From 78daac51b3097ca887817825324f7a1e99a46fbf Mon Sep 17 00:00:00 2001 From: Jokcer <519548295@qq.com> Date: Sun, 29 Dec 2024 23:09:28 +0800 Subject: [PATCH 01/23] feat: add unitable torch inference --- .github/workflows/publish_whl_torch.yml | 65 +++ .gitignore | 3 + README.md | 213 +++++++++- demo_torch.py | 34 ++ outputs/table.html | 4 - outputs/table_ocr_vis.jpg | Bin 95057 -> 0 bytes outputs/table_table_vis.jpg | Bin 79191 -> 0 bytes rapid_table/utils.py | 2 +- rapid_table_torch/__init__.py | 2 + rapid_table_torch/download_model.py | 59 +++ rapid_table_torch/logger.py | 21 + rapid_table_torch/main.py | 155 +++++++ rapid_table_torch/table_matcher/__init__.py | 4 + rapid_table_torch/table_matcher/matcher.py | 192 +++++++++ rapid_table_torch/table_matcher/utils.py | 248 +++++++++++ rapid_table_torch/table_structure/__init__.py | 1 + .../table_structure/components.py | 388 ++++++++++++++++++ .../table_structure/table_structure.py | 213 ++++++++++ rapid_table_torch/table_structure/utils.py | 28 ++ rapid_table_torch/utils.py | 210 ++++++++++ requirements_torch.txt | 8 + setup_torch.py | 66 +++ tests/test_table_torch.py | 35 ++ 23 files changed, 1933 insertions(+), 18 deletions(-) create mode 100644 .github/workflows/publish_whl_torch.yml create mode 100644 demo_torch.py delete mode 100644 outputs/table.html delete mode 100644 outputs/table_ocr_vis.jpg delete mode 100644 outputs/table_table_vis.jpg create mode 100644 rapid_table_torch/__init__.py create mode 100644 rapid_table_torch/download_model.py create mode 100644 rapid_table_torch/logger.py create mode 100644 rapid_table_torch/main.py create mode 100644 rapid_table_torch/table_matcher/__init__.py create mode 100644 rapid_table_torch/table_matcher/matcher.py create mode 100644 rapid_table_torch/table_matcher/utils.py create mode 100644 rapid_table_torch/table_structure/__init__.py create mode 100644 rapid_table_torch/table_structure/components.py create mode 100644 rapid_table_torch/table_structure/table_structure.py create mode 100644 rapid_table_torch/table_structure/utils.py create mode 100644 rapid_table_torch/utils.py create mode 100644 requirements_torch.txt create mode 100644 setup_torch.py create mode 100644 tests/test_table_torch.py diff --git a/.github/workflows/publish_whl_torch.yml b/.github/workflows/publish_whl_torch.yml new file mode 100644 index 0000000..ac7aecc --- /dev/null +++ b/.github/workflows/publish_whl_torch.yml @@ -0,0 +1,65 @@ +name: Push rapidocr_table to pypi + +on: + push: + tags: + - torch_v* + +env: + RESOURCES_URL: https://github.com/RapidAI/RapidTable/releases/download/assets/unitable.zip + + +jobs: + UnitTesting: + runs-on: ubuntu-latest + steps: + - name: Pull latest code + uses: actions/checkout@v3 + + - name: Set up Python 3.10 + uses: actions/setup-python@v4 + with: + python-version: '3.10' + architecture: 'x64' + + - name: Display Python version + run: python -c "import sys; print(sys.version)" + + - name: Unit testings + run: | + wget $RESOURCES_URL + ZIP_NAME=${RESOURCES_URL##*/} + DIR_NAME=${ZIP_NAME%.*} + unzip $DIR_NAME + cp $DIR_NAME/*.pth rapid_table_torch/models/ + cp $DIR_NAME/*.json rapid_table_torch/models/ + pip install -r requirements_torch.txt + pip install rapidocr_onnxruntime + pip install pytest + pytest tests/test_table_torch.py + + GenerateWHL_PushPyPi: + needs: UnitTesting + runs-on: ubuntu-latest + + steps: + - uses: actions/checkout@v3 + + - name: Set up Python 3.10 + uses: actions/setup-python@v4 + with: + python-version: '3.10' + architecture: 'x64' + + - name: Run setup_torch.py + run: | + pip install -r requirements_torch.txt + python -m pip install --upgrade pip + pip install wheel get_pypi_latest_version + python setup.py bdist_wheel ${{ github.ref_name }} + + - name: Publish distribution 📦 to PyPI + uses: pypa/gh-action-pypi-publish@v1.5.0 + with: + password: ${{ secrets.RAPID_TABLE }} + packages_dir: dist/ diff --git a/.gitignore b/.gitignore index edaddfb..378c8a8 100644 --- a/.gitignore +++ b/.gitignore @@ -156,3 +156,6 @@ long1.jpg *.pdmodel .DS_Store +*.pth +/rapid_table_torch/models/*.pth +/rapid_table_torch/models/*.json diff --git a/README.md b/README.md index 6a62216..a621571 100644 --- a/README.md +++ b/README.md @@ -15,21 +15,23 @@ ### 简介 -RapidTable库是专门用来文档类图像的表格结构还原，结合RapidOCR，将给定图像中的表格转化对应的HTML格式。 - -目前支持两种类别的表格识别模型：中文和英文表格识别模型，具体可参见下面表格： +RapidTable库是专门用来文档类图像的表格结构还原，表格结构模型均属于序列预测方法，结合RapidOCR，将给定图像中的表格转化对应的HTML格式。 slanet_plus是paddlex内置的SLANet升级版模型，准确率有大幅提升 - | 模型类型 | 模型名称 | 模型大小 | - |:--------------:|:--------------------------------------:| :------: | - | 英文 | `en_ppstructure_mobile_v2_SLANet.onnx` | 7.3M | - | 中文 | `ch_ppstructure_mobile_v2_SLANet.onnx` | 7.4M | - | slanet_plus 中文 | `slanet-plus.onnx` | 6.8M | +unitable是来源unitable的transformer模型，精度最高，暂仅支持pytorch推理，支持gpu推理加速,训练权重来源于 [OhMyTable项目](https://github.com/Sanster/OhMyTable) +| 模型类型 | 模型名称 | 推理框架 |模型大小 |推理耗时(单图 60KB)| + |:--------------:|:--------------------------------------:| :------: |:------: |:------: | +| 英文 | `en_ppstructure_mobile_v2_SLANet.onnx` | onnxruntime |7.3M |0.15s | +| 中文 | `ch_ppstructure_mobile_v2_SLANet.onnx` | onnxruntime |7.4M |0.15s | +| slanet_plus 中文 | `slanet-plus.onnx` | onnxruntime |6.8M |0.15s | +| unitable 中文 | `unitable(encoder.pth,decoder.pth)` | pytorch |500M |cpu(6s) gpu-4090(1.5s)| -模型来源：[PaddleOCR 表格识别](https://github.com/PaddlePaddle/PaddleOCR/blob/133d67f27dc8a241d6b2e30a9f047a0fb75bebbe/ppstructure/table/README_ch.md) -[PaddleX-SlaNetPlus 表格识别](https://github.com/PaddlePaddle/PaddleX/blob/release/3.0-beta1/docs/module_usage/tutorials/ocr_modules/table_structure_recognition.md) +模型来源\ +[PaddleOCR 表格识别](https://github.com/PaddlePaddle/PaddleOCR/blob/133d67f27dc8a241d6b2e30a9f047a0fb75bebbe/ppstructure/table/README_ch.md) \ +[PaddleX-SlaNetPlus 表格识别](https://github.com/PaddlePaddle/PaddleX/blob/release/3.0-beta1/docs/module_usage/tutorials/ocr_modules/table_structure_recognition.md) \ +[Unitable](https://github.com/poloclub/unitable?tab=readme-ov-file) 模型下载地址为：[link](https://github.com/RapidAI/RapidTable/releases/tag/assets) @@ -43,10 +45,16 @@ slanet_plus是paddlex内置的SLANet升级版模型，准确率有大幅提升
+#### 2024.12.30 update + +- 支持Unitable模型的表格识别，使用pytorch框架 + #### 2024.11.24 update + - 支持gpu推理，适配 rapidOCR 单字识别匹配,支持逻辑坐标返回及可视化 #### 2024.10.13 update + - 补充最新paddlex-SLANet-plus 模型(paddle2onnx原因暂不能支持onnx) #### 2023-12-29 v0.1.3 update @@ -78,7 +86,6 @@ slanet_plus是paddlex内置的SLANet升级版模型，准确率有大幅提升
- ### 与[TableStructureRec](https://github.com/RapidAI/TableStructureRec)关系 TableStructureRec库是一个表格识别算法的集合库，当前有`wired_table_rec`有线表格识别算法和`lineless_table_rec`无线表格识别算法的推理包。 @@ -98,6 +105,7 @@ RapidTable是整理自PP-Structure中表格识别部分而来。由于PP-Structu ```bash pip install rapidocr_onnxruntime pip install rapid_table +#pip install rapid_table_torch # for unitable inference #pip install onnxruntime-gpu # for gpu inference ``` @@ -113,6 +121,7 @@ table_engine = RapidTable() 完整示例： +#### onnx版本 ```python from pathlib import Path @@ -120,7 +129,6 @@ from rapid_table import RapidTable, VisTable from rapidocr_onnxruntime import RapidOCR from rapid_table.table_structure.utils import trans_char_ocr_res - table_engine = RapidTable() # 开启onnx-gpu推理 # table_engine = RapidTable(use_cuda=True) @@ -151,8 +159,41 @@ viser(img_path, table_html_str, save_html_path, table_cell_bboxes, save_drawed_p print(table_html_str) ``` +#### torch版本 +```python +from pathlib import Path +from rapidocr_onnxruntime import RapidOCR + +from rapid_table_torch import RapidTable, VisTable +from rapid_table_torch.table_structure.utils import trans_char_ocr_res + +if __name__ == '__main__': +# Init +ocr_engine = RapidOCR() +table_engine = RapidTable(device="cpu") # 默认使用cpu，若使用cuda，则传入device="cuda:0" +viser = VisTable() +img_path = "tests/test_files/image34.png" +# OCR,本模型检测框比较精准，配合单字匹配效果更好 +ocr_result, _ = ocr_engine(img_path, return_word_box=True) +ocr_result = trans_char_ocr_res(ocr_result) +boxes, txts, scores = list(zip(*ocr_result)) +# Save +save_dir = Path("outputs") +save_dir.mkdir(parents=True, exist_ok=True) + +save_html_path = save_dir / f"{Path(img_path).stem}.html" +save_drawed_path = save_dir / f"{Path(img_path).stem}_table_vis{Path(img_path).suffix}" +# 返回逻辑坐标 +table_html_str, table_cell_bboxes, logic_points, elapse = table_engine(img_path, ocr_result) +save_logic_path = save_dir / f"vis_logic_{Path(img_path).name}" +vis_imged = viser(img_path, table_html_str, save_html_path, table_cell_bboxes, save_drawed_path, logic_points, + save_logic_path) +print(f"elapse:{elapse}") +``` + #### 终端运行 +##### onnx: - 用法: ```bash @@ -174,12 +215,158 @@ print(table_html_str) rapid_table -v -img test_images/table.jpg ``` +##### pytorch: +- 用法: + + ```bash + $ rapid_table_torch -h + usage: rapid_table_torch [-h] [-v] -img IMG_PATH [-d DEVICE] + + optional arguments: + -h, --help show this help message and exit + -v, --vis Whether to visualize the layout results. + -img IMG_PATH, --img_path IMG_PATH + Path to image for layout. + -d DEVICE, --device device + The model device used for inference. + ``` + +- 示例: + + ```bash + rapid_table_torch -v -img test_images/table.jpg + ``` + ### 结果 #### 返回结果 ```html -<>
MethodsFPS
SegLink [26]70.086d>77.08.9
PixelLink [4]73.283.077.8
TextSnake [18]73.983.278.31.1
TextField [37]75.987.481.35.2
MSR[38]76.787.87.481.7
FTSN [3]77.187.682.0
LSE[30]81.784.282.9
CRAFT [2]78.288.282.98.6
MCN[16]798883
ATRR[35]82.185.283.6
PAN [34]83.884.484.130.2
DB[12]79.291.584.932.0
DRRG[41]82.3088.0585.08
Ours (SynText)80.688582.9712.68
Ours (MLT-17)84.5486.6285.5712.31
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + <> + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
MethodsFPS
SegLink [26]70.086d> + + 77.08.9
PixelLink [4]73.283.077.8
TextSnake [18]73.983.278.31.1
TextField [37]75.987.481.35.2
MSR[38]76.787.87.481.7
FTSN [3]77.187.682.0
LSE[30]81.784.282.9
CRAFT [2]78.288.282.98.6
MCN[16]798883
ATRR[35]82.185.283.6
PAN [34]83.884.484.130.2
DB[12]79.2 + 91.584.932.0
DRRG[41]82.3088.0585.08
Ours (SynText)80.6885 + + 82.9712.68
Ours (MLT-17)84.5486.6285.5712.31
+ + ``` #### 可视化结果 diff --git a/demo_torch.py b/demo_torch.py new file mode 100644 index 0000000..e8dc1e0 --- /dev/null +++ b/demo_torch.py @@ -0,0 +1,34 @@ +# -*- encoding: utf-8 -*- +# @Author: SWHL +# @Contact: liekkaskono@163.com +from pathlib import Path +from rapidocr_onnxruntime import RapidOCR + +from rapid_table_torch import RapidTable, VisTable +from rapid_table_torch.table_structure.utils import trans_char_ocr_res + +if __name__ == '__main__': + # Init + ocr_engine = RapidOCR() + table_engine = RapidTable(encoder_path="rapid_table_torch/models/encoder.pth", + decoder_path="rapid_table_torch/models/decoder.pth", + vocab_path="rapid_table_torch/models/vocab.json", + device="cpu") + viser = VisTable() + img_path = "tests/test_files/image34.png" + # OCR + ocr_result, _ = ocr_engine(img_path, return_word_box=True) + ocr_result = trans_char_ocr_res(ocr_result) + boxes, txts, scores = list(zip(*ocr_result)) + # Save + save_dir = Path("outputs") + save_dir.mkdir(parents=True, exist_ok=True) + + save_html_path = save_dir / f"{Path(img_path).stem}.html" + save_drawed_path = save_dir / f"{Path(img_path).stem}_table_vis{Path(img_path).suffix}" + # 返回逻辑坐标 + table_html_str, table_cell_bboxes, logic_points, elapse = table_engine(img_path, ocr_result) + save_logic_path = save_dir / f"vis_logic_{Path(img_path).name}" + vis_imged = viser(img_path, table_html_str, save_html_path, table_cell_bboxes, save_drawed_path, logic_points, + save_logic_path) + print(f"elapse:{elapse}") diff --git a/outputs/table.html b/outputs/table.html deleted file mode 100644 index 1d26243..0000000 --- a/outputs/table.html +++ /dev/null @@ -1,4 +0,0 @@ -
MethodsRPFFPS
SegLink[26]70.086.077.08.9
PixelLink[4]73.283.077.8-
TextSnake[18]73.983.278.31.1
TextField[37]75.987.481.35.2
MSR[38]76.787.481.7
FTSN[3]77.187.682.0
LSE[30]81.784.282.9"
CRAFT[2]78.288.282.98.6
MCN[16]798883-
ATRR[35]82.185.283.6"
PAN [34]83.884.484.130.2
DB[12]79.291.584.932.0
DRRG[41]82.3088.0585.08-
Ours (SynText)80.6885.4082.9712.68
Ours (MLT-17)84.5486.6285.5712.31
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- -#### 2024.12.30 update - -- 支持Unitable模型的表格识别，使用pytorch框架 - -#### 2024.11.24 update - -- 支持gpu推理，适配 rapidOCR 单字识别匹配,支持逻辑坐标返回及可视化 - -#### 2024.10.13 update - -- 补充最新paddlex-SLANet-plus 模型(paddle2onnx原因暂不能支持onnx) - -#### 2023-12-29 v0.1.3 update - -- 优化可视化结果部分 - -#### 2023-12-27 v0.1.2 update - -- 添加返回cell坐标框参数 -- 完善可视化函数 - -#### 2023-07-17 v0.1.0 update - -- 将`rapidocr_onnxruntime`部分从`rapid_table`中解耦合出来，给出选项是否依赖，更加灵活。 - -- 增加接口输入参数`ocr_result`： - - 如果在调用函数时，事先指定了`ocr_result`参数值，则不会再走OCR。其中`ocr_result`格式需要和`rapidocr_onnxruntime`返回值一致。 - - 如果未指定`ocr_result`参数值，但是事先安装了`rapidocr_onnxruntime`库，则会自动调用该库，进行识别。 - - 如果`ocr_result`未指定，且`rapidocr_onnxruntime`未安装，则会报错。必须满足两个条件中一个。 - -#### 2023-07-10 v0.0.13 updata - -- 更改传入表格还原中OCR的实例接口，可以传入其他OCR实例，前提要与`rapidocr_onnxruntime`接口一致 - -#### 2023-07-06 v0.0.12 update - -- 去掉返回表格的html字符串中的``元素，便于后续统一。 -- 采用Black工具优化代码 - -
- ### 与[TableStructureRec](https://github.com/RapidAI/TableStructureRec)关系 TableStructureRec库是一个表格识别算法的集合库，当前有`wired_table_rec`有线表格识别算法和`lineless_table_rec`无线表格识别算法的推理包。 @@ -105,8 +60,8 @@ RapidTable是整理自PP-Structure中表格识别部分而来。由于PP-Structu ```bash pip install rapidocr_onnxruntime pip install rapid_table -#pip install rapid_table[torch] # for unitable inference -#pip install onnxruntime-gpu # for onnx gpu inference +# pip install rapid_table[torch] # for unitable inference +# pip install onnxruntime-gpu # for onnx gpu inference ``` ### 使用方式 @@ -130,10 +85,13 @@ from rapidocr_onnxruntime import RapidOCR from rapid_table.table_structure.utils import trans_char_ocr_res table_engine = RapidTable() + # 开启onnx-gpu推理 # table_engine = RapidTable(use_cuda=True) + # 使用torch推理版本的unitable模型 # table_engine = RapidTable(use_cuda=True, device="cuda:0", model_type="unitable") + ocr_engine = RapidOCR() viser = VisTable() @@ -143,6 +101,7 @@ ocr_result, _ = ocr_engine(img_path) # 单字匹配 # ocr_result, _ = ocr_engine(img_path, return_word_box=True) # ocr_result = trans_char_ocr_res(ocr_result) + table_html_str, table_cell_bboxes, elapse = table_engine(img_path, ocr_result) save_dir = Path("./inference_results/") @@ -155,6 +114,7 @@ viser(img_path, table_html_str, save_html_path, table_cell_bboxes, save_drawed_p # 返回逻辑坐标 # table_html_str, table_cell_bboxes, logic_points, elapse = table_engine(img_path, ocr_result, return_logic_points=True) + # save_logic_path = save_dir / f"vis_logic_{Path(img_path).name}" # viser(img_path, table_html_str, save_html_path, table_cell_bboxes, save_drawed_path,logic_points, save_logic_path) @@ -163,33 +123,32 @@ print(table_html_str) #### 终端运行 -- 用法: - - ```bash - $ rapid_table -h - usage: rapid_table [-h] [-v] -img IMG_PATH [-m MODEL_PATH] +```bash +$ rapid_table -h +usage: rapid_table [-h] [-v] -img IMG_PATH [-m MODEL_PATH] + +optional arguments: +-h, --help show this help message and exit +-v, --vis Whether to visualize the layout results. +-img IMG_PATH, --img_path IMG_PATH + Path to image for layout. +-m MODEL_PATH, --model_path MODEL_PATH + The model path used for inference. +``` - optional arguments: - -h, --help show this help message and exit - -v, --vis Whether to visualize the layout results. - -img IMG_PATH, --img_path IMG_PATH - Path to image for layout. - -m MODEL_PATH, --model_path MODEL_PATH - The model path used for inference. - ``` +示例: -- 示例: +```bash +rapid_table -v -img test_images/table.jpg +``` - ```bash - rapid_table -v -img test_images/table.jpg - ``` - ### 结果 #### 返回结果 -```html +

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zw=1~pgxM?-^D_~tE?6vISMhm@@c5`*mdU%#{C}wWwaae)|F115x!7$yeXavfZ4x66 z&7K_Bzt$1Ihtc+G^ozr*!}%5E!N=}C{&v{%Oc&gD^;y|Xn`*lA>H+RU=W2N2Tv}qE{J@=P0p_9oo zA;STb6d3wlz&NE`!j9TvM6k4{Bfo`yDrV|_{4L~XQ7yQ&f~C#?5TOJ2KlcM;ts)@J z0lwN4^}0YY3Pjp@&o|TlFnhB+DBGZ-6Kvcqcu@4MVyC(agnd%QG8xgMtTVsxZb3`h z(Z*=tvWlhD#JPZDp`qJ$JIZ np.ndarray: diff --git a/rapid_table_torch/__init__.py b/rapid_table_torch/__init__.py new file mode 100644 index 0000000..86c7b1c --- /dev/null +++ b/rapid_table_torch/__init__.py @@ -0,0 +1,2 @@ +from .main import RapidTable +from .utils import VisTable diff --git a/rapid_table_torch/download_model.py b/rapid_table_torch/download_model.py new file mode 100644 index 0000000..d636f94 --- /dev/null +++ b/rapid_table_torch/download_model.py @@ -0,0 +1,59 @@ +import io +from pathlib import Path +from typing import Optional, Union + +import requests +from tqdm import tqdm + +from .logger import get_logger + +logger = get_logger("DownloadModel") +CUR_DIR = Path(__file__).resolve() +PROJECT_DIR = CUR_DIR.parent + + +class DownloadModel: + cur_dir = PROJECT_DIR + + @classmethod + def download(cls, model_full_url: Union[str, Path]) -> str: + save_dir = cls.cur_dir / "models" + save_dir.mkdir(parents=True, exist_ok=True) + + model_name = Path(model_full_url).name + save_file_path = save_dir / model_name + if save_file_path.exists(): + logger.debug("%s already exists", save_file_path) + return str(save_file_path) + + try: + logger.info("Download %s to %s", model_full_url, save_dir) + file = cls.download_as_bytes_with_progress(model_full_url, model_name) + cls.save_file(save_file_path, file) + except Exception as exc: + raise DownloadModelError from exc + return str(save_file_path) + + @staticmethod + def download_as_bytes_with_progress( + url: Union[str, Path], name: Optional[str] = None + ) -> bytes: + resp = requests.get(str(url), stream=True, allow_redirects=True, timeout=180) + total = int(resp.headers.get("content-length", 0)) + bio = io.BytesIO() + with tqdm( + desc=name, total=total, unit="b", unit_scale=True, unit_divisor=1024 + ) as pbar: + for chunk in resp.iter_content(chunk_size=65536): + pbar.update(len(chunk)) + bio.write(chunk) + return bio.getvalue() + + @staticmethod + def save_file(save_path: Union[str, Path], file: bytes): + with open(save_path, "wb") as f: + f.write(file) + + +class DownloadModelError(Exception): + pass diff --git a/rapid_table_torch/logger.py b/rapid_table_torch/logger.py new file mode 100644 index 0000000..2950987 --- /dev/null +++ b/rapid_table_torch/logger.py @@ -0,0 +1,21 @@ +# -*- encoding: utf-8 -*- +# @Author: Jocker1212 +# @Contact: xinyijianggo@gmail.com +import logging +from functools import lru_cache + + +@lru_cache(maxsize=32) +def get_logger(name: str) -> logging.Logger: + logger = logging.getLogger(name) + logger.setLevel(logging.DEBUG) + + fmt = "%(asctime)s - %(name)s - %(levelname)s: %(message)s" + format_str = logging.Formatter(fmt) + + sh = logging.StreamHandler() + sh.setLevel(logging.DEBUG) + + logger.addHandler(sh) + sh.setFormatter(format_str) + return logger diff --git a/rapid_table_torch/main.py b/rapid_table_torch/main.py new file mode 100644 index 0000000..5ef3dcc --- /dev/null +++ b/rapid_table_torch/main.py @@ -0,0 +1,155 @@ +# -*- encoding: utf-8 -*- +# @Author: SWHL +# @Contact: liekkaskono@163.com +import argparse +import copy +import importlib +import os +import time +from pathlib import Path +from typing import List, Union + +import cv2 +import numpy as np + +from .download_model import DownloadModel +from .logger import get_logger +from .table_matcher import TableMatch +from .table_structure import TableStructurer +from .utils import LoadImage, VisTable + +root_dir = Path(__file__).resolve().parent +model_dir = os.path.join(root_dir, "models") +logger = get_logger("rapid_table_torch") +default_config = os.path.join(root_dir, "config.yaml") +ROOT_URL = "https://www.modelscope.cn/studio/jockerK/TableRec/resolve/master/models/table_rec/unitable/" +KEY_TO_MODEL_URL = { + "unitable": { + "encoder": f"{ROOT_URL}/encoder.pth", + "decoder": f"{ROOT_URL}/decoder.pth", + "vocab": f"{ROOT_URL}/vocab.json", + } +} + + +class RapidTable: + def __init__(self, encoder_path: str = None, decoder_path: str = None, vocab_path: str = None, + model_type: str = "unitable", + device: str = "cpu"): + self.model_type = model_type + self.load_img = LoadImage() + encoder_path = self.get_model_path(model_type, "encoder", encoder_path) + decoder_path = self.get_model_path(model_type, "decoder", decoder_path) + vocab_path = self.get_model_path(model_type, "vocab", vocab_path) + self.table_structure = TableStructurer(encoder_path, decoder_path, vocab_path, device) + self.table_matcher = TableMatch() + try: + self.ocr_engine = importlib.import_module("rapidocr_onnxruntime").RapidOCR() + except ModuleNotFoundError: + self.ocr_engine = None + + def __call__( + self, + img_content: Union[str, np.ndarray, bytes, Path], + ocr_result: List[Union[List[List[float]], str, str]] = None + ): + if self.ocr_engine is None and ocr_result is None: + raise ValueError( + "One of two conditions must be met: ocr_result is not empty, or rapidocr_onnxruntime is installed." + ) + + img = self.load_img(img_content) + + s = time.time() + h, w = img.shape[:2] + if ocr_result is None: + ocr_result, _ = self.ocr_engine(img) + dt_boxes, rec_res = self.get_boxes_recs(ocr_result, h, w) + + pred_structures, pred_bboxes, _ = self.table_structure(copy.deepcopy(img)) + pred_html = self.table_matcher(pred_structures, pred_bboxes, dt_boxes, rec_res) + logic_points = self.table_matcher.decode_logic_points(pred_structures) + elapse = time.time() - s + return pred_html, pred_bboxes, logic_points, elapse + + def get_boxes_recs( + self, ocr_result: List[Union[List[List[float]], str, str]], h: int, w: int + ): + dt_boxes, rec_res, scores = list(zip(*ocr_result)) + rec_res = list(zip(rec_res, scores)) + + r_boxes = [] + for box in dt_boxes: + box = np.array(box) + x_min = max(0, box[:, 0].min() - 1) + x_max = min(w, box[:, 0].max() + 1) + y_min = max(0, box[:, 1].min() - 1) + y_max = min(h, box[:, 1].max() + 1) + box = [x_min, y_min, x_max, y_max] + r_boxes.append(box) + dt_boxes = np.array(r_boxes) + return dt_boxes, rec_res + + @staticmethod + def get_model_path(model_type: str, sub_file_type: str, path: Union[str, Path, None]) -> str: + if path is not None: + return path + + model_url = KEY_TO_MODEL_URL.get(model_type, {}).get(sub_file_type, None) + if model_url: + model_path = DownloadModel.download(model_url) + return model_path + + logger.info( + "model url is None, using the default download model %s", path + ) + return path +def main(): + parser = argparse.ArgumentParser() + parser.add_argument( + "-v", + "--vis", + action="store_true", + help="Wheter to visualize the layout results.", + ) + parser.add_argument( + "-img", "--img_path", type=str, required=True, help="Path to image for layout." + ) + parser.add_argument( + "-d", "--device", type=str, default="cpu", help="device to use" + ) + args = parser.parse_args() + + try: + ocr_engine = importlib.import_module("rapidocr_onnxruntime").RapidOCR() + except ModuleNotFoundError as exc: + raise ModuleNotFoundError( + "Please install the rapidocr_onnxruntime by pip install rapidocr_onnxruntime." + ) from exc + + rapid_table = RapidTable(device=args.device) + + img = cv2.imread(args.img_path) + + ocr_result, _ = ocr_engine(img) + table_html_str, table_cell_bboxes, elapse = rapid_table(img, ocr_result) + print(table_html_str) + + viser = VisTable() + if args.vis: + img_path = Path(args.img_path) + + save_dir = img_path.resolve().parent + save_html_path = save_dir / f"{Path(img_path).stem}.html" + save_drawed_path = save_dir / f"vis_{Path(img_path).name}" + viser( + img_path, + table_html_str, + save_html_path, + table_cell_bboxes, + save_drawed_path, + ) + + +if __name__ == "__main__": + main() \ No newline at end of file diff --git a/rapid_table_torch/table_matcher/__init__.py b/rapid_table_torch/table_matcher/__init__.py new file mode 100644 index 0000000..9bff7d7 --- /dev/null +++ b/rapid_table_torch/table_matcher/__init__.py @@ -0,0 +1,4 @@ +# -*- encoding: utf-8 -*- +# @Author: SWHL +# @Contact: liekkaskono@163.com +from .matcher import TableMatch diff --git a/rapid_table_torch/table_matcher/matcher.py b/rapid_table_torch/table_matcher/matcher.py new file mode 100644 index 0000000..0453929 --- /dev/null +++ b/rapid_table_torch/table_matcher/matcher.py @@ -0,0 +1,192 @@ +# copyright (c) 2022 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. +# -*- encoding: utf-8 -*- +import numpy as np + +from .utils import compute_iou, distance + + +class TableMatch: + def __init__(self, filter_ocr_result=True, use_master=False): + self.filter_ocr_result = filter_ocr_result + self.use_master = use_master + + def __call__(self, pred_structures, pred_bboxes, dt_boxes, rec_res): + if self.filter_ocr_result: + dt_boxes, rec_res = self._filter_ocr_result(pred_bboxes, dt_boxes, rec_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 + + def match_result(self, dt_boxes, pred_bboxes): + matched = {} + for i, gt_box in enumerate(dt_boxes): + distances = [] + for j, pred_box in enumerate(pred_bboxes): + if len(pred_box) == 8: + pred_box = [ + np.min(pred_box[0::2]), + np.min(pred_box[1::2]), + np.max(pred_box[0::2]), + np.max(pred_box[1::2]), + ] + distances.append( + (distance(gt_box, pred_box), 1.0 - compute_iou(gt_box, pred_box)) + ) # compute iou and l1 distance + sorted_distances = distances.copy() + # select det box by iou and l1 distance + 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 "" not in tag: + end_html.append(tag) + continue + + if "" == tag: + end_html.extend("") + + if td_index in matched_index.keys(): + b_with = False + if ( + "" in ocr_contents[matched_index[td_index][0]] + and len(matched_index[td_index]) > 1 + ): + b_with = True + end_html.extend("") + + 