Merge branch 'dygraph' into bm_dyg

ppstructure/paddlestructure.py

+# 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 logging
+import os
+import sys
+
+__dir__ = os.path.dirname(__file__)
+sys.path.append(__dir__)
+sys.path.append(os.path.join(__dir__, '..'))
+
+import cv2
+import numpy as np
+from pathlib import Path
+
+from ppocr.utils.logging import get_logger
+from ppstructure.predict_system import OCRSystem, save_res
+from ppstructure.table.predict_table import to_excel
+from ppstructure.utility import init_args, draw_result
+
+logger = get_logger()
+from ppocr.utils.utility import check_and_read_gif, get_image_file_list
+from ppocr.utils.network import maybe_download, download_with_progressbar, confirm_model_dir_url, is_link
+
+__all__ = ['PaddleStructure', 'draw_result', 'to_excel']
+
+VERSION = '2.1'
+BASE_DIR = os.path.expanduser("~/.paddlestructure/")
+
+model_urls = {
+    'det': 'https://paddleocr.bj.bcebos.com/dygraph_v2.0/table/en_ppocr_mobile_v2.0_table_det_infer.tar',
+    'rec': 'https://paddleocr.bj.bcebos.com/dygraph_v2.0/table/en_ppocr_mobile_v2.0_table_rec_infer.tar',
+    'structure': 'https://paddleocr.bj.bcebos.com/dygraph_v2.0/table/en_ppocr_mobile_v2.0_table_structure_infer.tar'
+
+}
+
+
+def parse_args(mMain=True):
+    import argparse
+    parser = init_args()
+    parser.add_help = mMain
+
+    for action in parser._actions:
+        if action.dest in ['rec_char_dict_path', 'structure_char_dict_path']:
+            action.default = None
+    if mMain:
+        return parser.parse_args()
+    else:
+        inference_args_dict = {}
+        for action in parser._actions:
+            inference_args_dict[action.dest] = action.default
+        return argparse.Namespace(**inference_args_dict)
+
+
+class PaddleStructure(OCRSystem):
+    def __init__(self, **kwargs):
+        params = parse_args(mMain=False)
+        params.__dict__.update(**kwargs)
+        if params.show_log:
+            logger.setLevel(logging.DEBUG)
+        params.use_angle_cls = False
+        # init model dir
+        params.det_model_dir, det_url = confirm_model_dir_url(params.det_model_dir,
+                                                              os.path.join(BASE_DIR, VERSION, 'det'),
+                                                              model_urls['det'])
+        params.rec_model_dir, rec_url = confirm_model_dir_url(params.rec_model_dir,
+                                                              os.path.join(BASE_DIR, VERSION, 'rec'),
+                                                              model_urls['rec'])
+        params.structure_model_dir, structure_url = confirm_model_dir_url(params.structure_model_dir,
+                                                                          os.path.join(BASE_DIR, VERSION, 'structure'),
+                                                                          model_urls['structure'])
+        # download model
+        maybe_download(params.det_model_dir, det_url)
+        maybe_download(params.rec_model_dir, rec_url)
+        maybe_download(params.structure_model_dir, structure_url)
+
+        if params.rec_char_dict_path is None:
+            params.rec_char_type = 'EN'
+            if os.path.exists(str(Path(__file__).parent / 'ppocr/utils/dict/table_dict.txt')):
+                params.rec_char_dict_path = str(Path(__file__).parent / 'ppocr/utils/dict/table_dict.txt')
+            else:
+                params.rec_char_dict_path = str(Path(__file__).parent.parent / 'ppocr/utils/dict/table_dict.txt')
+        if params.structure_char_dict_path is None:
+            if os.path.exists(str(Path(__file__).parent / 'ppocr/utils/dict/table_structure_dict.txt')):
+                params.structure_char_dict_path = str(
+                    Path(__file__).parent / 'ppocr/utils/dict/table_structure_dict.txt')
+            else:
+                params.structure_char_dict_path = str(
+                    Path(__file__).parent.parent / 'ppocr/utils/dict/table_structure_dict.txt')
+
+        print(params)
+        super().__init__(params)
+
+    def __call__(self, img):
+        if isinstance(img, str):
+            # download net image
+            if img.startswith('http'):
+                download_with_progressbar(img, 'tmp.jpg')
+                img = 'tmp.jpg'
+            image_file = img
+            img, flag = check_and_read_gif(image_file)
+            if not flag:
+                with open(image_file, 'rb') as f:
+                    np_arr = np.frombuffer(f.read(), dtype=np.uint8)
+                    img = cv2.imdecode(np_arr, cv2.IMREAD_COLOR)
+            if img is None:
+                logger.error("error in loading image:{}".format(image_file))
+                return None
+        if isinstance(img, np.ndarray) and len(img.shape) == 2:
+            img = cv2.cvtColor(img, cv2.COLOR_GRAY2BGR)
+
+        res = super().__call__(img)
+        return res
+
+
+def main():
+    # for cmd
+    args = parse_args(mMain=True)
+    image_dir = args.image_dir
+    save_folder = args.output
+    if image_dir.startswith('http'):
+        download_with_progressbar(image_dir, 'tmp.jpg')
+        image_file_list = ['tmp.jpg']
+    else:
+        image_file_list = get_image_file_list(args.image_dir)
+    if len(image_file_list) == 0:
+        logger.error('no images find in {}'.format(args.image_dir))
+        return
+
+    structure_engine = PaddleStructure(**(args.__dict__))
+    for img_path in image_file_list:
+        img_name = os.path.basename(img_path).split('.')[0]
+        logger.info('{}{}{}'.format('*' * 10, img_path, '*' * 10))
+        result = structure_engine(img_path)
+        for item in result:
+            logger.info(item['res'])
+        save_res(result, save_folder, img_name)
+        logger.info('result save to {}'.format(os.path.join(save_folder, img_name)))
\ No newline at end of file

