diff --git a/MANIFEST.in b/MANIFEST.in index cd1c9636d4d23cc4d0f745403ec8ca407d1cc1a8..e16f157d6e9dd249d6c6a14ae54313759a6752c4 100644 --- a/MANIFEST.in +++ b/MANIFEST.in @@ -1,7 +1,7 @@ -include LICENSE +include LICENSE.txt include README.md -recursive-include ppocr/utils *.txt utility.py logging.py network.py +recursive-include ppocr/utils *.txt utility.py logging.py recursive-include ppocr/data/ *.py recursive-include ppocr/postprocess *.py recursive-include tools/infer *.py diff --git a/doc/table/1.png b/doc/table/1.png deleted file mode 100644 index 47df618ab1bef431a5dd94418c01be16b09d31aa..0000000000000000000000000000000000000000 Binary files a/doc/table/1.png and /dev/null differ diff --git a/paddleocr.py b/paddleocr.py index 48c8c9c6523dc3f813189477e641f0e51b740885..1e4d94ff4e72da951e1ffb92edb50715482581ae 100644 --- a/paddleocr.py +++ b/paddleocr.py @@ -19,16 +19,17 @@ __dir__ = os.path.dirname(__file__) sys.path.append(os.path.join(__dir__, '')) import cv2 -import logging import numpy as np from pathlib import Path +import tarfile +import requests +from tqdm import tqdm from tools.infer import predict_system from ppocr.utils.logging import get_logger logger = get_logger() from ppocr.utils.utility import check_and_read_gif, get_image_file_list -from ppocr.utils.network import maybe_download, download_with_progressbar from tools.infer.utility import draw_ocr, init_args, str2bool __all__ = ['PaddleOCR'] @@ -36,84 +37,84 @@ __all__ = ['PaddleOCR'] model_urls = { 'det': { 'ch': - 'https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_det_infer.tar', + 'https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_det_infer.tar', 'en': - 'https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/en_ppocr_mobile_v2.0_det_infer.tar' + 'https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/en_ppocr_mobile_v2.0_det_infer.tar' }, 'rec': { 'ch': { 'url': - 'https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_rec_infer.tar', + 'https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_rec_infer.tar', 'dict_path': './ppocr/utils/ppocr_keys_v1.txt' }, 'en': { 'url': - 'https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/en_number_mobile_v2.0_rec_infer.tar', + 'https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/en_number_mobile_v2.0_rec_infer.tar', 'dict_path': './ppocr/utils/en_dict.txt' }, 'french': { 'url': - 'https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/french_mobile_v2.0_rec_infer.tar', + 'https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/french_mobile_v2.0_rec_infer.tar', 'dict_path': './ppocr/utils/dict/french_dict.txt' }, 'german': { 'url': - 'https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/german_mobile_v2.0_rec_infer.tar', + 'https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/german_mobile_v2.0_rec_infer.tar', 'dict_path': './ppocr/utils/dict/german_dict.txt' }, 'korean': { 'url': - 'https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/korean_mobile_v2.0_rec_infer.tar', + 'https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/korean_mobile_v2.0_rec_infer.tar', 'dict_path': './ppocr/utils/dict/korean_dict.txt' }, 'japan': { 'url': - 'https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/japan_mobile_v2.0_rec_infer.tar', + 'https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/japan_mobile_v2.0_rec_infer.tar', 'dict_path': './ppocr/utils/dict/japan_dict.txt' }, 'chinese_cht': { 'url': - 'https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/chinese_cht_mobile_v2.0_rec_infer.tar', + 'https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/chinese_cht_mobile_v2.0_rec_infer.tar', 'dict_path': './ppocr/utils/dict/chinese_cht_dict.txt' }, 'ta': { 'url': - 'https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/ta_mobile_v2.0_rec_infer.tar', + 'https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/ta_mobile_v2.0_rec_infer.tar', 'dict_path': './ppocr/utils/dict/ta_dict.txt' }, 'te': { 'url': - 'https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/te_mobile_v2.0_rec_infer.tar', + 'https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/te_mobile_v2.0_rec_infer.tar', 'dict_path': './ppocr/utils/dict/te_dict.txt' }, 'ka': { 'url': - 'https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/ka_mobile_v2.0_rec_infer.tar', + 'https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/ka_mobile_v2.0_rec_infer.tar', 'dict_path': './ppocr/utils/dict/ka_dict.txt' }, 'latin': { 'url': - 'https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/latin_ppocr_mobile_v2.0_rec_infer.tar', + 'https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/latin_ppocr_mobile_v2.0_rec_infer.tar', 'dict_path': './ppocr/utils/dict/latin_dict.txt' }, 'arabic': { 'url': - 'https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/arabic_ppocr_mobile_v2.0_rec_infer.tar', + 'https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/arabic_ppocr_mobile_v2.0_rec_infer.tar', 'dict_path': './ppocr/utils/dict/arabic_dict.txt' }, 'cyrillic': { 'url': - 'https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/cyrillic_ppocr_mobile_v2.0_rec_infer.tar', + 'https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/cyrillic_ppocr_mobile_v2.0_rec_infer.tar', 'dict_path': './ppocr/utils/dict/cyrillic_dict.txt' }, 'devanagari': { 'url': - 'https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/devanagari_ppocr_mobile_v2.0_rec_infer.tar', + 'https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/devanagari_ppocr_mobile_v2.0_rec_infer.tar', 'dict_path': './ppocr/utils/dict/devanagari_dict.txt' } }, 'cls': - 'https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_cls_infer.tar' + 'https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_cls_infer.tar' } SUPPORT_DET_MODEL = ['DB'] @@ -122,6 +123,50 @@ SUPPORT_REC_MODEL = ['CRNN'] BASE_DIR = os.path.expanduser("~/.paddleocr/") +def download_with_progressbar(url, save_path): + response = requests.get(url, stream=True) + total_size_in_bytes = int(response.headers.get('content-length', 0)) + block_size = 1024 # 1 Kibibyte + progress_bar = tqdm(total=total_size_in_bytes, unit='iB', unit_scale=True) + with open(save_path, 'wb') as file: + for data in response.iter_content(block_size): + progress_bar.update(len(data)) + file.write(data) + progress_bar.close() + if total_size_in_bytes == 0 or progress_bar.n != total_size_in_bytes: + logger.error("Something went wrong while downloading models") + sys.exit(0) + + +def maybe_download(model_storage_directory, url): + # using custom model + tar_file_name_list = [ + 'inference.pdiparams', 'inference.pdiparams.info', 'inference.pdmodel' + ] + if not os.path.exists( + os.path.join(model_storage_directory, 'inference.pdiparams') + ) or not os.path.exists( + os.path.join(model_storage_directory, 'inference.pdmodel')): + tmp_path = os.path.join(model_storage_directory, url.split('/')[-1]) + print('download {} to {}'.format(url, tmp_path)) + os.makedirs(model_storage_directory, exist_ok=True) + download_with_progressbar(url, tmp_path) + with tarfile.open(tmp_path, 'r') as tarObj: + for member in tarObj.getmembers(): + filename = None + for tar_file_name in tar_file_name_list: + if tar_file_name in member.name: + filename = tar_file_name + if filename is None: + continue + file = tarObj.extractfile(member) + with open( + os.path.join(model_storage_directory, filename), + 'wb') as f: + f.write(file.read()) + os.remove(tmp_path) + + def parse_args(mMain=True): import argparse parser = init_args() @@ -149,12 +194,10 @@ class PaddleOCR(predict_system.TextSystem): args: **kwargs: other params show in paddleocr --help """ - params = parse_args(mMain=False) - params.__dict__.update(**kwargs) - if params.show_log: - logger.setLevel(logging.DEBUG) - self.use_angle_cls = params.use_angle_cls - lang = params.lang + postprocess_params = parse_args(mMain=False) + postprocess_params.__dict__.update(**kwargs) + self.use_angle_cls = postprocess_params.use_angle_cls + lang = postprocess_params.lang latin_lang = [ 'af', 'az', 'bs', 'cs', 'cy', 'da', 'de', 'es', 'et', 'fr', 'ga', 'hr', 'hu', 'id', 'is', 'it', 'ku', 'la', 'lt', 'lv', 'mi', 'ms', @@ -180,46 +223,46 @@ class PaddleOCR(predict_system.TextSystem): lang = "devanagari" assert lang in model_urls[ 'rec'], 'param lang must in {}, but got {}'.format( - model_urls['rec'].keys(), lang) + model_urls['rec'].keys(), lang) if lang == "ch": det_lang = "ch" else: det_lang = "en" use_inner_dict = False - if params.rec_char_dict_path is None: + if postprocess_params.rec_char_dict_path is None: use_inner_dict = True - params.rec_char_dict_path = model_urls['rec'][lang][ + postprocess_params.rec_char_dict_path = model_urls['rec'][lang][ 'dict_path'] # init model dir - if params.det_model_dir is None: - params.det_model_dir = os.path.join(BASE_DIR, VERSION, + if postprocess_params.det_model_dir is None: + postprocess_params.det_model_dir = os.path.join(BASE_DIR, VERSION, 'det', det_lang) - if params.rec_model_dir is None: - params.rec_model_dir = os.path.join(BASE_DIR, VERSION, + if postprocess_params.rec_model_dir is None: + postprocess_params.rec_model_dir = os.path.join(BASE_DIR, VERSION, 'rec', lang) - if params.cls_model_dir is None: - params.cls_model_dir = os.path.join(BASE_DIR, 'cls') + if postprocess_params.cls_model_dir is None: + postprocess_params.cls_model_dir = os.path.join(BASE_DIR, 'cls') + print(postprocess_params) # download model - maybe_download(params.det_model_dir, + maybe_download(postprocess_params.det_model_dir, model_urls['det'][det_lang]) - maybe_download(params.rec_model_dir, + maybe_download(postprocess_params.rec_model_dir, model_urls['rec'][lang]['url']) - maybe_download(params.cls_model_dir, model_urls['cls']) + maybe_download(postprocess_params.cls_model_dir, model_urls['cls']) - if params.det_algorithm not in SUPPORT_DET_MODEL: + if postprocess_params.det_algorithm not in SUPPORT_DET_MODEL: logger.error('det_algorithm must in {}'.format(SUPPORT_DET_MODEL)) sys.exit(0) - if params.rec_algorithm not in SUPPORT_REC_MODEL: + if postprocess_params.rec_algorithm not in SUPPORT_REC_MODEL: logger.error('rec_algorithm must in {}'.format(SUPPORT_REC_MODEL)) sys.exit(0) if use_inner_dict: - params.rec_char_dict_path = str( - Path(__file__).parent / params.rec_char_dict_path) + postprocess_params.rec_char_dict_path = str( + Path(__file__).parent / postprocess_params.rec_char_dict_path) - print(params) # init det_model and rec_model - super().