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https://github.com/PaddlePaddle/FastDeploy.git
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[Doc] add doxygen docs for c sharp api (#1495)
add doxygen docs for c sharp api Co-authored-by: DefTruth <31974251+DefTruth@users.noreply.github.com>
This commit is contained in:
chenjian
@@ -23,9 +23,17 @@ namespace fastdeploy {
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namespace vision {
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namespace detection {
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// PPYOLOE
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/*! @brief PPYOLOE model
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*/
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public class PPYOLOE {
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/** \brief Set path of model file and configuration file, and the configuration of runtime
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*
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* \param[in] model_file Path of model file, e.g ppyoloe/model.pdmodel
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* \param[in] params_file Path of parameter file, e.g picodet/model.pdiparams, if the model format is ONNX, this parameter will be ignored
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* \param[in] config_file Path of configuration file for deployment, e.g picodet/infer_cfg.yml
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* \param[in] custom_option RuntimeOption for inference, the default will use cpu, and choose the backend defined in `valid_cpu_backends`
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* \param[in] model_format Model format of the loaded model, default is Paddle format
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*/
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public PPYOLOE(string model_file, string params_file, string config_file,
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RuntimeOption custom_option = null,
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ModelFormat model_format = ModelFormat.PADDLE) {
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@@ -39,6 +47,11 @@ public class PPYOLOE {
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~PPYOLOE() { FD_C_DestroyPPYOLOEWrapper(fd_ppyoloe_wrapper); }
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/** \brief Predict the detection result for an input image
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* \param[in] im The input image data, comes from cv::imread(), is a 3-D array with layout HWC, BGR format
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*
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* \return DetectionResult
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*/
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public DetectionResult Predict(Mat img) {
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FD_DetectionResult fd_detection_result = new FD_DetectionResult();
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if(! FD_C_PPYOLOEWrapperPredict(fd_ppyoloe_wrapper, img.CvPtr,
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@@ -53,6 +66,11 @@ public class PPYOLOE {
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return detection_result;
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}
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/** \brief Predict the detection result for an input image list
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* \param[in] im The input image list, all the elements come from cv::imread(), is a 3-D array with layout HWC, BGR format
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*
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* \return List<DetectionResult>
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*/
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public List<DetectionResult> BatchPredict(List<Mat> imgs){
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FD_OneDimMat imgs_in = new FD_OneDimMat();
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imgs_in.size = (nuint)imgs.Count;
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@@ -80,6 +98,7 @@ public class PPYOLOE {
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return results_out;
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}
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/// Check whether model is initialized successfully
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public bool Initialized() {
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return FD_C_PPYOLOEWrapperInitialized(fd_ppyoloe_wrapper);
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}
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@@ -129,6 +148,9 @@ public class PPYOLOE {
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}
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// PicoDet
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/*! @brief PicoDet model
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*/
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public class PicoDet {
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public PicoDet(string model_file, string params_file, string config_file,
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@@ -144,6 +166,12 @@ public class PicoDet {
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~PicoDet() { FD_C_DestroyPicoDetWrapper(fd_picodet_wrapper); }
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/** \brief Predict the detection result for an input image
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* \param[in] im The input image data, comes from cv::imread(), is a 3-D array with layout HWC, BGR format
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*
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* \return DetectionResult
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*/
