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Merge pull request #8013 from WenmuZhou/whl

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add recovery requirements to whl
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Evezerest 2022-10-21 09:58:17 +08:00 committed by GitHub
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2
doc/doc_ch/algorithm_det_drrg.md
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@ -23,7 +23,7 @@
| 模型 |骨干网络|配置文件|precision|recall|Hmean|下载链接|
|-----| --- | --- | --- | --- | --- | --- |
| DRRG | ResNet50_vd | [configs/det/det_r50_drrg_ctw.yml](../../configs/det/det_r50_drrg_ctw.yml)| 89.92%|80.91%|85.18%|[训练模型](https://paddleocr.bj.bcebos.com/contribution/det_r50_drrg_ctw.tar)|
| DRRG | ResNet50_vd | [configs/det/det_r50_drrg_ctw.yml](../../configs/det/det_r50_drrg_ctw.yml)| 89.92%|80.91%|85.18%|[训练模型](https://paddleocr.bj.bcebos.com/contribution/det_r50_drrg_ctw_train.tar)|
<a name="2"></a>
## 2. 环境配置

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doc/doc_ch/algorithm_overview.md
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@ -55,7 +55,7 @@ PaddleOCR将**持续新增**支持OCR领域前沿算法与模型，**欢迎广
|模型|骨干网络|precision|recall|Hmean|下载链接|
| --- | --- | --- | --- | --- | --- |
|FCE|ResNet50_dcn|88.39%|82.18%|85.27%|[训练模型](https://paddleocr.bj.bcebos.com/contribution/det_r50_dcn_fce_ctw_v2.0_train.tar)|
|DRRG|ResNet50_vd|89.92%|80.91%|85.18%|[训练模型](https://paddleocr.bj.bcebos.com/contribution/det_r50_drrg_ctw.tar)|
|DRRG|ResNet50_vd|89.92%|80.91%|85.18%|[训练模型](https://paddleocr.bj.bcebos.com/contribution/det_r50_drrg_ctw_train.tar)|
**说明：** SAST模型训练额外加入了icdar2013、icdar2017、COCO-Text、ArT等公开数据集进行调优。PaddleOCR用到的经过整理格式的英文公开数据集下载：
* [百度云地址](https://pan.baidu.com/s/12cPnZcVuV1zn5DOd4mqjVw) (提取码: 2bpi)

2
doc/doc_ch/algorithm_rec_can.md
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@ -27,7 +27,7 @@
|模型 |骨干网络|配置文件|ExpRate|下载链接|
| ----- | ----- | ----- | ----- | ----- |
|CAN|DenseNet|[rec_d28_can.yml](../../configs/rec/rec_d28_can.yml)|51.72|[训练模型](https://paddleocr.bj.bcebos.com/contribution/can_train.tar)|
|CAN|DenseNet|[rec_d28_can.yml](../../configs/rec/rec_d28_can.yml)|51.72|[训练模型](https://paddleocr.bj.bcebos.com/contribution/rec_d28_can_train.tar)|
<a name="2"></a>
## 2. 环境配置

