# 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.insert(0, os.path.abspath(os.path.join(__dir__, "../")))
os.environ["FLAGS_allocator_strategy"] = "auto_growth"
import cv2
import json
import numpy as np
import time
import logging
from copy import deepcopy
from paddle.utils import try_import
from ppocr.utils.utility import get_image_file_list, check_and_read
from ppocr.utils.logging import get_logger
from ppocr.utils.visual import draw_ser_results, draw_re_results
from tools.infer.predict_system import TextSystem
from tools.infer.predict_rec import TextRecognizer
from ppstructure.layout.predict_layout import LayoutPredictor
from ppstructure.table.predict_table import TableSystem, to_excel
from ppstructure.utility import parse_args, draw_structure_result, cal_ocr_word_box
logger = get_logger()
class StructureSystem(object):
def __init__(self, args):
self.mode = args.mode
self.recovery = args.recovery
self.image_orientation_predictor = None
if args.image_orientation:
import paddleclas
self.image_orientation_predictor = paddleclas.PaddleClas(
model_name="text_image_orientation"
)
if self.mode == "structure":
if not args.show_log:
logger.setLevel(logging.INFO)
if args.layout == False and args.ocr == True:
args.ocr = False
logger.warning(
"When args.layout is false, args.ocr is automatically set to false"
)
# init model
self.layout_predictor = None
self.text_system = None
self.table_system = None
self.formula_system = None
if args.layout:
self.layout_predictor = LayoutPredictor(args)
if args.ocr:
self.text_system = TextSystem(args)
if args.table:
if self.text_system is not None:
self.table_system = TableSystem(
args,
self.text_system.text_detector,
self.text_system.text_recognizer,
)
else:
self.table_system = TableSystem(args)
if args.formula:
args_formula = deepcopy(args)
args_formula.rec_algorithm = args.formula_algorithm
args_formula.rec_model_dir = args.formula_model_dir
args_formula.rec_char_dict_path = args.formula_char_dict_path
args_formula.rec_batch_num = args.formula_batch_num
self.formula_system = TextRecognizer(args_formula)
elif self.mode == "kie":
from ppstructure.kie.predict_kie_token_ser_re import SerRePredictor
self.kie_predictor = SerRePredictor(args)
self.return_word_box = args.return_word_box
def __call__(self, img, return_ocr_result_in_table=False, img_idx=0):
time_dict = {
"image_orientation": 0,
"layout": 0,
"table": 0,
"table_match": 0,
"formula": 0,
"det": 0,
"rec": 0,
"kie": 0,
"all": 0,
}
start = time.time()
if self.image_orientation_predictor is not None:
tic = time.time()
cls_result = self.image_orientation_predictor.predict(input_data=img)
cls_res = next(cls_result)
angle = cls_res[0]["label_names"][0]
cv_rotate_code = {
"90": cv2.ROTATE_90_COUNTERCLOCKWISE,
"180": cv2.ROTATE_180,
"270": cv2.ROTATE_90_CLOCKWISE,
}
if angle in cv_rotate_code:
img = cv2.rotate(img, cv_rotate_code[angle])
toc = time.time()
time_dict["image_orientation"] = toc - tic
if self.mode == "structure":
ori_im = img.copy()
if self.layout_predictor is not None:
layout_res, elapse = self.layout_predictor(img)
time_dict["layout"] += elapse
else:
h, w = ori_im.shape[:2]
layout_res = [dict(bbox=None, label="table", score=0.0)]
# As reported in issues such as #10270 and #11665, the old
# implementation, which recognizes texts from the layout regions,
# has problems with OCR recognition accuracy.
#
# To enhance the OCR recognition accuracy, we implement a patch fix
# that first use text_system to detect and recognize all text information
# and then filter out relevant texts according to the layout regions.
text_res = None
if self.text_system is not None:
text_res, ocr_time_dict = self._predict_text(img)
time_dict["det"] += ocr_time_dict["det"]
time_dict["rec"] += ocr_time_dict["rec"]
res_list = []
for region in layout_res:
res = ""
if region["bbox"] is not None:
x1, y1, x2, y2 = region["bbox"]
x1, y1, x2, y2 = int(x1), int(y1), int(x2), int(y2)
roi_img = ori_im[y1:y2, x1:x2, :]
else:
x1, y1, x2, y2 = 0, 0, w, h
roi_img = ori_im
bbox = [x1, y1, x2, y2]
if region["label"] == "table":
if self.table_system is not None:
res, table_time_dict = self.table_system(
roi_img, return_ocr_result_in_table
)
time_dict["table"] += table_time_dict["table"]
time_dict["table_match"] += table_time_dict["match"]
time_dict["det"] += table_time_dict["det"]
time_dict["rec"] += table_time_dict["rec"]
elif region["label"] == "equation" and self.formula_system is not None:
latex_res, formula_time = self.formula_system([roi_img])
time_dict["formula"] += formula_time
res = {"latex": latex_res[0]}
else:
if text_res is not None:
