Skip to content

P
PaddleOCR

weixin_41840029 / PaddleOCR
与 Fork 源项目一致

Fork自 PaddlePaddle / PaddleOCR

通知 1
Star 1
Fork 0
P
PaddleOCR
未验证 提交 e93735a2 编写于 作者: M MissPenguin 提交者： GitHub
浏览文件
操作

Merge pull request #3083 from WenmuZhou/table1 

[DO NOT MERGE]Table
上级 6127aad9 b2260182
隐藏空白更改
内联 并排
Showing with 5615 addition and 108 deletion
MANIFEST.in
浏览文件 @ e93735a2
include LICENSE.txt
include LICENSE
include README.md
recursive-include ppocr/utils *.txt utility.py logging.py
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
......
doc/doc_ch/whl.md
浏览文件 @ e93735a2
......@@ -355,3 +355,4 @@ im_show.save('result.jpg')
| det | 前向时使用启动检测 | TRUE |
| rec | 前向时是否启动识别 | TRUE |
| cls | 前向时是否启动分类 (命令行模式下使用use_angle_cls控制前向是否启动分类) | FALSE |
| show_log | 是否打印det和rec等信息 | FALSE |
doc/doc_en/whl_en.md
浏览文件 @ e93735a2
......@@ -362,3 +362,5 @@ im_show.save('result.jpg')
| det | Enable detction when `ppocr.ocr` func exec | TRUE |
| rec | Enable recognition when `ppocr.ocr` func exec | TRUE |
| cls | Enable classification when `ppocr.ocr` func exec((Use use_angle_cls in command line mode to control whether to start classification in the forward direction) | FALSE |
| show_log | Whether to print log in det and rec
| FALSE |
\ No newline at end of file
paddleocr.py
浏览文件 @ e93735a2
......@@ -19,17 +19,16 @@ __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, is_link, confirm_model_dir_url
from tools.infer.utility import draw_ocr, init_args, str2bool
__all__ = ['PaddleOCR']
......@@ -37,84 +36,84 @@ __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']
......@@ -123,50 +122,6 @@ 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()
......@@ -194,10 +149,12 @@ class PaddleOCR(predict_system.TextSystem):
args:
**kwargs: other params show in paddleocr --help
"""
postprocess_params = parse_args(mMain=False)
postprocess_params.__dict__.update(**kwargs)
self.use_angle_cls = postprocess_params.use_angle_cls
lang = postprocess_params.lang
params = parse_args(mMain=False)
params.__dict__.update(**kwargs)
if not params.show_log:
logger.setLevel(logging.INFO)
self.use_angle_cls = params.use_angle_cls
lang = 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',
......@@ -223,46 +180,45 @@ 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 postprocess_params.rec_char_dict_path is None:
if params.rec_char_dict_path is None:
use_inner_dict = True
postprocess_params.rec_char_dict_path = model_urls['rec'][lang][
params.rec_char_dict_path = model_urls['rec'][lang][
'dict_path']
# init model dir
if postprocess_params.det_model_dir is None:
postprocess_params.det_model_dir = os.path.join(BASE_DIR, VERSION,
'det', det_lang)
if postprocess_params.rec_model_dir is None:
postprocess_params.rec_model_dir = os.path.join(BASE_DIR, VERSION,
'rec', lang)
if postprocess_params.cls_model_dir is None:
postprocess_params.cls_model_dir = os.path.join(BASE_DIR, 'cls')
print(postprocess_params)
params.det_model_dir, det_url = confirm_model_dir_url(params.det_model_dir,
os.path.join(BASE_DIR, VERSION, 'det', det_lang),
model_urls['det'][det_lang])
params.rec_model_dir, rec_url = confirm_model_dir_url(params.rec_model_dir,
os.path.join(BASE_DIR, VERSION, 'rec', lang),
model_urls['rec'][lang]['url'])
params.cls_model_dir, cls_url = confirm_model_dir_url(params.cls_model_dir,
os.path.join(BASE_DIR, VERSION, 'cls'),
model_urls['cls'])
# download model
maybe_download(postprocess_params.det_model_dir,
model_urls['det'][det_lang])
maybe_download(postprocess_params.rec_model_dir,
model_urls['rec'][lang]['url'])
maybe_download(postprocess_params.cls_model_dir, model_urls['cls'])
maybe_download(params.det_model_dir, det_url)
maybe_download(params.rec_model_dir, rec_url)
maybe_download(params.cls_model_dir, cls_url)
if postprocess_params.det_algorithm not in SUPPORT_DET_MODEL:
if params.det_algorithm not in SUPPORT_DET_MODEL:
logger.error('det_algorithm must in {}'.format(SUPPORT_DET_MODEL))
sys.exit(0)
if postprocess_params.rec_algorithm not in SUPPORT_REC_MODEL:
if 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:
postprocess_params.rec_char_dict_path = str(
Path(__file__).parent / postprocess_params.rec_char_dict_path)
params.rec_char_dict_path = str(
Path(__file__).parent / params.rec_char_dict_path)
print(params)
# init det_model and rec_model
super().__init__(postprocess_params)
super().__init__(params)
def ocr(self, img, det=True, rec=True, cls=True):
"""
......@@ -320,7 +276,7 @@ def main():
# for cmd
args = parse_args(mMain=True)
image_dir = args.image_dir
if image_dir.startswith('http'):
if is_link(image_dir):
download_with_progressbar(image_dir, 'tmp.jpg')
image_file_list = ['tmp.jpg']
else:
......
ppocr/data/imaug/__init__.py
浏览文件 @ e93735a2
......@@ -29,6 +29,7 @@ from .label_ops import *
from .east_process import *
from .sast_process import *
from .pg_process import *
from .gen_table_mask import *
def transform(data, ops=None):
......
ppocr/data/imaug/gen_table_mask.py 0 → 100644
浏览文件 @ e93735a2
"""
