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[Doc] add doxygen docs for c sharp api (#1495)

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add doxygen docs for c sharp api

Co-authored-by: DefTruth <31974251+DefTruth@users.noreply.github.com>
This commit is contained in:
chenjian
2023-04-09 12:32:22 +08:00
committed by GitHub
parent ed19c759df
commit fc15124800
11 changed files with 3201 additions and 36 deletions
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115
csharp/fastdeploy/vision/ocr/model.cs
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@@ -27,8 +27,18 @@ namespace ocr {
// Recognizer
/*! @brief Recognizer object is used to load the recognition model provided by PaddleOCR.
*/
public class Recognizer {
/** \brief Set path of model file, and the configuration of runtime
*
* \param[in] model_file Path of model file, e.g ./ch_PP-OCRv3_rec_infer/model.pdmodel.
* \param[in] params_file Path of parameter file, e.g ./ch_PP-OCRv3_rec_infer/model.pdiparams, if the model format is ONNX, this parameter will be ignored.
* \param[in] label_path Path of label file used by OCR recognition model. e.g ./ppocr_keys_v1.txt
* \param[in] custom_option RuntimeOption for inference, the default will use cpu, and choose the backend defined in `valid_cpu_backends`.
* \param[in] model_format Model format of the loaded model, default is Paddle format.
*/
public Recognizer(string model_file, string params_file,
string label_path,
RuntimeOption custom_option = null,
@@ -45,11 +55,17 @@ public class Recognizer {
FD_C_DestroyRecognizerWrapper(fd_recognizer_model_wrapper);
}
/// Get model's name
public string ModelName() {
return "ppocr/ocr_rec";
}
/** \brief Predict the input image and get OCR recognition model result.
*
* \param[in] img The input image data, comes from cv::imread(), is a 3-D array with layout HWC, BGR format.
*
* \return The output of OCR recognition model result
*/
public OCRRecognizerResult Predict(Mat img) {
OCRRecognizerResult ocr_recognizer_result = new OCRRecognizerResult();
FD_Cstr text = new FD_Cstr();
@@ -64,6 +80,12 @@ public class Recognizer {
return ocr_recognizer_result;
}
/** \brief BatchPredict the input image and get OCR recognition model result.
*
* \param[in] images The list of input image data, comes from cv::imread(), is a 3-D array with layout HWC, BGR format.
*
* \return The output of OCR recognition model result.
*/
public List<OCRRecognizerResult> BatchPredict(List<Mat> imgs){
FD_OneDimMat imgs_in = new FD_OneDimMat();
imgs_in.size = (nuint)imgs.Count;
@@ -152,6 +174,7 @@ public class Recognizer {
return results_out;
}
/// Check whether model is initialized successfully
public bool Initialized() {
return FD_C_RecognizerWrapperInitialized(fd_recognizer_model_wrapper);
}
@@ -219,8 +242,17 @@ public class Recognizer {
// Classifier
/*! @brief Classifier object is used to load the classification model provided by PaddleOCR.
*/
public class Classifier {
/** \brief Set path of model file, and the configuration of runtime
*
* \param[in] model_file Path of model file, e.g ./ch_ppocr_mobile_v2.0_cls_infer/model.pdmodel.
* \param[in] params_file Path of parameter file, e.g ./ch_ppocr_mobile_v2.0_cls_infer/model.pdiparams, if the model format is ONNX, this parameter will be ignored.
* \param[in] custom_option RuntimeOption for inference, the default will use cpu, and choose the backend defined in `valid_cpu_backends`.
* \param[in] model_format Model format of the loaded model, default is Paddle format.
*/
public Classifier(string model_file, string params_file,
RuntimeOption custom_option = null,
ModelFormat model_format = ModelFormat.PADDLE) {
@@ -236,11 +268,17 @@ public class Classifier {
FD_C_DestroyClassifierWrapper(fd_classifier_model_wrapper);
}
/// Get model's name
public string ModelName() {
return "ppocr/ocr_cls";
}
/** \brief Predict the input image and get OCR classification model cls_result.
