feat: add broken detection script
Browse files- font_ds_detect_broken.py +96 -0
font_ds_detect_broken.py
ADDED
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| 1 |
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import sys
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| 2 |
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import traceback
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import pickle
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import os
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import concurrent.futures
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from tqdm import tqdm
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import time
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from font_dataset.font import load_fonts
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import cv2
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cjk_ratio = 3
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train_cnt = 100
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val_cnt = 5
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test_cnt = 30
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train_cnt_cjk = int(train_cnt * cjk_ratio)
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val_cnt_cjk = int(val_cnt * cjk_ratio)
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test_cnt_cjk = int(test_cnt * cjk_ratio)
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dataset_path = "./dataset/font_img"
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os.makedirs(dataset_path, exist_ok=True)
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unqualified_log_file_name = f"unqualified_font_{time.time()}.txt"
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runtime_exclusion_list = []
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fonts, exclusion_rule = load_fonts()
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def generate_dataset(dataset_type: str, cnt: int):
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dataset_bath_dir = os.path.join(dataset_path, dataset_type)
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os.makedirs(dataset_bath_dir, exist_ok=True)
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def _generate_single(args):
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i, j, font = args
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print(
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f"Checking {dataset_type} font: {font.path} {i} / {len(fonts)}, image {j}",
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end="\r",
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)
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if exclusion_rule(font):
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print(f"Excluded font: {font.path}")
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return
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if font.path in runtime_exclusion_list:
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print(f"Excluded font: {font.path}")
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return
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image_file_name = f"font_{i}_img_{j}.jpg"
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label_file_name = f"font_{i}_img_{j}.bin"
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image_file_path = os.path.join(dataset_bath_dir, image_file_name)
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label_file_path = os.path.join(dataset_bath_dir, label_file_name)
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# detect cache
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if (not os.path.exists(image_file_path)) or (
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not os.path.exists(label_file_path)
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):
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print(
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f"Missing {dataset_type} font: {font.path} {i} / {len(fonts)}, image {j}"
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)
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# detect broken
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try:
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# check image
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cv2.imread(image_file_path)
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# check label
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with open(label_file_path, "rb") as f:
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pickle.load(f)
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except Exception as e:
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print(
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f"Broken {dataset_type} font: {font.path} {i} / {len(fonts)}, image {j}"
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)
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os.remove(image_file_path)
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os.remove(label_file_path)
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return
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work_list = []
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# divide len(fonts) into 64 parts and choose the third part for this script
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for i in range(len(fonts)):
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font = fonts[i]
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if font.language == "CJK":
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true_cnt = cnt * cjk_ratio
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else:
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true_cnt = cnt
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for j in range(true_cnt):
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work_list.append((i, j, font))
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for i in tqdm(range(len(work_list))):
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_generate_single(work_list[i])
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generate_dataset("train", train_cnt)
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generate_dataset("val", val_cnt)
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generate_dataset("test", test_cnt)
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