#if !UNITY_WSA_10_0 using OpenCVForUnity.CoreModule; using OpenCVForUnity.UtilsModule; using System; using System.Collections.Generic; using System.Runtime.InteropServices; namespace OpenCVForUnity.DnnModule { // C++: class Model /** * This class is presented high-level API for neural networks. * * Model allows to set params for preprocessing input image. * Model creates net from file with trained weights and config, * sets preprocessing input and runs forward pass. */ public class Model : DisposableOpenCVObject { protected override void Dispose(bool disposing) { try { if (disposing) { } if (IsEnabledDispose) { if (nativeObj != IntPtr.Zero) dnn_Model_delete(nativeObj); nativeObj = IntPtr.Zero; } } finally { base.Dispose(disposing); } } protected internal Model(IntPtr addr) : base(addr) { } public IntPtr getNativeObjAddr() { return nativeObj; } // internal usage only public static Model __fromPtr__(IntPtr addr) { return new Model(addr); } // // C++: cv::dnn::Model::Model(String model, String config = "") // /** * Create model from deep learning network represented in one of the supported formats. * An order of {code model} and {code config} arguments does not matter. * param model Binary file contains trained weights. * param config Text file contains network configuration. */ public Model(string model, string config) { nativeObj = DisposableObject.ThrowIfNullIntPtr(dnn_Model_Model_10(model, config)); } /** * Create model from deep learning network represented in one of the supported formats. * An order of {code model} and {code config} arguments does not matter. * param model Binary file contains trained weights. */ public Model(string model) { nativeObj = DisposableObject.ThrowIfNullIntPtr(dnn_Model_Model_11(model)); } // // C++: cv::dnn::Model::Model(Net network) // /** * Create model from deep learning network. * param network Net object. */ public Model(Net network) { if (network != null) network.ThrowIfDisposed(); nativeObj = DisposableObject.ThrowIfNullIntPtr(dnn_Model_Model_12(network.nativeObj)); } // // C++: Model cv::dnn::Model::setInputSize(Size size) // /** * Set input size for frame. * param size New input size. * Note: If shape of the new blob less than 0, then frame size not change. * return automatically generated */ public Model setInputSize(Size size) { ThrowIfDisposed(); return new Model(DisposableObject.ThrowIfNullIntPtr(dnn_Model_setInputSize_10(nativeObj, size.width, size.height))); } // // C++: Model cv::dnn::Model::setInputSize(int width, int height) // /** * * param width New input width. * param height New input height. * return automatically generated */ public Model setInputSize(int width, int height) { ThrowIfDisposed(); return new Model(DisposableObject.ThrowIfNullIntPtr(dnn_Model_setInputSize_11(nativeObj, width, height))); } // // C++: Model cv::dnn::Model::setInputMean(Scalar mean) // /** * Set mean value for frame. * param mean Scalar with mean values which are subtracted from channels. * return automatically generated */ public Model setInputMean(Scalar mean) { ThrowIfDisposed(); return new Model(DisposableObject.ThrowIfNullIntPtr(dnn_Model_setInputMean_10(nativeObj, mean.val[0], mean.val[1], mean.val[2], mean.val[3]))); } // // C++: Model cv::dnn::Model::setInputScale(Scalar scale) // /** * Set scalefactor value for frame. * param scale Multiplier for frame values. * return automatically generated */ public Model setInputScale(Scalar scale) { ThrowIfDisposed(); return new Model(DisposableObject.ThrowIfNullIntPtr(dnn_Model_setInputScale_10(nativeObj, scale.val[0], scale.val[1], scale.val[2], scale.val[3]))); } // // C++: Model cv::dnn::Model::setInputCrop(bool