Merge branch 'dygraph' into bm_dyg

test1/api.md

+# PaddleStructure
+
+install layoutparser
+```sh
+wget  https://paddleocr.bj.bcebos.com/whl/layoutparser-0.0.0-py3-none-any.whl
+pip3 install layoutparser-0.0.0-py3-none-any.whl
+```
+
+## 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. Predictive by inference engine
+
+Use the following commands to complete the inference
+```python
+python3 table/predict_system.py --det_model_dir=path/to/det_model_dir --rec_model_dir=path/to/rec_model_dir --table_model_dir=path/to/table_model_dir --image_dir=../doc/table/1.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 --output ../output/table
+```
+After running, each image will have a directory with the same name under the directory specified in the output field. Each table in the picture will be stored as an excel, and the excel file name will be the coordinates of the table in the image.
+
+## 5. PaddleStructure whl package introduction
+
+### 5.1 Use
+
+5.1.1 Use by code
+```python
+import os
+import cv2
+from paddlestructure import PaddleStructure,draw_result,save_res
+
+table_engine = PaddleStructure(show_log=True)
+
+save_folder = './output/table'
+img_path = '../doc/table/1.png'
+img = cv2.imread(img_path)
+result = table_engine(img)
+save_res(result, save_folder,os.path.basename(img_path).split('.')[0])
+
+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')
+```
+
+5.1.2 Use by command line
+```bash
+paddlestructure --image_dir=../doc/table/1.png
+```
+
+### Parameter Description
+Most of the parameters are consistent with the paddleocr whl package, see [whl package documentation](../doc/doc_ch/whl.md)
+
+| Parameter                    | Description                                            | Default           |
+|------------------------|------------------------------------------------------|------------------|
+| output                 | The path where excel and recognition results are saved                    | ./output/table            |
+| structure_max_len      |  When the table structure model predicts, the long side of the image is resized             |  488            |
+| structure_model_dir      |  Table structure inference model path             |  None            |
+| structure_char_type      | Dictionary path used by table structure model             |  ../ppocr/utils/dict/table_structure_dict.tx            |
+
+

test1/api_ch.md

+# PaddleStructure
+
+安装layoutparser
+```sh
+wget  https://paddleocr.bj.bcebos.com/whl/layoutparser-0.0.0-py3-none-any.whl
+pip3 install layoutparser-0.0.0-py3-none-any.whl
+```
+
+## 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
+
+[文档](layout/README.md)
+
+## 3. Table OCR
+
+[文档](table/README_ch.md)
+
+## 4. 预测引擎推理
+
+使用如下命令即可完成预测引擎的推理
+```python
+python3 table/predict_system.py --det_model_dir=path/to/det_model_dir --rec_model_dir=path/to/rec_model_dir --table_model_dir=path/to/table_model_dir --image_dir=../doc/table/1.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 --output ../output/table
+```
+运行完成后，每张图片会output字段指定的目录下有一个同名目录，图片里的每个表格会存储为一个excel，excel文件名为表格在图片里的坐标。
+
+## 5. PaddleStructure whl包介绍
+
+### 5.1 使用
+
+5.1.1 代码使用
+```python
+import os
+import cv2
+from paddlestructure import PaddleStructure,draw_result,save_res
+
+table_engine = PaddleStructure(show_log=True)
+
+save_folder = './output/table'
+img_path = '../doc/table/1.png'
+img = cv2.imread(img_path)
+result = table_engine(img)
+save_res(result, save_folder,os.path.basename(img_path).split('.')[0])
+
+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')
+```
+
+5.1.2 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            |
+| table_max_len      |  表格结构模型预测时，图像的长边resize尺度             |  488            |
+| table_model_dir      |  表格结构模型 inference 模型地址             |  None            |
+| table_char_type      |  表格结构模型所用字典地址             |  ../ppocr/utils/dict/table_structure_dict.tx            |
+
+