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 "" in content: + content = content[3:] + + if "" 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("") + + if "" == tag: + end_html.append("") + else: + end_html.append(tag) + + td_index += 1 + + # Filter elements + filter_elements = ["", "", "", ""] + end_html = [v for v in end_html if v not in filter_elements] + return "".join(end_html), end_html + def decode_logic_points(self, pred_structures): + logic_points = [] + current_row = 0 + current_col = 0 + max_rows = 0 + max_cols = 0 + occupied_cells = {} # 用于记录已经被占用的单元格 + + def is_occupied(row, col): + return (row, col) in occupied_cells + + def mark_occupied(row, col, rowspan, colspan): + for r in range(row, row + rowspan): + for c in range(col, col + colspan): + occupied_cells[(r, c)] = True + + i = 0 + while i < len(pred_structures): + token = pred_structures[i] + + if token == '': + current_col = 0 # 每次遇到 时，重置当前列号 + elif token == '': + current_row += 1 # 行结束，行号增加 + elif token .startswith(''): + if 'colspan=' in pred_structures[j]: + colspan = int(pred_structures[j].split('=')[1].strip('"\'')) + elif 'rowspan=' in pred_structures[j]: + rowspan = int(pred_structures[j].split('=')[1].strip('"\'')) + j += 1 + + # 跳过已经处理过的属性 token + i = j + + # 找到下一个未被占用的列 + while is_occupied(current_row, current_col): + current_col += 1 + + # 计算逻辑坐标 + r_start = current_row + r_end = current_row + rowspan - 1 + col_start = current_col + col_end = current_col + colspan - 1 + + # 记录逻辑坐标 + logic_points.append([r_start, r_end, col_start, col_end]) + + # 标记占用的单元格 + mark_occupied(r_start, col_start, rowspan, colspan) + + # 更新当前列号 + current_col += colspan + + # 更新最大行数和列数 + max_rows = max(max_rows, r_end + 1) + max_cols = max(max_cols, col_end + 1) + + i += 1 + + return logic_points + + def _filter_ocr_result(self, pred_bboxes, dt_boxes, rec_res): + y1 = pred_bboxes[:, 1::2].min() + new_dt_boxes = [] + new_rec_res = [] + + for box, rec in zip(dt_boxes, rec_res): + if np.max(box[1::2]) < y1: + continue + new_dt_boxes.append(box) + new_rec_res.append(rec) + return new_dt_boxes, new_rec_res diff --git a/rapid_table_torch/table_matcher/utils.py b/rapid_table_torch/table_matcher/utils.py new file mode 100644 index 0000000..3ec8fcc --- /dev/null +++ b/rapid_table_torch/table_matcher/utils.py @@ -0,0 +1,248 @@ +# copyright (c) 2022 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. +# -*- encoding: utf-8 -*- +# @Author: SWHL +# @Contact: liekkaskono@163.com +import re + + +def deal_isolate_span(thead_part): + """ + Deal with isolate span cases in this function. + It causes by wrong prediction in structure recognition model. + eg. predict to rowspan="2">. + :param thead_part: + :return: + """ + # 1. find out isolate span tokens. + isolate_pattern = ( + ' rowspan="(\d)+" colspan="(\d)+">|' + ' colspan="(\d)+" rowspan="(\d)+">|' + ' rowspan="(\d)+">|' + ' colspan="(\d)+">' + ) + isolate_iter = re.finditer(isolate_pattern, thead_part) + isolate_list = [i.group() for i in isolate_iter] + + # 2. find out span number, by step 1 results. + span_pattern = ( + ' rowspan="(\d)+" colspan="(\d)+"|' + ' colspan="(\d)+" rowspan="(\d)+"|' + ' rowspan="(\d)+"|' + ' colspan="(\d)+"' + ) + corrected_list = [] + for isolate_item in isolate_list: + span_part = re.search(span_pattern, isolate_item) + spanStr_in_isolateItem = span_part.group() + # 3. merge the span number into the span token format string. + if spanStr_in_isolateItem is not None: + corrected_item = "".format(spanStr_in_isolateItem) + corrected_list.append(corrected_item) + else: + corrected_list.append(None) + + # 4. replace original isolated token. + for corrected_item, isolate_item in zip(corrected_list, isolate_list): + if corrected_item is not None: + thead_part = thead_part.replace(isolate_item, corrected_item) + else: + pass + return thead_part + + +def deal_duplicate_bb(thead_part): + """ + Deal duplicate or after replace. + Keep one in a token. + :param thead_part: + :return: + """ + # 1. find out in . + td_pattern = ( + '(.+?)|' + '(.+?)|' + '(.+?)|' + '(.+?)|' + "(.*?)" + ) + td_iter = re.finditer(td_pattern, thead_part) + td_list = [t.group() for t in td_iter] + + # 2. is multiply in or not? + new_td_list = [] + for td_item in td_list: + if td_item.count("") > 1 or td_item.count("") > 1: + # multiply in case. + # 1. remove all + td_item = td_item.replace("", "").replace("", "") + # 2. replace -> , -> . + td_item = td_item.replace("", "").replace("", "") + new_td_list.append(td_item) + else: + new_td_list.append(td_item) + + # 3. replace original thead part. + for td_item, new_td_item in zip(td_list, new_td_list): + thead_part = thead_part.replace(td_item, new_td_item) + return thead_part + + +def deal_bb(result_token): + """ + In our opinion, always occurs in text's context. + This function will find out all tokens in and insert by manual. + :param result_token: + :return: + """ + # find out parts. + thead_pattern = "(.*?)" + if re.search(thead_pattern, result_token) is None: + return result_token + thead_part = re.search(thead_pattern, result_token).group() + origin_thead_part = copy.deepcopy(thead_part) + + # check "rowspan" or "colspan" occur in parts or not . + span_pattern = '|||' + span_iter = re.finditer(span_pattern, thead_part) + span_list = [s.group() for s in span_iter] + has_span_in_head = True if len(span_list) > 0 else False + + if not has_span_in_head: + # not include "rowspan" or "colspan" branch 1. + # 1. replace to , and to + # 2. it is possible to predict text include or by Text-line recognition, + # so we replace to , and to + thead_part = ( + thead_part.replace("", "") + .replace("", "") + .replace("", "") + .replace("", "") + ) + else: + # include "rowspan" or "colspan" branch 2. + # Firstly, we deal rowspan or colspan cases. + # 1. replace > to > + # 2. replace to + # 3. it is possible to predict text include or by Text-line recognition, + # so we replace to , and to + + # Secondly, deal ordinary cases like branch 1 + + # replace ">" to "" + replaced_span_list = [] + for sp in span_list: + replaced_span_list.append(sp.replace(">", ">")) + for sp, rsp in zip(span_list, replaced_span_list): + thead_part = thead_part.replace(sp, rsp) + + # replace "" to "" + thead_part = thead_part.replace("", "") + + # remove duplicated by re.sub + mb_pattern = "()+" + single_b_string = "" + thead_part = re.sub(mb_pattern, single_b_string, thead_part) + + mgb_pattern = "()+" + single_gb_string = "" + thead_part = re.sub(mgb_pattern, single_gb_string, thead_part) + + # ordinary cases like branch 1 + thead_part = thead_part.replace("", "").replace("", "") + + # convert back to , empty cell has no . + # but space cell( ) is suitable for + thead_part = thead_part.replace("", "") + # deal with duplicated + thead_part = deal_duplicate_bb(thead_part) + # deal with isolate span tokens, which causes by wrong predict by structure prediction. + # eg.PMC5994107_011_00.png + thead_part = deal_isolate_span(thead_part) + # replace original result with new thead part. + result_token = result_token.replace(origin_thead_part, thead_part) + return result_token + + +def deal_eb_token(master_token): + """ + post process with , , ... + emptyBboxTokenDict = { + "[]": '', + "[' ']": '', + "['', ' ', '']": '', + "['\\u2028', '\\u2028']": '', + "['', ' ', '']": '', + "['', '']": '', + "['', ' ', '']": '', + "['', '', '', '']": '', + "['', '', ' ', '', '']": '', + "['', '']": '', + "['', ' ', '\\u2028', ' ', '\\u2028', ' ', '']": '', + } + :param master_token: + :return: + """ + master_token = master_token.replace("", "") + master_token = master_token.replace("", " ") + master_token = master_token.replace("", " ") + master_token = master_token.replace("", "\u2028\u2028") + master_token = master_token.replace("", " ") + master_token = master_token.replace("", "") + master_token = master_token.replace("", " ") + master_token = master_token.replace("", "") + master_token = master_token.replace("", " ") + master_token = master_token.replace("", "") + master_token = master_token.replace( + "", " \u2028 \u2028 " + ) + return master_token + + +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 diff --git a/rapid_table_torch/table_structure/__init__.py b/rapid_table_torch/table_structure/__init__.py new file mode 100644 index 0000000..4582f3d --- /dev/null +++ b/rapid_table_torch/table_structure/__init__.py @@ -0,0 +1 @@ +from .table_structure import TableStructurer diff --git a/rapid_table_torch/table_structure/components.py b/rapid_table_torch/table_structure/components.py new file mode 100644 index 0000000..367bd57 --- /dev/null +++ b/rapid_table_torch/table_structure/components.py @@ -0,0 +1,388 @@ +from functools import partial +from dataclasses import dataclass +from typing import Optional +import torch +import torch.nn as nn +from torch import Tensor +from torch.nn import functional as F +from torch.nn.modules.transformer import _get_activation_fn + +TOKEN_WHITE_LIST = [1, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 183, 184, 185, 186, 187, 188, 189, 190, 191, 192, 193, 194, 195, 196, 197, 198, 199, 200, 201, 202, 203, 204, 205, 206, 207, 208, 209, 210, 211, 212, 213, 214, 215, 216, 217, 218, 219, 220, 221, 222, 223, 224, 225, 226, 227, 228, 229, 230, 231, 232, 233, 234, 235, 236, 237, 238, 239, 240, 241, 242, 243, 244, 245, 246, 247, 248, 249, 250, 251, 252, 253, 254, 255, 256, 257, 258, 259, 260, 261, 262, 263, 264, 265, 266, 267, 268, 269, 270, 271, 272, 273, 274, 275, 276, 277, 278, 279, 280, 281, 282, 283, 284, 285, 286, 287, 288, 289, 290, 291, 292, 293, 294, 295, 296, 297, 298, 299, 300, 301, 302, 303, 304, 305, 306, 307, 308, 309, 310, 311, 312, 313, 314, 315, 316, 317, 318, 319, 320, 321, 322, 323, 324, 325, 326, 327, 328, 329, 330, 331, 332, 333, 334, 335, 336, 337, 338, 339, 340, 341, 342, 343, 344, 345, 346, 347, 348, 349, 350, 351, 352, 353, 354, 355, 356, 357, 358, 359, 360, 361, 362, 363, 364, 365, 366, 367, 368, 369, 370, 371, 372, 373, 374, 375, 376, 377, 378, 379, 380, 381, 382, 383, 384, 385, 386, 387, 388, 389, 390, 391, 392, 393, 394, 395, 396, 397, 398, 399, 400, 401, 402, 403, 404, 405, 406, 407, 408, 409, 410, 411, 412, 413, 414, 415, 416, 417, 418, 419, 420, 421, 422, 423, 424, 425, 426, 427, 428, 429, 430, 431, 432, 433, 434, 435, 436, 437, 438, 439, 440, 441, 442, 443, 444, 445, 446, 447, 448, 449, 450, 451, 452, 453, 454, 455, 456, 457, 458, 459, 460, 461, 462, 463, 464, 465, 466, 467, 468, 469, 470, 471, 472, 473, 474, 475, 476, 477, 478, 479, 480, 481, 482, 483, 484, 485, 486, 487, 488, 489, 490, 491, 492, 493, 494, 495, 496, 497, 498, 499, 500, 501, 502, 503, 504, 505, 506, 507, 508, 509] + + +class ImgLinearBackbone(nn.Module): + def __init__( + self, + d_model: int, + patch_size: int, + in_chan: int = 3, + ) -> None: + super().__init__() + + self.conv_proj = nn.Conv2d( + in_chan, + out_channels=d_model, + kernel_size=patch_size, + stride=patch_size, + ) + self.d_model = d_model + + def forward(self, x: Tensor) -> Tensor: + x = self.conv_proj(x) + x = x.flatten(start_dim=-2).transpose(1, 2) + return x + + +class Encoder(nn.Module): + def __init__( + self + ) -> None: + super().__init__() + + self.patch_size = 16 + self.d_model = 768 + self.dropout = 0 + self.activation = "gelu" + self.norm_first = True + self.ff_ratio = 4 + self.nhead = 12 + self.max_seq_len = 1024 + self.n_encoder_layer = 12 + encoder_layer = nn.TransformerEncoderLayer( + self.d_model, + nhead=self.nhead, + dim_feedforward=self.ff_ratio * self.d_model, + dropout=self.dropout, + activation=self.activation, + batch_first=True, + norm_first=self.norm_first, + ) + norm_layer=partial(nn.LayerNorm, eps=1e-6) + self.norm = norm_layer(self.d_model) + self.backbone = ImgLinearBackbone(d_model=self.d_model, patch_size=self.patch_size) + self.pos_embed = PositionEmbedding(max_seq_len=self.max_seq_len, d_model=self.d_model, dropout=self.dropout) + self.encoder = nn.TransformerEncoder(encoder_layer, num_layers=self.n_encoder_layer, enable_nested_tensor=False) + + def forward(self, x: Tensor) -> Tensor: + src_feature = self.backbone(x) + src_feature = self.pos_embed(src_feature) + memory = self.encoder(src_feature) + memory = self.norm(memory) + return memory + + +class PositionEmbedding(nn.Module): + def __init__(self, max_seq_len: int, d_model: int, dropout: float) -> None: + super().__init__() + self.embedding = nn.Embedding(max_seq_len, d_model) + self.dropout = nn.Dropout(dropout) + + def forward(self, x: Tensor, input_pos: Optional[Tensor] = None) -> Tensor: + # assume x is batch first + if input_pos is None: + _pos = torch.arange(x.shape[1], device=x.device) + else: + _pos = input_pos + out = self.embedding(_pos) + return self.dropout(out + x) + + +class TokenEmbedding(nn.Module): + def __init__( + self, + vocab_size: int, + d_model: int, + padding_idx: int, + ) -> None: + super().__init__() + assert vocab_size > 0 + self.embedding = nn.Embedding(vocab_size, d_model, padding_idx=padding_idx) + + def forward(self, x: Tensor) -> Tensor: + return self.embedding(x) + + +def find_multiple(n: int, k: int) -> int: + if n % k == 0: + return n + return n + k - (n % k) + + +@dataclass +class ModelArgs: + n_layer: int = 4 + n_head: int = 12 + dim: int = 768 + intermediate_size: int = None + head_dim: int = 64 + activation: str = "gelu" + norm_first: bool = True + + def __post_init__(self): + if self.intermediate_size is None: + hidden_dim = 4 * self.dim + n_hidden = int(2 * hidden_dim / 3) + self.intermediate_size = find_multiple(n_hidden, 256) + self.head_dim = self.dim // self.n_head + +class KVCache(nn.Module): + def __init__( + self, + max_batch_size, + max_seq_length, + n_heads, + head_dim, + dtype=torch.bfloat16, + device="cpu", + ): + super().__init__() + cache_shape = (max_batch_size, n_heads, max_seq_length, head_dim) + self.register_buffer("k_cache", torch.zeros(cache_shape, dtype=dtype, device=device), persistent=False) + self.register_buffer("v_cache", torch.zeros(cache_shape, dtype=dtype, device=device), persistent=False) + + def update(self, input_pos, k_val, v_val): + # input_pos: [S], k_val: [B, H, S, D] + # assert input_pos.shape[0] == k_val.shape[2] + + bs = k_val.shape[0] + k_out = self.k_cache + v_out = self.v_cache + k_out[:bs, :, input_pos] = k_val + v_out[:bs, :, input_pos] = v_val + + return k_out[:bs], v_out[:bs] + +class GPTFastDecoder(nn.Module): + def __init__( + self + ) -> None: + super().__init__() + + + self.vocab_size = 960 + self.padding_idx = 2 + self.prefix_token_id = 11 + self.eos_id = 1 + self.max_seq_len = 1024 + self.dropout = 0 + self.d_model = 768 + self.nhead = 12 + self.activation = "gelu" + self.norm_first = True + self.n_decoder_layer = 4 + config = ModelArgs( + n_layer=self.n_decoder_layer, + n_head=self.nhead, + dim=self.d_model, + intermediate_size=self.d_model * 4, + activation=self.activation, + norm_first=self.norm_first, + ) + self.config = config + self.layers = nn.ModuleList(TransformerBlock(config) for _ in range(config.n_layer)) + self.token_embed = TokenEmbedding(vocab_size=self.vocab_size, d_model=self.d_model, padding_idx=self.padding_idx) + self.pos_embed = PositionEmbedding(max_seq_len=self.max_seq_len, d_model=self.d_model, dropout=self.dropout) + self.generator = nn.Linear(self.d_model, self.vocab_size) + self.token_white_list = TOKEN_WHITE_LIST + self.mask_cache: Optional[Tensor] = None + self.max_batch_size = -1 + self.max_seq_length = -1 + + def setup_caches(self, max_batch_size, max_seq_length, dtype, device): + for b in self.layers: + b.multihead_attn.k_cache = None + b.multihead_attn.v_cache = None + + if self.max_seq_length >= max_seq_length and self.max_batch_size >= max_batch_size: + return + head_dim = self.config.dim // self.config.n_head + max_seq_length = find_multiple(max_seq_length, 8) + self.max_seq_length = max_seq_length + self.max_batch_size = max_batch_size + + for b in self.layers: + b.self_attn.kv_cache = KVCache( + max_batch_size, + max_seq_length, + self.config.n_head, + head_dim, + dtype, + device + ) + b.multihead_attn.k_cache = None + b.multihead_attn.v_cache = None + + self.causal_mask = torch.tril(torch.ones(self.max_seq_length, self.max_seq_length, dtype=torch.bool)).to(device) + + def forward( + self, + memory: Tensor, + tgt: Tensor + ) -> Tensor: + + input_pos = torch.tensor([tgt.shape[1] - 1], device=tgt.device, dtype=torch.int) + tgt = tgt[:, -1:] + tgt_feature = self.pos_embed(self.token_embed(tgt), input_pos=input_pos) + # tgt = self.decoder(tgt_feature, memory, input_pos) + with torch.backends.cuda.sdp_kernel(enable_flash=False, enable_mem_efficient=False, enable_math=True): + logits = tgt_feature + tgt_mask = self.causal_mask[None, None, input_pos] + for i, layer in enumerate(self.layers): + logits = layer(logits, memory, input_pos=input_pos, tgt_mask=tgt_mask) + # return output + logits = self.generator(logits)[:, -1, :] + total = set([i for i in range(logits.shape[-1])]) + black_list = list(total.difference(set(self.token_white_list))) + logits[..., black_list] = -1e9 + probs = F.softmax(logits, dim=-1) + _, next_tokens = probs.topk(1) + return next_tokens + +class TransformerBlock(nn.Module): + def __init__(self, config: ModelArgs) -> None: + super().__init__() + self.self_attn = Attention(config) + self.multihead_attn = CrossAttention(config) + + layer_norm_eps = 1e-5 + + d_model = config.dim + dim_feedforward = config.intermediate_size + + self.linear1 = nn.Linear(d_model, dim_feedforward) + self.linear2 = nn.Linear(dim_feedforward, d_model) + + self.norm_first = config.norm_first + self.norm1 = nn.LayerNorm(d_model, eps=layer_norm_eps) + self.norm2 = nn.LayerNorm(d_model, eps=layer_norm_eps) + self.norm3 = nn.LayerNorm(d_model, eps=layer_norm_eps) + + self.activation = _get_activation_fn(config.activation) + + def forward( + self, + tgt: Tensor, + memory: Tensor, + tgt_mask: Tensor, + input_pos: Tensor, + ) -> Tensor: + if self.norm_first: + x = tgt + x = x + self.self_attn(self.norm1(x), tgt_mask, input_pos) + x = x + self.multihead_attn(self.norm2(x), memory) + x = x + self._ff_block(self.norm3(x)) + else: + x = tgt + x = self.norm1(x + self.self_attn(x, tgt_mask, input_pos)) + x = self.norm2(x + self.multihead_attn(x, memory)) + x = self.norm3(x + self._ff_block(x)) + return x + + def _ff_block(self, x: Tensor) -> Tensor: + x = self.linear2(self.activation(self.linear1(x))) + return x + + +class Attention(nn.Module): + def __init__(self, config: ModelArgs): + super().__init__() + assert config.dim % config.n_head == 0 + + # key, query, value projections for all heads, but in a batch + self.wqkv = nn.Linear(config.dim, 3 * config.dim) + self.wo = nn.Linear(config.dim, config.dim) + + self.kv_cache: Optional[KVCache] = None + + self.n_head = config.n_head + self.head_dim = config.head_dim + self.dim = config.dim + + def forward( + self, + x: Tensor, + mask: Tensor, + input_pos: Optional[Tensor] = None, + ) -> Tensor: + bsz, seqlen, _ = x.shape + + kv_size = self.n_head * self.head_dim + q, k, v = self.wqkv(x).split([self.dim, kv_size, kv_size], dim=-1) + + q = q.view(bsz, seqlen, self.n_head, self.head_dim) + k = k.view(bsz, seqlen, self.n_head, self.head_dim) + v = v.view(bsz, seqlen, self.n_head, self.head_dim) + + q, k, v = map(lambda x: x.transpose(1, 2), (q, k, v)) + + if self.kv_cache is not None: + k, v = self.kv_cache.update(input_pos, k, v) + + y = F.scaled_dot_product_attention(q, k, v, attn_mask=mask, dropout_p=0.0) + + y = y.transpose(1, 2).contiguous().view(bsz, seqlen, self.dim) + + y = self.wo(y) + return y + + +class CrossAttention(nn.Module): + def __init__(self, config: ModelArgs): + super().__init__() + assert config.dim % config.n_head == 0 + + self.query = nn.Linear(config.dim, config.dim) + self.key = nn.Linear(config.dim, config.dim) + self.value = nn.Linear(config.dim, config.dim) + self.out = nn.Linear(config.dim, config.dim) + + self.k_cache = None + self.v_cache = None + + self.n_head = config.n_head + self.head_dim = config.head_dim + + def get_kv(self, xa: torch.Tensor): + if self.k_cache is not None and self.v_cache is not None: + return self.k_cache, self.v_cache + + k = self.key(xa) + v = self.value(xa) + + # Reshape for correct format + batch_size, source_seq_len, _ = k.shape + k = k.view(batch_size, source_seq_len, self.n_head, self.head_dim) + v = v.view(batch_size, source_seq_len, self.n_head, self.head_dim) + + if self.k_cache is None: + self.k_cache = k + if self.v_cache is None: + self.v_cache = v + + return k, v + + def forward( + self, + x: Tensor, + xa: Tensor, + ): + q = self.query(x) + batch_size, target_seq_len, _ = q.shape + q = q.view(batch_size, target_seq_len, self.n_head, self.head_dim) + k, v = self.get_kv(xa) + + q, k, v = map(lambda x: x.transpose(1, 2), (q, k, v)) + + wv = F.scaled_dot_product_attention( + query=q, + key=k, + value=v, + is_causal=False, + ) + wv = wv.transpose(1, 2).reshape( + batch_size, + target_seq_len, + self.n_head * self.head_dim, + ) + + return self.out(wv) diff --git a/rapid_table_torch/table_structure/table_structure.py b/rapid_table_torch/table_structure/table_structure.py new file mode 100644 index 0000000..32f3fa0 --- /dev/null +++ b/rapid_table_torch/table_structure/table_structure.py @@ -0,0 +1,213 @@ +import re +import time + +import cv2 +import numpy as np +import torch +from PIL import Image +from tokenizers import Tokenizer + +from .components import Encoder, GPTFastDecoder +from torchvision import transforms + +IMG_SIZE = 448 +EOS_TOKEN = "" +BBOX_TOKENS = [f"bbox-{i}" for i in range(IMG_SIZE + 1)] + +HTML_BBOX_HTML_TOKENS = [ + "", + "[", + "]", + "[", + ">", + "", + "", + "", + "", + "", + "", + ' rowspan="2"', + ' rowspan="3"', + ' rowspan="4"', + ' rowspan="5"', + ' rowspan="6"', + ' rowspan="7"', + ' rowspan="8"', + ' rowspan="9"', + ' rowspan="10"', + ' rowspan="11"', + ' rowspan="12"', + ' rowspan="13"', + ' rowspan="14"', + ' rowspan="15"', + ' rowspan="16"', + ' rowspan="17"', + ' rowspan="18"', + ' rowspan="19"', + ' colspan="2"', + ' colspan="3"', + ' colspan="4"', + ' colspan="5"', + ' colspan="6"', + ' colspan="7"', + ' colspan="8"', + ' colspan="9"', + ' colspan="10"', + ' colspan="11"', + ' colspan="12"', + ' colspan="13"', + ' colspan="14"', + ' colspan="15"', + ' colspan="16"', + ' colspan="17"', + ' colspan="18"', + ' colspan="19"', + ' colspan="25"', +] + +VALID_HTML_BBOX_TOKENS = [EOS_TOKEN] + HTML_BBOX_HTML_TOKENS + BBOX_TOKENS +TASK_TOKENS = [ + "[table]", + "[html]", + "[cell]", + "[bbox]", + "[cell+bbox]", + "[html+bbox]", +] + + +class TableStructurer: + def __init__(self, encoder_path: str, decoder_path: str, vocab_path: str, device: str): + self.vocab = Tokenizer.from_file(vocab_path) + self.token_white_list = [self.vocab.token_to_id(i) for i in VALID_HTML_BBOX_TOKENS] + self.bbox_token_ids = set([self.vocab.token_to_id(i) for i in BBOX_TOKENS]) + self.bbox_close_html_token = self.vocab.token_to_id("]") + self.prefix_token_id = self.vocab.token_to_id("[html+bbox]") + self.eos_id = self.vocab.token_to_id(EOS_TOKEN) + self.max_seq_len = 1024 + self.device = device + self.img_size = IMG_SIZE + # init encoder + encoder_state_dict = torch.load(encoder_path, map_location=device) + self.encoder = Encoder() + self.encoder.load_state_dict(encoder_state_dict) + self.encoder.eval().to(device) + # init decoder + decoder_state_dict = torch.load(decoder_path, map_location=device) + self.decoder = GPTFastDecoder() + self.decoder.load_state_dict(decoder_state_dict) + self.decoder.eval().to(device) + # define img transform + self.transform = transforms.Compose( + [ + transforms.Resize((448, 448)), + transforms.ToTensor(), + transforms.Normalize( + mean=[0.86597056, 0.88463002, 0.87491087], + std=[0.20686628, 0.18201602, 0.18485524], + ), + ] + ) + + + @torch.inference_mode() + def __call__( + self, + image: np.ndarray, + ): + start_time = time.time() + ori_h, ori_w = image.shape[:2] + image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB) + image = Image.fromarray(image) + image = self.transform(image).unsqueeze(0).to(self.device) + self.decoder.setup_caches( + max_batch_size=1, + max_seq_length=self.max_seq_len, + dtype=image.dtype, + device=self.device + ) + context = torch.tensor([self.prefix_token_id], dtype=torch.int32).repeat(1, 1).to(self.device) + eos_id_tensor = torch.tensor(self.eos_id, dtype=torch.int32).to(self.device) + memory = self.encoder(image) + context = self.loop_decode(context, eos_id_tensor, memory) + bboxes, html_tokens = self.decode_tokens(context) + bboxes = bboxes.astype(np.float32) + # rescale boxes + scale_h = ori_h / self.img_size + scale_w = ori_w / self.img_size + bboxes[:, 0::2] *= scale_w # 缩放 x 坐标 + bboxes[:, 1::2] *= scale_h # 缩放 y 坐标 + bboxes[:, 0::2] = np.clip(bboxes[:, 0::2], 0, ori_w - 1) + bboxes[:, 1::2] = np.clip(bboxes[:, 1::2], 0, ori_h - 1) + structure_str_list = ( + ["", "", ""] + + html_tokens + + ["
