# 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.insert(0, os.path.abspath(os.path.join(__dir__, "..")))
sys.path.insert(0, os.path.abspath(os.path.join(__dir__, "../..")))
os.environ["FLAGS_allocator_strategy"] = "auto_growth"
import cv2
import copy
import logging
import numpy as np
import time
import tools.infer.predict_rec as predict_rec
import tools.infer.predict_det as predict_det
import tools.infer.utility as utility
from tools.infer.predict_system import sorted_boxes
from ppocr.utils.utility import get_image_file_list, check_and_read
from ppocr.utils.logging import get_logger
from ppstructure.table.matcher import TableMatch
from ppstructure.table.table_master_match import TableMasterMatcher
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.args = args
if not args.show_log:
logger.setLevel(logging.INFO)
benchmark_tmp = False
if args.benchmark:
benchmark_tmp = args.benchmark
args.benchmark = False
self.text_detector = (
predict_det.TextDetector(copy.deepcopy(args))
if text_detector is None
else text_detector
)
self.text_recognizer = (
predict_rec.TextRecognizer(copy.deepcopy(args))
if text_recognizer is None
else text_recognizer
)
if benchmark_tmp:
args.benchmark = True
self.table_structurer = predict_strture.TableStructurer(args)
if args.table_algorithm in ["TableMaster"]:
self.match = TableMasterMatcher()
else:
self.match = TableMatch(filter_ocr_result=True)
(
self.predictor,
self.input_tensor,
self.output_tensors,
self.config,
) = utility.create_predictor(args, "table", logger)
def __call__(self, img, return_ocr_result_in_table=False):
result = dict()
time_dict = {"det": 0, "rec": 0, "table": 0, "all": 0, "match": 0}
start = time.time()
structure_res, elapse = self._structure(copy.deepcopy(img))
result["cell_bbox"] = structure_res[1].tolist()
time_dict["table"] = elapse
dt_boxes, rec_res, det_elapse, rec_elapse = self._ocr(copy.deepcopy(img))
time_dict["det"] = det_elapse
time_dict["rec"] = rec_elapse
if return_ocr_result_in_table:
result["boxes"] = [x.tolist() for x in dt_boxes]
result["rec_res"] = rec_res
tic = time.time()
pred_html = self.match(structure_res, dt_boxes, rec_res)
toc = time.time()
time_dict["match"] = toc - tic
result["html"] = pred_html
end = time.time()
time_dict["all"] = end - start
return result, time_dict
def _structure(self, img):
structure_res, elapse = self.table_structurer(copy.deepcopy(img))
return structure_res, elapse
def _ocr(self, img):
h, w = img.shape[:2]
dt_boxes, det_elapse = self.text_detector(copy.deepcopy(img))
dt_boxes = sorted_boxes(dt_boxes)
r_boxes = []
for box in dt_boxes:
x_min = max(0, box[:, 0].min() - 1)
x_max = min(w, box[:, 0].max() + 1)
y_min = max(0, box[:, 1].min() - 1)
y_max = min(h, 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), det_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, img.shape)
text_rect = img[int(y0) : int(y1), int(x0) : int(x1), :]
img_crop_list.append(text_rect)
rec_res, rec_elapse = self.text_recognizer(img_crop_list)
logger.debug("rec_res num : {}, elapse : {}".format(len(rec_res), rec_elapse))
return dt_boxes, rec_res, det_elapse, rec_elapse
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)
table_sys = TableSystem(args)
img_num = len(image_file_list)
f_html = open(os.path.join(args.output, "show.html"), mode="w", encoding="utf-8")
f_html.write("<html>\n<body>\n")
f_html.write('<table border="1">\n')
f_html.write(
'<meta http-equiv="Content-Type" content="text/html; charset=utf-8" />'
)
f_html.write("<tr>\n")
f_html.write("<td>Crop_img name\n")
f_html.write("<td>ori image</td>")
f_html.write("<td>table html</td>")
f_html.write("<td>cell box</td>")
f_html.write("</tr>\n")
for i, image_file in enumerate(image_file_list):
logger.info("[{}/{}] {}".format(i, img_num, image_file))
img, flag, _ = check_and_read(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_res, _ = table_sys(img)
pred_html = pred_res["html"]
logger.info(pred_html)
to_excel(pred_html, excel_path)
logger.info("excel saved to {}".format(excel_path))
elapse = time.time() - starttime
logger.info("Predict time : {:.3f}s".format(elapse))
if len(pred_res["cell_bbox"]) > 0 and len(pred_res["cell_bbox"][0]) == 4:
img = predict_strture.draw_rectangle(image_file, pred_res["cell_bbox"])
else:
img = utility.draw_boxes(img, pred_res["cell_bbox"])
img_save_path = os.path.join(args.output, os.path.basename(image_file))
cv2.imwrite(img_save_path, img)
f_html.write("<tr>\n")
f_html.write(f"<td> {os.path.basename(image_file)} <br/>\n")
f_html.write(f'<td><Crop_img src="{image_file}" width=640></td>\n')
f_html.write(
'<td><table border="1">'
+ pred_html.replace("<html><body><table>", "").replace(
"</table></body></html>", ""
)
+ "</table></td>\n"
)
f_html.write(f'<td><Crop_img src="{os.path.basename(image_file)}" width=640></td>\n')
f_html.write("</tr>\n")
f_html.write("</table>\n")
f_html.close()
if args.benchmark:
table_sys.table_structurer.autolog.report()
if __name__ == "__main__":
args = parse_args()
if args.use_mp:
import subprocess
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)