__init__(params) + super().__init__(postprocess_params) def ocr(self, img, det=True, rec=True, cls=True): """ diff --git a/ppocr/data/imaug/operators.py b/ppocr/data/imaug/operators.py index 2535b4420c503f2e9e9cc5a677ef70c4dd9c36be..9c48b09647527cf718113ea1b5df152ff7befa04 100644 --- a/ppocr/data/imaug/operators.py +++ b/ppocr/data/imaug/operators.py @@ -81,7 +81,7 @@ class NormalizeImage(object): assert isinstance(img, np.ndarray), "invalid input 'img' in NormalizeImage" data['image'] = ( - img.astype('float32') * self.scale - self.mean) / self.std + img.astype('float32') * self.scale - self.mean) / self.std return data @@ -163,7 +163,7 @@ class DetResizeForTest(object): img, (ratio_h, ratio_w) """ limit_side_len = self.limit_side_len - h, w, c = img.shape + h, w, _ = img.shape # limit the max side if self.limit_type == 'max': @@ -174,7 +174,7 @@ class DetResizeForTest(object): ratio = float(limit_side_len) / w else: ratio = 1. - elif self.limit_type == 'min': + else: if min(h, w) < limit_side_len: if h < w: ratio = float(limit_side_len) / h @@ -182,10 +182,6 @@ class DetResizeForTest(object): ratio = float(limit_side_len) / w else: ratio = 1. - elif self.limit_type == 'resize_long': - ratio = float(limit_side_len) / max(h,w) - else: - raise Exception('not support limit type, image ') resize_h = int(h * ratio) resize_w = int(w * ratio) diff --git a/ppocr/postprocess/rec_postprocess.py b/ppocr/postprocess/rec_postprocess.py index 164dec557a672842df868e2f6a01fc0fbc3e4946..ae5470a520eb90464c548bbe8a00d54840cc529c 100644 --- a/ppocr/postprocess/rec_postprocess.py +++ b/ppocr/postprocess/rec_postprocess.py @@ -44,16 +44,16 @@ class BaseRecLabelDecode(object): self.character_str = string.printable[:-6] dict_character = list(self.character_str) elif character_type in support_character_type: - self.character_str = [] + self.character_str = "" assert character_dict_path is not None, "character_dict_path should not be None when character_type is {}".format( character_type) with open(character_dict_path, "rb") as fin: lines = fin.readlines() for line in lines: line = line.decode('utf-8').strip("\n").strip("\r\n") - self.character_str.append(line) + self.character_str += line if use_space_char: - self.character_str.append(" ") + self.character_str += " " dict_character = list(self.character_str) else: @@ -319,156 +319,3 @@ class SRNLabelDecode(BaseRecLabelDecode): assert False, "unsupport type %s in get_beg_end_flag_idx" \ % beg_or_end return idx - - -class TableLabelDecode(object): - """ """ - - def __init__(self, - max_text_length, - max_elem_length, - max_cell_num, - character_dict_path, - **kwargs): - self.max_text_length = max_text_length - self.max_elem_length = max_elem_length - self.max_cell_num = max_cell_num - list_character, list_elem = self.load_char_elem_dict(character_dict_path) - list_character = self.add_special_char(list_character) - list_elem = self.add_special_char(list_elem) - self.dict_character = {} - self.dict_idx_character = {} - for i, char in enumerate(list_character): - self.dict_idx_character[i] = char - self.dict_character[char] = i - self.dict_elem = {} - self.dict_idx_elem = {} - for i, elem in enumerate(list_elem): - self.dict_idx_elem[i] = elem - self.dict_elem[elem] = i - - def load_char_elem_dict(self, character_dict_path): - list_character = [] - list_elem = [] - with open(character_dict_path, "rb") as fin: - lines = fin.readlines() - substr = lines[0].decode('utf-8').strip("\n").split("\t") - character_num = int(substr[0]) - elem_num = int(substr[1]) - for cno in range(1, 1 + character_num): - character = lines[cno].decode('utf-8').strip("\n") - list_character.append(character) - for eno in range(1 + character_num, 1 + character_num + elem_num): - elem = lines[eno].decode('utf-8').strip("\n") - list_elem.append(elem) - return list_character, list_elem - - def add_special_char(self, list_character): - self.beg_str = "sos" - self.end_str = "eos" - list_character = [self.beg_str] + list_character + [self.end_str] - return list_character - - def get_sp_tokens(self): - char_beg_idx = self.get_beg_end_flag_idx('beg', 'char') - char_end_idx = self.get_beg_end_flag_idx('end', 'char') - elem_beg_idx = self.get_beg_end_flag_idx('beg', 'elem') - elem_end_idx = self.get_beg_end_flag_idx('end', 'elem') - elem_char_idx1 = self.dict_elem[''] - elem_char_idx2 = self.dict_elem['', ' 0 and tmp_elem_idx == end_idx: - break - if tmp_elem_idx in ignored_tokens: - continue - - char_list.append(current_dict[tmp_elem_idx]) - elem_pos_list.append(idx) - score_list.append(structure_probs[batch_idx, idx]) - elem_idx_list.append(tmp_elem_idx) - result_list.append(char_list) - result_pos_list.append(elem_pos_list) - result_score_list.append(score_list) - result_elem_idx_list.append(elem_idx_list) - return result_list, result_pos_list, result_score_list, result_elem_idx_list - - def get_ignored_tokens(self, char_or_elem): - beg_idx = self.get_beg_end_flag_idx("beg", char_or_elem) - end_idx = self.get_beg_end_flag_idx("end", char_or_elem) - return [beg_idx, end_idx] - - def get_beg_end_flag_idx(self, beg_or_end, char_or_elem): - if char_or_elem == "char": - if beg_or_end == "beg": - idx = self.dict_character[self.beg_str] - elif beg_or_end == "end": - idx = self.dict_character[self.end_str] - else: - assert False, "Unsupport type %s in get_beg_end_flag_idx of char" \ - % beg_or_end - elif char_or_elem == "elem": - if beg_or_end == "beg": - idx = self.dict_elem[self.beg_str] - elif beg_or_end == "end": - idx = self.dict_elem[self.end_str] - else: - assert False, "Unsupport type %s in get_beg_end_flag_idx of elem" \ - % beg_or_end - else: - assert False, "Unsupport type %s in char_or_elem" \ - % char_or_elem - return idx diff --git a/ppocr/utils/dict/table_dict.txt b/ppocr/utils/dict/table_dict.txt deleted file mode 100644 index 2ef028c786cbce6d1e25856c62986d757b31f93b..0000000000000000000000000000000000000000 --- a/ppocr/utils/dict/table_dict.txt +++ /dev/null @@ -1,277 +0,0 @@ -← - -☆ -─ -α - - -⋅ -$ -ω -ψ -χ -( -υ -≥ -σ -, -ρ -ε -0 -■ -4 -8 -✗ -b -< -✓ -Ψ -Ω -€ -D -3 -Π -H -║ - -L -Φ -Χ -θ -P -κ -λ -μ -T -ξ -X -β -γ -δ -\ -ζ -η -` -d - -h -f -l -Θ -p -√ -t - -x -Β -Γ -Δ -| -ǂ -ɛ -j -̧ -➢ -⁡ -̌ -′ -« -△ -▲ -# - -' -Ι -+ -¶ -/ -▼ -⇑ -□ -· -7 -▪ -; -? -➔ -∩ -C -÷ -G -⇒ -K - -O -S -С -W -Α -[ -○ -_ -● -‡ -c -z -g - -o - -〈 -〉 -s -⩽ -w -φ -ʹ -{ -» -∣ -̆ -e -ˆ -∈ -τ -◆ -ι -∅ -∆ -∙ -∘ -Ø -ß -✔ -∞ -∑ -− -× -◊ -∗ -∖ -˃ -˂ -∫ -" -i -& -π -↔ -* -∥ -æ -∧ -. -⁄ -ø -Q -∼ -6 -⁎ -: -★ -> -a -B -≈ -F -J -̄ -N -♯ -R -V - -― -Z -♣ -^ -¤ -¥ -§ - -¢ -£ -≦ -­ -≤ -‖ -Λ -© -n -↓ -→ -↑ -r -° -± -v - -♂ -k -♀ -~ -ᅟ -̇ -@ -” -♦ -ł -® -⊕ -„ -! - -% -⇓ -) -- -1 -5 -9 -= -А -A -‰ -⋆ -Σ -E -◦ -I -※ -M -m -̨ -⩾ -† - -• -U -Y -
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1 -1576 1 -1590 1 -1594 1 -1604 1 -1608 1 -1614 1 -1622 1 -1624 2 -1628 1 -1629 1 -1636 1 -1642 1 -1654 2 -1660 1 -1664 1 -1670 1 -1684 4 -1698 1 -1732 3 -1742 1 -1752 1 -1760 1 -1764 1 -1772 2 -1798 1 -1808 1 -1820 1 -1852 1 -1856 1 -1874 1 -1902 1 -1908 1 -1952 1 -2004 1 -2018 1 -2020 1 -2028 1 -2174 1 -2233 1 -2244 1 -2280 1 -2290 1 -2352 1 -2604 1 -4190 1 diff --git a/ppocr/utils/network.py b/ppocr/utils/network.py deleted file mode 100644 index 453abb693d4c0ed370c1031b677d5bf51661add9..0000000000000000000000000000000000000000 --- a/ppocr/utils/network.py +++ /dev/null @@ -1,82 +0,0 @@ -# copyright (c) 2020 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. - -import os -import sys -import tarfile -import requests -from tqdm import tqdm - -from ppocr.utils.logging import get_logger - - -def download_with_progressbar(url, save_path): - logger = get_logger() - response = requests.get(url, stream=True) - total_size_in_bytes = int(response.headers.get('content-length', 0)) - block_size = 1024 # 1 Kibibyte - progress_bar = tqdm(total=total_size_in_bytes, unit='iB', unit_scale=True) - with open(save_path, 'wb') as file: - for data in response.iter_content(block_size): - progress_bar.update(len(data)) - file.write(data) - progress_bar.close() - if total_size_in_bytes == 0 or progress_bar.n != total_size_in_bytes: - logger.error("Something went wrong while downloading models") - sys.exit(0) - - -def maybe_download(model_storage_directory, url): - # using custom model - tar_file_name_list = [ - 'inference.pdiparams', 'inference.pdiparams.info', 'inference.pdmodel' - ] - if not os.path.exists( - os.path.join(model_storage_directory, 'inference.pdiparams') - ) or not os.path.exists( - os.path.join(model_storage_directory, 'inference.pdmodel')): - assert url.endswith('.tar'), 'Only supports tar compressed package' - tmp_path = os.path.join(model_storage_directory, url.split('/')[-1]) - print('download {} to {}'.format(url, tmp_path)) - os.makedirs(model_storage_directory, exist_ok=True) - download_with_progressbar(url, tmp_path) - with tarfile.open(tmp_path, 'r') as tarObj: - for member in tarObj.getmembers(): - filename = None - for tar_file_name in tar_file_name_list: - if tar_file_name in member.name: - filename = tar_file_name - if filename is None: - continue - file = tarObj.extractfile(member) - with open( - os.path.join(model_storage_directory, filename), - 'wb') as f: - f.write(file.read()) - os.remove(tmp_path) - - -def is_link(s): - return s is not None and s.startswith('http') - - -def confirm_model_dir_url(model_dir, default_model_dir, default_url): - url = default_url - if model_dir is None or is_link(model_dir): - if is_link(model_dir): - url = model_dir - file_name = url.split('/')[-1][:-4] - model_dir = default_model_dir - model_dir = os.path.join(model_dir, file_name) - return model_dir, url diff --git a/ppstructure/MANIFEST.in b/ppstructure/MANIFEST.in deleted file mode 100644 index 2961e722b7cebe8e1912be2dd903fcdecb694019..0000000000000000000000000000000000000000 --- a/ppstructure/MANIFEST.in +++ /dev/null @@ -1,9 +0,0 @@ -include LICENSE -include README.md - -recursive-include ppocr/utils *.txt utility.py logging.py network.py -recursive-include ppocr/data/ *.py -recursive-include ppocr/postprocess *.py -recursive-include tools/infer *.py -recursive-include ppstructure *.py - diff --git a/ppstructure/README_ch.md b/ppstructure/README_ch.md index 22505ad83c6dc58adf472f3db94cbf608b9bbd01..