[Doc] Rename PPOCRSystem to PPOCR and update comments. (#395)

* Imporve OCR Readme * Improve OCR Readme * Improve OCR Readme * Improve OCR Readme * Improve OCR Readme * Add Initialize function to PP-OCR * Add Initialize function to PP-OCR * Add Initialize function to PP-OCR * Make all the model links come from PaddleOCR * Improve OCR readme * Improve OCR readme * Improve OCR readme * Improve OCR readme * Add Readme for vision results * Add Readme for vision results * Add Readme for vision results * Add Readme for vision results * Add Readme for vision results * Add Readme for vision results * Add Readme for vision results * Add Readme for vision results * Add Readme for vision results * Add Readme for vision results * Add check for label file in postprocess of Rec model * Add check for label file in postprocess of Rec model * Add check for label file in postprocess of Rec model * Add check for label file in postprocess of Rec model * Add check for label file in postprocess of Rec model * Add check for label file in postprocess of Rec model * Add comments to create API docs * Improve OCR comments * Rename OCR and add comments * Make sure previous python example works * Make sure previous python example works Co-authored-by: Jason <jiangjiajun@baidu.com>
2025-12-24 13:28:13 +08:00 · 2022-10-19 17:21:48 +08:00
parent 85e1c647f6
commit 24317e1a14
23 changed files with 380 additions and 186 deletions
--- a/examples/vision/ocr/PP-OCRv2/cpp/README.md
+++ b/examples/vision/ocr/PP-OCRv2/cpp/README.md
@@ -1,6 +1,6 @@
-# PPOCRSystemv2 C++部署示例
+# PPOCRv2 C++部署示例

-本目录下提供`infer.cc`快速完成PPOCRSystemv2在CPU/GPU，以及GPU上通过TensorRT加速部署的示例。
+本目录下提供`infer.cc`快速完成PPOCRv2在CPU/GPU，以及GPU上通过TensorRT加速部署的示例。

 在部署前，需确认以下两个步骤

@@ -19,14 +19,14 @@ make -j


 # 下载模型,图片和字典文件
-wget https://bj.bcebos.com/paddlehub/fastdeploy/ch_PP-OCRv2_det_infer.tar.gz
-tar -xvf ch_PP-OCRv2_det_infer.tar.gz
+wget https://paddleocr.bj.bcebos.com/PP-OCRv2/chinese/ch_PP-OCRv2_det_infer.tar
+tar -xvf ch_PP-OCRv2_det_infer.tar

-wget https://bj.bcebos.com/paddlehub/fastdeploy/ch_ppocr_mobile_v2.0_cls_infer.tar.gz
-tar -xvf ch_ppocr_mobile_v2.0_cls_infer.tar.gz
+https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_cls_infer.tar
+tar -xvf ch_ppocr_mobile_v2.0_cls_infer.tar

-wget https://bj.bcebos.com/paddlehub/fastdeploy/ch_PP-OCRv2_rec_infer.tar.gz
-tar -xvf ch_PP-OCRv2_rec_infer.tar.gz
+wgethttps://paddleocr.bj.bcebos.com/PP-OCRv2/chinese/ch_PP-OCRv2_rec_infer.tar
+tar -xvf ch_PP-OCRv2_rec_infer.tar

 wget https://gitee.com/paddlepaddle/PaddleOCR/raw/release/2.6/doc/imgs/12.jpg

@@ -48,17 +48,17 @@ wget https://gitee.com/paddlepaddle/PaddleOCR/raw/release/2.6/ppocr/utils/ppocr_
 <img width="640" src="https://user-images.githubusercontent.com/109218879/185826024-f7593a0c-1bd2-4a60-b76c-15588484fa08.jpg">


-## PPOCRSystemv2 C++接口
+## PPOCRv2 C++接口

-### PPOCRSystemv2类
+### PPOCRv2类

 ```
-fastdeploy::application::ocrsystem::PPOCRSystemv2(fastdeploy::vision::ocr::DBDetector* det_model,
+fastdeploy::pipeline::PPOCRv2(fastdeploy::vision::ocr::DBDetector* det_model,
                fastdeploy::vision::ocr::Classifier* cls_model,
                fastdeploy::vision::ocr::Recognizer* rec_model);
 ```

