import sys import traceback import pickle import os import concurrent.futures from tqdm import tqdm from font_dataset.font import load_fonts from font_dataset.layout import generate_font_image from font_dataset.text import CorpusGeneratorManager from font_dataset.background import background_image_generator global_script_index = int(sys.argv[1]) global_script_index_total = int(sys.argv[2]) print(f"Mission {global_script_index} / {global_script_index_total}") num_workers = 32 cjk_ratio = 3 train_cnt = 100 val_cnt = 5 test_cnt = 30 train_cnt_cjk = int(train_cnt * cjk_ratio) val_cnt_cjk = int(val_cnt * cjk_ratio) test_cnt_cjk = int(test_cnt * cjk_ratio) dataset_path = "./dataset/font_img" os.makedirs(dataset_path, exist_ok=True) fonts = load_fonts() corpus_manager = CorpusGeneratorManager() images = background_image_generator() def generate_dataset(dataset_type: str, cnt: int): dataset_bath_dir = os.path.join(dataset_path, dataset_type) os.makedirs(dataset_bath_dir, exist_ok=True) def _generate_single(args): while True: try: i, j, font = args image_file_name = f"font_{i}_img_{j}.jpg" label_file_name = f"font_{i}_img_{j}.bin" image_file_path = os.path.join(dataset_bath_dir, image_file_name) label_file_path = os.path.join(dataset_bath_dir, label_file_name) # detect cache if os.path.exists(image_file_path) and os.path.exists(label_file_path): return im = next(images) im, label = generate_font_image( im, font, corpus_manager, ) im.save(image_file_path) pickle.dump(label, open(label_file_path, "wb")) return except Exception as e: traceback.print_exc() continue work_list = [] # divide len(fonts) into 64 parts and choose the third part for this script for i in range( (global_script_index - 1) * len(fonts) // global_script_index_total, global_script_index * len(fonts) // global_script_index_total, ): font = fonts[i] if font.language == "CJK": true_cnt = cnt * cjk_ratio else: true_cnt = cnt for j in range(true_cnt): work_list.append((i, j, font)) # with concurrent.futures.ThreadPoolExecutor(max_workers=num_workers) as executor: # _ = list( # tqdm( # executor.map(_generate_single, work_list), # total=len(work_list), # leave=True, # desc=dataset_type, # miniters=1, # ) # ) for i in tqdm(range(len(work_list))): _generate_single(work_list[i]) generate_dataset("train", train_cnt) generate_dataset("val", val_cnt) generate_dataset("test", test_cnt)