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 100644 --- a/ppstructure/README_ch.md +++ b/ppstructure/README_ch.md @@ -1,30 +0,0 @@ -# TableStructurer - -1. 代码使用 -```python -import cv2 -from paddlestructure import PaddleStructure,draw_result - -table_engine = PaddleStructure( - output='./output/table', - show_log=True) - -img_path = '../doc/table/1.png' -img = cv2.imread(img_path) -result = table_engine(img) -for line in result: - print(line) - -from PIL import Image - -font_path = 'path/tp/PaddleOCR/doc/fonts/simfang.ttf' -image = Image.open(img_path).convert('RGB') -im_show = draw_result(image, result,font_path=font_path) -im_show = Image.fromarray(im_show) -im_show.save('result.jpg') -``` - -2. 命令行使用 -```bash -paddlestructure --image_dir=../doc/table/1.png -``` diff --git a/ppstructure/__init__.py b/ppstructure/__init__.py deleted file mode 100644 index 7055bee443fb86648b80bcb892778a114bc47d71..0000000000000000000000000000000000000000 --- a/ppstructure/__init__.py +++ /dev/null @@ -1,17 +0,0 @@ -# copyright (c) 2020 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 .paddlestructure import PaddleStructure, draw_result, to_excel - -__all__ = ['PaddleStructure', 'draw_result', 'to_excel'] diff --git a/ppstructure/layout/README.md b/ppstructure/layout/README.md new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/ppstructure/layout/README_ch.md b/ppstructure/layout/README_ch.md new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/ppstructure/paddlestructure.py b/ppstructure/paddlestructure.py deleted file mode 100644 index 57a53d6496f66771f1f6f7628751b4f0ac0fc3b5..0000000000000000000000000000000000000000 --- a/ppstructure/paddlestructure.py +++ /dev/null @@ -1,148 +0,0 @@ -# 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. -import logging -import os -import sys - -__dir__ = os.path.dirname(__file__) -sys.path.append(__dir__) -sys.path.append(os.path.join(__dir__, '..')) - -import cv2 -import numpy as np -from pathlib import Path - -from ppocr.utils.logging import get_logger -from ppstructure.predict_system import OCRSystem, save_res -from ppstructure.table.predict_table import to_excel -from ppstructure.utility import init_args, draw_result - -logger = get_logger() -from ppocr.utils.utility import check_and_read_gif, get_image_file_list -from ppocr.utils.network import maybe_download, download_with_progressbar, confirm_model_dir_url, is_link - -__all__ = ['PaddleStructure', 'draw_result', 'to_excel'] - -VERSION = '2.1' -BASE_DIR = os.path.expanduser("~/.paddlestructure/") - -model_urls = { - 'det': 'https://paddleocr.bj.bcebos.com/dygraph_v2.0/table/en_ppocr_mobile_v2.0_table_det_infer.tar', - 'rec': 'https://paddleocr.bj.bcebos.com/dygraph_v2.0/table/en_ppocr_mobile_v2.0_table_rec_infer.tar', - 'structure': 'https://paddleocr.bj.bcebos.com/dygraph_v2.0/table/en_ppocr_mobile_v2.0_table_structure_infer.tar' - -} - - -def parse_args(mMain=True): - import argparse - parser = init_args() - parser.add_help = mMain - - for action in parser._actions: - if action.dest in ['rec_char_dict_path', 'structure_char_dict_path']: - action.default = None - if mMain: - return parser.parse_args() - else: - inference_args_dict = {} - for action in parser._actions: - inference_args_dict[action.dest] = action.default - return argparse.Namespace(**inference_args_dict) - - -class PaddleStructure(OCRSystem): - def __init__(self, **kwargs): - params = parse_args(mMain=False) - params.__dict__.update(**kwargs) - if params.show_log: - logger.setLevel(logging.DEBUG) - params.use_angle_cls = False - # init model dir - params.det_model_dir, det_url = confirm_model_dir_url(params.det_model_dir, - os.path.join(BASE_DIR, VERSION, 'det'), - model_urls['det']) - params.rec_model_dir, rec_url = confirm_model_dir_url(params.rec_model_dir, - os.path.join(BASE_DIR, VERSION, 'rec'), - model_urls['rec']) - params.structure_model_dir, structure_url = confirm_model_dir_url(params.structure_model_dir, - os.path.join(BASE_DIR, VERSION, 'structure'), - model_urls['structure']) - # download model - maybe_download(params.det_model_dir, det_url) - maybe_download(params.rec_model_dir, rec_url) - maybe_download(params.structure_model_dir, structure_url) - - if params.rec_char_dict_path is None: - params.rec_char_type = 'EN' - if os.path.exists(str(Path(__file__).parent / 'ppocr/utils/dict/table_dict.txt')): - params.rec_char_dict_path = str(Path(__file__).parent / 'ppocr/utils/dict/table_dict.txt') - else: - params.rec_char_dict_path = str(Path(__file__).parent.parent / 'ppocr/utils/dict/table_dict.txt') - if params.structure_char_dict_path is None: - if os.path.exists(str(Path(__file__).parent / 'ppocr/utils/dict/table_structure_dict.txt')): - params.structure_char_dict_path = str( - Path(__file__).parent / 'ppocr/utils/dict/table_structure_dict.txt') - else: - params.structure_char_dict_path = str( - Path(__file__).parent.parent / 'ppocr/utils/dict/table_structure_dict.txt') - - print(params) - super().__init__(params) - - def __call__(self, img): - if isinstance(img, str): - # download net image - if img.startswith('http'): - download_with_progressbar(img, 'tmp.jpg') - img = 'tmp.jpg' - image_file = img - img, flag = check_and_read_gif(image_file) - if not flag: - with open(image_file, 'rb') as f: - np_arr = np.frombuffer(f.read(), dtype=np.uint8) - img = cv2.imdecode(np_arr, cv2.IMREAD_COLOR) - if img is None: - logger.error("error in loading image:{}".format(image_file)) - return None - if isinstance(img, np.ndarray) and len(img.shape) == 2: - img = cv2.cvtColor(img, cv2.COLOR_GRAY2BGR) - - res = super().__call__(img) - return res - - -def main(): - # for cmd - args = parse_args(mMain=True) - image_dir = args.image_dir - save_folder = args.output - if image_dir.startswith('http'): - download_with_progressbar(image_dir, 'tmp.jpg') - image_file_list = ['tmp.jpg'] - else: - image_file_list = get_image_file_list(args.image_dir) - if len(image_file_list) == 0: - logger.error('no images find in {}'.format(args.image_dir)) - return - - structure_engine = PaddleStructure(**(args.__dict__)) - for img_path in image_file_list: - img_name = os.path.basename(img_path).split('.')[0] - logger.info('{}{}{}'.format('*' * 10, img_path, '*' * 10)) - result = structure_engine(img_path) - for item in result: - logger.info(item['res']) - save_res(result, save_folder, img_name) - logger.info('result save to {}'.format(os.path.join(save_folder, img_name))) \ No newline at end of file diff --git a/ppstructure/predict_system.py b/ppstructure/predict_system.py index 2cdfcce2eb3ad4abe4407f781eb99e3591ecebde..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 100644 --- a/ppstructure/predict_system.py +++ b/ppstructure/predict_system.py @@ -1,132 +0,0 @@ -# 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. - -import os -import sys -import subprocess - -__dir__ = os.path.dirname(os.path.abspath(__file__)) -sys.path.append(__dir__) -sys.path.append(os.path.abspath(os.path.join(__dir__, '..'))) - -os.environ["FLAGS_allocator_strategy"] = 'auto_growth' -import cv2 -import numpy as np -import time - -import layoutparser as lp - -from ppocr.utils.utility import get_image_file_list, check_and_read_gif -from ppocr.utils.logging import get_logger -from tools.infer.predict_system import TextSystem -from ppstructure.table.predict_table import TableSystem, to_excel -from ppstructure.utility import parse_args,draw_result - -logger = get_logger() - - -class OCRSystem(object): - def __init__(self, args): - args.det_limit_type = 'resize_long' - args.drop_score = 0 - self.text_system = TextSystem(args) - self.table_system = TableSystem(args, self.text_system.text_detector, self.text_system.text_recognizer) - self.table_layout = lp.PaddleDetectionLayoutModel("lp://PubLayNet/ppyolov2_r50vd_dcn_365e_publaynet/config", - threshold=0.5, enable_mkldnn=args.enable_mkldnn, - enforce_cpu=not args.use_gpu, thread_num=args.cpu_threads) - self.use_angle_cls = args.use_angle_cls - self.drop_score = args.drop_score - - def __call__(self, img): - ori_im = img.copy() - layout_res = self.table_layout.detect(img[..., ::-1]) - res_list = [] - for region in layout_res: - x1, y1, x2, y2 = region.coordinates - x1, y1, x2, y2 = int(x1), int(y1), int(x2), int(y2) - roi_img = ori_im[y1:y2, x1:x2, :] - if region.type == 'Table': - res = self.table_system(roi_img) - elif region.type == 'Figure': - continue - else: - filter_boxes, filter_rec_res = self.text_system(roi_img) - filter_boxes = [x + [x1, y1] for x in filter_boxes] - filter_boxes = [x.reshape(-1).tolist() for x in filter_boxes] - - res = (filter_boxes, filter_rec_res) - res_list.append({'type': region.type, 'bbox': [x1, y1, x2, y2], 'res': res}) - return res_list - -def save_res(res, save_folder, img_name): - excel_save_folder = os.path.join(save_folder, img_name) - os.makedirs(excel_save_folder, exist_ok=True) - # save res - for region in res: - if region['type'] == 'Table': - excel_path = os.path.join(excel_save_folder, '{}.xlsx'.format(region['bbox'])) - to_excel(region['res'], excel_path) - elif region['type'] == 'Figure': - pass - else: - with open(os.path.join(excel_save_folder, 'res.txt'), 'a', encoding='utf8') as f: - for box, rec_res in zip(region['res'][0], region['res'][1]): - f.write('{}\t{}\n'.format(np.array(box).reshape(-1).tolist(), rec_res)) - - -def main(args): - image_file_list = get_image_file_list(args.image_dir) - image_file_list = image_file_list - image_file_list = image_file_list[args.process_id::args.total_process_num] - save_folder = args.output - os.makedirs(save_folder, exist_ok=True) - - structure_sys = OCRSystem(args) - img_num = len(image_file_list) - for i, image_file in enumerate(image_file_list): - logger.info("[{}/{}] {}".format(i, img_num, image_file)) - img, flag = check_and_read_gif(image_file) - img_name = os.path.basename(image_file).split('.')