-PPOCRSystemv2 的初始化，由检测，分类和识别模型串联构成
+PPOCRv2 的初始化，由检测，分类和识别模型串联构成

 **参数**

@@ -67,10 +67,10 @@ PPOCRSystemv2 的初始化，由检测，分类和识别模型串联构成
 > * **Recognizer**(model): OCR中的识别模型

 ```
-fastdeploy::application::ocrsystem::PPOCRSystemv2(fastdeploy::vision::ocr::DBDetector* det_model,
+fastdeploy::pipeline::PPOCRv2(fastdeploy::vision::ocr::DBDetector* det_model,
                fastdeploy::vision::ocr::Recognizer* rec_model);
 ```
-PPOCRSystemv2 的初始化，由检测，识别模型串联构成(无分类器)
+PPOCRv2 的初始化，由检测，识别模型串联构成(无分类器)

 **参数**

--- a/examples/vision/ocr/PP-OCRv2/cpp/infer.cc
+++ b/examples/vision/ocr/PP-OCRv2/cpp/infer.cc
@@ -37,12 +37,12 @@ void InitAndInfer(const std::string& det_model_dir, const std::string& cls_model
  assert(cls_model.Initialized());
  assert(rec_model.Initialized());
  
-  // The classification model is optional, so the OCR system can also be connected in series as follows
-  // auto ocr_system_v2 = fastdeploy::application::ocrsystem::PPOCRSystemv2(&det_model, &rec_model);
-  auto ocr_system_v2 = fastdeploy::application::ocrsystem::PPOCRSystemv2(&det_model, &cls_model, &rec_model);
+  // The classification model is optional, so the PP-OCR can also be connected in series as follows
+  // auto ppocr_v2 = fastdeploy::pipeline::PPOCRv2(&det_model, &rec_model);
+  auto ppocr_v2 = fastdeploy::pipeline::PPOCRv2(&det_model, &cls_model, &rec_model);

-  if(!ocr_system_v2.Initialized()){
-    std::cerr << "Failed to initialize OCR system." << std::endl;
+  if(!ppocr_v2.Initialized()){
+    std::cerr << "Failed to initialize PP-OCR." << std::endl;
    return;
  }

@@ -50,14 +50,14 @@ void InitAndInfer(const std::string& det_model_dir, const std::string& cls_model
  auto im_bak = im.clone();
  
  fastdeploy::vision::OCRResult result;
-  if (!ocr_system_v2.Predict(&im, &result)) {
+  if (!ppocr_v2.Predict(&im, &result)) {
    std::cerr << "Failed to predict." << std::endl;
    return;
  }

  std::cout << result.Str() << std::endl;

-  auto vis_im = fastdeploy::vision::Visualize::VisOcr(im_bak, result);
+  auto vis_im = fastdeploy::vision::VisOcr(im_bak, result);
  cv::imwrite("vis_result.jpg", vis_im);
  std::cout << "Visualized result saved in ./vis_result.jpg" << std::endl;
 }
--- a/examples/vision/ocr/PP-OCRv2/python/README.md
+++ b/examples/vision/ocr/PP-OCRv2/python/README.md
@@ -1,23 +1,23 @@
-# PPOCRSystemv2 Python部署示例
+# PPOCRv2 Python部署示例

 在部署前，需确认以下两个步骤

 - 1. 软硬件环境满足要求，参考[FastDeploy环境要求](../../../../../docs/cn/build_and_install/download_prebuilt_libraries.md)  
 - 2. FastDeploy Python whl包安装，参考[FastDeploy Python安装](../../../../../docs/cn/build_and_install/download_prebuilt_libraries.md)

