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| ## Text Detect Metric | ||
| <p align="left"> | ||
| <a href=""><img src="https://img.shields.io/badge/OS-Linux%2C%20Win%2C%20Mac-pink.svg"></a> | ||
| <a href=""><img src="https://img.shields.io/badge/python->=3.6,<3.12-aff.svg"></a> | ||
| <a href="https://pypi.org/project/text_det_metric/"><img alt="PyPI" src="https://img.shields.io/pypi/v/text_det_metric"></a> | ||
| <a href="https://pepy.tech/project/text_det_metric"><img src="https://static.pepy.tech/personalized-badge/text_det_metric?period=total&units=abbreviation&left_color=grey&right_color=blue&left_text=Downloads "></a> | ||
| <a href="https://semver.org/"><img alt="SemVer2.0" src="https://img.shields.io/badge/SemVer-2.0-brightgreen"></a> | ||
| <a href="https://github.com/psf/black"><img src="https://img.shields.io/badge/code%20style-black-000000.svg"></a> | ||
| </p> | ||
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| - This library is used to calculate the three metric `Precision`, `Recall` and `H-mean` to evaluate the effect of text detection algorithms. It is used in conjunction with [Modelscope-Text Detection Test Set](https://www.modelscope.cn/datasets/liekkas/text_det_test_dataset/summary). | ||
| - Indicator calculation code reference: [PaddleOCR](https://github.com/PaddlePaddle/PaddleOCR/blob/b13f99607653c220ba94df2a8650edac086b0f37/ppocr/metrics/eval_det_iou.py) and [DB](https://github.com/MhLiao/DB/blob/3c32b808d4412680310d3d28eeb6a2d5bf1566c5/concern/icdar2015_eval/detection/iou.py#L8) | ||
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| ### Evaluate on the custom dataset. | ||
| - Here we use the evaluation code of `ch_ppocr_v3_det` on the text detection test set [liekkas/text_det_test_dataset](https://www.modelscope.cn/datasets/liekkas/text_det_test_dataset/summary), and you can use the same analogy. | ||
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| ### Usage | ||
| 1. Install packages. | ||
| ```bash | ||
| pip install modelscope==1.5.2 | ||
| pip install text_det_metric | ||
| ``` | ||
| 2. Run `get_pred_txt.py` to get `pred.txt` | ||
| <details> | ||
| <summary>Click to expand</summary> | ||
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| ```python | ||
| from pathlib import Path | ||
| import cv2 | ||
| import yaml | ||
| from modelscope.msdatasets import MsDataset | ||
| from tqdm import tqdm | ||
| from det_demos.ch_ppocr_v3_det import TextDetector | ||
| root_dir = Path(__file__).resolve().parent | ||
| def read_yaml(yaml_path): | ||
| with open(yaml_path, "rb") as f: | ||
| data = yaml.load(f, Loader=yaml.Loader) | ||
| return data | ||
| test_data = MsDataset.load( | ||
| "text_det_test_dataset", | ||
| namespace="liekkas", | ||
| subset_name="default", | ||
| split="test", | ||
| ) | ||
| config_path = root_dir / 'det_demos' / 'ch_ppocr_v3_det' / 'config.yaml' | ||
| config = read_yaml(str(config_path)) | ||
| # Configure the onnx model path. | ||
| config['model_path'] = str(root_dir / 'det_demos' / config['model_path']) | ||
| text_detector = TextDetector(config) | ||
| content = [] | ||
| for one_data in tqdm(test_data): | ||
| img_path = one_data.get("image:FILE") | ||
| img = cv2.imread(str(img_path)) | ||
| dt_boxes, elapse = text_detector(img) | ||
| content.append(f"{img_path}\t{dt_boxes.tolist()}\t{elapse}") | ||
| with open("pred.txt", "w", encoding="utf-8") as f: | ||
| for v in content: | ||
| f.write(f"{v}\n") | ||
| ``` | ||
| </details> | ||
| 3. Run `compute_metric.py` to get the metrics on the dataset | ||
| ```python | ||
| from text_det_metric import DetectionIoUEvaluator | ||
| metric = DetectionIoUEvaluator() | ||
| # pred_path | ||
| pred_path = "pred.txt" | ||
| metric = metric(pred_path) | ||
| print(metric) | ||
| ``` | ||
| 4. Output | ||
| ```python | ||
| { | ||
| 'precision': 0.6958333333333333, | ||
| 'recall': 0.8608247422680413, | ||
| 'hmean': 0.7695852534562212, | ||
| 'avg_elapse': 2.0107483345529307 | ||
| } | ||
| ``` | ||
| ### See details for [TextDetMetric](https://github.com/SWHL/TextDetMetric). |