[Feature] Add CharRecallPrecision for OCR Task

mmeval/metrics/__init__.py

@@ -5,6 +5,7 @@
 from .accuracy import Accuracy
 from .ava_map import AVAMeanAP
 from .bleu import BLEU
+from .char_recall_precision import CharRecallPrecision
 from .coco_detection import COCODetection
 from .connectivity_error import ConnectivityError
 from .dota_map import DOTAMeanAP
@@ -36,7 +37,7 @@
     'StructuralSimilarity', 'SignalNoiseRatio', 'MultiLabelMetric',
     'AveragePrecision', 'AVAMeanAP', 'BLEU', 'DOTAMeanAP',
     'SumAbsoluteDifferences', 'GradientError', 'MattingMeanSquaredError',
-    'ConnectivityError', 'ROUGE'
+    'ConnectivityError', 'ROUGE', 'CharRecallPrecision'
 ]
 
 _deprecated_msg = (

mmeval/metrics/char_recall_precision.py

@@ -0,0 +1,102 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import re
+from difflib import SequenceMatcher
+from typing import Dict, Sequence, Tuple
+
+from mmeval.core import BaseMetric
+
+
+class CharRecallPrecision(BaseMetric):
+    """Calculate the char level recall & precision.
+
+    Args:
+        letter_case (str): There are three options to alter the letter cases
+            - unchanged: Do not change prediction texts and labels.
+            - upper: Convert prediction texts and labels into uppercase
+                     characters.
+            - lower: Convert prediction texts and labels into lowercase
+                     characters.
+            Usually, it only works for English characters. Defaults to
+            'unchanged'.
+        valid_symbol (str): Valid characters. Defaults to
+            '[^A-Z^a-z^0-9^\u4e00-\u9fa5]'.
+
+    Examples:
+        >>> from mmeval import CharRecallPrecision
+        >>> metric = CharRecallPrecision()
+        >>> metric(['helL', 'HEL'], ['hello', 'HELLO'])
+        {'char_recall': 0.6, 'char_precision': 0.8571428571428571}
+        >>> metric = CharRecallPrecision(letter_case='upper')
+        >>> metric(['helL', 'HEL'], ['hello', 'HELLO'])
+        {'char_recall': 0.7, 'char_precision': 1.0}
+    """
+
+    def __init__(self,
+                 letter_case: str = 'unchanged',
+                 valid_symbol: str = '[^A-Z^a-z^0-9^\u4e00-\u9fa5]',
+                 **kwargs):
+        super().__init__(**kwargs)
+        assert letter_case in ['unchanged', 'upper', 'lower']
+        self.letter_case = letter_case
+        self.valid_symbol = re.compile(valid_symbol)
+
+    def add(self, predictions: Sequence[str], labels: Sequence[str]) -> None:  # type: ignore # yapf: disable # noqa: E501
+        """Process one batch of data and predictions.
+
+        Args:
+            predictions (list[str]): The prediction texts.
+            labels (list[str]): The ground truth texts.
+        """
+        for pred, label in zip(predictions, labels):
+            if self.letter_case in ['upper', 'lower']:
+                pred = getattr(pred, self.letter_case)()
+                label = getattr(label, self.letter_case)()
+            label_ignore = self.valid_symbol.sub('', label)
+            pred_ignore = self.valid_symbol.sub('', pred)
+            # number to calculate char level recall & precision
+            true_positive_char_num = self._cal_true_positive_char(
+                pred_ignore, label_ignore)
+            self._results.append(
+                (len(label_ignore), len(pred_ignore), true_positive_char_num))
+
+    def compute_metric(self, results: Sequence[Tuple[int, int, int]]) -> Dict:
+        """Compute the metrics from processed results.
+
+        Args:
+            results (list[tuple]): The processed results of each batch.
+
+        Returns:
+            Dict: The computed metrics. The keys are the names of the
+            metrics, and the values are corresponding results.
+        """
+        gt_sum, pred_sum, true_positive_sum = 0.0, 0.0, 0.0
+        for gt, pred, true_positive in results:
+            gt_sum += gt
+            pred_sum += pred
+            true_positive_sum += true_positive
+        char_recall = true_positive_sum / max(gt_sum, 1.0)
+        char_precision = true_positive_sum / max(pred_sum, 1.0)
+        eval_res = {}
+        eval_res['recall'] = char_recall
+        eval_res['precision'] = char_precision
+        return eval_res
+
+    def _cal_true_positive_char(self, pred: str, gt: str) -> int:
+        """Calculate correct character number in prediction.
+
+        Args:
+            pred (str): Prediction text.
+            gt (str): Ground truth text.
+
+        Returns:
+            true_positive_char_num (int): The true positive number.
+        """
+
+        all_opt = SequenceMatcher(None, pred, gt)
+        true_positive_char_num = 0
+        for opt, _, _, s2, e2 in all_opt.get_opcodes():
+            if opt == 'equal':
+                true_positive_char_num += (e2 - s2)
+            else:
+                pass
+        return true_positive_char_num

requirements/optional.txt

@@ -1,3 +1,4 @@
+difflib
 opencv-python!=4.5.5.62,!=4.5.5.64
 pycocotools
 scipy

tests/test_metrics/test_char_recall_precision.py

@@ -0,0 +1,25 @@
+import pytest
+
+from mmeval import CharRecallPrecision
+
+
+def test_init():
+    with pytest.raises(AssertionError):
+        CharRecallPrecision(letter_case='fake')
+
+
+def test_char_recall_precision_metric():
+    metric = CharRecallPrecision(letter_case='lower')
+    res = metric(['helL', 'HEL'], ['hello', 'HELLO'])
+    assert abs(res['recall'] - 0.7) < 1e-7
+    assert abs(res['precision'] - 1) < 1e-7
+
+    metric = CharRecallPrecision(letter_case='upper')
+    res = metric(['helL', 'HEL'], ['hello', 'HELLO'])
+    assert abs(res['recall'] - 0.7) < 1e-7
+    assert abs(res['precision'] - 1) < 1e-7
+
+    metric = CharRecallPrecision(letter_case='unchanged')
+    res = metric(['helL', 'HEL'], ['hello', 'HELLO'])
+    assert abs(res['recall'] - 0.6) < 1e-7
+    assert abs(res['precision'] - 6.0 / 7) < 1e-7