# 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.
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from __future__ import unicode_literals
import sys
import six
import cv2
import numpy as np
class GenTableMask(object):
""" gen table mask """
def __init__(self, shrink_h_max, shrink_w_max, mask_type=0, **kwargs):
self.shrink_h_max = 5
self.shrink_w_max = 5
self.mask_type = mask_type
def projection(self, erosion, h, w, spilt_threshold=0):
# 水平投影
projection_map = np.ones_like(erosion)
project_val_array = [0 for _ in range(0, h)]
for j in range(0, h):
for i in range(0, w):
if erosion[j, i] == 255:
project_val_array[j] += 1
# 根据数组，获取切割点
start_idx = 0 # 记录进入字符区的索引
end_idx = 0 # 记录进入空白区域的索引
in_text = False # 是否遍历到了字符区内
box_list = []
for i in range(len(project_val_array)):
if in_text == False and project_val_array[i] > spilt_threshold: # 进入字符区了
in_text = True
start_idx = i
elif project_val_array[i] <= spilt_threshold and in_text == True: # 进入空白区了
end_idx = i
in_text = False
if end_idx - start_idx <= 2:
continue
box_list.append((start_idx, end_idx + 1))
if in_text:
box_list.append((start_idx, h - 1))
# 绘制投影直方图
for j in range(0, h):
for i in range(0, project_val_array[j]):
projection_map[j, i] = 0
return box_list, projection_map
def projection_cx(self, box_img):
box_gray_img = cv2.cvtColor(box_img, cv2.COLOR_BGR2GRAY)
h, w = box_gray_img.shape
# 灰度图片进行二值化处理
ret, thresh1 = cv2.threshold(box_gray_img, 200, 255, cv2.THRESH_BINARY_INV)
# 纵向腐蚀
if h < w:
kernel = np.ones((2, 1), np.uint8)
erode = cv2.erode(thresh1, kernel, iterations=1)
else:
erode = thresh1
# 水平膨胀
kernel = np.ones((1, 5), np.uint8)
erosion = cv2.dilate(erode, kernel, iterations=1)
# 水平投影
projection_map = np.ones_like(erosion)
project_val_array = [0 for _ in range(0, h)]
for j in range(0, h):
for i in range(0, w):
if erosion[j, i] == 255:
project_val_array[j] += 1
# 根据数组，获取切割点
start_idx = 0 # 记录进入字符区的索引
end_idx = 0 # 记录进入空白区域的索引
in_text = False # 是否遍历到了字符区内
box_list = []
spilt_threshold = 0
for i in range(len(project_val_array)):
if in_text == False and project_val_array[i] > spilt_threshold: # 进入字符区了
in_text = True
start_idx = i
elif project_val_array[i] <= spilt_threshold and in_text == True: # 进入空白区了
end_idx = i
in_text = False
if end_idx - start_idx <= 2:
continue
box_list.append((start_idx, end_idx + 1))
if in_text:
box_list.append((start_idx, h - 1))
# 绘制投影直方图
for j in range(0, h):
for i in range(0, project_val_array[j]):
projection_map[j, i] = 0
split_bbox_list = []
if len(box_list) > 1:
for i, (h_start, h_end) in enumerate(box_list):
if i == 0:
h_start = 0
if i == len(box_list):
h_end = h
word_img = erosion[h_start:h_end + 1, :]
word_h, word_w = word_img.shape
w_split_list, w_projection_map = self.projection(word_img.T, word_w, word_h)
w_start, w_end = w_split_list[0][0], w_split_list[-1][1]
if h_start > 0:
h_start -= 1
h_end += 1
word_img = box_img[h_start:h_end + 1:, w_start:w_end + 1, :]
split_bbox_list.append([w_start, h_start, w_end, h_end])
else:
split_bbox_list.append([0, 0, w, h])
return split_bbox_list
def shrink_bbox(self, bbox):
left, top, right, bottom = bbox
sh_h = min(max(int((bottom - top) * 0.1), 1), self.shrink_h_max)
sh_w = min(max(int((right - left) * 0.1), 1), self.shrink_w_max)
left_new = left + sh_w
right_new = right - sh_w
top_new = top + sh_h
bottom_new = bottom - sh_h
if left_new >= right_new:
left_new = left
right_new = right
if top_new >= bottom_new:
top_new = top
bottom_new = bottom
return [left_new, top_new, right_new, bottom_new]
def __call__(self, data):
img = data['image']
cells = data['cells']
height, width = img.shape[0:2]
if self.mask_type == 1:
mask_img = np.zeros((height, width), dtype=np.float32)
else:
mask_img = np.zeros((height, width, 3), dtype=np.float32)
cell_num = len(cells)
for cno in range(cell_num):
if "bbox" in cells[cno]:
bbox = cells[cno]['bbox']
left, top, right, bottom = bbox
box_img = img[top:bottom, left:right, :].copy()
split_bbox_list = self.projection_cx(box_img)
for sno in range(len(split_bbox_list)):
split_bbox_list[sno][0] += left
split_bbox_list[sno][1] += top
split_bbox_list[sno][2] += left
split_bbox_list[sno][3] += top
for sno in range(len(split_bbox_list)):
left, top, right, bottom = split_bbox_list[sno]
left, top, right, bottom = self.shrink_bbox([left, top, right, bottom])
if self.mask_type == 1:
mask_img[top:bottom, left:right] = 1.0
data['mask_img'] = mask_img
else:
mask_img[top:bottom, left:right, :] = (255, 255, 255)
data['image'] = mask_img
return data
class ResizeTableImage(object):
def __init__(self, max_len, **kwargs):
super(ResizeTableImage, self).__init__()
self.max_len = max_len
def get_img_bbox(self, cells):
bbox_list = []
if len(cells) == 0:
return bbox_list
cell_num = len(cells)
for cno in range(cell_num):
if "bbox" in cells[cno]:
bbox = cells[cno]['bbox']
bbox_list.append(bbox)
return bbox_list
def resize_img_table(self, img, bbox_list, max_len):
height, width = img.shape[0:2]
ratio = max_len / (max(height, width) * 1.0)
resize_h = int(height * ratio)
resize_w = int(width * ratio)
img_new = cv2.resize(img, (resize_w, resize_h))
bbox_list_new = []
for bno in range(len(bbox_list)):
left, top, right, bottom = bbox_list[bno].copy()
left = int(left * ratio)
top = int(top * ratio)
right = int(right * ratio)
bottom = int(bottom * ratio)
bbox_list_new.append([left, top, right, bottom])
return img_new, bbox_list_new
def __call__(self, data):
img = data['image']
if 'cells' not in data:
cells = []
else:
cells = data['cells']
bbox_list = self.get_img_bbox(cells)
img_new, bbox_list_new = self.resize_img_table(img, bbox_list, self.max_len)
data['image'] = img_new
cell_num = len(cells)
bno = 0
for cno in range(cell_num):
if "bbox" in data['cells'][cno]:
data['cells'][cno]['bbox'] = bbox_list_new[bno]
bno += 1
data['max_len'] = self.max_len
return data
class PaddingTableImage(object):
def __init__(self, **kwargs):
super(PaddingTableImage, self).__init__()
def __call__(self, data):
img = data['image']
max_len = data['max_len']
padding_img = np.zeros((max_len, max_len, 3), dtype=np.float32)
height, width = img.shape[0:2]
padding_img[0:height, 0:width, :] = img.copy()
data['image'] = padding_img
return data
\ No newline at end of file
ppocr/data/imaug/operators.py
浏览文件 @ e93735a2
......@@ -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, _ = img.shape
h, w, c = 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.
else:
elif self.limit_type == 'min':
if min(h, w) < limit_side_len:
if h < w:
ratio = float(limit_side_len) / h
......@@ -182,6 +182,10 @@ 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)
......
ppocr/postprocess/__init__.py
浏览文件 @ e93735a2
......@@ -24,7 +24,8 @@ __all__ = ['build_post_process']
from .db_postprocess import DBPostProcess
from .east_postprocess import EASTPostProcess
from .sast_postprocess import SASTPostProcess
from .rec_postprocess import CTCLabelDecode, AttnLabelDecode, SRNLabelDecode, DistillationCTCLabelDecode
from .rec_postprocess import CTCLabelDecode, AttnLabelDecode, SRNLabelDecode, DistillationCTCLabelDecode, \
TableLabelDecode
from .cls_postprocess import ClsPostProcess
from .pg_postprocess import PGPostProcess
......@@ -33,7 +34,7 @@ def build_post_process(config, global_config=None):
support_dict = [
'DBPostProcess', 'EASTPostProcess', 'SASTPostProcess', 'CTCLabelDecode',
'AttnLabelDecode', 'ClsPostProcess', 'SRNLabelDecode', 'PGPostProcess',
'DistillationCTCLabelDecode'
'DistillationCTCLabelDecode', 'TableLabelDecode'
]
config = copy.deepcopy(config)
......
ppocr/postprocess/rec_postprocess.py
浏览文件 @ e93735a2
......@@ -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 += line
self.character_str.append(line)
if use_space_char:
self.character_str += " "
self.character_str.append(" ")
dict_character = list(self.character_str)
else:
......@@ -319,3 +319,138 @@ 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,
character_dict_path,
**kwargs):
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 __call__(self, preds):
structure_probs = preds['structure_probs']
loc_preds = preds['loc_preds']
if isinstance(structure_probs,paddle.Tensor):
structure_probs = structure_probs.numpy()
if isinstance(loc_preds,paddle.Tensor):
loc_preds = loc_preds.numpy()
structure_idx = structure_probs.argmax(axis=2)
structure_probs = structure_probs.max(axis=2)
structure_str, structure_pos, result_score_list, result_elem_idx_list = self.decode(structure_idx,
structure_probs, 'elem')
res_html_code_list = []
res_loc_list = []
batch_num = len(structure_str)
for bno in range(batch_num):
res_loc = []
for sno in range(len(structure_str[bno])):
text = structure_str[bno][sno]
if text in ['<td>', '<td']:
pos = structure_pos[bno][sno]
res_loc.append(loc_preds[bno, pos])
res_html_code = ''.join(structure_str[bno])
res_loc = np.array(res_loc)
res_html_code_list.append(res_html_code)
res_loc_list.append(res_loc)
return {'res_html_code': res_html_code_list, 'res_loc': res_loc_list, 'res_score_list': result_score_list,
'res_elem_idx_list': result_elem_idx_list,'structure_str_list':structure_str}
def decode(self, text_index, structure_probs, char_or_elem):
"""convert text-label into text-index.
