# 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)