285 lines
11 KiB
Python
285 lines
11 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'] = 7
|
|
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.
|
|
|
|
opt['dest'] = 'file'
|
|
opt['input_folder'] = 'F:\\4k6k\\datasets\\images\youtube\\images_cook'
|
|
opt['save_folder'] = 'F:\\4k6k\\datasets\\images\\youtube_massive_cook'
|
|
opt['crop_sz'] = [512, 1024, 2048] # the size of each sub-image
|
|
opt['step'] = [256, 512, 1024] # step of the sliding crop window
|
|
opt['exclusions'] = [[],[],[]] # image names matching these terms wont be included in the processing.
|
|
opt['thres_sz'] = 128 # size threshold
|
|
opt['resize_final_img'] = [.5, .25, .125]
|
|
opt['only_resize'] = False
|
|
opt['vertical_split'] = False
|
|
opt['input_image_max_size_before_being_halved'] = 5500 # As described, images larger than this dimensional size will be halved before anything else is done.
|
|
# This helps prevent images from cameras with "false-megapixels" from polluting the dataset.
|
|
# False-megapixel=lots of noise at ultra-high res.
|
|
|
|
save_folder = opt['save_folder']
|
|
if not osp.exists(save_folder):
|
|
os.makedirs(save_folder)
|
|
print('mkdir [{:s}] ...'.format(save_folder))
|
|
|
|
if opt['dest'] == 'lmdb':
|
|
writer = LmdbWriter(save_folder)
|
|
else:
|
|
writer = FileWriter(save_folder)
|
|
|
|
extract_single(opt, writer)
|
|
|
|
|
|
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 FileWriter:
|
|
def __init__(self, folder):
|
|
self.folder = folder
|
|
self.next_unique_id = 0
|
|
self.ref_center_points = {} # Maps ref_img basename to a dict of image IDs:center points
|
|
self.ref_ids_to_names = {}
|
|
|
|
def get_next_unique_id(self):
|
|
id = self.next_unique_id
|
|
self.next_unique_id += 1
|
|
return id
|
|
|
|
def save_image(self, ref_path, img_name, img):
|
|
save_path = osp.join(self.folder, ref_path)
|
|
os.makedirs(save_path, exist_ok=True)
|
|
f = open(osp.join(save_path, img_name), "wb")
|
|
f.write(img)
|
|
f.close()
|
|
|
|
# Writes the given reference image to the db and returns its ID.
|
|
def write_reference_image(self, ref_img, path):
|
|
ref_img, _, _ = ref_img # Encoded with a center point, which is irrelevant for the reference image.
|
|
img_name = osp.basename(path).replace(".jpg", "").replace(".png", "")
|
|
self.ref_center_points[img_name] = {}
|
|
self.save_image(img_name, "ref.jpg", ref_img)
|
|
id = self.get_next_unique_id()
|
|
self.ref_ids_to_names[id] = img_name
|
|
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):
|
|
id = self.get_next_unique_id()
|
|
ref_name = self.ref_ids_to_names[ref_id]
|
|
img, center, tile_sz = tile_image
|
|
self.ref_center_points[ref_name][id] = center, tile_sz
|
|
self.save_image(ref_name, "%08i.jpg" % (id,), img)
|
|
return id
|
|
|
|
def flush(self):
|
|
for ref_name, cps in self.ref_center_points.items():
|
|
torch.save(cps, osp.join(self.folder, ref_name, "centers.pt"))
|
|
self.ref_center_points = {}
|
|
|
|
def close(self):
|
|
self.flush()
|
|
|
|
class TiledDataset(data.Dataset):
|
|
def __init__(self, opt):
|
|
self.split_mode = opt['vertical_split']
|
|
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), self.get(index, True, False))
|
|
else:
|
|
# Wrap in a tuple to align with split mode.
|
|
return (self.get(index, False, False), None)
|
|
|
|
def get_for_scale(self, img, crop_sz, step, resize_factor, ref_resize_factor):
|
|
thres_sz = self.opt['thres_sz']
|
|
h, w, c = img.shape
|
|
|
|
if crop_sz > h:
|
|
return []
|
|
|
|
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)
|
|
|
|
index = 0
|
|
tile_dim = int(crop_sz * resize_factor)
|
|
dsize = (tile_dim, tile_dim)
|
|
results = []
|
|
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 needs to be resized by ref_resize_factor - since it is relative to the reference image.