"""
if char_or_elem == "char":
current_dict = self.dict_idx_character
else:
current_dict = self.dict_idx_elem
ignored_tokens = self.get_ignored_tokens('elem')
beg_idx, end_idx = ignored_tokens
result_list = []
result_pos_list = []
result_score_list = []
result_elem_idx_list = []
batch_size = len(text_index)
for batch_idx in range(batch_size):
char_list = []
elem_pos_list = []
elem_idx_list = []
score_list = []
for idx in range(len(text_index[batch_idx])):
tmp_elem_idx = int(text_index[batch_idx][idx])
if idx > 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
ppocr/utils/dict/table_dict.txt 0 → 100644
浏览文件 @ e93735a2
←
</overline>
☆
─
α

⋅
$
ω
ψ
χ
(
υ
≥
σ
,
ρ
ε
0
■
4
8
✗
b
<
✓
Ψ
Ω
€
D
3
Π
H
║
</strike>
L
Φ
Χ
θ
P
κ
λ
μ
T
ξ
X
β
γ
δ
\
ζ
η
`
d
<strike>
h
f
l
Θ
p
√
t
</sub>
x
Β
Γ
Δ
|
ǂ
ɛ
j
̧
➢
⁡
̌
′
«
△
▲
#
</b>
'
Ι
+
/
▼
⇑
□
·
7
▪
;
?
➔
∩
C
÷
G
⇒
K
<sup>
O
S
С
W
Α
[
○
_
●
‡
c
z
g
<i>
o
<sub>
〈
〉
s
⩽
w
φ
ʹ
{
»
∣
̆
e
ˆ
∈
τ
◆
ι
∅
∆
∙
∘
Ø
ß
✔
∞
∑
−
×
◊
∗
∖
˃
˂
∫
"
i
&
π
↔
*
∥
æ
∧
.
⁄
ø
Q
∼
6
⁎
:
★
>
a
B
≈
F
J
̄
N
♯
R
V
<overline>
―
Z
♣
^
¤
¥
§
<underline>
¢
£
≦
­
≤
‖
Λ
©
n
↓
→
↑
r
°
±
v
<b>
♂
k
♀
~
ᅟ
̇
@
”
♦
ł
®
⊕
„
!
</sup>
%
⇓
)
-
1
5
9
=
А
A
‰
⋆
Σ
E
◦
I
※
M
m
̨
⩾
†
</i>
•
U
Y


]
̸
2
‐
–
‒
̂
—
̀
́
’
‘
⋮
⋯
̊
“
̈
≧
q
u
ı
y
</underline>
​
̃
}
ν
ppocr/utils/dict/table_structure_dict.txt 0 → 100644
浏览文件 @ e93735a2
277 28 1267 1186
<b>
V
a
r
i
b
l
e
</b>
H
z
d
t
o
9
5
%
C
I
<i>
p
</i>
v
u
*
A
g
(
m
n
)
0
.
7
1
6
≤
>
8
3
–
2
G
4
M
F
T
y
f
s
L
w
c
U
h
D
S
Q
R
x
P
-
E
O
/
k
,
+
N
K
q
′
[
]
<
≥
<sup>
−
</sup>
μ
±
J
j
W
_
Δ
B
“
:
Y
α
λ
;
<sub>
</sub>
?
∼
=
°
#
̊
̈
̂
’
Z
X
∗
—
β
'
†
~
@
"
γ
↓
↑
&
‡
χ
”
σ
§
|
‐
×
$
→
√
✓
‘
\
∞
π
•
®
^
∆
≧
<underline>
</underline>
́
♀
♂
‒
⁎
▲
·
£
φ
Ψ
ß
△
☆
▪
η
€
∧
̃
Φ
ρ
̄
δ
‰
̧
Ω
♦
{
}
̀
∑
∫
ø
κ
ε
¥
※
`
ω
Σ
➔
‖
Β
̸


─
●
⩾
Χ
Α
⋅
◆
★
■
ψ
ǂ
□
ζ
!
Γ
↔
θ
⁄
〈
〉
―
υ
τ
⋆
Ø
©
∥
С
˂
➢
ɛ
⁡
✗
←
○
¢
⩽
∖
˃
­
≈
Π
̌
≦
∅
ᅟ
<overline>
</overline>
∣
¤
♯
̆
ξ
÷
▼

ι
ν
║
<strike>
</strike>
◦
​
◊
∙
«
»
ł
ı
Θ
∈
„
∘
✔
̇
æ
ʹ
ˆ
♣
⇓
∩
⊕
⇒
⇑
̨
Ι
Λ
⋯
А
⋮
<thead>
<tr>
<td>
</td>
</tr>
</thead>
<tbody>
</tbody>
<td
colspan="5"
>
colspan="2"
colspan="3"
rowspan="2"
colspan="4"
colspan="6"
rowspan="3"
colspan="9"
colspan="10"
colspan="7"
rowspan="4"
rowspan="5"
rowspan="9"
colspan="8"
rowspan="8"
rowspan="6"
rowspan="7"
rowspan="10"
0 2924682
1 3405345
2 2363468
3 2709165
4 4078680
5 3250792
6 1923159
7 1617890
8 1450532
9 1717624
10 1477550
11 1489223
12 915528
13 819193
14 593660
15 518924
16 682065
17 494584
18 400591
19 396421
20 340994
21 280688
22 250328
23 226786
24 199927
25 182707
26 164629
27 141613
28 127554
29 116286
30 107682
31 96367
32 88002
33 79234
34 72186
35 65921
36 60374
37 55976
38 52166
39 47414
40 44932
41 41279
42 38232
43 35463
44 33703
45 30557
46 29639
47 27000
48 25447
49 23186
50 22093
51 20412
52 19844
53 18261
54 17561
55 16499
56 15597
57 14558
58 14372
59 13445
60 13514
61 12058
62 11145
63 10767
64 10370
65 9630
66 9337
67 8881
68 8727
69 8060
70 7994
71 7740
72 7189
73 6729
74 6749
75 6548
76 6321
77 5957
78 5740
79 5407
80 5370
81 5035
82 4921
83 4656
84 4600
85 4519
86 4277
87 4023
88 3939
89 3910
90 3861
91 3560
92 3483
93 3406
94 3346
95 3229
96 3122
97 3086
98 3001
99 2884
100 2822
101 2677
102 2670
103 2610
104 2452
105 2446
106 2400
107 2300
108 2316
109 2196
110 2089
111 2083
112 2041
113 1881
114 1838
115 1896
116 1795
117 1786
118 1743
119 1765
120 1750
121 1683
122 1563
123 1499
124 1513
125 1462
126 1388
127 1441
128 1417
129 1392
130 1306
131 1321
132 1274
133 1294
134 1240
135 1126
136 1157
137 1130
138 1084
139 1130
140 1083
141 1040
142 980
143 1031
144 974
145 980
146 932
147 898
148 960
149 907
150 852
151 912
152 859
153 847
154 876
155 792
156 791
157 765
158 788
159 787
160 744
161 673
162 683
163 697
164 666
165 680
166 632
167 677
168 657
169 618
170 587
171 585
172 567
173 549
174 562
175 548
176 542
177 539
178 542
179 549
180 547
181 526
182 525
183 514
184 512
185 505
186 515
187 467
188 475
189 458
190 435
191 443
192 427
193 424
194 404
195 389
196 429
197 404
198 386
199 351
200 388
201 408
202 361
203 346
204 324
205 361
206 363
207 364
208 323
209 336
210 342
211 315
212 325
213 328
214 314
215 327
216 320
217 300
218 295
219 315
220 310
221 295
222 275
223 248
224 274
225 232
226 293
227 259
228 286
229 263
230 242
231 214
232 261
233 231
234 211
235 250
236 233
237 206
238 224
239 210
240 233
241 223
242 216
243 222
244 207
245 212
246 196
247 205
248 201
249 202
250 211
251 201
252 215
253 179
254 163
255 179
256 191
257 188
258 196
259 150
260 154
261 176
262 211
263 166
264 171
265 165
266 149
267 182
268 159
269 161
270 164
271 161
272 141
273 151
274 127
275 129
276 142
277 158
278 148
279 135
280 127
281 134
282 138
283 131
284 126
285 125
286 130
287 126
288 135
289 125
290 135
291 131
292 95
293 135
294 106
295 117
296 136
297 128
298 128
299 118
300 109
301 112
302 117
303 108
304 120
305 100
306 95
307 108
308 112
309 77
310 120
311 104
312 109
313 89
314 98
