https://github.com/swhl/textdetmetric
Compute the metric of text detection algorithm.
https://github.com/swhl/textdetmetric
h-mean ocr text-detection
Last synced: 8 months ago
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Compute the metric of text detection algorithm.
- Host: GitHub
- URL: https://github.com/swhl/textdetmetric
- Owner: SWHL
- License: apache-2.0
- Created: 2023-07-23T00:22:11.000Z (almost 3 years ago)
- Default Branch: main
- Last Pushed: 2024-10-31T00:30:26.000Z (over 1 year ago)
- Last Synced: 2025-08-21T06:55:24.921Z (10 months ago)
- Topics: h-mean, ocr, text-detection
- Language: Python
- Homepage:
- Size: 6.33 MB
- Stars: 1
- Watchers: 1
- Forks: 1
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
Text Detect Metric
### 简介
该库用于计算`Precision`、`Recall`和`H-mean`三个指标，用来快速评测文本检测算法效果，与[text_det_test_dataset](https://huggingface.co/datasets/SWHL/text_det_test_dataset)配套使用。
指标计算代码参考：[PaddleOCR](https://github.com/PaddlePaddle/PaddleOCR/blob/b13f99607653c220ba94df2a8650edac086b0f37/ppocr/metrics/eval_det_iou.py) 和 [DB](https://github.com/MhLiao/DB/blob/3c32b808d4412680310d3d28eeb6a2d5bf1566c5/concern/icdar2015_eval/detection/iou.py#L8)
### 整体框架
```mermaid
flowchart LR
A([Text Detect Algorithm]) --get_pred_txt.py--> B([pred_txt])
B --compute_metric.py--> C([TextDetMetric]) --> D([Precision])
C --> E([Recall])
C --> F([H-mean])
```
### 指定数据集上评测
如果想要评测其他文本检测算法，需要将预测结果写入`pred.txt`中，格式为`预测框坐标\t真实框坐标\t耗时`，详细可参考[link](./pred.txt)。示例如下：
```text
[[[85.0, 43.0], [164.0, 44.0], [164.0, 72.0], [85.0, 70.0]]] [[[473.36082474226805, 271.2938144329896], [520.7835051546391, 290.8814432989691]]] 0.14536070823669434
```
### 示例（评测`rapidocr_onnxruntime==1.3.16`）
1. 安装运行环境
```bash
pip install rapidocr_onnxruntime==1.3.16
pip install datasets
pip install text_det_metric
```
2. 获得`pred.txt`文本文件
```python
from pathlib import Path
import cv2
import numpy as np
from datasets import load_dataset
from rapidocr_onnxruntime import RapidOCR
from tqdm import tqdm
engine = RapidOCR()
dataset = load_dataset("SWHL/text_det_test_dataset")
test_data = dataset["test"]
content = []
for i, one_data in enumerate(tqdm(test_data)):
img = np.array(one_data.get("image"))
img = cv2.cvtColor(img, cv2.COLOR_RGB2BGR)
dt_boxes, elapse = engine(img, use_det=True, use_cls=False, use_rec=False)
dt_boxes = [] if dt_boxes is None else dt_boxes
elapse = 0 if elapse is None else elapse[0]
gt_boxes = [v["points"] for v in one_data["shapes"]]
content.append(f"{dt_boxes}\t{gt_boxes}\t{elapse}")
with open("pred.txt", "w", encoding="utf-8") as f:
for v in content:
f.write(f"{v}\n")
```
3. 计算指标
```python
from text_det_metric import TextDetMetric
metric = TextDetMetric()
pred_path = "pred.txt"
metric = metric(pred_path)
print(metric)
```
4. 得到结果
```bash
{'precision': 0.8301, 'recall': 0.8659, 'hmean': 0.8476, 'avg_elapse': 0.2246}
```