*
* \param[in] img The input image data, comes from cv::imread(), is a 3-D array with layout HWC, BGR format.
*
* \return OCRClassifierResult
*/
public OCRClassifierResult Predict(Mat img) {
OCRClassifierResult ocr_classify_result = new OCRClassifierResult();
if(! FD_C_ClassifierWrapperPredict(
@@ -252,6 +290,12 @@ public class Classifier {
return ocr_classify_result;
}
/** \brief BatchPredict the input image and get OCR classification model result.
*
* \param[in] img The input image data, comes from cv::imread(), is a 3-D array with layout HWC, BGR format.
*
* \return List<OCRClassifierResult>
*/
public List<OCRClassifierResult> BatchPredict(List<Mat> imgs){
FD_OneDimMat imgs_in = new FD_OneDimMat();
imgs_in.size = (nuint)imgs.Count;
@@ -334,6 +378,7 @@ public class Classifier {
return results_out;
}
/// Check whether model is initialized successfully
public bool Initialized() {
return FD_C_ClassifierWrapperInitialized(fd_classifier_model_wrapper);
}
@@ -395,8 +440,17 @@ public class Classifier {
// DBDetector
/*! @brief DBDetector object is used to load the detection model provided by PaddleOCR.
*/
public class DBDetector {
/** \brief Set path of model file, and the configuration of runtime
*
* \param[in] model_file Path of model file, e.g ./ch_PP-OCRv3_det_infer/model.pdmodel.
* \param[in] params_file Path of parameter file, e.g ./ch_PP-OCRv3_det_infer/model.pdiparams, if the model format is ONNX, this parameter will be ignored.
* \param[in] custom_option RuntimeOption for inference, the default will use cpu, and choose the backend defined in `valid_cpu_backends`.
* \param[in] model_format Model format of the loaded model, default is Paddle format.
*/
public DBDetector(string model_file, string params_file,
RuntimeOption custom_option = null,
ModelFormat model_format = ModelFormat.PADDLE) {
@@ -412,11 +466,17 @@ public class DBDetector {
FD_C_DestroyDBDetectorWrapper(fd_dbdetector_model_wrapper);
}
/// Get model's name
public string ModelName() {
return "ppocr/ocr_det";
}
/** \brief Predict the input image and get OCR detection model result.
*
* \param[in] img The input image data, comes from cv::imread(), is a 3-D array with layout HWC, BGR format.
*
* \return OCRDBDetectorResult
*/
public OCRDBDetectorResult Predict(Mat img) {
OCRDBDetectorResult ocr_detector_result = new OCRDBDetectorResult();
FD_TwoDimArrayInt32 fd_box_result = new FD_TwoDimArrayInt32();
@@ -441,6 +501,12 @@ public class DBDetector {
return ocr_detector_result;
}
/** \brief BatchPredict the input image and get OCR detection model result.
*
* \param[in] images The list input of image data, comes from cv::imread(), is a 3-D array with layout HWC, BGR format.
*
* \return List<OCRDBDetectorResult>
*/
public List<OCRDBDetectorResult> BatchPredict(List<Mat> imgs){
FD_OneDimMat imgs_in = new FD_OneDimMat();
imgs_in.size = (nuint)imgs.Count;
@@ -484,6 +550,7 @@ public class DBDetector {
return results_out;
}
/// Check whether model is initialized successfully
public bool Initialized() {
return FD_C_DBDetectorWrapperInitialized(fd_dbdetector_model_wrapper);
}
@@ -541,8 +608,16 @@ namespace pipeline {
// PPOCRv2
/*! @brief PPOCRv2 is used to load PP-OCRv2 series models provided by PaddleOCR.