", "", ""] + ) + return structure_str_list, bboxes, time.time() - start_time + + def decode_tokens(self, context): + pred_html = context[0] + pred_html = pred_html.detach().cpu().numpy() + pred_html = self.vocab.decode(pred_html, skip_special_tokens=False) + # pred_html = html_str_to_token_list(pred_html) + seq = pred_html.split("")[0] + token_black_list = ["", "", *TASK_TOKENS] + for i in token_black_list: + seq = seq.replace(i, "") + + # 正则表达式模式 + tr_pattern = re.compile(r'(.*?)', re.DOTALL) + td_pattern = re.compile(r'(.*?)', re.DOTALL) + bbox_pattern = re.compile(r'\[ bbox-(\d+) bbox-(\d+) bbox-(\d+) bbox-(\d+) \]') + + # 存储解码后的结果 + decoded_list = [] + bbox_coords = [] + + # 查找所有的 标签 + for tr_match in tr_pattern.finditer(pred_html): + tr_content = tr_match.group(1) + decoded_list.append('') + + # 查找所有的 标签 + for td_match in td_pattern.finditer(tr_content): + td_attrs = td_match.group(1).strip() + td_content = td_match.group(2).strip() + if td_attrs: + decoded_list.append('') + decoded_list.append('') + else: + decoded_list.append('') + + # 查找 bbox 坐标 + bbox_match = bbox_pattern.search(td_content) + if bbox_match: + xmin, ymin, xmax, ymax = map(int, bbox_match.groups()) + # 将坐标转换为从左上角开始顺时针到左下角的点的坐标 + coords = np.array([xmin, ymin, xmax, ymin, xmax, ymax, xmin, ymax]) + bbox_coords.append(coords) + + decoded_list.append('') + + # 将 bbox_coords 转换为 numpy 数组 + bbox_coords_array = np.array(bbox_coords) + return bbox_coords_array, decoded_list + + def loop_decode(self, context, eos_id_tensor, memory): + box_token_count = 0 + for _ in range(self.max_seq_len): + eos_flag = (context == eos_id_tensor).any(dim=1) + if torch.all(eos_flag): + break + next_tokens = self.decoder(memory, context) + if next_tokens[0] in self.bbox_token_ids: + box_token_count += 1 + if box_token_count > 4: + next_tokens = torch.tensor([self.bbox_close_html_token], dtype=torch.int32) + box_token_count = 0 + context = torch.cat([context, next_tokens], dim=1) + return context diff --git a/rapid_table_torch/table_structure/utils.py b/rapid_table_torch/table_structure/utils.py new file mode 100644 index 0000000..f2d76df --- /dev/null +++ b/rapid_table_torch/table_structure/utils.py @@ -0,0 +1,28 @@ +# 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. +# -*- encoding: utf-8 -*- +# @Author: SWHL +# @Contact: liekkaskono@163.com + +def trans_char_ocr_res(ocr_res): + word_result = [] + for res in ocr_res: + score = res[2] + for word_box, word in zip(res[3], res[4]): + word_res = [] + word_res.append(word_box) + word_res.append(word) + word_res.append(score) + word_result.append(word_res) + return word_result diff --git a/rapid_table_torch/utils.py b/rapid_table_torch/utils.py new file mode 100644 index 0000000..e7135c5 --- /dev/null +++ b/rapid_table_torch/utils.py @@ -0,0 +1,210 @@ +# -*- encoding: utf-8 -*- +# @Author: SWHL +# @Contact: liekkaskono@163.com +import os +from io import BytesIO +from pathlib import Path +from typing import Optional, Union, List + +import cv2 +import numpy as np +from PIL import Image, UnidentifiedImageError + +InputType = Union[str, np.ndarray, bytes, Path] + + +class LoadImage: + def __init__( + self, + ): + pass + + def __call__(self, img: InputType) -> np.ndarray: + if not isinstance(img, InputType.__args__): + raise LoadImageError( + f"The img type {type(img)} does not in {InputType.__args__}" + ) + + img = self.load_img(img) + + if img.ndim == 2: + return cv2.cvtColor(img, cv2.COLOR_GRAY2BGR) + + if img.ndim == 3 and img.shape[2] == 4: + return self.cvt_four_to_three(img) + + return img + + def load_img(self, img: InputType) -> np.ndarray: + if isinstance(img, (str, Path)): + self.verify_exist(img) + try: + img = np.array(Image.open(img)) + img = cv2.cvtColor(img, cv2.COLOR_RGB2BGR) + except UnidentifiedImageError as e: + raise LoadImageError(f"cannot identify image file {img}") from e + return img + + if isinstance(img, bytes): + img = np.array(Image.open(BytesIO(img))) + img = cv2.cvtColor(img, cv2.COLOR_RGB2BGR) + return img + + if isinstance(img, np.ndarray): + return img + + raise LoadImageError(f"{type(img)} is not supported!") + + @staticmethod + def cvt_four_to_three(img: np.ndarray) -> np.ndarray: + """RGBA → RGB""" + r, g, b, a = cv2.split(img) + new_img = cv2.merge((b, g, r)) + + not_a = cv2.bitwise_not(a) + not_a = cv2.cvtColor(not_a, cv2.COLOR_GRAY2BGR) + + new_img = cv2.bitwise_and(new_img, new_img, mask=a) + new_img = cv2.add(new_img, not_a) + return new_img + + @staticmethod + def verify_exist(file_path: Union[str, Path]): + if not Path(file_path).exists(): + raise LoadImageError(f"{file_path} does not exist.") + + +class LoadImageError(Exception): + pass + + +class VisTable: + def __init__( + self, + ): + self.load_img = LoadImage() + + def __call__( + self, + img_path: Union[str, Path], + table_html_str: str, + save_html_path: Optional[str] = None, + table_cell_bboxes: Optional[np.ndarray] = None, + save_drawed_path: Optional[str] = None, + logic_points: List[List[float]] = None, + save_logic_path: Optional[str] = None, + ) -> None: + if save_html_path: + html_with_border = self.insert_border_style(table_html_str) + self.save_html(save_html_path, html_with_border) + + if table_cell_bboxes is None: + return None + + img = self.load_img(img_path) + + dims_bboxes = table_cell_bboxes.shape[1] + if dims_bboxes == 4: + drawed_img = self.draw_rectangle(img, table_cell_bboxes) + elif dims_bboxes == 8: + drawed_img = self.draw_polylines(img, table_cell_bboxes) + else: + raise ValueError("Shape of table bounding boxes is not between in 4 or 8.") + + if save_drawed_path: + self.save_img(save_drawed_path, drawed_img) + if save_logic_path and logic_points: + polygons = [[box[0],box[1], box[4], box[5]] for box in table_cell_bboxes] + self.plot_rec_box_with_logic_info(img_path, save_logic_path, logic_points, polygons) + return drawed_img + + def insert_border_style(self, table_html_str: str): + style_res = f"""""" + prefix_table, suffix_table = table_html_str.split("") + html_with_border = f"{prefix_table}{style_res}{suffix_table}" + return html_with_border + + def plot_rec_box_with_logic_info(self, img_path, output_path, logic_points, sorted_polygons): + """ + :param img_path + :param output_path + :param logic_points: [row_start,row_end,col_start,col_end] + :param sorted_polygons: [xmin,ymin,xmax,ymax] + :return: + """ + # 读取原图 + img = cv2.imread(img_path) + img = cv2.copyMakeBorder( + img, 0, 0, 0, 100, cv2.BORDER_CONSTANT, value=[255, 255, 255] + ) + # 绘制 polygons 矩形 + for idx, polygon in enumerate(sorted_polygons): + x0, y0, x1, y1 = polygon[0], polygon[1], polygon[2], polygon[3] + x0 = round(x0) + y0 = round(y0) + x1 = round(x1) + y1 = round(y1) + cv2.rectangle(img, (x0, y0), (x1, y1), (0, 0, 255), 1) + # 增大字体大小和线宽 + font_scale = 0.9 # 原先是0.5 + thickness = 1 # 原先是1 + logic_point = logic_points[idx] + cv2.putText( + img, + f"row: {logic_point[0]}-{logic_point[1]}", + (x0 + 3, y0 + 8), + cv2.FONT_HERSHEY_PLAIN, + font_scale, + (0, 0, 255), + thickness, + ) + cv2.putText( + img, + f"col: {logic_point[2]}-{logic_point[3]}", + (x0 + 3, y0 + 18), + cv2.FONT_HERSHEY_PLAIN, + font_scale, + (0, 0, 255), + thickness, + ) + os.makedirs(os.path.dirname(output_path), exist_ok=True) + # 保存绘制后的图像 + self.save_img(output_path, img) + + @staticmethod + def draw_rectangle(img: np.ndarray, boxes: np.ndarray) -> np.ndarray: + img_copy = img.copy() + for box in boxes.astype(int): + x1, y1, x2, y2 = box + cv2.rectangle(img_copy, (x1, y1), (x2, y2), (255, 0, 0), 2) + return img_copy + + @staticmethod + def draw_polylines(img: np.ndarray, points) -> np.ndarray: + img_copy = img.copy() + for point in points.astype(int): + point = point.reshape(4, 2) + cv2.polylines(img_copy, [point.astype(int)], True, (255, 0, 0), 2) + return img_copy + + @staticmethod + def save_img(save_path: Union[str, Path], img: np.ndarray): + cv2.imwrite(str(save_path), img) + + @staticmethod + def save_html(save_path: Union[str, Path], html: str): + with open(save_path, "w", encoding="utf-8") as f: + f.write(html) diff --git a/requirements_torch.txt b/requirements_torch.txt new file mode 100644 index 0000000..4ba3572 --- /dev/null +++ b/requirements_torch.txt @@ -0,0 +1,8 @@ +onnxruntime>=1.7.0 +opencv_python>=4.5.1.48 +numpy>=1.21.6,<2 +Pillow +requests +torch +torchvision +tokenizers \ No newline at end of file diff --git a/setup_torch.py b/setup_torch.py new file mode 100644 index 0000000..e7b73c5 --- /dev/null +++ b/setup_torch.py @@ -0,0 +1,66 @@ +# -*- encoding: utf-8 -*- +# @Author: SWHL +# @Contact: liekkaskono@163.com +import sys +from pathlib import Path + +import setuptools +from get_pypi_latest_version import GetPyPiLatestVersion + + +def get_readme(): + root_dir = Path(__file__).resolve().parent + readme_path = str(root_dir / "docs" / "doc_whl_rapid_table.md") + with open(readme_path, "r", encoding="utf-8") as f: + readme = f.read() + return readme + + +MODULE_NAME = "rapid_table_torch" +obtainer = GetPyPiLatestVersion() +latest_version = obtainer(MODULE_NAME) +VERSION_NUM = obtainer.version_add_one(latest_version) + +if len(sys.argv) > 2: + match_str = " ".join(sys.argv[2:]) + matched_versions = obtainer.extract_version(match_str) + if matched_versions: + VERSION_NUM = matched_versions +sys.argv = sys.argv[:2] + +setuptools.setup( + name=MODULE_NAME, + version=VERSION_NUM, + platforms="Any", + long_description=get_readme(), + long_description_content_type="text/markdown", + description="Tools for parsing table structures based pytorch.", + author="SWHL", + author_email="liekkaskono@163.com", + url="https://github.com/RapidAI/RapidTable", + license="Apache-2.0", + include_package_data=True, + install_requires=[ + "opencv_python>=4.5.1.48", + "numpy>=1.21.6", + "Pillow", + "torch", + "torchvision", + "tokenizers" + ], + packages=[ + MODULE_NAME, + f"{MODULE_NAME}.models", + f"{MODULE_NAME}.table_matcher", + f"{MODULE_NAME}.table_structure", + ], + package_data={"": [".gitkeep"]}, + keywords=["unitable,table,rapidocr,rapid_table"], + classifiers=[ + "Programming Language :: Python :: 3.10", + "Programming Language :: Python :: 3.11", + "Programming Language :: Python :: 3.12", + ], + python_requires=">=3.10,<3.13", + entry_points={"console_scripts": [f"{MODULE_NAME}={MODULE_NAME}.main:main"]}, +) diff --git a/tests/test_table_torch.py b/tests/test_table_torch.py new file mode 100644 index 0000000..f143f17 --- /dev/null +++ b/tests/test_table_torch.py @@ -0,0 +1,35 @@ +# -*- encoding: utf-8 -*- +# @Author: SWHL +# @Contact: liekkaskono@163.com +import sys +from pathlib import Path + +from rapidocr_onnxruntime import RapidOCR + +cur_dir = Path(__file__).resolve().parent +root_dir = cur_dir.parent + +sys.path.append(str(root_dir)) + +from rapid_table_torch import RapidTable + +ocr_engine = RapidOCR() +table_engine = RapidTable() + +test_file_dir = cur_dir / "test_files" +img_path = str(test_file_dir / "table.jpg") + + +def test_ocr_input(): + ocr_res, _ = ocr_engine(img_path) + table_html_str, table_cell_bboxes, logic_points, elapse = table_engine(img_path, ocr_res) + assert table_html_str.count("") == 16 + + +def test_input_ocr_none(): + table_html_str, table_cell_bboxes, logic_points, elapse = table_engine(img_path) + assert table_html_str.count("") == 16 + +def test_logic_points_out(): + table_html_str, table_cell_bboxes, logic_points, elapse = table_engine(img_path, return_logic_points=True) + assert len(table_cell_bboxes) == len(logic_points) From b96126798869bcd7efa78f5f2d5463d822007959 Mon Sep 17 00:00:00 2001 From: Joker1212 <519548295@qq.com> Date: Mon, 30 Dec 2024 14:38:01 +0800 Subject: [PATCH 02/23] chore: fix publish workflow --- .github/workflows/publish_whl_torch.yml | 8 +------- 1 file changed, 1 insertion(+), 7 deletions(-) diff --git a/.github/workflows/publish_whl_torch.yml b/.github/workflows/publish_whl_torch.yml index ac7aecc..5e836a9 100644 --- a/.github/workflows/publish_whl_torch.yml +++ b/.github/workflows/publish_whl_torch.yml @@ -1,4 +1,4 @@ -name: Push rapidocr_table to pypi +name: Push rapidocr_table_torch to pypi on: push: @@ -27,12 +27,6 @@ jobs: - name: Unit testings run: | - wget $RESOURCES_URL - ZIP_NAME=${RESOURCES_URL##*/} - DIR_NAME=${ZIP_NAME%.*} - unzip $DIR_NAME - cp $DIR_NAME/*.pth rapid_table_torch/models/ - cp $DIR_NAME/*.json rapid_table_torch/models/ pip install -r requirements_torch.txt pip install rapidocr_onnxruntime pip install pytest From ba681367bdc7bc1fac614ef4f93079d5c8873337 Mon Sep 17 00:00:00 2001 From: Joker1212 <519548295@qq.com> Date: Mon, 30 Dec 2024 14:44:29 +0800 Subject: [PATCH 03/23] test: fix base test --- tests/test_table_torch.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tests/test_table_torch.py b/tests/test_table_torch.py index f143f17..640257c 100644 --- a/tests/test_table_torch.py +++ b/tests/test_table_torch.py @@ -31,5 +31,5 @@ def test_input_ocr_none(): assert table_html_str.count("") == 16 def test_logic_points_out(): - table_html_str, table_cell_bboxes, logic_points, elapse = table_engine(img_path, return_logic_points=True) + table_html_str, table_cell_bboxes, logic_points, elapse = table_engine(img_path) assert len(table_cell_bboxes) == len(logic_points) From 5d4b1821da883c6656785bdd216a9bfb5f94da83 Mon Sep 17 00:00:00 2001 From: Joker1212 <519548295@qq.com> Date: Mon, 30 Dec 2024 15:03:54 +0800 Subject: [PATCH 04/23] chore: fix rapid_table_torch push workflow --- .github/workflows/publish_whl_torch.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/publish_whl_torch.yml b/.github/workflows/publish_whl_torch.yml index 5e836a9..09bee91 100644 --- a/.github/workflows/publish_whl_torch.yml +++ b/.github/workflows/publish_whl_torch.yml @@ -50,7 +50,7 @@ jobs: pip install -r requirements_torch.txt python -m pip install --upgrade pip pip install wheel get_pypi_latest_version - python setup.py bdist_wheel ${{ github.ref_name }} + python setup_torch.py bdist_wheel ${{ github.ref_name }} - name: Publish distribution 📦 to PyPI uses: pypa/gh-action-pypi-publish@v1.5.0 From 01869dbeb4a34faac7cc2554736a85ad1ebc20de Mon Sep 17 00:00:00 2001 From: Joker1212 <519548295@qq.com> Date: Mon, 30 Dec 2024 15:17:57 +0800 Subject: [PATCH 05/23] chore: fix setup_torch --- setup_torch.py | 5 ++++- 1 file changed, 4 insertions(+), 1 deletion(-) diff --git a/setup_torch.py b/setup_torch.py index e7b73c5..b332a20 100644 --- a/setup_torch.py +++ b/setup_torch.py @@ -18,7 +18,10 @@ def get_readme(): MODULE_NAME = "rapid_table_torch" obtainer = GetPyPiLatestVersion() -latest_version = obtainer(MODULE_NAME) +try: + latest_version = obtainer(MODULE_NAME) +except Exception: + latest_version = "0.0.0" VERSION_NUM = obtainer.version_add_one(latest_version) if len(sys.argv) > 2: From 97b685ff187a7f76c2deee0f9c265eb4b178f4a5 Mon Sep 17 00:00:00 2001 From: Joker1212 <519548295@qq.com> Date: Mon, 30 Dec 2024 17:07:04 +0800 Subject: [PATCH 06/23] feat: optimize torch inference --- .github/workflows/publish_whl.yml | 4 + .github/workflows/publish_whl_torch.yml | 59 ----- README.md | 64 +---- demo_torch.py | 50 ++-- .../download_model.py | 24 +- {rapid_table_torch => rapid_table}/logger.py | 0 rapid_table/main.py | 29 +- rapid_table/params.py | 40 +++ rapid_table/table_structure/__init__.py | 1 + .../table_structure_unitable.py | 13 +- .../table_structure/unitable_modules.py | 0 rapid_table/utils.py | 1 + rapid_table_torch/__init__.py | 2 - rapid_table_torch/main.py | 155 ----------- rapid_table_torch/table_matcher/__init__.py | 4 - rapid_table_torch/table_matcher/matcher.py | 192 -------------- rapid_table_torch/table_matcher/utils.py | 248 ------------------ rapid_table_torch/table_structure/__init__.py | 1 - rapid_table_torch/table_structure/utils.py | 28 -- rapid_table_torch/utils.py | 210 --------------- requirements_torch.txt | 8 - setup.py | 1 + setup_torch.py | 69 ----- tests/test_table_torch.py | 10 +- 24 files changed, 130 insertions(+), 1083 deletions(-) delete mode 100644 .github/workflows/publish_whl_torch.yml rename {rapid_table_torch => rapid_table}/download_model.py (70%) rename {rapid_table_torch => rapid_table}/logger.py (100%) create mode 100644 rapid_table/params.py rename rapid_table_torch/table_structure/table_structure.py => rapid_table/table_structure/table_structure_unitable.py (93%) rename rapid_table_torch/table_structure/components.py => rapid_table/table_structure/unitable_modules.py (100%) delete mode 100644 rapid_table_torch/__init__.py delete mode 100644 rapid_table_torch/main.py delete mode 100644 rapid_table_torch/table_matcher/__init__.py delete mode 100644 rapid_table_torch/table_matcher/matcher.py delete mode 100644 rapid_table_torch/table_matcher/utils.py delete mode 100644 rapid_table_torch/table_structure/__init__.py delete mode 100644 rapid_table_torch/table_structure/utils.py delete mode 100644 rapid_table_torch/utils.py delete mode 100644 requirements_torch.txt delete mode 100644 setup_torch.py diff --git a/.github/workflows/publish_whl.yml b/.github/workflows/publish_whl.yml index 99e137c..7e135b3 100644 --- a/.github/workflows/publish_whl.yml +++ b/.github/workflows/publish_whl.yml @@ -34,8 +34,12 @@ jobs: pip install -r requirements.txt pip install rapidocr_onnxruntime + pip install torch + pip install torchvision + pip install tokenizers pip install pytest pytest tests/test_table.py + pytest tests/test_table_torch.py GenerateWHL_PushPyPi: needs: UnitTesting diff --git a/.github/workflows/publish_whl_torch.yml b/.github/workflows/publish_whl_torch.yml deleted file mode 100644 index 09bee91..0000000 --- a/.github/workflows/publish_whl_torch.yml +++ /dev/null @@ -1,59 +0,0 @@ -name: Push rapidocr_table_torch to pypi - -on: - push: - tags: - - torch_v* - -env: - RESOURCES_URL: https://github.com/RapidAI/RapidTable/releases/download/assets/unitable.zip - - -jobs: - UnitTesting: - runs-on: ubuntu-latest - steps: - - name: Pull latest code - uses: actions/checkout@v3 - - - name: Set up Python 3.10 - uses: actions/setup-python@v4 - with: - python-version: '3.10' - architecture: 'x64' - - - name: Display Python version - run: python -c "import sys; print(sys.version)" - - - name: Unit testings - run: | - pip install -r requirements_torch.txt - pip install rapidocr_onnxruntime - pip install pytest - pytest tests/test_table_torch.py - - GenerateWHL_PushPyPi: - needs: UnitTesting - runs-on: ubuntu-latest - - steps: - - uses: actions/checkout@v3 - - - name: Set up Python 3.10 - uses: actions/setup-python@v4 - with: - python-version: '3.10' - architecture: 'x64' - - - name: Run setup_torch.py - run: | - pip install -r requirements_torch.txt - python -m pip install --upgrade pip - pip install wheel get_pypi_latest_version - python setup_torch.py bdist_wheel ${{ github.ref_name }} - - - name: Publish distribution 📦 to PyPI - uses: pypa/gh-action-pypi-publish@v1.5.0 - with: - password: ${{ secrets.RAPID_TABLE }} - packages_dir: dist/ diff --git a/README.md b/README.md index a621571..89b0f59 100644 --- a/README.md +++ b/README.md @@ -105,8 +105,8 @@ RapidTable是整理自PP-Structure中表格识别部分而来。由于PP-Structu ```bash pip install rapidocr_onnxruntime pip install rapid_table -#pip install rapid_table_torch # for unitable inference -#pip install onnxruntime-gpu # for gpu inference +#pip install rapid_table[torch] # for unitable inference +#pip install onnxruntime-gpu # for onnx gpu inference ``` ### 使用方式 @@ -117,6 +117,7 @@ RapidTable类提供model_path参数，可以自行指定上述2个模型，默 ```python table_engine = RapidTable() +# table_engine = RapidTable(use_cuda=True, device="cuda:0", model_type="unitable") ``` 完整示例： @@ -132,6 +133,8 @@ from rapid_table.table_structure.utils import trans_char_ocr_res table_engine = RapidTable() # 开启onnx-gpu推理 # table_engine = RapidTable(use_cuda=True) +# 使用torch推理版本的unitable模型 +# table_engine = RapidTable(use_cuda=True, device="cuda:0", model_type="unitable") ocr_engine = RapidOCR() viser = VisTable() @@ -159,41 +162,8 @@ viser(img_path, table_html_str, save_html_path, table_cell_bboxes, save_drawed_p print(table_html_str) ``` -#### torch版本 -```python -from pathlib import Path -from rapidocr_onnxruntime import RapidOCR - -from rapid_table_torch import RapidTable, VisTable -from rapid_table_torch.table_structure.utils import trans_char_ocr_res - -if __name__ == '__main__': -# Init -ocr_engine = RapidOCR() -table_engine = RapidTable(device="cpu") # 默认使用cpu，若使用cuda，则传入device="cuda:0" -viser = VisTable() -img_path = "tests/test_files/image34.png" -# OCR,本模型检测框比较精准，配合单字匹配效果更好 -ocr_result, _ = ocr_engine(img_path, return_word_box=True) -ocr_result = trans_char_ocr_res(ocr_result) -boxes, txts, scores = list(zip(*ocr_result)) -# Save -save_dir = Path("outputs") -save_dir.mkdir(parents=True, exist_ok=True) - -save_html_path = save_dir / f"{Path(img_path).stem}.html" -save_drawed_path = save_dir / f"{Path(img_path).stem}_table_vis{Path(img_path).suffix}" -# 返回逻辑坐标 -table_html_str, table_cell_bboxes, logic_points, elapse = table_engine(img_path, ocr_result) -save_logic_path = save_dir / f"vis_logic_{Path(img_path).name}" -vis_imged = viser(img_path, table_html_str, save_html_path, table_cell_bboxes, save_drawed_path, logic_points, - save_logic_path) -print(f"elapse:{elapse}") -``` - #### 终端运行 -##### onnx: - 用法: ```bash @@ -214,29 +184,7 @@ print(f"elapse:{elapse}") ```bash rapid_table -v -img test_images/table.jpg ``` - -##### pytorch: -- 用法: - - ```bash - $ rapid_table_torch -h - usage: rapid_table_torch [-h] [-v] -img IMG_PATH [-d DEVICE] - - optional arguments: - -h, --help show this help message and exit - -v, --vis Whether to visualize the layout results. - -img IMG_PATH, --img_path IMG_PATH - Path to image for layout. - -d DEVICE, --device device - The model device used for inference. - ``` - -- 示例: - - ```bash - rapid_table_torch -v -img test_images/table.jpg - ``` - + ### 结果 #### 返回结果 diff --git a/demo_torch.py b/demo_torch.py index e8dc1e0..72a2d44 100644 --- a/demo_torch.py +++ b/demo_torch.py @@ -4,31 +4,29 @@ from pathlib import Path from rapidocr_onnxruntime import RapidOCR -from rapid_table_torch import RapidTable, VisTable -from rapid_table_torch.table_structure.utils import trans_char_ocr_res +from rapid_table import RapidTable, VisTable +from rapid_table.table_structure.utils import trans_char_ocr_res -if __name__ == '__main__': - # Init - ocr_engine = RapidOCR() - table_engine = RapidTable(encoder_path="rapid_table_torch/models/encoder.pth", - decoder_path="rapid_table_torch/models/decoder.pth", - vocab_path="rapid_table_torch/models/vocab.json", - device="cpu") - viser = VisTable() - img_path = "tests/test_files/image34.png" - # OCR - ocr_result, _ = ocr_engine(img_path, return_word_box=True) - ocr_result = trans_char_ocr_res(ocr_result) - boxes, txts, scores = list(zip(*ocr_result)) - # Save - save_dir = Path("outputs") - save_dir.mkdir(parents=True, exist_ok=True) +ocr_engine = RapidOCR() +table_engine = RapidTable(use_cuda=True, device="cuda:0", model_type="unitable") +viser = VisTable() +img_path = "tests/test_files/table.jpg" +# OCR +ocr_result, _ = ocr_engine(img_path, return_word_box=True) +ocr_result = trans_char_ocr_res(ocr_result) +boxes, txts, scores = list(zip(*ocr_result)) +# Save +save_dir = Path("outputs") +save_dir.mkdir(parents=True, exist_ok=True) - save_html_path = save_dir / f"{Path(img_path).stem}.html" - save_drawed_path = save_dir / f"{Path(img_path).stem}_table_vis{Path(img_path).suffix}" - # 返回逻辑坐标 - table_html_str, table_cell_bboxes, logic_points, elapse = table_engine(img_path, ocr_result) - save_logic_path = save_dir / f"vis_logic_{Path(img_path).name}" - vis_imged = viser(img_path, table_html_str, save_html_path, table_cell_bboxes, save_drawed_path, logic_points, - save_logic_path) - print(f"elapse:{elapse}") +save_html_path = save_dir / f"{Path(img_path).stem}.html" +save_drawed_path = save_dir / f"{Path(img_path).stem}_table_vis{Path(img_path).suffix}" + +table_html_str, table_cell_bboxes, elapse = table_engine(img_path, ocr_result) +viser(img_path, table_html_str, save_html_path, table_cell_bboxes, save_drawed_path) +# 返回逻辑坐标 +# table_html_str, table_cell_bboxes, logic_points, elapse = table_engine(img_path, ocr_result, return_logic_points=True) +# save_logic_path = save_dir / f"vis_logic_{Path(img_path).name}" +# vis_imged = viser(img_path, table_html_str, save_html_path, table_cell_bboxes, save_drawed_path, logic_points, +# save_logic_path) +print(f"elapse:{elapse}") diff --git a/rapid_table_torch/download_model.py b/rapid_table/download_model.py similarity index 70% rename from rapid_table_torch/download_model.py rename to rapid_table/download_model.py index d636f94..9e9c496 100644 --- a/rapid_table_torch/download_model.py +++ b/rapid_table/download_model.py @@ -10,11 +10,33 @@ logger = get_logger("DownloadModel") CUR_DIR = Path(__file__).resolve() PROJECT_DIR = CUR_DIR.parent - +ROOT_URL = "https://www.modelscope.cn/studio/jockerK/TableRec/resolve/master/models/table_rec/unitable/" +KEY_TO_MODEL_URL = { + "unitable": { + "encoder": f"{ROOT_URL}/encoder.pth", + "decoder": f"{ROOT_URL}/decoder.pth", + "vocab": f"{ROOT_URL}/vocab.json", + } +} class DownloadModel: cur_dir = PROJECT_DIR + @staticmethod + def get_model_path(model_type: str, sub_file_type: str, path: Union[str, Path, None]) -> str: + if path is not None: + return path + + model_url = KEY_TO_MODEL_URL.get(model_type, {}).get(sub_file_type, None) + if model_url: + model_path = DownloadModel.download(model_url) + return model_path + + logger.info( + "model url is None, using the default download model %s", path + ) + return path + @classmethod def download(cls, model_full_url: Union[str, Path]) -> str: save_dir = cls.cur_dir / "models" diff --git a/rapid_table_torch/logger.py b/rapid_table/logger.py similarity index 100% rename from rapid_table_torch/logger.py rename to rapid_table/logger.py diff --git a/rapid_table/main.py b/rapid_table/main.py index d620fe5..202f6e0 100644 --- a/rapid_table/main.py +++ b/rapid_table/main.py @@ -5,33 +5,34 @@ import copy import importlib import time +from dataclasses import asdict from pathlib import Path -from typing import List, Optional, Tuple, Union +from typing import List, Tuple, Union import cv2 import numpy as np +from .download_model import DownloadModel +from .params import accept_kwargs_as_dataclass, BaseConfig from .table_matcher import TableMatch -from .table_structure import TableStructurer +from .table_structure import TableStructurer, TableStructureUnitable from .utils import LoadImage, VisTable root_dir = Path(__file__).resolve().parent class RapidTable: - def __init__(self, model_path: Optional[str] = None, model_type: str = None, use_cuda: bool = False): - if model_path is None: - model_path = str( - root_dir / "models" / "slanet-plus.onnx" - ) - model_type = "slanet-plus" - self.model_type = model_type + @accept_kwargs_as_dataclass(BaseConfig) + def __init__(self, config: BaseConfig): + self.model_type = config.model_type self.load_img = LoadImage() - config = { - "model_path": model_path, - "use_cuda": use_cuda, - } - self.table_structure = TableStructurer(config) + if self.model_type == "unitable": + config.encoder_path = DownloadModel.get_model_path(self.model_type, "encoder", config.encoder_path) + config.decoder_path = DownloadModel.get_model_path(self.model_type, "decoder", config.decoder_path) + config.vocab_path = DownloadModel.get_model_path(self.model_type, "vocab", config.vocab_path) + self.table_structure = TableStructureUnitable(asdict(config)) + else: + self.table_structure = TableStructurer(asdict(config)) self.table_matcher = TableMatch() try: diff --git a/rapid_table/params.py b/rapid_table/params.py new file mode 100644 index 0000000..c5756aa --- /dev/null +++ b/rapid_table/params.py @@ -0,0 +1,40 @@ +from dataclasses import dataclass, fields +from functools import wraps +from pathlib import Path + +from rapid_table.logger import get_logger + +root_dir = Path(__file__).resolve().parent +logger = get_logger("params") + +@dataclass +class BaseConfig: + model_type: str = "slanet-plus" + model_path: str = str(root_dir / "models" / "slanet-plus.onnx") + use_cuda: bool = False + device: str = "cpu" + encoder_path: str = None + decoder_path: str = None + vocab_path: str = None + + +def accept_kwargs_as_dataclass(cls): + def decorator(func): + @wraps(func) + def wrapper(*args, **kwargs): + if len(args) == 2 and isinstance(args[1], cls): + # 如果已经传递了 ModelConfig 实例，直接调用函数 + return func(*args, **kwargs) + else: + # 提取 cls 中定义的字段 + cls_fields = {field.name for field in fields(cls)} + # 过滤掉未定义的字段 + filtered_kwargs = {k: v for k, v in kwargs.items() if k in cls_fields} + # 发出警告对于未定义的字段 + for k in (kwargs.keys() - cls_fields): + logger.warning(f"Warning: '{k}' is not a valid field in {cls.