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Commit 83303bc7 authored by LDOUBLEV's avatar LDOUBLEV
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fix conflicts

parents 3af943f3 af0bac58
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ppstructure/table/predict_structure.py 0 → 100755
View file @ 83303bc7
# 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 time
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 ppstructure.utility import parse_args
logger = get_logger()
class TableStructurer(object):
def __init__(self, args):
pre_process_list = [{
'ResizeTableImage': {
'max_len': args.table_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.table_char_type,
"character_dict_path": args.table_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, 'table', 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())
ppstructure/table/predict_table.py 0 → 100644
View file @ 83303bc7
# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import os
import sys
import subprocess
__dir__ = os.path.dirname(os.path.abspath(__file__))
sys.path.append(__dir__)
sys.path.append(os.path.abspath(os.path.join(__dir__, '..')))
sys.path.append(os.path.abspath(os.path.join(__dir__, '../..')))
os.environ["FLAGS_allocator_strategy"] = 'auto_growth'
import cv2
import copy
import numpy as np
import time
import tools.infer.predict_rec as predict_rec
import tools.infer.predict_det as predict_det
from ppocr.utils.utility import get_image_file_list, check_and_read_gif
from ppocr.utils.logging import get_logger
from ppstructure.table.matcher import distance, compute_iou
from ppstructure.utility import parse_args
import ppstructure.table.predict_structure as predict_strture
logger = get_logger()
def expand(pix, det_box, shape):
x0, y0, x1, y1 = det_box
# print(shape)
h, w, c = shape
tmp_x0 = x0 - pix
tmp_x1 = x1 + pix
tmp_y0 = y0 - pix
tmp_y1 = y1 + pix
x0_ = tmp_x0 if tmp_x0 >= 0 else 0
x1_ = tmp_x1 if tmp_x1 <= w else w
y0_ = tmp_y0 if tmp_y0 >= 0 else 0
y1_ = tmp_y1 if tmp_y1 <= h else h
return x0_, y0_, x1_, y1_
class TableSystem(object):
def __init__(self, args, text_detector=None, text_recognizer=None):
self.text_detector = predict_det.TextDetector(args) if text_detector is None else text_detector
self.text_recognizer = predict_rec.TextRecognizer(args) if text_recognizer is None else text_recognizer
self.table_structurer = predict_strture.TableStructurer(args)
def __call__(self, img):
ori_im = img.copy()
structure_res, elapse = self.table_structurer(copy.deepcopy(img))
dt_boxes, elapse = self.text_detector(copy.deepcopy(img))
dt_boxes = sorted_boxes(dt_boxes)
r_boxes = []
for box in dt_boxes:
x_min = box[:, 0].min() - 1
x_max = box[:, 0].max() + 1
y_min = box[:, 1].min() - 1
y_max = box[:, 1].max() + 1
box = [x_min, y_min, x_max, y_max]
r_boxes.append(box)
dt_boxes = np.array(r_boxes)
logger.debug("dt_boxes num : {}, elapse : {}".format(
len(dt_boxes), elapse))
if dt_boxes is None:
return None, None
img_crop_list = []
for i in range(len(dt_boxes)):
det_box = dt_boxes[i]
x0, y0, x1, y1 = expand(2, det_box, ori_im.shape)
text_rect = ori_im[int(y0):int(y1), int(x0):int(x1), :]
img_crop_list.append(text_rect)
rec_res, elapse = self.text_recognizer(img_crop_list)
logger.debug("rec_res num : {}, elapse : {}".format(
len(rec_res), elapse))
pred_html, pred = self.rebuild_table(structure_res, dt_boxes, rec_res)
return pred_html
def rebuild_table(self, structure_res, dt_boxes, rec_res):
pred_structures, pred_bboxes = structure_res
matched_index = self.match_result(dt_boxes, pred_bboxes)
pred_html, pred = self.get_pred_html(pred_structures, matched_index, rec_res)
return pred_html, pred
def match_result(self, dt_boxes, pred_bboxes):