|
|
center_point = (int((x + crop_sz // 2) // ref_resize_factor), int((y + crop_sz // 2) // ref_resize_factor))
|
|
crop_img = np.ascontiguousarray(crop_img)
|
|
if 'resize_final_img' in self.opt.keys():
|
|
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, int(crop_sz // ref_resize_factor)))
|
|
return results
|
|
|
|
def get(self, index, split_mode, left_img):
|
|
path = self.images[index]
|
|
img = cv2.imread(path, cv2.IMREAD_UNCHANGED)
|
|
|
|
if img is None or len(img.shape) == 2:
|
|
return None
|
|
|
|
h, w, c = img.shape
|
|
|
|
if max(h,w) > self.opt['input_image_max_size_before_being_halved']:
|
|
h = h // 2
|
|
w = w // 2
|
|
img = cv2.resize(img, (w, h), interpolation=cv2.INTER_AREA)
|
|
#print("Resizing to ", img.shape)
|
|
|
|
# Uncomment to filter any image that doesnt meet a threshold size.
|
|
if min(h,w) < 512:
|
|
return None
|
|
# Greyscale not supported.
|
|
if len(img.shape) == 2:
|
|
return None
|
|
|
|
# Handle splitting the image if needed.
|
|
left = 0
|
|
right = w
|
|
if split_mode:
|
|
if left_img:
|
|
left = 0
|
|
right = w//2
|
|
else:
|
|
left = w//2
|
|
right = w
|
|
img = img[:, left:right]
|
|
|
|
# We must convert the image into a square.
|
|
dim = min(h, w)
|
|
if split_mode:
|
|
# Crop the image towards the center, which makes more sense in split mode.
|
|
if left_img:
|
|
img = img[-dim:, -dim:, :]
|
|
else:
|
|
img = img[:dim, :dim, :]
|
|
else:
|
|
# 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, :]
|
|
|
|
h, w, c = img.shape
|
|
|
|
tile_dim = int(self.opt['crop_sz'][0] * self.opt['resize_final_img'][0])
|
|
dsize = (tile_dim, tile_dim)
|
|
ref_resize_factor = h / tile_dim
|
|
|
|
# Reference image should always be first entry in results.
|
|
ref_img = cv2.resize(img, dsize, interpolation=cv2.INTER_AREA)
|
|
success, ref_buffer = cv2.imencode(".jpg", ref_img, [cv2.IMWRITE_JPEG_QUALITY, self.opt['compression_level']])
|
|
assert success
|
|
results = [(ref_buffer, (-1,-1), (-1,-1))]
|
|
|
|
for crop_sz, exclusions, resize_factor, step in zip(self.opt['crop_sz'], self.opt['exclusions'], self.opt['resize_final_img'], self.opt['step']):
|
|
excluded = False
|
|
for exc in exclusions:
|
|
if exc in path:
|
|
excluded = True
|
|
break;
|
|
if excluded:
|
|
continue
|
|
results.extend(self.get_for_scale(img, crop_sz, step, resize_factor, ref_resize_factor))
|
|
return results, path
|
|
|
|
def __len__(self):
|
|
return len(self.images)
|
|
|
|
|
|
def identity(x):
|
|
return x
|
|
|
|
def extract_single(opt, writer):
|
|
dataset = TiledDataset(opt)
|
|
dataloader = data.DataLoader(dataset, num_workers=opt['n_thread'], collate_fn=identity)
|
|
tq = tqdm(dataloader)
|
|
for spl_imgs in tq:
|
|
if spl_imgs is None:
|
|
continue
|
|
spl_imgs = spl_imgs[0]
|
|
for imgs, lbl in zip(list(spl_imgs), ['left', 'right']):
|
|
if imgs is None:
|
|
continue
|
|
imgs, path = imgs
|
|
if imgs is None or len(imgs) <= 1:
|
|
continue
|
|
path = path + "_" + lbl
|
|
ref_id = writer.write_reference_image(imgs[0], path)
|
|
for tile in imgs[1:]:
|
|
writer.write_tile_image(ref_id, tile)
|
|
writer.flush()
|
|
writer.close()
|
|
|
|
|
|
if __name__ == '__main__':
|
|
main()
|