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public DetectionResult Predict(Mat img) {
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FD_DetectionResult fd_detection_result = new FD_DetectionResult();
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if(! FD_C_PicoDetWrapperPredict(fd_picodet_wrapper, img.CvPtr,
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@@ -158,6 +186,12 @@ public class PicoDet {
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return detection_result;
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}
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/** \brief Predict the detection result for an input image list
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* \param[in] im The input image list, all the elements come from cv::imread(), is a 3-D array with layout HWC, BGR format
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*
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* \return List<DetectionResult>
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*/
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public List<DetectionResult> BatchPredict(List<Mat> imgs){
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FD_OneDimMat imgs_in = new FD_OneDimMat();
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imgs_in.size = (nuint)imgs.Count;
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@@ -185,6 +219,8 @@ public class PicoDet {
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return results_out;
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}
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/// Check whether model is initialized successfully
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public bool Initialized() {
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return FD_C_PicoDetWrapperInitialized(fd_picodet_wrapper);
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}
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@@ -236,6 +272,9 @@ public class PicoDet {
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// PPYOLO
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/*! @brief PPYOLO model
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*/
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public class PPYOLO {
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public PPYOLO(string model_file, string params_file, string config_file,
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@@ -251,6 +290,12 @@ public class PPYOLO {
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~PPYOLO() { FD_C_DestroyPPYOLOWrapper(fd_ppyolo_wrapper); }
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/** \brief Predict the detection result for an input image
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* \param[in] im The input image data, comes from cv::imread(), is a 3-D array with layout HWC, BGR format
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*
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* \return DetectionResult
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*/
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public DetectionResult Predict(Mat img) {
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FD_DetectionResult fd_detection_result = new FD_DetectionResult();
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if(! FD_C_PPYOLOWrapperPredict(fd_ppyolo_wrapper, img.CvPtr,
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@@ -265,6 +310,12 @@ public class PPYOLO {
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return detection_result;
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}
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/** \brief Predict the detection result for an input image list
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* \param[in] im The input image list, all the elements come from cv::imread(), is a 3-D array with layout HWC, BGR format
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*
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* \return List<DetectionResult>
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*/
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public List<DetectionResult> BatchPredict(List<Mat> imgs){
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FD_OneDimMat imgs_in = new FD_OneDimMat();
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imgs_in.size = (nuint)imgs.Count;
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@@ -292,6 +343,8 @@ public class PPYOLO {
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return results_out;
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}
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/// Check whether model is initialized successfully
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public bool Initialized() {
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return FD_C_PPYOLOWrapperInitialized(fd_ppyolo_wrapper);
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}
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@@ -342,6 +395,9 @@ public class PPYOLO {
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// YOLOv3
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/*! @brief YOLOv3 model
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*/
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public class YOLOv3 {
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public YOLOv3(string model_file, string params_file, string config_file,
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@@ -357,6 +413,12 @@ public class YOLOv3 {
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~YOLOv3() { FD_C_DestroyYOLOv3Wrapper(fd_yolov3_wrapper); }
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/** \brief Predict the detection result for an input image
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* \param[in] im The input image data, comes from cv::imread(), is a 3-D array with layout HWC, BGR format
