Merge pull request #8013 from WenmuZhou/whl

add recovery requirements to whl

doc/doc_ch/algorithm_det_drrg.md

@@ -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

doc/doc_ch/algorithm_overview.md

@@ -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)

doc/doc_ch/algorithm_rec_can.md

@@ -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

doc/doc_ch/algorithm_sr_telescope.md

@@ -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={},

doc/doc_en/algorithm_det_drrg_en.md

@@ -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

doc/doc_en/algorithm_overview_en.md

@@ -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).

doc/doc_en/algorithm_rec_can_en.md

@@ -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

doc/doc_en/algorithm_sr_telescope_en.md

@@ -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={},

paddleocr.py

@@ -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/")
 

ppstructure/docs/quickstart.md

@@ -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>

ppstructure/docs/quickstart_en.md

@@ -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>

setup.py

@@ -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(),