matched = {}
for i, gt_box in enumerate(dt_boxes):
# gt_box = [np.min(gt_box[:, 0]), np.min(gt_box[:, 1]), np.max(gt_box[:, 0]), np.max(gt_box[:, 1])]
distances = []
for j, pred_box in enumerate(pred_bboxes):
distances.append(
(distance(gt_box, pred_box), 1. - compute_iou(gt_box, pred_box))) # 获取两两cell之间的L1距离和 1- IOU
sorted_distances = distances.copy()
# 根据距离和IOU挑选最"近"的cell
sorted_distances = sorted(sorted_distances, key=lambda item: (item[1], item[0]))
if distances.index(sorted_distances[0]) not in matched.keys():
matched[distances.index(sorted_distances[0])] = [i]
else:
matched[distances.index(sorted_distances[0])].append(i)
return matched
def get_pred_html(self, pred_structures, matched_index, ocr_contents):
end_html = []
td_index = 0
for tag in pred_structures:
if '</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)
ppstructure/table/table_metric/__init__.py 0 → 100755
View file @ 83303bc7
# 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
ppstructure/table/table_metric/parallel.py 0 → 100755
View file @ 83303bc7
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
ppstructure/table/table_metric/table_metric.py 0 → 100755
View file @ 83303bc7
# 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)
ppstructure/table/tablepyxl/__init__.py 0 → 100644
View file @ 83303bc7
# 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
ppstructure/table/tablepyxl/style.py 0 → 100644
View file @ 83303bc7
# 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
ppstructure/table/tablepyxl/tablepyxl.py 0 → 100644
View file @ 83303bc7
# 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
ppstructure/utility.py 0 → 100644
View file @ 83303bc7
# 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("--table_max_len", type=int, default=488)
parser.add_argument("--table_model_dir", type=str)
parser.add_argument("--table_char_type", type=str, default='en')
parser.add_argument("--table_char_dict_path", type=str, default="../ppocr/utils/dict/table_structure_dict.txt")
parser.add_argument("--layout_path_model", type=str, default="lp://PubLayNet/ppyolov2_r50vd_dcn_365e_publaynet/config")
return parser
def parse_args():
parser = init_args()
return parser.parse_args()
def draw_structure_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
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
requirements.txt
View file @ 83303bc7
...@@ -3,9 +3,13 @@ scikit-image==0.17.2 ...@@ -3,9 +3,13 @@ scikit-image==0.17.2
imgaug==0.4.0 imgaug==0.4.0
pyclipper pyclipper
lmdb lmdb
opencv-python==4.2.0.32
tqdm tqdm
numpy numpy
visualdl visualdl
python-Levenshtein python-Levenshtein
opencv-contrib-python opencv-contrib-python==4.4.0.46
\ No newline at end of file cython
lxml
premailer
openpyxl
fasttext==0.9.1
\ No newline at end of file
setup.py
View file @ 83303bc7
...@@ -14,6 +14,7 @@ ...@@ -14,6 +14,7 @@
from setuptools import setup from setuptools import setup
from io import open from io import open
from paddleocr import VERSION
with open('requirements.txt', encoding="utf-8-sig") as f: with open('requirements.txt', encoding="utf-8-sig") as f:
requirements = f.readlines() requirements = f.readlines()
...@@ -32,7 +33,7 @@ setup( ...@@ -32,7 +33,7 @@ setup(
package_dir={'paddleocr': ''}, package_dir={'paddleocr': ''},
include_package_data=True, include_package_data=True,
entry_points={"console_scripts": ["paddleocr= paddleocr.paddleocr:main"]}, entry_points={"console_scripts": ["paddleocr= paddleocr.paddleocr:main"]},
version='2.0.6', version=VERSION,
install_requires=requirements, install_requires=requirements,
license='Apache License 2.0', 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', 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',
......