[0] - - if not flag: - img = cv2.imread(image_file) - if img is None: - logger.error("error in loading image:{}".format(image_file)) - continue - starttime = time.time() - res = structure_sys(img) - save_res(res, save_folder, img_name) - draw_img = draw_result(img,res, args.vis_font_path) - cv2.imwrite(os.path.join(save_folder, img_name, 'show.jpg'), draw_img) - logger.info('result save to {}'.format(os.path.join(save_folder, img_name))) - elapse = time.time() - starttime - logger.info("Predict time : {:.3f}s".format(elapse)) - - -if __name__ == "__main__": - args = parse_args() - if args.use_mp: - p_list = [] - total_process_num = args.total_process_num - for process_id in range(total_process_num): - cmd = [sys.executable, "-u"] + sys.argv + [ - "--process_id={}".format(process_id), - "--use_mp={}".format(False) - ] - p = subprocess.Popen(cmd, stdout=sys.stdout, stderr=sys.stdout) - p_list.append(p) - for p in p_list: - p.wait() - else: - main(args) diff --git a/ppstructure/setup.py b/ppstructure/setup.py deleted file mode 100644 index 8e68b2e44140f6ad5a13661349666d17cfe45524..0000000000000000000000000000000000000000 --- a/ppstructure/setup.py +++ /dev/null @@ -1,72 +0,0 @@ -# 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. -import os - -from setuptools import setup -from io import open -import shutil - -with open('../requirements.txt', encoding="utf-8-sig") as f: - requirements = f.readlines() - requirements.append('tqdm') - requirements.append('layoutparser') - requirements.append('iopath') - - -def readme(): - with open('README_ch.md', encoding="utf-8-sig") as f: - README = f.read() - return README - - -shutil.copytree('../ppstructure/table', './ppstructure/table') -shutil.copyfile('../ppstructure/predict_system.py', './ppstructure/predict_system.py') -shutil.copyfile('../ppstructure/utility.py', './ppstructure/utility.py') -shutil.copytree('../ppocr', './ppocr') -shutil.copytree('../tools', './tools') -shutil.copyfile('../LICENSE', './LICENSE') - -setup( - name='paddlestructure', - packages=['paddlestructure'], - package_dir={'paddlestructure': ''}, - include_package_data=True, - entry_points={"console_scripts": ["paddlestructure= paddlestructure.paddlestructure:main"]}, - version='1.0', - install_requires=requirements, - license='Apache License 2.0', - description='Awesome OCR toolkits based on PaddlePaddle （8.6M ultra-lightweight pre-trained model, support training and deployment among server, mobile, embeded and IoT devices', - long_description=readme(), - long_description_content_type='text/markdown', - url='https://github.com/PaddlePaddle/PaddleOCR', - download_url='https://github.com/PaddlePaddle/PaddleOCR.git', - keywords=[ - 'ocr textdetection textrecognition paddleocr crnn east star-net rosetta ocrlite db chineseocr chinesetextdetection chinesetextrecognition' - ], - classifiers=[ - 'Intended Audience :: Developers', 'Operating System :: OS Independent', - 'Natural Language :: Chinese (Simplified)', - 'Programming Language :: Python :: 3', - 'Programming Language :: Python :: 3.2', - 'Programming Language :: Python :: 3.3', - 'Programming Language :: Python :: 3.4', - 'Programming Language :: Python :: 3.5', - 'Programming Language :: Python :: 3.6', - 'Programming Language :: Python :: 3.7', 'Topic :: Utilities' - ], ) - -shutil.rmtree('ppocr') -shutil.rmtree('tools') -shutil.rmtree('ppstructure') -os.remove('LICENSE') diff --git a/ppstructure/table/README_ch.md b/ppstructure/table/README_ch.md index 105231068a99eb6c012a125ba3fb65934c5d4ac6..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 100644 --- a/ppstructure/table/README_ch.md +++ b/ppstructure/table/README_ch.md @@ -1,15 +0,0 @@ -# 表格结构和内容预测 - -先cd到PaddleOCR/ppstructure目录下 - -预测 -```python -python3 table/predict_table.py --det_model_dir=../inference/db --rec_model_dir=../inference/rec_mv3_large1.0/infer --table_model_dir=../inference/explite3/infer --image_dir=../table/imgs/PMC3006023_004_00.png --rec_char_dict_path=../ppocr/utils/dict/table_dict.txt --table_char_dict_path=../ppocr/utils/dict/table_structure_dict.txt --rec_char_type=EN --det_limit_side_len=736 --det_limit_type=min --table_output ../output/table -``` -运行完成后，每张图片的excel表格会保存到table_output字段指定的目录下 - -评估 - -```python -python3 table/eval_table.py --det_model_dir=../inference/db --rec_model_dir=../inference/rec_mv3_large1.0/infer --table_model_dir=../inference/explite3/infer --image_dir=../table/imgs --rec_char_dict_path=../ppocr/utils/dict/table_dict.txt --table_char_dict_path=../ppocr/utils/dict/table_structure_dict.txt --rec_char_type=EN --det_limit_side_len=736 --det_limit_type=min --gt_path=path/to/gt.json -``` diff --git a/ppstructure/table/__init__.py b/ppstructure/table/__init__.py deleted file mode 100644 index 1d11e265597c7c8e39098a228108da3bb954b892..0000000000000000000000000000000000000000 --- a/ppstructure/table/__init__.py +++ /dev/null @@ -1,13 +0,0 @@ -# copyright (c) 2020 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. diff --git a/ppstructure/table/eval_table.py b/ppstructure/table/eval_table.py deleted file mode 100755 index 1bcbaa8d0d0b2669828dc6b19c3370a30c522ede..0000000000000000000000000000000000000000 --- a/ppstructure/table/eval_table.py +++ /dev/null @@ -1,69 +0,0 @@ -# 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. -import os -import sys -__dir__ = os.path.dirname(os.path.abspath(__file__)) -sys.path.append(__dir__) -sys.path.append(os.path.abspath(os.path.join(__dir__, '../..'))) - -import cv2 -import json -from tqdm import tqdm -from ppstructure.table.table_metric import TEDS -from ppstructure.table.predict_table import TableSystem -from ppstructure.utility import init_args - - -def parse_args(): - parser = init_args() - parser.add_argument("--gt_path", type=str) - return parser.parse_args() - -def main(gt_path, img_root, args): - teds = TEDS(n_jobs=16) - - text_sys = TableSystem(args) - jsons_gt = json.load(open(gt_path)) # gt - pred_htmls = [] - gt_htmls = [] - for img_name in tqdm(jsons_gt): - # read image - img = cv2.imread(os.path.join(img_root,img_name)) - pred_html = text_sys(img) - pred_htmls.append(pred_html) - - gt_structures, gt_bboxes, gt_contents, contents_with_block = jsons_gt[img_name] - gt_html, gt = get_gt_html(gt_structures, contents_with_block) - gt_htmls.append(gt_html) - scores = teds.batch_evaluate_html(gt_htmls, pred_htmls) - print('teds:', sum(scores) / len(scores)) - - -def get_gt_html(gt_structures, contents_with_block): - end_html = [] - td_index = 0 - for tag in gt_structures: - if '' in tag: - if contents_with_block[td_index] != []: - end_html.extend(contents_with_block[td_index]) - end_html.append(tag) - td_index += 1 - else: - end_html.append(tag) - return ''.join(end_html), end_html - - -if __name__ == '__main__': - args = parse_args() - main(args.gt_path,args.image_dir, args) diff --git a/ppstructure/table/matcher.py b/ppstructure/table/matcher.py deleted file mode 100755 index c3b56384403f5fd92a8db4b4bb378a6d55e5a76c..0000000000000000000000000000000000000000 --- a/ppstructure/table/matcher.py +++ /dev/null @@ -1,192 +0,0 @@ -import json -def distance(box_1, box_2): - x1, y1, x2, y2 = box_1 - x3, y3, x4, y4 = box_2 - dis = abs(x3 - x1) + abs(y3 - y1) + abs(x4- x2) + abs(y4 - y2) - dis_2 = abs(x3 - x1) + abs(y3 - y1) - dis_3 = abs(x4- x2) + abs(y4 - y2) - return dis + min(dis_2, dis_3) - -def compute_iou(rec1, rec2): - """ - computing IoU - :param rec1: (y0, x0, y1, x1), which reflects - (top, left, bottom, right) - :param rec2: (y0, x0, y1, x1) - :return: scala value of IoU - """ - # computing area of each rectangles - S_rec1 = (rec1[2] - rec1[0]) * (rec1[3] - rec1[1]) - S_rec2 = (rec2[2] - rec2[0]) * (rec2[3] - rec2[1]) - - # computing the sum_area - sum_area = S_rec1 + S_rec2 - - # find the each edge of intersect rectangle - left_line = max(rec1[1], rec2[1]) - right_line = min(rec1[3], rec2[3]) - top_line = max(rec1[0], rec2[0]) - bottom_line = min(rec1[2], rec2[2]) - - # judge if there is an intersect - if left_line >= right_line or top_line >= bottom_line: - return 0.0 - else: - intersect = (right_line - left_line) * (bottom_line - top_line) - return (intersect / (sum_area - intersect))*1.0 - - - -def matcher_merge(ocr_bboxes, pred_bboxes): - all_dis = [] - ious = [] - matched = {} - for i, gt_box in enumerate(ocr_bboxes): - distances = [] - for j, pred_box in enumerate(pred_bboxes): - # compute l1 distence and IOU between two boxes - distances.append((distance(gt_box, pred_box), 1. - compute_iou(gt_box, pred_box))) - sorted_distances = distances.copy() - # select nearest cell - sorted_distances = sorted(sorted_distances, key = lambda item: (item[1], item[0])) - if distances.index(sorted_distances[0]) not in matched.keys(): - matched[distances.index(sorted_distances[0])] = [i] - else: - matched[distances.index(sorted_distances[0])].append(i) - return matched#, sum(ious) / len(ious) - -def complex_num(pred_bboxes): - complex_nums = [] - for bbox in pred_bboxes: - distances = [] - temp_ious = [] - for pred_bbox in pred_bboxes: - if bbox != pred_bbox: - distances.append(distance(bbox, pred_bbox)) - temp_ious.append(compute_iou(bbox, pred_bbox)) - complex_nums.append(temp_ious[distances.index(min(distances))]) - return sum(complex_nums) / len(complex_nums) - -def get_rows(pred_bboxes): - pre_bbox = pred_bboxes[0] - res = [] - step = 0 - for i in range(len(pred_bboxes)): - bbox = pred_bboxes[i] - if bbox[1] - pre_bbox[1] > 2 or bbox[0] - pre_bbox[0] < 0: - break - else: - res.append(bbox) - step += 1 - for i in range(step): - pred_bboxes.pop(0) - return res, pred_bboxes -def refine_rows(pred_bboxes): # 微调整行的框，使在一条水平线上 - ys_1 = [] - ys_2 = [] - for box in pred_bboxes: - ys_1.append(box[1]) - ys_2.append(box[3]) - min_y_1 = sum(ys_1) / len(ys_1) - min_y_2 = sum(ys_2) / len(ys_2) - re_boxes = [] - for box in pred_bboxes: - box[1] = min_y_1 - box[3] = min_y_2 - re_boxes.append(box) - return re_boxes - -def matcher_refine_row(gt_bboxes, pred_bboxes): - before_refine_pred_bboxes = pred_bboxes.copy() - pred_bboxes = [] - while(len(before_refine_pred_bboxes) != 0): - row_bboxes, before_refine_pred_bboxes = get_rows(before_refine_pred_bboxes) - print(row_bboxes) - pred_bboxes.extend(refine_rows(row_bboxes)) - all_dis = [] - ious = [] - matched = {} - for i, gt_box in enumerate(gt_bboxes): - distances = [] - #temp_ious = [] - for j, pred_box in enumerate(pred_bboxes): - distances.append(distance(gt_box, pred_box)) - #temp_ious.append(compute_iou(gt_box, pred_box)) - #all_dis.append(min(distances)) - #ious.append(temp_ious[distances.index(min(distances))]) - if distances.index(min(distances)) not in matched.keys(): - matched[distances.index(min(distances))] = [i] - else: - matched[distances.index(min(distances))].append(i) - return matched#, sum(ious) / len(ious) - - - -#先挑选出一行，再进行匹配 -def matcher_structure_1(gt_bboxes, pred_bboxes_rows, pred_bboxes): - gt_box_index = 0 - delete_gt_bboxes = gt_bboxes.copy() - match_bboxes_ready = [] - matched = {} - while(len(delete_gt_bboxes) != 0): - row_bboxes, delete_gt_bboxes = get_rows(delete_gt_bboxes) - row_bboxes = sorted(row_bboxes, key = lambda key: key[0]) - if len(pred_bboxes_rows) > 0: - match_bboxes_ready.extend(pred_bboxes_rows.pop(0)) - print(row_bboxes) - for i, gt_box in enumerate(row_bboxes): - #print(gt_box) - pred_distances = [] - distances = [] - for pred_bbox in pred_bboxes: - pred_distances.append(distance(gt_box, pred_bbox)) - for j, pred_box in enumerate(match_bboxes_ready): - distances.append(distance(gt_box, pred_box)) - index = pred_distances.index(min(distances)) - #print('index', index) - if index not in matched.keys(): - matched[index] = [gt_box_index] - else: - matched[index].append(gt_box_index) - gt_box_index += 1 - return matched - -def matcher_structure(gt_bboxes, pred_bboxes_rows, pred_bboxes): - ''' - gt_bboxes: 排序后 - pred_bboxes: - ''' - pre_bbox = gt_bboxes[0] - matched = {} - match_bboxes_ready = [] - match_bboxes_ready.extend(pred_bboxes_rows.pop(0)) - for i, gt_box in enumerate(gt_bboxes): - - pred_distances = [] - for pred_bbox in pred_bboxes: - pred_distances.append(distance(gt_box, pred_bbox)) - distances = [] - gap_pre = gt_box[1] - pre_bbox[1] - gap_pre_1 = gt_box[0] - pre_bbox[2] - #print(gap_pre, len(pred_bboxes_rows)) - if (gap_pre_1 < 0 and len(pred_bboxes_rows) > 0): - match_bboxes_ready.extend(pred_bboxes_rows.pop(0)) - if len(pred_bboxes_rows) == 1: - match_bboxes_ready.extend(pred_bboxes_rows.pop(0)) - if len(match_bboxes_ready) == 0 and len(pred_bboxes_rows) > 0: - match_bboxes_ready.extend(pred_bboxes_rows.pop(0)) - if len(match_bboxes_ready) == 0 and len(pred_bboxes_rows) == 0: - break - #print(match_bboxes_ready) - for j, pred_box in enumerate(match_bboxes_ready): - distances.append(distance(gt_box, pred_box)) - index = pred_distances.index(min(distances)) - #print(gt_box, index) - #match_bboxes_ready.pop(distances.index(min(distances))) - print(gt_box, match_bboxes_ready[distances.index(min(distances))]) - if index not in matched.keys(): - matched[index] = [i] - else: - matched[index].append(i) - pre_bbox = gt_box - return matched diff --git a/ppstructure/table/predict_structure.py b/ppstructure/table/predict_structure.py deleted file mode 100755 index 6e680b3574ba28b439acad34424b51dfdc02078c..0000000000000000000000000000000000000000 --- a/ppstructure/table/predict_structure.py +++ /dev/null @@ -1,141 +0,0 @@ -# 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. -import os -import sys - -__dir__ = os.path.dirname(os.path.abspath(__file__)) -sys.path.append(__dir__) -sys.path.append(os.path.abspath(os.path.join(__dir__, '../..'))) - -os.environ["FLAGS_allocator_strategy"] = 'auto_growth' - -import cv2 -import numpy as np -import math -import time -import traceback -import paddle - -import tools.infer.utility as utility -from ppocr.data import create_operators, transform -from ppocr.postprocess import build_post_process -from ppocr.utils.logging import get_logger -from ppocr.utils.utility import get_image_file_list, check_and_read_gif - -logger = get_logger() - - -class TableStructurer(object): - def __init__(self, args): - pre_process_list = [{ - 'ResizeTableImage': { - 'max_len': args.structure_max_len - } - }, { - 'NormalizeImage': { - 'std': [0.229, 0.224, 0.225], - 'mean': [0.485, 0.456, 0.406], - 'scale': '1./255.', - 'order': 'hwc' - } - }, { - 'PaddingTableImage': None - }, { - 'ToCHWImage': None - }, { - 'KeepKeys': { - 'keep_keys': ['image'] - } - }] - postprocess_params = { - 'name': 'TableLabelDecode', - "character_type": args.structure_char_type, - "character_dict_path": args.structure_char_dict_path, - "max_text_length": args.structure_max_text_length, - "max_elem_length": args.structure_max_elem_length, - "max_cell_num": args.structure_max_cell_num - } - - self.preprocess_op = create_operators(pre_process_list) - self.postprocess_op = build_post_process(postprocess_params) - self.predictor, self.input_tensor, self.output_tensors = \ - utility.create_predictor(args, 'structure', logger) - - def __call__(self, img): - ori_im = img.copy() - data = {'image': img} - data = transform(data, self.preprocess_op) - img = data[0] - if img is None: - return None, 0 - img = np.expand_dims(img, axis=0) - img = img.copy() - starttime = time.time() - - self.input_tensor.copy_from_cpu(img) - self.predictor.run() - outputs = [] - for output_tensor in self.output_tensors: - output = output_tensor.copy_to_cpu() - outputs.append(output) - - preds = {} - preds['structure_probs'] = outputs[1] - preds['loc_preds'] = outputs[0] - - post_result = self.postprocess_op(preds) - - structure_str_list = post_result['structure_str_list'] - res_loc = post_result['res_loc'] - imgh, imgw = ori_im.shape[0:2] - res_loc_final = [] - for rno in range(len(res_loc[0])): - x0, y0, x1, y1 = res_loc[0][rno] - left = max(int(imgw * x0), 0) - top = max(int(imgh * y0), 0) - right = min(int(imgw * x1), imgw - 1) - bottom = min(int(imgh * y1), imgh - 1) - res_loc_final.append([left, top, right, bottom]) - - structure_str_list = structure_str_list[0][:-1] - structure_str_list = ['', '', ''] + structure_str_list + ['
', '', ''] - - elapse = time.time() - starttime - return (structure_str_list, res_loc_final), elapse - - -def main(args): - image_file_list = get_image_file_list(args.image_dir) - table_structurer = TableStructurer(args) - count = 0 - total_time = 0 - for image_file in image_file_list: - img, flag = check_and_read_gif(image_file) - if not flag: - img = cv2.imread(image_file) - if img is None: - logger.info("error in loading image:{}".format(image_file)) - continue - structure_res, elapse = table_structurer(img) - - logger.info("result: {}".format(structure_res)) - - if count > 0: - total_time += elapse - count += 1 - logger.info("Predict time of {}: {}".format(image_file, elapse)) - - -if __name__ == "__main__": - main(utility.parse_args()) diff --git a/ppstructure/table/predict_table.py b/ppstructure/table/predict_table.py deleted file mode 100644 index c4edd22c3de4df5f0ba3e0a1e28a8c346a48d4ee..0000000000000000000000000000000000000000 --- a/ppstructure/table/predict_table.py +++ /dev/null @@ -1,221 +0,0 @@ -# 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. - -import os -import sys -import subprocess - -__dir__ = os.path.dirname(os.path.abspath(__file__)) -sys.path.append(__dir__) -sys.path.append(os.path.abspath(os.path.join(__dir__, '..'))) -sys.path.append(os.path.abspath(os.path.join(__dir__, '../..'))) - -os.environ["FLAGS_allocator_strategy"] = 'auto_growth' -import cv2 -import copy -import numpy as np -import time -import tools.infer.predict_rec as predict_rec -import tools.infer.predict_det as predict_det -from ppocr.utils.utility import get_image_file_list, check_and_read_gif -from ppocr.utils.logging import get_logger -from ppstructure.table.matcher import distance, compute_iou -from ppstructure.utility import parse_args -import ppstructure.table.predict_structure as predict_strture - -logger = get_logger() - - -def expand(pix, det_box, shape): - x0, y0, x1, y1 = det_box - # print(shape) - h, w, c = shape - tmp_x0 = x0 - pix - tmp_x1 = x1 + pix - tmp_y0 = y0 - pix - tmp_y1 = y1 + pix - x0_ = tmp_x0 if tmp_x0 >= 0 else 0 - x1_ = tmp_x1 if tmp_x1 <= w else w - y0_ = tmp_y0 if tmp_y0 >= 0 else 0 - y1_ = tmp_y1 if tmp_y1 <= h else h - return x0_, y0_, x1_, y1_ - - -class TableSystem(object): - def __init__(self, args, text_detector=None, text_recognizer=None): - self.text_detector = predict_det.TextDetector(args) if text_detector is None else text_detector - self.text_recognizer = predict_rec.TextRecognizer(args) if text_recognizer is None else text_recognizer - self.table_structurer = predict_strture.TableStructurer(args) - - def __call__(self, img): - ori_im = img.copy() - structure_res, elapse = self.table_structurer(copy.deepcopy(img)) - dt_boxes, elapse = self.text_detector(copy.deepcopy(img)) - dt_boxes = sorted_boxes(dt_boxes) - - r_boxes = [] - for box in dt_boxes: - x_min = box[:, 0].min() - 1 - x_max = box[:, 0].max() + 1 - y_min = box[:, 1].min() - 1 - y_max = box[:, 1].max() + 1 - box = [x_min, y_min, x_max, y_max] - r_boxes.append(box) - dt_boxes = np.array(r_boxes) - - logger.debug("dt_boxes num : {}, elapse : {}".format( - len(dt_boxes), elapse)) - if dt_boxes is None: - return None, None - img_crop_list = [] - - for i in range(len(dt_boxes)): - det_box = dt_boxes[i] - x0, y0, x1, y1 = expand(2, det_box, ori_im.shape) - text_rect = ori_im[int(y0):int(y1), int(x0):int(x1), :] - img_crop_list.append(text_rect) - rec_res, elapse = self.text_recognizer(img_crop_list) - logger.debug("rec_res num : {}, elapse : {}".format( - len(rec_res), elapse)) - - pred_html, pred = self.rebuild_table(structure_res, dt_boxes, rec_res) - return pred_html - - def rebuild_table(self, structure_res, dt_boxes, rec_res): - pred_structures, pred_bboxes = structure_res - matched_index = self.match_result(dt_boxes, pred_bboxes) - pred_html, pred = self.get_pred_html(pred_structures, matched_index, rec_res) - return pred_html, pred - - def match_result(self, dt_boxes, pred_bboxes): - matched = {} - for i, gt_box in enumerate(dt_boxes): - # gt_box = [np.min(gt_box[:, 0]), np.min(gt_box[:, 1]), np.max(gt_box[:, 0]), np.max(gt_box[:, 1])] - distances = [] - for j, pred_box in enumerate(pred_bboxes): - distances.append( - (distance(gt_box, pred_box), 1. - compute_iou(gt_box, pred_box))) # 获取两两cell之间的L1距离和 1- IOU - sorted_distances = distances.copy() - # 根据距离和IOU挑选最"近"的cell - sorted_distances = sorted(sorted_distances, key=lambda item: (item[1], item[0])) - if distances.index(sorted_distances[0]) not in matched.keys(): - matched[distances.index(sorted_distances[0])] = [i] - else: - matched[distances.index(sorted_distances[0])].append(i) - return matched - - def get_pred_html(self, pred_structures, matched_index, ocr_contents): - end_html = [] - td_index = 0 - for tag in pred_structures: - if '' in tag: - if td_index in matched_index.keys(): - b_with = False - if '' in ocr_contents[matched_index[td_index][0]] and len(matched_index[td_index]) > 1: - b_with = True - end_html.extend('') - for i, td_index_index in enumerate(matched_index[td_index]): - content = ocr_contents[td_index_index][0] - if len(matched_index[td_index]) > 1: - if len(content) == 0: - continue - if content[0] == ' ': - content = content[1:] - if '' in content: - content = content[3:] - if '' in content: - content = content[:-4] - if len(content) == 0: - continue - if i != len(matched_index[td_index]) - 1 and ' ' != content[-1]: - content += ' ' - end_html.extend(content) - if b_with: - end_html.extend('') - - end_html.append(tag) - td_index += 1 - else: - end_html.append(tag) - return ''.join(end_html), end_html - - -def sorted_boxes(dt_boxes): - """ - Sort text boxes in order from top to bottom, left to right - args: - dt_boxes(array):detected text boxes with shape [4, 2] - return: - sorted boxes(array) with shape [4, 2] - """ - num_boxes = dt_boxes.shape[0] - sorted_boxes = sorted(dt_boxes, key=lambda x: (x[0][1], x[0][0])) - _boxes = list(sorted_boxes) - - for i in range(num_boxes - 1): - if abs(_boxes[i + 1][0][1] - _boxes[i][0][1]) < 10 and \ - (_boxes[i + 1][0][0] < _boxes[i][0][0]): - tmp = _boxes[i] - _boxes[i] = _boxes[i + 1] - _boxes[i + 1] = tmp - return _boxes - - -def to_excel(html_table, excel_path): - from tablepyxl import tablepyxl - tablepyxl.document_to_xl(html_table, excel_path) - - -def main(args): - image_file_list = get_image_file_list(args.image_dir) - image_file_list = image_file_list[args.process_id::args.total_process_num] - os.makedirs(args.output, exist_ok=True) - - text_sys = TableSystem(args) - img_num = len(image_file_list) - for i, image_file in enumerate(image_file_list): - logger.info("[{}/{}] {}".format(i, img_num, image_file)) - img, flag = check_and_read_gif(image_file) - excel_path = os.path.join(args.table_output, os.path.basename(image_file).split('.')