# Filter the text results whose regions intersect with the current layout bbox.
res = self._filter_text_res(text_res, bbox)
res_list.append(
{
"type": region["label"].lower(),
"bbox": bbox,
"img": roi_img,
"res": res,
"img_idx": img_idx,
"score": region["score"],
}
)
end = time.time()
time_dict["all"] = end - start
return res_list, time_dict
elif self.mode == "kie":
re_res, elapse = self.kie_predictor(img)
time_dict["kie"] = elapse
time_dict["all"] = elapse
return re_res[0], time_dict
return None, None
def _predict_text(self, img):
filter_boxes, filter_rec_res, ocr_time_dict = self.text_system(img)
# remove style char,
# when using the recognition model trained on the PubtabNet dataset,
# it will recognize the text format in the table, such as <b>
style_token = [
"<strike>",
"<strike>",
"<sup>",
"</sub>",
"<b>",
"</b>",
"<sub>",
"</sup>",
"<overline>",
"</overline>",
"<underline>",
"</underline>",
"<i>",
"</i>",
]
res = []
for box, rec_res in zip(filter_boxes, filter_rec_res):
rec_str, rec_conf = rec_res[0], rec_res[1]
for token in style_token:
if token in rec_str:
rec_str = rec_str.replace(token, "")
if self.return_word_box:
word_box_content_list, word_box_list = cal_ocr_word_box(
rec_str, box, rec_res[2]
)
res.append(
{
"text": rec_str,
"confidence": float(rec_conf),
"text_region": box.tolist(),
"text_word": word_box_content_list,
"text_word_region": word_box_list,
}
)
else:
res.append(
{
"text": rec_str,
"confidence": float(rec_conf),
"text_region": box.tolist(),
}
)
return res, ocr_time_dict
def _filter_text_res(self, text_res, bbox):
res = []
for r in text_res:
box = r["text_region"]
rect = box[0][0], box[0][1], box[2][0], box[2][1]
if self._has_intersection(bbox, rect):
res.append(r)
return res
def _has_intersection(self, rect1, rect2):
x_min1, y_min1, x_max1, y_max1 = rect1
x_min2, y_min2, x_max2, y_max2 = rect2
if x_min1 > x_max2 or x_max1 < x_min2:
return False
if y_min1 > y_max2 or y_max1 < y_min2:
return False
return True
def save_structure_res(res, save_folder, img_name, img_idx=0):
excel_save_folder = os.path.join(save_folder, img_name)
os.makedirs(excel_save_folder, exist_ok=True)
res_cp = deepcopy(res)
# save res
with open(
os.path.join(excel_save_folder, "res_{}.txt".format(img_idx)),
"w",
encoding="utf8",
) as f:
for region in res_cp:
roi_img = region.pop("img")
f.write("{}\n".format(json.dumps(region)))
if (
region["type"].lower() == "table"
and len(region["res"]) > 0
and "html" in region["res"]
):
excel_path = os.path.join(
excel_save_folder, "{}_{}.xlsx".format(region["bbox"], img_idx)
)
to_excel(region["res"]["html"], excel_path)
elif region["type"].lower() == "figure":
img_path = os.path.join(
excel_save_folder, "{}_{}.jpg".format(region["bbox"], img_idx)
)
cv2.imwrite(img_path, roi_img)
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]
if not args.use_pdf2docx_api:
structure_sys = StructureSystem(args)
save_folder = os.path.join(args.output, structure_sys.mode)
os.makedirs(save_folder, exist_ok=True)
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_gif, flag_pdf = check_and_read(image_file)
img_name = os.path.basename(image_file).split(".")[0]
if args.recovery and args.use_pdf2docx_api and flag_pdf:
try_import("pdf2docx")
from pdf2docx.converter import Converter
os.makedirs(args.output, exist_ok=True)
docx_file = os.path.join(args.output, "{}_api.docx".format(img_name))
cv = Converter(image_file)
cv.convert(docx_file)
cv.close()
logger.info("docx save to {}".format(docx_file))
continue
if not flag_gif and not flag_pdf:
img = cv2.imread(image_file)
if not flag_pdf:
if img is None:
logger.error("error in loading image:{}".format(image_file))
continue
imgs = [img]
else:
imgs = img
all_res = []
for index, img in enumerate(imgs):
res, time_dict = structure_sys(img, img_idx=index)
img_save_path = os.path.join(
save_folder, img_name, "show_{}.jpg".format(index)
)
os.makedirs(os.path.join(save_folder, img_name), exist_ok=True)
if structure_sys.mode == "structure" and res != []:
draw_img = draw_structure_result(img, res, args.vis_font_path)
save_structure_res(res, save_folder, img_name, index)
elif structure_sys.mode == "kie":
if structure_sys.kie_predictor.predictor is not None:
draw_img = draw_re_results(img, res, font_path=args.vis_font_path)
else:
draw_img = draw_ser_results(img, res, font_path=args.vis_font_path)
with open(
os.path.join(save_folder, img_name, "res_{}_kie.txt".format(index)),
"w",
encoding="utf8",
) as f:
res_str = "{}\t{}\n".format(
image_file, json.dumps({"ocr_info": res}, ensure_ascii=False)
)
f.write(res_str)
if res != []:
cv2.imwrite(img_save_path, draw_img)
logger.info("result save to {}".format(img_save_path))
if args.recovery and res != []:
from ppstructure.recovery.recovery_to_doc import (
sorted_layout_boxes,
convert_info_docx,
)
from ppstructure.recovery.recovery_to_markdown import (
convert_info_markdown,
)
h, w, _ = img.shape
res = sorted_layout_boxes(res, w)
all_res += res
if args.recovery and all_res != []:
try:
convert_info_docx(img, all_res, save_folder, img_name)
if args.recovery_to_markdown:
convert_info_markdown(all_res, save_folder, img_name)
except Exception as ex:
logger.error(
"error in layout recovery image:{}, err msg: {}".format(
image_file, ex
)
)
continue
logger.info("Predict time : {:.3f}s".format(time_dict["all"]))
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)