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ppocr/metrics/det_metric.py

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+# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+__all__ = ["DetMetric", "DetFCEMetric"]
+
+from .eval_det_iou import DetectionIoUEvaluator
+
+
+class DetMetric(object):
+    def __init__(self, main_indicator="hmean", **kwargs):
+        self.evaluator = DetectionIoUEvaluator()
+        self.main_indicator = main_indicator
+        self.reset()
+
+    def __call__(self, preds, batch, **kwargs):
+        """
+        batch: a list produced by dataloaders.
+            image: np.ndarray  of shape (N, C, H, W).
+            ratio_list: np.ndarray  of shape(N,2)
+            polygons: np.ndarray  of shape (N, K, 4, 2), the polygons of objective regions.
+            ignore_tags: np.ndarray  of shape (N, K), indicates whether a region is ignorable or not.
+        preds: a list of dict produced by post process
+             points: np.ndarray of shape (N, K, 4, 2), the polygons of objective regions.
+        """
+        gt_polyons_batch = batch[2]
+        ignore_tags_batch = batch[3]
+        for pred, gt_polyons, ignore_tags in zip(
+            preds, gt_polyons_batch, ignore_tags_batch
+        ):
+            # prepare gt
+            gt_info_list = [
+                {"points": gt_polyon, "text": "", "ignore": ignore_tag}
+                for gt_polyon, ignore_tag in zip(gt_polyons, ignore_tags)
+            ]
+            # prepare det
+            det_info_list = [
+                {"points": det_polyon, "text": ""} for det_polyon in pred["points"]
+            ]
+            result = self.evaluator.evaluate_image(gt_info_list, det_info_list)
+            self.results.append(result)
+
+    def get_metric(self):
+        """
+        return metrics {
+                 'precision': 0,
+                 'recall': 0,
+                 'hmean': 0
+            }
+        """
+
+        metrics = self.evaluator.combine_results(self.results)
+        self.reset()
+        return metrics
+
+    def reset(self):
+        self.results = []  # clear results
+
+
+class DetFCEMetric(object):
+    def __init__(self, main_indicator="hmean", **kwargs):
+        self.evaluator = DetectionIoUEvaluator()
+        self.main_indicator = main_indicator
+        self.reset()
+
+    def __call__(self, preds, batch, **kwargs):
+        """
+        batch: a list produced by dataloaders.
+            image: np.ndarray  of shape (N, C, H, W).
+            ratio_list: np.ndarray  of shape(N,2)
+            polygons: np.ndarray  of shape (N, K, 4, 2), the polygons of objective regions.
+            ignore_tags: np.ndarray  of shape (N, K), indicates whether a region is ignorable or not.
+        preds: a list of dict produced by post process
+             points: np.ndarray of shape (N, K, 4, 2), the polygons of objective regions.
+        """
+        gt_polyons_batch = batch[2]
+        ignore_tags_batch = batch[3]
+
+        for pred, gt_polyons, ignore_tags in zip(
+            preds, gt_polyons_batch, ignore_tags_batch
+        ):
+            # prepare gt
+            gt_info_list = [
+                {"points": gt_polyon, "text": "", "ignore": ignore_tag}
+                for gt_polyon, ignore_tag in zip(gt_polyons, ignore_tags)
+            ]
+            # prepare det
+            det_info_list = [
+                {"points": det_polyon, "text": "", "score": score}
+                for det_polyon, score in zip(pred["points"], pred["scores"])
+            ]
+
+            for score_thr in self.results.keys():
+                det_info_list_thr = [
+                    det_info
+                    for det_info in det_info_list
+                    if det_info["score"] >= score_thr
+                ]
+                result = self.evaluator.evaluate_image(gt_info_list, det_info_list_thr)
+                self.results[score_thr].append(result)
+
+    def get_metric(self):
+        """
+        return metrics {'heman':0,
+            'thr 0.3':'precision: 0 recall: 0 hmean: 0',
+            'thr 0.4':'precision: 0 recall: 0 hmean: 0',
+            'thr 0.5':'precision: 0 recall: 0 hmean: 0',
+            'thr 0.6':'precision: 0 recall: 0 hmean: 0',
+            'thr 0.7':'precision: 0 recall: 0 hmean: 0',
+            'thr 0.8':'precision: 0 recall: 0 hmean: 0',
+            'thr 0.9':'precision: 0 recall: 0 hmean: 0',
+            }
+        """
+        metrics = {}
+        hmean = 0
+        for score_thr in self.results.keys():
+            metric = self.evaluator.combine_results(self.results[score_thr])
+            # for key, value in metric.items():
+            #     metrics['{}_{}'.format(key, score_thr)] = value
+            metric_str = "precision:{:.5f} recall:{:.5f} hmean:{:.5f}".format(
+                metric["precision"], metric["recall"], metric["hmean"]
+            )
+            metrics["thr {}".format(score_thr)] = metric_str
+            hmean = max(hmean, metric["hmean"])
+        metrics["hmean"] = hmean
+
+        self.reset()
+        return metrics
+
+    def reset(self):
+        self.results = {
+            0.3: [],
+            0.4: [],
+            0.5: [],
+            0.6: [],
+            0.7: [],
+            0.8: [],
+            0.9: [],
+        }  # clear results