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*
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* \return DetectionResult
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*/
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public DetectionResult Predict(Mat img) {
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FD_DetectionResult fd_detection_result = new FD_DetectionResult();
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if(! FD_C_YOLOv3WrapperPredict(fd_yolov3_wrapper, img.CvPtr,
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@@ -371,6 +433,12 @@ public class YOLOv3 {
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return detection_result;
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}
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/** \brief Predict the detection result for an input image list
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* \param[in] im The input image list, all the elements come from cv::imread(), is a 3-D array with layout HWC, BGR format
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*
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* \return List<DetectionResult>
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*/
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public List<DetectionResult> BatchPredict(List<Mat> imgs){
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FD_OneDimMat imgs_in = new FD_OneDimMat();
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imgs_in.size = (nuint)imgs.Count;
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@@ -398,6 +466,8 @@ public class YOLOv3 {
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return results_out;
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}
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/// Check whether model is initialized successfully
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public bool Initialized() {
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return FD_C_YOLOv3WrapperInitialized(fd_yolov3_wrapper);
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}
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@@ -448,6 +518,9 @@ public class YOLOv3 {
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// PaddleYOLOX
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/*! @brief PaddleYOLOX model
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*/
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public class PaddleYOLOX {
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public PaddleYOLOX(string model_file, string params_file, string config_file,
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@@ -463,6 +536,12 @@ public class PaddleYOLOX {
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~PaddleYOLOX() { FD_C_DestroyPaddleYOLOXWrapper(fd_paddleyolox_wrapper); }
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/** \brief Predict the detection result for an input image
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* \param[in] im The input image data, comes from cv::imread(), is a 3-D array with layout HWC, BGR format
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*
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* \return DetectionResult
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*/
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public DetectionResult Predict(Mat img) {
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FD_DetectionResult fd_detection_result = new FD_DetectionResult();
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if(! FD_C_PaddleYOLOXWrapperPredict(fd_paddleyolox_wrapper, img.CvPtr,
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@@ -477,6 +556,12 @@ public class PaddleYOLOX {
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return detection_result;
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}
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/** \brief Predict the detection result for an input image list
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* \param[in] im The input image list, all the elements come from cv::imread(), is a 3-D array with layout HWC, BGR format
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*
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* \return List<DetectionResult>
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*/
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public List<DetectionResult> BatchPredict(List<Mat> imgs){
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FD_OneDimMat imgs_in = new FD_OneDimMat();
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imgs_in.size = (nuint)imgs.Count;
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@@ -504,6 +589,8 @@ public class PaddleYOLOX {
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return results_out;
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}
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/// Check whether model is initialized successfully
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public bool Initialized() {
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return FD_C_PaddleYOLOXWrapperInitialized(fd_paddleyolox_wrapper);
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}
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@@ -554,6 +641,9 @@ public class PaddleYOLOX {
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// FasterRCNN
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/*! @brief FasterRCNN model
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*/
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public class FasterRCNN {
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public FasterRCNN(string model_file, string params_file, string config_file,
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@@ -569,6 +659,12 @@ public class FasterRCNN {
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~FasterRCNN() { FD_C_DestroyFasterRCNNWrapper(fd_fasterrcnn_wrapper); }
