mirror of
https://github.com/PaddlePaddle/PaddleOCR.git
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436 lines
16 KiB
Python
436 lines
16 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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import cv2
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import copy
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import numpy as np
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import math
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import re
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import sys
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import argparse
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import string
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from copy import deepcopy
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class DetResizeForTest(object):
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def __init__(self, **kwargs):
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super(DetResizeForTest, self).__init__()
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self.resize_type = 0
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if 'image_shape' in kwargs:
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self.image_shape = kwargs['image_shape']
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self.resize_type = 1
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elif 'limit_side_len' in kwargs:
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self.limit_side_len = kwargs['limit_side_len']
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self.limit_type = kwargs.get('limit_type', 'min')
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elif 'resize_short' in kwargs:
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self.limit_side_len = 736
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self.limit_type = 'min'
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else:
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self.resize_type = 2
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self.resize_long = kwargs.get('resize_long', 960)
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def __call__(self, data):
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img = deepcopy(data)
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src_h, src_w, _ = img.shape
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if self.resize_type == 0:
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img, [ratio_h, ratio_w] = self.resize_image_type0(img)
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elif self.resize_type == 2:
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img, [ratio_h, ratio_w] = self.resize_image_type2(img)
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else:
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img, [ratio_h, ratio_w] = self.resize_image_type1(img)
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return img
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def resize_image_type1(self, img):
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resize_h, resize_w = self.image_shape
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ori_h, ori_w = img.shape[:2] # (h, w, c)
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ratio_h = float(resize_h) / ori_h
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ratio_w = float(resize_w) / ori_w
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img = cv2.resize(img, (int(resize_w), int(resize_h)))
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return img, [ratio_h, ratio_w]
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def resize_image_type0(self, img):
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"""
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resize image to a size multiple of 32 which is required by the network
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args:
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img(array): array with shape [h, w, c]
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return(tuple):
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img, (ratio_h, ratio_w)
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"""
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limit_side_len = self.limit_side_len
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h, w, _ = img.shape
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# limit the max side
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if self.limit_type == 'max':
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if max(h, w) > limit_side_len:
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if h > w:
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ratio = float(limit_side_len) / h
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else:
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ratio = float(limit_side_len) / w
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else:
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ratio = 1.
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else:
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if min(h, w) < limit_side_len:
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if h < w:
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ratio = float(limit_side_len) / h
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else:
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ratio = float(limit_side_len) / w
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else:
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ratio = 1.
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resize_h = int(h * ratio)
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resize_w = int(w * ratio)
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resize_h = int(round(resize_h / 32) * 32)
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resize_w = int(round(resize_w / 32) * 32)
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try:
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if int(resize_w) <= 0 or int(resize_h) <= 0:
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return None, (None, None)
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img = cv2.resize(img, (int(resize_w), int(resize_h)))
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except:
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print(img.shape, resize_w, resize_h)
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sys.exit(0)
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ratio_h = resize_h / float(h)
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ratio_w = resize_w / float(w)
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# return img, np.array([h, w])
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return img, [ratio_h, ratio_w]
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def resize_image_type2(self, img):
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h, w, _ = img.shape
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resize_w = w
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resize_h = h
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# Fix the longer side
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if resize_h > resize_w:
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ratio = float(self.resize_long) / resize_h
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else:
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ratio = float(self.resize_long) / resize_w
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resize_h = int(resize_h * ratio)
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resize_w = int(resize_w * ratio)
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max_stride = 128
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resize_h = (resize_h + max_stride - 1) // max_stride * max_stride
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resize_w = (resize_w + max_stride - 1) // max_stride * max_stride
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img = cv2.resize(img, (int(resize_w), int(resize_h)))
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ratio_h = resize_h / float(h)
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ratio_w = resize_w / float(w)
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return img, [ratio_h, ratio_w]
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class BaseRecLabelDecode(object):
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""" Convert between text-label and text-index """
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def __init__(self, config):
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support_character_type = [
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'ch', 'en', 'EN_symbol', 'french', 'german', 'japan', 'korean',
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'it', 'xi', 'pu', 'ru', 'ar', 'ta', 'ug', 'fa', 'ur', 'rs', 'oc',
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'rsc', 'bg', 'uk', 'be', 'te', 'ka', 'chinese_cht', 'hi', 'mr',
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'ne', 'EN'
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]
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character_type = config['character_type']
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character_dict_path = config['character_dict_path']
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use_space_char = True
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assert character_type in support_character_type, "Only {} are supported now but get {}".format(
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support_character_type, character_type)
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self.beg_str = "sos"
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self.end_str = "eos"
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if character_type == "en":
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self.character_str = "0123456789abcdefghijklmnopqrstuvwxyz"
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dict_character = list(self.character_str)
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elif character_type == "EN_symbol":
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# same with ASTER setting (use 94 char).