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doc/doc_ch/algorithm_sr_telescope.md
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@ -27,7 +27,7 @@
|模型|骨干网络|PSNR_Avg|SSIM_Avg|配置文件|下载链接|
|---|---|---|---|---|---|
|Text Telescope|tbsrn|21.56|0.7411| [configs/sr/sr_telescope.yml](../../configs/sr/sr_telescope.yml)|[训练模型](https://paddleocr.bj.bcebos.com/contribution/Telescope_train.tar.gz)|
|Text Telescope|tbsrn|21.56|0.7411| [configs/sr/sr_telescope.yml](../../configs/sr/sr_telescope.yml)|[训练模型](https://paddleocr.bj.bcebos.com/contribution/sr_telescope_train.tar)|
[TextZoom数据集](https://paddleocr.bj.bcebos.com/dataset/TextZoom.tar) 来自两个超分数据集RealSR和SR-RAW，两个数据集都包含LR-HR对，TextZoom有17367对训数据和4373对测试数据。
@ -118,8 +118,8 @@ python3 tools/infer/predict_sr.py --sr_model_dir=./inference/sr_out --image_dir=
```bibtex
@INPROCEEDINGS{9578891,
author={Chen, Jingye and Li, Bin and Xue, Xiangyang},
booktitle={2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
title={Scene Text Telescope: Text-Focused Scene Image Super-Resolution},
booktitle={2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
title={Scene Text Telescope: Text-Focused Scene Image Super-Resolution},
year={2021},
volume={},
number={},

2
doc/doc_en/algorithm_det_drrg_en.md
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@ -25,7 +25,7 @@ On the CTW1500 dataset, the text detection result is as follows:
|Model|Backbone|Configuration|Precision|Recall|Hmean|Download|
| --- | --- | --- | --- | --- | --- | --- |
| DRRG | ResNet50_vd | [configs/det/det_r50_drrg_ctw.yml](../../configs/det/det_r50_drrg_ctw.yml)| 89.92%|80.91%|85.18%|[trained model](https://paddleocr.bj.bcebos.com/contribution/det_r50_drrg_ctw.tar)|
| DRRG | ResNet50_vd | [configs/det/det_r50_drrg_ctw.yml](../../configs/det/det_r50_drrg_ctw.yml)| 89.92%|80.91%|85.18%|[trained model](https://paddleocr.bj.bcebos.com/contribution/det_r50_drrg_ctw_train.tar)|
<a name="2"></a>
## 2. Environment

2
doc/doc_en/algorithm_overview_en.md
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@ -53,7 +53,7 @@ On CTW1500 dataset, the text detection result is as follows:
|Model|Backbone|Precision|Recall|Hmean| Download link|
| --- | --- | --- | --- | --- |---|
|FCE|ResNet50_dcn|88.39%|82.18%|85.27%| [trained model](https://paddleocr.bj.bcebos.com/contribution/det_r50_dcn_fce_ctw_v2.0_train.tar) |
|DRRG|ResNet50_vd|89.92%|80.91%|85.18%|[trained model](https://paddleocr.bj.bcebos.com/contribution/det_r50_drrg_ctw.tar)|
|DRRG|ResNet50_vd|89.92%|80.91%|85.18%|[trained model](https://paddleocr.bj.bcebos.com/contribution/det_r50_drrg_ctw_train.tar)|
**Note：** Additional data, like icdar2013, icdar2017, COCO-Text, ArT, was added to the model training of SAST. Download English public dataset in organized format used by PaddleOCR from:
* [Baidu Drive](https://pan.baidu.com/s/12cPnZcVuV1zn5DOd4mqjVw) (download code: 2bpi).

2
doc/doc_en/algorithm_rec_can_en.md
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@ -25,7 +25,7 @@ Using CROHME handwrittem mathematical expression recognition datasets for traini
|Model|Backbone|config|exprate|Download link|
| --- | --- | --- | --- | --- |
|CAN|DenseNet|[rec_d28_can.yml](../../configs/rec/rec_d28_can.yml)|51.72|[trained model](https://paddleocr.bj.bcebos.com/contribution/can_train.tar)|
|CAN|DenseNet|[rec_d28_can.yml](../../configs/rec/rec_d28_can.yml)|51.72|[trained model](https://paddleocr.bj.bcebos.com/contribution/rec_d28_can_train.tar)|
<a name="2"></a>
## 2. Environment

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doc/doc_en/algorithm_sr_telescope_en.md
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@ -28,7 +28,7 @@ Referring to the [FudanOCR](https://github.com/FudanVI/FudanOCR/tree/main/scene-
|Model|Backbone|config|Acc|Download link|
|---|---|---|---|---|---|
|Text Gestalt|tsrn|21.56|0.7411| [configs/sr/sr_telescope.yml](../../configs/sr/sr_telescope.yml)|[train model](https://paddleocr.bj.bcebos.com/contribution/Telescope_train.tar.gz)|
|Text Gestalt|tsrn|21.56|0.7411| [configs/sr/sr_telescope.yml](../../configs/sr/sr_telescope.yml)|[train model](https://paddleocr.bj.bcebos.com/contribution/sr_telescope_train.tar)|
The [TextZoom dataset](https://paddleocr.bj.bcebos.com/dataset/TextZoom.tar) comes from two superfraction data sets, RealSR and SR-RAW, both of which contain LR-HR pairs. TextZoom has 17367 pairs of training data and 4373 pairs of test data.
@ -127,8 +127,8 @@ Not supported
```bibtex
@INPROCEEDINGS{9578891,
author={Chen, Jingye and Li, Bin and Xue, Xiangyang},
booktitle={2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
title={Scene Text Telescope: Text-Focused Scene Image Super-Resolution},
booktitle={2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
title={Scene Text Telescope: Text-Focused Scene Image Super-Resolution},
year={2021},
volume={},
number={},

