init

backend/ppocr/postprocess/pse_postprocess/pse_postprocess.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.
+"""
+This code is refer from:
+https://github.com/whai362/PSENet/blob/python3/models/head/psenet_head.py
+"""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import numpy as np
+import cv2
+import paddle
+from paddle.nn import functional as F
+
+from ppocr.postprocess.pse_postprocess.pse import pse
+
+
+class PSEPostProcess(object):
+    """
+    The post process for PSE.
+    """
+
+    def __init__(self,
+                 thresh=0.5,
+                 box_thresh=0.85,
+                 min_area=16,
+                 box_type='quad',
+                 scale=4,
+                 **kwargs):
+        assert box_type in ['quad', 'poly'], 'Only quad and poly is supported'
+        self.thresh = thresh
+        self.box_thresh = box_thresh
+        self.min_area = min_area
+        self.box_type = box_type
+        self.scale = scale
+
+    def __call__(self, outs_dict, shape_list):
+        pred = outs_dict['maps']
+        if not isinstance(pred, paddle.Tensor):
+            pred = paddle.to_tensor(pred)
+        pred = F.interpolate(
+            pred, scale_factor=4 // self.scale, mode='bilinear')
+
+        score = F.sigmoid(pred[:, 0, :, :])
+
+        kernels = (pred > self.thresh).astype('float32')
+        text_mask = kernels[:, 0, :, :]
+        kernels[:, 0:, :, :] = kernels[:, 0:, :, :] * text_mask
+
+        score = score.numpy()
+        kernels = kernels.numpy().astype(np.uint8)
+
+        boxes_batch = []
+        for batch_index in range(pred.shape[0]):
+            boxes, scores = self.boxes_from_bitmap(score[batch_index],
+                                                   kernels[batch_index],
+                                                   shape_list[batch_index])
+
+            boxes_batch.append({'points': boxes, 'scores': scores})
+        return boxes_batch
+
+    def boxes_from_bitmap(self, score, kernels, shape):
+        label = pse(kernels, self.min_area)
+        return self.generate_box(score, label, shape)
+
+    def generate_box(self, score, label, shape):
+        src_h, src_w, ratio_h, ratio_w = shape
+        label_num = np.max(label) + 1
+
+        boxes = []
+        scores = []
+        for i in range(1, label_num):
+            ind = label == i
+            points = np.array(np.where(ind)).transpose((1, 0))[:, ::-1]
+
+            if points.shape[0] < self.min_area:
+                label[ind] = 0
+                continue
+
+            score_i = np.mean(score[ind])
+            if score_i < self.box_thresh:
+                label[ind] = 0
+                continue
+
+            if self.box_type == 'quad':
+                rect = cv2.minAreaRect(points)
+                bbox = cv2.boxPoints(rect)
+            elif self.box_type == 'poly':
+                box_height = np.max(points[:, 1]) + 10
+                box_width = np.max(points[:, 0]) + 10
+
+                mask = np.zeros((box_height, box_width), np.uint8)
+                mask[points[:, 1], points[:, 0]] = 255
+
+                contours, _ = cv2.findContours(mask, cv2.RETR_EXTERNAL,
+                                               cv2.CHAIN_APPROX_SIMPLE)
+                bbox = np.squeeze(contours[0], 1)
+            else:
+                raise NotImplementedError
+
+            bbox[:, 0] = np.clip(np.round(bbox[:, 0] / ratio_w), 0, src_w)
+            bbox[:, 1] = np.clip(np.round(bbox[:, 1] / ratio_h), 0, src_h)
+            boxes.append(bbox)
+            scores.append(score_i)
+        return boxes, scores