tests/compare_results.py 0 → 100644
View file @ 83303bc7
import numpy as np
import os
import subprocess
import json
import argparse
import glob
def init_args():
parser = argparse.ArgumentParser()
# params for testing assert allclose
parser.add_argument("--atol", type=float, default=1e-3)
parser.add_argument("--rtol", type=float, default=1e-3)
parser.add_argument("--gt_file", type=str, default="")
parser.add_argument("--log_file", type=str, default="")
parser.add_argument("--precision", type=str, default="fp32")
return parser
def parse_args():
parser = init_args()
return parser.parse_args()
def run_shell_command(cmd):
p = subprocess.Popen(
cmd, stdout=subprocess.PIPE, stderr=subprocess.PIPE, shell=True)
out, err = p.communicate()
if p.returncode == 0:
return out.decode('utf-8')
else:
return None
def parser_results_from_log_by_name(log_path, names_list):
if not os.path.exists(log_path):
raise ValueError("The log file {} does not exists!".format(log_path))
if names_list is None or len(names_list) < 1:
return []
parser_results = {}
for name in names_list:
cmd = "grep {} {}".format(name, log_path)
outs = run_shell_command(cmd)
outs = outs.split("\n")[0]
result = outs.split("{}".format(name))[-1]
try:
result = json.loads(result)
except:
result = np.array([int(r) for r in result.split()]).reshape(-1, 4)
parser_results[name] = result
return parser_results
def load_gt_from_file(gt_file):
if not os.path.exists(gt_file):
raise ValueError("The log file {} does not exists!".format(gt_file))
with open(gt_file, 'r') as f:
data = f.readlines()
f.close()
parser_gt = {}
for line in data:
image_name, result = line.strip("\n").split("\t")
image_name = image_name.split('/')[-1]
try:
result = json.loads(result)
except:
result = np.array([int(r) for r in result.split()]).reshape(-1, 4)
parser_gt[image_name] = result
return parser_gt
def load_gt_from_txts(gt_file):
gt_list = glob.glob(gt_file)
gt_collection = {}
for gt_f in gt_list:
gt_dict = load_gt_from_file(gt_f)
basename = os.path.basename(gt_f)
if "fp32" in basename:
gt_collection["fp32"] = [gt_dict, gt_f]
elif "fp16" in basename:
gt_collection["fp16"] = [gt_dict, gt_f]
elif "int8" in basename:
gt_collection["int8"] = [gt_dict, gt_f]
else:
continue
return gt_collection
def collect_predict_from_logs(log_path, key_list):
log_list = glob.glob(log_path)
pred_collection = {}
for log_f in log_list:
pred_dict = parser_results_from_log_by_name(log_f, key_list)
key = os.path.basename(log_f)
pred_collection[key] = pred_dict
return pred_collection
def testing_assert_allclose(dict_x, dict_y, atol=1e-7, rtol=1e-7):
for k in dict_x:
np.testing.assert_allclose(
np.array(dict_x[k]), np.array(dict_y[k]), atol=atol, rtol=rtol)
if __name__ == "__main__":
# Usage:
# python3.7 tests/compare_results.py --gt_file=./tests/results/*.txt --log_file=./tests/output/infer_*.log
args = parse_args()
gt_collection = load_gt_from_txts(args.gt_file)
key_list = gt_collection["fp32"][0].keys()
pred_collection = collect_predict_from_logs(args.log_file, key_list)
for filename in pred_collection.keys():
if "fp32" in filename:
gt_dict, gt_filename = gt_collection["fp32"]
elif "fp16" in filename:
gt_dict, gt_filename = gt_collection["fp16"]
elif "int8" in filename:
gt_dict, gt_filename = gt_collection["int8"]
else:
continue
pred_dict = pred_collection[filename]
try:
testing_assert_allclose(
gt_dict, pred_dict, atol=args.atol, rtol=args.rtol)
print(
"Assert allclose passed! The results of {} and {} are consistent!".
format(filename, gt_filename))
except Exception as E:
print(E)
raise ValueError(
"The results of {} and the results of {} are inconsistent!".
format(filename, gt_filename))
tests/configs/det_mv3_db.yml 0 → 100644
View file @ 83303bc7
Global:
use_gpu: false
epoch_num: 5
log_smooth_window: 20
print_batch_step: 1
save_model_dir: ./output/db_mv3/
save_epoch_step: 1200
# evaluation is run every 2000 iterations
eval_batch_step: [0, 400]
cal_metric_during_train: False
pretrained_model: ./pretrain_models/MobileNetV3_large_x0_5_pretrained
checkpoints:
save_inference_dir:
use_visualdl: False
infer_img: doc/imgs_en/img_10.jpg
save_res_path: ./output/det_db/predicts_db.txt
Architecture:
model_type: det
algorithm: DB
Transform:
Backbone:
name: MobileNetV3
scale: 0.5
model_name: large
disable_se: False
Neck:
name: DBFPN
out_channels: 256
Head:
name: DBHead
k: 50
Loss:
name: DBLoss
balance_loss: true
main_loss_type: DiceLoss
alpha: 5
beta: 10
ohem_ratio: 3
Optimizer:
name: Adam #Momentum
#momentum: 0.9
beta1: 0.9
beta2: 0.999
lr:
learning_rate: 0.001
regularizer:
name: 'L2'
factor: 0
PostProcess:
name: DBPostProcess
thresh: 0.3
box_thresh: 0.6
max_candidates: 1000
unclip_ratio: 1.5
Metric:
name: DetMetric
main_indicator: hmean
Train:
dataset:
name: SimpleDataSet
data_dir: ./train_data/icdar2015/text_localization/
label_file_list:
- ./train_data/icdar2015/text_localization/train_icdar2015_label.txt
ratio_list: [1.0]
transforms:
- DecodeImage: # load image
img_mode: BGR
channel_first: False
- DetLabelEncode: # Class handling label
- Resize:
size: [640, 640]
- MakeBorderMap:
shrink_ratio: 0.4
thresh_min: 0.3
thresh_max: 0.7
- MakeShrinkMap:
shrink_ratio: 0.4
min_text_size: 8
- NormalizeImage:
scale: 1./255.
mean: [0.485, 0.456, 0.406]
std: [0.229, 0.224, 0.225]
order: 'hwc'
- ToCHWImage:
- KeepKeys:
keep_keys: ['image', 'threshold_map', 'threshold_mask', 'shrink_map', 'shrink_mask'] # the order of the dataloader list
loader:
shuffle: False
drop_last: False
batch_size_per_card: 1
num_workers: 0
use_shared_memory: False
Eval:
dataset:
name: SimpleDataSet
data_dir: ./train_data/icdar2015/text_localization/
label_file_list:
- ./train_data/icdar2015/text_localization/test_icdar2015_label.txt
transforms:
- DecodeImage: # load image
img_mode: BGR
channel_first: False
- DetLabelEncode: # Class handling label
- DetResizeForTest:
image_shape: [736, 1280]
- NormalizeImage:
scale: 1./255.