[0] + '.xlsx') - if not flag: - img = cv2.imread(image_file) - if img is None: - logger.error("error in loading image:{}".format(image_file)) - continue - starttime = time.time() - pred_html = text_sys(img) - - to_excel(pred_html, excel_path) - logger.info('excel saved to {}'.format(excel_path)) - logger.info(pred_html) - elapse = time.time() - starttime - logger.info("Predict time : {:.3f}s".format(elapse)) - - -if __name__ == "__main__": - args = parse_args() - if args.use_mp: - p_list = [] - total_process_num = args.total_process_num - for process_id in range(total_process_num): - cmd = [sys.executable, "-u"] + sys.argv + [ - "--process_id={}".format(process_id), - "--use_mp={}".format(False) - ] - p = subprocess.Popen(cmd, stdout=sys.stdout, stderr=sys.stdout) - p_list.append(p) - for p in p_list: - p.wait() - else: - main(args) diff --git a/ppstructure/table/table_metric/__init__.py b/ppstructure/table/table_metric/__init__.py deleted file mode 100755 index de2d307430f68881ece1e41357d3b2f423e07ddd..0000000000000000000000000000000000000000 --- a/ppstructure/table/table_metric/__init__.py +++ /dev/null @@ -1,16 +0,0 @@ -# copyright (c) 2020 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. - -__all__ = ['TEDS'] -from .table_metric import TEDS \ No newline at end of file diff --git a/ppstructure/table/table_metric/parallel.py b/ppstructure/table/table_metric/parallel.py deleted file mode 100755 index f7326a1f506ca5fb7b3e97b0d077dc016e7eb7c7..0000000000000000000000000000000000000000 --- a/ppstructure/table/table_metric/parallel.py +++ /dev/null @@ -1,51 +0,0 @@ -from tqdm import tqdm -from concurrent.futures import ProcessPoolExecutor, as_completed - - -def parallel_process(array, function, n_jobs=16, use_kwargs=False, front_num=0): - """ - A parallel version of the map function with a progress bar. - Args: - array (array-like): An array to iterate over. - function (function): A python function to apply to the elements of array - n_jobs (int, default=16): The number of cores to use - use_kwargs (boolean, default=False): Whether to consider the elements of array as dictionaries of - keyword arguments to function - front_num (int, default=3): The number of iterations to run serially before kicking off the parallel job. - Useful for catching bugs - Returns: - [function(array[0]), function(array[1]), ...] - """ - # We run the first few iterations serially to catch bugs - if front_num > 0: - front = [function(**a) if use_kwargs else function(a) - for a in array[:front_num]] - else: - front = [] - # If we set n_jobs to 1, just run a list comprehension. This is useful for benchmarking and debugging. - if n_jobs == 1: - return front + [function(**a) if use_kwargs else function(a) for a in tqdm(array[front_num:])] - # Assemble the workers - with ProcessPoolExecutor(max_workers=n_jobs) as pool: - # Pass the elements of array into function - if use_kwargs: - futures = [pool.submit(function, **a) for a in array[front_num:]] - else: - futures = [pool.submit(function, a) for a in array[front_num:]] - kwargs = { - 'total': len(futures), - 'unit': 'it', - 'unit_scale': True, - 'leave': True - } - # Print out the progress as tasks complete - for f in tqdm(as_completed(futures), **kwargs): - pass - out = [] - # Get the results from the futures. - for i, future in tqdm(enumerate(futures)): - try: - out.append(future.result()) - except Exception as e: - out.append(e) - return front + out diff --git a/ppstructure/table/table_metric/table_metric.py b/ppstructure/table/table_metric/table_metric.py deleted file mode 100755 index 9aca98ad785d4614a803fa5a277a6e4a27b3b078..0000000000000000000000000000000000000000 --- a/ppstructure/table/table_metric/table_metric.py +++ /dev/null @@ -1,247 +0,0 @@ -# Copyright 2020 IBM -# Author: peter.zhong@au1.ibm.com -# -# This is free software; you can redistribute it and/or modify -# it under the terms of the Apache 2.0 License. -# -# This software is distributed in the hope that it will be useful, -# but WITHOUT ANY WARRANTY; without even the implied warranty of -# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the -# Apache 2.0 License for more details. - -import distance -from apted import APTED, Config -from apted.helpers import Tree -from lxml import etree, html -from collections import deque -from .parallel import parallel_process -from tqdm import tqdm - - -class TableTree(Tree): - def __init__(self, tag, colspan=None, rowspan=None, content=None, *children): - self.tag = tag - self.colspan = colspan - self.rowspan = rowspan - self.content = content - self.children = list(children) - - def bracket(self): - """Show tree using brackets notation""" - if self.tag == 'td': - result = '"tag": %s, "colspan": %d, "rowspan": %d, "text": %s' % \ - (self.tag, self.colspan, self.rowspan, self.content) - else: - result = '"tag": %s' % self.tag - for child in self.children: - result += child.bracket() - return "{{{}}}".format(result) - - -class CustomConfig(Config): - @staticmethod - def maximum(*sequences): - """Get maximum possible value - """ - return max(map(len, sequences)) - - def normalized_distance(self, *sequences): - """Get distance from 0 to 1 - """ - return float(distance.levenshtein(*sequences)) / self.maximum(*sequences) - - def rename(self, node1, node2): - """Compares attributes of trees""" - #print(node1.tag) - if (node1.tag != node2.tag) or (node1.colspan != node2.colspan) or (node1.rowspan != node2.rowspan): - return 1. - if node1.tag == 'td': - if node1.content or node2.content: - #print(node1.content, ) - return self.normalized_distance(node1.content, node2.content) - return 0. - - - -class CustomConfig_del_short(Config): - @staticmethod - def maximum(*sequences): - """Get maximum possible value - """ - return max(map(len, sequences)) - - def normalized_distance(self, *sequences): - """Get distance from 0 to 1 - """ - return float(distance.levenshtein(*sequences)) / self.maximum(*sequences) - - def rename(self, node1, node2): - """Compares attributes of trees""" - if (node1.tag != node2.tag) or (node1.colspan != node2.colspan) or (node1.rowspan != node2.rowspan): - return 1. - if node1.tag == 'td': - if node1.content or node2.content: - #print('before') - #print(node1.content, node2.content) - #print('after') - node1_content = node1.content - node2_content = node2.content - if len(node1_content) < 3: - node1_content = ['####'] - if len(node2_content) < 3: - node2_content = ['####'] - return self.normalized_distance(node1_content, node2_content) - return 0. - -class CustomConfig_del_block(Config): - @staticmethod - def maximum(*sequences): - """Get maximum possible value - """ - return max(map(len, sequences)) - - def normalized_distance(self, *sequences): - """Get distance from 0 to 1 - """ - return float(distance.levenshtein(*sequences)) / self.maximum(*sequences) - - def rename(self, node1, node2): - """Compares attributes of trees""" - if (node1.tag != node2.tag) or (node1.colspan != node2.colspan) or (node1.rowspan != node2.rowspan): - return 1. - if node1.tag == 'td': - if node1.content or node2.content: - - node1_content = node1.content - node2_content = node2.content - while ' ' in node1_content: - print(node1_content.index(' ')) - node1_content.pop(node1_content.index(' ')) - while ' ' in node2_content: - print(node2_content.index(' ')) - node2_content.pop(node2_content.index(' ')) - return self.normalized_distance(node1_content, node2_content) - return 0. - -class TEDS(object): - ''' Tree Edit Distance basead Similarity - ''' - - def __init__(self, structure_only=False, n_jobs=1, ignore_nodes=None): - assert isinstance(n_jobs, int) and ( - n_jobs >= 1), 'n_jobs must be an integer greather than 1' - self.structure_only = structure_only - self.n_jobs = n_jobs - self.ignore_nodes = ignore_nodes - self.__tokens__ = [] - - def tokenize(self, node): - ''' Tokenizes table cells - ''' - self.__tokens__.append('<%s>' % node.tag) - if node.text is not None: - self.__tokens__ += list(node.text) - for n in node.getchildren(): - self.tokenize(n) - if node.tag != 'unk': - self.__tokens__.append('' % node.tag) - if node.tag != 'td' and node.tail is not None: - self.__tokens__ += list(node.tail) - - def load_html_tree(self, node, parent=None): - ''' Converts HTML tree to the format required by apted - ''' - global __tokens__ - if node.tag == 'td': - if self.structure_only: - cell = [] - else: - self.__tokens__ = [] - self.tokenize(node) - cell = self.