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import os
import shutil
import gc
import json
import pickle
import cv2
import numpy as np
from tqdm import tqdm
import threading
from concurrent.futures import ProcessPoolExecutor
from multi_fractal_db import ifs
from multi_fractal_db import serach_ifs_systems
from multi_fractal_db.multi_fractal_dataset import MultiFractalDataset
from multi_fractal_db.multi_fractal_generator import MultiGenerator
from PIL import Image
class Generator():
@classmethod
def get_params(cls, params_path):
# デバッグ表示
cls.debug = True
# Conner's FractalDBのパラメータ
with open(os.path.join(params_path, 'multi_fractal_ifs_params.json')) as f:
cls.ifs_params = json.load(f)
# IFSシステムの探索パラメータ
kwargs = dict(
# IFSシステム数
num_systems=cls.ifs_params["num_systems"],
# 連立方程式の数
n=(2, 4),
bval=1,
beta=None,
sample_fn=None,
)
# 全IFSシステムのパラメータ作成
sys = serach_ifs_systems.random_systems(**kwargs)
cls.ifs_systems = {'params': sys, 'hparams': kwargs}
print(f"ifs_systems length {len(cls.ifs_systems['params'])}")
# デバッグモード
cls.debug = True
return True
@classmethod
def generate(cls, out_path, start_index : int = None, end_index : int = None, jpeg_quality : int = 95):
if cls.debug:
print(out_path)
# MixUp元フォルダ作成
base_path = out_path.replace("pretrain", "base")
# クラス数
num_classes = cls.ifs_params['num_classes']
# 1クラスあたりの画像枚数
num_image_per_class = cls.ifs_params['num_image_per_class']
if start_index is None:
start_index = 0
if end_index is None:
end_index = num_classes
# 全クラス分の画像作成
for iclass in range(start_index, end_index):
print(f"iclass = {iclass:05}")
class_dir = os.path.join(out_path, f"{iclass:05}")
if os.path.exists(class_dir):
files = os.listdir(class_dir)
files = [f for f in files if f.endswith(".jpg")]
if len(files) == num_image_per_class:
print(f"this iclass already processed = {iclass:05}")
once_load_failed = False
for f in files:
path = os.path.join(class_dir, f)
try:
img = Image.open(path)
except:
once_load_failed = True
break
if not once_load_failed:
continue
else:
print(f"[RE] this iclass already processed = {iclass:05}, but file corrupted. ")
for f in files:
os.remove(os.path.join(class_dir, f))
else:
for f in files:
os.remove(os.path.join(class_dir, f))
base_images = []
# MixUp元ベースクラス作成
for ib, ibase in enumerate([iclass*2, iclass*2+1]):
# クラスフォルダ
# 1クラスあたりのIFSシステム数
num_systems_per_calss = cls.ifs_params['num_systems_per_calss']
# 使用するIFSシステムパラメータ
st = ibase * num_systems_per_calss
en = (ibase+1)*num_systems_per_calss
# print(f"ib={ib}, ibase={ibase}, st={st}, en={en}")
ifs_syss = {'params':cls.ifs_systems['params'][st:en],
'hparams': cls.ifs_systems['hparams']}
# chaceサイズ
#cache_size = num_systems_per_calss * num_image_per_class
cache_size = min(500, num_image_per_class*num_systems_per_calss)
# 別スレッドで実行
future = make_multi_fractal_images(
ifs_syss, cls.ifs_params, num_systems_per_calss,
num_image_per_class, cache_size, cls.debug, out_path, ibase)
base_images.append(future)
# MixUp画像作成
# クラスフォルダ
class_dir = os.path.join(out_path, f"{iclass:05}")
if os.path.exists(class_dir)==False:
os.makedirs(class_dir, exist_ok=True)
# 全画像作成
for idx in tqdm(range(num_image_per_class)):
# MixUp元画像の読み込み
image_base1 = base_images[0][idx]
image_base2 = base_images[1][idx]
# MixUp
alpha = 1.0
lam = np.clip(np.random.beta(alpha, alpha), 0.4, 0.6)