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/** \brief Predict the detection result for an input image
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* \param[in] im The input image data, comes from cv::imread(), is a 3-D array with layout HWC, BGR format
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*
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* \return DetectionResult
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*/
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public DetectionResult Predict(Mat img) {
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FD_DetectionResult fd_detection_result = new FD_DetectionResult();
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if(! FD_C_FasterRCNNWrapperPredict(fd_fasterrcnn_wrapper, img.CvPtr,
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@@ -583,6 +679,12 @@ public class FasterRCNN {
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return detection_result;
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}
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/** \brief Predict the detection result for an input image list
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* \param[in] im The input image list, all the elements come from cv::imread(), is a 3-D array with layout HWC, BGR format
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*
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* \return List<DetectionResult>
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*/
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public List<DetectionResult> BatchPredict(List<Mat> imgs){
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FD_OneDimMat imgs_in = new FD_OneDimMat();
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imgs_in.size = (nuint)imgs.Count;
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@@ -610,6 +712,8 @@ public class FasterRCNN {
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return results_out;
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}
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/// Check whether model is initialized successfully
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public bool Initialized() {
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return FD_C_FasterRCNNWrapperInitialized(fd_fasterrcnn_wrapper);
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}
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@@ -660,6 +764,9 @@ public class FasterRCNN {
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// MaskRCNN
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/*! @brief MaskRCNN model
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*/
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public class MaskRCNN {
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public MaskRCNN(string model_file, string params_file, string config_file,
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@@ -675,6 +782,12 @@ public class MaskRCNN {
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~MaskRCNN() { FD_C_DestroyMaskRCNNWrapper(fd_maskrcnn_wrapper); }
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/** \brief Predict the detection result for an input image
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* \param[in] im The input image data, comes from cv::imread(), is a 3-D array with layout HWC, BGR format
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*
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* \return DetectionResult
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*/
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public DetectionResult Predict(Mat img) {
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FD_DetectionResult fd_detection_result = new FD_DetectionResult();
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if(! FD_C_MaskRCNNWrapperPredict(fd_maskrcnn_wrapper, img.CvPtr,
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@@ -689,6 +802,12 @@ public class MaskRCNN {
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return detection_result;
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}
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/** \brief Predict the detection result for an input image list
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* \param[in] im The input image list, all the elements come from cv::imread(), is a 3-D array with layout HWC, BGR format
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*
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* \return List<DetectionResult>
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*/
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public List<DetectionResult> BatchPredict(List<Mat> imgs){
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FD_OneDimMat imgs_in = new FD_OneDimMat();
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imgs_in.size = (nuint)imgs.Count;
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@@ -716,6 +835,8 @@ public class MaskRCNN {
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return results_out;
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}
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/// Check whether model is initialized successfully
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public bool Initialized() {
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return FD_C_MaskRCNNWrapperInitialized(fd_maskrcnn_wrapper);
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}
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@@ -766,6 +887,9 @@ public class MaskRCNN {
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// SSD
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/*! @brief SSD model
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*/
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public class SSD {