__name__} and will be ignored.") + # 创建 ModelConfig 实例并调用函数 + config = cls(**filtered_kwargs) + return func(args[0], config=config) + return wrapper + return decorator \ No newline at end of file diff --git a/rapid_table/table_structure/__init__.py b/rapid_table/table_structure/__init__.py index a548391..3e638d9 100644 --- a/rapid_table/table_structure/__init__.py +++ b/rapid_table/table_structure/__init__.py @@ -12,3 +12,4 @@ # See the License for the specific language governing permissions and # limitations under the License. from .table_structure import TableStructurer +from .table_structure_unitable import TableStructureUnitable diff --git a/rapid_table_torch/table_structure/table_structure.py b/rapid_table/table_structure/table_structure_unitable.py similarity index 93% rename from rapid_table_torch/table_structure/table_structure.py rename to rapid_table/table_structure/table_structure_unitable.py index 32f3fa0..da9ed7f 100644 --- a/rapid_table_torch/table_structure/table_structure.py +++ b/rapid_table/table_structure/table_structure_unitable.py @@ -1,5 +1,6 @@ import re import time +from typing import Dict, Any import cv2 import numpy as np @@ -7,7 +8,7 @@ from PIL import Image from tokenizers import Tokenizer -from .components import Encoder, GPTFastDecoder +from .unitable_modules import Encoder, GPTFastDecoder from torchvision import transforms IMG_SIZE = 448 @@ -77,8 +78,14 @@ ] -class TableStructurer: - def __init__(self, encoder_path: str, decoder_path: str, vocab_path: str, device: str): +class TableStructureUnitable: + def __init__(self, config:Dict[str, Any]): + # encoder_path: str, decoder_path: str, vocab_path: str, device: str + vocab_path = config["vocab_path"] + encoder_path = config["encoder_path"] + decoder_path = config["decoder_path"] + device = config.get("device", "cuda:0") if config["use_cuda"] else "cpu" + self.vocab = Tokenizer.from_file(vocab_path) self.token_white_list = [self.vocab.token_to_id(i) for i in VALID_HTML_BBOX_TOKENS] self.bbox_token_ids = set([self.vocab.token_to_id(i) for i in BBOX_TOKENS]) diff --git a/rapid_table_torch/table_structure/components.py b/rapid_table/table_structure/unitable_modules.py similarity index 100% rename from rapid_table_torch/table_structure/components.py rename to rapid_table/table_structure/unitable_modules.py diff --git a/rapid_table/utils.py b/rapid_table/utils.py index e7135c5..77224ce 100644 --- a/rapid_table/utils.py +++ b/rapid_table/utils.py @@ -208,3 +208,4 @@ def save_img(save_path: Union[str, Path], img: np.ndarray): def save_html(save_path: Union[str, Path], html: str): with open(save_path, "w", encoding="utf-8") as f: f.write(html) + diff --git a/rapid_table_torch/__init__.py b/rapid_table_torch/__init__.py deleted file mode 100644 index 86c7b1c..0000000 --- a/rapid_table_torch/__init__.py +++ /dev/null @@ -1,2 +0,0 @@ -from .main import RapidTable -from .utils import VisTable diff --git a/rapid_table_torch/main.py b/rapid_table_torch/main.py deleted file mode 100644 index 5ef3dcc..0000000 --- a/rapid_table_torch/main.py +++ /dev/null @@ -1,155 +0,0 @@ -# -*- encoding: utf-8 -*- -# @Author: SWHL -# @Contact: liekkaskono@163.com -import argparse -import copy -import importlib -import os -import time -from pathlib import Path -from typing import List, Union - -import cv2 -import numpy as np - -from .download_model import DownloadModel -from .logger import get_logger -from .table_matcher import TableMatch -from .table_structure import TableStructurer -from .utils import LoadImage, VisTable - -root_dir = Path(__file__).resolve().parent -model_dir = os.path.join(root_dir, "models") -logger = get_logger("rapid_table_torch") -default_config = os.path.join(root_dir, "config.yaml") -ROOT_URL = "https://www.modelscope.cn/studio/jockerK/TableRec/resolve/master/models/table_rec/unitable/" -KEY_TO_MODEL_URL = { - "unitable": { - "encoder": f"{ROOT_URL}/encoder.pth", - "decoder": f"{ROOT_URL}/decoder.pth", - "vocab": f"{ROOT_URL}/vocab.json", - } -} - - -class RapidTable: - def __init__(self, encoder_path: str = None, decoder_path: str = None, vocab_path: str = None, - model_type: str = "unitable", - device: str = "cpu"): - self.model_type = model_type - self.load_img = LoadImage() - encoder_path = self.get_model_path(model_type, "encoder", encoder_path) - decoder_path = self.get_model_path(model_type, "decoder", decoder_path) - vocab_path = self.get_model_path(model_type, "vocab", vocab_path) - self.table_structure = TableStructurer(encoder_path, decoder_path, vocab_path, device) - self.table_matcher = TableMatch() - try: - self.ocr_engine = importlib.import_module("rapidocr_onnxruntime").RapidOCR() - except ModuleNotFoundError: - self.ocr_engine = None - - def __call__( - self, - img_content: Union[str, np.ndarray, bytes, Path], - ocr_result: List[Union[List[List[float]], str, str]] = None - ): - if self.ocr_engine is None and ocr_result is None: - raise ValueError( - "One of two conditions must be met: ocr_result is not empty, or rapidocr_onnxruntime is installed." - ) - - img = self.load_img(img_content) - - s = time.time() - h, w = img.shape[:2] - if ocr_result is None: - ocr_result, _ = self.ocr_engine(img) - dt_boxes, rec_res = self.get_boxes_recs(ocr_result, h, w) - - pred_structures, pred_bboxes, _ = self.table_structure(copy.deepcopy(img)) - pred_html = self.table_matcher(pred_structures, pred_bboxes, dt_boxes, rec_res) - logic_points = self.table_matcher.decode_logic_points(pred_structures) - elapse = time.time() - s - return pred_html, pred_bboxes, logic_points, elapse - - def get_boxes_recs( - self, ocr_result: List[Union[List[List[float]], str, str]], h: int, w: int - ): - dt_boxes, rec_res, scores = list(zip(*ocr_result)) - rec_res = list(zip(rec_res, scores)) - - r_boxes = [] - for box in dt_boxes: - box = np.array(box) - x_min = max(0, box[:, 0].min() - 1) - x_max = min(w, box[:, 0].max() + 1) - y_min = max(0, box[:, 1].min() - 1) - y_max = min(h, box[:, 1].max() + 1) - box = [x_min, y_min, x_max, y_max] - r_boxes.append(box) - dt_boxes = np.array(r_boxes) - return dt_boxes, rec_res - - @staticmethod - def get_model_path(model_type: str, sub_file_type: str, path: Union[str, Path, None]) -> str: - if path is not None: - return path - - model_url = KEY_TO_MODEL_URL.get(model_type, {}).get(sub_file_type, None) - if model_url: - model_path = DownloadModel.download(model_url) - return model_path - - logger.info( - "model url is None, using the default download model %s", path - ) - return path -def main(): - parser = argparse.ArgumentParser() - parser.add_argument( - "-v", - "--vis", - action="store_true", - help="Wheter to visualize the layout results.", - ) - parser.add_argument( - "-img", "--img_path", type=str, required=True, help="Path to image for layout." - ) - parser.add_argument( - "-d", "--device", type=str, default="cpu", help="device to use" - ) - args = parser.parse_args() - - try: - ocr_engine = importlib.import_module("rapidocr_onnxruntime").RapidOCR() - except ModuleNotFoundError as exc: - raise ModuleNotFoundError( - "Please install the rapidocr_onnxruntime by pip install rapidocr_onnxruntime." - ) from exc - - rapid_table = RapidTable(device=args.device) - - img = cv2.imread(args.img_path) - - ocr_result, _ = ocr_engine(img) - table_html_str, table_cell_bboxes, elapse = rapid_table(img, ocr_result) - print(table_html_str) - - viser = VisTable() - if args.vis: - img_path = Path(args.img_path) - - save_dir = img_path.resolve().parent - save_html_path = save_dir / f"{Path(img_path).stem}.html" - save_drawed_path = save_dir / f"vis_{Path(img_path).name}" - viser( - img_path, - table_html_str, - save_html_path, - table_cell_bboxes, - save_drawed_path, - ) - - -if __name__ == "__main__": - main() \ No newline at end of file diff --git a/rapid_table_torch/table_matcher/__init__.py b/rapid_table_torch/table_matcher/__init__.py deleted file mode 100644 index 9bff7d7..0000000 --- a/rapid_table_torch/table_matcher/__init__.py +++ /dev/null @@ -1,4 +0,0 @@ -# -*- encoding: utf-8 -*- -# @Author: SWHL -# @Contact: liekkaskono@163.com -from .matcher import TableMatch diff --git a/rapid_table_torch/table_matcher/matcher.py b/rapid_table_torch/table_matcher/matcher.py deleted file mode 100644 index 0453929..0000000 --- a/rapid_table_torch/table_matcher/matcher.py +++ /dev/null @@ -1,192 +0,0 @@ -# copyright (c) 2022 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. -# -*- encoding: utf-8 -*- -import numpy as np - -from .utils import compute_iou, distance - - -class TableMatch: - def __init__(self, filter_ocr_result=True, use_master=False): - self.filter_ocr_result = filter_ocr_result - self.use_master = use_master - - def __call__(self, pred_structures, pred_bboxes, dt_boxes, rec_res): - if self.filter_ocr_result: - dt_boxes, rec_res = self._filter_ocr_result(pred_bboxes, dt_boxes, rec_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 - - def match_result(self, dt_boxes, pred_bboxes): - matched = {} - for i, gt_box in enumerate(dt_boxes): - distances = [] - for j, pred_box in enumerate(pred_bboxes): - if len(pred_box) == 8: - pred_box = [ - np.min(pred_box[0::2]), - np.min(pred_box[1::2]), - np.max(pred_box[0::2]), - np.max(pred_box[1::2]), - ] - distances.append( - (distance(gt_box, pred_box), 1.0 - compute_iou(gt_box, pred_box)) - ) # compute iou and l1 distance - sorted_distances = distances.copy() - # select det box by iou and l1 distance - 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 "" not in tag: - end_html.append(tag) - continue - - if "" == tag: - end_html.extend("") - - if td_index in matched_index.keys(): - b_with = False - if ( - "" in ocr_contents[matched_index[td_index][0]] - and len(matched_index[td_index]) > 1 - ): - b_with = True - end_html.extend("") - - 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 "" in content: - content = content[3:] - - if "" 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("") - - if "" == tag: - end_html.append("") - else: - end_html.append(tag) - - td_index += 1 - - # Filter elements - filter_elements = ["", "", "", ""] - end_html = [v for v in end_html if v not in filter_elements] - return "".join(end_html), end_html - def decode_logic_points(self, pred_structures): - logic_points = [] - current_row = 0 - current_col = 0 - max_rows = 0 - max_cols = 0 - occupied_cells = {} # 用于记录已经被占用的单元格 - - def is_occupied(row, col): - return (row, col) in occupied_cells - - def mark_occupied(row, col, rowspan, colspan): - for r in range(row, row + rowspan): - for c in range(col, col + colspan): - occupied_cells[(r, c)] = True - - i = 0 - while i < len(pred_structures): - token = pred_structures[i] - - if token == '': - current_col = 0 # 每次遇到 时，重置当前列号 - elif token == '': - current_row += 1 # 行结束，行号增加 - elif token .startswith(''): - if 'colspan=' in pred_structures[j]: - colspan = int(pred_structures[j].split('=')[1].strip('"\'')) - elif 'rowspan=' in pred_structures[j]: - rowspan = int(pred_structures[j].split('=')[1].strip('"\'')) - j += 1 - - # 跳过已经处理过的属性 token - i = j - - # 找到下一个未被占用的列 - while is_occupied(current_row, current_col): - current_col += 1 - - # 计算逻辑坐标 - r_start = current_row - r_end = current_row + rowspan - 1 - col_start = current_col - col_end = current_col + colspan - 1 - - # 记录逻辑坐标 - logic_points.append([r_start, r_end, col_start, col_end]) - - # 标记占用的单元格 - mark_occupied(r_start, col_start, rowspan, colspan) - - # 更新当前列号 - current_col += colspan - - # 更新最大行数和列数 - max_rows = max(max_rows, r_end + 1) - max_cols = max(max_cols, col_end + 1) - - i += 1 - - return logic_points - - def _filter_ocr_result(self, pred_bboxes, dt_boxes, rec_res): - y1 = pred_bboxes[:, 1::2].min() - new_dt_boxes = [] - new_rec_res = [] - - for box, rec in zip(dt_boxes, rec_res): - if np.max(box[1::2]) < y1: - continue - new_dt_boxes.append(box) - new_rec_res.append(rec) - return new_dt_boxes, new_rec_res diff --git a/rapid_table_torch/table_matcher/utils.py b/rapid_table_torch/table_matcher/utils.py deleted file mode 100644 index 3ec8fcc..0000000 --- a/rapid_table_torch/table_matcher/utils.py +++ /dev/null @@ -1,248 +0,0 @@ -# copyright (c) 2022 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. -# -*- encoding: utf-8 -*- -# @Author: SWHL -# @Contact: liekkaskono@163.com -import re - - -def deal_isolate_span(thead_part): - """ - Deal with isolate span cases in this function. - It causes by wrong prediction in structure recognition model. - eg. predict to rowspan="2">. - :param thead_part: - :return: - """ - # 1. find out isolate span tokens. - isolate_pattern = ( - ' rowspan="(\d)+" colspan="(\d)+">|' - ' colspan="(\d)+" rowspan="(\d)+">|' - ' rowspan="(\d)+">|' - ' colspan="(\d)+">' - ) - isolate_iter = re.finditer(isolate_pattern, thead_part) - isolate_list = [i.group() for i in isolate_iter] - - # 2. find out span number, by step 1 results. - span_pattern = ( - ' rowspan="(\d)+" colspan="(\d)+"|' - ' colspan="(\d)+" rowspan="(\d)+"|' - ' rowspan="(\d)+"|' - ' colspan="(\d)+"' - ) - corrected_list = [] - for isolate_item in isolate_list: - span_part = re.search(span_pattern, isolate_item) - spanStr_in_isolateItem = span_part.group() - # 3. merge the span number into the span token format string. - if spanStr_in_isolateItem is not None: - corrected_item = "".format(spanStr_in_isolateItem) - corrected_list.append(corrected_item) - else: - corrected_list.append(None) - - # 4. replace original isolated token. - for corrected_item, isolate_item in zip(corrected_list, isolate_list): - if corrected_item is not None: - thead_part = thead_part.replace(isolate_item, corrected_item) - else: - pass - return thead_part - - -def deal_duplicate_bb(thead_part): - """ - Deal duplicate or after replace. - Keep one in a token. - :param thead_part: - :return: - """ - # 1. find out in . - td_pattern = ( - '(.+?)|' - '(.+?)|' - '(.+?)|' - '(.+?)|' - "(.*?)" - ) - td_iter = re.finditer(td_pattern, thead_part) - td_list = [t.group() for t in td_iter] - - # 2. is multiply in or not? - new_td_list = [] - for td_item in td_list: - if td_item.count("") > 1 or td_item.count("") > 1: - # multiply in case. - # 1. remove all - td_item = td_item.replace("", "").replace("", "") - # 2. replace -> , -> . - td_item = td_item.replace("", "").replace("", "") - new_td_list.append(td_item) - else: - new_td_list.append(td_item) - - # 3. replace original thead part. - for td_item, new_td_item in zip(td_list, new_td_list): - thead_part = thead_part.replace(td_item, new_td_item) - return thead_part - - -def deal_bb(result_token): - """ - In our opinion, always occurs in text's context. - This function will find out all tokens in and insert by manual. - :param result_token: - :return: - """ - # find out parts. - thead_pattern = "(.*?)" - if re.search(thead_pattern, result_token) is None: - return result_token - thead_part = re.search(thead_pattern, result_token).group() - origin_thead_part = copy.deepcopy(thead_part) - - # check "rowspan" or "colspan" occur in parts or not . - span_pattern = '|||' - span_iter = re.finditer(span_pattern, thead_part) - span_list = [s.group() for s in span_iter] - has_span_in_head = True if len(span_list) > 0 else False - - if not has_span_in_head: - # not include "rowspan" or "colspan" branch 1. - # 1. replace to , and to - # 2. it is possible to predict text include or by Text-line recognition, - # so we replace to , and to - thead_part = ( - thead_part.replace("", "") - .replace("", "") - .replace("", "") - .replace("", "") - ) - else: - # include "rowspan" or "colspan" branch 2. - # Firstly, we deal rowspan or colspan cases. - # 1. replace > to > - # 2. replace to - # 3. it is possible to predict text include or by Text-line recognition, - # so we replace to , and to - - # Secondly, deal ordinary cases like branch 1 - - # replace ">" to "" - replaced_span_list = [] - for sp in span_list: - replaced_span_list.append(sp.replace(">", ">")) - for sp, rsp in zip(span_list, replaced_span_list): - thead_part = thead_part.replace(sp, rsp) - - # replace "" to "" - thead_part = thead_part.replace("", "") - - # remove duplicated by re.sub - mb_pattern = "()+" - single_b_string = "" - thead_part = re.sub(mb_pattern, single_b_string, thead_part) - - mgb_pattern = "()+" - single_gb_string = "" - thead_part = re.sub(mgb_pattern, single_gb_string, thead_part) - - # ordinary cases like branch 1 - thead_part = thead_part.replace("", "").replace("", "") - - # convert back to , empty cell has no . - # but space cell( ) is suitable for - thead_part = thead_part.replace("", "") - # deal with duplicated - thead_part = deal_duplicate_bb(thead_part) - # deal with isolate span tokens, which causes by wrong predict by structure prediction. - # eg.PMC5994107_011_00.png - thead_part = deal_isolate_span(thead_part) - # replace original result with new thead part. - result_token = result_token.replace(origin_thead_part, thead_part) - return result_token - - -def deal_eb_token(master_token): - """ - post process with , , ... - emptyBboxTokenDict = { - "[]": '', - "[' ']": '', - "['', ' ', '']": '', - "['\\u2028', '\\u2028']": '', - "['', ' ', '']": '', - "['', '']": '', - "['', ' ', '']": '', - "['', '', '', '']": '', - "['', '', ' ', '', '']": '', - "['', '']": '', - "['', ' ', '\\u2028', ' ', '\\u2028', ' ', '']": '', - } - :param master_token: - :return: - """ - master_token = master_token.replace("", "") - master_token = master_token.replace("", " ") - master_token = master_token.replace("", " ") - master_token = master_token.replace("", "\u2028\u2028") - master_token = master_token.replace("", " ") - master_token = master_token.replace("", "") - master_token = master_token.replace("", " ") - master_token = master_token.replace("", "") - master_token = master_token.replace("", " ") - master_token = master_token.replace("", "") - master_token = master_token.replace( - "", " \u2028 \u2028 " - ) - return master_token - - -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 diff --git a/rapid_table_torch/table_structure/__init__.py b/rapid_table_torch/table_structure/__init__.py deleted file mode 100644 index 4582f3d..0000000 --- a/rapid_table_torch/table_structure/__init__.py +++ /dev/null @@ -1 +0,0 @@ -from .table_structure import TableStructurer diff --git a/rapid_table_torch/table_structure/utils.py b/rapid_table_torch/table_structure/utils.py deleted file mode 100644 index f2d76df..0000000 --- a/rapid_table_torch/table_structure/utils.py +++ /dev/null @@ -1,28 +0,0 @@ -# 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. -# -*- encoding: utf-8 -*- -# @Author: SWHL -# @Contact: liekkaskono@163.com - -def trans_char_ocr_res(ocr_res): - word_result = [] - for res in ocr_res: - score = res[2] - for word_box, word in zip(res[3], res[4]): - word_res = [] - word_res.append(word_box) - word_res.append(word) - word_res.append(score) - word_result.append(word_res) - return word_result diff --git a/rapid_table_torch/utils.py b/rapid_table_torch/utils.py deleted file mode 100644 index e7135c5..0000000 --- a/rapid_table_torch/utils.py +++ /dev/null @@ -1,210 +0,0 @@ -# -*- encoding: utf-8 -*- -# @Author: SWHL -# @Contact: liekkaskono@163.com -import os -from io import BytesIO -from pathlib import Path -from typing import Optional, Union, List - -import cv2 -import numpy as np -from PIL import Image, UnidentifiedImageError - -InputType = Union[str, np.ndarray, bytes, Path] - - -class LoadImage: - def __init__( - self, - ): - pass - - def __call__(self, img: InputType) -> np.ndarray: - if not isinstance(img, InputType.__args__): - raise LoadImageError( - f"The img type {type(img)} does not in {InputType.