mean: [0.485, 0.456, 0.406]
std: [0.229, 0.224, 0.225]
order: 'hwc'
- ToCHWImage:
- KeepKeys:
keep_keys: ['image', 'shape', 'polys', 'ignore_tags']
loader:
shuffle: False
drop_last: False
batch_size_per_card: 1 # must be 1
num_workers: 0
use_shared_memory: False
tests/configs/det_r50_vd_db.yml 0 → 100644
View file @ 83303bc7
Global:
use_gpu: false
epoch_num: 5
log_smooth_window: 20
print_batch_step: 1
save_model_dir: ./output/db_mv3/
save_epoch_step: 1200
# evaluation is run every 2000 iterations
eval_batch_step: [0, 400]
cal_metric_during_train: False
pretrained_model: ./pretrain_models/MobileNetV3_large_x0_5_pretrained
checkpoints:
save_inference_dir:
use_visualdl: False
infer_img: doc/imgs_en/img_10.jpg
save_res_path: ./output/det_db/predicts_db.txt
Architecture:
model_type: det
algorithm: DB
Transform:
Backbone:
name: ResNet #MobileNetV3
layers: 50
Neck:
name: DBFPN
out_channels: 256
Head:
name: DBHead
k: 50
Loss:
name: DBLoss
balance_loss: true
main_loss_type: DiceLoss
alpha: 5 #5
beta: 10 #10
ohem_ratio: 3
Optimizer:
name: Adam #Momentum
#momentum: 0.9
beta1: 0.9
beta2: 0.999
lr:
learning_rate: 0.001
regularizer:
name: 'L2'
factor: 0
PostProcess:
name: DBPostProcess
thresh: 0.3
box_thresh: 0.6
max_candidates: 1000
unclip_ratio: 1.5
Metric:
name: DetMetric
main_indicator: hmean
Train:
dataset:
name: SimpleDataSet
data_dir: ./train_data/icdar2015/text_localization/
label_file_list:
- ./train_data/icdar2015/text_localization/train_icdar2015_label.txt
ratio_list: [1.0]
transforms:
- DecodeImage: # load image
img_mode: BGR
channel_first: False
- DetLabelEncode: # Class handling label
- Resize:
# size: [640, 640]
- MakeBorderMap:
shrink_ratio: 0.4
thresh_min: 0.3
thresh_max: 0.7
- MakeShrinkMap:
shrink_ratio: 0.4
min_text_size: 8
- NormalizeImage:
scale: 1./255.
mean: [0.485, 0.456, 0.406]
std: [0.229, 0.224, 0.225]
order: 'hwc'
- ToCHWImage:
- KeepKeys:
keep_keys: ['image', 'threshold_map', 'threshold_mask', 'shrink_map', 'shrink_mask'] # the order of the dataloader list
loader:
shuffle: False
drop_last: False
batch_size_per_card: 1
num_workers: 0
use_shared_memory: False
Eval:
dataset:
name: SimpleDataSet
data_dir: ./train_data/icdar2015/text_localization/
label_file_list:
- ./train_data/icdar2015/text_localization/test_icdar2015_label.txt
transforms:
- DecodeImage: # load image
img_mode: BGR
channel_first: False
- DetLabelEncode: # Class handling label
- DetResizeForTest:
image_shape: [736, 1280]
- NormalizeImage:
scale: 1./255.
mean: [0.485, 0.456, 0.406]
std: [0.229, 0.224, 0.225]
order: 'hwc'
- ToCHWImage:
- KeepKeys:
keep_keys: ['image', 'shape', 'polys', 'ignore_tags']
loader:
shuffle: False
drop_last: False
batch_size_per_card: 1 # must be 1
num_workers: 0
use_shared_memory: False
tests/configs/rec_icdar15_r34_train.yml 0 → 100644
View file @ 83303bc7
Global:
use_gpu: true
epoch_num: 72
log_smooth_window: 20
print_batch_step: 10
save_model_dir: ./output/rec/ic15/
save_epoch_step: 3
# evaluation is run every 2000 iterations
eval_batch_step: [0, 2000]
cal_metric_during_train: True
pretrained_model:
checkpoints:
save_inference_dir: ./
use_visualdl: False
infer_img: doc/imgs_words_en/word_10.png
# for data or label process
character_dict_path: ppocr/utils/en_dict.txt
character_type: EN
max_text_length: 25
infer_mode: False
use_space_char: False
save_res_path: ./output/rec/predicts_ic15.txt
Optimizer:
name: Adam
beta1: 0.9
beta2: 0.999
lr:
learning_rate: 0.0005
regularizer:
name: 'L2'
factor: 0
Architecture:
model_type: rec
algorithm: CRNN
Transform:
Backbone:
name: ResNet
layers: 34
Neck:
name: SequenceEncoder
