## Text Detect Metric
<p align="left">
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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)
### 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.
### 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>
```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).
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"description": "## Text Detect Metric\n<p align=\"left\">\n <a href=\"\"><img src=\"https://img.shields.io/badge/OS-Linux%2C%20Win%2C%20Mac-pink.svg\"></a>\n <a href=\"\"><img src=\"https://img.shields.io/badge/python->=3.6,<3.12-aff.svg\"></a>\n <a href=\"https://pypi.org/project/text_det_metric/\"><img alt=\"PyPI\" src=\"https://img.shields.io/pypi/v/text_det_metric\"></a>\n <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>\n<a href=\"https://semver.org/\"><img alt=\"SemVer2.0\" src=\"https://img.shields.io/badge/SemVer-2.0-brightgreen\"></a>\n <a href=\"https://github.com/psf/black\"><img src=\"https://img.shields.io/badge/code%20style-black-000000.svg\"></a>\n</p>\n\n\n- 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).\n- 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)\n\n\n### Evaluate on the custom dataset.\n- 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.\n\n\n### Usage\n1. Install packages.\n ```bash\n pip install modelscope==1.5.2\n pip install text_det_metric\n ```\n2. Run `get_pred_txt.py` to get `pred.txt`\n <details>\n <summary>Click to expand</summary>\n\n ```python\n from pathlib import Path\n\n import cv2\n import yaml\n from modelscope.msdatasets import MsDataset\n from tqdm import tqdm\n\n from det_demos.ch_ppocr_v3_det import TextDetector\n\n root_dir = Path(__file__).resolve().parent\n\n\n def read_yaml(yaml_path):\n with open(yaml_path, \"rb\") as f:\n data = yaml.load(f, Loader=yaml.Loader)\n return data\n\n\n test_data = MsDataset.load(\n \"text_det_test_dataset\",\n namespace=\"liekkas\",\n subset_name=\"default\",\n split=\"test\",\n )\n\n config_path = root_dir / 'det_demos' / 'ch_ppocr_v3_det' / 'config.yaml'\n config = read_yaml(str(config_path))\n\n # Configure the onnx model path.\n config['model_path'] = str(root_dir / 'det_demos' / config['model_path'])\n\n text_detector = TextDetector(config)\n\n content = []\n for one_data in tqdm(test_data):\n img_path = one_data.get(\"image:FILE\")\n\n img = cv2.imread(str(img_path))\n dt_boxes, elapse = text_detector(img)\n content.append(f\"{img_path}\\t{dt_boxes.tolist()}\\t{elapse}\")\n\n with open(\"pred.txt\", \"w\", encoding=\"utf-8\") as f:\n for v in content:\n f.write(f\"{v}\\n\")\n ```\n </details>\n3. Run `compute_metric.py` to get the metrics on the dataset\n ```python\n from text_det_metric import DetectionIoUEvaluator\n\n metric = DetectionIoUEvaluator()\n\n # pred_path\n pred_path = \"pred.txt\"\n metric = metric(pred_path)\n print(metric)\n ```\n4. Output\n ```python\n {\n 'precision': 0.6958333333333333,\n 'recall': 0.8608247422680413,\n 'hmean': 0.7695852534562212,\n 'avg_elapse': 2.0107483345529307\n }\n ```\n\n### See details for [TextDetMetric](https://github.com/SWHL/TextDetMetric).\n\n\n",
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