315 82
316 98
317 93
318 77
319 93
320 77
321 98
322 93
323 86
324 89
325 73
326 70
327 71
328 77
329 87
330 77
331 93
332 100
333 83
334 72
335 74
336 69
337 77
338 68
339 78
340 90
341 98
342 75
343 80
344 63
345 71
346 83
347 66
348 71
349 70
350 62
351 62
352 59
353 63
354 62
355 52
356 64
357 64
358 56
359 49
360 57
361 63
362 60
363 68
364 62
365 55
366 54
367 40
368 75
369 70
370 53
371 58
372 57
373 55
374 69
375 57
376 53
377 43
378 45
379 47
380 56
381 51
382 59
383 51
384 43
385 34
386 57
387 49
388 39
389 46
390 48
391 43
392 40
393 54
394 50
395 41
396 43
397 33
398 27
399 49
400 44
401 44
402 38
403 30
404 32
405 37
406 39
407 42
408 53
409 39
410 34
411 31
412 32
413 52
414 27
415 41
416 34
417 36
418 50
419 35
420 32
421 33
422 45
423 35
424 40
425 29
426 41
427 40
428 39
429 32
430 31
431 34
432 29
433 27
434 26
435 22
436 34
437 28
438 30
439 38
440 35
441 36
442 36
443 27
444 24
445 33
446 31
447 25
448 33
449 27
450 32
451 46
452 31
453 35
454 35
455 34
456 26
457 21
458 25
459 26
460 24
461 27
462 33
463 30
464 35
465 21
466 32
467 19
468 27
469 16
470 28
471 26
472 27
473 26
474 25
475 25
476 27
477 20
478 28
479 22
480 23
481 16
482 25
483 27
484 19
485 23
486 19
487 15
488 15
489 23
490 24
491 19
492 20
493 18
494 17
495 30
496 28
497 20
498 29
499 17
500 19
501 21
502 15
503 24
504 15
505 19
506 25
507 16
508 23
509 26
510 21
511 15
512 12
513 16
514 18
515 24
516 26
517 18
518 8
519 25
520 14
521 8
522 24
523 20
524 18
525 15
526 13
527 17
528 18
529 22
530 21
531 9
532 16
533 17
534 13
535 17
536 15
537 13
538 20
539 13
540 19
541 29
542 10
543 8
544 18
545 13
546 9
547 18
548 10
549 18
550 18
551 9
552 9
553 15
554 13
555 15
556 14
557 14
558 18
559 8
560 13
561 9
562 7
563 12
564 6
565 9
566 9
567 18
568 9
569 10
570 13
571 14
572 13
573 21
574 8
575 16
576 12
577 9
578 16
579 17
580 22
581 6
582 14
583 13
584 15
585 11
586 13
587 5
588 12
589 13
590 15
591 13
592 15
593 12
594 7
595 18
596 12
597 13
598 13
599 13
600 12
601 12
602 10
603 11
604 6
605 6
606 2
607 9
608 8
609 12
610 9
611 12
612 13
613 12
614 14
615 9
616 8
617 9
618 14
619 13
620 12
621 6
622 8
623 8
624 8
625 12
626 8
627 7
628 5
629 8
630 12
631 6
632 10
633 10
634 7
635 8
636 9
637 6
638 9
639 4
640 12
641 4
642 3
643 11
644 10
645 6
646 12
647 12
648 4
649 4
650 9
651 8
652 6
653 5
654 14
655 10
656 11
657 8
658 5
659 5
660 9
661 13
662 4
663 5
664 9
665 11
666 12
667 7
668 13
669 2
670 1
671 7
672 7
673 7
674 10
675 9
676 6
677 5
678 7
679 6
680 3
681 3
682 4
683 9
684 8
685 5
686 3
687 11
688 9
689 2
690 6
691 5
692 9
693 5
694 6
695 5
696 9
697 8
698 3
699 7
700 5
701 9
702 8
703 7
704 2
705 3
706 7
707 6
708 6
709 10
710 2
711 10
712 6
713 7
714 5
715 6
716 4
717 6
718 8
719 4
720 6
721 7
722 5
723 7
724 3
725 10
726 10
727 3
728 7
729 7
730 5
731 2
732 1
733 5
734 1
735 5
736 6
737 2
738 2
739 3
740 7
741 2
742 7
743 4
744 5
745 4
746 5
747 3
748 1
749 4
750 4
751 2
752 4
753 6
754 6
755 6
756 3
757 2
758 5
759 5
760 3
761 4
762 2
763 1
764 8
765 3
766 4
767 3
768 1
769 5
770 3
771 3
772 4
773 4
774 1
775 3
776 2
777 2
778 3
779 3
780 1
781 4
782 3
783 4
784 6
785 3
786 5
787 4
788 2
789 4
790 5
791 4
792 6
794 4
795 1
796 1
797 4
798 2
799 3
800 3
801 1
802 5
803 5
804 3
805 3
806 3
807 4
808 4
809 2
811 5
812 4
813 6
814 3
815 2
816 2
817 3
818 5
819 3
820 1
821 1
822 4
823 3
824 4
825 8
826 3
827 5
828 5
829 3
830 6
831 3
832 4
833 8
834 5
835 3
836 3
837 2
838 4
839 2
840 1
841 3
842 2
843 1
844 3
846 4
847 4
848 3
849 3
850 2
851 3
853 1
854 4
855 4
856 2
857 4
858 1
859 2
860 5
861 1
862 1
863 4
864 2
865 2
867 5
868 1
869 4
870 1
871 1
872 1
873 2
875 5
876 3
877 1
878 3
879 3
880 3
881 2
882 1
883 6
884 2
885 2
886 1
887 1
888 3
889 2
890 2
891 3
892 1
893 3
894 1
895 5
896 1
897 3
899 2
900 2
902 1
903 2
904 4
905 4
906 3
907 1
908 1
909 2
910 5
911 2
912 3
914 1
915 1
916 2
918 2
919 2
920 4
921 4
922 1
923 1
924 4
925 5
926 1
928 2
929 1
930 1
931 1
932 1
933 1
934 2
935 1
936 1
937 1
938 2
939 1
941 1
942 4
944 2
945 2
946 2
947 1
948 1
950 1
951 2
953 1
954 2
955 1
956 1
957 2
958 1
960 3
962 4
963 1
964 1
965 3
966 2
967 2
968 1
969 3
970 3
972 1
974 4
975 3
976 3
977 2
979 2
980 1
981 1
983 5
984 1
985 3
986 1
987 2
988 4
989 2
991 2
992 2
993 1
994 1
996 2
997 2
998 1
999 3
1000 2
1001 1
1002 3
1003 3
1004 2
1005 3
1006 1
1007 2
1009 1
1011 1
1013 3
1014 1
1016 2
1017 1
1018 1
1019 1
1020 4
1021 1
1022 2
1025 1
1026 1
1027 2
1028 1
1030 1
1031 2
1032 4
1034 3
1035 2
1036 1
1038 1
1039 1
1040 1
1041 1
1042 2
1043 1
1044 2
1045 4
1048 1
1050 1
1051 1
1052 2
1054 1
1055 3
1056 2
1057 1
1059 1
1061 2
1063 1
1064 1
1065 1
1066 1
1067 1
1068 1
1069 2
1074 1
1075 1
1077 1
1078 1
1079 1
1082 1
1085 1
1088 1
1090 1
1091 1
1092 2
1094 2
1097 2
1098 1
1099 2
1101 2
1102 1
1104 1
1105 1
1107 1
1109 1
1111 2
1112 1
1114 2
1115 2
1116 2
1117 1
1118 1
1119 1
1120 1
1122 1
1123 1
1127 1
1128 3
1132 2
1138 3
1142 1
1145 4