encoder_type: rnn
hidden_size: 256
Head:
name: CTCHead
fc_decay: 0
Loss:
name: CTCLoss
PostProcess:
name: CTCLabelDecode
Metric:
name: RecMetric
main_indicator: acc
Train:
dataset:
name: SimpleDataSet
data_dir: ./train_data/ic15_data/
label_file_list: ["./train_data/ic15_data/rec_gt_train.txt"]
transforms:
- DecodeImage: # load image
img_mode: BGR
channel_first: False
- CTCLabelEncode: # Class handling label
- RecResizeImg:
image_shape: [3, 32, 100]
- KeepKeys:
keep_keys: ['image', 'label', 'length'] # dataloader will return list in this order
loader:
shuffle: True
batch_size_per_card: 256
drop_last: True
num_workers: 8
use_shared_memory: False
Eval:
dataset:
name: SimpleDataSet
data_dir: ./train_data/ic15_data
label_file_list: ["./train_data/ic15_data/rec_gt_test.txt"]
transforms:
- DecodeImage: # load image
img_mode: BGR
channel_first: False
- CTCLabelEncode: # Class handling label
- RecResizeImg:
image_shape: [3, 32, 100]
- KeepKeys:
keep_keys: ['image', 'label', 'length'] # dataloader will return list in this order
loader:
shuffle: False
drop_last: False
batch_size_per_card: 256
num_workers: 4
use_shared_memory: False
tests/ocr_det_params.txt 0 → 100644
View file @ 83303bc7
===========================train_params===========================
model_name:ocr_det
python:python3.7
gpu_list:0|0,1
Global.use_gpu:True|True
Global.auto_cast:null
Global.epoch_num:lite_train_infer=1|whole_train_infer=300
Global.save_model_dir:./output/
Train.loader.batch_size_per_card:lite_train_infer=2|whole_train_infer=4
Global.pretrained_model:null
train_model_name:latest
train_infer_img_dir:./train_data/icdar2015/text_localization/ch4_test_images/
null:null
##
trainer:norm_train|pact_train|fpgm_train
norm_train:tools/train.py -c tests/configs/det_mv3_db.yml -o Global.pretrained_model=./pretrain_models/MobileNetV3_large_x0_5_pretrained
pact_train:deploy/slim/quantization/quant.py -c tests/configs/det_mv3_db.yml -o
fpgm_train:deploy/slim/prune/sensitivity_anal.py -c tests/configs/det_mv3_db.yml -o Global.pretrained_model=./pretrain_models/det_mv3_db_v2.0_train/best_accuracy
distill_train:null
null:null
null:null
##
===========================eval_params===========================
eval:null
null:null
##
===========================infer_params===========================
Global.save_inference_dir:./output/
Global.pretrained_model:
norm_export:tools/export_model.py -c tests/configs/det_mv3_db.yml -o
quant_export:deploy/slim/quantization/export_model.py -c tests/configs/det_mv3_db.yml -o
fpgm_export:deploy/slim/prune/export_prune_model.py -c tests/configs/det_mv3_db.yml -o
distill_export:null
export1:null
export2:null
##
train_model:./inference/ch_ppocr_mobile_v2.0_det_train/best_accuracy
infer_export:tools/export_model.py -c configs/det/ch_ppocr_v2.0/ch_det_mv3_db_v2.0.yml -o
infer_quant:False
inference:tools/infer/predict_det.py
--use_gpu:True|False
--enable_mkldnn:True|False
--cpu_threads:6
--rec_batch_num:1
--use_tensorrt:False|True
--precision:fp32|fp16|int8
--det_model_dir:
--image_dir:./inference/ch_det_data_50/all-sum-510/
--save_log_path:null
--benchmark:True
null:null
===========================cpp_infer_params===========================
use_opencv:True
infer_model:./inference/ch_ppocr_mobile_v2.0_det_infer/
infer_quant:False
inference:./deploy/cpp_infer/build/ppocr det
--use_gpu:True|False
--enable_mkldnn:True|False
--cpu_threads:1|6
--rec_batch_num:1
--use_tensorrt:False|True
--precision:fp32|fp16
--det_model_dir:
--image_dir:./inference/ch_det_data_50/all-sum-510/
null:null
--benchmark:True
