mirror of
https://github.com/PaddlePaddle/PaddleOCR.git
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222 lines
8.4 KiB
Python
222 lines
8.4 KiB
Python
# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import os
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import sys
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import subprocess
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__dir__ = os.path.dirname(os.path.abspath(__file__))
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sys.path.append(__dir__)
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sys.path.append(os.path.abspath(os.path.join(__dir__, '..')))
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sys.path.append(os.path.abspath(os.path.join(__dir__, '../..')))
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os.environ["FLAGS_allocator_strategy"] = 'auto_growth'
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import cv2
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import copy
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import numpy as np
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import time
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import tools.infer.predict_rec as predict_rec
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import tools.infer.predict_det as predict_det
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from ppocr.utils.utility import get_image_file_list, check_and_read_gif
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from ppocr.utils.logging import get_logger
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from ppstructure.table.matcher import distance, compute_iou
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from ppstructure.utility import parse_args
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import ppstructure.table.predict_structure as predict_strture
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logger = get_logger()
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def expand(pix, det_box, shape):
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x0, y0, x1, y1 = det_box
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# print(shape)
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h, w, c = shape
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tmp_x0 = x0 - pix
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tmp_x1 = x1 + pix
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tmp_y0 = y0 - pix
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tmp_y1 = y1 + pix
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x0_ = tmp_x0 if tmp_x0 >= 0 else 0
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x1_ = tmp_x1 if tmp_x1 <= w else w
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y0_ = tmp_y0 if tmp_y0 >= 0 else 0
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y1_ = tmp_y1 if tmp_y1 <= h else h
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return x0_, y0_, x1_, y1_
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class TableSystem(object):
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def __init__(self, args, text_detector=None, text_recognizer=None):
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self.text_detector = predict_det.TextDetector(args) if text_detector is None else text_detector
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self.text_recognizer = predict_rec.TextRecognizer(args) if text_recognizer is None else text_recognizer
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self.table_structurer = predict_strture.TableStructurer(args)
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def __call__(self, img):
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ori_im = img.copy()
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structure_res, elapse = self.table_structurer(copy.deepcopy(img))
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dt_boxes, elapse = self.text_detector(copy.deepcopy(img))
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dt_boxes = sorted_boxes(dt_boxes)
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r_boxes = []
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for box in dt_boxes:
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x_min = box[:, 0].min() - 1
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x_max = box[:, 0].max() + 1
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y_min = box[:, 1].min() - 1
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y_max = box[:, 1].max() + 1
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box = [x_min, y_min, x_max, y_max]
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r_boxes.append(box)
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dt_boxes = np.array(r_boxes)
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logger.debug("dt_boxes num : {}, elapse : {}".format(
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len(dt_boxes), elapse))
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if dt_boxes is None:
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return None, None
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img_crop_list = []
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for i in range(len(dt_boxes)):
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det_box = dt_boxes[i]
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x0, y0, x1, y1 = expand(2, det_box, ori_im.shape)
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text_rect = ori_im[int(y0):int(y1), int(x0):int(x1), :]
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img_crop_list.append(text_rect)
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rec_res, elapse = self.text_recognizer(img_crop_list)
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logger.debug("rec_res num : {}, elapse : {}".format(
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len(rec_res), elapse))
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pred_html, pred = self.rebuild_table(structure_res, dt_boxes, rec_res)
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return pred_html
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def rebuild_table(self, structure_res, dt_boxes, rec_res):
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pred_structures, pred_bboxes = structure_res
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matched_index = self.match_result(dt_boxes, pred_bboxes)
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pred_html, pred = self.get_pred_html(pred_structures, matched_index, rec_res)
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return pred_html, pred
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def match_result(self, dt_boxes, pred_bboxes):
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matched = {}
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for i, gt_box in enumerate(dt_boxes):
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# gt_box = [np.min(gt_box[:, 0]), np.min(gt_box[:, 1]), np.max(gt_box[:, 0]), np.max(gt_box[:, 1])]
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distances = []
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for j, pred_box in enumerate(pred_bboxes):
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distances.append(
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(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
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sorted_distances = distances.copy()
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# 根据距离和IOU挑选最"近"的cell
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sorted_distances = sorted(sorted_distances, key=lambda item: (item[1], item[0]))
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if distances.index(sorted_distances[0]) not in matched.keys():
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matched[distances.index(sorted_distances[0])] = [i]
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else:
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matched[distances.index(sorted_distances[0])].append(i)
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return matched
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def get_pred_html(self, pred_structures, matched_index, ocr_contents):
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end_html = []
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td_index = 0
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for tag in pred_structures:
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if '</td>' in tag:
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if td_index in matched_index.keys():
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b_with = False
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if '<b>' in ocr_contents[matched_index[td_index][0]] and len(matched_index[td_index]) > 1:
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b_with = True
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end_html.extend('<b>')
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for i, td_index_index in enumerate(matched_index[td_index]):
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content = ocr_contents[td_index_index][0]
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if len(matched_index[td_index]) > 1:
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if len(content) == 0:
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continue
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if content[0] == ' ':
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content = content[1:]
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if '<b>' in content:
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content = content[3:]
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if '</b>' in content:
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content = content[:-4]
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if len(content) == 0:
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continue
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if i != len(matched_index[td_index]) - 1 and ' ' != content[-1]:
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content += ' '
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end_html.extend(content)
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if b_with:
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end_html.extend('</b>')
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end_html.append(tag)
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td_index += 1
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else:
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end_html.append(tag)
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return ''.join(end_html), end_html
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def sorted_boxes(dt_boxes):
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"""
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Sort text boxes in order from top to bottom, left to right
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args:
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dt_boxes(array):detected text boxes with shape [4, 2]
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return:
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sorted boxes(array) with shape [4, 2]
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"""
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num_boxes = dt_boxes.shape[0]
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sorted_boxes = sorted(dt_boxes, key=lambda x: (x[0][1], x[0][0]))
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_boxes = list(sorted_boxes)
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for i in range(num_boxes - 1):
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if abs(_boxes[i + 1][0][1] - _boxes[i][0][1]) < 10 and \
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(_boxes[i + 1][0][0] < _boxes[i][0][0]):
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tmp = _boxes[i]
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_boxes[i] = _boxes[i + 1]
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_boxes[i + 1] = tmp
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return _boxes
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def to_excel(html_table, excel_path):
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from tablepyxl import tablepyxl
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tablepyxl.document_to_xl(html_table, excel_path)
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def main(args):
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image_file_list = get_image_file_list(args.image_dir)
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image_file_list = image_file_list[args.process_id::args.total_process_num]
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os.makedirs(args.output, exist_ok=True)
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text_sys = TableSystem(args)
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img_num = len(image_file_list)
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for i, image_file in enumerate(image_file_list):
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logger.info("[{}/{}] {}".format(i, img_num, image_file))
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img, flag = check_and_read_gif(image_file)
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excel_path = os.path.join(args.output, os.path.basename(image_file).split('.')[0] + '.xlsx')
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if not flag:
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img = cv2.imread(image_file)
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if img is None:
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logger.error("error in loading image:{}".format(image_file))
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continue
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starttime = time.time()
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pred_html = text_sys(img)
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to_excel(pred_html, excel_path)
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logger.info('excel saved to {}'.format(excel_path))
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logger.info(pred_html)
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elapse = time.time() - starttime
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logger.info("Predict time : {:.3f}s".format(elapse))
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if __name__ == "__main__":
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args = parse_args()
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if args.use_mp:
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p_list = []
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total_process_num = args.total_process_num
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for process_id in range(total_process_num):
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cmd = [sys.executable, "-u"] + sys.argv + [
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"--process_id={}".format(process_id),
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"--use_mp={}".format(False)
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]
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p = subprocess.Popen(cmd, stdout=sys.stdout, stderr=sys.stdout)
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p_list.append(p)
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for p in p_list:
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p.wait()
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else:
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main(args)
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