1150 1
1153 2
1154 1
1158 1
1159 1
1163 1
1165 1
1169 2
1174 1
1176 1
1177 1
1178 2
1179 1
1180 2
1181 1
1182 1
1183 2
1185 1
1187 1
1191 2
1193 1
1195 3
1196 1
1201 3
1203 1
1206 1
1210 1
1213 1
1214 1
1215 2
1218 1
1220 1
1221 1
1225 1
1226 1
1233 2
1241 1
1243 1
1249 1
1250 2
1251 1
1254 1
1255 2
1260 1
1268 1
1270 1
1273 1
1274 1
1277 1
1284 1
1287 1
1291 1
1292 2
1294 1
1295 2
1297 1
1298 1
1301 1
1307 1
1308 3
1311 2
1313 1
1316 1
1321 1
1324 1
1325 1
1330 1
1333 1
1334 1
1338 2
1340 1
1341 1
1342 1
1343 1
1345 1
1355 1
1357 1
1360 2
1375 1
1376 1
1380 1
1383 1
1387 1
1389 1
1393 1
1394 1
1396 1
1398 1
1410 1
1414 1
1419 1
1425 1
1434 1
1435 1
1438 1
1439 1
1447 1
1455 2
1460 1
1461 1
1463 1
1466 1
1470 1
1473 1
1478 1
1480 1
1483 1
1484 1
1485 2
1492 2
1499 1
1509 1
1512 1
1513 1
1523 1
1524 1
1525 2
1529 1
1539 1
1544 1
1568 1
1584 1
1591 1
1598 1
1600 1
1604 1
1614 1
1617 1
1621 1
1622 1
1626 1
1638 1
1648 1
1658 1
1661 1
1679 1
1682 1
1693 1
1700 1
1705 1
1707 1
1722 1
1728 1
1758 1
1762 1
1763 1
1775 1
1776 1
1801 1
1810 1
1812 1
1827 1
1834 1
1846 1
1847 1
1848 1
1851 1
1862 1
1866 1
1877 2
1884 1
1888 1
1903 1
1912 1
1925 1
1938 1
1955 1
1998 1
2054 1
2058 1
2065 1
2069 1
2076 1
2089 1
2104 1
2111 1
2133 1
2138 1
2156 1
2204 1
2212 1
2237 1
2246 2
2298 1
2304 1
2360 1
2400 1
2481 1
2544 1
2586 1
2622 1
2666 1
2682 1
2725 1
2920 1
3997 1
4019 1
5211 1
12 19
14 1
16 401
18 2
20 421
22 557
24 625
26 50
28 4481
30 52
32 550
34 5840
36 4644
38 87
40 5794
41 33
42 571
44 11805
46 4711
47 7
48 597
49 12
50 678
51 2
52 14715
53 3
54 7322
55 3
56 508
57 39
58 3486
59 11
60 8974
61 45
62 1276
63 4
64 15693
65 15
66 657
67 13
68 6409
69 10
70 3188
71 25
72 1889
73 27
74 10370
75 9
76 12432
77 23
78 520
79 15
80 1534
81 29
82 2944
83 23
84 12071
85 36
86 1502
87 10
88 10978
89 11
90 889
91 16
92 4571
93 17
94 7855
95 21
96 2271
97 33
98 1423
99 15
100 11096
101 21
102 4082
103 13
104 5442
105 25
106 2113
107 26
108 3779
109 43
110 1294
111 29
112 7860
113 29
114 4965
115 22
116 7898
117 25
118 1772
119 28
120 1149
121 38
122 1483
123 32
124 10572
125 25
126 1147
127 31
128 1699
129 22
130 5533
131 22
132 4669
133 34
134 3777
135 10
136 5412
137 21
138 855
139 26
140 2485
141 46
142 1970
143 27
144 6565
145 40
146 933
147 15
148 7923
149 16
150 735
151 23
152 1111
153 33
154 3714
155 27
156 2445
157 30
158 3367
159 10
160 4646
161 27
162 990
163 23
164 5679
165 25
166 2186
167 17
168 899
169 32
170 1034
171 22
172 6185
173 32
174 2685
175 17
176 1354
177 38
178 1460
179 15
180 3478
181 20
182 958
183 20
184 6055
185 23
186 2180
187 15
188 1416
189 30
190 1284
191 22
192 1341
193 21
194 2413
195 18
196 4984
197 13
198 830
199 22
200 1834
201 19
202 2238
203 9
204 3050
205 22
206 616
207 17
208 2892
209 22
210 711
211 30
212 2631
213 19
214 3341
215 21
216 987
217 26
218 823
219 9
220 3588
221 20
222 692
223 7
224 2925
225 31
226 1075
227 16
228 2909
229 18
230 673
231 20
232 2215
233 14
234 1584
235 21
236 1292
237 29
238 1647
239 25
240 1014
241 30
242 1648
243 19
244 4465
245 10
246 787
247 11
248 480
249 25
250 842
251 15
252 1219
253 23
254 1508
255 8
256 3525
257 16
258 490
259 12
260 1678
261 14
262 822
263 16
264 1729
265 28
266 604
267 11
268 2572
269 7
270 1242
271 15
272 725
273 18
274 1983
275 13
276 1662
277 19
278 491
279 12
280 1586
281 14
282 563
283 10
284 2363
285 10
286 656
287 14
288 725
289 28
290 871
291 9
292 2606
293 12
294 961
295 9
296 478
297 13
298 1252
299 10
300 736
301 19
302 466
303 13
304 2254
305 12
306 486
307 14
308 1145
309 13
310 955
311 13
312 1235
313 13
314 931
315 14
316 1768
317 11
318 330
319 10
320 539
321 23
322 570
323 12
324 1789
325 13
326 884
327 5
328 1422
329 14
330 317
331 11
332 509
333 13
334 1062
335 12
336 577
337 27
338 378
339 10
340 2313
341 9
342 391
343 13
344 894
345 17
346 664
347 9
348 453
349 6
350 363
351 15
352 1115
353 13
354 1054
355 8
356 1108
357 12
358 354
359 7
360 363
361 16
362 344
363 11
364 1734
365 12
366 265
367 10
368 969
369 16
370 316
371 12
372 757
373 7
374 563
375 15
376 857
377 9
378 469
379 9
380 385
381 12
382 921
383 15
384 764
385 14
386 246
387 6
388 1108
389 14
390 230
391 8
392 266
393 11
394 641
395 8
396 719
397 9
398 243
399 4
400 1108
401 7
402 229
403 7
404 903
405 7
406 257
407 12
408 244
409 3
410 541
411 6
412 744
413 8
414 419
415 8
416 388
417 19
418 470
419 14
420 612
421 6
422 342
423 3
424 1179
425 3
426 116
427 14
428 207
429 6
430 255
431 4
432 288
433 12
434 343
435 6
436 1015
437 3
438 538
439 10
440 194
441 6
442 188
443 15
444 524
445 7
446 214
447 7
448 574
449 6
450 214
451 5
452 635
453 9
454 464
455 5
456 205
457 9
458 163
459 2
460 558
461 4
462 171
463 14
464 444
465 11
466 543
467 5
468 388
469 6
470 141
471 4
472 647
473 3
474 210
475 4
476 193
477 7
478 195
479 7
480 443
481 10
482 198
483 3
484 816
485 6
486 128
487 9
488 215
489 9
490 328
491 7
492 158
493 11
494 335
495 8
496 435
497 6
498 174
499 1
500 373