===========================serving_params===========================
trans_model:-m paddle_serving_client.convert
--dirname:./inference/ch_ppocr_mobile_v2.0_det_infer/
--model_filename:inference.pdmodel
--params_filename:inference.pdiparams
--serving_server:./deploy/pdserving/ppocr_det_mobile_2.0_serving/
--serving_client:./deploy/pdserving/ppocr_det_mobile_2.0_client/
serving_dir:./deploy/pdserving
web_service:web_service_det.py --config=config.yml --opt op.det.concurrency=1
op.det.local_service_conf.devices:null|0
op.det.local_service_conf.use_mkldnn:True|False
op.det.local_service_conf.thread_num:1|6
op.det.local_service_conf.use_trt:False|True
op.det.local_service_conf.precision:fp32|fp16|int8
pipline:pipeline_http_client.py --image_dir=../../doc/imgs
tests/ocr_det_server_params.txt 0 → 100644
View file @ 83303bc7
===========================train_params===========================
model_name:ocr_server_det
python:python3.7
gpu_list:0|0,1
Global.use_gpu:True|True
Global.auto_cast:null
Global.epoch_num:lite_train_infer=2|whole_train_infer=300
Global.save_model_dir:./output/
Train.loader.batch_size_per_card:lite_train_infer=2|whole_train_infer=4
Global.pretrained_model:null
train_model_name:latest
train_infer_img_dir:./train_data/icdar2015/text_localization/ch4_test_images/
null:null
##
trainer:norm_train|pact_train
norm_train:tools/train.py -c tests/configs/det_r50_vd_db.yml -o Global.pretrained_model=""
pact_train:null
fpgm_train:null
distill_train:null
null:null
null:null
##
===========================eval_params===========================
eval:tools/eval.py -c tests/configs/det_r50_vd_db.yml -o
null:null
##
===========================infer_params===========================
Global.save_inference_dir:./output/
Global.pretrained_model:
norm_export:tools/export_model.py -c tests/configs/det_r50_vd_db.yml -o
quant_export:null
fpgm_export:null
distill_export:null
export1:null
export2:null
##
infer_model:./inference/ch_ppocr_server_v2.0_det_infer/
infer_export:null
infer_quant:False
inference:tools/infer/predict_det.py
--use_gpu:True|False
--enable_mkldnn:True|False
--cpu_threads:1|6
--rec_batch_num:1
--use_tensorrt:False|True
--precision:fp32|fp16|int8
--det_model_dir:
--image_dir:./inference/ch_det_data_50/all-sum-510/
--save_log_path:null
--benchmark:True
null:null
tests/ocr_kl_quant_params.txt 0 → 100644
View file @ 83303bc7
===========================train_params===========================
model_name:ocr_system
python:python3.7
gpu_list:null
Global.use_gpu:null
Global.auto_cast:null
Global.epoch_num:null
Global.save_model_dir:./output/
Train.loader.batch_size_per_card:null
Global.pretrained_model:null
train_model_name:null
train_infer_img_dir:null
null:null
##
trainer:
norm_train:null
pact_train:null
fpgm_train:null
distill_train:null
null:null
null:null
##
===========================eval_params===========================
eval:null
null:null
##
===========================infer_params===========================
Global.save_inference_dir:./output/
Global.pretrained_model:
norm_export:null
quant_export:null
fpgm_export:null
distill_export:null
export1:null
export2:null
##
infer_model:./inference/ch_ppocr_mobile_v2.0_det_infer/
kl_quant:deploy/slim/quantization/quant_kl.py -c configs/det/ch_ppocr_v2.0/ch_det_mv3_db_v2.0.yml -o
infer_quant:True
inference:tools/infer/predict_det.py
--use_gpu:TrueFalse
--enable_mkldnn:True|False
--cpu_threads:1|6
--rec_batch_num:1
--use_tensorrt:False|True
--precision:fp32|fp16|int8
--det_model_dir:
--image_dir:./inference/ch_det_data_50/all-sum-510/
--save_log_path:null
--benchmark:True
null:null