crop) // /** * Set flag crop for frame. * param crop Flag which indicates whether image will be cropped after resize or not. * return automatically generated */ public Model setInputCrop(bool crop) { ThrowIfDisposed(); return new Model(DisposableObject.ThrowIfNullIntPtr(dnn_Model_setInputCrop_10(nativeObj, crop))); } // // C++: Model cv::dnn::Model::setInputSwapRB(bool swapRB) // /** * Set flag swapRB for frame. * param swapRB Flag which indicates that swap first and last channels. * return automatically generated */ public Model setInputSwapRB(bool swapRB) { ThrowIfDisposed(); return new Model(DisposableObject.ThrowIfNullIntPtr(dnn_Model_setInputSwapRB_10(nativeObj, swapRB))); } // // C++: void cv::dnn::Model::setInputParams(double scale = 1.0, Size size = Size(), Scalar mean = Scalar(), bool swapRB = false, bool crop = false) // /** * Set preprocessing parameters for frame. * param size New input size. * param mean Scalar with mean values which are subtracted from channels. * param scale Multiplier for frame values. * param swapRB Flag which indicates that swap first and last channels. * param crop Flag which indicates whether image will be cropped after resize or not. * blob(n, c, y, x) = scale * resize( frame(y, x, c) ) - mean(c) ) */ public void setInputParams(double scale, Size size, Scalar mean, bool swapRB, bool crop) { ThrowIfDisposed(); dnn_Model_setInputParams_10(nativeObj, scale, size.width, size.height, mean.val[0], mean.val[1], mean.val[2], mean.val[3], swapRB, crop); } /** * Set preprocessing parameters for frame. * param size New input size. * param mean Scalar with mean values which are subtracted from channels. * param scale Multiplier for frame values. * param swapRB Flag which indicates that swap first and last channels. * blob(n, c, y, x) = scale * resize( frame(y, x, c) ) - mean(c) ) */ public void setInputParams(double scale, Size size, Scalar mean, bool swapRB) { ThrowIfDisposed(); dnn_Model_setInputParams_11(nativeObj, scale, size.width, size.height, mean.val[0], mean.val[1], mean.val[2], mean.val[3], swapRB); } /** * Set preprocessing parameters for frame. * param size New input size. * param mean Scalar with mean values which are subtracted from channels. * param scale Multiplier for frame values. * blob(n, c, y, x) = scale * resize( frame(y, x, c) ) - mean(c) ) */ public void setInputParams(double scale, Size size, Scalar mean) { ThrowIfDisposed(); dnn_Model_setInputParams_12(nativeObj, scale, size.width, size.height, mean.val[0], mean.val[1], mean.val[2], mean.val[3]); } /** * Set preprocessing parameters for frame. * param size New input size. * param scale Multiplier for frame values. * blob(n, c, y, x) = scale * resize( frame(y, x, c) ) - mean(c) ) */ public void setInputParams(double scale, Size size) { ThrowIfDisposed(); dnn_Model_setInputParams_13(nativeObj, scale, size.width, size.height); } /** * Set preprocessing parameters for frame. * param scale Multiplier for frame values. * blob(n, c, y, x) = scale * resize( frame(y, x, c) ) - mean(c) ) */ public void setInputParams(double scale) { ThrowIfDisposed(); dnn_Model_setInputParams_14(nativeObj, scale); } /** * Set preprocessing parameters for frame. * blob(n, c, y, x) = scale * resize( frame(y, x, c) ) - mean(c) ) */ public void setInputParams() { ThrowIfDisposed(); dnn_Model_setInputParams_15(nativeObj); } // // C++: void cv::dnn::Model::predict(Mat frame, vector_Mat& outs) // /** * Given the {code input} frame, create input blob, run net and return the output {code blobs}. * param outs Allocated output blobs, which will store results of the computation. * param frame automatically generated */ public