*/
public class PPOCRv2 {
/** \brief Set up the detection model path, classification model path and recognition model path respectively.
*
* \param[in] det_model Path of detection model, e.g ./ch_PP-OCRv2_det_infer
* \param[in] cls_model Path of classification model, e.g ./ch_ppocr_mobile_v2.0_cls_infer
* \param[in] rec_model Path of recognition model, e.g ./ch_PP-OCRv2_rec_infer
*/
public PPOCRv2(DBDetector ppocrv2, Classifier classifier,
Recognizer recognizer) {
fd_ppocrv2_wrapper = FD_C_CreatePPOCRv2Wrapper(
@@ -560,6 +635,12 @@ public class PPOCRv2 {
return "PPOCRv2";
}
/** \brief Predict the input image and get OCR result.
*
* \param[in] im The input image data, comes from cv::imread(), is a 3-D array with layout HWC, BGR format.
*
* \return OCRResult
*/
public OCRResult Predict(Mat img) {
FD_OCRResult fd_ocr_result = new FD_OCRResult();
if(! FD_C_PPOCRv2WrapperPredict(
@@ -573,6 +654,12 @@ public class PPOCRv2 {
return ocr_detector_result;
}
/** \brief BatchPredict the input image and get OCR result.
*
* \param[in] images The list of input image data, comes from cv::imread(), is a 3-D array with layout HWC, BGR format.
*
* \return List<OCRResult>
*/
public List<OCRResult> BatchPredict(List<Mat> imgs){
FD_OneDimMat imgs_in = new FD_OneDimMat();
imgs_in.size = (nuint)imgs.Count;
@@ -601,6 +688,7 @@ public class PPOCRv2 {
return results_out;
}
/// Check whether model is initialized successfully
public bool Initialized() {
return FD_C_PPOCRv2WrapperInitialized(fd_ppocrv2_wrapper);
}
@@ -643,8 +731,16 @@ public class PPOCRv2 {
// PPOCRv3
/*! @brief PPOCRv3 is used to load PP-OCRv3 series models provided by PaddleOCR.
*/
public class PPOCRv3 {
/** \brief Set up the detection model path, classification model path and recognition model path respectively.
*
* \param[in] det_model Path of detection model, e.g ./ch_PP-OCRv3_det_infer
* \param[in] cls_model Path of classification model, e.g ./ch_ppocr_mobile_v2.0_cls_infer
* \param[in] rec_model Path of recognition model, e.g ./ch_PP-OCRv3_rec_infer
*/
public PPOCRv3(DBDetector ppocrv3, Classifier classifier,
Recognizer recognizer) {
fd_ppocrv3_wrapper = FD_C_CreatePPOCRv3Wrapper(
@@ -662,6 +758,12 @@ public class PPOCRv3 {
return "PPOCRv3";
}
/** \brief Predict the input image and get OCR result.
*
* \param[in] im The input image data, comes from cv::imread(), is a 3-D array with layout HWC, BGR format.
*
* \return OCRResult
*/
public OCRResult Predict(Mat img) {
FD_OCRResult fd_ocr_result = new FD_OCRResult();
if(! FD_C_PPOCRv3WrapperPredict(
@@ -675,6 +777,12 @@ public class PPOCRv3 {
return ocr_detector_result;
}
/** \brief BatchPredict the input image and get OCR result.
*
* \param[in] images The list of input image data, comes from cv::imread(), is a 3-D array with layout HWC, BGR format.
*
* \return List<OCRResult>
*/
public List<OCRResult> BatchPredict(List<Mat> imgs){
FD_OneDimMat imgs_in = new FD_OneDimMat();
imgs_in.size = (nuint)imgs.Count;
@@ -703,6 +811,7 @@ public class PPOCRv3 {
return results_out;
}
/// Check whether model is initialized successfully
public bool Initialized() {
return FD_C_PPOCRv3WrapperInitialized(fd_ppocrv3_wrapper);
}