150 lines
4.5 KiB
C#
150 lines
4.5 KiB
C#
#if !UNITY_WSA_10_0
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using OpenCVForUnity.CoreModule;
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using OpenCVForUnity.UtilsModule;
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using System;
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using System.Collections.Generic;
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using System.Runtime.InteropServices;
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namespace OpenCVForUnity.DnnModule
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{
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// C++: class SegmentationModel
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/**
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* This class represents high-level API for segmentation models
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*
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* SegmentationModel allows to set params for preprocessing input image.
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* SegmentationModel creates net from file with trained weights and config,
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* sets preprocessing input, runs forward pass and returns the class prediction for each pixel.
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*/
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public class SegmentationModel : Model
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{
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protected override void Dispose(bool disposing)
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{
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try
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{
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if (disposing)
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{
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}
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if (IsEnabledDispose)
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{
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if (nativeObj != IntPtr.Zero)
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dnn_SegmentationModel_delete(nativeObj);
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nativeObj = IntPtr.Zero;
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}
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}
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finally
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{
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base.Dispose(disposing);
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}
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}
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protected internal SegmentationModel(IntPtr addr) : base(addr) { }
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// internal usage only
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public static new SegmentationModel __fromPtr__(IntPtr addr) { return new SegmentationModel(addr); }
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//
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// C++: cv::dnn::SegmentationModel::SegmentationModel(String model, String config = "")
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//
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/**
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* Create segmentation model from network represented in one of the supported formats.
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* An order of {code model} and {code config} arguments does not matter.
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* param model Binary file contains trained weights.
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* param config Text file contains network configuration.
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*/
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public SegmentationModel(string model, string config) :
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base(DisposableObject.ThrowIfNullIntPtr(dnn_SegmentationModel_SegmentationModel_10(model, config)))
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{
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}
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/**
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* Create segmentation model from network represented in one of the supported formats.
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* An order of {code model} and {code config} arguments does not matter.
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* param model Binary file contains trained weights.
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*/
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public SegmentationModel(string model) :
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base(DisposableObject.ThrowIfNullIntPtr(dnn_SegmentationModel_SegmentationModel_11(model)))
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{
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}
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//
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// C++: cv::dnn::SegmentationModel::SegmentationModel(Net network)
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//
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/**
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* Create model from deep learning network.
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* param network Net object.
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*/
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public SegmentationModel(Net network) :
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base(DisposableObject.ThrowIfNullIntPtr(dnn_SegmentationModel_SegmentationModel_12(network.nativeObj)))
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{
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}
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//
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// C++: void cv::dnn::SegmentationModel::segment(Mat frame, Mat& mask)
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//
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/**
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* Given the {code input} frame, create input blob, run net
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* param mask Allocated class prediction for each pixel
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* param frame automatically generated
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*/
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public void segment(Mat frame, Mat mask)
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{
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ThrowIfDisposed();
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if (frame != null) frame.ThrowIfDisposed();
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if (mask != null) mask.ThrowIfDisposed();
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dnn_SegmentationModel_segment_10(nativeObj, frame.nativeObj, mask.nativeObj);
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}
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#if (UNITY_IOS || UNITY_WEBGL) && !UNITY_EDITOR
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const string LIBNAME = "__Internal";
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#else
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const string LIBNAME = "opencvforunity";
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#endif
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// C++: cv::dnn::SegmentationModel::SegmentationModel(String model, String config = "")
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[DllImport(LIBNAME)]
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private static extern IntPtr dnn_SegmentationModel_SegmentationModel_10(string model, string config);
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[DllImport(LIBNAME)]
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private static extern IntPtr dnn_SegmentationModel_SegmentationModel_11(string model);
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// C++: cv::dnn::SegmentationModel::SegmentationModel(Net network)
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[DllImport(LIBNAME)]
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private static extern IntPtr dnn_SegmentationModel_SegmentationModel_12(IntPtr network_nativeObj);
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// C++: void cv::dnn::SegmentationModel::segment(Mat frame, Mat& mask)
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[DllImport(LIBNAME)]
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private static extern void dnn_SegmentationModel_segment_10(IntPtr nativeObj, IntPtr frame_nativeObj, IntPtr mask_nativeObj);
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// native support for java finalize()
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[DllImport(LIBNAME)]
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private static extern void dnn_SegmentationModel_delete(IntPtr nativeObj);
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}
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}
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#endif |