__tokens__[1:-1].copy() - new_node = TableTree(node.tag, - int(node.attrib.get('colspan', '1')), - int(node.attrib.get('rowspan', '1')), - cell, *deque()) - else: - new_node = TableTree(node.tag, None, None, None, *deque()) - if parent is not None: - parent.children.append(new_node) - if node.tag != 'td': - for n in node.getchildren(): - self.load_html_tree(n, new_node) - if parent is None: - return new_node - - def evaluate(self, pred, true): - ''' Computes TEDS score between the prediction and the ground truth of a - given sample - ''' - if (not pred) or (not true): - return 0.0 - parser = html.HTMLParser(remove_comments=True, encoding='utf-8') - pred = html.fromstring(pred, parser=parser) - true = html.fromstring(true, parser=parser) - if pred.xpath('body/table') and true.xpath('body/table'): - pred = pred.xpath('body/table')[0] - true = true.xpath('body/table')[0] - if self.ignore_nodes: - etree.strip_tags(pred, *self.ignore_nodes) - etree.strip_tags(true, *self.ignore_nodes) - n_nodes_pred = len(pred.xpath(".//*")) - n_nodes_true = len(true.xpath(".//*")) - n_nodes = max(n_nodes_pred, n_nodes_true) - tree_pred = self.load_html_tree(pred) - tree_true = self.load_html_tree(true) - distance = APTED(tree_pred, tree_true, - CustomConfig()).compute_edit_distance() - return 1.0 - (float(distance) / n_nodes) - else: - return 0.0 - - def batch_evaluate(self, pred_json, true_json): - ''' Computes TEDS score between the prediction and the ground truth of - a batch of samples - @params pred_json: {'FILENAME': 'HTML CODE', ...} - @params true_json: {'FILENAME': {'html': 'HTML CODE'}, ...} - @output: {'FILENAME': 'TEDS SCORE', ...} - ''' - samples = true_json.keys() - if self.n_jobs == 1: - scores = [self.evaluate(pred_json.get( - filename, ''), true_json[filename]['html']) for filename in tqdm(samples)] - else: - inputs = [{'pred': pred_json.get( - filename, ''), 'true': true_json[filename]['html']} for filename in samples] - scores = parallel_process( - inputs, self.evaluate, use_kwargs=True, n_jobs=self.n_jobs, front_num=1) - scores = dict(zip(samples, scores)) - return scores - - def batch_evaluate_html(self, pred_htmls, true_htmls): - ''' Computes TEDS score between the prediction and the ground truth of - a batch of samples - ''' - if self.n_jobs == 1: - scores = [self.evaluate(pred_html, true_html) for ( - pred_html, true_html) in zip(pred_htmls, true_htmls)] - else: - inputs = [{"pred": pred_html, "true": true_html} for( - pred_html, true_html) in zip(pred_htmls, true_htmls)] - - scores = parallel_process( - inputs, self.evaluate, use_kwargs=True, n_jobs=self.n_jobs, front_num=1) - return scores - - -if __name__ == '__main__': - import json - import pprint - with open('sample_pred.json') as fp: - pred_json = json.load(fp) - with open('sample_gt.json') as fp: - true_json = json.load(fp) - teds = TEDS(n_jobs=4) - scores = teds.batch_evaluate(pred_json, true_json) - pp = pprint.PrettyPrinter() - pp.pprint(scores) diff --git a/ppstructure/table/tablepyxl/__init__.py b/ppstructure/table/tablepyxl/__init__.py deleted file mode 100644 index dc0085071cf4497b01fc648e7c38f2e8d9d173d0..0000000000000000000000000000000000000000 --- a/ppstructure/table/tablepyxl/__init__.py +++ /dev/null @@ -1,13 +0,0 @@ -# copyright (c) 2020 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. \ No newline at end of file diff --git a/ppstructure/table/tablepyxl/style.py b/ppstructure/table/tablepyxl/style.py deleted file mode 100644 index ebd794b1b47d7f9e4f9294dde7330f592d613656..0000000000000000000000000000000000000000 --- a/ppstructure/table/tablepyxl/style.py +++ /dev/null @@ -1,283 +0,0 @@ -# This is where we handle translating css styles into openpyxl styles -# and cascading those from parent to child in the dom. - -from openpyxl.cell import cell -from openpyxl.styles import Font, Alignment, PatternFill, NamedStyle, Border, Side, Color -from openpyxl.styles.fills import FILL_SOLID -from openpyxl.styles.numbers import FORMAT_CURRENCY_USD_SIMPLE, FORMAT_PERCENTAGE -from openpyxl.styles.colors import BLACK - -FORMAT_DATE_MMDDYYYY = 'mm/dd/yyyy' - - -def colormap(color): - """ - Convenience for looking up known colors - """ - cmap = {'black': BLACK} - return cmap.get(color, color) - - -def style_string_to_dict(style): - """ - Convert css style string to a python dictionary - """ - def clean_split(string, delim): - return (s.strip() for s in string.split(delim)) - styles = [clean_split(s, ":") for s in style.split(";") if ":" in s] - return dict(styles) - - -def get_side(style, name): - return {'border_style': style.get('border-{}-style'.format(name)), - 'color': colormap(style.get('border-{}-color'.format(name)))} - -known_styles = {} - - -def style_dict_to_named_style(style_dict, number_format=None): - """ - Change css style (stored in a python dictionary) to openpyxl NamedStyle - """ - - style_and_format_string = str({ - 'style_dict': style_dict, - 'parent': style_dict.parent, - 'number_format': number_format, - }) - - if style_and_format_string not in known_styles: - # Font - font = Font(bold=style_dict.get('font-weight') == 'bold', - color=style_dict.get_color('color', None), - size=style_dict.get('font-size')) - - # Alignment - alignment = Alignment(horizontal=style_dict.get('text-align', 'general'), - vertical=style_dict.get('vertical-align'), - wrap_text=style_dict.get('white-space', 'nowrap') == 'normal') - - # Fill - bg_color = style_dict.get_color('background-color') - fg_color = style_dict.get_color('foreground-color', Color()) - fill_type = style_dict.get('fill-type') - if bg_color and bg_color != 'transparent': - fill = PatternFill(fill_type=fill_type or FILL_SOLID, - start_color=bg_color, - end_color=fg_color) - else: - fill = PatternFill() - - # Border - border = Border(left=Side(**get_side(style_dict, 'left')), - right=Side(**get_side(style_dict, 'right')), - top=Side(**get_side(style_dict, 'top')), - bottom=Side(**get_side(style_dict, 'bottom')), - diagonal=Side(**get_side(style_dict, 'diagonal')), - diagonal_direction=None, - outline=Side(**get_side(style_dict, 'outline')), - vertical=None, - horizontal=None) - - name = 'Style {}'.format(len(known_styles) + 1) - - pyxl_style = NamedStyle(name=name, font=font, fill=fill, alignment=alignment, border=border, - number_format=number_format) - - known_styles[style_and_format_string] = pyxl_style - - return known_styles[style_and_format_string] - - -class StyleDict(dict): - """ - It's like a dictionary, but it looks for items in the parent dictionary - """ - def __init__(self, *args, **kwargs): - self.parent = kwargs.pop('parent', None) - super(StyleDict, self).__init__(*args, **kwargs) - - def __getitem__(self, item): - if item in self: - return super(StyleDict, self).__getitem__(item) - elif self.parent: - return self.parent[item] - else: - raise KeyError('{} not found'.format(item)) - - def __hash__(self): - return hash(tuple([(k, self.get(k)) for k in self._keys()])) - - # Yielding the keys avoids creating unnecessary data structures - # and happily works with both python2 and python3 where the - # .keys() method is a dictionary_view in python3 and a list in python2. - def _keys(self): - yielded = set() - for k in self.keys(): - yielded.add(k) - yield k - if self.parent: - for k in self.parent._keys(): - if k not in yielded: - yielded.add(k) - yield k - - def get(self, k, d=None): - try: - return self[k] - except KeyError: - return d - - def get_color(self, k, d=None): - """ - Strip leading # off colors if necessary - """ - color = self.get(k, d) - if hasattr(color, 'startswith') and color.startswith('#'): - color = color[1:] - if len(color) == 3: # Premailers reduces colors like #00ff00 to #0f0, openpyxl doesn't like that - color = ''.join(2 * c for c in color) - return color - - -class Element(object): - """ - Our base class for representing an html element along with a cascading style. - The element is created along with a parent so that the StyleDict that we store - can point to the parent's StyleDict. - """ - def __init__(self, element, parent=None): - self.element = element - self.number_format = None - parent_style = parent.style_dict if parent else None - self.style_dict = StyleDict(style_string_to_dict(element.get('style', '')), parent=parent_style) - self._style_cache = None - - def style(self): - """ - Turn the css styles for this element into an openpyxl NamedStyle. - """ - if not self._style_cache: - self._style_cache = style_dict_to_named_style(self.style_dict, number_format=self.number_format) - return self._style_cache - - def get_dimension(self, dimension_key): - """ - Extracts the dimension from the style dict of the Element and returns it as a float. - """ - dimension = self.style_dict.get(dimension_key) - if dimension: - if dimension[-2:] in ['px', 'em', 'pt', 'in', 'cm']: - dimension = dimension[:-2] - dimension = float(dimension) - return dimension - - -class Table(Element): - """ - The concrete implementations of Elements are semantically named for the types of elements we are interested in. - This defines a very concrete tree structure for html tables that we expect to deal with. I prefer this compared to - allowing Element to have an arbitrary number of children and dealing with an abstract element tree. - """ - def __init__(self, table): - """ - takes an html table object (from lxml) - """ - super(Table, self).__init__(table) - table_head = table.find('thead') - self.head = TableHead(table_head, parent=self) if table_head is not None else None - table_body = table.find('tbody') - self.body = TableBody(table_body if table_body is not None else table, parent=self) - - -class TableHead(Element): - """ - This class maps to the `` element of the html table. - """ - def __init__(self, head, parent=None): - super(TableHead, self).__init__(head, parent=parent) - self.rows = [TableRow(tr, parent=self) for tr in head.findall('tr')] - - -class TableBody(Element): - """ - This class maps to the `` element of the html table. - """ - def __init__(self, body, parent=None): - super(TableBody, self).__init__(body, parent=parent) - self.rows = [TableRow(tr, parent=self) for tr in body.findall('tr')] - - -class TableRow(Element): - """ - This class maps to the `` element of the html table. - """ - def __init__(self, tr, parent=None): - super(TableRow, self).__init__(tr, parent=parent) - self.cells = [TableCell(cell, parent=self) for cell in tr.findall('th') + tr.findall('td')] - - -def element_to_string(el): - return _element_to_string(el).strip() - - -def _element_to_string(el): - string = '' - - for x in el.iterchildren(): - string += '\n' + _element_to_string(x) - - text = el.text.strip() if el.text else '' - tail = el.tail.strip() if el.tail else '' - - return text + string + '\n' + tail - - -class TableCell(Element): - """ - This class maps to the `` element of the html table. - """ - CELL_TYPES = {'TYPE_STRING', 'TYPE_FORMULA', 'TYPE_NUMERIC', 'TYPE_BOOL', 'TYPE_CURRENCY', 'TYPE_PERCENTAGE', - 'TYPE_NULL', 'TYPE_INLINE', 'TYPE_ERROR', 'TYPE_FORMULA_CACHE_STRING', 'TYPE_INTEGER'} - - def __init__(self, cell, parent=None): - super(TableCell, self).