
|
self.ref_id = 0
|
|
self.tile_ids = {}
|
|
self.writes = 0
|
|
self.write_freq = write_freq
|
|
self.keys = []
|
|
|
|
# Writes the given reference image to the db and returns its ID.
|
|
def write_reference_image(self, ref_img):
|
|
id = self.ref_id
|
|
self.ref_id += 1
|
|
self.write_image(id, ref_img[0], ref_img[1])
|
|
return id
|
|
|
|
# Writes a tile image to the db given a reference image and returns its ID.
|
|
def write_tile_image(self, ref_id, tile_image):
|
|
next_tile_id = 0 if ref_id not in self.tile_ids.keys() else self.tile_ids[ref_id]
|
|
self.tile_ids[ref_id] = next_tile_id+1
|
|
full_id = "%i_%i" % (ref_id, next_tile_id)
|
|
self.write_image(full_id, tile_image[0], tile_image[1])
|
|
self.keys.append(full_id)
|
|
return full_id
|
|
|
|
# Writes an image directly to the db with the given reference image and center point.
|
|
def write_image(self, id, img, center_point):
|
|
self.txn.put(u'{}'.format(id).encode('ascii'), pyarrow.serialize(img).to_buffer(), pyarrow.serialize(center_point).to_buffer())
|
|
self.writes += 1
|
|
if self.writes % self.write_freq == 0:
|
|
self.txn.commit()
|
|
self.txn = self.db.begin(write=True)
|
|
|
|
def close(self):
|
|
self.txn.commit()
|
|
with self.db.begin(write=True) as txn:
|
|
txn.put(b'__keys__', pyarrow.serialize(self.keys).to_buffer())
|
|
txn.put(b'__len__', pyarrow.serialize(len(self.keys)).to_buffer())
|
|
self.db.sync()
|
|
self.db.close()
|
|
|
|
|
|
class TiledDataset(data.Dataset):
|
|
def __init__(self, opt, split_mode=False):
|
|
self.split_mode = split_mode
|
|
self.opt = opt
|
|
input_folder = opt['input_folder']
|
|
self.images = data_util._get_paths_from_images(input_folder)
|
|
|
|
def __getitem__(self, index):
|
|
if self.split_mode:
|
|
return self.get(index, True, True).extend(self.get(index, True, False))
|
|
else:
|
|
return self.get(index, False, False)
|
|
|
|
def get(self, index, split_mode, left_img):
|
|
path = self.images[index]
|
|
crop_sz = self.opt['crop_sz']
|
|
step = self.opt['step']
|
|
thres_sz = self.opt['thres_sz']
|
|
only_resize = self.opt['only_resize']
|
|
img = cv2.imread(path, cv2.IMREAD_UNCHANGED)
|
|
|
|
# We must convert the image into a square. Crop the image so that only the center is left, since this is often
|
|
# the most salient part of the image.
|
|
h, w, c = img.shape
|
|
dim = min(h, w)
|
|
img = img[(h - dim) // 2:dim + (h - dim) // 2, (w - dim) // 2:dim + (w - dim) // 2, :]
|
|
|
|
h, w, c = img.shape
|
|
# Uncomment to filter any image that doesnt meet a threshold size.
|
|
if min(h,w) < 1024:
|
|
return
|
|
left = 0
|
|
right = w
|
|
if split_mode:
|
|
if left_img:
|
|
left = 0
|
|
right = int(w/2)
|
|
else:
|
|
left = int(w/2)
|
|
right = w
|
|
w = int(w/2)
|
|
img = img[:, left:right]
|
|
|
|
h_space = np.arange(0, h - crop_sz + 1, step)
|
|
if h - (h_space[-1] + crop_sz) > thres_sz:
|
|
h_space = np.append(h_space, h - crop_sz)
|
|
w_space = np.arange(0, w - crop_sz + 1, step)
|
|
if w - (w_space[-1] + crop_sz) > thres_sz:
|
|
w_space = np.append(w_space, w - crop_sz)
|
|
|
|
dsize = None
|
|
if only_resize:
|
|
dsize = (crop_sz, crop_sz)
|
|
if h < w:
|
|
h_space = [0]
|
|
w_space = [(w - h) // 2]
|
|
crop_sz = h
|
|
else:
|
|
h_space = [(h - w) // 2]
|
|
w_space = [0]
|
|
crop_sz = w
|
|
|
|
index = 0
|
|
resize_factor = self.opt['resize_final_img'] if 'resize_final_img' in self.opt.keys() else 1
|
|
dsize = (int(crop_sz * resize_factor), int(crop_sz * resize_factor))
|
|
# Reference image should always be first.
|
|
results = [(cv2.resize(img, dsize, interpolation=cv2.INTER_AREA), (-1,-1))]
|
|
for x in h_space:
|
|
for y in w_space:
|
|
index += 1
|
|
crop_img = img[x:x + crop_sz, y:y + crop_sz, :]
|
|
center_point = (x + crop_sz // 2, y + crop_sz // 2)
|
|
crop_img = np.ascontiguousarray(crop_img)
|
|
if 'resize_final_img' in self.opt.keys():
|
|
# Resize too.
|
|
resize_factor = self.opt['resize_final_img']
|
|
center_point = (int(center_point[0] * resize_factor), int(center_point[1] * resize_factor))
|
|
crop_img = cv2.resize(crop_img, dsize, interpolation=cv2.INTER_AREA)
|
|
success, buffer = cv2.imencode(".jpg", crop_img, [cv2.IMWRITE_JPEG_QUALITY, self.opt['compression_level']])
|
|
assert success
|
|
results.append((buffer, center_point))
|
|
return results
|
|
|
|
def __len__(self):
|
|
return len(self.images)
|
|
|
|
|
|
def identity(x):
|
|
return x
|
|
|
|
def extract_single(opt, split_img=False):
|
|
save_folder = opt['save_folder']
|
|
if not osp.exists(save_folder):
|
|
os.makedirs(save_folder)
|
|
print('mkdir [{:s}] ...'.format(save_folder))
|
|
lmdb = LmdbWriter(save_folder)
|
|
|
|
dataset = TiledDataset(opt, split_img)
|
|
dataloader = data.DataLoader(dataset, num_workers=opt['n_thread'], collate_fn=identity)
|
|
tq = tqdm(dataloader)
|
|
for imgs in tq:
|
|
if imgs is None or len(imgs) <= 1:
|
|
continue
|
|
ref_id = lmdb.write_reference_image(imgs[0])
|
|
for tile in imgs[1:]:
|
|
lmdb.write_tile_image(ref_id, tile)
|
|
lmdb.close()
|
|
|
|
|
|
if __name__ == '__main__':
|
|
main()
|