[Feature] Add CharRecallPrecision for OCR Task

docs/en/api/metrics.rst

@@ -50,6 +50,7 @@ Metrics
    ROUGE
    NaturalImageQualityEvaluator
    Perplexity
+   CharRecallPrecision
    KeypointEndPointError
    KeypointAUC
    KeypointNME

docs/zh_cn/api/metrics.rst

@@ -50,6 +50,7 @@ Metrics
    ROUGE
    NaturalImageQualityEvaluator
    Perplexity
+   CharRecallPrecision
    KeypointEndPointError
    KeypointAUC
    KeypointNME

mmeval/metrics/__init__.py

@@ -6,6 +6,7 @@
 from .ava_map import AVAMeanAP
 from .average_precision import AveragePrecision
 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
@@ -46,7 +47,8 @@
     'ConnectivityError', 'ROUGE', 'Perplexity', 'KeypointEndPointError',
     'KeypointAUC', 'KeypointNME', 'NaturalImageQualityEvaluator',
     'WordAccuracy', 'PrecisionRecallF1score',
-    'SingleLabelPrecisionRecallF1score', 'MultiLabelPrecisionRecallF1score'
+    'SingleLabelPrecisionRecallF1score', 'MultiLabelPrecisionRecallF1score',
+    'CharRecallPrecision'
 ]
 
 _deprecated_msg = (

mmeval/metrics/char_recall_precision.py

@@ -0,0 +1,105 @@
+# 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):
+    r"""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'.
+        invalid_symbol (str): A regular expression to filter out invalid or
+            not cared characters. Defaults to '[^A-Za-z0-9\u4e00-\u9fa5]'.
+        **kwargs: Keyword parameters passed to :class:`BaseMetric`.
+
+    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',
+                 invalid_symbol: str = '[^A-Za-z0-9\u4e00-\u9fa5]',
+                 **kwargs):
+        super().__init__(**kwargs)
+        assert letter_case in ['unchanged', 'upper', 'lower']
+        self.letter_case = letter_case
+        self.invalid_symbol = re.compile(invalid_symbol)
+
+    def add(self, predictions: Sequence[str], groundtruths: Sequence[str]) -> None:  # type: ignore # yapf: disable # noqa: E501
+        """Process one batch of data and predictions.
+
+        Args:
+            predictions (list[str]): The prediction texts.
+            groundtruths (list[str]): The ground truth texts.
+        """
+        for pred, label in zip(predictions, groundtruths):
+            if self.letter_case in ['upper', 'lower']:
+                pred = getattr(pred, self.letter_case)()
+                label = getattr(label, self.letter_case)()
+            valid_label = self.invalid_symbol.sub('', label)
+            valid_pred = self.invalid_symbol.sub('', pred)
+            # number to calculate char level recall & precision
+            true_positive_char_num = self._cal_true_positive_char(
+                valid_pred, valid_label)
+            self._results.append(
+                (len(valid_label), len(valid_pred), 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)
+        metric_results = {}
+        metric_results['recall'] = char_recall
+        metric_results['precision'] = char_precision
+        return metric_results
+
+    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

tests/test_metrics/test_char_recall_precision.py

@@ -0,0 +1,28 @@
+import pytest
+
+from mmeval import CharRecallPrecision
+
+
+def test_init():
+    with pytest.raises(AssertionError):
+        CharRecallPrecision(letter_case='fake')
+
+
+@pytest.mark.parametrize(
+    argnames=['letter_case', 'recall', 'precision'],
+    argvalues=[
+        ('lower', 0.7, 1),
+        ('upper', 0.7, 1),
+        ('unchanged', 0.6, 6.0 / 7),
+    ])
+def test_char_recall_precision_metric(letter_case, recall, precision):
+    metric = CharRecallPrecision(letter_case=letter_case)
+    res = metric(['helL', 'HEL'], ['hello', 'HELLO'])
+    assert abs(res['recall'] - recall) < 1e-7
+    assert abs(res['precision'] - precision) < 1e-7
+    metric.reset()
+    for pred, label in zip(['helL', 'HEL'], ['hello', 'HELLO']):
+        metric.add([pred], [label])
+    res = metric.compute()
+    assert abs(res['recall'] - recall) < 1e-7
+    assert abs(res['precision'] - precision) < 1e-7