ppstructure/predict_system.py

+# 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__, '..')))
+
+os.environ["FLAGS_allocator_strategy"] = 'auto_growth'
+import cv2
+import numpy as np
+import time
+
+import layoutparser as lp
+
+from ppocr.utils.utility import get_image_file_list, check_and_read_gif
+from ppocr.utils.logging import get_logger
+from tools.infer.predict_system import TextSystem
+from ppstructure.table.predict_table import TableSystem, to_excel
+from ppstructure.utility import parse_args,draw_result
+
+logger = get_logger()
+
+
+class OCRSystem(object):
+    def __init__(self, args):
+        args.det_limit_type = 'resize_long'
+        args.drop_score = 0
+        self.text_system = TextSystem(args)
+        self.table_system = TableSystem(args, self.text_system.text_detector, self.text_system.text_recognizer)
+        self.table_layout = lp.PaddleDetectionLayoutModel("lp://PubLayNet/ppyolov2_r50vd_dcn_365e_publaynet/config",
+                                                          threshold=0.5, enable_mkldnn=args.enable_mkldnn,
+                                                          enforce_cpu=not args.use_gpu, thread_num=args.cpu_threads)
+        self.use_angle_cls = args.use_angle_cls
+        self.drop_score = args.drop_score
+
+    def __call__(self, img):
+        ori_im = img.copy()
+        layout_res = self.table_layout.detect(img[..., ::-1])
+        res_list = []
+        for region in layout_res:
+            x1, y1, x2, y2 = region.coordinates
+            x1, y1, x2, y2 = int(x1), int(y1), int(x2), int(y2)
+            roi_img = ori_im[y1:y2, x1:x2, :]
+            if region.type == 'Table':
+                res = self.table_system(roi_img)
+            elif region.type == 'Figure':
+                continue
+            else:
+                filter_boxes, filter_rec_res = self.text_system(roi_img)
+                filter_boxes = [x + [x1, y1] for x in filter_boxes]
+                filter_boxes = [x.reshape(-1).tolist() for x in filter_boxes]
+
+                res = (filter_boxes, filter_rec_res)
+            res_list.append({'type': region.type, 'bbox': [x1, y1, x2, y2], 'res': res})
+        return res_list
+
+def save_res(res, save_folder, img_name):
+    excel_save_folder = os.path.join(save_folder, img_name)
+    os.makedirs(excel_save_folder, exist_ok=True)
+    # save res
+    for region in res:
+        if region['type'] == 'Table':
+            excel_path = os.path.join(excel_save_folder, '{}.xlsx'.format(region['bbox']))
+            to_excel(region['res'], excel_path)
+        elif region['type'] == 'Figure':
+            pass
+        else:
+            with open(os.path.join(excel_save_folder, 'res.txt'), 'a', encoding='utf8') as f:
+                for box, rec_res in zip(region['res'][0], region['res'][1]):
+                    f.write('{}\t{}\n'.format(np.array(box).reshape(-1).tolist(), rec_res))
+
+
+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]
+    save_folder = args.output
+    os.makedirs(save_folder, exist_ok=True)
+
+    structure_sys = OCRSystem(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)
+        img_name = os.path.basename(image_file).split('.')[0]
+
+        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()
+        res = structure_sys(img)
+        save_res(res, save_folder, img_name)
+        draw_img = draw_result(img,res, args.vis_font_path)
+        cv2.imwrite(os.path.join(save_folder, img_name, 'show.jpg'), draw_img)
+        logger.info('result save to {}'.format(os.path.join(save_folder, img_name)))
+        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)

ppstructure/setup.py

+# 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
+
+from setuptools import setup
+from io import open
+import shutil
+
+with open('../requirements.txt', encoding="utf-8-sig") as f:
+    requirements = f.readlines()
+    requirements.append('tqdm')
+    requirements.append('layoutparser')
+    requirements.append('iopath')
+
+
+def readme():
+    with open('README_ch.md', encoding="utf-8-sig") as f:
+        README = f.read()
+    return README
+
+
+shutil.copytree('../ppstructure/table', './ppstructure/table')
+shutil.copyfile('../ppstructure/predict_system.py', './ppstructure/predict_system.py')
+shutil.copyfile('../ppstructure/utility.py', './ppstructure/utility.py')
+shutil.copytree('../ppocr', './ppocr')
+shutil.copytree('../tools', './tools')
+shutil.copyfile('../LICENSE', './LICENSE')
+
+setup(
+    name='paddlestructure',
+    packages=['paddlestructure'],
+    package_dir={'paddlestructure': ''},
+    include_package_data=True,
+    entry_points={"console_scripts": ["paddlestructure= paddlestructure.paddlestructure:main"]},
+    version='1.0',
+    install_requires=requirements,
+    license='Apache License 2.0',
+    description='Awesome OCR toolkits based on PaddlePaddle （8.6M ultra-lightweight pre-trained model, support training and deployment among server, mobile, embeded and IoT devices',
+    long_description=readme(),
+    long_description_content_type='text/markdown',
+    url='https://github.com/PaddlePaddle/PaddleOCR',
+    download_url='https://github.com/PaddlePaddle/PaddleOCR.git',
+    keywords=[
+        'ocr textdetection textrecognition paddleocr crnn east star-net rosetta ocrlite db chineseocr chinesetextdetection chinesetextrecognition'
+    ],
+    classifiers=[
+        'Intended Audience :: Developers', 'Operating System :: OS Independent',
+        'Natural Language :: Chinese (Simplified)',
+        'Programming Language :: Python :: 3',
+        'Programming Language :: Python :: 3.2',
+        'Programming Language :: Python :: 3.3',
+        'Programming Language :: Python :: 3.4',
+        'Programming Language :: Python :: 3.5',
+        'Programming Language :: Python :: 3.6',
+        'Programming Language :: Python :: 3.7', 'Topic :: Utilities'
+    ], )
+
+shutil.rmtree('ppocr')
+shutil.rmtree('tools')
+shutil.rmtree('ppstructure')
+os.remove('LICENSE')

ppstructure/table/README_ch.md

+# 表格结构和内容预测
+
+先cd到PaddleOCR/ppstructure目录下
+
+预测
+```python
+python3 table/predict_table.py --det_model_dir=../inference/db --rec_model_dir=../inference/rec_mv3_large1.0/infer --table_model_dir=../inference/explite3/infer --image_dir=../table/imgs/PMC3006023_004_00.png --rec_char_dict_path=../ppocr/utils/dict/table_dict.txt --table_char_dict_path=../ppocr/utils/dict/table_structure_dict.txt --rec_char_type=EN --det_limit_side_len=736 --det_limit_type=min --table_output ../output/table
+```
+运行完成后，每张图片的excel表格会保存到table_output字段指定的目录下
+
+评估
+
+```python
+python3 table/eval_table.py --det_model_dir=../inference/db --rec_model_dir=../inference/rec_mv3_large1.0/infer --table_model_dir=../inference/explite3/infer --image_dir=../table/imgs --rec_char_dict_path=../ppocr/utils/dict/table_dict.txt --table_char_dict_path=../ppocr/utils/dict/table_structure_dict.txt --rec_char_type=EN --det_limit_side_len=736 --det_limit_type=min --gt_path=path/to/gt.json
+```

ppstructure/table/__init__.py

+# copyright (c) 2020 PaddlePaddle Authors. All Rights Reserve.
+#
+# 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.

ppstructure/table/eval_table.py

+# 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.append(os.path.abspath(os.path.join(__dir__, '../..')))
+
+import cv2
+import json
+from tqdm import tqdm
+from ppstructure.table.table_metric import TEDS
+from ppstructure.table.predict_table import TableSystem
+from ppstructure.utility import init_args
+
+
+def parse_args():
+    parser = init_args()
+    parser.add_argument("--gt_path", type=str)
+    return parser.parse_args()
+
+def main(gt_path, img_root, args):
+    teds = TEDS(n_jobs=16)
+
+    text_sys = TableSystem(args)
+    jsons_gt = json.load(open(gt_path))  # gt
+    pred_htmls = []
+    gt_htmls = []
+    for img_name in tqdm(jsons_gt):
+        # read image
+        img = cv2.imread(os.path.join(img_root,img_name))
+        pred_html = text_sys(img)
+        pred_htmls.append(pred_html)
+
+        gt_structures, gt_bboxes, gt_contents, contents_with_block = jsons_gt[img_name]
+        gt_html, gt = get_gt_html(gt_structures, contents_with_block)
+        gt_htmls.append(gt_html)
+    scores = teds.batch_evaluate_html(gt_htmls, pred_htmls)
+    print('teds:', sum(scores) / len(scores))
+
+
+def get_gt_html(gt_structures, contents_with_block):
+    end_html = []
+    td_index = 0
+    for tag in gt_structures:
+        if '</td>' in tag:
+            if contents_with_block[td_index] != []:
+                end_html.extend(contents_with_block[td_index])
+            end_html.append(tag)
+            td_index += 1
+        else:
+            end_html.append(tag)
+    return ''.join(end_html), end_html
+
+
+if __name__ == '__main__':
+    args = parse_args()
+    main(args.gt_path,args.image_dir, args)

ppstructure/table/matcher.py

+import json
+def distance(box_1, box_2):
+        x1, y1, x2, y2 = box_1
+        x3, y3, x4, y4 = box_2
+        dis = abs(x3 - x1) + abs(y3 - y1) + abs(x4- x2) + abs(y4 - y2)
+        dis_2 = abs(x3 - x1) + abs(y3 - y1)
+        dis_3 = abs(x4- x2) + abs(y4 - y2)
+        return dis + min(dis_2, dis_3)
+
+def compute_iou(rec1, rec2):
+    """
+    computing IoU
+    :param rec1: (y0, x0, y1, x1), which reflects
+            (top, left, bottom, right)
+    :param rec2: (y0, x0, y1, x1)
+    :return: scala value of IoU
+    """
+    # computing area of each rectangles
+    S_rec1 = (rec1[2] - rec1[0]) * (rec1[3] - rec1[1])
+    S_rec2 = (rec2[2] - rec2[0]) * (rec2[3] - rec2[1])
+ 
+    # computing the sum_area
+    sum_area = S_rec1 + S_rec2
+ 
+    # find the each edge of intersect rectangle
+    left_line = max(rec1[1], rec2[1])
+    right_line = min(rec1[3], rec2[3])
+    top_line = max(rec1[0], rec2[0])
+    bottom_line = min(rec1[2], rec2[2])
+ 
+    # judge if there is an intersect
+    if left_line >= right_line or top_line >= bottom_line:
+        return 0.0
+    else:
+        intersect = (right_line - left_line) * (bottom_line - top_line)
+        return (intersect / (sum_area - intersect))*1.0
+ 
+
+
+def matcher_merge(ocr_bboxes, pred_bboxes):
+    all_dis = []
+    ious = []
+    matched = {}
+    for i, gt_box in enumerate(ocr_bboxes):
+        distances = []
+        for j, pred_box in enumerate(pred_bboxes):
+            # compute l1 distence and IOU between two boxes
+            distances.append((distance(gt_box, pred_box), 1. - compute_iou(gt_box, pred_box)))
+        sorted_distances = distances.copy()
+        # select nearest 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#, sum(ious) / len(ious)
+
+def complex_num(pred_bboxes):
+    complex_nums = []
+    for bbox in pred_bboxes:
+        distances = []
+        temp_ious = []
+        for pred_bbox in pred_bboxes:
+            if bbox != pred_bbox:
+                distances.append(distance(bbox, pred_bbox))
+                temp_ious.append(compute_iou(bbox, pred_bbox))
+        complex_nums.append(temp_ious[distances.index(min(distances))])
+    return sum(complex_nums) / len(complex_nums)
+
+def get_rows(pred_bboxes):
+    pre_bbox = pred_bboxes[0]
+    res = []
+    step = 0
+    for i in range(len(pred_bboxes)):
+        bbox = pred_bboxes[i]
+        if bbox[1] - pre_bbox[1] > 2 or bbox[0] - pre_bbox[0] < 0:
+            break
+        else:
+            res.append(bbox)
+            step += 1
+    for i in range(step):
+        pred_bboxes.pop(0)
+    return res, pred_bboxes
+def refine_rows(pred_bboxes): # 微调整行的框，使在一条水平线上
+    ys_1 = []
+    ys_2 = []
+    for box in pred_bboxes:
+        ys_1.append(box[1])
+        ys_2.append(box[3])
+    min_y_1 = sum(ys_1) / len(ys_1)
+    min_y_2 = sum(ys_2) / len(ys_2)
+    re_boxes = []
+    for box in pred_bboxes:
+        box[1] = min_y_1
+        box[3] = min_y_2
+        re_boxes.append(box)
+    return re_boxes
+    
+def matcher_refine_row(gt_bboxes, pred_bboxes):
+    before_refine_pred_bboxes = pred_bboxes.copy()
+    pred_bboxes = []
+    while(len(before_refine_pred_bboxes) != 0):
+        row_bboxes, before_refine_pred_bboxes = get_rows(before_refine_pred_bboxes)
+        print(row_bboxes)
+        pred_bboxes.extend(refine_rows(row_bboxes))
+    all_dis = []
+    ious = []
+    matched = {}
