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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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25
csharp/fastdeploy/vision/segmentation/model.cs
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@@ -23,8 +23,18 @@ namespace fastdeploy {
namespace vision {
namespace segmentation {
/*! @brief PaddleSeg serials model object used when to load a PaddleSeg model exported by PaddleSeg repository
*/
public class PaddleSegModel {
/** \brief Set path of model file and configuration file, and the configuration of runtime
*
* \param[in] model_file Path of model file, e.g unet/model.pdmodel
* \param[in] params_file Path of parameter file, e.g unet/model.pdiparams, if the model format is ONNX, this parameter will be ignored
* \param[in] config_file Path of configuration file for deployment, e.g unet/deploy.yml
* \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 PaddleSegModel(string model_file, string params_file,
string config_file, RuntimeOption custom_option = null,
ModelFormat model_format = ModelFormat.PADDLE) {
@@ -40,11 +50,17 @@ public class PaddleSegModel {
FD_C_DestroyPaddleSegModelWrapper(fd_paddleseg_model_wrapper);
}
/// Get model's name
public string ModelName() {
return "PaddleSeg";
}
/** \brief DEPRECATED Predict the segmentation result for an input image
*
* \param[in] im The input image data, comes from cv::imread(), is a 3-D array with layout HWC, BGR format
*
* \return SegmentationResult
*/
public SegmentationResult Predict(Mat img) {
FD_SegmentationResult fd_segmentation_result = new FD_SegmentationResult();
if(! FD_C_PaddleSegModelWrapperPredict(
@@ -59,6 +75,12 @@ public class PaddleSegModel {
return segmentation_result;
}
/** \brief Predict the segmentation results for a batch of input images
*
* \param[in] imgs, The input image list, each element comes from cv::imread()
*
* \return List<SegmentationResult>
*/
public List<SegmentationResult> BatchPredict(List<Mat> imgs){
FD_OneDimMat imgs_in = new FD_OneDimMat();
imgs_in.size = (nuint)imgs.Count;
@@ -86,6 +108,7 @@ public class PaddleSegModel {
return results_out;
}
/// Check whether model is initialized successfully
public bool Initialized() {
return FD_C_PaddleSegModelWrapperInitialized(fd_paddleseg_model_wrapper);
}