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self.character_str = string.printable[:-6]
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dict_character = list(self.character_str)
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elif character_type in support_character_type:
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self.character_str = ""
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assert character_dict_path is not None, "character_dict_path should not be None when character_type is {}".format(
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character_type)
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with open(character_dict_path, "rb") as fin:
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lines = fin.readlines()
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for line in lines:
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line = line.decode('utf-8').strip("\n").strip("\r\n")
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self.character_str += line
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if use_space_char:
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self.character_str += " "
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dict_character = list(self.character_str)
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else:
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raise NotImplementedError
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self.character_type = character_type
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dict_character = self.add_special_char(dict_character)
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self.dict = {}
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for i, char in enumerate(dict_character):
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self.dict[char] = i
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self.character = dict_character
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def add_special_char(self, dict_character):
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return dict_character
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def decode(self, text_index, text_prob=None, is_remove_duplicate=False):
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""" convert text-index into text-label. """
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result_list = []
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ignored_tokens = self.get_ignored_tokens()
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batch_size = len(text_index)
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for batch_idx in range(batch_size):
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char_list = []
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conf_list = []
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for idx in range(len(text_index[batch_idx])):
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if text_index[batch_idx][idx] in ignored_tokens:
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continue
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if is_remove_duplicate:
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# only for predict
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if idx > 0 and text_index[batch_idx][idx - 1] == text_index[
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batch_idx][idx]:
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continue
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char_list.append(self.character[int(text_index[batch_idx][
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idx])])
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if text_prob is not None:
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conf_list.append(text_prob[batch_idx][idx])
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else:
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conf_list.append(1)
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text = ''.join(char_list)
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result_list.append((text, np.mean(conf_list)))
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return result_list
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def get_ignored_tokens(self):
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return [0] # for ctc blank
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class CTCLabelDecode(BaseRecLabelDecode):
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""" Convert between text-label and text-index """
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def __init__(
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self,
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config,
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#character_dict_path=None,
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#character_type='ch',
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#use_space_char=False,
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**kwargs):
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super(CTCLabelDecode, self).__init__(config)
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def __call__(self, preds, label=None, *args, **kwargs):
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preds_idx = preds.argmax(axis=2)
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preds_prob = preds.max(axis=2)
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text = self.decode(preds_idx, preds_prob, is_remove_duplicate=True)
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if label is None:
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return text
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label = self.decode(label)
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return text, label
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def add_special_char(self, dict_character):
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dict_character = ['blank'] + dict_character
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return dict_character
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class CharacterOps(object):
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""" Convert between text-label and text-index """
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def __init__(self, config):
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self.character_type = config['character_type']
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self.loss_type = config['loss_type']
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if self.character_type == "en":
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self.character_str = "0123456789abcdefghijklmnopqrstuvwxyz"
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dict_character = list(self.character_str)
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elif self.character_type == "ch":
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character_dict_path = config['character_dict_path']
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self.character_str = ""
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with open(character_dict_path, "rb") as fin:
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lines = fin.readlines()
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for line in lines:
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line = line.decode('utf-8').strip("\n").strip("\r\n")
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self.character_str += line
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dict_character = list(self.character_str)
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elif self.character_type == "en_sensitive":
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# same with ASTER setting (use 94 char).
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self.character_str = string.printable[:-6]
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dict_character = list(self.character_str)
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else:
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self.character_str = None
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assert self.character_str is not None, \
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"Nonsupport type of the character: {}".format(self.character_str)
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self.beg_str = "sos"
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self.end_str = "eos"
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if self.loss_type == "attention":
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dict_character = [self.beg_str, self.end_str] + dict_character
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self.dict = {}
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for i, char in enumerate(dict_character):
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self.dict[char] = i
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self.character = dict_character
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def encode(self, text):
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"""convert text-label into text-index.
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input:
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text: text labels of each image. [batch_size]
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output:
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text: concatenated text index for CTCLoss.