__args__}" - ) - - img = self.load_img(img) - - if img.ndim == 2: - return cv2.cvtColor(img, cv2.COLOR_GRAY2BGR) - - if img.ndim == 3 and img.shape[2] == 4: - return self.cvt_four_to_three(img) - - return img - - def load_img(self, img: InputType) -> np.ndarray: - if isinstance(img, (str, Path)): - self.verify_exist(img) - try: - img = np.array(Image.open(img)) - img = cv2.cvtColor(img, cv2.COLOR_RGB2BGR) - except UnidentifiedImageError as e: - raise LoadImageError(f"cannot identify image file {img}") from e - return img - - if isinstance(img, bytes): - img = np.array(Image.open(BytesIO(img))) - img = cv2.cvtColor(img, cv2.COLOR_RGB2BGR) - return img - - if isinstance(img, np.ndarray): - return img - - raise LoadImageError(f"{type(img)} is not supported!") - - @staticmethod - def cvt_four_to_three(img: np.ndarray) -> np.ndarray: - """RGBA → RGB""" - r, g, b, a = cv2.split(img) - new_img = cv2.merge((b, g, r)) - - not_a = cv2.bitwise_not(a) - not_a = cv2.cvtColor(not_a, cv2.COLOR_GRAY2BGR) - - new_img = cv2.bitwise_and(new_img, new_img, mask=a) - new_img = cv2.add(new_img, not_a) - return new_img - - @staticmethod - def verify_exist(file_path: Union[str, Path]): - if not Path(file_path).exists(): - raise LoadImageError(f"{file_path} does not exist.") - - -class LoadImageError(Exception): - pass - - -class VisTable: - def __init__( - self, - ): - self.load_img = LoadImage() - - def __call__( - self, - img_path: Union[str, Path], - table_html_str: str, - save_html_path: Optional[str] = None, - table_cell_bboxes: Optional[np.ndarray] = None, - save_drawed_path: Optional[str] = None, - logic_points: List[List[float]] = None, - save_logic_path: Optional[str] = None, - ) -> None: - if save_html_path: - html_with_border = self.insert_border_style(table_html_str) - self.save_html(save_html_path, html_with_border) - - if table_cell_bboxes is None: - return None - - img = self.load_img(img_path) - - dims_bboxes = table_cell_bboxes.shape[1] - if dims_bboxes == 4: - drawed_img = self.draw_rectangle(img, table_cell_bboxes) - elif dims_bboxes == 8: - drawed_img = self.draw_polylines(img, table_cell_bboxes) - else: - raise ValueError("Shape of table bounding boxes is not between in 4 or 8.") - - if save_drawed_path: - self.save_img(save_drawed_path, drawed_img) - if save_logic_path and logic_points: - polygons = [[box[0],box[1], box[4], box[5]] for box in table_cell_bboxes] - self.plot_rec_box_with_logic_info(img_path, save_logic_path, logic_points, polygons) - return drawed_img - - def insert_border_style(self, table_html_str: str): - style_res = f"""""" - prefix_table, suffix_table = table_html_str.split("") - html_with_border = f"{prefix_table}{style_res}{suffix_table}" - return html_with_border - - def plot_rec_box_with_logic_info(self, img_path, output_path, logic_points, sorted_polygons): - """ - :param img_path - :param output_path - :param logic_points: [row_start,row_end,col_start,col_end] - :param sorted_polygons: [xmin,ymin,xmax,ymax] - :return: - """ - # 读取原图 - img = cv2.imread(img_path) - img = cv2.copyMakeBorder( - img, 0, 0, 0, 100, cv2.BORDER_CONSTANT, value=[255, 255, 255] - ) - # 绘制 polygons 矩形 - for idx, polygon in enumerate(sorted_polygons): - x0, y0, x1, y1 = polygon[0], polygon[1], polygon[2], polygon[3] - x0 = round(x0) - y0 = round(y0) - x1 = round(x1) - y1 = round(y1) - cv2.rectangle(img, (x0, y0), (x1, y1), (0, 0, 255), 1) - # 增大字体大小和线宽 - font_scale = 0.9 # 原先是0.5 - thickness = 1 # 原先是1 - logic_point = logic_points[idx] - cv2.putText( - img, - f"row: {logic_point[0]}-{logic_point[1]}", - (x0 + 3, y0 + 8), - cv2.FONT_HERSHEY_PLAIN, - font_scale, - (0, 0, 255), - thickness, - ) - cv2.putText( - img, - f"col: {logic_point[2]}-{logic_point[3]}", - (x0 + 3, y0 + 18), - cv2.FONT_HERSHEY_PLAIN, - font_scale, - (0, 0, 255), - thickness, - ) - os.makedirs(os.path.dirname(output_path), exist_ok=True) - # 保存绘制后的图像 - self.save_img(output_path, img) - - @staticmethod - def draw_rectangle(img: np.ndarray, boxes: np.ndarray) -> np.ndarray: - img_copy = img.copy() - for box in boxes.astype(int): - x1, y1, x2, y2 = box - cv2.rectangle(img_copy, (x1, y1), (x2, y2), (255, 0, 0), 2) - return img_copy - - @staticmethod - def draw_polylines(img: np.ndarray, points) -> np.ndarray: - img_copy = img.copy() - for point in points.astype(int): - point = point.reshape(4, 2) - cv2.polylines(img_copy, [point.astype(int)], True, (255, 0, 0), 2) - return img_copy - - @staticmethod - def save_img(save_path: Union[str, Path], img: np.ndarray): - cv2.imwrite(str(save_path), img) - - @staticmethod - def save_html(save_path: Union[str, Path], html: str): - with open(save_path, "w", encoding="utf-8") as f: - f.write(html) diff --git a/requirements_torch.txt b/requirements_torch.txt deleted file mode 100644 index 4ba3572..0000000 --- a/requirements_torch.txt +++ /dev/null @@ -1,8 +0,0 @@ -onnxruntime>=1.7.0 -opencv_python>=4.5.1.48 -numpy>=1.21.6,<2 -Pillow -requests -torch -torchvision -tokenizers \ No newline at end of file diff --git a/setup.py b/setup.py index 0539473..0489760 100644 --- a/setup.py +++ b/setup.py @@ -66,4 +66,5 @@ def get_readme(): ], python_requires=">=3.6,<3.13", entry_points={"console_scripts": [f"{MODULE_NAME}={MODULE_NAME}.main:main"]}, + extras_require={"torch": ["torch", "torchvision", "tokenizers"]}, ) diff --git a/setup_torch.py b/setup_torch.py deleted file mode 100644 index b332a20..0000000 --- a/setup_torch.py +++ /dev/null @@ -1,69 +0,0 @@ -# -*- encoding: utf-8 -*- -# @Author: SWHL -# @Contact: liekkaskono@163.com -import sys -from pathlib import Path - -import setuptools -from get_pypi_latest_version import GetPyPiLatestVersion - - -def get_readme(): - root_dir = Path(__file__).resolve().parent - readme_path = str(root_dir / "docs" / "doc_whl_rapid_table.md") - with open(readme_path, "r", encoding="utf-8") as f: - readme = f.read() - return readme - - -MODULE_NAME = "rapid_table_torch" -obtainer = GetPyPiLatestVersion() -try: - latest_version = obtainer(MODULE_NAME) -except Exception: - latest_version = "0.0.0" -VERSION_NUM = obtainer.version_add_one(latest_version) - -if len(sys.argv) > 2: - match_str = " ".join(sys.argv[2:]) - matched_versions = obtainer.extract_version(match_str) - if matched_versions: - VERSION_NUM = matched_versions -sys.argv = sys.argv[:2] - -setuptools.setup( - name=MODULE_NAME, - version=VERSION_NUM, - platforms="Any", - long_description=get_readme(), - long_description_content_type="text/markdown", - description="Tools for parsing table structures based pytorch.", - author="SWHL", - author_email="liekkaskono@163.com", - url="https://github.com/RapidAI/RapidTable", - license="Apache-2.0", - include_package_data=True, - install_requires=[ - "opencv_python>=4.5.1.48", - "numpy>=1.21.6", - "Pillow", - "torch", - "torchvision", - "tokenizers" - ], - packages=[ - MODULE_NAME, - f"{MODULE_NAME}.models", - f"{MODULE_NAME}.table_matcher", - f"{MODULE_NAME}.table_structure", - ], - package_data={"": [".gitkeep"]}, - keywords=["unitable,table,rapidocr,rapid_table"], - classifiers=[ - "Programming Language :: Python :: 3.10", - "Programming Language :: Python :: 3.11", - "Programming Language :: Python :: 3.12", - ], - python_requires=">=3.10,<3.13", - entry_points={"console_scripts": [f"{MODULE_NAME}={MODULE_NAME}.main:main"]}, -) diff --git a/tests/test_table_torch.py b/tests/test_table_torch.py index 640257c..1eb4505 100644 --- a/tests/test_table_torch.py +++ b/tests/test_table_torch.py @@ -11,10 +11,10 @@ sys.path.append(str(root_dir)) -from rapid_table_torch import RapidTable +from rapid_table import RapidTable ocr_engine = RapidOCR() -table_engine = RapidTable() +table_engine = RapidTable(model_type="unitable") test_file_dir = cur_dir / "test_files" img_path = str(test_file_dir / "table.jpg") @@ -22,14 +22,14 @@ def test_ocr_input(): ocr_res, _ = ocr_engine(img_path) - table_html_str, table_cell_bboxes, logic_points, elapse = table_engine(img_path, ocr_res) + table_html_str, table_cell_bboxes, elapse = table_engine(img_path, ocr_res) assert table_html_str.count("") == 16 def test_input_ocr_none(): - table_html_str, table_cell_bboxes, logic_points, elapse = table_engine(img_path) + table_html_str, table_cell_bboxes, elapse = table_engine(img_path) assert table_html_str.count("") == 16 def test_logic_points_out(): - table_html_str, table_cell_bboxes, logic_points, elapse = table_engine(img_path) + table_html_str, table_cell_bboxes, logic_points, elapse = table_engine(img_path, return_logic_points=True) assert len(table_cell_bboxes) == len(logic_points) From 2e514121079bcca130c09482cfd2365014f778ad Mon Sep 17 00:00:00 2001 From: Joker1212 <519548295@qq.com> Date: Mon, 30 Dec 2024 17:09:03 +0800 Subject: [PATCH 07/23] chore: rm extra words --- README.md | 1 - 1 file changed, 1 deletion(-) diff --git a/README.md b/README.md index 89b0f59..d0f6594 100644 --- a/README.md +++ b/README.md @@ -122,7 +122,6 @@ table_engine = RapidTable() 完整示例： -#### onnx版本 ```python from pathlib import Path From 4a70f601ee1120841fc0661b0959305f9df1914d Mon Sep 17 00:00:00 2001 From: Jokcer <519548295@qq.com> Date: Mon, 30 Dec 2024 22:36:56 +0800 Subject: [PATCH 08/23] fix: adapt unitable no content label token --- rapid_table/main.py | 3 +++ rapid_table/table_matcher/matcher.py | 5 ++++- rapid_table/table_structure/table_structure_unitable.py | 8 +++++--- 3 files changed, 12 insertions(+), 4 deletions(-) diff --git a/rapid_table/main.py b/rapid_table/main.py index 202f6e0..b412790 100644 --- a/rapid_table/main.py +++ b/rapid_table/main.py @@ -65,6 +65,9 @@ def __call__( if self.model_type == "slanet-plus": pred_bboxes = self.adapt_slanet_plus(img, pred_bboxes) pred_html = self.table_matcher(pred_structures, pred_bboxes, dt_boxes, rec_res) + # 过滤掉占位的bbox + mask = ~np.all(pred_bboxes == 0, axis=1) + pred_bboxes = pred_bboxes[mask] # 避免低版本升级后出现问题,默认不打开 if return_logic_points: logic_points = self.table_matcher.decode_logic_points(pred_structures) diff --git a/rapid_table/table_matcher/matcher.py b/rapid_table/table_matcher/matcher.py index 0453929..bc976ed 100644 --- a/rapid_table/table_matcher/matcher.py +++ b/rapid_table/table_matcher/matcher.py @@ -29,7 +29,7 @@ def __call__(self, pred_structures, pred_bboxes, dt_boxes, rec_res): pred_html, pred = self.get_pred_html(pred_structures, matched_index, rec_res) return pred_html - def match_result(self, dt_boxes, pred_bboxes): + def match_result(self, dt_boxes, pred_bboxes, min_iou=0.1 ** 8): matched = {} for i, gt_box in enumerate(dt_boxes): distances = [] @@ -49,6 +49,9 @@ def match_result(self, dt_boxes, pred_bboxes): sorted_distances = sorted( sorted_distances, key=lambda item: (item[1], item[0]) ) + # must > min_iou + if sorted_distances[0][1] >= 1 - min_iou: + continue if distances.index(sorted_distances[0]) not in matched.keys(): matched[distances.index(sorted_distances[0])] = [i] else: diff --git a/rapid_table/table_structure/table_structure_unitable.py b/rapid_table/table_structure/table_structure_unitable.py index da9ed7f..fdc42b4 100644 --- a/rapid_table/table_structure/table_structure_unitable.py +++ b/rapid_table/table_structure/table_structure_unitable.py @@ -183,8 +183,8 @@ def decode_tokens(self, context): td_attrs = td_match.group(1).strip() td_content = td_match.group(2).strip() if td_attrs: - decoded_list.append('') decoded_list.append('') else: @@ -197,7 +197,9 @@ def decode_tokens(self, context): # 将坐标转换为从左上角开始顺时针到左下角的点的坐标 coords = np.array([xmin, ymin, xmax, ymin, xmax, ymax, xmin, ymax]) bbox_coords.append(coords) - + else: + # 填充占位的bbox，保证后续流程统一 + bbox_coords.append(np.array([0, 0, 0,0,0,0, 0, 0])) decoded_list.append('') # 将 bbox_coords 转换为 numpy 数组 From 8904b15db071de9b5d5b9703a6508d65f4d2ad85 Mon Sep 17 00:00:00 2001 From: SWHL Date: Wed, 1 Jan 2025 22:44:20 +0800 Subject: [PATCH 09/23] chore: optimize code --- README.md | 154 +++-- rapid_table/download_model.py | 9 +- rapid_table/main.py | 23 +- rapid_table/params.py | 29 +- rapid_table/table_matcher/matcher.py | 26 +- rapid_table/table_matcher/utils.py | 9 +- .../table_structure/table_structure.py | 2 +- .../table_structure/unitable_modules.py | 635 ++++++++++++++++-- rapid_table/table_structure/utils.py | 5 +- rapid_table/utils.py | 37 +- setup.py | 2 +- 11 files changed, 742 insertions(+), 189 deletions(-) diff --git a/README.md b/README.md index d0f6594..3f2397a 100644 --- a/README.md +++ b/README.md @@ -21,16 +21,16 @@ slanet_plus是paddlex内置的SLANet升级版模型，准确率有大幅提升 unitable是来源unitable的transformer模型，精度最高，暂仅支持pytorch推理，支持gpu推理加速,训练权重来源于 [OhMyTable项目](https://github.com/Sanster/OhMyTable) -| 模型类型 | 模型名称 | 推理框架 |模型大小 |推理耗时(单图 60KB)| - |:--------------:|:--------------------------------------:| :------: |:------: |:------: | -| 英文 | `en_ppstructure_mobile_v2_SLANet.onnx` | onnxruntime |7.3M |0.15s | -| 中文 | `ch_ppstructure_mobile_v2_SLANet.onnx` | onnxruntime |7.4M |0.15s | -| slanet_plus 中文 | `slanet-plus.onnx` | onnxruntime |6.8M |0.15s | -| unitable 中文 | `unitable(encoder.pth,decoder.pth)` | pytorch |500M |cpu(6s) gpu-4090(1.5s)| +| `model_type` | 模型名称 | 推理框架 |模型大小 |推理耗时(单图 60KB)| + |:--------------|:--------------------------------------| :------: |:------ |:------ | +| `ppstructure_en` | `en_ppstructure_mobile_v2_SLANet.onnx` | onnxruntime |7.3M |0.15s | +| `ppstructure_zh` | `ch_ppstructure_mobile_v2_SLANet.onnx` | onnxruntime |7.4M |0.15s | +| `slanet_plus` | `slanet-plus.onnx` | onnxruntime |6.8M |0.15s | +| `unitable` | `unitable(encoder.pth,decoder.pth)` | pytorch |500M |cpu(6s) gpu-4090(1.5s)| 模型来源\ -[PaddleOCR 表格识别](https://github.com/PaddlePaddle/PaddleOCR/blob/133d67f27dc8a241d6b2e30a9f047a0fb75bebbe/ppstructure/table/README_ch.md) \ -[PaddleX-SlaNetPlus 表格识别](https://github.com/PaddlePaddle/PaddleX/blob/release/3.0-beta1/docs/module_usage/tutorials/ocr_modules/table_structure_recognition.md) \ +[PaddleOCR 表格识别](https://github.com/PaddlePaddle/PaddleOCR/blob/133d67f27dc8a241d6b2e30a9f047a0fb75bebbe/ppstructure/table/README_ch.md)\ +[PaddleX-SlaNetPlus 表格识别](https://github.com/PaddlePaddle/PaddleX/blob/release/3.0-beta1/docs/module_usage/tutorials/ocr_modules/table_structure_recognition.md)\ [Unitable](https://github.com/poloclub/unitable?tab=readme-ov-file) 模型下载地址为：[link](https://github.com/RapidAI/RapidTable/releases/tag/assets) @@ -41,51 +41,6 @@ unitable是来源unitable的transformer模型，精度最高，暂仅支持pytor Demo -### 更新日志 - -

+```html @@ -316,9 +275,56 @@ print(table_html_str) ``` + + #### 可视化结果
<>
MethodsFPS
SegLink [26]70.086d>77.08.9
PixelLink [4]73.283.077.8
TextSnake [18]73.983.278.31.1
TextField [37]75.987.481.35.2
MSR[38]76.787.87.481.7
FTSN [3]77.187.682.0
LSE[30]81.784.282.9
CRAFT [2]78.288.282.98.6
MCN[16]798883
ATRR[35]82.185.283.6
PAN [34]83.884.484.130.2
DB[12]79.291.584.932.0
DRRG[41]82.3088.0585.08
Ours (SynText)80.688582.9712.68
Ours (MLT-17)84.5486.6285.5712.31
+ +### 更新日志 + +
+ +#### 2024.12.30 update + +- 支持Unitable模型的表格识别，使用pytorch框架 + +#### 2024.11.24 update + +- 支持gpu推理，适配 rapidOCR 单字识别匹配,支持逻辑坐标返回及可视化 + +#### 2024.10.13 update + +- 补充最新paddlex-SLANet-plus 模型(paddle2onnx原因暂不能支持onnx) + +#### 2023-12-29 v0.1.3 update + +- 优化可视化结果部分 + +#### 2023-12-27 v0.1.2 update + +- 添加返回cell坐标框参数 +- 完善可视化函数 + +#### 2023-07-17 v0.1.0 update + +- 将`rapidocr_onnxruntime`部分从`rapid_table`中解耦合出来，给出选项是否依赖，更加灵活。 + +- 增加接口输入参数`ocr_result`： + - 如果在调用函数时，事先指定了`ocr_result`参数值，则不会再走OCR。其中`ocr_result`格式需要和`rapidocr_onnxruntime`返回值一致。 + - 如果未指定`ocr_result`参数值，但是事先安装了`rapidocr_onnxruntime`库，则会自动调用该库，进行识别。 + - 如果`ocr_result`未指定，且`rapidocr_onnxruntime`未安装，则会报错。必须满足两个条件中一个。 + +#### 2023-07-10 v0.0.13 updata + +- 更改传入表格还原中OCR的实例接口，可以传入其他OCR实例，前提要与`rapidocr_onnxruntime`接口一致 + +#### 2023-07-06 v0.0.12 update + +- 去掉返回表格的html字符串中的``元素，便于后续统一。 +- 采用Black工具优化代码 + +
diff --git a/rapid_table/download_model.py b/rapid_table/download_model.py index 9e9c496..ab65fe3 100644 --- a/rapid_table/download_model.py +++ b/rapid_table/download_model.py @@ -19,11 +19,14 @@ } } + class DownloadModel: cur_dir = PROJECT_DIR @staticmethod - def get_model_path(model_type: str, sub_file_type: str, path: Union[str, Path, None]) -> str: + def get_model_path( + model_type: str, sub_file_type: str, path: Union[str, Path, None] + ) -> str: if path is not None: return path @@ -32,9 +35,7 @@ def get_model_path(model_type: str, sub_file_type: str, path: Union[str, Path, N model_path = DownloadModel.download(model_url) return model_path - logger.info( - "model url is None, using the default download model %s", path - ) + logger.info("model url is None, using the default download model %s", path) return path @classmethod diff --git a/rapid_table/main.py b/rapid_table/main.py index b412790..9567e6c 100644 --- a/rapid_table/main.py +++ b/rapid_table/main.py @@ -13,7 +13,7 @@ import numpy as np from .download_model import DownloadModel -from .params import accept_kwargs_as_dataclass, BaseConfig +from .params import BaseConfig, accept_kwargs_as_dataclass from .table_matcher import TableMatch from .table_structure import TableStructurer, TableStructureUnitable from .utils import LoadImage, VisTable @@ -26,13 +26,21 @@ class RapidTable: def __init__(self, config: BaseConfig): self.model_type = config.model_type self.load_img = LoadImage() + if self.model_type == "unitable": - config.encoder_path = DownloadModel.get_model_path(self.model_type, "encoder", config.encoder_path) - config.decoder_path = DownloadModel.get_model_path(self.model_type, "decoder", config.decoder_path) - config.vocab_path = DownloadModel.get_model_path(self.model_type, "vocab", config.vocab_path) + config.encoder_path = DownloadModel.get_model_path( + self.model_type, "encoder", config.encoder_path + ) + config.decoder_path = DownloadModel.get_model_path( + self.model_type, "decoder", config.decoder_path + ) + config.vocab_path = DownloadModel.get_model_path( + self.model_type, "vocab", config.vocab_path + ) self.table_structure = TableStructureUnitable(asdict(config)) else: self.table_structure = TableStructurer(asdict(config)) + self.table_matcher = TableMatch() try: @@ -44,7 +52,7 @@ def __call__( self, img_content: Union[str, np.ndarray, bytes, Path], ocr_result: List[Union[List[List[float]], str, str]] = None, - return_logic_points = False + return_logic_points=False, ) -> Tuple[str, float]: if self.ocr_engine is None and ocr_result is None: raise ValueError( @@ -65,14 +73,17 @@ def __call__( if self.model_type == "slanet-plus": pred_bboxes = self.adapt_slanet_plus(img, pred_bboxes) pred_html = self.table_matcher(pred_structures, pred_bboxes, dt_boxes, rec_res) + # 过滤掉占位的bbox mask = ~np.all(pred_bboxes == 0, axis=1) pred_bboxes = pred_bboxes[mask] + # 避免低版本升级后出现问题,默认不打开 if return_logic_points: logic_points = self.table_matcher.decode_logic_points(pred_structures) elapse = time.time() - s return pred_html, pred_bboxes, logic_points, elapse + elapse = time.time() - s return pred_html, pred_bboxes, elapse @@ -93,6 +104,7 @@ def get_boxes_recs( r_boxes.append(box) dt_boxes = np.array(r_boxes) return dt_boxes, rec_res + def adapt_slanet_plus(self, img: np.ndarray, pred_bboxes: np.ndarray) -> np.ndarray: h, w = img.shape[:2] resized = 488 @@ -103,6 +115,7 @@ def adapt_slanet_plus(self, img: np.ndarray, pred_bboxes: np.ndarray) -> np.ndar pred_bboxes[:, 1::2] *= h_ratio return pred_bboxes + def main(): parser = argparse.ArgumentParser() parser.add_argument( diff --git a/rapid_table/params.py b/rapid_table/params.py index c5756aa..cbbaaee 100644 --- a/rapid_table/params.py +++ b/rapid_table/params.py @@ -7,6 +7,7 @@ root_dir = Path(__file__).resolve().parent logger = get_logger("params") + @dataclass class BaseConfig: model_type: str = "slanet-plus" @@ -25,16 +26,20 @@ def wrapper(*args, **kwargs): if len(args) == 2 and isinstance(args[1], cls): # 如果已经传递了 ModelConfig 实例，直接调用函数 return func(*args, **kwargs) - else: - # 提取 cls 中定义的字段 - cls_fields = {field.name for field in fields(cls)} - # 过滤掉未定义的字段 - filtered_kwargs = {k: v for k, v in kwargs.items() if k in cls_fields} - # 发出警告对于未定义的字段 - for k in (kwargs.keys() - cls_fields): - logger.warning(f"Warning: '{k}' is not a valid field in {cls.__name__} and will be ignored.") - # 创建 ModelConfig 实例并调用函数 - config = cls(**filtered_kwargs) - return func(args[0], config=config) + + # 提取 cls 中定义的字段 + cls_fields = {field.name for field in fields(cls)} + # 过滤掉未定义的字段 + filtered_kwargs = {k: v for k, v in kwargs.items() if k in cls_fields} + # 发出警告对于未定义的字段 + for k in kwargs.keys() - cls_fields: + logger.warning( + f"Warning: '{k}' is not a valid field in {cls.__name__} and will be ignored." + ) + # 创建 ModelConfig 实例并调用函数 + config = cls(**filtered_kwargs) + return func(args[0], config=config) + return wrapper - return decorator \ No newline at end of file + + return decorator diff --git a/rapid_table/table_matcher/matcher.py b/rapid_table/table_matcher/matcher.py index bc976ed..00bea6a 100644 --- a/rapid_table/table_matcher/matcher.py +++ b/rapid_table/table_matcher/matcher.py @@ -29,7 +29,7 @@ def __call__(self, pred_structures, pred_bboxes, dt_boxes, rec_res): pred_html, pred = self.get_pred_html(pred_structures, matched_index, rec_res) return pred_html - def match_result(self, dt_boxes, pred_bboxes, min_iou=0.1 ** 8): + def match_result(self, dt_boxes, pred_bboxes, min_iou=0.1**8): matched = {} for i, gt_box in enumerate(dt_boxes): distances = [] @@ -52,7 +52,8 @@ def match_result(self, dt_boxes, pred_bboxes, min_iou=0.1 ** 8): # must > min_iou if sorted_distances[0][1] >= 1 - min_iou: continue - if distances.index(sorted_distances[0]) not in matched.keys(): + + if distances.index(sorted_distances[0]) not in matched: matched[distances.index(sorted_distances[0])] = [i] else: matched[distances.index(sorted_distances[0])].append(i) @@ -114,6 +115,7 @@ def get_pred_html(self, pred_structures, matched_index, ocr_contents): filter_elements = ["", "", "", ""] end_html = [v for v in end_html if v not in filter_elements] return "".join(end_html), end_html + def decode_logic_points(self, pred_structures): logic_points = [] current_row = 0 @@ -134,22 +136,24 @@ def mark_occupied(row, col, rowspan, colspan): while i < len(pred_structures): token = pred_structures[i] - if token == '': + if token == "": current_col = 0 # 每次遇到 时，重置当前列号 - elif token == '': + elif token == "": current_row += 1 # 行结束，行号增加 - elif token .startswith(''): - if 'colspan=' in pred_structures[j]: - colspan = int(pred_structures[j].split('=')[1].strip('"\'')) - elif 'rowspan=' in pred_structures[j]: - rowspan = int(pred_structures[j].split('=')[1].strip('"\'')) + while j < len(pred_structures) and not pred_structures[ + j + ].startswith(">"): + if "colspan=" in pred_structures[j]: + colspan = int(pred_structures[j].split("=")[1].strip("\"'")) + elif "rowspan=" in pred_structures[j]: + rowspan = int(pred_structures[j].split("=")[1].strip("\"'")) j += 1 # 跳过已经处理过的属性 token diff --git a/rapid_table/table_matcher/utils.py b/rapid_table/table_matcher/utils.py index 3ec8fcc..57a613c 100644 --- a/rapid_table/table_matcher/utils.py +++ b/rapid_table/table_matcher/utils.py @@ -14,6 +14,7 @@ # -*- encoding: utf-8 -*- # @Author: SWHL # @Contact: liekkaskono@163.com +import copy import re @@ -48,7 +49,7 @@ def deal_isolate_span(thead_part): spanStr_in_isolateItem = span_part.group() # 3. merge the span number into the span token format string. if spanStr_in_isolateItem is not None: - corrected_item = "".format(spanStr_in_isolateItem) + corrected_item = f"" corrected_list.append(corrected_item) else: corrected_list.append(None) @@ -243,6 +244,6 @@ def compute_iou(rec1, rec2): # 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 + + intersect = (right_line - left_line) * (bottom_line - top_line) + return (intersect / (sum_area - intersect)) * 1.0 diff --git a/rapid_table/table_structure/table_structure.py b/rapid_table/table_structure/table_structure.py index c2509d8..9152603 100644 --- a/rapid_table/table_structure/table_structure.py +++ b/rapid_table/table_structure/table_structure.py @@ -12,7 +12,7 @@ # See the License for the specific language governing permissions and # limitations under the License. import time -from typing import Dict, Any +from typing import Any, Dict import numpy as np diff --git a/rapid_table/table_structure/unitable_modules.py b/rapid_table/table_structure/unitable_modules.py index 367bd57..5b8dac3 100644 --- a/rapid_table/table_structure/unitable_modules.py +++ b/rapid_table/table_structure/unitable_modules.py @@ -1,21 +1,522 @@ -from functools import partial from dataclasses import dataclass +from functools import partial from typing import Optional + import torch import torch.nn as nn from torch import Tensor from torch.nn import functional as F from torch.nn.modules.transformer import _get_activation_fn -TOKEN_WHITE_LIST = [1, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 183, 184, 185, 186, 187, 188, 189, 190, 191, 192, 193, 194, 195, 196, 197, 198, 199, 200, 201, 202, 203, 204, 205, 206, 207, 208, 209, 210, 211, 212, 213, 214, 215, 216, 217, 218, 219, 220, 221, 222, 223, 224, 225, 226, 227, 228, 229, 230, 231, 232, 233, 234, 235, 236, 237, 238, 239, 240, 241, 242, 243, 244, 245, 246, 247, 248, 249, 250, 251, 252, 253, 254, 255, 256, 257, 258, 259, 260, 261, 262, 263, 264, 265, 266, 267, 268, 269, 270, 271, 272, 273, 274, 275, 276, 277, 278, 279, 280, 281, 282, 283, 284, 285, 286, 287, 288, 289, 290, 291, 292, 293, 294, 295, 296, 297, 298, 299, 300, 301, 302, 303, 304, 305, 306, 307, 308, 309, 310, 311, 312, 313, 314, 315, 316, 317, 318, 319, 320, 321, 322, 323, 324, 325, 326, 327, 328, 329, 330, 331, 332, 333, 334, 335, 336, 337, 338, 339, 340, 341, 342, 343, 344, 345, 346, 347, 348, 349, 350, 351, 352, 353, 354, 355, 356, 357, 358, 359, 360, 361, 362, 363, 364, 365, 366, 367, 368, 369, 370, 371, 372, 373, 374, 375, 376, 377, 378, 379, 380, 381, 382, 383, 384, 385, 386, 387, 388, 389, 390, 391, 392, 393, 394, 395, 396, 397, 398, 399, 400, 401, 402, 403, 404, 405, 406, 407, 408, 409, 410, 411, 412, 413, 414, 415, 416, 417, 418, 419, 420, 421, 422, 423, 424, 425, 426, 427, 428, 429, 430, 431, 432, 433, 434, 435, 436, 437, 438, 439, 440, 441, 442, 443, 444, 445, 446, 447, 448, 449, 450, 451, 452, 453, 454, 455, 456, 457, 458, 459, 460, 461, 462, 463, 464, 465, 466, 467, 468, 469, 470, 471, 472, 473, 474, 475, 476, 477, 478, 479, 480, 481, 482, 483, 484, 485, 486, 487, 488, 489, 490, 491, 492, 493, 494, 495, 496, 497, 498, 499, 500, 501, 502, 503, 504, 505, 506, 507, 508, 509] +TOKEN_WHITE_LIST = [ + 1, + 12, + 13, + 14, + 15, + 16, + 17, + 18, + 19, + 20, + 21, + 22, + 23, + 24, + 25, + 26, + 27, + 28, + 29, + 30, + 31, + 32, + 33, + 34, + 35, + 36, + 37, + 38, + 39, + 40, + 41, + 42, + 43, + 44, + 45, + 46, + 47, + 48, + 49, + 50, + 51, + 52, + 53, + 54, + 55, + 56, + 57, + 58, + 59, + 60, + 61, + 62, + 63, + 64, + 65, + 66, + 67, + 68, + 69, + 70, + 71, + 72, + 73, + 74, + 75, + 76, + 77, + 78, + 79, + 80, + 81, + 82, + 83, + 84, + 85, + 86, + 87, + 88, + 89, + 90, + 91, + 92, + 93, + 94, + 95, + 96, + 97, + 98, + 99, + 100, + 101, + 102, + 103, + 104, + 105, + 106, + 107, + 108, + 109, + 110, + 111, + 112, + 113, + 114, + 115, + 116, + 117, + 118, + 119, + 120, + 121, + 122, + 123, + 124, + 125, + 126, + 127, + 128, + 129, + 130, + 131, + 132, + 133, + 134, + 135, + 136, + 137, + 138, + 139, + 140, + 141, + 142, + 143, + 144, + 145, + 146, + 147, + 148, + 149, + 150, + 151, + 152, + 153, + 154, + 155, + 156, + 157, + 158, + 159, + 160, + 161, + 162, + 163, + 164, + 165, + 166, + 167, + 168, + 169, + 170, + 171, + 172, + 173, + 174, + 175, + 176, + 177, + 178, + 179, + 180, + 181, + 182, + 183, + 184, + 185, + 186, + 187, + 188, + 189, + 190, + 191, + 192, + 193, + 194, + 195, + 196, + 197, + 198, + 199, + 200, + 201, + 202, + 203, + 204, + 205, + 206, + 207, + 208, + 209, + 210, + 211, + 212, + 213, + 214, + 215, + 216, + 217, + 218, + 219, + 220, + 221, + 222, + 223, + 224, + 225, + 226, + 227, + 228, + 229, + 230, + 231, + 232, + 233, + 234, + 235, + 236, + 237, + 238, + 239, + 240, + 241, + 242, + 243, + 244, + 245, + 246, + 247, + 248, + 249, + 250, + 251, + 252, + 253, + 254, + 255, + 256, + 257, + 258, + 259, + 260, + 261, + 262, + 263, + 264, + 265, + 266, + 267, + 268, + 269, + 270, + 271, + 272, + 273, + 274, + 275, + 276, + 277, + 278, + 279, + 280, + 281, + 282, + 283, + 284, + 285, + 286, + 287, + 288, + 289, + 290, + 291, + 292, + 293, + 294, + 295, + 296, + 297, + 298, + 299, + 300, + 301, + 302, + 303, + 304, + 305, + 306, + 307, + 308, + 309, + 310, + 311, + 312, + 313, + 314, + 315, + 316, + 317, + 318, + 319, + 320, + 321, + 322, + 323, + 324, + 325, + 326, + 327, + 328, + 329, + 330, + 331, + 332, + 333, + 334, + 335, + 336, + 337, + 338, + 339, + 340, + 341, + 342, + 343, + 344, + 345, + 346, + 347, + 348, + 349, + 350, + 351, + 352, + 353, + 354, + 355, + 356, + 357, + 358, + 359, + 360, + 361, + 362, + 363, + 364, + 365, + 366, + 367, + 368, + 369, + 370, + 371, + 372, + 373, + 374, + 375, + 376, + 377, + 378, + 379, + 380, + 381, + 382, + 383, + 384, + 385, + 386, + 387, + 388, + 389, + 390, + 391, + 392, + 393, + 394, + 395, + 396, + 397, + 398, + 399, + 400, + 401, + 402, + 403, + 404, + 405, + 406, + 407, + 408, + 409, + 410, + 411, + 412, + 413, + 414, + 415, + 416, + 417, + 418, + 419, + 420, + 421, + 422, + 423, + 424, + 425, + 426, + 427, + 428, + 429, + 430, + 431, + 432, + 433, + 434, + 435, + 436, + 437, + 438, + 439, + 440, + 441, + 442, + 443, + 444, + 445, + 446, + 447, + 448, + 449, + 450, + 451, + 452, + 453, + 454, + 455, + 456, + 457, + 458, + 459, + 460, + 461, + 462, + 463, + 464, + 465, + 466, + 467, + 468, + 469, + 470, + 471, + 472, + 473, + 474, + 475, + 476, + 477, + 478, + 479, + 480, + 481, + 482, + 483, + 484, + 485, + 486, + 487, + 488, + 489, + 490, + 491, + 492, + 493, + 494, + 495, + 496, + 497, + 498, + 499, + 500, + 501, + 502, + 503, + 504, + 505, + 506, + 507, + 508, + 509, +] class ImgLinearBackbone(nn.Module): def __init__( - self, - d_model: int, - patch_size: int, - in_chan: int = 3, + self, + d_model: int, + patch_size: int, + in_chan: int = 3, ) -> None: super().