init

backend/ppocr/data/imaug/make_pse_gt.py

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+# copyright (c) 2021 PaddlePaddle Authors. All Rights Reserve.
+#
+# 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
+from __future__ import unicode_literals
+
+import cv2
+import numpy as np
+import pyclipper
+from shapely.geometry import Polygon
+
+__all__ = ['MakePseGt']
+
+
+class MakePseGt(object):
+    def __init__(self, kernel_num=7, size=640, min_shrink_ratio=0.4, **kwargs):
+        self.kernel_num = kernel_num
+        self.min_shrink_ratio = min_shrink_ratio
+        self.size = size
+
+    def __call__(self, data):
+
+        image = data['image']
+        text_polys = data['polys']
+        ignore_tags = data['ignore_tags']
+
+        h, w, _ = image.shape
+        short_edge = min(h, w)
+        if short_edge < self.size:
+            # keep short_size >= self.size
+            scale = self.size / short_edge
+            image = cv2.resize(image, dsize=None, fx=scale, fy=scale)
+            text_polys *= scale
+
+        gt_kernels = []
+        for i in range(1, self.kernel_num + 1):
+            # s1->sn, from big to small
+            rate = 1.0 - (1.0 - self.min_shrink_ratio) / (self.kernel_num - 1
+                                                          ) * i
+            text_kernel, ignore_tags = self.generate_kernel(
+                image.shape[0:2], rate, text_polys, ignore_tags)
+            gt_kernels.append(text_kernel)
+
+        training_mask = np.ones(image.shape[0:2], dtype='uint8')
+        for i in range(text_polys.shape[0]):
+            if ignore_tags[i]:
+                cv2.fillPoly(training_mask,
+                             text_polys[i].astype(np.int32)[np.newaxis, :, :],
+                             0)
+
+        gt_kernels = np.array(gt_kernels)
+        gt_kernels[gt_kernels > 0] = 1
+
+        data['image'] = image
+        data['polys'] = text_polys
+        data['gt_kernels'] = gt_kernels[0:]
+        data['gt_text'] = gt_kernels[0]
+        data['mask'] = training_mask.astype('float32')
+        return data
+
+    def generate_kernel(self,
+                        img_size,
+                        shrink_ratio,
+                        text_polys,
+                        ignore_tags=None):
+        """
+        Refer to part of the code:
+        https://github.com/open-mmlab/mmocr/blob/main/mmocr/datasets/pipelines/textdet_targets/base_textdet_targets.py
+        """
+
+        h, w = img_size
+        text_kernel = np.zeros((h, w), dtype=np.float32)
+        for i, poly in enumerate(text_polys):
+            polygon = Polygon(poly)
+            distance = polygon.area * (1 - shrink_ratio * shrink_ratio) / (
+                polygon.length + 1e-6)
+            subject = [tuple(l) for l in poly]
+            pco = pyclipper.PyclipperOffset()
+            pco.AddPath(subject, pyclipper.JT_ROUND, pyclipper.ET_CLOSEDPOLYGON)
+            shrinked = np.array(pco.Execute(-distance))
+
+            if len(shrinked) == 0 or shrinked.size == 0:
+                if ignore_tags is not None:
+                    ignore_tags[i] = True
+                continue
+            try:
+                shrinked = np.array(shrinked[0]).reshape(-1, 2)
+            except:
+                if ignore_tags is not None:
+                    ignore_tags[i] = True
+                continue
+            cv2.fillPoly(text_kernel, [shrinked.astype(np.int32)], i + 1)
+        return text_kernel, ignore_tags