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public SSD(string model_file, string params_file, string config_file,
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@@ -781,6 +905,12 @@ public class SSD {
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~SSD() { FD_C_DestroySSDWrapper(fd_ssd_wrapper); }
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/** \brief Predict the detection result for an input image
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* \param[in] im The input image data, comes from cv::imread(), is a 3-D array with layout HWC, BGR format
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*
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* \return DetectionResult
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*/
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public DetectionResult Predict(Mat img) {
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FD_DetectionResult fd_detection_result = new FD_DetectionResult();
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if(! FD_C_SSDWrapperPredict(fd_ssd_wrapper, img.CvPtr,
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@@ -795,6 +925,12 @@ public class SSD {
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return detection_result;
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}
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/** \brief Predict the detection result for an input image list
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* \param[in] im The input image list, all the elements come from cv::imread(), is a 3-D array with layout HWC, BGR format
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*
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* \return List<DetectionResult>
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*/
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public List<DetectionResult> BatchPredict(List<Mat> imgs){
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FD_OneDimMat imgs_in = new FD_OneDimMat();
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imgs_in.size = (nuint)imgs.Count;
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@@ -822,6 +958,8 @@ public class SSD {
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return results_out;
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}
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/// Check whether model is initialized successfully
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public bool Initialized() {
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return FD_C_SSDWrapperInitialized(fd_ssd_wrapper);
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}
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@@ -872,6 +1010,9 @@ public class SSD {
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// PaddleYOLOv5
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/*! @brief PaddleYOLOv5 model
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*/
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public class PaddleYOLOv5 {
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public PaddleYOLOv5(string model_file, string params_file, string config_file,
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@@ -887,6 +1028,12 @@ public class PaddleYOLOv5 {
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~PaddleYOLOv5() { FD_C_DestroyPaddleYOLOv5Wrapper(fd_paddleyolov5_wrapper); }
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/** \brief Predict the detection result for an input image
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* \param[in] im The input image data, comes from cv::imread(), is a 3-D array with layout HWC, BGR format
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*
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* \return DetectionResult
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*/
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public DetectionResult Predict(Mat img) {
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FD_DetectionResult fd_detection_result = new FD_DetectionResult();
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if(! FD_C_PaddleYOLOv5WrapperPredict(fd_paddleyolov5_wrapper, img.CvPtr,
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@@ -901,6 +1048,12 @@ public class PaddleYOLOv5 {
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return detection_result;
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}
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/** \brief Predict the detection result for an input image list
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* \param[in] im The input image list, all the elements come from cv::imread(), is a 3-D array with layout HWC, BGR format
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*
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* \return List<DetectionResult>
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*/
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public List<DetectionResult> BatchPredict(List<Mat> imgs){
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FD_OneDimMat imgs_in = new FD_OneDimMat();
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imgs_in.size = (nuint)imgs.Count;
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@@ -928,6 +1081,8 @@ public class PaddleYOLOv5 {
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return results_out;
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}
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/// Check whether model is initialized successfully
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public bool Initialized() {
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return FD_C_PaddleYOLOv5WrapperInitialized(fd_paddleyolov5_wrapper);
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}
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@@ -978,6 +1133,9 @@ public class PaddleYOLOv5 {
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// PaddleYOLOv6