test1/layout/README.md

+# 版面分析使用说明
+
+* [1. 安装whl包](#安装whl包)
+* [2. 使用](#使用)
+* [3. 后处理](#后处理)
+* [4. 指标](#指标)
+* [5. 训练版面分析模型](#训练版面分析模型)
+
+<a name="安装whl包"></a>
+
+## 1.  安装whl包
+```bash
+wget https://paddleocr.bj.bcebos.com/whl/layoutparser-0.0.0-py3-none-any.whl
+pip install -U layoutparser-0.0.0-py3-none-any.whl
+```
+
+<a name="使用"></a>
+
+## 2. 使用
+
+使用layoutparser识别给定文档的布局：
+
+```python
+import layoutparser as lp
+image = cv2.imread("imags/paper-image.jpg")
+image = image[..., ::-1]
+
+# 加载模型
+model = lp.PaddleDetectionLayoutModel(config_path="lp://PubLayNet/ppyolov2_r50vd_dcn_365e_publaynet/config", 
+                                threshold=0.5,
+                                label_map={0: "Text", 1: "Title", 2: "List", 3:"Table", 4:"Figure"},
+                                enforce_cpu=False, 
+                                enable_mkldnn=True)
+# 检测
+layout = model.detect(image)
+
+# 显示结果
+lp.draw_box(image, layout, box_width=3, show_element_type=True)
+```
+
+下图展示了结果，不同颜色的检测框表示不同的类别，并通过`show_element_type`在框的左上角显示具体类别：
+
+<div align="center">
+<img src="../../doc/table/result_all.jpg"  width = "600" />
+</div>
+
+`PaddleDetectionLayoutModel`函数参数说明如下:
+
+|      参数      |            含义             |   默认值    |                             备注                             |
+| :------------: | :-------------------------: | :---------: | :----------------------------------------------------------: |
+|  config_path   |        模型配置路径         |    None     | 指定config_path会自动下载模型(仅第一次，之后模型存在，不会再下载) |
+|   model_path   |          模型路径           |    None     | 本地模型路径，config_path和model_path必须设置一个，不能同时为None |
+|   threshold    |       预测得分的阈值        |     0.5     |                              \                               |
+|  input_shape   |     reshape之后图片尺寸     | [3,640,640] |                              \                               |
+|   batch_size   |       测试batch size        |      1      |                              \                               |
+|   label_map    |         类别映射表          |    None     | 设置config_path时，可以为None，根据数据集名称自动获取label_map |
+|  enforce_cpu   |     代码是否使用CPU运行     |    False    |         设置为False表示使用GPU，True表示强制使用CPU          |
+| enforce_mkldnn | CPU预测中是否开启MKLDNN加速 |    True     |                              \                               |
+|   thread_num   |        设置CPU线程数        |     10      |                              \                               |
+
+目前支持以下几种模型配置和label map，您可以通过修改 `--config_path`和 `--label_map`使用这些模型，从而检测不同类型的内容：
+
+| dataset                                                      | config_path                                                  | label_map                                                 |
+| ------------------------------------------------------------ | ------------------------------------------------------------ | --------------------------------------------------------- |
+| [TableBank](https://doc-analysis.github.io/tablebank-page/index.html) word | lp://TableBank/ppyolov2_r50vd_dcn_365e_tableBank_word/config | {0:"Table"}                                               |
+| TableBank latex                                              | lp://TableBank/ppyolov2_r50vd_dcn_365e_tableBank_latex/config | {0:"Table"}                                               |
+| [PubLayNet](https://github.com/ibm-aur-nlp/PubLayNet)        | lp://PubLayNet/ppyolov2_r50vd_dcn_365e_publaynet/config      | {0: "Text", 1: "Title", 2: "List", 3:"Table", 4:"Figure"} |
+
+* TableBank word和TableBank latex分别在word文档、latex文档数据集训练；
+* 下载TableBank数据集同时包含word和latex。
+
+<a name="后处理"></a>
+
+## 3. 后处理
+
+版面分析检测包含多个类别，如果只想获取指定类别(如"Text"类别)的检测框、可以使用下述代码：
+
+```python
+# 首先过滤特定文本类型的区域
+text_blocks = lp.Layout([b for b in layout if b.type=='Text'])
+figure_blocks = lp.Layout([b for b in layout if b.type=='Figure'])
+
+# 因为在图像区域内可能检测到文本区域，所以只需要删除它们
+text_blocks = lp.Layout([b for b in text_blocks \
+                   if not any(b.is_in(b_fig) for b_fig in figure_blocks)])
+
+# 对文本区域排序并分配id 
+h, w = image.shape[:2]
+
+left_interval = lp.Interval(0, w/2*1.05, axis='x').put_on_canvas(image)
+
+left_blocks = text_blocks.filter_by(left_interval, center=True)
+left_blocks.sort(key = lambda b:b.coordinates[1])
+
+right_blocks = [b for b in text_blocks if b not in left_blocks]
+right_blocks.sort(key = lambda b:b.coordinates[1])
+
+# 最终合并两个列表，并按顺序添加索引
+text_blocks = lp.Layout([b.set(id = idx) for idx, b in enumerate(left_blocks + right_blocks)])
+
+# 显示结果
+lp.draw_box(image, text_blocks,
+            box_width=3, 
+            show_element_id=True)
+```
+
+显示只有"Text"类别的结果：
+
+<div align="center">
+<img src="../../doc/table/result_text.jpg"  width = "600" />
+</div>
+
+<a name="指标"></a>
+
+## 4. 指标
+
+| Dataset   | mAP  | CPU time cost | GPU time cost |
+| --------- | ---- | ------------- | ------------- |
+| PubLayNet | 93.6 | 1713.7ms      | 66.6ms        |
+| TableBank | 96.2 | 1968.4ms      | 65.1ms        |
+
+**Envrionment：**	
+
+​	**CPU：**  Intel(R) Xeon(R) CPU E5-2650 v4 @ 2.20GHz，24core
+
+​	**GPU：**  a single NVIDIA Tesla P40
+
+<a name="训练版面分析模型"></a>
+
+## 5. 训练版面分析模型
+
+上述模型基于[PaddleDetection](https://github.com/PaddlePaddle/PaddleDetection) 训练，如果您想训练自己的版面分析模型，请参考：[train_layoutparser_model](train_layoutparser_model.md)
+

