mirror of
https://github.com/PaddlePaddle/PaddleOCR.git
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163 lines
6.2 KiB
Python
163 lines
6.2 KiB
Python
# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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import paddle
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import os
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import sys
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__dir__ = os.path.dirname(__file__)
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sys.path.append(__dir__)
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sys.path.append(os.path.join(__dir__, '..'))
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from extract_textpoint_slow import *
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from extract_textpoint_fast import generate_pivot_list_fast, restore_poly
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class PGNet_PostProcess(object):
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# two different post-process
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def __init__(self, character_dict_path, valid_set, score_thresh, outs_dict,
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shape_list):
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self.Lexicon_Table = get_dict(character_dict_path)
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self.valid_set = valid_set
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self.score_thresh = score_thresh
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self.outs_dict = outs_dict
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self.shape_list = shape_list
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def pg_postprocess_fast(self):
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p_score = self.outs_dict['f_score']
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p_border = self.outs_dict['f_border']
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p_char = self.outs_dict['f_char']
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p_direction = self.outs_dict['f_direction']
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if isinstance(p_score, paddle.Tensor):
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p_score = p_score[0].numpy()
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p_border = p_border[0].numpy()
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p_direction = p_direction[0].numpy()
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p_char = p_char[0].numpy()
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else:
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p_score = p_score[0]
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p_border = p_border[0]
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p_direction = p_direction[0]
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p_char = p_char[0]
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src_h, src_w, ratio_h, ratio_w = self.shape_list[0]
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instance_yxs_list, seq_strs = generate_pivot_list_fast(
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p_score,
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p_char,
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p_direction,
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self.Lexicon_Table,
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score_thresh=self.score_thresh)
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poly_list, keep_str_list = restore_poly(instance_yxs_list, seq_strs,
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p_border, ratio_w, ratio_h,
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src_w, src_h, self.valid_set)
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data = {
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'points': poly_list,
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'texts': keep_str_list,
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}
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return data
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def pg_postprocess_slow(self):
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p_score = self.outs_dict['f_score']
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p_border = self.outs_dict['f_border']
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p_char = self.outs_dict['f_char']
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p_direction = self.outs_dict['f_direction']
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if isinstance(p_score, paddle.Tensor):
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p_score = p_score[0].numpy()
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p_border = p_border[0].numpy()
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p_direction = p_direction[0].numpy()
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p_char = p_char[0].numpy()
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else:
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p_score = p_score[0]
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p_border = p_border[0]
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p_direction = p_direction[0]
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p_char = p_char[0]
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src_h, src_w, ratio_h, ratio_w = self.shape_list[0]
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is_curved = self.valid_set == "totaltext"
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char_seq_idx_set, instance_yxs_list = generate_pivot_list_slow(
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p_score,
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p_char,
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p_direction,
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score_thresh=self.score_thresh,
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is_backbone=True,
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is_curved=is_curved)
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seq_strs = []
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for char_idx_set in char_seq_idx_set:
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pr_str = ''.join([self.Lexicon_Table[pos] for pos in char_idx_set])
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seq_strs.append(pr_str)
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poly_list = []
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keep_str_list = []
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all_point_list = []
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all_point_pair_list = []
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for yx_center_line, keep_str in zip(instance_yxs_list, seq_strs):
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if len(yx_center_line) == 1:
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yx_center_line.append(yx_center_line[-1])
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offset_expand = 1.0
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if self.valid_set == 'totaltext':
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offset_expand = 1.2
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point_pair_list = []
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for batch_id, y, x in yx_center_line:
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offset = p_border[:, y, x].reshape(2, 2)
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if offset_expand != 1.0:
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offset_length = np.linalg.norm(
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offset, axis=1, keepdims=True)
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expand_length = np.clip(
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offset_length * (offset_expand - 1),
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a_min=0.5,
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a_max=3.0)
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offset_detal = offset / offset_length * expand_length
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offset = offset + offset_detal
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ori_yx = np.array([y, x], dtype=np.float32)
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point_pair = (ori_yx + offset)[:, ::-1] * 4.0 / np.array(
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[ratio_w, ratio_h]).reshape(-1, 2)
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point_pair_list.append(point_pair)
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all_point_list.append([
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int(round(x * 4.0 / ratio_w)),
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int(round(y * 4.0 / ratio_h))
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])
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all_point_pair_list.append(point_pair.round().astype(np.int32)
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.tolist())
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detected_poly, pair_length_info = point_pair2poly(point_pair_list)
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detected_poly = expand_poly_along_width(
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detected_poly, shrink_ratio_of_width=0.2)
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detected_poly[:, 0] = np.clip(
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detected_poly[:, 0], a_min=0, a_max=src_w)
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detected_poly[:, 1] = np.clip(
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detected_poly[:, 1], a_min=0, a_max=src_h)
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if len(keep_str) < 2:
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continue
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keep_str_list.append(keep_str)
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detected_poly = np.round(detected_poly).astype('int32')
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if self.valid_set == 'partvgg':
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middle_point = len(detected_poly) // 2
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detected_poly = detected_poly[
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[0, middle_point - 1, middle_point, -1], :]
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poly_list.append(detected_poly)
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elif self.valid_set == 'totaltext':
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poly_list.append(detected_poly)
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else:
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print('--> Not supported format.')
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exit(-1)
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data = {
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'points': poly_list,
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'texts': keep_str_list,
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}
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return data
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