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/*! @brief PaddleYOLOv6 model
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*/
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public class PaddleYOLOv6 {
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public PaddleYOLOv6(string model_file, string params_file, string config_file,
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@@ -993,6 +1151,12 @@ public class PaddleYOLOv6 {
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~PaddleYOLOv6() { FD_C_DestroyPaddleYOLOv6Wrapper(fd_paddleyolov6_wrapper); }
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/** \brief Predict the detection result for an input image
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* \param[in] im The input image data, comes from cv::imread(), is a 3-D array with layout HWC, BGR format
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*
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* \return DetectionResult
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*/
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public DetectionResult Predict(Mat img) {
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FD_DetectionResult fd_detection_result = new FD_DetectionResult();
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if(! FD_C_PaddleYOLOv6WrapperPredict(fd_paddleyolov6_wrapper, img.CvPtr,
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@@ -1007,6 +1171,12 @@ public class PaddleYOLOv6 {
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return detection_result;
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}
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/** \brief Predict the detection result for an input image list
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* \param[in] im The input image list, all the elements come from cv::imread(), is a 3-D array with layout HWC, BGR format
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*
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* \return List<DetectionResult>
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*/
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public List<DetectionResult> BatchPredict(List<Mat> imgs){
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FD_OneDimMat imgs_in = new FD_OneDimMat();
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imgs_in.size = (nuint)imgs.Count;
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@@ -1034,6 +1204,8 @@ public class PaddleYOLOv6 {
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||||
return results_out;
|
||||
}
|
||||
|
||||
|
||||
/// Check whether model is initialized successfully
|
||||
public bool Initialized() {
|
||||
return FD_C_PaddleYOLOv6WrapperInitialized(fd_paddleyolov6_wrapper);
|
||||
}
|
||||
@@ -1084,6 +1256,9 @@ public class PaddleYOLOv6 {
|
||||
|
||||
// PaddleYOLOv7
|
||||
|
||||
|
||||
/*! @brief PaddleYOLOv7 model
|
||||
*/
|
||||
public class PaddleYOLOv7 {
|
||||
|
||||
public PaddleYOLOv7(string model_file, string params_file, string config_file,
|
||||
@@ -1099,6 +1274,12 @@ public class PaddleYOLOv7 {
|
||||
|
||||
~PaddleYOLOv7() { FD_C_DestroyPaddleYOLOv7Wrapper(fd_paddleyolov7_wrapper); }
|
||||
|
||||
|
||||
/** \brief Predict the detection 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 DetectionResult
|
||||
*/
|
||||
public DetectionResult Predict(Mat img) {
|
||||
FD_DetectionResult fd_detection_result = new FD_DetectionResult();
|
||||
if(! FD_C_PaddleYOLOv7WrapperPredict(fd_paddleyolov7_wrapper, img.CvPtr,
|
||||
@@ -1113,6 +1294,12 @@ public class PaddleYOLOv7 {
|
||||
return detection_result;
|
||||
}
|
||||
|
||||
|
||||
/** \brief Predict the detection result for an input image list
|
||||
* \param[in] im The input image list, all the elements come from cv::imread(), is a 3-D array with layout HWC, BGR format
|
||||
*
|
||||
* \return List<DetectionResult>
|
||||
*/
|
||||
public List<DetectionResult> BatchPredict(List<Mat> imgs){
|
||||
FD_OneDimMat imgs_in = new FD_OneDimMat();
|
||||
imgs_in.size = (nuint)imgs.Count;
|
||||
@@ -1140,6 +1327,8 @@ public class PaddleYOLOv7 {
|
||||
return results_out;
|
||||
}
|
||||
|
||||
|
||||
/// Check whether model is initialized successfully
|
||||
public bool Initialized() {
|
||||
return FD_C_PaddleYOLOv7WrapperInitialized(fd_paddleyolov7_wrapper);
|
||||
}
|
||||
@@ -1190,6 +1379,9 @@ public class PaddleYOLOv7 {
|
||||
|
||||
// PaddleYOLOv8
|
||||
|
||||
|
||||
/*! @brief PaddleYOLOv8 model
|
||||
*/
|
||||
public class PaddleYOLOv8 {
|
||||
|
||||
public PaddleYOLOv8(string model_file, string params_file, string config_file,
|
||||
@@ -1205,6 +1397,12 @@ public class PaddleYOLOv8 {
|
||||
|
||||
~PaddleYOLOv8() { FD_C_DestroyPaddleYOLOv8Wrapper(fd_paddleyolov8_wrapper); }
|
||||
|
||||
|
||||
/** \brief Predict the detection 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 DetectionResult
|
||||
*/
|
||||
public DetectionResult Predict(Mat img) {
|
||||
FD_DetectionResult fd_detection_result = new FD_DetectionResult();
|
||||
if(! FD_C_PaddleYOLOv8WrapperPredict(fd_paddleyolov8_wrapper, img.CvPtr,
|
||||
@@ -1219,6 +1417,12 @@ public class PaddleYOLOv8 {
|
||||
return detection_result;
|
||||
}
|
||||
|
||||
|
||||
/** \brief Predict the detection result for an input image list
|
||||
* \param[in] im The input image list, all the elements come from cv::imread(), is a 3-D array with layout HWC, BGR format
|
||||
*
|
||||
* \return List<DetectionResult>
|
||||
*/
|
||||
public List<DetectionResult> BatchPredict(List<Mat> imgs){
|
||||
FD_OneDimMat imgs_in = new FD_OneDimMat();
|
||||
imgs_in.size = (nuint)imgs.Count;
|
||||
@@ -1246,6 +1450,8 @@ public class PaddleYOLOv8 {
|
||||
return results_out;
|
||||
}
|
||||
|
||||
|
||||
/// Check whether model is initialized successfully
|
||||
public bool Initialized() {
|
||||
return FD_C_PaddleYOLOv8WrapperInitialized(fd_paddleyolov8_wrapper);
|
||||
}
|
||||
@@ -1296,6 +1502,9 @@ public class PaddleYOLOv8 {