__init__() @@ -34,9 +535,7 @@ def forward(self, x: Tensor) -> Tensor: class Encoder(nn.Module): - def __init__( - self - ) -> None: + def __init__(self) -> None: super().__init__() self.patch_size = 16 @@ -57,11 +556,17 @@ def __init__( batch_first=True, norm_first=self.norm_first, ) - norm_layer=partial(nn.LayerNorm, eps=1e-6) + norm_layer = partial(nn.LayerNorm, eps=1e-6) self.norm = norm_layer(self.d_model) - self.backbone = ImgLinearBackbone(d_model=self.d_model, patch_size=self.patch_size) - self.pos_embed = PositionEmbedding(max_seq_len=self.max_seq_len, d_model=self.d_model, dropout=self.dropout) - self.encoder = nn.TransformerEncoder(encoder_layer, num_layers=self.n_encoder_layer, enable_nested_tensor=False) + self.backbone = ImgLinearBackbone( + d_model=self.d_model, patch_size=self.patch_size + ) + self.pos_embed = PositionEmbedding( + max_seq_len=self.max_seq_len, d_model=self.d_model, dropout=self.dropout + ) + self.encoder = nn.TransformerEncoder( + encoder_layer, num_layers=self.n_encoder_layer, enable_nested_tensor=False + ) def forward(self, x: Tensor) -> Tensor: src_feature = self.backbone(x) @@ -89,10 +594,10 @@ def forward(self, x: Tensor, input_pos: Optional[Tensor] = None) -> Tensor: class TokenEmbedding(nn.Module): def __init__( - self, - vocab_size: int, - d_model: int, - padding_idx: int, + self, + vocab_size: int, + d_model: int, + padding_idx: int, ) -> None: super().__init__() assert vocab_size > 0 @@ -125,20 +630,29 @@ def __post_init__(self): self.intermediate_size = find_multiple(n_hidden, 256) self.head_dim = self.dim // self.n_head + class KVCache(nn.Module): def __init__( - self, - max_batch_size, - max_seq_length, - n_heads, - head_dim, - dtype=torch.bfloat16, - device="cpu", + self, + max_batch_size, + max_seq_length, + n_heads, + head_dim, + dtype=torch.bfloat16, + device="cpu", ): super().__init__() cache_shape = (max_batch_size, n_heads, max_seq_length, head_dim) - self.register_buffer("k_cache", torch.zeros(cache_shape, dtype=dtype, device=device), persistent=False) - self.register_buffer("v_cache", torch.zeros(cache_shape, dtype=dtype, device=device), persistent=False) + self.register_buffer( + "k_cache", + torch.zeros(cache_shape, dtype=dtype, device=device), + persistent=False, + ) + self.register_buffer( + "v_cache", + torch.zeros(cache_shape, dtype=dtype, device=device), + persistent=False, + ) def update(self, input_pos, k_val, v_val): # input_pos: [S], k_val: [B, H, S, D] @@ -152,13 +666,11 @@ def update(self, input_pos, k_val, v_val): return k_out[:bs], v_out[:bs] + class GPTFastDecoder(nn.Module): - def __init__( - self - ) -> None: + def __init__(self) -> None: super().__init__() - self.vocab_size = 960 self.padding_idx = 2 self.prefix_token_id = 11 @@ -179,9 +691,17 @@ def __init__( norm_first=self.norm_first, ) self.config = config - self.layers = nn.ModuleList(TransformerBlock(config) for _ in range(config.n_layer)) - self.token_embed = TokenEmbedding(vocab_size=self.vocab_size, d_model=self.d_model, padding_idx=self.padding_idx) - self.pos_embed = PositionEmbedding(max_seq_len=self.max_seq_len, d_model=self.d_model, dropout=self.dropout) + self.layers = nn.ModuleList( + TransformerBlock(config) for _ in range(config.n_layer) + ) + self.token_embed = TokenEmbedding( + vocab_size=self.vocab_size, + d_model=self.d_model, + padding_idx=self.padding_idx, + ) + self.pos_embed = PositionEmbedding( + max_seq_len=self.max_seq_len, d_model=self.d_model, dropout=self.dropout + ) self.generator = nn.Linear(self.d_model, self.vocab_size) self.token_white_list = TOKEN_WHITE_LIST self.mask_cache: Optional[Tensor] = None @@ -193,7 +713,10 @@ def setup_caches(self, max_batch_size, max_seq_length, dtype, device): b.multihead_attn.k_cache = None b.multihead_attn.v_cache = None - if self.max_seq_length >= max_seq_length and self.max_batch_size >= max_batch_size: + if ( + self.max_seq_length >= max_seq_length + and self.max_batch_size >= max_batch_size + ): return head_dim = self.config.dim // self.config.n_head max_seq_length = find_multiple(max_seq_length, 8) @@ -207,24 +730,23 @@ def setup_caches(self, max_batch_size, max_seq_length, dtype, device): self.config.n_head, head_dim, dtype, - device + device, ) b.multihead_attn.k_cache = None b.multihead_attn.v_cache = None - self.causal_mask = torch.tril(torch.ones(self.max_seq_length, self.max_seq_length, dtype=torch.bool)).to(device) - - def forward( - self, - memory: Tensor, - tgt: Tensor - ) -> Tensor: + self.causal_mask = torch.tril( + torch.ones(self.max_seq_length, self.max_seq_length, dtype=torch.bool) + ).to(device) + def forward(self, memory: Tensor, tgt: Tensor) -> Tensor: input_pos = torch.tensor([tgt.shape[1] - 1], device=tgt.device, dtype=torch.int) tgt = tgt[:, -1:] tgt_feature = self.pos_embed(self.token_embed(tgt), input_pos=input_pos) # tgt = self.decoder(tgt_feature, memory, input_pos) - with torch.backends.cuda.sdp_kernel(enable_flash=False, enable_mem_efficient=False, enable_math=True): + with torch.backends.cuda.sdp_kernel( + enable_flash=False, enable_mem_efficient=False, enable_math=True + ): logits = tgt_feature tgt_mask = self.causal_mask[None, None, input_pos] for i, layer in enumerate(self.layers): @@ -238,6 +760,7 @@ def forward( _, next_tokens = probs.topk(1) return next_tokens + class TransformerBlock(nn.Module): def __init__(self, config: ModelArgs) -> None: super().__init__() @@ -260,11 +783,11 @@ def __init__(self, config: ModelArgs) -> None: self.activation = _get_activation_fn(config.activation) def forward( - self, - tgt: Tensor, - memory: Tensor, - tgt_mask: Tensor, - input_pos: Tensor, + self, + tgt: Tensor, + memory: Tensor, + tgt_mask: Tensor, + input_pos: Tensor, ) -> Tensor: if self.norm_first: x = tgt @@ -299,10 +822,10 @@ def __init__(self, config: ModelArgs): self.dim = config.dim def forward( - self, - x: Tensor, - mask: Tensor, - input_pos: Optional[Tensor] = None, + self, + x: Tensor, + mask: Tensor, + input_pos: Optional[Tensor] = None, ) -> Tensor: bsz, seqlen, _ = x.shape @@ -362,9 +885,9 @@ def get_kv(self, xa: torch.Tensor): return k, v def forward( - self, - x: Tensor, - xa: Tensor, + self, + x: Tensor, + xa: Tensor, ): q = self.query(x) batch_size, target_seq_len, _ = q.shape @@ -383,6 +906,6 @@ def forward( batch_size, target_seq_len, self.n_head * self.head_dim, - ) + ) return self.out(wv) diff --git a/rapid_table/table_structure/utils.py b/rapid_table/table_structure/utils.py index c88cf3c..e06f610 100644 --- a/rapid_table/table_structure/utils.py +++ b/rapid_table/table_structure/utils.py @@ -17,12 +17,11 @@ import os import platform import traceback - -import cv2 from enum import Enum from pathlib import Path from typing import Any, Dict, List, Tuple, Union +import cv2 import numpy as np from onnxruntime import ( GraphOptimizationLevel, @@ -79,10 +78,12 @@ def _init_sess_opts(config: Dict[str, Any]) -> SessionOptions: sess_opt.inter_op_num_threads = inter_op_num_threads return sess_opt + def get_metadata(self, key: str = "character") -> list: meta_dict = self.session.get_modelmeta().custom_metadata_map content_list = meta_dict[key].splitlines() return content_list + def _get_ep_list(self) -> List[Tuple[str, Dict[str, Any]]]: cpu_provider_opts = { "arena_extend_strategy": "kSameAsRequested", diff --git a/rapid_table/utils.py b/rapid_table/utils.py index 77224ce..82d7cef 100644 --- a/rapid_table/utils.py +++ b/rapid_table/utils.py @@ -4,7 +4,7 @@ import os from io import BytesIO from pathlib import Path -from typing import Optional, Union, List +from typing import List, Optional, Union import cv2 import numpy as np @@ -14,9 +14,7 @@ class LoadImage: - def __init__( - self, - ): + def __init__(self): pass def __call__(self, img: InputType) -> np.ndarray: @@ -79,20 +77,18 @@ class LoadImageError(Exception): class VisTable: - def __init__( - self, - ): + def __init__(self): self.load_img = LoadImage() def __call__( - self, - img_path: Union[str, Path], - table_html_str: str, - save_html_path: Optional[str] = None, - table_cell_bboxes: Optional[np.ndarray] = None, - save_drawed_path: Optional[str] = None, - logic_points: List[List[float]] = None, - save_logic_path: Optional[str] = None, + self, + img_path: Union[str, Path], + table_html_str: str, + save_html_path: Optional[str] = None, + table_cell_bboxes: Optional[np.ndarray] = None, + save_drawed_path: Optional[str] = None, + logic_points: List[List[float]] = None, + save_logic_path: Optional[str] = None, ) -> None: if save_html_path: html_with_border = self.insert_border_style(table_html_str) @@ -114,8 +110,10 @@ def __call__( if save_drawed_path: self.save_img(save_drawed_path, drawed_img) if save_logic_path and logic_points: - polygons = [[box[0],box[1], box[4], box[5]] for box in table_cell_bboxes] - self.plot_rec_box_with_logic_info(img_path, save_logic_path, logic_points, polygons) + polygons = [[box[0], box[1], box[4], box[5]] for box in table_cell_bboxes] + self.plot_rec_box_with_logic_info( + img_path, save_logic_path, logic_points, polygons + ) return drawed_img def insert_border_style(self, table_html_str: str): @@ -137,7 +135,9 @@ def insert_border_style(self, table_html_str: str): html_with_border = f"{prefix_table}{style_res}{suffix_table}" return html_with_border - def plot_rec_box_with_logic_info(self, img_path, output_path, logic_points, sorted_polygons): + def plot_rec_box_with_logic_info( + self, img_path, output_path, logic_points, sorted_polygons + ): """ :param img_path :param output_path @@ -208,4 +208,3 @@ def save_img(save_path: Union[str, Path], img: np.ndarray): def save_html(save_path: Union[str, Path], html: str): with open(save_path, "w", encoding="utf-8") as f: f.write(html) - diff --git a/setup.py b/setup.py index 0489760..8dc2e60 100644 --- a/setup.py +++ b/setup.py @@ -19,7 +19,7 @@ def get_readme(): MODULE_NAME = "rapid_table" obtainer = GetPyPiLatestVersion() latest_version = obtainer(MODULE_NAME) -VERSION_NUM = obtainer.version_add_one(latest_version) +VERSION_NUM = obtainer.version_add_one(latest_version, add_patch=True) if len(sys.argv) > 2: match_str = " ".join(sys.argv[2:]) From 7810dfdb0411eddf53ff27fb0c5c379a20a2e496 Mon Sep 17 00:00:00 2001 From: SWHL Date: Wed, 1 Jan 2025 22:47:58 +0800 Subject: [PATCH 10/23] docs: update README --- README.md | 9 +++++++-- 1 file changed, 7 insertions(+), 2 deletions(-) diff --git a/README.md b/README.md index 3f2397a..82285cf 100644 --- a/README.md +++ b/README.md @@ -60,8 +60,13 @@ RapidTable是整理自PP-Structure中表格识别部分而来。由于PP-Structu ```bash pip install rapidocr_onnxruntime pip install rapid_table -# pip install rapid_table[torch] # for unitable inference -# pip install onnxruntime-gpu # for onnx gpu inference + +# 基于torch来推理unitable模型 +pip install rapid_table[torch] # for unitable inference + +# onnxruntime-gpu推理 +pip uninstall onnxruntime +pip install onnxruntime-gpu # for onnx gpu inference ``` ### 使用方式 From d7e8d1af386080360f6a89e22445191741b47f28 Mon Sep 17 00:00:00 2001 From: SWHL Date: Mon, 6 Jan 2025 22:32:55 +0800 Subject: [PATCH 11/23] chore: optimize code --- rapid_table/__init__.py | 2 +- rapid_table/download_model.py | 9 +-- rapid_table/main.py | 32 +++++++-- rapid_table/params.py | 10 +++ .../table_structure_unitable.py | 65 ++++++++++--------- rapid_table/utils.py | 15 +++-- tests/test_table_torch.py | 5 +- 7 files changed, 85 insertions(+), 53 deletions(-) diff --git a/rapid_table/__init__.py b/rapid_table/__init__.py index 93c6a5d..702f7d0 100644 --- a/rapid_table/__init__.py +++ b/rapid_table/__init__.py @@ -1,5 +1,5 @@ # -*- encoding: utf-8 -*- # @Author: SWHL # @Contact: liekkaskono@163.com -from .main import RapidTable +from .main import RapidTable, RapidTableInput from .utils import VisTable diff --git a/rapid_table/download_model.py b/rapid_table/download_model.py index ab65fe3..bce876a 100644 --- a/rapid_table/download_model.py +++ b/rapid_table/download_model.py @@ -8,16 +8,9 @@ from .logger import get_logger logger = get_logger("DownloadModel") + CUR_DIR = Path(__file__).resolve() PROJECT_DIR = CUR_DIR.parent -ROOT_URL = "https://www.modelscope.cn/studio/jockerK/TableRec/resolve/master/models/table_rec/unitable/" -KEY_TO_MODEL_URL = { - "unitable": { - "encoder": f"{ROOT_URL}/encoder.pth", - "decoder": f"{ROOT_URL}/decoder.pth", - "vocab": f"{ROOT_URL}/vocab.json", - } -} class DownloadModel: diff --git a/rapid_table/main.py b/rapid_table/main.py index 9567e6c..9e7cdeb 100644 --- a/rapid_table/main.py +++ b/rapid_table/main.py @@ -5,25 +5,43 @@ import copy import importlib import time -from dataclasses import asdict +from dataclasses import asdict, dataclass from pathlib import Path -from typing import List, Tuple, Union +from typing import List, Optional, Tuple, Union import cv2 import numpy as np from .download_model import DownloadModel -from .params import BaseConfig, accept_kwargs_as_dataclass from .table_matcher import TableMatch from .table_structure import TableStructurer, TableStructureUnitable from .utils import LoadImage, VisTable root_dir = Path(__file__).resolve().parent +ROOT_URL = "https://www.modelscope.cn/models/RapidAI-NG/RapidTable/resolve/master/" +KEY_TO_MODEL_URL = { + "ppstructure_en": f"{ROOT_URL}/en_ppstructure_mobile_v2_SLANet.onnx", + "ppstructure_zh": f"{ROOT_URL}/ch_ppstructure_mobile_v2_SLANet.onnx", + "slanet_plus": f"{ROOT_URL}/slanet-plus.onnx", + "unitable": { + "encoder": f"{ROOT_URL}/unitable/encoder.pth", + "decoder": f"{ROOT_URL}/unitable/decoder.pth", + "vocab": f"{ROOT_URL}/unitable/vocab.json", + }, +} + + +@dataclass +class RapidTableInput: + model_type: Optional[str] = None + model_path_or_dir: Union[str, Path, None] = None + use_cuda: bool = False + device: str = "cpu" + class RapidTable: - @accept_kwargs_as_dataclass(BaseConfig) - def __init__(self, config: BaseConfig): + def __init__(self, config: RapidTableInput): self.model_type = config.model_type self.load_img = LoadImage() @@ -52,7 +70,7 @@ def __call__( self, img_content: Union[str, np.ndarray, bytes, Path], ocr_result: List[Union[List[List[float]], str, str]] = None, - return_logic_points=False, + return_logic_points: bool = False, ) -> Tuple[str, float]: if self.ocr_engine is None and ocr_result is None: raise ValueError( @@ -69,9 +87,11 @@ def __call__( dt_boxes, rec_res = self.get_boxes_recs(ocr_result, h, w) pred_structures, pred_bboxes, _ = self.table_structure(copy.deepcopy(img)) + # 适配slanet-plus模型输出的box缩放还原 if self.model_type == "slanet-plus": pred_bboxes = self.adapt_slanet_plus(img, pred_bboxes) + pred_html = self.table_matcher(pred_structures, pred_bboxes, dt_boxes, rec_res) # 过滤掉占位的bbox diff --git a/rapid_table/params.py b/rapid_table/params.py index cbbaaee..b831587 100644 --- a/rapid_table/params.py +++ b/rapid_table/params.py @@ -1,6 +1,8 @@ +# -*- encoding: utf-8 -*- from dataclasses import dataclass, fields from functools import wraps from pathlib import Path +from typing import Optional, Union from rapid_table.logger import get_logger @@ -8,6 +10,14 @@ logger = get_logger("params") +@dataclass +class RapidTableInput: + model_type: Optional[str] = None + model_path_or_dir: Union[str, Path, None] = None + use_cuda: bool = False + device: str = "cpu" + + @dataclass class BaseConfig: model_type: str = "slanet-plus" diff --git a/rapid_table/table_structure/table_structure_unitable.py b/rapid_table/table_structure/table_structure_unitable.py index fdc42b4..e14f548 100644 --- a/rapid_table/table_structure/table_structure_unitable.py +++ b/rapid_table/table_structure/table_structure_unitable.py @@ -1,15 +1,15 @@ import re import time -from typing import Dict, Any +from typing import Any, Dict import cv2 import numpy as np import torch from PIL import Image from tokenizers import Tokenizer +from torchvision import transforms from .unitable_modules import Encoder, GPTFastDecoder -from torchvision import transforms IMG_SIZE = 448 EOS_TOKEN = "" @@ -79,7 +79,7 @@ class TableStructureUnitable: - def __init__(self, config:Dict[str, Any]): + def __init__(self, config: Dict[str, Any]): # encoder_path: str, decoder_path: str, vocab_path: str, device: str vocab_path = config["vocab_path"] encoder_path = config["encoder_path"] @@ -87,24 +87,29 @@ def __init__(self, config:Dict[str, Any]): device = config.get("device", "cuda:0") if config["use_cuda"] else "cpu" self.vocab = Tokenizer.from_file(vocab_path) - self.token_white_list = [self.vocab.token_to_id(i) for i in VALID_HTML_BBOX_TOKENS] - self.bbox_token_ids = set([self.vocab.token_to_id(i) for i in BBOX_TOKENS]) + self.token_white_list = [ + self.vocab.token_to_id(i) for i in VALID_HTML_BBOX_TOKENS + ] + self.bbox_token_ids = set(self.vocab.token_to_id(i) for i in BBOX_TOKENS) self.bbox_close_html_token = self.vocab.token_to_id("]") self.prefix_token_id = self.vocab.token_to_id("[html+bbox]") self.eos_id = self.vocab.token_to_id(EOS_TOKEN) self.max_seq_len = 1024 self.device = device self.img_size = IMG_SIZE + # init encoder encoder_state_dict = torch.load(encoder_path, map_location=device) self.encoder = Encoder() self.encoder.load_state_dict(encoder_state_dict) self.encoder.eval().to(device) + # init decoder decoder_state_dict = torch.load(decoder_path, map_location=device) self.decoder = GPTFastDecoder() self.decoder.load_state_dict(decoder_state_dict) self.decoder.eval().to(device) + # define img transform self.transform = transforms.Compose( [ @@ -117,12 +122,8 @@ def __init__(self, config:Dict[str, Any]): ] ) - @torch.inference_mode() - def __call__( - self, - image: np.ndarray, - ): + def __call__(self, image: np.ndarray): start_time = time.time() ori_h, ori_w = image.shape[:2] image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB) @@ -132,14 +133,19 @@ def __call__( max_batch_size=1, max_seq_length=self.max_seq_len, dtype=image.dtype, - device=self.device + device=self.device, + ) + context = ( + torch.tensor([self.prefix_token_id], dtype=torch.int32) + .repeat(1, 1) + .to(self.device) ) - context = torch.tensor([self.prefix_token_id], dtype=torch.int32).repeat(1, 1).to(self.device) eos_id_tensor = torch.tensor(self.eos_id, dtype=torch.int32).to(self.device) memory = self.encoder(image) context = self.loop_decode(context, eos_id_tensor, memory) bboxes, html_tokens = self.decode_tokens(context) bboxes = bboxes.astype(np.float32) + # rescale boxes scale_h = ori_h / self.img_size scale_w = ori_w / self.img_size @@ -148,9 +154,9 @@ def __call__( bboxes[:, 0::2] = np.clip(bboxes[:, 0::2], 0, ori_w - 1) bboxes[:, 1::2] = np.clip(bboxes[:, 1::2], 0, ori_h - 1) structure_str_list = ( - ["", "", ""] - + html_tokens - + ["
", "", ""] + ["", "", ""] + + html_tokens + + ["
", "", ""] ) return structure_str_list, bboxes, time.time() - start_time @@ -158,37 +164,34 @@ def decode_tokens(self, context): pred_html = context[0] pred_html = pred_html.detach().cpu().numpy() pred_html = self.vocab.decode(pred_html, skip_special_tokens=False) - # pred_html = html_str_to_token_list(pred_html) seq = pred_html.split("")[0] token_black_list = ["", "", *TASK_TOKENS] for i in token_black_list: seq = seq.replace(i, "") - # 正则表达式模式 - tr_pattern = re.compile(r'(.*?)', re.DOTALL) - td_pattern = re.compile(r'(.*?)', re.DOTALL) - bbox_pattern = re.compile(r'\[ bbox-(\d+) bbox-(\d+) bbox-(\d+) bbox-(\d+) \]') + tr_pattern = re.compile(r"(.*?)", re.DOTALL) + td_pattern = re.compile(r"(.*?)", re.DOTALL) + bbox_pattern = re.compile(r"\[ bbox-(\d+) bbox-(\d+) bbox-(\d+) bbox-(\d+) \]") - # 存储解码后的结果 decoded_list = [] bbox_coords = [] # 查找所有的 标签 for tr_match in tr_pattern.finditer(pred_html): tr_content = tr_match.group(1) - decoded_list.append('') + decoded_list.append("") # 查找所有的 标签 for td_match in td_pattern.finditer(tr_content): td_attrs = td_match.group(1).strip() td_content = td_match.group(2).strip() if td_attrs: - decoded_list.append('') - decoded_list.append('') + decoded_list.append(">") + decoded_list.append("") else: - decoded_list.append('') + decoded_list.append("") # 查找 bbox 坐标 bbox_match = bbox_pattern.search(td_content) @@ -199,10 +202,9 @@ def decode_tokens(self, context): bbox_coords.append(coords) else: # 填充占位的bbox，保证后续流程统一 - bbox_coords.append(np.array([0, 0, 0,0,0,0, 0, 0])) - decoded_list.append('') + bbox_coords.append(np.array([0, 0, 0, 0, 0, 0, 0, 0])) + decoded_list.append("") - # 将 bbox_coords 转换为 numpy 数组 bbox_coords_array = np.array(bbox_coords) return bbox_coords_array, decoded_list @@ -212,11 +214,14 @@ def loop_decode(self, context, eos_id_tensor, memory): eos_flag = (context == eos_id_tensor).any(dim=1) if torch.all(eos_flag): break + next_tokens = self.decoder(memory, context) if next_tokens[0] in self.bbox_token_ids: box_token_count += 1 if box_token_count > 4: - next_tokens = torch.tensor([self.bbox_close_html_token], dtype=torch.int32) + next_tokens = torch.tensor( + [self.bbox_close_html_token], dtype=torch.int32 + ) box_token_count = 0 context = torch.cat([context, next_tokens], dim=1) return context diff --git a/rapid_table/utils.py b/rapid_table/utils.py index 82d7cef..7370016 100644 --- a/rapid_table/utils.py +++ b/rapid_table/utils.py @@ -117,20 +117,21 @@ def __call__( return drawed_img def insert_border_style(self, table_html_str: str): - style_res = f"""""" + prefix_table, suffix_table = table_html_str.split("") html_with_border = f"{prefix_table}{style_res}{suffix_table}" return html_with_border diff --git a/tests/test_table_torch.py b/tests/test_table_torch.py index 1eb4505..3526d11 100644 --- a/tests/test_table_torch.py +++ b/tests/test_table_torch.py @@ -30,6 +30,9 @@ def test_input_ocr_none(): table_html_str, table_cell_bboxes, elapse = table_engine(img_path) assert table_html_str.count("") == 16 + def test_logic_points_out(): - table_html_str, table_cell_bboxes, logic_points, elapse = table_engine(img_path, return_logic_points=True) + table_html_str, table_cell_bboxes, logic_points, elapse = table_engine( + img_path, return_logic_points=True + ) assert len(table_cell_bboxes) == len(logic_points) From 8ec990d3894ea7f498124155822fb1fa6dea4454 Mon Sep 17 00:00:00 2001 From: SWHL Date: Tue, 7 Jan 2025 23:10:06 +0800 Subject: [PATCH 12/23] chore: optimize code --- LICENSE | 2 +- demo.py | 9 +- demo_torch.py | 32 - rapid_table/download_model.py | 25 +- rapid_table/main.py | 91 +- rapid_table/models/unitable_vocab.json | 9650 +++++++++++++++++ rapid_table/models/vocab.json | 9650 +++++++++++++++++ rapid_table/table_structure/logger.py | 21 - .../table_structure_unitable.py | 9 +- rapid_table/table_structure/utils.py | 2 +- setup.py | 19 +- tests/test_main.py | 50 + tests/test_table.py | 35 - tests/test_table_torch.py | 38 - 14 files changed, 19455 insertions(+), 178 deletions(-) delete mode 100644 demo_torch.py create mode 100644 rapid_table/models/unitable_vocab.json create mode 100644 rapid_table/models/vocab.json delete mode 100644 rapid_table/table_structure/logger.py create mode 100644 tests/test_main.py delete mode 100644 tests/test_table.py delete mode 100644 tests/test_table_torch.py diff --git a/LICENSE b/LICENSE index a13bf2b..16c91c5 100644 --- a/LICENSE +++ b/LICENSE @@ -186,7 +186,7 @@ same "printed page" as the copyright notice for easier identification within third-party archives. - Copyright 2024 RapidAI + Copyright 2025 RapidAI Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. diff --git a/demo.py b/demo.py index fb13d21..22022a7 100644 --- a/demo.py +++ b/demo.py @@ -6,12 +6,14 @@ import cv2 from rapidocr_onnxruntime import RapidOCR, VisRes -from rapid_table import RapidTable, VisTable +from rapid_table import RapidTable, RapidTableInput, VisTable # Init ocr_engine = RapidOCR() vis_ocr = VisRes() -table_engine = RapidTable() + +input_args = RapidTableInput(model_type="unitable") +table_engine = RapidTable(input_args) viser = VisTable() img_path = "tests/test_files/table.jpg" @@ -21,7 +23,8 @@ boxes, txts, scores = list(zip(*ocr_result)) # Table Rec -table_html_str, table_cell_bboxes, _ = table_engine(img_path, ocr_result) +table_results = table_engine(img_path, ocr_result) +table_html_str, table_cell_bboxes = table_results.pred_html, table_results.pred_bboxes # Save save_dir = Path("outputs") diff --git a/demo_torch.py b/demo_torch.py deleted file mode 100644 index 72a2d44..0000000 --- a/demo_torch.py +++ /dev/null @@ -1,32 +0,0 @@ -# -*- encoding: utf-8 -*- -# @Author: SWHL -# @Contact: liekkaskono@163.com -from pathlib import Path -from rapidocr_onnxruntime import RapidOCR - -from rapid_table import RapidTable, VisTable -from rapid_table.table_structure.utils import trans_char_ocr_res - -ocr_engine = RapidOCR() -table_engine = RapidTable(use_cuda=True, device="cuda:0", model_type="unitable") -viser = VisTable() -img_path = "tests/test_files/table.jpg" -# OCR -ocr_result, _ = ocr_engine(img_path, return_word_box=True) -ocr_result = trans_char_ocr_res(ocr_result) -boxes, txts, scores = list(zip(*ocr_result)) -# Save -save_dir = Path("outputs") -save_dir.mkdir(parents=True, exist_ok=True) - -save_html_path = save_dir / f"{Path(img_path).stem}.html" -save_drawed_path = save_dir / f"{Path(img_path).stem}_table_vis{Path(img_path).suffix}" - -table_html_str, table_cell_bboxes, elapse = table_engine(img_path, ocr_result) -viser(img_path, table_html_str, save_html_path, table_cell_bboxes, save_drawed_path) -# 返回逻辑坐标 -# table_html_str, table_cell_bboxes, logic_points, elapse = table_engine(img_path, ocr_result, return_logic_points=True) -# save_logic_path = save_dir / f"vis_logic_{Path(img_path).name}" -# vis_imged = viser(img_path, table_html_str, save_html_path, table_cell_bboxes, save_drawed_path, logic_points, -# save_logic_path) -print(f"elapse:{elapse}") diff --git a/rapid_table/download_model.py b/rapid_table/download_model.py index bce876a..312afa2 100644 --- a/rapid_table/download_model.py +++ b/rapid_table/download_model.py @@ -9,13 +9,11 @@ logger = get_logger("DownloadModel") -CUR_DIR = Path(__file__).resolve() -PROJECT_DIR = CUR_DIR.parent +PROJECT_DIR = Path(__file__).resolve().parent +DEFAULT_MODEL_DIR = PROJECT_DIR / "models" class DownloadModel: - cur_dir = PROJECT_DIR - @staticmethod def get_model_path( model_type: str, sub_file_type: str, path: Union[str, Path, None] @@ -32,19 +30,28 @@ def get_model_path( return path @classmethod - def download(cls, model_full_url: Union[str, Path]) -> str: - save_dir = cls.cur_dir / "models" + def download( + cls, + model_full_url: Union[str, Path], + save_dir: Union[str, Path, None] = None, + save_model_name: Optional[str] = None, + ) -> str: + if save_dir is None: + save_dir = DEFAULT_MODEL_DIR + save_dir.mkdir(parents=True, exist_ok=True) - model_name = Path(model_full_url).name - save_file_path = save_dir / model_name + if save_model_name is None: + save_model_name = Path(model_full_url).name + + save_file_path = save_dir / save_model_name if save_file_path.exists(): logger.debug("%s already exists", save_file_path) return str(save_file_path) try: logger.info("Download %s to %s", model_full_url, save_dir) - file = cls.download_as_bytes_with_progress(model_full_url, model_name) + file = cls.download_as_bytes_with_progress(model_full_url, save_model_name) cls.save_file(save_file_path, file) except Exception as exc: raise DownloadModelError from exc diff --git a/rapid_table/main.py b/rapid_table/main.py index 9e7cdeb..30068fc 100644 --- a/rapid_table/main.py +++ b/rapid_table/main.py @@ -6,25 +6,37 @@ import importlib import time from dataclasses import asdict, dataclass +from enum import Enum from pathlib import Path -from typing import List, Optional, Tuple, Union +from typing import Dict, List, Optional, Tuple, Union import cv2 import numpy as np +from rapid_table.logger import get_logger + from .download_model import DownloadModel from .table_matcher import TableMatch from .table_structure import TableStructurer, TableStructureUnitable from .utils import LoadImage, VisTable +logger = get_logger("main") root_dir = Path(__file__).resolve().parent + +class ModelType(Enum): + PPSTRUCTURE_EN = "ppstructure_en" + PPSTRUCTURE_ZH = "ppstructure_zh" + SLANETPLUS = "slanet_plus" + UNITABLE = "unitable" + + ROOT_URL = "https://www.modelscope.cn/models/RapidAI-NG/RapidTable/resolve/master/" KEY_TO_MODEL_URL = { - "ppstructure_en": f"{ROOT_URL}/en_ppstructure_mobile_v2_SLANet.onnx", - "ppstructure_zh": f"{ROOT_URL}/ch_ppstructure_mobile_v2_SLANet.onnx", - "slanet_plus": f"{ROOT_URL}/slanet-plus.onnx", - "unitable": { + ModelType.PPSTRUCTURE_EN.value: f"{ROOT_URL}/en_ppstructure_mobile_v2_SLANet.onnx", + ModelType.PPSTRUCTURE_ZH.value: f"{ROOT_URL}/ch_ppstructure_mobile_v2_SLANet.onnx", + ModelType.SLANETPLUS.value: f"{ROOT_URL}/slanet-plus.onnx", + ModelType.UNITABLE.value: { "encoder": f"{ROOT_URL}/unitable/encoder.pth", "decoder": f"{ROOT_URL}/unitable/decoder.pth", "vocab": f"{ROOT_URL}/unitable/vocab.json", @@ -34,27 +46,32 @@ @dataclass class RapidTableInput: - model_type: Optional[str] = None - model_path_or_dir: Union[str, Path, None] = None + model_type: Optional[str] = ModelType.SLANETPLUS.value + model_path: Union[str, Path, None, Dict[str, str]] = None use_cuda: bool = False device: str = "cpu" +@dataclass +class RapidTableOutput: + pred_html: Optional[str] = None + pred_bboxes: Optional[np.ndarray] = None + logic_points: Optional[np.ndarray] = None + elapse: Optional[float] = None + + class RapidTable: def __init__(self, config: RapidTableInput): self.model_type = config.model_type - self.load_img = LoadImage() - if self.model_type == "unitable": - config.encoder_path = DownloadModel.get_model_path( - self.model_type, "encoder", config.encoder_path - ) - config.decoder_path = DownloadModel.get_model_path( - self.model_type, "decoder", config.decoder_path - ) - config.vocab_path = DownloadModel.get_model_path( - self.model_type, "vocab", config.vocab_path + if self.model_type not in KEY_TO_MODEL_URL: + model_list = ",".join(KEY_TO_MODEL_URL.keys()) + raise ValueError( + f"{self.model_type} is not supported. The currently supported models are {model_list}." ) + + config.model_path = self.get_model_path(config.model_type, config.model_path) + if self.model_type == ModelType.UNITABLE.value: self.table_structure = TableStructureUnitable(asdict(config)) else: self.table_structure = TableStructurer(asdict(config)) @@ -66,12 +83,14 @@ def __init__(self, config: RapidTableInput): except ModuleNotFoundError: self.ocr_engine = None + self.load_img = LoadImage() + def __call__( self, img_content: Union[str, np.ndarray, bytes, Path], ocr_result: List[Union[List[List[float]], str, str]] = None, return_logic_points: bool = False, - ) -> Tuple[str, float]: + ) -> RapidTableOutput: if self.ocr_engine is None and ocr_result is None: raise ValueError( "One of two conditions must be met: ocr_result is not empty, or rapidocr_onnxruntime is installed." @@ -79,7 +98,7 @@ def __call__( img = self.load_img(img_content) - s = time.time() + s = time.perf_counter() h, w = img.shape[:2] if ocr_result is None: @@ -89,7 +108,7 @@ def __call__( pred_structures, pred_bboxes, _ = self.table_structure(copy.deepcopy(img)) # 适配slanet-plus模型输出的box缩放还原 - if self.model_type == "slanet-plus": + if self.model_type == ModelType.SLANETPLUS.value: pred_bboxes = self.adapt_slanet_plus(img, pred_bboxes) pred_html = self.table_matcher(pred_structures, pred_bboxes, dt_boxes, rec_res) @@ -101,11 +120,13 @@ def __call__( # 避免低版本升级后出现问题,默认不打开 if return_logic_points: logic_points = self.table_matcher.decode_logic_points(pred_structures) - elapse = time.time() - s - return pred_html, pred_bboxes, logic_points, elapse + elapse = time.perf_counter() - s + return RapidTableOutput(pred_html, pred_bboxes, logic_points, elapse) - elapse = time.time() - s - return pred_html, pred_bboxes, elapse + elapse = time.perf_counter() - s + return RapidTableOutput( + pred_html=pred_html, pred_bboxes=pred_bboxes, elapse=elapse + ) def get_boxes_recs( self, ocr_result: List[Union[List[List[float]], str, str]], h: int, w: int @@ -135,6 +156,28 @@ def adapt_slanet_plus(self, img: np.ndarray, pred_bboxes: np.ndarray) -> np.ndar pred_bboxes[:, 1::2] *= h_ratio return pred_bboxes + @staticmethod + def get_model_path( + model_type: str, model_path: Union[str, Path, None] + ) -> Union[str, Dict[str, str]]: + if model_path is not None: + return model_path + + model_url = KEY_TO_MODEL_URL.get(model_type, None) + if isinstance(model_url, str): + model_path = DownloadModel.download(model_url) + return model_path + + if isinstance(model_url, dict): + model_paths = {} + for k, url in model_url.items(): + model_paths[k] = DownloadModel.download( + url, save_model_name=f"{model_type}_{Path(url).name}" + ) + return model_paths + + raise ValueError(f"Model URL: {type(model_url)} is not between str and dict.") + def main(): parser = argparse.ArgumentParser() diff --git a/rapid_table/models/unitable_vocab.json b/rapid_table/models/unitable_vocab.json new file mode 100644 index 0000000..c13ccef --- /dev/null +++ b/rapid_table/models/unitable_vocab.json @@ -0,0 +1,9650 @@ +{ + "version": "1.0", + "truncation": null, + "padding": { + "strategy": "BatchLongest", + "direction": "Right", + "pad_to_multiple_of": null, + "pad_id": 2, + "pad_type_id": 0, + "pad_token": "" + }, + "added_tokens": [ + { + "id": 0, + "content": "", + "single_word": false, + "lstrip": false, + "rstrip": false, + "normalized": false, + "special": true + }, + { + "id": 1, + "content": "", + "single_word": false, + "lstrip": false, + "rstrip": false, + "normalized": false, + "special": true + }, + { + "id": 2, + "content": "", + "single_word": false, + "lstrip": false, + "rstrip": false, + "normalized": false, + "special": true + }, + { + "id": 3, + "content": "", + "single_word": false, + "lstrip": false, + "rstrip": false, + "normalized": false, + "special": true + }, + { + "id": 4, + "content": "", + "single_word": false, + "lstrip": false, + "rstrip": false, + "normalized": false, + "special": true + }, + { + "id": 5, + "content": "", + "single_word": false, + "lstrip": false, + "rstrip": false, + "normalized": false, + "special": true + }, + { + "id": 6, + "content": "[table]", + "single_word": false, + "lstrip": false, + "rstrip": false, + "normalized": false, + "special": true + }, + { + "id": 7, + "content": "[html]", + "single_word": false, + "lstrip": false, + "rstrip": false, + "normalized": false, + "special": true + }, + { + "id": 8, + "content": "[cell]", + "single_word": false, + "lstrip": false, + "rstrip": false, + "normalized": false, + "special": true + }, + { + "id": 9, + "content": "[bbox]", + "single_word": false, + "lstrip": false, + "rstrip": false, + "normalized": false, + "special": true + }, + { + "id": 10, + "content": "[cell+bbox]", + "single_word": false, + "lstrip": false, + "rstrip": false, + "normalized": false, + "special": true + }, + { + "id": 11, + "content": "[html+bbox]", + "single_word": false, + "lstrip": false, + "rstrip": false, + "normalized": false, + 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-1,21 +0,0 @@ -# -*- encoding: utf-8 -*- -# @Author: Jocker1212 -# @Contact: xinyijianggo@gmail.com -import logging -from functools import lru_cache - - -@lru_cache(maxsize=32) -def get_logger(name: str) -> logging.Logger: - logger = logging.getLogger(name) - logger.setLevel(logging.DEBUG) - - fmt = "%(asctime)s - %(name)s - %(levelname)s: %(message)s" - format_str = logging.Formatter(fmt) - - sh = logging.StreamHandler() - sh.setLevel(logging.DEBUG) - - logger.addHandler(sh) - sh.setFormatter(format_str) - return logger diff --git a/rapid_table/table_structure/table_structure_unitable.py b/rapid_table/table_structure/table_structure_unitable.py index e14f548..c04e8f8 100644 --- a/rapid_table/table_structure/table_structure_unitable.py +++ b/rapid_table/table_structure/table_structure_unitable.py @@ -1,6 +1,5 @@ import re import time -from typing import Any, Dict import cv2 import numpy as np @@ -79,11 +78,11 @@ class TableStructureUnitable: - def __init__(self, config: Dict[str, Any]): + def __init__(self, config): # encoder_path: str, decoder_path: str, vocab_path: str, device: str - vocab_path = config["vocab_path"] - encoder_path = config["encoder_path"] - decoder_path = config["decoder_path"] + vocab_path = config["model_path"]["vocab"] + encoder_path = config["model_path"]["encoder"] + decoder_path = config["model_path"]["decoder"] device = config.get("device", "cuda:0") if config["use_cuda"] else "cpu" self.vocab = Tokenizer.from_file(vocab_path) diff --git a/rapid_table/table_structure/utils.py b/rapid_table/table_structure/utils.py index e06f610..1f1d8dd 100644 --- a/rapid_table/table_structure/utils.py +++ b/rapid_table/table_structure/utils.py @@ -31,7 +31,7 @@ get_device, ) -from rapid_table.table_structure.logger import get_logger +from rapid_table.logger import get_logger class EP(Enum): diff --git a/setup.py b/setup.py index 8dc2e60..caf2114 100644 --- a/setup.py +++ b/setup.py @@ -3,11 +3,18 @@ # @Contact: liekkaskono@163.com import sys from pathlib import Path +from typing import List, Union import setuptools from get_pypi_latest_version import GetPyPiLatestVersion +def read_txt(txt_path: Union[Path, str]) -> List[str]: + with open(txt_path, "r", encoding="utf-8") as f: + data = [v.rstrip("\n") for v in f] + return data + + def get_readme(): root_dir = Path(__file__).resolve().parent readme_path = str(root_dir / "docs" / "doc_whl_rapid_table.md") @@ -34,19 +41,13 @@ def get_readme(): platforms="Any", long_description=get_readme(), long_description_content_type="text/markdown", - description="Tools for parsing table structures based ONNXRuntime.", + description="Table Recognition", author="SWHL", author_email="liekkaskono@163.com", - url="https://github.com/RapidAI/RapidStructure", + url="https://github.com/RapidAI/RapidTable", license="Apache-2.0", include_package_data=True, - install_requires=[ - "onnxruntime>=1.7.0", - "PyYAML>=6.0", - "opencv_python>=4.5.1.48", - "numpy>=1.21.6", - "Pillow", - ], + install_requires=read_txt("requirements.txt"), packages=[ MODULE_NAME, f"{MODULE_NAME}.models", diff --git a/tests/test_main.py b/tests/test_main.py new file mode 100644 index 0000000..11db74c --- /dev/null +++ b/tests/test_main.py @@ -0,0 +1,50 @@ +# -*- encoding: utf-8 -*- +# @Author: SWHL +# @Contact: liekkaskono@163.com +import sys +from pathlib import Path + +import pytest +from rapidocr_onnxruntime import RapidOCR + +cur_dir = Path(__file__).resolve().parent +root_dir = cur_dir.parent + +sys.path.append(str(root_dir)) + +from rapid_table import RapidTable, RapidTableInput + +ocr_engine = RapidOCR() + +input_args = RapidTableInput() +table_engine = RapidTable(input_args) + +test_file_dir = cur_dir / "test_files" +img_path = str(test_file_dir / "table.jpg") + + +@pytest.mark.parametrize("model_type", ["slanet_plus", "unitable"]) +def test_ocr_input(model_type): + ocr_res, _ = ocr_engine(img_path) + + input_args = RapidTableInput(model_type=model_type) + table_engine = RapidTable(input_args) + + table_results = table_engine(img_path, ocr_res) + assert table_results.pred_html.count("") == 16 + + +@pytest.mark.parametrize("model_type", ["slanet_plus", "unitable"]) +def test_input_ocr_none(model_type): + input_args = RapidTableInput(model_type=model_type) + table_engine = RapidTable(input_args) + table_results = table_engine(img_path) + assert table_results.pred_html.count("") == 16 + + +@pytest.mark.parametrize("model_type", ["slanet_plus", "unitable"]) +def test_logic_points_out(model_type): + input_args = RapidTableInput(model_type=model_type) + table_engine = RapidTable(input_args) + table_results = table_engine(img_path, return_logic_points=True) + assert len(table_results.pred_bboxes) == len(table_results.logic_points) diff --git a/tests/test_table.py b/tests/test_table.py deleted file mode 100644 index 41a2060..0000000 --- a/tests/test_table.py +++ /dev/null @@ -1,35 +0,0 @@ -# -*- encoding: utf-8 -*- -# @Author: SWHL -# @Contact: liekkaskono@163.com -import sys -from pathlib import Path - -from rapidocr_onnxruntime import RapidOCR - -cur_dir = Path(__file__).resolve().parent -root_dir = cur_dir.parent - -sys.path.append(str(root_dir)) - -from rapid_table import RapidTable - -ocr_engine = RapidOCR() -table_engine = RapidTable() - -test_file_dir = cur_dir / "test_files" -img_path = str(test_file_dir / "table.jpg") - - -def test_ocr_input(): - ocr_res, _ = ocr_engine(img_path) - table_html_str, table_cell_bboxes, elapse = table_engine(img_path, ocr_res) - assert table_html_str.count("") == 16 - - -def test_input_ocr_none(): - table_html_str, table_cell_bboxes, elapse = table_engine(img_path) - assert table_html_str.count("") == 16 - -def test_logic_points_out(): - table_html_str, table_cell_bboxes, logic_points, elapse = table_engine(img_path, return_logic_points=True) - assert len(table_cell_bboxes) == len(logic_points) diff --git a/tests/test_table_torch.py b/tests/test_table_torch.py deleted file mode 100644 index 3526d11..0000000 --- a/tests/test_table_torch.py +++ /dev/null @@ -1,38 +0,0 @@ -# -*- encoding: utf-8 -*- -# @Author: SWHL -# @Contact: liekkaskono@163.com -import sys -from pathlib import Path - -from rapidocr_onnxruntime import RapidOCR - -cur_dir = Path(__file__).resolve().parent -root_dir = cur_dir.parent - -sys.path.append(str(root_dir)) - -from rapid_table import RapidTable - -ocr_engine = RapidOCR() -table_engine = RapidTable(model_type="unitable") - -test_file_dir = cur_dir / "test_files" -img_path = str(test_file_dir / "table.jpg") - - -def test_ocr_input(): - ocr_res, _ = ocr_engine(img_path) - table_html_str, table_cell_bboxes, elapse = table_engine(img_path, ocr_res) - assert table_html_str.count("") == 16 - - -def test_input_ocr_none(): - table_html_str, table_cell_bboxes, elapse = table_engine(img_path) - assert table_html_str.count("") == 16 - - -def test_logic_points_out(): - table_html_str, table_cell_bboxes, logic_points, elapse = table_engine( - img_path, return_logic_points=True - ) - assert len(table_cell_bboxes) == len(logic_points) From e9b6456414b09f3a0075bb037fa3fb2ed0da29c9 Mon Sep 17 00:00:00 2001 From: SWHL Date: Tue, 7 Jan 2025 23:11:20 +0800 Subject: [PATCH 13/23] ci: update unit testing --- .github/workflows/publish_whl.yml | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/.github/workflows/publish_whl.yml b/.github/workflows/publish_whl.yml index 7e135b3..c668a7a 100644 --- a/.github/workflows/publish_whl.yml +++ b/.github/workflows/publish_whl.yml @@ -38,8 +38,7 @@ jobs: pip install torchvision pip install tokenizers pip install pytest - pytest tests/test_table.py - pytest tests/test_table_torch.py + pytest tests/test_main.py GenerateWHL_PushPyPi: needs: UnitTesting From 269baf84dfa1733c8585ce7065292d8b73ded3ff Mon Sep 17 00:00:00 2001 From: SWHL Date: Wed, 8 Jan 2025 09:11:15 +0800 Subject: [PATCH 14/23] chore: optimize code --- README.md | 2 +- rapid_table/__init__.py | 2 +- rapid_table/main.py | 24 +- rapid_table/params.py | 55 - rapid_table/table_structure/utils.py | 2 +- rapid_table/utils/__init__.py | 3 + rapid_table/{ => utils}/download_model.py | 15 - rapid_table/{ => utils}/logger.py | 0 rapid_table/utils/models/unitable_vocab.json | 9650 ++++++++++++++++++ rapid_table/{ => utils}/utils.py | 0 10 files changed, 9670 insertions(+), 83 deletions(-) delete mode 100644 rapid_table/params.py create mode 100644 rapid_table/utils/__init__.py rename rapid_table/{ => utils}/download_model.py (80%) rename rapid_table/{ => utils}/logger.py (100%) create mode 100644 rapid_table/utils/models/unitable_vocab.json rename rapid_table/{ => utils}/utils.py (100%) diff --git a/README.md b/README.md index 82285cf..685d8c2 100644 --- a/README.md +++ b/README.md @@ -33,7 +33,7 @@ unitable是来源unitable的transformer模型，精度最高，暂仅支持pytor [PaddleX-SlaNetPlus 表格识别](https://github.com/PaddlePaddle/PaddleX/blob/release/3.0-beta1/docs/module_usage/tutorials/ocr_modules/table_structure_recognition.md)\ [Unitable](https://github.com/poloclub/unitable?tab=readme-ov-file) -模型下载地址为：[link](https://github.com/RapidAI/RapidTable/releases/tag/assets) +模型托管在modelscope上，具体下载地址为：[link](https://www.modelscope.cn/models/RapidAI-NG/RapidTable/files) ### 效果展示 diff --git a/rapid_table/__init__.py b/rapid_table/__init__.py index 702f7d0..e152860 100644 --- a/rapid_table/__init__.py +++ b/rapid_table/__init__.py @@ -2,4 +2,4 @@ # @Author: SWHL # @Contact: liekkaskono@163.com from .main import RapidTable, RapidTableInput -from .utils import VisTable +from .utils.utils import VisTable diff --git a/rapid_table/main.py b/rapid_table/main.py index 30068fc..3d496f1 100644 --- a/rapid_table/main.py +++ b/rapid_table/main.py @@ -13,12 +13,12 @@ import cv2 import numpy as np -from rapid_table.logger import get_logger +from rapid_table.utils.download_model import DownloadModel +from rapid_table.utils.logger import get_logger +from rapid_table.utils.utils import LoadImage, VisTable -from .download_model import DownloadModel from .table_matcher import TableMatch from .table_structure import TableStructurer, TableStructureUnitable -from .utils import LoadImage, VisTable logger = get_logger("main") root_dir = Path(__file__).resolve().parent @@ -63,9 +63,8 @@ class RapidTableOutput: class RapidTable: def __init__(self, config: RapidTableInput): self.model_type = config.model_type - if self.model_type not in KEY_TO_MODEL_URL: - model_list = ",".join(KEY_TO_MODEL_URL.keys()) + model_list = ",".join(KEY_TO_MODEL_URL) raise ValueError( f"{self.model_type} is not supported. The currently supported models are {model_list}." ) @@ -185,6 +184,7 @@ def main(): "-v", "--vis", action="store_true", + default=False, help="Wheter to visualize the layout results.", ) parser.add_argument( @@ -192,10 +192,10 @@ def main(): ) parser.add_argument( "-m", - "--model_path", + "--model_type", type=str, - default=str(root_dir / "models" / "en_ppstructure_mobile_v2_SLANet.onnx"), - help="The model path used for inference.", + default=ModelType.SLANETPLUS.value, + choices=list(KEY_TO_MODEL_URL), ) args = parser.parse_args() @@ -206,12 +206,16 @@ def main(): "Please install the rapidocr_onnxruntime by pip install rapidocr_onnxruntime." ) from exc - rapid_table = RapidTable(args.model_path) + table_engine = RapidTable(args.model_path) img = cv2.imread(args.img_path) ocr_result, _ = ocr_engine(img) - table_html_str, table_cell_bboxes, elapse = rapid_table(img, ocr_result) + table_results = table_engine(img, ocr_result) + table_html_str, table_cell_bboxes = ( + table_results.pred_html, + table_results.pred_bboxes, + ) print(table_html_str) viser = VisTable() diff --git a/rapid_table/params.py b/rapid_table/params.py deleted file mode 100644 index b831587..0000000 --- a/rapid_table/params.py +++ /dev/null @@ -1,55 +0,0 @@ -# -*- encoding: utf-8 -*- -from dataclasses import dataclass, fields -from functools import wraps -from pathlib import Path -from typing import Optional, Union - -from rapid_table.logger import get_logger - -root_dir = Path(__file__).resolve().parent -logger = get_logger("params") - - -@dataclass -class RapidTableInput: - model_type: Optional[str] = None - model_path_or_dir: Union[str, Path, None] = None - use_cuda: bool = False - device: str = "cpu" - - -@dataclass -class BaseConfig: - model_type: str = "slanet-plus" - model_path: str = str(root_dir / "models" / "slanet-plus.onnx") - use_cuda: bool = False - device: str = "cpu" - encoder_path: str = None - decoder_path: str = None - vocab_path: str = None - - -def accept_kwargs_as_dataclass(cls): - def decorator(func): - @wraps(func) - def wrapper(*args, **kwargs): - if len(args) == 2 and isinstance(args[1], cls): - # 如果已经传递了 ModelConfig 实例，直接调用函数 - return func(*args, **kwargs) - - # 提取 cls 中定义的字段 - cls_fields = {field.name for field in fields(cls)} - # 过滤掉未定义的字段 - filtered_kwargs = {k: v for k, v in kwargs.items() if k in cls_fields} - # 发出警告对于未定义的字段 - for k in kwargs.keys() - cls_fields: - logger.warning( - f"Warning: '{k}' is not a valid field in {cls.__name__} and will be ignored." - ) - # 创建 ModelConfig 实例并调用函数 - config = cls(**filtered_kwargs) - return func(args[0], config=config) - - return wrapper - - return decorator diff --git a/rapid_table/table_structure/utils.py b/rapid_table/table_structure/utils.py index 1f1d8dd..78f41d2 100644 --- a/rapid_table/table_structure/utils.py +++ b/rapid_table/table_structure/utils.py @@ -31,7 +31,7 @@ get_device, ) -from rapid_table.logger import get_logger +from rapid_table.utils.logger import get_logger class EP(Enum): diff --git a/rapid_table/utils/__init__.py b/rapid_table/utils/__init__.py new file mode 100644 index 0000000..0ecdd4f --- /dev/null +++ b/rapid_table/utils/__init__.py @@ -0,0 +1,3 @@ +# -*- encoding: utf-8 -*- +# @Author: SWHL +# @Contact: liekkaskono@163.com diff --git a/rapid_table/download_model.py b/rapid_table/utils/download_model.py similarity index 80% rename from rapid_table/download_model.py rename to rapid_table/utils/download_model.py index 312afa2..a228238 100644 --- a/rapid_table/download_model.py +++ b/rapid_table/utils/download_model.py @@ -14,21 +14,6 @@ class DownloadModel: - @staticmethod - def get_model_path( - model_type: str, sub_file_type: str, path: Union[str, Path, None] - ) -> str: - if path is not None: - return path - - model_url = KEY_TO_MODEL_URL.get(model_type, {}).get(sub_file_type, None) - if model_url: - model_path = DownloadModel.download(model_url) - return model_path - - logger.info("model url is None, using the default download model %s", path) - return path - @classmethod def download( cls, diff --git a/rapid_table/logger.py b/rapid_table/utils/logger.py similarity index 100% rename from rapid_table/logger.py rename to rapid_table/utils/logger.py diff --git a/rapid_table/utils/models/unitable_vocab.json b/rapid_table/utils/models/unitable_vocab.json new file mode 100644 index 0000000..c13ccef --- /dev/null +++ b/rapid_table/utils/models/unitable_vocab.json @@ -0,0 +1,9650 @@ +{ + "version": "1.0", + "truncation": null, + "padding": { + "strategy": "BatchLongest", + "direction": "Right", + "pad_to_multiple_of": null, + "pad_id": 2, + "pad_type_id": 0, + "pad_token": "" + }, + "added_tokens": [ + { + "id": 0, + "content": "", + "single_word": false, + "lstrip": false, + "rstrip": false, + "normalized": false, + "special": true + }, + { + "id": 1, + "content": "", + "single_word": false, + "lstrip": false, + "rstrip": false, + "normalized": false, + "special": true + }, + { + "id": 2, + "content": "", 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4806b4bb80c5dffbb92c192a9a4705c754ed7e99 Mon Sep 17 00:00:00 2001 From: SWHL Date: Wed, 8 Jan 2025 21:54:57 +0800 Subject: [PATCH 16/23] chore: optimize code --- .github/workflows/publish_whl.yml | 15 +- README.md | 97 +- rapid_table/main.py | 2 +- rapid_table/models/unitable_vocab.json | 9650 ------------------ rapid_table/utils/download_model.py | 2 +- rapid_table/utils/models/unitable_vocab.json | 9650 ------------------ 6 files changed, 63 insertions(+), 19353 deletions(-) delete mode 100644 rapid_table/models/unitable_vocab.json delete mode 100644 rapid_table/utils/models/unitable_vocab.json diff --git a/.github/workflows/publish_whl.yml b/.github/workflows/publish_whl.yml index c668a7a..c2f6b44 100644 --- a/.github/workflows/publish_whl.yml +++ b/.github/workflows/publish_whl.yml @@ -6,7 +6,7 @@ on: - v* env: - RESOURCES_URL: https://github.com/RapidAI/RapidTable/releases/download/assets/rapid_table_models.zip + DEFAULT_MODEL: https://www.modelscope.cn/models/RapidAI/RapidTable/resolve/master/slanet-plus.onnx jobs: UnitTesting: @@ -26,11 +26,7 @@ jobs: - name: Unit testings run: | - wget $RESOURCES_URL - ZIP_NAME=${RESOURCES_URL##*/} - DIR_NAME=${ZIP_NAME%.*} - unzip $DIR_NAME - cp $DIR_NAME/*.onnx rapid_table/models/ + wget $DEFAULT_MODEL -P rapid_table/models pip install -r requirements.txt pip install rapidocr_onnxruntime @@ -58,11 +54,8 @@ jobs: pip install -r requirements.txt python -m pip install --upgrade pip pip install wheel get_pypi_latest_version - wget $RESOURCES_URL - ZIP_NAME=${RESOURCES_URL##*/} - DIR_NAME=${ZIP_NAME%.*} - unzip $ZIP_NAME - mv $DIR_NAME/slanet-plus.onnx rapid_table/models/ + + wget $DEFAULT_MODEL -P rapid_table/models python setup.py bdist_wheel ${{ github.ref_name }} - name: Publish distribution 📦 to PyPI diff --git a/README.md b/README.md index 685d8c2..f482210 100644 --- a/README.md +++ b/README.md @@ -21,6 +21,14 @@ slanet_plus是paddlex内置的SLANet升级版模型，准确率有大幅提升 unitable是来源unitable的transformer模型，精度最高，暂仅支持pytorch推理，支持gpu推理加速,训练权重来源于 [OhMyTable项目](https://github.com/Sanster/OhMyTable) +### 效果展示 + +
