Linguistic_Prior / scripts /3_calu_metric_v1.py
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import json
import Levenshtein
def compute_edit_distance(pred: str, gt: str) -> float:
"""
计算两个字符串的归一化编辑距离 (Normalized Edit Distance, NED)
值域为 [0, 1],越小表示越相似。
"""
pred = pred.replace("\n", "").replace(" ", "")
gt = gt.replace("\n", "").replace(" ", "")
if not pred and not gt:
return 0.0
dist = Levenshtein.distance(pred, gt)
return round(dist / max(len(pred), len(gt)), 4)
def compute_accuracy(json_path: str):
"""从 JSON 文件批量计算平均 NED 及每条样本的编辑距离"""
with open(json_path, 'r', encoding='utf-8') as f:
data = json.load(f)
total_ned = 0
sample_accuracies = []
for sample in data:
# Ground truth 选择逻辑
if "normal" in json_path:
gt = sample.get("content", "")
elif "tiny_shuffled" in json_path:
gt = sample.get("tiny_shuffled_content", "")
elif "shuffled" in json_path:
gt = sample.get("shuffled_content", "")
else:
raise ValueError("无法确定使用哪个字段作为 ground truth")
pred = sample.get("ocr", "")
ned = compute_edit_distance(pred, gt)
total_ned += ned
sample_accuracies.append({
"id": sample["id"],
"image_path": sample["image_path"],
"content": gt,
"ocr": sample["ocr"],
"NED": ned
})
overall_ned = round(total_ned / len(data), 4)
print(f"共 {len(data)} 条样本, 平均 NED = {overall_ned}")
return overall_ned, sample_accuracies
if __name__ == "__main__":
# json_path = "/vol/zhaoy/ds-ocr/data/CCI3-Data/sample200_len1.0-1.2k/tiny_shuffled/input_small_ocr.json"
# overall_ned, sample_accuracies = compute_accuracy(json_path)
# # 保存每条样本的编辑距离
# out_path = json_path.replace(".json", "_acc.json")
# with open(out_path, "w", encoding="utf-8") as f:
# json.dump(sample_accuracies, f, ensure_ascii=False, indent=2)
# print(f"结果已保存至:{out_path}")
origin = "四月飞雪《四月飞雪》是四月菲雪创作的网络小说,发表于起点网。作品简介大千世界,两亿年前究竟发生了什么,在我们身边,好似缺少着什么,,一个生活在这个世界的小孩,究竟经历了什么,一步步走上了永无止境的通天大道。。接下来就让我们一起见证。。。。。"
mod = "四月飞雪《四月飞雪雪》是四月菲雪作创作的网络小说,发表于起点网。作作品简大简介世大千世界,两两亿年前究竟发竟发什生了什么,在我们身身边,好似缺什少着什么,,一个生活活在这个世世小界的小孩,经究竟经历了什么,一步步步上走上了永止无止天境的通天大道。。接下来就让我们一起见见证。。。。。"
ned = compute_edit_distance(origin, mod)
print(ned)