test1/layout/train_layoutparser_model.md

+# 训练版面分析
+
+* [1. 安装](#安装)
+  * [1.1 环境要求](#环境要求)
+  * [1.2 安装PaddleDetection](#安装PaddleDetection)
+* [2. 准备数据](#准备数据)
+* [3. 配置文件改动和说明](#配置文件改动和说明)
+* [4. PaddleDetection训练](#训练)
+* [5. PaddleDetection预测](#预测)
+* [6. 预测部署](#预测部署)
+  * [6.1 模型导出](#模型导出)
+  * [6.2 layout parser预测](#layout_parser预测)
+
+<a name="安装"></a>
+
+## 1. 安装
+
+<a name="环境要求"></a>
+
+### 1.1 环境要求
+
+- PaddlePaddle 2.1
+- OS 64 bit
+- Python 3(3.5.1+/3.6/3.7/3.8/3.9)，64 bit
+- pip/pip3(9.0.1+), 64 bit
+- CUDA >= 10.1
+- cuDNN >= 7.6
+
+<a name="安装PaddleDetection"></a>
+
+### 1.2 安装PaddleDetection
+
+```bash
+# 克隆PaddleDetection仓库
+cd <path/to/clone/PaddleDetection>
+git clone https://github.com/PaddlePaddle/PaddleDetection.git
+
+cd PaddleDetection
+# 安装其他依赖
+pip install -r requirements.txt
+```
+
+更多安装教程，请参考: [Install doc](https://github.com/PaddlePaddle/PaddleDetection/blob/release/2.1/docs/tutorials/INSTALL_cn.md)
+
+<a name="数据准备"></a>
+
+## 2. 准备数据
+
+下载 [PubLayNet](https://github.com/ibm-aur-nlp/PubLayNet) 数据集：
+
+```bash
+cd PaddleDetection/dataset/
+mkdir publaynet
+# 执行命令，下载
+wget -O publaynet.tar.gz https://dax-cdn.cdn.appdomain.cloud/dax-publaynet/1.0.0/publaynet.tar.gz?_ga=2.104193024.1076900768.1622560733-649911202.1622560733
+# 解压
+tar -xvf publaynet.tar.gz
+```
+
+解压之后PubLayNet目录结构：
+
+| File or Folder | Description                                      | num     |
+| :------------- | :----------------------------------------------- | ------- |
+| `train/`       | Images in the training subset                    | 335,703 |
+| `val/`         | Images in the validation subset                  | 11,245  |
+| `test/`        | Images in the testing subset                     | 11,405  |
+| `train.json`   | Annotations for training images                  |         |
+| `val.json`     | Annotations for validation images                |         |
+| `LICENSE.txt`  | Plaintext version of the CDLA-Permissive license |         |
+| `README.txt`   | Text file with the file names and description    |         |
+
+如果使用其它数据集，请参考[准备训练数据](https://github.com/PaddlePaddle/PaddleDetection/blob/release/2.1/docs/tutorials/PrepareDataSet.md)
+
+<a name="配置文件改动和说明"></a>
+
+## 3. 配置文件改动和说明
+
+我们使用 `configs/ppyolo/ppyolov2_r50vd_dcn_365e_coco.yml`配置进行训练，配置文件摘要如下：
+
+<div align='center'>
+  <img src='../../doc/table/PaddleDetection_config.png' width='600px'/>
+</div>
+
+从上图看到 `ppyolov2_r50vd_dcn_365e_coco.yml` 配置需要依赖其他的配置文件，在该例子中需要依赖:
+
+```
+coco_detection.yml：主要说明了训练数据和验证数据的路径
+
+runtime.yml：主要说明了公共的运行参数，比如是否使用GPU、每多少个epoch存储checkpoint等
+
+optimizer_365e.yml：主要说明了学习率和优化器的配置
+
+ppyolov2_r50vd_dcn.yml：主要说明模型和主干网络的情况
+
+ppyolov2_reader.yml：主要说明数据读取器配置，如batch size，并发加载子进程数等，同时包含读取后预处理操作，如resize、数据增强等等
+```
+
+根据实际情况，修改上述文件，比如数据集路径、batch size等。
+
+<a name="训练"></a>
+