image_mixup = lam * image_base1 + (1 - lam) * image_base2
image_mixup = image_mixup.astype(np.uint8)
# 画像書き出し
image_file = os.path.join(class_dir, f"{idx:05}.jpg")
cv2.imwrite(image_file, image_mixup, [cv2.IMWRITE_JPEG_QUALITY, jpeg_quality])
# MixUp元フォルダの削除
base_images = None
del base_images
futures = None
del futures
gc.collect()
def make_multi_fractal_images(ifs_systems, ifs_params,
num_systems_per_calss, num_image_per_class, cache_size,
debug, out_path, ibase):
# Conner's Multi-FractalDB
multi_fractal_dataset = MultiFractalDataset(
ifs_params=ifs_systems,
num_systems=num_systems_per_calss,
num_class=1,
per_class=num_image_per_class,
generator=MultiGenerator(
color=ifs_params["color"],
background=ifs_params["background"],
niter=ifs_params["niter"],
patch=ifs_params["patch"],
n_objects=ifs_params["n_objects"],
size_range=ifs_params["size_range"],
jitter_params=ifs_params["jitter_params"],
cache_size=cache_size,
size=ifs_params["image_size"]
),
period=2)
if debug:
# 確認用フォルダ
check_dir = out_path.replace("pretrain", "check")
if os.path.exists(check_dir)==False:
os.makedirs(check_dir, exist_ok=True)
# 使用するIFSフラクタルを描画
for i, sys in enumerate(ifs_systems['params']):
image_gray = multi_fractal_dataset.generator.render(sys['system'])
image_gray = (image_gray * 255).astype(np.uint8)
#image_gray = cv2.applyColorMap(image_gray, cv2.COLORMAP_BONE)
image_file = os.path.join(check_dir, f"{ibase:05}_{i:02}.jpg")
cv2.imwrite(image_file, image_gray)
# 全画像数
base_images = []
num_fractal_images = len(multi_fractal_dataset)
class_dir = os.path.join(check_dir, f"{ibase:05}")
os.makedirs(class_dir, exist_ok=True)
for idx in range(num_fractal_images):
# 画像とラベルの取得
image, labels = multi_fractal_dataset[idx]
# 画像書き出し
image_file = os.path.join(class_dir, f"{idx:05}.png")
cv2.imwrite(image_file, image)
base_images.append(image)
# メモリ解放
multi_fractal_dataset = None
del multi_fractal_dataset
gc.collect()
return base_images
def multifractal_main(outputdir, start_index, end_index, jpeg_quality):
Generator.get_params('../params')
Generator.generate(outputdir, start_index, end_index, jpeg_quality)
if __name__ == "__main__":
import argparse
from tqdm import tqdm
from copy import deepcopy
import concurrent.futures
import time
from typing import List
import multiprocessing
worker_num=multiprocessing.cpu_count()
print("workers : ", worker_num)
parser = argparse.ArgumentParser()
parser.add_argument('--fpath', type=str, default="../output/pretrain")
parser.add_argument('--total', type=int, default=1000)
parser.add_argument('--step', type=int, default=1000//worker_num+1)
parser.add_argument('--offset', type=int, default=0)
parser.add_argument('--jpeg_quality', type=int, default=95)
args = parser.parse_args()
os.makedirs(args.fpath, exist_ok=True)
executor = concurrent.futures.ProcessPoolExecutor(max_workers=worker_num)
futures : List[concurrent.futures.Future] = []
for i in range(args.offset, args.total, args.step):
start_index = i
end_index = i + args.step
futures.append(executor.submit(multifractal_main, args.fpath, start_index, end_index, args.jpeg_quality))
for future in tqdm(concurrent.futures.as_completed(futures)):
try:
rr = future.result()
except Exception as exc:
print('generated an exception: %s' % (exc))
print("All done!") |