__init__(cell, parent=parent) - self.value = element_to_string(cell) - self.number_format = self.get_number_format() - - def data_type(self): - cell_types = self.CELL_TYPES & set(self.element.get('class', '').split()) - if cell_types: - if 'TYPE_FORMULA' in cell_types: - # Make sure TYPE_FORMULA takes precedence over the other classes in the set. - cell_type = 'TYPE_FORMULA' - elif cell_types & {'TYPE_CURRENCY', 'TYPE_INTEGER', 'TYPE_PERCENTAGE'}: - cell_type = 'TYPE_NUMERIC' - else: - cell_type = cell_types.pop() - else: - cell_type = 'TYPE_STRING' - return getattr(cell, cell_type) - - def get_number_format(self): - if 'TYPE_CURRENCY' in self.element.get('class', '').split(): - return FORMAT_CURRENCY_USD_SIMPLE - if 'TYPE_INTEGER' in self.element.get('class', '').split(): - return '#,##0' - if 'TYPE_PERCENTAGE' in self.element.get('class', '').split(): - return FORMAT_PERCENTAGE - if 'TYPE_DATE' in self.element.get('class', '').split(): - return FORMAT_DATE_MMDDYYYY - if self.data_type() == cell.TYPE_NUMERIC: - try: - int(self.value) - except ValueError: - return '#,##0.##' - else: - return '#,##0' - - def format(self, cell): - cell.style = self.style() - data_type = self.data_type() - if data_type: - cell.data_type = data_type \ No newline at end of file diff --git a/ppstructure/table/tablepyxl/tablepyxl.py b/ppstructure/table/tablepyxl/tablepyxl.py deleted file mode 100644 index ba3cc0fc499fccd93ffe3993a99296bc6603ed8a..0000000000000000000000000000000000000000 --- a/ppstructure/table/tablepyxl/tablepyxl.py +++ /dev/null @@ -1,118 +0,0 @@ -# Do imports like python3 so our package works for 2 and 3 -from __future__ import absolute_import - -from lxml import html -from openpyxl import Workbook -from openpyxl.utils import get_column_letter -from premailer import Premailer -from tablepyxl.style import Table - - -def string_to_int(s): - if s.isdigit(): - return int(s) - return 0 - - -def get_Tables(doc): - tree = html.fromstring(doc) - comments = tree.xpath('//comment()') - for comment in comments: - comment.drop_tag() - return [Table(table) for table in tree.xpath('//table')] - - -def write_rows(worksheet, elem, row, column=1): - """ - Writes every tr child element of elem to a row in the worksheet - returns the next row after all rows are written - """ - from openpyxl.cell.cell import MergedCell - - initial_column = column - for table_row in elem.rows: - for table_cell in table_row.cells: - cell = worksheet.cell(row=row, column=column) - while isinstance(cell, MergedCell): - column += 1 - cell = worksheet.cell(row=row, column=column) - - colspan = string_to_int(table_cell.element.get("colspan", "1")) - rowspan = string_to_int(table_cell.element.get("rowspan", "1")) - if rowspan > 1 or colspan > 1: - worksheet.merge_cells(start_row=row, start_column=column, - end_row=row + rowspan - 1, end_column=column + colspan - 1) - - cell.value = table_cell.value - table_cell.format(cell) - min_width = table_cell.get_dimension('min-width') - max_width = table_cell.get_dimension('max-width') - - if colspan == 1: - # Initially, when iterating for the first time through the loop, the width of all the cells is None. - # As we start filling in contents, the initial width of the cell (which can be retrieved by: - # worksheet.column_dimensions[get_column_letter(column)].width) is equal to the width of the previous - # cell in the same column (i.e. width of A2 = width of A1) - width = max(worksheet.column_dimensions[get_column_letter(column)].width or 0, len(table_cell.value) + 2) - if max_width and width > max_width: - width = max_width - elif min_width and width < min_width: - width = min_width - worksheet.column_dimensions[get_column_letter(column)].width = width - column += colspan - row += 1 - column = initial_column - return row - - -def table_to_sheet(table, wb): - """ - Takes a table and workbook and writes the table to a new sheet. - The sheet title will be the same as the table attribute name. - """ - ws = wb.create_sheet(title=table.element.get('name')) - insert_table(table, ws, 1, 1) - - -def document_to_workbook(doc, wb=None, base_url=None): - """ - Takes a string representation of an html document and writes one sheet for - every table in the document. - The workbook is returned - """ - if not wb: - wb = Workbook() - wb.remove(wb.active) - - inline_styles_doc = Premailer(doc, base_url=base_url, remove_classes=False).transform() - tables = get_Tables(inline_styles_doc) - - for table in tables: - table_to_sheet(table, wb) - - return wb - - -def document_to_xl(doc, filename, base_url=None): - """ - Takes a string representation of an html document and writes one sheet for - every table in the document. The workbook is written out to a file called filename - """ - wb = document_to_workbook(doc, base_url=base_url) - wb.save(filename) - - -def insert_table(table, worksheet, column, row): - if table.head: - row = write_rows(worksheet, table.head, row, column) - if table.body: - row = write_rows(worksheet, table.body, row, column) - - -def insert_table_at_cell(table, cell): - """ - Inserts a table at the location of an openpyxl Cell object. - """ - ws = cell.parent - column, row = cell.column, cell.row - insert_table(table, ws, column, row) \ No newline at end of file diff --git a/ppstructure/utility.py b/ppstructure/utility.py deleted file mode 100644 index 8112b9efd2155d69784ebc9915d9c3ec30e94f9c..0000000000000000000000000000000000000000 --- a/ppstructure/utility.py +++ /dev/null @@ -1,59 +0,0 @@ -# copyright (c) 2020 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 PIL import Image -import numpy as np -from tools.infer.utility import draw_ocr_box_txt, init_args as infer_args - - -def init_args(): - parser = infer_args() - - # params for output - parser.add_argument("--output", type=str, default='./output/table') - # params for table structure - parser.add_argument("--structure_max_len", type=int, default=488) - parser.add_argument("--structure_max_text_length", type=int, default=100) - parser.add_argument("--structure_max_elem_length", type=int, default=800) - parser.add_argument("--structure_max_cell_num", type=int, default=500) - parser.add_argument("--structure_model_dir", type=str) - parser.add_argument("--structure_char_type", type=str, default='en') - parser.add_argument("--structure_char_dict_path", type=str, default="../ppocr/utils/dict/table_structure_dict.txt") - - # params for layout detector - parser.add_argument("--layout_model_dir", type=str) - return parser - - -def parse_args(): - parser = init_args() - return parser.parse_args() - - -def draw_result(image, result, font_path): - if isinstance(image, np.ndarray): - image = Image.fromarray(image) - boxes, txts, scores = [], [], [] - for region in result: - if region['type'] == 'Table': - pass - elif region['type'] == 'Figure': - pass - else: - for box, rec_res in zip(region['res'][0], region['res'][1]): - boxes.append(np.array(box).reshape(-1, 2)) - txts.append(rec_res[0]) - scores.append(rec_res[1]) - im_show = draw_ocr_box_txt(image, boxes, txts, scores, font_path=font_path,drop_score=0) - return im_show \ No newline at end of file diff --git a/tools/infer/predict_det.py b/tools/infer/predict_det.py index baa89be130084d98628656fe4e309728a0e9f661..f5bade36315fbe321927df82cdd7cd8bf40b2ae5 100755 --- a/tools/infer/predict_det.py +++ b/tools/infer/predict_det.py @@ -43,7 +43,7 @@ class TextDetector(object): pre_process_list = [{ 'DetResizeForTest': { 'limit_side_len': args.det_limit_side_len, - 'limit_type': args.det_limit_type, + 'limit_type': args.det_limit_type } }, { 'NormalizeImage': { diff --git a/tools/infer/predict_system.py b/tools/infer/predict_system.py index 58a363e3e8fa852ce37cd5a44a19e460da00c2bc..ad1b7d4ef432924f1781a16eae07c171e571826b 100755 --- a/tools/infer/predict_system.py +++ b/tools/infer/predict_system.py @@ -88,7 +88,8 @@ class TextSystem(object): ori_im = img.copy() dt_boxes, elapse = self.text_detector(img) - logger.debug("dt_boxes num : {}, elapse : {}".format( + logger.info("dt_boxes num : {}, elapse : {}".format( + len(dt_boxes), elapse)) if dt_boxes is None: return None, None @@ -103,11 +104,11 @@ class TextSystem(object): if self.use_angle_cls and cls: img_crop_list, angle_list, elapse = self.text_classifier( img_crop_list) - logger.debug("cls num : {}, elapse : {}".format( + logger.info("cls num : {}, elapse : {}".format( len(img_crop_list), elapse)) rec_res, elapse = self.text_recognizer(img_crop_list) - logger.debug("rec_res num : {}, elapse : {}".format( + logger.info("rec_res num : {}, elapse : {}".format( len(rec_res), elapse)) # self.print_draw_crop_rec_res(img_crop_list, rec_res) filter_boxes, filter_rec_res = [], [] @@ -155,7 +156,7 @@ def main(args): if not flag: img = cv2.imread(image_file) if img is None: - logger.error("error in loading image:{}".format(image_file)) + logger.info("error in loading image:{}".format(image_file)) continue starttime = time.time() dt_boxes, rec_res = text_sys(img) diff --git a/tools/infer/utility.py b/tools/infer/utility.py index 9210c45783029996f8d6fd105c8413d01c768806..69f28e00b39b657954902e1b4c851fe357ea3619 100755 --- a/tools/infer/utility.py +++ b/tools/infer/utility.py @@ -113,7 +113,6 @@ def init_args(): parser.add_argument("--benchmark", type=bool, default=False) parser.add_argument("--save_log_path", type=str, default="./log_output/") - parser.add_argument("--show_log", type=str2bool, default=True) return parser @@ -200,8 +199,6 @@ def create_predictor(args, mode, logger): model_dir = args.cls_model_dir elif mode == 'rec': model_dir = args.rec_model_dir - elif mode == 'structure': - model_dir = args.structure_model_dir else: model_dir = args.e2e_model_dir @@ -331,9 +328,7 @@ def create_predictor(args, mode, logger): config.delete_pass("conv_transpose_eltwiseadd_bn_fuse_pass") config.switch_use_feed_fetch_ops(False) - config.switch_ir_optim(True) - if mode == 'structure': - config.switch_ir_optim(False) + # create predictor predictor = inference.create_predictor(config) input_names = predictor.get_input_names()