+## 4. PaddleDetection训练
+
+PaddleDetection提供了单卡/多卡训练模式，满足用户多种训练需求
+
+* GPU 单卡训练
+
+```bash
+export CUDA_VISIBLE_DEVICES=0 #windows和Mac下不需要执行该命令
+python tools/train.py -c configs/ppyolo/ppyolov2_r50vd_dcn_365e_coco.yml
+```
+
+* GPU多卡训练
+
+```bash
+export CUDA_VISIBLE_DEVICES=0,1,2,3
+python -m paddle.distributed.launch --gpus 0,1,2,3 tools/train.py -c configs/ppyolo/ppyolov2_r50vd_dcn_365e_coco.yml --eval
+```
+
+--eval：表示边训练边验证
+
+* 模型恢复训练
+
+在日常训练过程中，有的用户由于一些原因导致训练中断，用户可以使用-r的命令恢复训练:
+
+```bash
+export CUDA_VISIBLE_DEVICES=0,1,2,3
+python -m paddle.distributed.launch --gpus 0,1,2,3 tools/train.py -c configs/ppyolo/ppyolov2_r50vd_dcn_365e_coco.yml --eval -r output/ppyolov2_r50vd_dcn_365e_coco/10000
+```
+
+注意：如果遇到 "`Out of memory error`" 问题, 尝试在 `ppyolov2_reader.yml` 文件中调小`batch_size`
+
+<a name="预测"></a>
+
+## 5. PaddleDetection预测
+
+设置参数，使用PaddleDetection预测：
+
+```bash
+export CUDA_VISIBLE_DEVICES=0
+python tools/infer.py -c configs/ppyolo/ppyolov2_r50vd_dcn_365e_coco.yml --infer_img=images/paper-image.jpg --output_dir=infer_output/ --draw_threshold=0.5 -o weights=output/ppyolov2_r50vd_dcn_365e_coco/model_final --use_vdl=Ture
+```
+
+`--draw_threshold` 是个可选参数. 根据 [NMS](https://ieeexplore.ieee.org/document/1699659) 的计算，不同阈值会产生不同的结果 `keep_top_k`表示设置输出目标的最大数量，默认值为100，用户可以根据自己的实际情况进行设定。
+
+<a name="预测部署"></a>
+
+## 6. 预测部署
+
+在layout parser中使用自己训练好的模型，
+
+<a name="模型导出"></a>
+
+### 6.1 模型导出
+
+在模型训练过程中保存的模型文件是包含前向预测和反向传播的过程，在实际的工业部署则不需要反向传播，因此需要将模型进行导成部署需要的模型格式。 在PaddleDetection中提供了 `tools/export_model.py`脚本来导出模型。
+
+导出模型名称默认是`model.*`，layout parser代码模型名称是`inference.*`,  所以修改[PaddleDetection/ppdet/engine/trainer.py ](https://github.com/PaddlePaddle/PaddleDetection/blob/b87a1ea86fa18ce69e44a17ad1b49c1326f19ff9/ppdet/engine/trainer.py#L512) (点开链接查看详细代码行)，将`model`改为`inference`即可。
+
+执行导出模型脚本：
+
+```bash
+python tools/export_model.py -c configs/ppyolo/ppyolov2_r50vd_dcn_365e_coco.yml --output_dir=./inference -o weights=output/ppyolov2_r50vd_dcn_365e_coco/model_final.pdparams
+```
+
+预测模型会导出到`inference/ppyolov2_r50vd_dcn_365e_coco`目录下，分别为`infer_cfg.yml`(预测不需要), `inference.pdiparams`, `inference.pdiparams.info`,`inference.pdmodel` 。
+
+更多模型导出教程，请参考：[EXPORT_MODEL](https://github.com/PaddlePaddle/PaddleDetection/blob/release/2.1/deploy/EXPORT_MODEL.md)
+
+<a name="layout parser预测"></a>
+
+### 6.2 layout_parser预测
+
+`model_path`指定训练好的模型路径，使用layout parser进行预测：
+
+```bash
+import layoutparser as lp
+model = lp.PaddleDetectionLayoutModel(model_path="inference/ppyolov2_r50vd_dcn_365e_coco", threshold=0.5,label_map={0: "Text", 1: "Title", 2: "List", 3:"Table", 4:"Figure"},enforce_cpu=True,enable_mkldnn=True)
+```
+
+
+
+***
+
+更多PaddleDetection训练教程，请参考：[PaddleDetection训练](https://github.com/PaddlePaddle/PaddleDetection/blob/release/2.1/docs/tutorials/GETTING_STARTED_cn.md)
+
+***
+