+    for i, gt_box in enumerate(gt_bboxes):
+        distances = []
+        #temp_ious = []
+        for j, pred_box in enumerate(pred_bboxes):
+            distances.append(distance(gt_box, pred_box))
+            #temp_ious.append(compute_iou(gt_box, pred_box))
+        #all_dis.append(min(distances))
+        #ious.append(temp_ious[distances.index(min(distances))])
+        if distances.index(min(distances)) not in matched.keys(): 
+            matched[distances.index(min(distances))] = [i]
+        else:
+            matched[distances.index(min(distances))].append(i)
+    return matched#, sum(ious) / len(ious)
+
+
+
+#先挑选出一行，再进行匹配
+def matcher_structure_1(gt_bboxes, pred_bboxes_rows, pred_bboxes):
+    gt_box_index = 0
+    delete_gt_bboxes = gt_bboxes.copy()
+    match_bboxes_ready = []
+    matched = {}
+    while(len(delete_gt_bboxes) != 0):
+        row_bboxes, delete_gt_bboxes = get_rows(delete_gt_bboxes)
+        row_bboxes = sorted(row_bboxes, key = lambda key: key[0])
+        if len(pred_bboxes_rows) > 0:
+            match_bboxes_ready.extend(pred_bboxes_rows.pop(0))
+        print(row_bboxes)
+        for i, gt_box in enumerate(row_bboxes):
+            #print(gt_box)
+            pred_distances = []
+            distances = []  
+            for pred_bbox in pred_bboxes:
+                pred_distances.append(distance(gt_box, pred_bbox))
+            for j, pred_box in enumerate(match_bboxes_ready):
+                distances.append(distance(gt_box, pred_box))
+            index = pred_distances.index(min(distances))
+            #print('index', index)
+            if index not in matched.keys(): 
+                matched[index] = [gt_box_index]
+            else:
+                matched[index].append(gt_box_index)
+            gt_box_index += 1
+    return matched
+
+def matcher_structure(gt_bboxes, pred_bboxes_rows, pred_bboxes):
+    '''
+    gt_bboxes: 排序后
+    pred_bboxes: 
+    '''
+    pre_bbox = gt_bboxes[0]
+    matched = {}
+    match_bboxes_ready = []
+    match_bboxes_ready.extend(pred_bboxes_rows.pop(0))
+    for i, gt_box in enumerate(gt_bboxes):
+        
+        pred_distances = []
+        for pred_bbox in pred_bboxes:
+            pred_distances.append(distance(gt_box, pred_bbox))
+        distances = []
+        gap_pre = gt_box[1] - pre_bbox[1]
+        gap_pre_1 = gt_box[0] - pre_bbox[2]
+        #print(gap_pre, len(pred_bboxes_rows))
+        if (gap_pre_1 < 0 and len(pred_bboxes_rows) > 0):
+            match_bboxes_ready.extend(pred_bboxes_rows.pop(0))
+        if len(pred_bboxes_rows) == 1:
+            match_bboxes_ready.extend(pred_bboxes_rows.pop(0))
+        if len(match_bboxes_ready) == 0 and len(pred_bboxes_rows) > 0:
+            match_bboxes_ready.extend(pred_bboxes_rows.pop(0))
+        if len(match_bboxes_ready) == 0 and len(pred_bboxes_rows) == 0:
+            break
+        #print(match_bboxes_ready)
+        for j, pred_box in enumerate(match_bboxes_ready):
+            distances.append(distance(gt_box, pred_box))
+        index = pred_distances.index(min(distances))
+        #print(gt_box, index)
+        #match_bboxes_ready.pop(distances.index(min(distances)))
+        print(gt_box, match_bboxes_ready[distances.index(min(distances))])
+        if index not in matched.keys(): 
+            matched[index] = [i]
+        else:
+            matched[index].append(i)
+        pre_bbox = gt_box
+    return matched

