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!")