Merge pull request #3083 from WenmuZhou/table1

[DO NOT MERGE]Table

test/MANIFEST.in

+include LICENSE
+include README.md
+
+recursive-include ppocr/utils *.txt utility.py logging.py network.py
+recursive-include ppocr/data/ *.py
+recursive-include ppocr/postprocess *.py
+recursive-include tools/infer *.py
+recursive-include ppstructure *.py
+

test/__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.
+
+from .paddlestructure import PaddleStructure, draw_result, to_excel
+
+__all__ = ['PaddleStructure', 'draw_result', 'to_excel']

test/api.md

+# PaddleStructure
+
+## 1. Introduction to pipeline
+
+PaddleStructure is a toolkit for complex layout text OCR, the process is as follows
+
+![pipeline](../doc/table/pipeline.png)
+
+In PaddleStructure, the image will be analyzed by layoutparser first. In the layout analysis, the area in the image will be classified, and the OCR process will be carried out according to the category.
+
+Currently layoutparser will output five categories:
+1. Text
+2. Title
+3. Figure
+4. List
+5. Table
+   
+Types 1-4 follow the traditional OCR process, and 5 follow the Table OCR process.
+
+## 2. LayoutParser
+
+
+## 3. Table OCR
+
+[doc](table/README.md)
+
+## 4. PaddleStructure whl package introduction
+
+### 4.1 Use
+
+4.1.1 Use by code
+```python
+import cv2
+from paddlestructure import PaddleStructure,draw_result
+
+table_engine = PaddleStructure(
+    output='./output/table',
+    show_log=True)
+
+img_path = '../doc/table/1.png'
+img = cv2.imread(img_path)
+result = table_engine(img)
+for line in result:
+    print(line)
+
+from PIL import Image
+
+font_path = 'path/tp/PaddleOCR/doc/fonts/simfang.ttf'
+image = Image.open(img_path).convert('RGB')
+im_show = draw_result(image, result,font_path=font_path)
+im_show = Image.fromarray(im_show)
+im_show.save('result.jpg')
+```
+
+4.1.2 Use by command line
+```bash
+paddlestructure --image_dir=../doc/table/1.png
+```
+
+### 参数说明
+大部分参数和paddleocr whl包保持一致，见 [whl包文档](../doc/doc_ch/whl.md)
+
+| 字段                    | 说明                                            | 默认值           |
+|------------------------|------------------------------------------------------|------------------|
+| output                 | excel和识别结果保存的地址                    | ./output/table            |
+| structure_max_len      |  structure模型预测时，图像的长边resize尺度             |  488            |
+| structure_model_dir      |  structure inference 模型地址             |  None            |
+| structure_char_type      |  structure 模型所用字典地址             |  ../ppocr/utils/dict/table_structure_dict.tx            |
+
+

test/api_ch.md

+# PaddleStructure
+
+## 1. pipeline介绍
+
+PaddleStructure 是一个用于复杂板式文字OCR的工具包，流程如下
+![pipeline](../doc/table/pipeline.png)
+
+在PaddleStructure中，图片会先经由layoutparser进行版面分析，在版面分析中，会对图片里的区域进行分类，根据根据类别进行对于的ocr流程。
+
+目前layoutparser会输出五个类别:
+1. Text
+2. Title
+3. Figure
+4. List
+5. Table
+   
+1-4类走传统的OCR流程，5走表格的OCR流程。
+
+## 2. LayoutParser
+
+
+## 3. Table OCR
+
+[文档](table/README_ch.md)
+
+## 4. PaddleStructure whl包介绍
+
+### 4.1 使用
+
+4.1.1 代码使用
+```python
+import cv2
+from paddlestructure import PaddleStructure,draw_result
+
+table_engine = PaddleStructure(
+    output='./output/table',
+    show_log=True)
+
+img_path = '../doc/table/1.png'
+img = cv2.imread(img_path)
+result = table_engine(img)
+for line in result:
+    print(line)
+
+from PIL import Image
+
+font_path = 'path/tp/PaddleOCR/doc/fonts/simfang.ttf'
+image = Image.open(img_path).convert('RGB')
+im_show = draw_result(image, result,font_path=font_path)
+im_show = Image.fromarray(im_show)
+im_show.save('result.jpg')
+```
+
+4.1.2 命令行使用
+```bash
+paddlestructure --image_dir=../doc/table/1.png
+```
+
+### 参数说明
+大部分参数和paddleocr whl包保持一致，见 [whl包文档](../doc/doc_ch/whl.md)
+
+| 字段                    | 说明                                            | 默认值           |
+|------------------------|------------------------------------------------------|------------------|
+| output                 | excel和识别结果保存的地址                    | ./output/table            |
+| structure_max_len      |  structure模型预测时，图像的长边resize尺度             |  488            |
+| structure_model_dir      |  structure inference 模型地址             |  None            |
+| structure_char_type      |  structure 模型所用字典地址             |  ../ppocr/utils/dict/table_structure_dict.tx            |
+
+