ppstructure/table/predict_structure.py

+# 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.append(os.path.abspath(os.path.join(__dir__, '../..')))
+
+os.environ["FLAGS_allocator_strategy"] = 'auto_growth'
+
+import cv2
+import numpy as np
+import math
+import time
+import traceback
+import paddle
+
+import tools.infer.utility as utility
+from ppocr.data import create_operators, transform
+from ppocr.postprocess import build_post_process
+from ppocr.utils.logging import get_logger
+from ppocr.utils.utility import get_image_file_list, check_and_read_gif
+
+logger = get_logger()
+
+
+class TableStructurer(object):
+    def __init__(self, args):
+        pre_process_list = [{
+            'ResizeTableImage': {
+                'max_len': args.structure_max_len
+            }
+        }, {
+            'NormalizeImage': {
+                'std': [0.229, 0.224, 0.225],
+                'mean': [0.485, 0.456, 0.406],
+                'scale': '1./255.',
+                'order': 'hwc'
+            }
+        }, {
+            'PaddingTableImage': None
+        }, {
+            'ToCHWImage': None
+        }, {
+            'KeepKeys': {
+                'keep_keys': ['image']
+            }
+        }]
+        postprocess_params = {
+            'name': 'TableLabelDecode',
+            "character_type": args.structure_char_type,
+            "character_dict_path": args.structure_char_dict_path,
+            "max_text_length": args.structure_max_text_length,
+            "max_elem_length": args.structure_max_elem_length,
+            "max_cell_num": args.structure_max_cell_num
+        }
+
+        self.preprocess_op = create_operators(pre_process_list)
+        self.postprocess_op = build_post_process(postprocess_params)
+        self.predictor, self.input_tensor, self.output_tensors = \
+            utility.create_predictor(args, 'structure', logger)
+
+    def __call__(self, img):
+        ori_im = img.copy()
+        data = {'image': img}
+        data = transform(data, self.preprocess_op)
+        img = data[0]
+        if img is None:
+            return None, 0
+        img = np.expand_dims(img, axis=0)
+        img = img.copy()
+        starttime = time.time()
+
+        self.input_tensor.copy_from_cpu(img)
+        self.predictor.run()
+        outputs = []
+        for output_tensor in self.output_tensors:
+            output = output_tensor.copy_to_cpu()
+            outputs.append(output)
+
+        preds = {}
+        preds['structure_probs'] = outputs[1]
+        preds['loc_preds'] = outputs[0]
+
+        post_result = self.postprocess_op(preds)
+
+        structure_str_list = post_result['structure_str_list']
+        res_loc = post_result['res_loc']
+        imgh, imgw = ori_im.shape[0:2]
+        res_loc_final = []
+        for rno in range(len(res_loc[0])):
+            x0, y0, x1, y1 = res_loc[0][rno]
+            left = max(int(imgw * x0), 0)
+            top = max(int(imgh * y0), 0)
+            right = min(int(imgw * x1), imgw - 1)
+            bottom = min(int(imgh * y1), imgh - 1)
+            res_loc_final.append([left, top, right, bottom])
+
+        structure_str_list = structure_str_list[0][:-1]
+        structure_str_list = ['<html>', '<body>', '<table>'] + structure_str_list + ['</table>', '</body>', '</html>']
+
+        elapse = time.time() - starttime
+        return (structure_str_list, res_loc_final), elapse
+
+
+def main(args):
+    image_file_list = get_image_file_list(args.image_dir)
+    table_structurer = TableStructurer(args)
+    count = 0
+    total_time = 0
+    for image_file in image_file_list:
+        img, flag = check_and_read_gif(image_file)
+        if not flag:
+            img = cv2.imread(image_file)
+        if img is None:
+            logger.info("error in loading image:{}".format(image_file))
+            continue
+        structure_res, elapse = table_structurer(img)
+
+        logger.info("result: {}".format(structure_res))
+
+        if count > 0:
+            total_time += elapse
+        count += 1
+        logger.info("Predict time of {}: {}".format(image_file, elapse))
+
+
+if __name__ == "__main__":
+    main(utility.parse_args())

ppstructure/table/predict_table.py

+# 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.table_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)

ppstructure/table/table_metric/__init__.py

+# copyright (c) 2020 PaddlePaddle Authors. All Rights Reserve.
+#
+# 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.
+
+__all__ = ['TEDS']
+from .table_metric import TEDS
\ No newline at end of file

ppstructure/table/table_metric/parallel.py

+from tqdm import tqdm
+from concurrent.futures import ProcessPoolExecutor, as_completed
+
+
+def parallel_process(array, function, n_jobs=16, use_kwargs=False, front_num=0):
+    """
+        A parallel version of the map function with a progress bar.
+        Args:
+            array (array-like): An array to iterate over.
+            function (function): A python function to apply to the elements of array
+            n_jobs (int, default=16): The number of cores to use
+            use_kwargs (boolean, default=False): Whether to consider the elements of array as dictionaries of
+                keyword arguments to function
+            front_num (int, default=3): The number of iterations to run serially before kicking off the parallel job.
+                Useful for catching bugs
+        Returns:
+            [function(array[0]), function(array[1]), ...]
+    """
+    # We run the first few iterations serially to catch bugs
+    if front_num > 0:
+        front = [function(**a) if use_kwargs else function(a)
+                 for a in array[:front_num]]
+    else:
+        front = []
+    # If we set n_jobs to 1, just run a list comprehension. This is useful for benchmarking and debugging.
+    if n_jobs == 1:
+        return front + [function(**a) if use_kwargs else function(a) for a in tqdm(array[front_num:])]
+    # Assemble the workers
+    with ProcessPoolExecutor(max_workers=n_jobs) as pool:
+        # Pass the elements of array into function
+        if use_kwargs:
+            futures = [pool.submit(function, **a) for a in array[front_num:]]
+        else:
+            futures = [pool.submit(function, a) for a in array[front_num:]]
+        kwargs = {
+            'total': len(futures),
+            'unit': 'it',
+            'unit_scale': True,
+            'leave': True
+        }
+        # Print out the progress as tasks complete
+        for f in tqdm(as_completed(futures), **kwargs):
+            pass
+    out = []
+    # Get the results from the futures.
+    for i, future in tqdm(enumerate(futures)):
+        try:
+            out.append(future.result())
+        except Exception as e:
+            out.append(e)
+    return front + out