ppstructure/paddlestructure.py → test1/paddlestructure.py

@@ -24,15 +24,15 @@ 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
+from test1.predict_system import OCRSystem, save_res
+from test1.table.predict_table import to_excel
+from test1.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']
+__all__ = ['PaddleStructure', 'draw_result', 'save_res']
 
 VERSION = '2.1'
 BASE_DIR = os.path.expanduser("~/.paddlestructure/")
@@ -40,7 +40,7 @@ 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'
+    'table': 'https://paddleocr.bj.bcebos.com/dygraph_v2.0/table/en_ppocr_mobile_v2.0_table_structure_infer.tar'
 
 }
 
@@ -51,7 +51,7 @@ def parse_args(mMain=True):
     parser.add_help = mMain
 
     for action in parser._actions:
-        if action.dest in ['rec_char_dict_path', 'structure_char_dict_path']:
+        if action.dest in ['rec_char_dict_path', 'table_char_dict_path']:
             action.default = None
     if mMain:
         return parser.parse_args()
@@ -66,8 +66,8 @@ class PaddleStructure(OCRSystem):
     def __init__(self, **kwargs):
         params = parse_args(mMain=False)
         params.__dict__.update(**kwargs)
-        if params.show_log:
-            logger.setLevel(logging.DEBUG)
+        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,
@@ -76,13 +76,13 @@ class PaddleStructure(OCRSystem):
         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'])
+        params.table_model_dir, table_url = confirm_model_dir_url(params.table_model_dir,
+                                                                          os.path.join(BASE_DIR, VERSION, 'table'),
+                                                                          model_urls['table'])
         # 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)
+        maybe_download(params.table_model_dir, table_url)
 
         if params.rec_char_dict_path is None:
             params.rec_char_type = 'EN'
@@ -90,12 +90,12 @@ class PaddleStructure(OCRSystem):
                 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 params.table_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(
+                params.table_char_dict_path = str(
                     Path(__file__).parent / 'ppocr/utils/dict/table_structure_dict.txt')
             else:
-                params.structure_char_dict_path = str(
+                params.table_char_dict_path = str(
                     Path(__file__).parent.parent / 'ppocr/utils/dict/table_structure_dict.txt')
 
         print(params)
@@ -145,4 +145,24 @@ def main():
         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
+        logger.info('result save to {}'.format(os.path.join(save_folder, img_name)))
+
+if __name__ == '__main__':
+    table_engine = PaddleStructure(show_log=True)
+
+    img_path = '../test/test_imgs/PMC1173095_006_00.png'
+    img = cv2.imread(img_path)
+    result = table_engine(img)
+    save_res(result, '/Users/zhoujun20/Desktop/工作相关/table/table_pr/PaddleOCR/output/table',
+             os.path.basename(img_path).split('.')[0])
+
+    for line in result:
+        print(line)
+
+    from PIL import Image
+
+    font_path = '../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')
\ No newline at end of file

ppstructure/predict_system.py → test1/predict_system.py

@@ -24,14 +24,15 @@ 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 ppstructure.table.predict_table import TableSystem, to_excel
-from ppstructure.utility import parse_args,draw_result
+from test1.table.predict_table import TableSystem, to_excel
+from test1.utility import parse_args, draw_result
 
 logger = get_logger()
 