-本目录下提供`infer.py`快速完成PPOCRSystemv2在CPU/GPU，以及GPU上通过TensorRT加速部署的示例。执行如下脚本即可完成
+本目录下提供`infer.py`快速完成PPOCRv2在CPU/GPU，以及GPU上通过TensorRT加速部署的示例。执行如下脚本即可完成

 ```

 # 下载模型,图片和字典文件
-wget https://bj.bcebos.com/paddlehub/fastdeploy/ch_PP-OCRv2_det_infer.tar.gz
-tar -xvf ch_PP-OCRv2_det_infer.tar.gz
+wget https://paddleocr.bj.bcebos.com/PP-OCRv2/chinese/ch_PP-OCRv2_det_infer.tar
+tar -xvf ch_PP-OCRv2_det_infer.tar

-wget https://bj.bcebos.com/paddlehub/fastdeploy/ch_ppocr_mobile_v2.0_cls_infer.tar.gz
-tar -xvf ch_ppocr_mobile_v2.0_cls_infer.tar.gz
+https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_cls_infer.tar
+tar -xvf ch_ppocr_mobile_v2.0_cls_infer.tar

-wget https://bj.bcebos.com/paddlehub/fastdeploy/ch_PP-OCRv2_rec_infer.tar.gz
-tar -xvf ch_PP-OCRv2_rec_infer.tar.gz
+wgethttps://paddleocr.bj.bcebos.com/PP-OCRv2/chinese/ch_PP-OCRv2_rec_infer.tar
+tar -xvf ch_PP-OCRv2_rec_infer.tar

 wget https://gitee.com/paddlepaddle/PaddleOCR/raw/release/2.6/doc/imgs/12.jpg

@@ -39,12 +39,12 @@ python infer.py --det_model ch_PP-OCRv2_det_infer --cls_model ch_ppocr_mobile_v2
 运行完成可视化结果如下图所示
 <img width="640" src="https://user-images.githubusercontent.com/109218879/185826024-f7593a0c-1bd2-4a60-b76c-15588484fa08.jpg">

-## PPOCRSystemv2 Python接口
+## PPOCRv2 Python接口

 ```
-fd.vision.ocr.PPOCRSystemv2(det_model=det_model, cls_model=cls_model, rec_model=rec_model)
+fd.vision.ocr.PPOCRv2(det_model=det_model, cls_model=cls_model, rec_model=rec_model)
 ```
-PPOCRSystemv2的初始化,输入的参数是检测模型，分类模型和识别模型，其中cls_model可选，如无需求，可设置为None
+PPOCRv2的初始化,输入的参数是检测模型，分类模型和识别模型，其中cls_model可选，如无需求，可设置为None

 **参数**

@@ -55,7 +55,7 @@ PPOCRSystemv2的初始化,输入的参数是检测模型，分类模型和识别
 ### predict函数

 > ```
-> result = ocr_system.predict(im)
+> result = ppocr_v2.predict(im)
 > ```
 >
 > 模型预测接口，输入是一张图片
--- a/examples/vision/ocr/PP-OCRv2/python/infer.py
+++ b/examples/vision/ocr/PP-OCRv2/python/infer.py
@@ -110,15 +110,15 @@ rec_model = fd.vision.ocr.Recognizer(
    rec_label_file,
    runtime_option=runtime_option)

-# 创建OCR系统，串联3个模型，其中cls_model可选，如无需求，可设置为None
-ocr_system = fd.vision.ocr.PPOCRSystemv2(
+# 创建PP-OCR，串联3个模型，其中cls_model可选，如无需求，可设置为None
+ppocr_v2 = fd.vision.ocr.PPOCRv2(
    det_model=det_model, cls_model=cls_model, rec_model=rec_model)

 # 预测图片准备
 im = cv2.imread(args.image)

 #预测并打印结果
-result = ocr_system.predict(im)
+result = ppocr_v2.predict(im)

 print(result)

--- a/examples/vision/ocr/PP-OCRv3/cpp/README.md
+++ b/examples/vision/ocr/PP-OCRv3/cpp/README.md
@@ -1,6 +1,6 @@
-# PPOCRSystemv3 C++部署示例
+# PPOCRv3 C++部署示例

-本目录下提供`infer.cc`快速完成PPOCRSystemv3在CPU/GPU，以及GPU上通过TensorRT加速部署的示例。
+本目录下提供`infer.cc`快速完成PPOCRv3在CPU/GPU，以及GPU上通过TensorRT加速部署的示例。

 在部署前，需确认以下两个步骤

@@ -22,8 +22,8 @@ make -j
 wget https://paddleocr.bj.bcebos.com/PP-OCRv3/chinese/ch_PP-OCRv3_det_infer.tar
 tar -xvf ch_PP-OCRv3_det_infer.tar

-wget https://bj.bcebos.com/paddlehub/fastdeploy/ch_ppocr_mobile_v2.0_cls_infer.tar.gz
-tar -xvf ch_ppocr_mobile_v2.0_cls_infer.tar.gz
+https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_cls_infer.tar
+tar -xvf ch_ppocr_mobile_v2.0_cls_infer.tar

 wget https://paddleocr.bj.bcebos.com/PP-OCRv3/chinese/ch_PP-OCRv3_rec_infer.tar
 tar -xvf ch_PP-OCRv3_rec_infer.tar
@@ -48,17 +48,17 @@ wget https://gitee.com/paddlepaddle/PaddleOCR/raw/release/2.6/ppocr/utils/ppocr_
 <img width="640" src="https://user-images.githubusercontent.com/109218879/185826024-f7593a0c-1bd2-4a60-b76c-15588484fa08.jpg">


-## PPOCRSystemv3 C++接口
+## PPOCRv3 C++接口

-### PPOCRSystemv3类
+### PPOCRv3类

