text-det-metric 0.0.7

Tool of computing the metric of text detection

Text Detect Metric

• 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.
• Indicator calculation code reference: PaddleOCR and DB

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, and you can use the same analogy.

Usage

1. Install packages.

   pip install modelscope==1.5.2
   pip install text_det_metric
   

2. Run get_pred_txt.py to get pred.txt

   Click to expand

   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")
   

3. Run compute_metric.py to get the metrics on the dataset

   from text_det_metric import DetectionIoUEvaluator
   
   metric = DetectionIoUEvaluator()
   
   # pred_path
   pred_path = "pred.txt"
   metric = metric(pred_path)
   print(metric)
   

4. Output

   {
       'precision': 0.6958333333333333,
       'recall': 0.8608247422680413,
       'hmean': 0.7695852534562212,
       'avg_elapse': 2.0107483345529307
   }
   

See details for TextDetMetric.