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[sum(text_lengths)] = [text_index_0 + text_index_1 + ... + text_index_(n - 1)]
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length: length of each text. [batch_size]
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"""
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if self.character_type == "en":
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text = text.lower()
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text_list = []
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for char in text:
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if char not in self.dict:
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continue
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text_list.append(self.dict[char])
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text = np.array(text_list)
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return text
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def decode(self, text_index, is_remove_duplicate=False):
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""" convert text-index into text-label. """
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char_list = []
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char_num = self.get_char_num()
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if self.loss_type == "attention":
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beg_idx = self.get_beg_end_flag_idx("beg")
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end_idx = self.get_beg_end_flag_idx("end")
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ignored_tokens = [beg_idx, end_idx]
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else:
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ignored_tokens = [char_num]
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for idx in range(len(text_index)):
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if text_index[idx] in ignored_tokens:
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continue
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if is_remove_duplicate:
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if idx > 0 and text_index[idx - 1] == text_index[idx]:
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continue
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char_list.append(self.character[text_index[idx]])
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text = ''.join(char_list)
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return text
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def get_char_num(self):
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return len(self.character)
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def get_beg_end_flag_idx(self, beg_or_end):
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if self.loss_type == "attention":
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if beg_or_end == "beg":
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idx = np.array(self.dict[self.beg_str])
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elif beg_or_end == "end":
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idx = np.array(self.dict[self.end_str])
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else:
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assert False, "Unsupport type %s in get_beg_end_flag_idx"\
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% beg_or_end
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return idx
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else:
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err = "error in get_beg_end_flag_idx when using the loss %s"\
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% (self.loss_type)
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assert False, err
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class OCRReader(object):
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def __init__(self,
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algorithm="CRNN",
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image_shape=[3, 32, 320],
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char_type="ch",
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batch_num=1,
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char_dict_path="./ppocr_keys_v1.txt"):
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self.rec_image_shape = image_shape
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self.character_type = char_type
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self.rec_batch_num = batch_num
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char_ops_params = {}
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char_ops_params["character_type"] = char_type
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char_ops_params["character_dict_path"] = char_dict_path
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char_ops_params['loss_type'] = 'ctc'
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self.char_ops = CharacterOps(char_ops_params)
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self.label_ops = CTCLabelDecode(char_ops_params)
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def resize_norm_img(self, img, max_wh_ratio):
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imgC, imgH, imgW = self.rec_image_shape
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if self.character_type == "ch":
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imgW = int(32 * max_wh_ratio)
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h = img.shape[0]
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w = img.shape[1]
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ratio = w / float(h)
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if math.ceil(imgH * ratio) > imgW:
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resized_w = imgW
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else:
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resized_w = int(math.ceil(imgH * ratio))
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resized_image = cv2.resize(img, (resized_w, imgH))
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resized_image = resized_image.astype('float32')
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resized_image = resized_image.transpose((2, 0, 1)) / 255
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resized_image -= 0.5
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resized_image /= 0.5
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padding_im = np.zeros((imgC, imgH, imgW), dtype=np.float32)
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padding_im[:, :, 0:resized_w] = resized_image
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return padding_im
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def preprocess(self, img_list):
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img_num = len(img_list)
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norm_img_batch = []
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max_wh_ratio = 0
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for ino in range(img_num):
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h, w = img_list[ino].shape[0:2]
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wh_ratio = w * 1.0 / h
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max_wh_ratio = max(max_wh_ratio, wh_ratio)
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for ino in range(img_num):
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norm_img = self.resize_norm_img(img_list[ino], max_wh_ratio)
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norm_img = norm_img[np.newaxis, :]
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norm_img_batch.append(norm_img)
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norm_img_batch = np.concatenate(norm_img_batch)
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norm_img_batch = norm_img_batch.copy()
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return norm_img_batch[0]
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def postprocess_old(self, outputs, with_score=False):
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rec_res = []
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rec_idx_lod = outputs["ctc_greedy_decoder_0.tmp_0.lod"]
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rec_idx_batch = outputs["ctc_greedy_decoder_0.tmp_0"]
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if with_score:
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predict_lod = outputs["softmax_0.tmp_0.lod"]
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for rno in range(len(rec_idx_lod) - 1):
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beg = rec_idx_lod[rno]
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end = rec_idx_lod[rno + 1]
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if isinstance(rec_idx_batch, list):
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rec_idx_tmp = [x[0] for x in rec_idx_batch[beg:end]]
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else: #nd array
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rec_idx_tmp = rec_idx_batch[beg:end, 0]
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preds_text = self.char_ops.decode(rec_idx_tmp)
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if with_score:
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beg = predict_lod[rno]
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end = predict_lod[rno + 1]
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if isinstance(outputs["softmax_0.tmp_0"], list):
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outputs["softmax_0.tmp_0"] = np.array(outputs[
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"softmax_0.tmp_0"]).astype(np.float32)
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probs = outputs["softmax_0.tmp_0"][beg:end, :]
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ind = np.argmax(probs, axis=1)
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blank = probs.shape[1]
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valid_ind = np.where(ind != (blank - 1))[0]
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score = np.mean(probs[valid_ind, ind[valid_ind]])
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rec_res.append([preds_text, score])
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else:
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rec_res.append([preds_text])
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return rec_res
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def postprocess(self, outputs, with_score=False):
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preds = outputs["save_infer_model/scale_0.tmp_1"]
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try:
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preds = preds.numpy()
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except:
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pass
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preds_idx = preds.argmax(axis=2)
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preds_prob = preds.max(axis=2)
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text = self.label_ops.decode(
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preds_idx, preds_prob, is_remove_duplicate=True)
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return text
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