261 lines
9.6 KiB
Python
261 lines
9.6 KiB
Python
"""A multi-thread tool to crop large images to sub-images for faster IO."""
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import os
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import os.path as osp
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import numpy as np
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import cv2
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from PIL import Image
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import data.util as data_util # noqa: E402
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import torch.utils.data as data
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from tqdm import tqdm
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import torch
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def main():
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split_img = False
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opt = {}
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opt['n_thread'] = 2
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opt['compression_level'] = 90 # JPEG compression quality rating.
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# CV_IMWRITE_PNG_COMPRESSION from 0 to 9. A higher value means a smaller size and longer
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# compression time. If read raw images during training, use 0 for faster IO speed.
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opt['dest'] = 'file'
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opt['input_folder'] = 'F:\\4k6k\\datasets\\ns_images\\vr\\images_sized'
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opt['save_folder'] = 'F:\\4k6k\\datasets\\ns_images\\vr\\paired_images'
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opt['crop_sz'] = [512, 1024] # the size of each sub-image
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opt['step'] = [512, 1024] # step of the sliding crop window
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opt['thres_sz'] = 128 # size threshold
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opt['resize_final_img'] = [.5, .25]
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opt['only_resize'] = False
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opt['vertical_split'] = True
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save_folder = opt['save_folder']
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if not osp.exists(save_folder):
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os.makedirs(save_folder)
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print('mkdir [{:s}] ...'.format(save_folder))
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if opt['dest'] == 'lmdb':
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writer = LmdbWriter(save_folder)
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else:
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writer = FileWriter(save_folder)
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extract_single(opt, writer)
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class LmdbWriter:
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def __init__(self, lmdb_path, max_mem_size=30*1024*1024*1024, write_freq=5000):
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self.db = lmdb.open(lmdb_path, subdir=True,
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map_size=max_mem_size, readonly=False,
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meminit=False, map_async=True)
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self.txn = self.db.begin(write=True)
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self.ref_id = 0
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self.tile_ids = {}
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self.writes = 0
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self.write_freq = write_freq
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self.keys = []
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# Writes the given reference image to the db and returns its ID.
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def write_reference_image(self, ref_img, _):
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id = self.ref_id
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self.ref_id += 1
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self.write_image(id, ref_img[0], ref_img[1])
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return id
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# Writes a tile image to the db given a reference image and returns its ID.
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def write_tile_image(self, ref_id, tile_image):
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next_tile_id = 0 if ref_id not in self.tile_ids.keys() else self.tile_ids[ref_id]
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self.tile_ids[ref_id] = next_tile_id+1
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full_id = "%i_%i" % (ref_id, next_tile_id)
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self.write_image(full_id, tile_image[0], tile_image[1])
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self.keys.append(full_id)
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return full_id
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# Writes an image directly to the db with the given reference image and center point.
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def write_image(self, id, img, center_point):
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self.txn.put(u'{}'.format(id).encode('ascii'), pyarrow.serialize(img).to_buffer(), pyarrow.serialize(center_point).to_buffer())
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self.writes += 1
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if self.writes % self.write_freq == 0:
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self.txn.commit()
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self.txn = self.db.begin(write=True)
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def close(self):
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self.txn.commit()
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with self.db.begin(write=True) as txn:
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txn.put(b'__keys__', pyarrow.serialize(self.keys).to_buffer())
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txn.put(b'__len__', pyarrow.serialize(len(self.keys)).to_buffer())
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self.db.sync()
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self.db.close()
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class FileWriter:
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def __init__(self, folder):
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self.folder = folder
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self.next_unique_id = 0
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self.ref_center_points = {} # Maps ref_img basename to a dict of image IDs:center points
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self.ref_ids_to_names = {}
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def get_next_unique_id(self):
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id = self.next_unique_id
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self.next_unique_id += 1
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return id
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def save_image(self, ref_path, img_name, img):
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save_path = osp.join(self.folder, ref_path)
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os.makedirs(save_path, exist_ok=True)
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f = open(osp.join(save_path, img_name), "wb")
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f.write(img)
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f.close()
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# Writes the given reference image to the db and returns its ID.
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def write_reference_image(self, ref_img, path):
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ref_img, _, _ = ref_img # Encoded with a center point, which is irrelevant for the reference image.
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img_name = osp.basename(path).replace(".jpg", "").replace(".png", "")
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self.ref_center_points[img_name] = {}
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self.save_image(img_name, "ref.jpg", ref_img)
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id = self.get_next_unique_id()
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self.ref_ids_to_names[id] = img_name
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return id
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# Writes a tile image to the db given a reference image and returns its ID.