501 5
502 140
503 7
504 330
505 9
506 149
507 5
508 642
509 3
510 179
511 3
512 159
513 8
514 204
515 7
516 306
517 4
518 110
519 5
520 326
521 6
522 305
523 6
524 294
525 7
526 268
527 5
528 149
529 4
530 133
531 2
532 513
533 10
534 116
535 5
536 258
537 4
538 113
539 4
540 138
541 6
542 116
544 485
545 4
546 93
547 9
548 299
549 3
550 256
551 6
552 92
553 3
554 175
555 6
556 253
557 7
558 95
559 2
560 128
561 4
562 206
563 2
564 465
565 3
566 69
567 3
568 157
569 7
570 97
571 8
572 118
573 5
574 130
575 4
576 301
577 6
578 177
579 2
580 397
581 3
582 80
583 1
584 128
585 5
586 52
587 2
588 72
589 1
590 84
591 6
592 323
593 11
594 77
595 5
596 205
597 1
598 244
599 4
600 69
601 3
602 89
603 5
604 254
605 6
606 147
607 3
608 83
609 3
610 77
611 3
612 194
613 1
614 98
615 3
616 243
617 3
618 50
619 8
620 188
621 4
622 67
623 4
624 123
625 2
626 50
627 1
628 239
629 2
630 51
631 4
632 65
633 5
634 188
636 81
637 3
638 46
639 3
640 103
641 1
642 136
643 3
644 188
645 3
646 58
648 122
649 4
650 47
651 2
652 155
653 4
654 71
655 1
656 71
657 3
658 50
659 2
660 177
661 5
662 66
663 2
664 183
665 3
666 50
667 2
668 53
669 2
670 115
672 66
673 2
674 47
675 1
676 197
677 2
678 46
679 3
680 95
681 3
682 46
683 3
684 107
685 1
686 86
687 2
688 158
689 4
690 51
691 1
692 80
694 56
695 4
696 40
698 43
699 3
700 95
701 2
702 51
703 2
704 133
705 1
706 100
707 2
708 121
709 2
710 15
711 3
712 35
713 2
714 20
715 3
716 37
717 2
718 78
720 55
721 1
722 42
723 2
724 218
725 3
726 23
727 2
728 26
729 1
730 64
731 2
732 65
734 24
735 2
736 53
737 1
738 32
739 1
740 60
742 81
743 1
744 77
745 1
746 47
747 1
748 62
749 1
750 19
751 1
752 86
753 3
754 40
756 55
757 2
758 38
759 1
760 101
761 1
762 22
764 67
765 2
766 35
767 1
768 38
769 1
770 22
771 1
772 82
773 1
774 73
776 29
777 1
778 55
780 23
781 1
782 16
784 84
785 3
786 28
788 59
789 1
790 33
791 3
792 24
794 13
795 1
796 110
797 2
798 15
800 22
801 3
802 29
803 1
804 87
806 21
808 29
810 48
812 28
813 1
814 58
815 1
816 48
817 1
818 31
819 1
820 66
822 17
823 2
824 58
826 10
827 2
828 25
829 1
830 29
831 1
832 63
833 1
834 26
835 3
836 52
837 1
838 18
840 27
841 2
842 12
843 1
844 83
845 1
846 7
847 1
848 10
850 26
852 25
853 1
854 15
856 27
858 32
859 1
860 15
862 43
864 32
865 1
866 6
868 39
870 11
872 25
873 1
874 10
875 1
876 20
877 2
878 19
879 1
880 30
882 11
884 53
886 25
887 1
888 28
890 6
892 36
894 10
896 13
898 14
900 31
902 14
903 2
904 43
906 25
908 9
910 11
911 1
912 16
913 1
914 24
916 27
918 6
920 15
922 27
923 1
924 23
926 13
928 42
929 1
930 3
932 27
934 17
936 8
937 1
938 11
940 33
942 4
943 1
944 18
946 15
948 13
950 18
952 12
954 11
956 21
958 10
960 13
962 5
964 32
966 13
968 8
970 8
971 1
972 23
973 2
974 12
975 1
976 22
978 7
979 1
980 14
982 8
984 22
985 1
986 6
988 17
989 1
990 6
992 13
994 19
996 11
998 4
1000 9
1002 2
1004 14
1006 5
1008 3
1010 9
1012 29
1014 6
1016 22
1017 1
1018 8
1019 1
1020 7
1022 6
1023 1
1024 10
1026 2
1028 8
1030 11
1031 2
1032 8
1034 9
1036 13
1038 12
1040 12
1042 3
1044 12
1046 3
1048 11
1050 2
1051 1
1052 2
1054 11
1056 6
1058 8
1059 1
1060 23
1062 6
1063 1
1064 8
1066 3
1068 6
1070 8
1071 1
1072 5
1074 3
1076 5
1078 3
1080 11
1081 1
1082 7
1084 18
1086 4
1087 1
1088 3
1090 3
1092 7
1094 3
1096 12
1098 6
1099 1
1100 2
1102 6
1104 14
1106 3
1108 6
1110 5
1112 2
1114 8
1116 3
1118 3
1120 7
1122 10
1124 6
1126 8
1128 1
1130 4
1132 3
1134 2
1136 5
1138 5
1140 8
1142 3
1144 7
1146 3
1148 11
1150 1
1152 5
1154 1
1156 5
1158 1
1160 5
1162 3
1164 6
1165 1
1166 1
1168 4
1169 1
1170 3
1171 1
1172 2
1174 5
1176 3
1177 1
1180 8
1182 2
1184 4
1186 2
1188 3
1190 2
1192 5
1194 6
1196 1
1198 2
1200 2
1204 10
1206 2
1208 9
1210 1
1214 6
1216 3
1218 4
1220 9
1221 2
1222 1
1224 5
1226 4
1228 8
1230 1
1232 1
1234 3
1236 5
1240 3
1242 1
1244 3
1245 1
1246 4
1248 6
1250 2
1252 7
1256 3
1258 2
1260 2
1262 3
1264 4
1265 1
1266 1
1270 1
1271 1
1272 2
1274 3
1276 3
1278 1
1280 3
1284 1
1286 1
1290 1
1292 3
1294 1
1296 7
1300 2
1302 4
1304 3
1306 2
1308 2
1312 1
1314 1
1316 3
1318 2
1320 1
1324 8
1326 1
1330 1
1331 1
1336 2
1338 1
1340 3
1341 1
1344 1
1346 2
1347 1
1348 3
1352 1
1354 2
1356 1
1358 1
1360 3
1362 1
1364 4
1366 1
1370 1
1372 3
1380 2
1384 2
1388 2
1390 2
1392 2
1394 1
1396 1
1398 1
1400 2
1402 1
1404 1
1406 1
1410 1
1412 5
1418 1
1420 1
1424 1
1432 2
1434 2
1442 3
1444 5
1448 1
1454 1
1456 1
1460 3
1462 4
1468 1
1474 1
1476 1
1478 2
1480 1
1486 2
1488 1
1492 1
1496 1
1500 3
1503 1
1506 1
1512 2
1516 1
1522 1
1524 2
1534 4
1536 1
1538 1
1540 2
1544 2
1548 1
1556 1
1560 1
1562 1
1564 2
1566 1
1568 1
1570 1
1572 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
ppocr/utils/logging.py
浏览文件 @ e93735a2
......@@ -22,7 +22,7 @@ logger_initialized = {}
@functools.lru_cache()
def get_logger(name='root', log_file=None, log_level=logging.INFO):
def get_logger(name='root', log_file=None, log_level=logging.DEBUG):
"""Initialize and get a logger by name.