test/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 test.predict_system import OCRSystem, save_res
+from test.table.predict_table import to_excel
+from test.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 not params.show_log:
+            logger.setLevel(logging.INFO)
+        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

test/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 logging
+
+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 test.table.predict_table import TableSystem, to_excel
+from test.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
+        if not args.show_log:
+            logger.setLevel(logging.INFO)
+        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)
+            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)

test/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('api_ch.md', encoding="utf-8-sig") as f:
+        README = f.read()
+    return README
+
+
+shutil.copytree('/table', './test/table')
+shutil.copyfile('/predict_system.py', './test/predict_system.py')
+shutil.copyfile('/utility.py', './test/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('test')
+os.remove('LICENSE')

test/table/README.md

+# Table structure and content prediction
+
+## 1. pipeline
+The ocr of the table mainly contains three models
+1. Single line text detection-DB
+2. Single line text recognition-CRNN
+3. Table structure and cell coordinate prediction-RARE
+
+The table ocr flow chart is as follows
+
+![tableocr_pipeline](../../doc/table/tableocr_pipeline.png)
+
+1. The coordinates of single-line text is detected by DB model, and then sends it to the recognition model to get the recognition result.
+2. The table structure and cell coordinates is predicted by RARE model.
+3. The recognition result of the cell is combined by the coordinates, recognition result of the single line and the coordinates of the cell.
+4. The cell recognition result and the table structure together construct the html string of the table.
+
+## 2. How to use
+
+
+### 2.1 Train
+TBD
+
+### 2.2 Eval
+First cd to the PaddleOCR/ppstructure directory
+
+The table uses TEDS (Tree-Edit-Distance-based Similarity) as the evaluation metric of the model. Before the model evaluation, the three models in the pipeline need to be exported as inference models (we have provided them), and the gt for evaluation needs to be prepared. Examples of gt are as follows:
+```json
+{"PMC4289340_004_00.png": [["<html>", "<body>", "<table>", "<thead>", "<tr>", "<td>", "</td>", "<td>", "</td>", "<td>", "</td>", "</tr>", "</thead>", "<tbody>", "<tr>", "<td>", "</td>", "<td>", "</td>", "<td>", "</td>", "</tr>",  "</tbody>", "</table>", "</body>", "</html>"], [[1, 4, 29, 13], [137, 4, 161, 13], [215, 4, 236, 13], [1, 17, 30, 27], [137, 17, 147, 27], [215, 17, 225, 27]], [["<b>", "F", "e", "a", "t", "u", "r", "e", "</b>"], ["<b>", "G", "b", "3", " ", "+", "</b>"], ["<b>", "G", "b", "3", " ", "-", "</b>"], ["<b>", "P", "a", "t", "i", "e", "n", "t", "s", "</b>"], ["6", "2"], ["4", "5"]]]}
+```
+In gt json, the key is the image name, the value is the corresponding gt, and gt is a list composed of four items, and each item is
+1. HTML string list of table structure
+2. The coordinates of each cell (not including the empty text in the cell)
+3. The text information in each cell (not including the empty text in the cell)
+4. The text information in each cell (including the empty text in the cell)
+
+Use the following command to evaluate. After the evaluation is completed, the teds indicator will be output.
+```python
+python3 table/eval_table.py --det_model_dir=path/to/det_model_dir --rec_model_dir=path/to/rec_model_dir --structure_model_dir=path/to/structure_model_dir --image_dir=../doc/table/1.png --rec_char_dict_path=../ppocr/utils/dict/table_dict.txt --structure_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
+```
+
+
+### 2.3 Inference
+First cd to the PaddleOCR/ppstructure directory
+
+```python
+python3 table/predict_table.py --det_model_dir=path/to/det_model_dir --rec_model_dir=path/to/rec_model_dir --structure_model_dir=path/to/structure_model_dir --image_dir=../doc/table/1.png --rec_char_dict_path=../ppocr/utils/dict/table_dict.txt --structure_char_dict_path=../ppocr/utils/dict/table_structure_dict.txt --rec_char_type=EN --det_limit_side_len=736 --det_limit_type=min --output ../output/table
+```
+After running, the excel sheet of each picture will be saved in the directory specified by the table_output field
\ No newline at end of file