ppstructure/table/table_metric/table_metric.py

+# Copyright 2020 IBM
+# Author: peter.zhong@au1.ibm.com
+#
+# This is free software; you can redistribute it and/or modify
+# it under the terms of the Apache 2.0 License.
+#
+# This software is distributed in the hope that it will be useful,
+# but WITHOUT ANY WARRANTY; without even the implied warranty of
+# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+# Apache 2.0 License for more details.
+
+import distance
+from apted import APTED, Config
+from apted.helpers import Tree
+from lxml import etree, html
+from collections import deque
+from .parallel import parallel_process
+from tqdm import tqdm
+
+
+class TableTree(Tree):
+    def __init__(self, tag, colspan=None, rowspan=None, content=None, *children):
+        self.tag = tag
+        self.colspan = colspan
+        self.rowspan = rowspan
+        self.content = content
+        self.children = list(children)
+
+    def bracket(self):
+        """Show tree using brackets notation"""
+        if self.tag == 'td':
+            result = '"tag": %s, "colspan": %d, "rowspan": %d, "text": %s' % \
+                     (self.tag, self.colspan, self.rowspan, self.content)
+        else:
+            result = '"tag": %s' % self.tag
+        for child in self.children:
+            result += child.bracket()
+        return "{{{}}}".format(result)
+
+
+class CustomConfig(Config):
+    @staticmethod
+    def maximum(*sequences):
+        """Get maximum possible value
+        """
+        return max(map(len, sequences))
+
+    def normalized_distance(self, *sequences):
+        """Get distance from 0 to 1
+        """
+        return float(distance.levenshtein(*sequences)) / self.maximum(*sequences)
+
+    def rename(self, node1, node2):
+        """Compares attributes of trees"""
+        #print(node1.tag)
+        if (node1.tag != node2.tag) or (node1.colspan != node2.colspan) or (node1.rowspan != node2.rowspan):
+            return 1.
+        if node1.tag == 'td':
+            if node1.content or node2.content:
+                #print(node1.content, )
+                return self.normalized_distance(node1.content, node2.content)
+        return 0.
+
+
+
+class CustomConfig_del_short(Config):
+    @staticmethod
+    def maximum(*sequences):
+        """Get maximum possible value
+        """
+        return max(map(len, sequences))
+
+    def normalized_distance(self, *sequences):
+        """Get distance from 0 to 1
+        """
+        return float(distance.levenshtein(*sequences)) / self.maximum(*sequences)
+
+    def rename(self, node1, node2):
+        """Compares attributes of trees"""
+        if (node1.tag != node2.tag) or (node1.colspan != node2.colspan) or (node1.rowspan != node2.rowspan):
+            return 1.
+        if node1.tag == 'td':
+            if node1.content or node2.content:
+                #print('before')
+                #print(node1.content, node2.content)
+                #print('after')
+                node1_content = node1.content
+                node2_content = node2.content
+                if len(node1_content) < 3:
+                    node1_content = ['####']
+                if len(node2_content) < 3:
+                    node2_content = ['####']   
+                return self.normalized_distance(node1_content, node2_content)
+        return 0.
+
+class CustomConfig_del_block(Config):
+    @staticmethod
+    def maximum(*sequences):
+        """Get maximum possible value
+        """
+        return max(map(len, sequences))
+
+    def normalized_distance(self, *sequences):
+        """Get distance from 0 to 1
+        """
+        return float(distance.levenshtein(*sequences)) / self.maximum(*sequences)
+
+    def rename(self, node1, node2):
+        """Compares attributes of trees"""
+        if (node1.tag != node2.tag) or (node1.colspan != node2.colspan) or (node1.rowspan != node2.rowspan):
+            return 1.
+        if node1.tag == 'td':
+            if node1.content or node2.content:
+                
+                node1_content = node1.content
+                node2_content = node2.content
+                while ' '  in node1_content:
+                    print(node1_content.index(' '))
+                    node1_content.pop(node1_content.index(' '))
+                while ' ' in node2_content:
+                    print(node2_content.index(' '))
+                    node2_content.pop(node2_content.index(' '))
+                return self.normalized_distance(node1_content, node2_content)
+        return 0.
+
+class TEDS(object):
+    ''' Tree Edit Distance basead Similarity
+    '''
+
+    def __init__(self, structure_only=False, n_jobs=1, ignore_nodes=None):
+        assert isinstance(n_jobs, int) and (
+            n_jobs >= 1), 'n_jobs must be an integer greather than 1'
+        self.structure_only = structure_only
+        self.n_jobs = n_jobs