If the logger has not been initialized, this method will initialize the
logger by adding one or two handlers, otherwise the initialized logger will
......
ppocr/utils/network.py 0 → 100644
浏览文件 @ e93735a2
# 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
test/MANIFEST.in 0 → 100644
浏览文件 @ e93735a2
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
test/__init__.py 0 → 100644
浏览文件 @ e93735a2
# 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']
test/api.md 0 → 100644
浏览文件 @ e93735a2
# PaddleStructure
## 1. Introduction to pipeline
PaddleStructure is a toolkit for complex layout text OCR, the process is as follows
![pipeline](../doc/table/pipeline.png)
In PaddleStructure, the image will be analyzed by layoutparser first. In the layout analysis, the area in the image will be classified, and the OCR process will be carried out according to the category.
Currently layoutparser will output five categories:
1. Text
2. Title
3. Figure
4. List
5. Table
Types 1-4 follow the traditional OCR process, and 5 follow the Table OCR process.
## 2. LayoutParser
## 3. Table OCR
[doc](table/README.md)
## 4. PaddleStructure whl package introduction
### 4.1 Use
4.1.1 Use by code
```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')
```
4.1.2 Use by command line
```bash
paddlestructure --image_dir=../doc/table/1.png
```
### 参数说明
大部分参数和paddleocr whl包保持一致，见 [whl包文档](../doc/doc_ch/whl.md)
| 字段 | 说明 | 默认值 |
|------------------------|------------------------------------------------------|------------------|
| output | excel和识别结果保存的地址 | ./output/table |
| structure_max_len | structure模型预测时，图像的长边resize尺度 | 488 |
| structure_model_dir | structure inference 模型地址 | None |
| structure_char_type | structure 模型所用字典地址 | ../ppocr/utils/dict/table_structure_dict.tx |
test/api_ch.md 0 → 100644
浏览文件 @ e93735a2
# PaddleStructure
## 1. pipeline介绍
PaddleStructure 是一个用于复杂板式文字OCR的工具包，流程如下
![pipeline](../doc/table/pipeline.png)
在PaddleStructure中，图片会先经由layoutparser进行版面分析，在版面分析中，会对图片里的区域进行分类，根据根据类别进行对于的ocr流程。
目前layoutparser会输出五个类别:
1. Text
2. Title
3. Figure
4. List
5. Table
1-4类走传统的OCR流程，5走表格的OCR流程。
## 2. LayoutParser
## 3. Table OCR
[文档](table/README_ch.md)
## 4. PaddleStructure whl包介绍
### 4.1 使用
4.1.1 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')
```
4.1.2 命令行使用
```bash
paddlestructure --image_dir=../doc/table/1.png
```
### 参数说明
大部分参数和paddleocr whl包保持一致，见 [whl包文档](../doc/doc_ch/whl.md)
| 字段 | 说明 | 默认值 |
|------------------------|------------------------------------------------------|------------------|
| output | excel和识别结果保存的地址 | ./output/table |
| structure_max_len | structure模型预测时，图像的长边resize尺度 | 488 |
| structure_model_dir | structure inference 模型地址 | None |
| structure_char_type | structure 模型所用字典地址 | ../ppocr/utils/dict/table_structure_dict.tx |
test/paddlestructure.py 0 → 100644
浏览文件 @ e93735a2
# 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 test.predict_system import OCRSystem, save_res
from test.table.predict_table import to_excel
from test.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 not params.show_log:
logger.setLevel(logging.INFO)
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
test/predict_system.py 0 → 100644
浏览文件 @ e93735a2
# 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 logging
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 test.table.predict_table import TableSystem, to_excel
from test.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
if not args.show_log:
logger.setLevel(logging.INFO)
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)
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)
test/setup.py 0 → 100644
浏览文件 @ e93735a2
# 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('api_ch.md', encoding="utf-8-sig") as f:
README = f.read()
return README
shutil.copytree('/table', './test/table')
shutil.copyfile('/predict_system.py', './test/predict_system.py')
shutil.copyfile('/utility.py', './test/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('test')
os.remove('LICENSE')
test/table/README.md 0 → 100644
浏览文件 @ e93735a2
# Table structure and content prediction
## 1. pipeline
The ocr of the table mainly contains three models
1. Single line text detection-DB
2. Single line text recognition-CRNN
3. Table structure and cell coordinate prediction-RARE
The table ocr flow chart is as follows
![tableocr_pipeline](../../doc/table/tableocr_pipeline.png)
1. The coordinates of single-line text is detected by DB model, and then sends it to the recognition model to get the recognition result.
2. The table structure and cell coordinates is predicted by RARE model.
3. The recognition result of the cell is combined by the coordinates, recognition result of the single line and the coordinates of the cell.
4. The cell recognition result and the table structure together construct the html string of the table.
## 2. How to use
### 2.1 Train
TBD
### 2.2 Eval
First cd to the PaddleOCR/ppstructure directory
The table uses TEDS (Tree-Edit-Distance-based Similarity) as the evaluation metric of the model. Before the model evaluation, the three models in the pipeline need to be exported as inference models (we have provided them), and the gt for evaluation needs to be prepared. Examples of gt are as follows:
```json
{"PMC4289340_004_00.png": [["<html>", "<body>", "<table>", "<thead>", "<tr>", "<td>", "</td>", "<td>", "</td>", "<td>", "</td>", "</tr>", "</thead>", "<tbody>", "<tr>", "<td>", "</td>", "<td>", "</td>", "<td>", "</td>", "</tr>", "</tbody>", "</table>", "</body>", "</html>"], [[1, 4, 29, 13], [137, 4, 161, 13], [215, 4, 236, 13], [1, 17, 30, 27], [137, 17, 147, 27], [215, 17, 225, 27]], [["<b>", "F", "e", "a", "t", "u", "r", "e", "</b>"], ["<b>", "G", "b", "3", " ", "+", "</b>"], ["<b>", "G", "b", "3", " ", "-", "</b>"], ["<b>", "P", "a", "t", "i", "e", "n", "t", "s", "</b>"], ["6", "2"], ["4", "5"]]]}
```
In gt json, the key is the image name, the value is the corresponding gt, and gt is a list composed of four items, and each item is
1. HTML string list of table structure
2. The coordinates of each cell (not including the empty text in the cell)
3. The text information in each cell (not including the empty text in the cell)
4. The text information in each cell (including the empty text in the cell)
Use the following command to evaluate. After the evaluation is completed, the teds indicator will be output.