test/table/README_ch.md

+# 表格结构和内容预测
+
+## 1. pipeline
+表格的ocr主要包含三个模型
+1. 单行文本检测-DB
+2. 单行文本识别-CRNN
+3. 表格结构和cell坐标预测-RARE
+
+具体流程图如下
+
+![tableocr_pipeline](../../doc/table/tableocr_pipeline.png)
+
+1. 图片由单行文字检测检测模型到单行文字的坐标，然后送入识别模型拿到识别结果。
+2. 图片由表格结构和cell坐标预测模型拿到表格的结构信息和单元格的坐标信息。
+3. 由单行文字的坐标、识别结果和单元格的坐标一起组合出单元格的识别结果。
+4. 单元格的识别结果和表格结构一起构造表格的html字符串。
+
+## 2. 使用
+
+
+### 2.1 训练
+TBD
+
+### 2.2 评估
+先cd到PaddleOCR/ppstructure目录下
+
+表格使用 TEDS(Tree-Edit-Distance-based Similarity) 作为模型的评估指标。在进行模型评估之前，需要将pipeline中的三个模型分别导出为inference模型(我们已经提供好)，还需要准备评估的gt， gt示例如下:
+```json
+{"PMC4289340_004_00.png": [["<html>", "<body>", "<table>", "<thead>", "<tr>", "<td>", "</td>", "<td>", "</td>", "<td>", "</td>", "</tr>", "</thead>", "<tbody>", "<tr>", "<td>", "</td>", "<td>", "</td>", "<td>", "</td>", "</tr>",  "</tbody>", "</table>", "</body>", "</html>"], [[1, 4, 29, 13], [137, 4, 161, 13], [215, 4, 236, 13], [1, 17, 30, 27], [137, 17, 147, 27], [215, 17, 225, 27]], [["<b>", "F", "e", "a", "t", "u", "r", "e", "</b>"], ["<b>", "G", "b", "3", " ", "+", "</b>"], ["<b>", "G", "b", "3", " ", "-", "</b>"], ["<b>", "P", "a", "t", "i", "e", "n", "t", "s", "</b>"], ["6", "2"], ["4", "5"]]]}
+```
+json 中，key为图片名，value为对于的gt，gt是一个由四个item组成的list，每个item分别为
+1. 表格结构的html字符串list
+2. 每个cell的坐标 (不包括cell里文字为空的)
+3. 每个cell里的文字信息 (不包括cell里文字为空的)
+4. 每个cell里的文字信息 (包括cell里文字为空的)
+
+准备完成后使用如下命令进行评估，评估完成后会输出teds指标。
+```python
+python3 table/eval_table.py --det_model_dir=path/to/det_model_dir --rec_model_dir=path/to/rec_model_dir --structure_model_dir=path/to/structure_model_dir --image_dir=../doc/table/1.png --rec_char_dict_path=../ppocr/utils/dict/table_dict.txt --structure_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
+```
+
+
+### 2.3 预测
+先cd到PaddleOCR/ppstructure目录下
+
+```python
+python3 table/predict_table.py --det_model_dir=path/to/det_model_dir --rec_model_dir=path/to/rec_model_dir --structure_model_dir=path/to/structure_model_dir --image_dir=../doc/table/1.png --rec_char_dict_path=../ppocr/utils/dict/table_dict.txt --structure_char_dict_path=../ppocr/utils/dict/table_structure_dict.txt --rec_char_type=EN --det_limit_side_len=736 --det_limit_type=min --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

test/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.

test/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 test.table.table_metric import TEDS
+from test.table.predict_table import TableSystem
+from test.utility import init_args
+from ppocr.utils.logging import get_logger
+
+logger = get_logger()
+
+
+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)
+    logger.info('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)

test/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

test/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
+from test.utility import parse_args
+
+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,
+        }
+
+        self.preprocess_op = create_operators(pre_process_list)
+        self.postprocess_op = build_post_process(postprocess_params)
+        self.predictor, self.input_tensor, self.output_tensors, self.config = \
+            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(parse_args())

test/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

test/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