+        self.ignore_nodes = ignore_nodes
+        self.__tokens__ = []
+
+    def tokenize(self, node):
+        ''' Tokenizes table cells
+        '''
+        self.__tokens__.append('<%s>' % node.tag)
+        if node.text is not None:
+            self.__tokens__ += list(node.text)
+        for n in node.getchildren():
+            self.tokenize(n)
+        if node.tag != 'unk':
+            self.__tokens__.append('</%s>' % node.tag)
+        if node.tag != 'td' and node.tail is not None:
+            self.__tokens__ += list(node.tail)
+
+    def load_html_tree(self, node, parent=None):
+        ''' Converts HTML tree to the format required by apted
+        '''
+        global __tokens__
+        if node.tag == 'td':
+            if self.structure_only:
+                cell = []
+            else:
+                self.__tokens__ = []
+                self.tokenize(node)
+                cell = self.__tokens__[1:-1].copy()
+            new_node = TableTree(node.tag,
+                                 int(node.attrib.get('colspan', '1')),
+                                 int(node.attrib.get('rowspan', '1')),
+                                 cell, *deque())
+        else:
+            new_node = TableTree(node.tag, None, None, None, *deque())
+        if parent is not None:
+            parent.children.append(new_node)
+        if node.tag != 'td':
+            for n in node.getchildren():
+                self.load_html_tree(n, new_node)
+        if parent is None:
+            return new_node
+
+    def evaluate(self, pred, true):
+        ''' Computes TEDS score between the prediction and the ground truth of a
+            given sample
+        '''
+        if (not pred) or (not true):
+            return 0.0
+        parser = html.HTMLParser(remove_comments=True, encoding='utf-8')
+        pred = html.fromstring(pred, parser=parser)
+        true = html.fromstring(true, parser=parser)
+        if pred.xpath('body/table') and true.xpath('body/table'):
+            pred = pred.xpath('body/table')[0]
+            true = true.xpath('body/table')[0]
+            if self.ignore_nodes:
+                etree.strip_tags(pred, *self.ignore_nodes)
+                etree.strip_tags(true, *self.ignore_nodes)
+            n_nodes_pred = len(pred.xpath(".//*"))
+            n_nodes_true = len(true.xpath(".//*"))
+            n_nodes = max(n_nodes_pred, n_nodes_true)
+            tree_pred = self.load_html_tree(pred)
+            tree_true = self.load_html_tree(true)
+            distance = APTED(tree_pred, tree_true,
+                             CustomConfig()).compute_edit_distance()
+            return 1.0 - (float(distance) / n_nodes)
+        else:
+            return 0.0
+
+    def batch_evaluate(self, pred_json, true_json):
+        ''' Computes TEDS score between the prediction and the ground truth of
+            a batch of samples
+            @params pred_json: {'FILENAME': 'HTML CODE', ...}
+            @params true_json: {'FILENAME': {'html': 'HTML CODE'}, ...}
+            @output: {'FILENAME': 'TEDS SCORE', ...}
+        '''
+        samples = true_json.keys()
+        if self.n_jobs == 1:
+            scores = [self.evaluate(pred_json.get(
+                filename, ''), true_json[filename]['html']) for filename in tqdm(samples)]
+        else:
+            inputs = [{'pred': pred_json.get(
+                filename, ''), 'true': true_json[filename]['html']} for filename in samples]
+            scores = parallel_process(
+                inputs, self.evaluate, use_kwargs=True, n_jobs=self.n_jobs, front_num=1)
+        scores = dict(zip(samples, scores))
+        return scores
+
+    def batch_evaluate_html(self, pred_htmls, true_htmls):
+        ''' Computes TEDS score between the prediction and the ground truth of
+            a batch of samples
+        '''
+        if self.n_jobs == 1:
+            scores = [self.evaluate(pred_html, true_html) for (
+                pred_html, true_html) in zip(pred_htmls, true_htmls)]
+        else:
+            inputs = [{"pred": pred_html, "true": true_html} for(
+                pred_html, true_html) in zip(pred_htmls, true_htmls)]
+
+            scores = parallel_process(
+                inputs, self.evaluate, use_kwargs=True, n_jobs=self.n_jobs, front_num=1)
+        return scores
+
+
+if __name__ == '__main__':
+    import json
+    import pprint
+    with open('sample_pred.json') as fp:
+        pred_json = json.load(fp)
+    with open('sample_gt.json') as fp:
+        true_json = json.load(fp)
+    teds = TEDS(n_jobs=4)
+    scores = teds.batch_evaluate(pred_json, true_json)
+    pp = pprint.PrettyPrinter()
+    pp.pprint(scores)

ppstructure/table/tablepyxl/__init__.py

+# copyright (c) 2020 PaddlePaddle Authors. All Rights Reserve.
+#
+# 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.
\ No newline at end of file