|
||||
|
||||
// RTMDet
|
||||
|
||||
|
||||
/*! @brief RTMDet model
|
||||
*/
|
||||
public class RTMDet {
|
||||
|
||||
public RTMDet(string model_file, string params_file, string config_file,
|
||||
@@ -1311,6 +1520,12 @@ public class RTMDet {
|
||||
|
||||
~RTMDet() { FD_C_DestroyRTMDetWrapper(fd_rtmdet_wrapper); }
|
||||
|
||||
|
||||
/** \brief Predict the detection 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 DetectionResult
|
||||
*/
|
||||
public DetectionResult Predict(Mat img) {
|
||||
FD_DetectionResult fd_detection_result = new FD_DetectionResult();
|
||||
if(! FD_C_RTMDetWrapperPredict(fd_rtmdet_wrapper, img.CvPtr,
|
||||
@@ -1325,6 +1540,12 @@ public class RTMDet {
|
||||
return detection_result;
|
||||
}
|
||||
|
||||
|
||||
/** \brief Predict the detection result for an input image list
|
||||
* \param[in] im The input image list, all the elements come from cv::imread(), is a 3-D array with layout HWC, BGR format
|
||||
*
|
||||
* \return List<DetectionResult>
|
||||
*/
|
||||
public List<DetectionResult> BatchPredict(List<Mat> imgs){
|
||||
FD_OneDimMat imgs_in = new FD_OneDimMat();
|
||||
imgs_in.size = (nuint)imgs.Count;
|
||||
@@ -1352,6 +1573,8 @@ public class RTMDet {
|
||||
return results_out;
|
||||
}
|
||||
|
||||
|
||||
/// Check whether model is initialized successfully
|
||||
public bool Initialized() {
|
||||
return FD_C_RTMDetWrapperInitialized(fd_rtmdet_wrapper);
|
||||
}
|
||||
@@ -1402,6 +1625,9 @@ public class RTMDet {
|
||||
|
||||
// CascadeRCNN
|
||||
|
||||
|
||||
/*! @brief CascadeRCNN model
|
||||
*/
|
||||
public class CascadeRCNN {
|
||||
|
||||
public CascadeRCNN(string model_file, string params_file, string config_file,
|
||||
@@ -1417,6 +1643,12 @@ public class CascadeRCNN {
|
||||
|
||||
~CascadeRCNN() { FD_C_DestroyCascadeRCNNWrapper(fd_cascadercnn_wrapper); }
|
||||
|
||||
|
||||
/** \brief Predict the detection 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 DetectionResult
|
||||
*/
|
||||
public DetectionResult Predict(Mat img) {
|
||||
FD_DetectionResult fd_detection_result = new FD_DetectionResult();
|
||||
if(! FD_C_CascadeRCNNWrapperPredict(fd_cascadercnn_wrapper, img.CvPtr,
|
||||
@@ -1431,6 +1663,12 @@ public class CascadeRCNN {
|
||||
return detection_result;
|
||||
}
|
||||
|
||||
|
||||
/** \brief Predict the detection result for an input image list
|
||||
* \param[in] im The input image list, all the elements come from cv::imread(), is a 3-D array with layout HWC, BGR format
|
||||
*
|
||||
* \return List<DetectionResult>
|
||||
*/
|
||||
public List<DetectionResult> BatchPredict(List<Mat> imgs){
|
||||
FD_OneDimMat imgs_in = new FD_OneDimMat();
|
||||
imgs_in.size = (nuint)imgs.Count;
|
||||
@@ -1458,6 +1696,8 @@ public class CascadeRCNN {
|
||||
return results_out;
|
||||
}
|
||||
|
||||
|
||||
/// Check whether model is initialized successfully
|
||||
public bool Initialized() {
|
||||
return FD_C_CascadeRCNNWrapperInitialized(fd_cascadercnn_wrapper);
|
||||
}
|
||||
@@ -1508,6 +1748,9 @@ public class CascadeRCNN {
|
||||
|
||||
// PSSDet
|
||||
|
||||
|
||||
/*! @brief PSSDet model
|
||||
*/
|
||||
public class PSSDet {
|
||||
|
||||
public PSSDet(string model_file, string params_file, string config_file,
|
||||
@@ -1523,6 +1766,12 @@ public class PSSDet {
|
||||
|
||||
~PSSDet() { FD_C_DestroyPSSDetWrapper(fd_pssdet_wrapper); }
|
||||
|
||||
|
||||
/** \brief Predict the detection 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 DetectionResult
|
||||
*/
|
||||
public DetectionResult Predict(Mat img) {
|
||||
FD_DetectionResult fd_detection_result = new FD_DetectionResult();
|
||||
if(! FD_C_PSSDetWrapperPredict(fd_pssdet_wrapper, img.CvPtr,
|
||||
@@ -1537,6 +1786,12 @@ public class PSSDet {
|
||||
return detection_result;
|
||||
}
|
||||
|
||||
|
||||
/** \brief Predict the detection result for an input image list
|
||||
* \param[in] im The input image list, all the elements come from cv::imread(), is a 3-D array with layout HWC, BGR format
|
||||
*
|
||||
* \return List<DetectionResult>
|
||||
*/
|
||||
public List<DetectionResult> BatchPredict(List<Mat> imgs){
|
||||
FD_OneDimMat imgs_in = new FD_OneDimMat();
|
||||
imgs_in.size = (nuint)imgs.Count;
|
||||
@@ -1564,6 +1819,8 @@ public class PSSDet {
|
||||
return results_out;
|
||||
}
|
||||
|
||||
|
||||
/// Check whether model is initialized successfully
|
||||
public bool Initialized() {
|
||||
return FD_C_PSSDetWrapperInitialized(fd_pssdet_wrapper);
|
||||
}
|
||||
@@ -1614,6 +1871,9 @@ public class PSSDet {
|
||||
|
||||
// RetinaNet
|
||||
|
||||
|
||||
/*! @brief RetinaNet model
|
||||
*/
|
||||
public class RetinaNet {
|
||||
|
||||
public RetinaNet(string model_file, string params_file, string config_file,
|
||||
@@ -1629,6 +1889,12 @@ public class RetinaNet {
|
||||
|
||||
~RetinaNet() { FD_C_DestroyRetinaNetWrapper(fd_retinanet_wrapper); }
|
||||
|
||||
|
||||
/** \brief Predict the detection 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 DetectionResult
|
||||
*/
|
||||
public DetectionResult Predict(Mat img) {
|
||||
FD_DetectionResult fd_detection_result = new FD_DetectionResult();
|
||||
if(! FD_C_RetinaNetWrapperPredict(fd_retinanet_wrapper, img.CvPtr,
|
||||
@@ -1643,6 +1909,12 @@ public class RetinaNet {
|
||||
return detection_result;
|
||||
}
|
||||
|
||||
|
||||
/** \brief Predict the detection result for an input image list
|
||||
* \param[in] im The input image list, all the elements come from cv::imread(), is a 3-D array with layout HWC, BGR format
|
||||
*
|
||||
* \return List<DetectionResult>
|
||||
*/
|
||||
public List<DetectionResult> BatchPredict(List<Mat> imgs){
|
||||
FD_OneDimMat imgs_in = new FD_OneDimMat();
|
||||
imgs_in.size = (nuint)imgs.Count;
|
||||
@@ -1670,6 +1942,8 @@ public class RetinaNet {
|
||||
return results_out;
|
||||
}
|
||||
|
||||
|
||||
/// Check whether model is initialized successfully
|
||||
public bool Initialized() {
|
||||