```python
python3 table/eval_table.py --det_model_dir=path/to/det_model_dir --rec_model_dir=path/to/rec_model_dir --structure_model_dir=path/to/structure_model_dir --image_dir=../doc/table/1.png --rec_char_dict_path=../ppocr/utils/dict/table_dict.txt --structure_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
```
### 2.3 Inference
First cd to the PaddleOCR/ppstructure directory
```python
python3 table/predict_table.py --det_model_dir=path/to/det_model_dir --rec_model_dir=path/to/rec_model_dir --structure_model_dir=path/to/structure_model_dir --image_dir=../doc/table/1.png --rec_char_dict_path=../ppocr/utils/dict/table_dict.txt --structure_char_dict_path=../ppocr/utils/dict/table_structure_dict.txt --rec_char_type=EN --det_limit_side_len=736 --det_limit_type=min --output ../output/table
```
After running, the excel sheet of each picture will be saved in the directory specified by the table_output field
\ No newline at end of file
test/table/README_ch.md 0 → 100644
浏览文件 @ e93735a2
# 表格结构和内容预测
## 1. pipeline
表格的ocr主要包含三个模型
1. 单行文本检测-DB
2. 单行文本识别-CRNN
3. 表格结构和cell坐标预测-RARE
具体流程图如下
![tableocr_pipeline](../../doc/table/tableocr_pipeline.png)
1. 图片由单行文字检测检测模型到单行文字的坐标，然后送入识别模型拿到识别结果。
2. 图片由表格结构和cell坐标预测模型拿到表格的结构信息和单元格的坐标信息。
3. 由单行文字的坐标、识别结果和单元格的坐标一起组合出单元格的识别结果。
4. 单元格的识别结果和表格结构一起构造表格的html字符串。
## 2. 使用
### 2.1 训练
TBD
### 2.2 评估
先cd到PaddleOCR/ppstructure目录下
表格使用 TEDS(Tree-Edit-Distance-based Similarity) 作为模型的评估指标。在进行模型评估之前，需要将pipeline中的三个模型分别导出为inference模型(我们已经提供好)，还需要准备评估的gt， gt示例如下:
```json
{"PMC4289340_004_00.png": [["<html>", "<body>", "<table>", "<thead>", "<tr>", "<td>", "</td>", "<td>", "</td>", "<td>", "</td>", "</tr>", "</thead>", "<tbody>", "<tr>", "<td>", "</td>", "<td>", "</td>", "<td>", "</td>", "</tr>", "</tbody>", "</table>", "</body>", "</html>"], [[1, 4, 29, 13], [137, 4, 161, 13], [215, 4, 236, 13], [1, 17, 30, 27], [137, 17, 147, 27], [215, 17, 225, 27]], [["<b>", "F", "e", "a", "t", "u", "r", "e", "</b>"], ["<b>", "G", "b", "3", " ", "+", "</b>"], ["<b>", "G", "b", "3", " ", "-", "</b>"], ["<b>", "P", "a", "t", "i", "e", "n", "t", "s", "</b>"], ["6", "2"], ["4", "5"]]]}
```
json 中，key为图片名，value为对于的gt，gt是一个由四个item组成的list，每个item分别为
1. 表格结构的html字符串list
2. 每个cell的坐标 (不包括cell里文字为空的)
3. 每个cell里的文字信息 (不包括cell里文字为空的)
4. 每个cell里的文字信息 (包括cell里文字为空的)
准备完成后使用如下命令进行评估，评估完成后会输出teds指标。
```python
python3 table/eval_table.py --det_model_dir=path/to/det_model_dir --rec_model_dir=path/to/rec_model_dir --structure_model_dir=path/to/structure_model_dir --image_dir=../doc/table/1.png --rec_char_dict_path=../ppocr/utils/dict/table_dict.txt --structure_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
```
### 2.3 预测
先cd到PaddleOCR/ppstructure目录下
```python
python3 table/predict_table.py --det_model_dir=path/to/det_model_dir --rec_model_dir=path/to/rec_model_dir --structure_model_dir=path/to/structure_model_dir --image_dir=../doc/table/1.png --rec_char_dict_path=../ppocr/utils/dict/table_dict.txt --structure_char_dict_path=../ppocr/utils/dict/table_structure_dict.txt --rec_char_type=EN --det_limit_side_len=736 --det_limit_type=min --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
test/table/__init__.py 0 → 100644
浏览文件 @ e93735a2
# 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.
test/table/eval_table.py 0 → 100755
浏览文件 @ e93735a2
# 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 test.table.table_metric import TEDS
from test.table.predict_table import TableSystem
from test.utility import init_args
from ppocr.utils.logging import get_logger
logger = get_logger()
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)
logger.info('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 '</td>' 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)
test/table/matcher.py 0 → 100755
浏览文件 @ e93735a2
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
test/table/predict_structure.py 0 → 100755
浏览文件 @ e93735a2
# 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
from test.utility import parse_args
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,
}
self.preprocess_op = create_operators(pre_process_list)
self.postprocess_op = build_post_process(postprocess_params)
self.predictor, self.input_tensor, self.output_tensors, self.config = \
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 = ['<html>', '<body>', '<table>'] + structure_str_list + ['</table>', '</body>', '</html>']
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(parse_args())
test/table/predict_table.py 0 → 100644
浏览文件 @ e93735a2
# 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 test.table.matcher import distance, compute_iou
from test.utility import parse_args
import test.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 '</td>' in tag:
if td_index in matched_index.keys():
b_with = False
if '<b>' in ocr_contents[matched_index[td_index][0]] and len(matched_index[td_index]) > 1:
b_with = True
end_html.extend('<b>')
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 '<b>' in content:
content = content[3:]
if '</b>' 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('</b>')
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.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)
test/table/table_metric/__init__.py 0 → 100755
浏览文件 @ e93735a2
# 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
test/table/table_metric/parallel.py 0 → 100755
浏览文件 @ e93735a2
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
test/table/table_metric/table_metric.py 0 → 100755
浏览文件 @ e93735a2
# 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('</%s>' % 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)
test/table/tablepyxl/__init__.py 0 → 100644
浏览文件 @ e93735a2
# 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
test/table/tablepyxl/style.py 0 → 100644
浏览文件 @ e93735a2
# 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 `<th>` 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 `<tbody>` 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 `<tr>` 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 `<td>` 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
test/table/tablepyxl/tablepyxl.py 0 → 100644
浏览文件 @ e93735a2
# 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
test/utility.py 0 → 100644
浏览文件 @ e93735a2
# 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_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")
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
tools/infer/predict_det.py
浏览文件 @ e93735a2
......@@ -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': {
......
tools/infer/predict_system.py
浏览文件 @ e93735a2
......@@ -24,6 +24,7 @@ import cv2
import copy
import numpy as np
import time
import logging
from PIL import Image
import tools.infer.utility as utility
import tools.infer.predict_rec as predict_rec
......@@ -38,6 +39,9 @@ logger = get_logger()
class TextSystem(object):
def __init__(self, args):
if not args.show_log:
logger.setLevel(logging.INFO)
self.text_detector = predict_det.TextDetector(args)
self.text_recognizer = predict_rec.TextRecognizer(args)
self.use_angle_cls = args.use_angle_cls
......@@ -88,7 +92,7 @@ class TextSystem(object):
ori_im = img.copy()
dt_boxes, elapse = self.text_detector(img)
logger.info("dt_boxes num : {}, elapse : {}".format(
logger.debug("dt_boxes num : {}, elapse : {}".format(
len(dt_boxes), elapse))
if dt_boxes is None:
......@@ -104,11 +108,11 @@ class TextSystem(object):
if self.use_angle_cls and cls:
img_crop_list, angle_list, elapse = self.text_classifier(
img_crop_list)
logger.info("cls num : {}, elapse : {}".format(
logger.debug("cls num : {}, elapse : {}".format(
len(img_crop_list), elapse))
rec_res, elapse = self.text_recognizer(img_crop_list)
logger.info("rec_res num : {}, elapse : {}".format(
logger.debug("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 = [], []
......
tools/infer/utility.py
浏览文件 @ e93735a2
......@@ -109,11 +109,12 @@ def init_args():
parser.add_argument("--use_mp", type=str2bool, default=False)
parser.add_argument("--total_process_num", type=int, default=1)
parser.add_argument("--process_id", type=int, default=0)
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
......@@ -199,6 +200,8 @@ 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
......@@ -328,7 +331,9 @@ 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()
......
Markdown is supported
0% 或 .
You are about to add 0 people to the discussion. Proceed with caution.
先完成此消息的编辑！
想要评论请 注册 或