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def write_tile_image(self, ref_id, tile_image):
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id = self.get_next_unique_id()
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ref_name = self.ref_ids_to_names[ref_id]
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img, center, tile_sz = tile_image
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self.ref_center_points[ref_name][id] = center, tile_sz
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self.save_image(ref_name, "%08i.jpg" % (id,), img)
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return id
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def flush(self):
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for ref_name, cps in self.ref_center_points.items():
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torch.save(cps, osp.join(self.folder, ref_name, "centers.pt"))
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self.ref_center_points = {}
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def close(self):
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self.flush()
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class TiledDataset(data.Dataset):
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def __init__(self, opt):
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self.split_mode = opt['vertical_split']
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self.opt = opt
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input_folder = opt['input_folder']
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self.images = data_util._get_paths_from_images(input_folder)
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def __getitem__(self, index):
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if self.split_mode:
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return (self.get(index, True, True), self.get(index, True, False))
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else:
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# Wrap in a tuple to align with split mode.
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return (self.get(index, False, False), None)
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def get_for_scale(self, img, crop_sz, step, resize_factor, ref_resize_factor):
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thres_sz = self.opt['thres_sz']
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h, w, c = img.shape
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h_space = np.arange(0, h - crop_sz + 1, step)
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if h - (h_space[-1] + crop_sz) > thres_sz:
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h_space = np.append(h_space, h - crop_sz)
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w_space = np.arange(0, w - crop_sz + 1, step)
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if w - (w_space[-1] + crop_sz) > thres_sz:
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w_space = np.append(w_space, w - crop_sz)
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index = 0
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tile_dim = int(crop_sz * resize_factor)
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dsize = (tile_dim, tile_dim)
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results = []
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for x in h_space:
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for y in w_space:
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index += 1
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crop_img = img[x:x + crop_sz, y:y + crop_sz, :]
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# Center point needs to be resized by ref_resize_factor - since it is relative to the reference image.
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center_point = (int((x + crop_sz // 2) // ref_resize_factor), int((y + crop_sz // 2) // ref_resize_factor))
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crop_img = np.ascontiguousarray(crop_img)
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if 'resize_final_img' in self.opt.keys():
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crop_img = cv2.resize(crop_img, dsize, interpolation=cv2.INTER_AREA)
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success, buffer = cv2.imencode(".jpg", crop_img, [cv2.IMWRITE_JPEG_QUALITY, self.opt['compression_level']])
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assert success
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results.append((buffer, center_point, int(crop_sz // ref_resize_factor)))
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return results
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def get(self, index, split_mode, left_img):
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path = self.images[index]
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img = cv2.imread(path, cv2.IMREAD_UNCHANGED)
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h, w, c = img.shape
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# Uncomment to filter any image that doesnt meet a threshold size.
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if min(h,w) < 1024:
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return None
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# Greyscale not supported.
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if len(img.shape) == 2:
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return None
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# Handle splitting the image if needed.
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left = 0
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right = w
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if split_mode:
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if left_img:
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left = 0
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right = w//2
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else:
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left = w//2
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right = w
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img = img[:, left:right]
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# We must convert the image into a square.
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dim = min(h, w)
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if split_mode:
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# Crop the image towards the center, which makes more sense in split mode.
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if left_img:
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img = img[-dim:, -dim:, :]
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else:
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img = img[:dim, :dim, :]
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else:
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# Crop the image so that only the center is left, since this is often the most salient part of the image.
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img = img[(h - dim) // 2:dim + (h - dim) // 2, (w - dim) // 2:dim + (w - dim) // 2, :]
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h, w, c = img.shape
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tile_dim = int(self.opt['crop_sz'][0] * self.opt['resize_final_img'][0])
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dsize = (tile_dim, tile_dim)
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ref_resize_factor = h / tile_dim
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# Reference image should always be first entry in results.
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ref_img = cv2.resize(img, dsize, interpolation=cv2.INTER_AREA)
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success, ref_buffer = cv2.imencode(".jpg", ref_img, [cv2.IMWRITE_JPEG_QUALITY, self.opt['compression_level']])
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assert success
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results = [(ref_buffer, (-1,-1), (-1,-1))]
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for crop_sz, resize_factor, step in zip(self.opt['crop_sz'], self.opt['resize_final_img'], self.opt['step']):
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results.extend(self.get_for_scale(img, crop_sz, step, resize_factor, ref_resize_factor))
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return results, path
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def __len__(self):
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return len(self.images)
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def identity(x):
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return x
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def extract_single(opt, writer):
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dataset = TiledDataset(opt)
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dataloader = data.DataLoader(dataset, num_workers=opt['n_thread'], collate_fn=identity)
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tq = tqdm(dataloader)
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for spl_imgs in tq:
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if spl_imgs is None:
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continue
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spl_imgs = spl_imgs[0]
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for imgs, lbl in zip(list(spl_imgs), ['left', 'right']):
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if imgs is None:
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continue
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imgs, path = imgs
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if imgs is None or len(imgs) <= 1:
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continue
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path = path + "_" + lbl
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ref_id = writer.write_reference_image(imgs[0], path)
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for tile in imgs[1:]:
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writer.write_tile_image(ref_id, tile)
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writer.flush()
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writer.close()
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if __name__ == '__main__':
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main()
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