 ```
-fastdeploy::application::ocrsystem::PPOCRSystemv3(fastdeploy::vision::ocr::DBDetector* det_model,
+fastdeploy::pipeline::PPOCRv3(fastdeploy::vision::ocr::DBDetector* det_model,
                fastdeploy::vision::ocr::Classifier* cls_model,
                fastdeploy::vision::ocr::Recognizer* rec_model);
 ```

-PPOCRSystemv3 的初始化，由检测，分类和识别模型串联构成
+PPOCRv3 的初始化，由检测，分类和识别模型串联构成

 **参数**

@@ -67,10 +67,10 @@ PPOCRSystemv3 的初始化，由检测，分类和识别模型串联构成
 > * **Recognizer**(model): OCR中的识别模型

 ```
-fastdeploy::application::ocrsystem::PPOCRSystemv3(fastdeploy::vision::ocr::DBDetector* det_model,
+fastdeploy::pipeline::PPOCRv3(fastdeploy::vision::ocr::DBDetector* det_model,
                fastdeploy::vision::ocr::Recognizer* rec_model);
 ```
-PPOCRSystemv3 的初始化，由检测，识别模型串联构成(无分类器)
+PPOCRv3 的初始化，由检测，识别模型串联构成(无分类器)

 **参数**

--- a/examples/vision/ocr/PP-OCRv3/cpp/infer.cc
+++ b/examples/vision/ocr/PP-OCRv3/cpp/infer.cc
@@ -37,12 +37,12 @@ void InitAndInfer(const std::string& det_model_dir, const std::string& cls_model
  assert(cls_model.Initialized());
  assert(rec_model.Initialized());

-  // The classification model is optional, so the OCR system can also be connected in series as follows
-  // auto ocr_system_v3 = fastdeploy::application::ocrsystem::PPOCRSystemv3(&det_model, &rec_model);
-  auto ocr_system_v3 = fastdeploy::application::ocrsystem::PPOCRSystemv3(&det_model, &cls_model, &rec_model);
+  // The classification model is optional, so the PP-OCR can also be connected in series as follows
+  // auto ppocr_v3 = fastdeploy::pipeline::PPOCRv3(&det_model, &rec_model);
+  auto ppocr_v3 = fastdeploy::pipeline::PPOCRv3(&det_model, &cls_model, &rec_model);

-  if(!ocr_system_v3.Initialized()){
-    std::cerr << "Failed to initialize OCR system." << std::endl;
+  if(!ppocr_v3.Initialized()){
+    std::cerr << "Failed to initialize PP-OCR." << std::endl;
    return;
  }

@@ -50,14 +50,14 @@ void InitAndInfer(const std::string& det_model_dir, const std::string& cls_model
  auto im_bak = im.clone();
  
  fastdeploy::vision::OCRResult result;
-  if (!ocr_system_v3.Predict(&im, &result)) {
+  if (!ppocr_v3.Predict(&im, &result)) {
    std::cerr << "Failed to predict." << std::endl;
    return;
  }

  std::cout << result.Str() << std::endl;

-  auto vis_im = fastdeploy::vision::Visualize::VisOcr(im_bak, result);
+  auto vis_im = fastdeploy::vision::VisOcr(im_bak, result);
  cv::imwrite("vis_result.jpg", vis_im);
  std::cout << "Visualized result saved in ./vis_result.jpg" << std::endl;
 }
--- a/examples/vision/ocr/PP-OCRv3/python/README.md
+++ b/examples/vision/ocr/PP-OCRv3/python/README.md
@@ -1,11 +1,11 @@
-# PPOCRSystemv3 Python部署示例
+# PPOCRv3 Python部署示例

 在部署前，需确认以下两个步骤

 - 1. 软硬件环境满足要求，参考[FastDeploy环境要求](../../../../../docs/cn/build_and_install/download_prebuilt_libraries.md)  
 - 2. FastDeploy Python whl包安装，参考[FastDeploy Python安装](../../../../../docs/cn/build_and_install/download_prebuilt_libraries.md)

-本目录下提供`infer.py`快速完成PPOCRSystemv3在CPU/GPU，以及GPU上通过TensorRT加速部署的示例。执行如下脚本即可完成
+本目录下提供`infer.py`快速完成PPOCRv3在CPU/GPU，以及GPU上通过TensorRT加速部署的示例。执行如下脚本即可完成