+ Demo +
+ +### 模型列表 + | `model_type` | 模型名称 | 推理框架 |模型大小 |推理耗时(单图 60KB)| |:--------------|:--------------------------------------| :------: |:------ |:------ | | `ppstructure_en` | `en_ppstructure_mobile_v2_SLANet.onnx` | onnxruntime |7.3M |0.15s | @@ -33,23 +41,7 @@ unitable是来源unitable的transformer模型，精度最高，暂仅支持pytor [PaddleX-SlaNetPlus 表格识别](https://github.com/PaddlePaddle/PaddleX/blob/release/3.0-beta1/docs/module_usage/tutorials/ocr_modules/table_structure_recognition.md)\ [Unitable](https://github.com/poloclub/unitable?tab=readme-ov-file) -模型托管在modelscope上，具体下载地址为：[link](https://www.modelscope.cn/models/RapidAI-NG/RapidTable/files) - -### 效果展示 - -
- Demo -
- -### 与[TableStructureRec](https://github.com/RapidAI/TableStructureRec)关系 - -TableStructureRec库是一个表格识别算法的集合库，当前有`wired_table_rec`有线表格识别算法和`lineless_table_rec`无线表格识别算法的推理包。 - -RapidTable是整理自PP-Structure中表格识别部分而来。由于PP-Structure较早，这个库命名就成了`rapid_table`。 - -总之，RapidTable和TabelStructureRec都是表格识别的仓库。大家可以都试试，哪个好用用哪个。由于每个算法都不太同，暂时不打算做统一处理。 - -关于表格识别算法的比较，可参见[TableStructureRec测评](https://github.com/RapidAI/TableStructureRec#指标结果) +模型托管在modelscope上，具体下载地址为：[link](https://www.modelscope.cn/models/RapidAI/RapidTable/files) ### 安装 @@ -73,11 +65,33 @@ pip install onnxruntime-gpu # for onnx gpu inference #### python脚本运行 -RapidTable类提供model_path参数，可以自行指定上述2个模型，默认是`slanet-plus.onnx`。举例如下： +> ⚠️注意：在`rapid_table>=1.0.0`之后，模型输入均采用dataclasses封装，简化和兼容参数传递。输入和输出定义如下： ```python -table_engine = RapidTable() -# table_engine = RapidTable(use_cuda=True, device="cuda:0", model_type="unitable") +# 输入 +@dataclass +class RapidTableInput: + model_type: Optional[str] = ModelType.SLANETPLUS.value + model_path: Union[str, Path, None, Dict[str, str]] = None + use_cuda: bool = False + device: str = "cpu" + +# 输出 +@dataclass +class RapidTableOutput: + pred_html: Optional[str] = None + pred_bboxes: Optional[np.ndarray] = None + logic_points: Optional[np.ndarray] = None + elapse: Optional[float] = None + +# 使用示例 +input_args = RapidTableInput(model_type="unitable") +table_engine = RapidTable(input_args) + +img_path = 'test_images/table.jpg' +table_results = table_engine(img_path) + +print(table_results.pred_html) ``` 完整示例： @@ -101,13 +115,13 @@ ocr_engine = RapidOCR() viser = VisTable() img_path = 'test_images/table.jpg' - ocr_result, _ = ocr_engine(img_path) # 单字匹配 # ocr_result, _ = ocr_engine(img_path, return_word_box=True) # ocr_result = trans_char_ocr_res(ocr_result) -table_html_str, table_cell_bboxes, elapse = table_engine(img_path, ocr_result) +table_results = table_engine(img_path, ocr_result) +table_html_str, table_cell_bboxes = table_results.pred_html, table_results.pred_bboxes save_dir = Path("./inference_results/") save_dir.mkdir(parents=True, exist_ok=True) @@ -118,31 +132,24 @@ save_drawed_path = save_dir / f"vis_{Path(img_path).name}" viser(img_path, table_html_str, save_html_path, table_cell_bboxes, save_drawed_path) # 返回逻辑坐标 -# table_html_str, table_cell_bboxes, logic_points, elapse = table_engine(img_path, ocr_result, return_logic_points=True) - +# table_results = table_engine(img_path, ocr_result, return_logic_points=True) +# table_html_str, table_cell_bboxes = table_results.pred_html, table_results.pred_bboxes # save_logic_path = save_dir / f"vis_logic_{Path(img_path).name}" -# viser(img_path, table_html_str, save_html_path, table_cell_bboxes, save_drawed_path,logic_points, save_logic_path) +# viser( +# img_path, +# table_results.pred_html, +# save_html_path, +# table_results.pred_bboxes, +# save_drawed_path, +# table_results.logic_points, +# save_logic_path, +# ) print(table_html_str) ``` #### 终端运行 -```bash -$ rapid_table -h -usage: rapid_table [-h] [-v] -img IMG_PATH [-m MODEL_PATH] - -optional arguments: --h, --help show this help message and exit --v, --vis Whether to visualize the layout results. --img IMG_PATH, --img_path IMG_PATH - Path to image for layout. --m MODEL_PATH, --model_path MODEL_PATH - The model path used for inference. -``` - -示例: - ```bash rapid_table -v -img test_images/table.jpg ``` @@ -289,6 +296,16 @@ rapid_table -v -img test_images/table.jpg +### 与[TableStructureRec](https://github.com/RapidAI/TableStructureRec)关系 + +TableStructureRec库是一个表格识别算法的集合库，当前有`wired_table_rec`有线表格识别算法和`lineless_table_rec`无线表格识别算法的推理包。 + +RapidTable是整理自PP-Structure中表格识别部分而来。由于PP-Structure较早，这个库命名就成了`rapid_table`。 + +总之，RapidTable和TabelStructureRec都是表格识别的仓库。大家可以都试试，哪个好用用哪个。由于每个算法都不太同，暂时不打算做统一处理。 + +关于表格识别算法的比较，可参见[TableStructureRec测评](https://github.com/RapidAI/TableStructureRec#指标结果) + ### 更新日志
diff --git a/rapid_table/main.py b/rapid_table/main.py index 3d496f1..a5a226f 100644 --- a/rapid_table/main.py +++ b/rapid_table/main.py @@ -31,7 +31,7 @@ class ModelType(Enum): UNITABLE = "unitable" -ROOT_URL = "https://www.modelscope.cn/models/RapidAI-NG/RapidTable/resolve/master/" +ROOT_URL = "https://www.modelscope.cn/models/RapidAI/RapidTable/resolve/master/" KEY_TO_MODEL_URL = { ModelType.PPSTRUCTURE_EN.value: f"{ROOT_URL}/en_ppstructure_mobile_v2_SLANet.onnx", ModelType.PPSTRUCTURE_ZH.value: f"{ROOT_URL}/ch_ppstructure_mobile_v2_SLANet.onnx", diff --git a/rapid_table/models/unitable_vocab.json b/rapid_table/models/unitable_vocab.json deleted file mode 100644 index c13ccef..0000000 --- a/rapid_table/models/unitable_vocab.json +++ /dev/null @@ -1,9650 +0,0 @@ -{ - "version": "1.0", - "truncation": null, - "padding": { - "strategy": "BatchLongest", - "direction": "Right", - "pad_to_multiple_of": null, - "pad_id": 2, - "pad_type_id": 0, - "pad_token": "" - }, - "added_tokens": [ - { - 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"bbox-890": 951, - "bbox-891": 952, - "bbox-892": 953, - "bbox-893": 954, - "bbox-894": 955, - "bbox-895": 956, - "bbox-896": 957, - "bbox-897": 958, - "bbox-898": 959 - } - } -} \ No newline at end of file diff --git a/rapid_table/utils/download_model.py b/rapid_table/utils/download_model.py index a228238..adedb5d 100644 --- a/rapid_table/utils/download_model.py +++ b/rapid_table/utils/download_model.py @@ -9,7 +9,7 @@ logger = get_logger("DownloadModel") -PROJECT_DIR = Path(__file__).resolve().parent +PROJECT_DIR = Path(__file__).resolve().parent.parent DEFAULT_MODEL_DIR = PROJECT_DIR / "models" diff --git a/rapid_table/utils/models/unitable_vocab.json b/rapid_table/utils/models/unitable_vocab.json deleted file mode 100644 index c13ccef..0000000 --- a/rapid_table/utils/models/unitable_vocab.json +++ /dev/null @@ -1,9650 +0,0 @@ -{ - "version": "1.0", - "truncation": null, - "padding": { - "strategy": "BatchLongest", - "direction": "Right", - "pad_to_multiple_of": null, - "pad_id": 2, - 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"bbox-887": 948, - "bbox-888": 949, - "bbox-889": 950, - "bbox-890": 951, - "bbox-891": 952, - "bbox-892": 953, - "bbox-893": 954, - "bbox-894": 955, - "bbox-895": 956, - "bbox-896": 957, - "bbox-897": 958, - "bbox-898": 959 - } - } -} \ No newline at end of file From 4560d6da274644dee0ced282102c5dc4877bedb9 Mon Sep 17 00:00:00 2001 From: SWHL Date: Wed, 8 Jan 2025 22:36:50 +0800 Subject: [PATCH 17/23] chore: optimize code --- README.md | 36 +++++++++++++--------------- demo.py | 2 +- rapid_table/main.py | 34 ++++++++++---------------- rapid_table/table_matcher/matcher.py | 14 +++++------ rapid_table/utils/utils.py | 13 +++++----- tests/test_main.py | 9 +------ 6 files changed, 45 insertions(+), 63 deletions(-) diff --git a/README.md b/README.md index f482210..9d4a65e 100644 --- a/README.md +++ b/README.md @@ -80,7 +80,7 @@ class RapidTableInput: @dataclass class RapidTableOutput: pred_html: Optional[str] = None - pred_bboxes: Optional[np.ndarray] = None + cell_bboxes: Optional[np.ndarray] = None logic_points: Optional[np.ndarray] = None elapse: Optional[float] = None @@ -106,22 +106,25 @@ from rapid_table.table_structure.utils import trans_char_ocr_res table_engine = RapidTable() # 开启onnx-gpu推理 -# table_engine = RapidTable(use_cuda=True) +# input_args = RapidTableInput(use_cuda=True) +# table_engine = RapidTable(input_args) # 使用torch推理版本的unitable模型 -# table_engine = RapidTable(use_cuda=True, device="cuda:0", model_type="unitable") +# input_args = RapidTableInput(model_type="unitable", use_cuda=True, device="cuda:0") +# table_engine = RapidTable(input_args) ocr_engine = RapidOCR() viser = VisTable() img_path = 'test_images/table.jpg' ocr_result, _ = ocr_engine(img_path) + # 单字匹配 # ocr_result, _ = ocr_engine(img_path, return_word_box=True) # ocr_result = trans_char_ocr_res(ocr_result) table_results = table_engine(img_path, ocr_result) -table_html_str, table_cell_bboxes = table_results.pred_html, table_results.pred_bboxes +table_html_str, table_cell_bboxes = table_results.pred_html, table_results.cell_bboxes save_dir = Path("./inference_results/") save_dir.mkdir(parents=True, exist_ok=True) @@ -129,22 +132,15 @@ save_dir.mkdir(parents=True, exist_ok=True) save_html_path = save_dir / f"{Path(img_path).stem}.html" save_drawed_path = save_dir / f"vis_{Path(img_path).name}" -viser(img_path, table_html_str, save_html_path, table_cell_bboxes, save_drawed_path) - -# 返回逻辑坐标 -# table_results = table_engine(img_path, ocr_result, return_logic_points=True) -# table_html_str, table_cell_bboxes = table_results.pred_html, table_results.pred_bboxes -# save_logic_path = save_dir / f"vis_logic_{Path(img_path).name}" -# viser( -# img_path, -# table_results.pred_html, -# save_html_path, -# table_results.pred_bboxes, -# save_drawed_path, -# table_results.logic_points, -# save_logic_path, -# ) - +viser( + img_path, + table_results.pred_html, + save_html_path, + table_results.cell_bboxes, + save_drawed_path, + table_results.logic_points, + save_logic_path, +) print(table_html_str) ``` diff --git a/demo.py b/demo.py index 22022a7..c8a4be8 100644 --- a/demo.py +++ b/demo.py @@ -24,7 +24,7 @@ # Table Rec table_results = table_engine(img_path, ocr_result) -table_html_str, table_cell_bboxes = table_results.pred_html, table_results.pred_bboxes +table_html_str, table_cell_bboxes = table_results.pred_html, table_results.cell_bboxes # Save save_dir = Path("outputs") diff --git a/rapid_table/main.py b/rapid_table/main.py index a5a226f..426a7ce 100644 --- a/rapid_table/main.py +++ b/rapid_table/main.py @@ -55,7 +55,7 @@ class RapidTableInput: @dataclass class RapidTableOutput: pred_html: Optional[str] = None - pred_bboxes: Optional[np.ndarray] = None + cell_bboxes: Optional[np.ndarray] = None logic_points: Optional[np.ndarray] = None elapse: Optional[float] = None @@ -88,7 +88,6 @@ def __call__( self, img_content: Union[str, np.ndarray, bytes, Path], ocr_result: List[Union[List[List[float]], str, str]] = None, - return_logic_points: bool = False, ) -> RapidTableOutput: if self.ocr_engine is None and ocr_result is None: raise ValueError( @@ -104,28 +103,21 @@ def __call__( ocr_result, _ = self.ocr_engine(img) dt_boxes, rec_res = self.get_boxes_recs(ocr_result, h, w) - pred_structures, pred_bboxes, _ = self.table_structure(copy.deepcopy(img)) + pred_structures, cell_bboxes, _ = self.table_structure(copy.deepcopy(img)) # 适配slanet-plus模型输出的box缩放还原 if self.model_type == ModelType.SLANETPLUS.value: - pred_bboxes = self.adapt_slanet_plus(img, pred_bboxes) + cell_bboxes = self.adapt_slanet_plus(img, cell_bboxes) - pred_html = self.table_matcher(pred_structures, pred_bboxes, dt_boxes, rec_res) + pred_html = self.table_matcher(pred_structures, cell_bboxes, dt_boxes, rec_res) # 过滤掉占位的bbox - mask = ~np.all(pred_bboxes == 0, axis=1) - pred_bboxes = pred_bboxes[mask] - - # 避免低版本升级后出现问题,默认不打开 - if return_logic_points: - logic_points = self.table_matcher.decode_logic_points(pred_structures) - elapse = time.perf_counter() - s - return RapidTableOutput(pred_html, pred_bboxes, logic_points, elapse) + mask = ~np.all(cell_bboxes == 0, axis=1) + cell_bboxes = cell_bboxes[mask] + logic_points = self.table_matcher.decode_logic_points(pred_structures) elapse = time.perf_counter() - s - return RapidTableOutput( - pred_html=pred_html, pred_bboxes=pred_bboxes, elapse=elapse - ) + return RapidTableOutput(pred_html, cell_bboxes, logic_points, elapse) def get_boxes_recs( self, ocr_result: List[Union[List[List[float]], str, str]], h: int, w: int @@ -145,15 +137,15 @@ def get_boxes_recs( dt_boxes = np.array(r_boxes) return dt_boxes, rec_res - def adapt_slanet_plus(self, img: np.ndarray, pred_bboxes: np.ndarray) -> np.ndarray: + def adapt_slanet_plus(self, img: np.ndarray, cell_bboxes: np.ndarray) -> np.ndarray: h, w = img.shape[:2] resized = 488 ratio = min(resized / h, resized / w) w_ratio = resized / (w * ratio) h_ratio = resized / (h * ratio) - pred_bboxes[:, 0::2] *= w_ratio - pred_bboxes[:, 1::2] *= h_ratio - return pred_bboxes + cell_bboxes[:, 0::2] *= w_ratio + cell_bboxes[:, 1::2] *= h_ratio + return cell_bboxes @staticmethod def get_model_path( @@ -214,7 +206,7 @@ def main(): table_results = table_engine(img, ocr_result) table_html_str, table_cell_bboxes = ( table_results.pred_html, - table_results.pred_bboxes, + table_results.cell_bboxes, ) print(table_html_str) diff --git a/rapid_table/table_matcher/matcher.py b/rapid_table/table_matcher/matcher.py index 00bea6a..f579a12 100644 --- a/rapid_table/table_matcher/matcher.py +++ b/rapid_table/table_matcher/matcher.py @@ -22,18 +22,18 @@ def __init__(self, filter_ocr_result=True, use_master=False): self.filter_ocr_result = filter_ocr_result self.use_master = use_master - def __call__(self, pred_structures, pred_bboxes, dt_boxes, rec_res): + def __call__(self, pred_structures, cell_bboxes, dt_boxes, rec_res): if self.filter_ocr_result: - dt_boxes, rec_res = self._filter_ocr_result(pred_bboxes, dt_boxes, rec_res) - matched_index = self.match_result(dt_boxes, pred_bboxes) + dt_boxes, rec_res = self._filter_ocr_result(cell_bboxes, dt_boxes, rec_res) + matched_index = self.match_result(dt_boxes, cell_bboxes) pred_html, pred = self.get_pred_html(pred_structures, matched_index, rec_res) return pred_html - def match_result(self, dt_boxes, pred_bboxes, min_iou=0.1**8): + def match_result(self, dt_boxes, cell_bboxes, min_iou=0.1**8): matched = {} for i, gt_box in enumerate(dt_boxes): distances = [] - for j, pred_box in enumerate(pred_bboxes): + for j, pred_box in enumerate(cell_bboxes): if len(pred_box) == 8: pred_box = [ np.min(pred_box[0::2]), @@ -186,8 +186,8 @@ def mark_occupied(row, col, rowspan, colspan): return logic_points - def _filter_ocr_result(self, pred_bboxes, dt_boxes, rec_res): - y1 = pred_bboxes[:, 1::2].min() + def _filter_ocr_result(self, cell_bboxes, dt_boxes, rec_res): + y1 = cell_bboxes[:, 1::2].min() new_dt_boxes = [] new_rec_res = [] diff --git a/rapid_table/utils/utils.py b/rapid_table/utils/utils.py index 7370016..5052d3f 100644 --- a/rapid_table/utils/utils.py +++ b/rapid_table/utils/utils.py @@ -83,18 +83,18 @@ def __init__(self): def __call__( self, img_path: Union[str, Path], - table_html_str: str, - save_html_path: Optional[str] = None, + table_results, table_cell_bboxes: Optional[np.ndarray] = None, - save_drawed_path: Optional[str] = None, logic_points: List[List[float]] = None, + save_html_path: Optional[str] = None, + save_drawed_path: Optional[str] = None, save_logic_path: Optional[str] = None, - ) -> None: + ): if save_html_path: - html_with_border = self.insert_border_style(table_html_str) + html_with_border = self.insert_border_style(table_results.pred_html) self.save_html(save_html_path, html_with_border) - if table_cell_bboxes is None: + if table_results.cell_bboxes is None: return None img = self.load_img(img_path) @@ -109,6 +109,7 @@ def __call__( if save_drawed_path: self.save_img(save_drawed_path, drawed_img) + if save_logic_path and logic_points: polygons = [[box[0], box[1], box[4], box[5]] for box in table_cell_bboxes] self.plot_rec_box_with_logic_info( diff --git a/tests/test_main.py b/tests/test_main.py index 11db74c..1138ef1 100644 --- a/tests/test_main.py +++ b/tests/test_main.py @@ -40,11 +40,4 @@ def test_input_ocr_none(model_type): table_engine = RapidTable(input_args) table_results = table_engine(img_path) assert table_results.pred_html.count("") == 16 - - -@pytest.mark.parametrize("model_type", ["slanet_plus", "unitable"]) -def test_logic_points_out(model_type): - input_args = RapidTableInput(model_type=model_type) - table_engine = RapidTable(input_args) - table_results = table_engine(img_path, return_logic_points=True) - assert len(table_results.pred_bboxes) == len(table_results.logic_points) + assert len(table_results.cell_bboxes) == len(table_results.logic_points) From f0a3dfefb9e77e91279c6b4182a0fbbe5303587e Mon Sep 17 00:00:00 2001 From: SWHL Date: Wed, 8 Jan 2025 22:40:36 +0800 Subject: [PATCH 18/23] chore: optimize code --- README.md | 1 - demo.py | 4 +--- rapid_table/utils/utils.py | 7 +++---- 3 files changed, 4 insertions(+), 8 deletions(-) diff --git a/README.md b/README.md index 9d4a65e..a84fb26 100644 --- a/README.md +++ b/README.md @@ -124,7 +124,6 @@ ocr_result, _ = ocr_engine(img_path) # ocr_result = trans_char_ocr_res(ocr_result) table_results = table_engine(img_path, ocr_result) -table_html_str, table_cell_bboxes = table_results.pred_html, table_results.cell_bboxes save_dir = Path("./inference_results/") save_dir.mkdir(parents=True, exist_ok=True) diff --git a/demo.py b/demo.py index c8a4be8..d9e8ba3 100644 --- a/demo.py +++ b/demo.py @@ -34,9 +34,7 @@ save_drawed_path = save_dir / f"{Path(img_path).stem}_table_vis{Path(img_path).suffix}" # Visualize table rec result -vis_imged = viser( - img_path, table_html_str, save_html_path, table_cell_bboxes, save_drawed_path -) +vis_imged = viser(img_path, table_results, save_html_path, save_drawed_path) # Visualize OCR result save_ocr_path = save_dir / f"{Path(img_path).stem}_ocr_vis{Path(img_path).suffix}" diff --git a/rapid_table/utils/utils.py b/rapid_table/utils/utils.py index 5052d3f..ad35a19 100644 --- a/rapid_table/utils/utils.py +++ b/rapid_table/utils/utils.py @@ -4,7 +4,7 @@ import os from io import BytesIO from pathlib import Path -from typing import List, Optional, Union +from typing import Optional, Union import cv2 import numpy as np @@ -84,8 +84,6 @@ def __call__( self, img_path: Union[str, Path], table_results, - table_cell_bboxes: Optional[np.ndarray] = None, - logic_points: List[List[float]] = None, save_html_path: Optional[str] = None, save_drawed_path: Optional[str] = None, save_logic_path: Optional[str] = None, @@ -94,7 +92,8 @@ def __call__( html_with_border = self.insert_border_style(table_results.pred_html) self.save_html(save_html_path, html_with_border) - if table_results.cell_bboxes is None: + table_cell_bboxes = table_results.cell_bboxes + if table_cell_bboxes is None: return None img = self.load_img(img_path) From 00375438c06c3c4d9248a970df812af1546cdcc4 Mon Sep 17 00:00:00 2001 From: Jokcer <519548295@qq.com> Date: Wed, 8 Jan 2025 23:13:31 +0800 Subject: [PATCH 19/23] fix: adapt row&col span decode for unitable --- rapid_table/table_structure/table_structure_unitable.py | 5 ++++- 1 file changed, 4 insertions(+), 1 deletion(-) diff --git a/rapid_table/table_structure/table_structure_unitable.py b/rapid_table/table_structure/table_structure_unitable.py index c04e8f8..2b98006 100644 --- a/rapid_table/table_structure/table_structure_unitable.py +++ b/rapid_table/table_structure/table_structure_unitable.py @@ -186,7 +186,10 @@ def decode_tokens(self, context): td_content = td_match.group(2).strip() if td_attrs: decoded_list.append("") decoded_list.append("") else: From 333f0628c5e2fab2d8319e17a7f661d7fbf0ce9a Mon Sep 17 00:00:00 2001 From: SWHL Date: Thu, 9 Jan 2025 08:10:33 +0800 Subject: [PATCH 20/23] docs: update online demo --- .gitignore | 1 + README.md | 3 ++- 2 files changed, 3 insertions(+), 1 deletion(-) diff --git a/.gitignore b/.gitignore index e7007dc..eee50b9 100644 --- a/.gitignore +++ b/.gitignore @@ -1,3 +1,4 @@ +outputs/ *.json # Created by .ignore support plugin (hsz.mobi) diff --git a/README.md b/README.md index a84fb26..a4c25df 100644 --- a/README.md +++ b/README.md @@ -3,7 +3,8 @@

📊 Rapid Table

- + + PyPI From 860e148ebba6f7114cd4d6a1ab252d01c1bf5584 Mon Sep 17 00:00:00 2001 From: SWHL Date: Thu, 9 Jan 2025 08:16:40 +0800 Subject: [PATCH 21/23] docs: add update recently --- README.md | 9 ++++++++- 1 file changed, 8 insertions(+), 1 deletion(-) diff --git a/README.md b/README.md index a4c25df..6560b60 100644 --- a/README.md +++ b/README.md @@ -22,6 +22,12 @@ slanet_plus是paddlex内置的SLANet升级版模型，准确率有大幅提升 unitable是来源unitable的transformer模型，精度最高，暂仅支持pytorch推理，支持gpu推理加速,训练权重来源于 [OhMyTable项目](https://github.com/Sanster/OhMyTable) +### 最近动态 + +2024.12.30 update：支持Unitable模型的表格识别，使用pytorch框架 \ +2024.11.24 update：支持gpu推理，适配 rapidOCR 单字识别匹配,支持逻辑坐标返回及可视化 \ +2024.10.13 update：补充最新paddlex-SLANet-plus 模型(paddle2onnx原因暂不能支持onnx) + ### 效果展示
@@ -46,7 +52,7 @@ unitable是来源unitable的transformer模型，精度最高，暂仅支持pytor ### 安装 -由于模型较小，预先将slanet-plus表格识别模型(`slanet-plus.onnx`)打包进了whl包内。 +由于模型较小，预先将slanet-plus表格识别模型(`slanet-plus.onnx`)打包进了whl包内。其余模型在初始化`RapidTable`类时，会根据`model_type`来自动下载模型到安装包所在`models`目录下。 > ⚠️注意：`rapid_table>=v0.1.0`之后，不再将`rapidocr_onnxruntime`依赖强制打包到`rapid_table`中。使用前，需要自行安装`rapidocr_onnxruntime`包。 @@ -104,6 +110,7 @@ from rapid_table import RapidTable, VisTable from rapidocr_onnxruntime import RapidOCR from rapid_table.table_structure.utils import trans_char_ocr_res +# 默认是slanet_plus模型 table_engine = RapidTable() # 开启onnx-gpu推理 From 953692a963a647a5fd2e4be07fdac685d6e9518d Mon Sep 17 00:00:00 2001 From: SWHL Date: Thu, 9 Jan 2025 08:19:39 +0800 Subject: [PATCH 22/23] docs: update README --- README.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/README.md b/README.md index 6560b60..f3c6f3f 100644 --- a/README.md +++ b/README.md @@ -48,11 +48,11 @@ unitable是来源unitable的transformer模型，精度最高，暂仅支持pytor [PaddleX-SlaNetPlus 表格识别](https://github.com/PaddlePaddle/PaddleX/blob/release/3.0-beta1/docs/module_usage/tutorials/ocr_modules/table_structure_recognition.md)\ [Unitable](https://github.com/poloclub/unitable?tab=readme-ov-file) -模型托管在modelscope上，具体下载地址为：[link](https://www.modelscope.cn/models/RapidAI/RapidTable/files) +模型下载地址：[link](https://www.modelscope.cn/models/RapidAI/RapidTable/files) ### 安装 -由于模型较小，预先将slanet-plus表格识别模型(`slanet-plus.onnx`)打包进了whl包内。其余模型在初始化`RapidTable`类时，会根据`model_type`来自动下载模型到安装包所在`models`目录下。 +由于模型较小，预先将slanet-plus表格识别模型(`slanet-plus.onnx`)打包进了whl包内。其余模型在初始化`RapidTable`类时，会根据`model_type`来自动下载模型到安装包所在`models`目录下。当然也可以通过`RapidTableInput(model_path='')`来指定自己模型路径。注意仅限于我们现支持的`model_type`。 > ⚠️注意：`rapid_table>=v0.1.0`之后，不再将`rapidocr_onnxruntime`依赖强制打包到`rapid_table`中。使用前，需要自行安装`rapidocr_onnxruntime`包。 From e9af31c90b4d8803b7ebaf8e3ce99b668a9fae95 Mon Sep 17 00:00:00 2001 From: SWHL Date: Thu, 9 Jan 2025 08:25:17 +0800 Subject: [PATCH 23/23] docs: update README --- README.md | 1 + 1 file changed, 1 insertion(+) diff --git a/README.md b/README.md index f3c6f3f..cafcd4a 100644 --- a/README.md +++ b/README.md @@ -24,6 +24,7 @@ unitable是来源unitable的transformer模型，精度最高，暂仅支持pytor ### 最近动态 +2025-01-09 update: 发布v1.0.2，全新接口升级。 \ 2024.12.30 update：支持Unitable模型的表格识别，使用pytorch框架 \ 2024.11.24 update：支持gpu推理，适配 rapidOCR 单字识别匹配,支持逻辑坐标返回及可视化 \ 2024.10.13 update：补充最新paddlex-SLANet-plus 模型(paddle2onnx原因暂不能支持onnx)