2
paddleocr.py
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@ -47,7 +47,7 @@ __all__ = [
]
SUPPORT_DET_MODEL = ['DB']
VERSION = '2.6.0.2'
VERSION = '2.6.0.3'
SUPPORT_REC_MODEL = ['CRNN', 'SVTR_LCNet']
BASE_DIR = os.path.expanduser("~/.paddleocr/")

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ppstructure/docs/quickstart.md
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@ -45,16 +45,10 @@
```bash
# 安装 paddleocr，推荐使用2.6版本
pip3 install "paddleocr>=2.6"
pip3 install "paddleocr>=2.6.0.3"
# 安装 图像方向分类依赖包paddleclas（如不需要图像方向分类功能，可跳过）
pip3 install paddleclas>=2.4.3
# 安装 关键信息抽取 依赖包（如不需要KIE功能，可跳过）
pip3 install -r ppstructure/kie/requirements.txt
# 安装 版面恢复 依赖包（如不需要版面恢复功能，可跳过）
pip3 install -r ppstructure/recovery/requirements.txt
```
<a name="2"></a>

8
ppstructure/docs/quickstart_en.md
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@ -47,16 +47,10 @@ For more software version requirements, please refer to the instructions in [Ins
```bash
# Install paddleocr, version 2.6 is recommended
pip3 install "paddleocr>=2.6"
pip3 install "paddleocr>=2.6.0.3"
# Install the image direction classification dependency package paddleclas (if you do not use the image direction classification, you can skip it)
pip3 install paddleclas>=2.4.3
# Install the KIE dependency packages (if you do not use the KIE, you can skip it)
pip3 install -r kie/requirements.txt
# Install the layout recovery dependency packages (if you do not use the layout recovery, you can skip it)
pip3 install -r recovery/requirements.txt
```
<a name="2"></a>

15
setup.py
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@ -16,9 +16,16 @@ from setuptools import setup
from io import open
from paddleocr import VERSION
with open('requirements.txt', encoding="utf-8-sig") as f:
requirements = f.readlines()
requirements.append('tqdm')
def load_requirements(file_list=None):
if file_list is None:
file_list = ['requirements.txt']
if isinstance(file_list,str):
file_list = [file_list]
requirements = []
for file in file_list:
with open(file, encoding="utf-8-sig") as f:
requirements.extend(f.readlines())
return requirements
def readme():
@ -34,7 +41,7 @@ setup(
include_package_data=True,
entry_points={"console_scripts": ["paddleocr= paddleocr.paddleocr:main"]},
version=VERSION,
install_requires=requirements,
install_requires=load_requirements(['requirements.txt', 'ppstructure/recovery/requirements.txt']),
license='Apache License 2.0',
description='Awesome OCR toolkits based on PaddlePaddle （8.6M ultra-lightweight pre-trained model, support training and deployment among server, mobile, embeded and IoT devices',
long_description=readme(),