tests/ocr_ppocr_mobile_params.txt 0 → 100644
View file @ 83303bc7
===========================train_params===========================
model_name:ocr_system_mobile
python:python3.7
gpu_list:null
Global.use_gpu:null
Global.auto_cast:null
Global.epoch_num:null
Global.save_model_dir:./output/
Train.loader.batch_size_per_card:null
Global.pretrained_model:null
train_model_name:null
train_infer_img_dir:null
null:null
##
trainer:
norm_train:null
pact_train:null
fpgm_train:null
distill_train:null
null:null
null:null
##
===========================eval_params===========================
eval:null
null:null
##
===========================infer_params===========================
Global.save_inference_dir:./output/
Global.pretrained_model:
norm_export:null
quant_export:null
fpgm_export:null
distill_export:null
export1:null
export2:null
##
infer_model:./inference/ch_ppocr_mobile_v2.0_det_infer/
infer_export:null
infer_quant:False
inference:tools/infer/predict_system.py
--use_gpu:True
--enable_mkldnn:True|False
--cpu_threads:1|6
--rec_batch_num:1
--use_tensorrt:False|True
--precision:fp32|fp16|int8
--det_model_dir:
--image_dir:./inference/ch_det_data_50/all-sum-510/
--save_log_path:null
--benchmark:True
--rec_model_dir:./inference/ch_ppocr_mobile_v2.0_rec_infer/
===========================cpp_infer_params===========================
use_opencv:True
infer_model:./inference/ch_ppocr_mobile_v2.0_det_infer/
infer_quant:False
inference:./deploy/cpp_infer/build/ppocr system
--use_gpu:True|False
--enable_mkldnn:True|False
--cpu_threads:1|6
--rec_batch_num:1
--use_tensorrt:False|True
--precision:fp32|fp16
--det_model_dir:
--image_dir:./inference/ch_det_data_50/all-sum-510/
--rec_model_dir:./inference/ch_ppocr_mobile_v2.0_rec_infer/
--benchmark:True
tests/ocr_ppocr_server_params.txt 0 → 100644
View file @ 83303bc7
===========================train_params===========================
model_name:ocr_system_server
python:python3.7
gpu_list:null
Global.use_gpu:null
Global.auto_cast:null
Global.epoch_num:null
Global.save_model_dir:./output/
Train.loader.batch_size_per_card:null
Global.pretrained_model:null
train_model_name:null
train_infer_img_dir:null
null:null
##
trainer:
norm_train:null
pact_train:null
fpgm_train:null
distill_train:null
null:null
null:null
##
===========================eval_params===========================
eval:null
null:null
##
===========================infer_params===========================
Global.save_inference_dir:./output/
Global.pretrained_model:
norm_export:null
quant_export:null
fpgm_export:null
distill_export:null
export1:null
export2:null
##
infer_model:./inference/ch_ppocr_server_v2.0_det_infer/
infer_export:null
infer_quant:False
inference:tools/infer/predict_system.py
--use_gpu:True
--enable_mkldnn:True|False
--cpu_threads:1|6
--rec_batch_num:1
--use_tensorrt:False|True
--precision:fp32|fp16|int8
--det_model_dir:
--image_dir:./inference/ch_det_data_50/all-sum-510/
--save_log_path:null
--benchmark:True
--rec_model_dir:./inference/ch_ppocr_server_v2.0_rec_infer/
===========================cpp_infer_params===========================
use_opencv:True
infer_model:./inference/ch_ppocr_server_v2.0_det_infer/
infer_quant:False
inference:./deploy/cpp_infer/build/ppocr system
--use_gpu:True|False
--enable_mkldnn:True|False
--cpu_threads:1|6
--rec_batch_num:1
--use_tensorrt:False|True
--precision:fp32|fp16
--det_model_dir:
--image_dir:./inference/ch_det_data_50/all-sum-510/
--rec_model_dir:./inference/ch_ppocr_server_v2.0_rec_infer/
--benchmark:True
\ No newline at end of file
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