 ```

@@ -13,8 +13,8 @@
 wget https://paddleocr.bj.bcebos.com/PP-OCRv3/chinese/ch_PP-OCRv3_det_infer.tar
 tar xvf ch_PP-OCRv3_det_infer.tar

-wget https://bj.bcebos.com/paddlehub/fastdeploy/ch_ppocr_mobile_v2.0_cls_infer.tar.gz
-tar -xvf ch_ppocr_mobile_v2.0_cls_infer.tar.gz
+https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_cls_infer.tar
+tar -xvf ch_ppocr_mobile_v2.0_cls_infer.tar

 wget https://paddleocr.bj.bcebos.com/PP-OCRv3/chinese/ch_PP-OCRv3_rec_infer.tar
 tar xvf ch_PP-OCRv3_rec_infer.tar
@@ -38,12 +38,12 @@ python infer.py --det_model ch_PP-OCRv3_det_infer --cls_model ch_ppocr_mobile_v2
 运行完成可视化结果如下图所示
 <img width="640" src="https://user-images.githubusercontent.com/109218879/185826024-f7593a0c-1bd2-4a60-b76c-15588484fa08.jpg">

-## PPOCRSystemv3 Python接口
+## PPOCRv3 Python接口

 ```
-fd.vision.ocr.PPOCRSystemv3(det_model=det_model, cls_model=cls_model, rec_model=rec_model)
+fd.vision.ocr.PPOCRv3(det_model=det_model, cls_model=cls_model, rec_model=rec_model)
 ```
-PPOCRSystemv3的初始化,输入的参数是检测模型，分类模型和识别模型，其中cls_model可选，如无需求，可设置为None
+PPOCRv3的初始化,输入的参数是检测模型，分类模型和识别模型，其中cls_model可选，如无需求，可设置为None

 **参数**

@@ -54,7 +54,7 @@ PPOCRSystemv3的初始化,输入的参数是检测模型，分类模型和识别
 ### predict函数

 > ```
-> result = ocr_system.predict(im)
+> result = ppocr_v3.predict(im)
 > ```
 >
 > 模型预测接口，输入是一张图片
--- a/examples/vision/ocr/PP-OCRv3/python/infer.py
+++ b/examples/vision/ocr/PP-OCRv3/python/infer.py
@@ -110,15 +110,15 @@ rec_model = fd.vision.ocr.Recognizer(
    rec_label_file,
    runtime_option=runtime_option)

-# 创建OCR系统，串联3个模型，其中cls_model可选，如无需求，可设置为None
-ocr_system = fd.vision.ocr.PPOCRSystemv3(
+# 创建PP-OCR，串联3个模型，其中cls_model可选，如无需求，可设置为None
+ppocr_v3 = fd.vision.ocr.PPOCRv3(
    det_model=det_model, cls_model=cls_model, rec_model=rec_model)

 # 预测图片准备
 im = cv2.imread(args.image)

 #预测并打印结果
-result = ocr_system.predict(im)
+result = ppocr_v3.predict(im)

 print(result)