@@ -40,6 +41,8 @@ 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",
@@ -58,8 +61,6 @@ class OCRSystem(object):
             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]
@@ -69,6 +70,7 @@ class OCRSystem(object):
             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)
@@ -107,7 +109,7 @@ def main(args):
         starttime = time.time()
         res = structure_sys(img)
         save_res(res, save_folder, img_name)
-        draw_img = draw_result(img,res, args.vis_font_path)
+        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

ppstructure/setup.py → test1/setup.py

@@ -25,14 +25,14 @@ with open('../requirements.txt', encoding="utf-8-sig") as f:
 
 
 def readme():
-    with open('README_ch.md', encoding="utf-8-sig") as f:
+    with open('api_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('./table', './test1/table')
+shutil.copyfile('./predict_system.py', './test1/predict_system.py')
+shutil.copyfile('./utility.py', './test1/utility.py')
 shutil.copytree('../ppocr', './ppocr')
 shutil.copytree('../tools', './tools')
 shutil.copyfile('../LICENSE', './LICENSE')
@@ -68,5 +68,5 @@ setup(
 
 shutil.rmtree('ppocr')
 shutil.rmtree('tools')
-shutil.rmtree('ppstructure')
+shutil.rmtree('test1')
 os.remove('LICENSE')

test1/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 --table_model_dir=path/to/table_model_dir --image_dir=../doc/table/1.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 --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 --table_model_dir=path/to/table_model_dir --image_dir=../doc/table/1.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 --output ../output/table
+```
+After running, the excel sheet of each picture will be saved in the directory specified by the output field
\ No newline at end of file

test1/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 --table_model_dir=path/to/table_model_dir --image_dir=../doc/table/1.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 --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 --table_model_dir=path/to/table_model_dir --image_dir=../doc/table/1.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 --output ../output/table
+```
+运行完成后，每张图片的excel表格会保存到output字段指定的目录下

ppstructure/table/eval_table.py → test1/table/eval_table.py

@@ -20,9 +20,12 @@ 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
+from test1.table.table_metric import TEDS
+from test1.table.predict_table import TableSystem
+from test1.utility import init_args
+from ppocr.utils.logging import get_logger
+
+logger = get_logger()
 
 
 def parse_args():
@@ -47,7 +50,7 @@ def main(gt_path, img_root, args):
         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))
+    logger.info('teds:', sum(scores) / len(scores))
 
 
 def get_gt_html(gt_structures, contents_with_block):

ppstructure/table/predict_structure.py → test1/table/predict_structure.py

@@ -32,6 +32,7 @@ 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 test1.utility import parse_args
 
 logger = get_logger()
 
@@ -40,7 +41,7 @@ class TableStructurer(object):
     def __init__(self, args):
         pre_process_list = [{
             'ResizeTableImage': {
-                'max_len': args.structure_max_len
+                'max_len': args.table_max_len
             }
         }, {
             'NormalizeImage': {
@@ -60,17 +61,14 @@ class TableStructurer(object):
         }]
         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
+            "character_type": args.table_char_type,
+            "character_dict_path": args.table_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 = \
-            utility.create_predictor(args, 'structure', logger)
+        self.predictor, self.input_tensor, self.output_tensors, self.config = \
+            utility.create_predictor(args, 'table', logger)
 
     def __call__(self, img):
         ori_im = img.copy()
@@ -138,4 +136,4 @@ def main(args):
 
 
 if __name__ == "__main__":
-    main(utility.parse_args())
+    main(parse_args())

ppstructure/table/predict_table.py → test1/table/predict_table.py

@@ -30,9 +30,9 @@ 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
+from test1.table.matcher import distance, compute_iou
+from test1.utility import parse_args
+import test1.table.predict_structure as predict_strture
 
 logger = get_logger()
 
@@ -187,7 +187,7 @@ def main(args):
     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')
+        excel_path = os.path.join(args.output, os.path.basename(image_file).split('.')[0] + '.xlsx')
         if not flag:
             img = cv2.imread(image_file)
         if img is None: