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