return FD_C_RetinaNetWrapperInitialized(fd_retinanet_wrapper);
|
||||
}
|
||||
@@ -1720,6 +1994,9 @@ public class RetinaNet {
|
||||
|
||||
// FCOS
|
||||
|
||||
|
||||
/*! @brief FCOS model
|
||||
*/
|
||||
public class FCOS {
|
||||
|
||||
public FCOS(string model_file, string params_file, string config_file,
|
||||
@@ -1735,6 +2012,12 @@ public class FCOS {
|
||||
|
||||
~FCOS() { FD_C_DestroyFCOSWrapper(fd_fcos_wrapper); }
|
||||
|
||||
|
||||
/** \brief Predict the detection 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 DetectionResult
|
||||
*/
|
||||
public DetectionResult Predict(Mat img) {
|
||||
FD_DetectionResult fd_detection_result = new FD_DetectionResult();
|
||||
if(! FD_C_FCOSWrapperPredict(fd_fcos_wrapper, img.CvPtr,
|
||||
@@ -1749,6 +2032,12 @@ public class FCOS {
|
||||
return detection_result;
|
||||
}
|
||||
|
||||
|
||||
/** \brief Predict the detection result for an input image list
|
||||
* \param[in] im The input image list, all the elements come from cv::imread(), is a 3-D array with layout HWC, BGR format
|
||||
*
|
||||
* \return List<DetectionResult>
|
||||
*/
|
||||
public List<DetectionResult> BatchPredict(List<Mat> imgs){
|
||||
FD_OneDimMat imgs_in = new FD_OneDimMat();
|
||||
imgs_in.size = (nuint)imgs.Count;
|
||||
@@ -1776,6 +2065,8 @@ public class FCOS {
|
||||
return results_out;
|
||||
}
|
||||
|
||||
|
||||
/// Check whether model is initialized successfully
|
||||
public bool Initialized() {
|
||||
return FD_C_FCOSWrapperInitialized(fd_fcos_wrapper);
|
||||
}
|
||||
@@ -1826,6 +2117,9 @@ public class FCOS {
|
||||
|
||||
// TTFNet
|
||||
|
||||
|
||||
/*! @brief TTFNet model
|
||||
*/
|
||||
public class TTFNet {
|
||||
|
||||
public TTFNet(string model_file, string params_file, string config_file,
|
||||
@@ -1841,6 +2135,12 @@ public class TTFNet {
|
||||
|
||||
~TTFNet() { FD_C_DestroyTTFNetWrapper(fd_ttfnet_wrapper); }
|
||||
|
||||
|
||||
/** \brief Predict the detection 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 DetectionResult
|
||||
*/
|
||||
public DetectionResult Predict(Mat img) {
|
||||
FD_DetectionResult fd_detection_result = new FD_DetectionResult();
|
||||
if(! FD_C_TTFNetWrapperPredict(fd_ttfnet_wrapper, img.CvPtr,
|
||||
@@ -1855,6 +2155,12 @@ public class TTFNet {
|
||||
return detection_result;
|
||||
}
|
||||
|
||||
|
||||
/** \brief Predict the detection result for an input image list
|
||||
* \param[in] im The input image list, all the elements come from cv::imread(), is a 3-D array with layout HWC, BGR format
|
||||
*
|
||||
* \return List<DetectionResult>
|
||||
*/
|
||||
public List<DetectionResult> BatchPredict(List<Mat> imgs){
|
||||
FD_OneDimMat imgs_in = new FD_OneDimMat();
|
||||
imgs_in.size = (nuint)imgs.Count;
|
||||
@@ -1882,6 +2188,8 @@ public class TTFNet {
|
||||
return results_out;
|
||||
}
|
||||
|
||||
|
||||
/// Check whether model is initialized successfully
|
||||
public bool Initialized() {
|
||||
return FD_C_TTFNetWrapperInitialized(fd_ttfnet_wrapper);
|
||||
}
|
||||
@@ -1932,6 +2240,9 @@ public class TTFNet {
|
||||
|
||||
// TOOD
|
||||
|
||||
|
||||
/*! @brief TOOD model
|
||||
*/
|
||||
public class TOOD {
|
||||
|
||||
public TOOD(string model_file, string params_file, string config_file,
|
||||
@@ -1947,6 +2258,12 @@ public class TOOD {
|
||||
|
||||
~TOOD() { FD_C_DestroyTOODWrapper(fd_tood_wrapper); }
|
||||
|
||||
|
||||
/** \brief Predict the detection 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 DetectionResult
|
||||
*/
|
||||
public DetectionResult Predict(Mat img) {
|
||||
FD_DetectionResult fd_detection_result = new FD_DetectionResult();
|
||||
if(! FD_C_TOODWrapperPredict(fd_tood_wrapper, img.CvPtr,
|
||||
@@ -1961,6 +2278,12 @@ public class TOOD {
|
||||
return detection_result;
|
||||
}
|
||||
|
||||
|
||||
/** \brief Predict the detection result for an input image list
|
||||
* \param[in] im The input image list, all the elements come from cv::imread(), is a 3-D array with layout HWC, BGR format
|
||||
*
|
||||
* \return List<DetectionResult>
|
||||
*/
|
||||
public List<DetectionResult> BatchPredict(List<Mat> imgs){
|
||||
FD_OneDimMat imgs_in = new FD_OneDimMat();
|
||||
imgs_in.size = (nuint)imgs.Count;
|
||||
@@ -1988,6 +2311,8 @@ public class TOOD {
|
||||
return results_out;
|
||||
}
|
||||
|
||||
|
||||
/// Check whether model is initialized successfully
|
||||
public bool Initialized() {
|
||||
return FD_C_TOODWrapperInitialized(fd_tood_wrapper);
|
||||
}
|
||||
@@ -2038,6 +2363,9 @@ public class TOOD {
|
||||
|
||||
// GFL
|
||||
|
||||
|
||||
/*! @brief GFL model
|
||||
*/
|
||||
public class GFL {
|
||||
|
||||
public GFL(string model_file, string params_file, string config_file,
|
||||
@@ -2053,6 +2381,12 @@ public class GFL {
|
||||
|
||||
~GFL() { FD_C_DestroyGFLWrapper(fd_gfl_wrapper); }
|
||||
|
||||
|
||||
/** \brief Predict the detection 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 DetectionResult
|
||||
*/
|
||||
public DetectionResult Predict(Mat img) {
|
||||
FD_DetectionResult fd_detection_result = new FD_DetectionResult();
|
||||
if(! FD_C_GFLWrapperPredict(fd_gfl_wrapper, img.CvPtr,
|
||||
@@ -2067,6 +2401,12 @@ public class GFL {
|
||||
return detection_result;
|
||||
}
|
||||
|
||||
|
||||
/** \brief Predict the detection result for an input image list
|
||||
* \param[in] im The input image list, all the elements come from cv::imread(), is a 3-D array with layout HWC, BGR format
|
||||
*
|
||||
* \return List<DetectionResult>
|
||||
*/
|
||||
public List<DetectionResult> BatchPredict(List<Mat> imgs){
|
||||
FD_OneDimMat imgs_in = new FD_OneDimMat();
|
||||
imgs_in.size = (nuint)imgs.Count;
|
||||
@@ -2094,6 +2434,8 @@ public class GFL {
|
||||
return results_out;
|
||||
}
|
||||
|
||||
|
||||
/// Check whether model is initialized successfully
|
||||
public bool Initialized() {
|
||||
return FD_C_GFLWrapperInitialized(fd_gfl_wrapper);
|
||||
}
|
||||
|
||||
Reference in New Issue
Block a user