void predict(Mat frame, List outs) { ThrowIfDisposed(); if (frame != null) frame.ThrowIfDisposed(); Mat outs_mat = new Mat(); dnn_Model_predict_10(nativeObj, frame.nativeObj, outs_mat.nativeObj); Converters.Mat_to_vector_Mat(outs_mat, outs); outs_mat.release(); } // // C++: Model cv::dnn::Model::setPreferableBackend(dnn_Backend backendId) // public Model setPreferableBackend(int backendId) { ThrowIfDisposed(); return new Model(DisposableObject.ThrowIfNullIntPtr(dnn_Model_setPreferableBackend_10(nativeObj, backendId))); } // // C++: Model cv::dnn::Model::setPreferableTarget(dnn_Target targetId) // public Model setPreferableTarget(int targetId) { ThrowIfDisposed(); return new Model(DisposableObject.ThrowIfNullIntPtr(dnn_Model_setPreferableTarget_10(nativeObj, targetId))); } #if (UNITY_IOS || UNITY_WEBGL) && !UNITY_EDITOR const string LIBNAME = "__Internal"; #else const string LIBNAME = "opencvforunity"; #endif // C++: cv::dnn::Model::Model(String model, String config = "") [DllImport(LIBNAME)] private static extern IntPtr dnn_Model_Model_10(string model, string config); [DllImport(LIBNAME)] private static extern IntPtr dnn_Model_Model_11(string model); // C++: cv::dnn::Model::Model(Net network) [DllImport(LIBNAME)] private static extern IntPtr dnn_Model_Model_12(IntPtr network_nativeObj); // C++: Model cv::dnn::Model::setInputSize(Size size) [DllImport(LIBNAME)] private static extern IntPtr dnn_Model_setInputSize_10(IntPtr nativeObj, double size_width, double size_height); // C++: Model cv::dnn::Model::setInputSize(int width, int height) [DllImport(LIBNAME)] private static extern IntPtr dnn_Model_setInputSize_11(IntPtr nativeObj, int width, int height); // C++: Model cv::dnn::Model::setInputMean(Scalar mean) [DllImport(LIBNAME)] private static extern IntPtr dnn_Model_setInputMean_10(IntPtr nativeObj, double mean_val0, double mean_val1, double mean_val2, double mean_val3); // C++: Model cv::dnn::Model::setInputScale(Scalar scale) [DllImport(LIBNAME)] private static extern IntPtr dnn_Model_setInputScale_10(IntPtr nativeObj, double scale_val0, double scale_val1, double scale_val2, double scale_val3); // C++: Model cv::dnn::Model::setInputCrop(bool crop) [DllImport(LIBNAME)] private static extern IntPtr dnn_Model_setInputCrop_10(IntPtr nativeObj, [MarshalAs(UnmanagedType.U1)] bool crop); // C++: Model cv::dnn::Model::setInputSwapRB(bool swapRB) [DllImport(LIBNAME)] private static extern IntPtr dnn_Model_setInputSwapRB_10(IntPtr nativeObj, [MarshalAs(UnmanagedType.U1)] bool swapRB); // C++: void cv::dnn::Model::setInputParams(double scale = 1.0, Size size = Size(), Scalar mean = Scalar(), bool swapRB = false, bool crop = false) [DllImport(LIBNAME)] private static extern void dnn_Model_setInputParams_10(IntPtr nativeObj, double scale, double size_width, double size_height, double mean_val0, double mean_val1, double mean_val2, double mean_val3, [MarshalAs(UnmanagedType.U1)] bool swapRB, [MarshalAs(UnmanagedType.U1)] bool crop); [DllImport(LIBNAME)] private static extern void dnn_Model_setInputParams_11(IntPtr nativeObj, double scale, double size_width, double size_height, double mean_val0, double mean_val1, double mean_val2, double mean_val3, [MarshalAs(UnmanagedType.U1)] bool swapRB); [DllImport(LIBNAME)] private static extern void dnn_Model_setInputParams_12(IntPtr nativeObj, double scale, double size_width, double size_height, double mean_val0, double mean_val1, double mean_val2, double mean_val3); [DllImport(LIBNAME)] private static extern void dnn_Model_setInputParams_13(IntPtr nativeObj, double scale, double size_width, double size_height); [DllImport(LIBNAME)] private static extern void dnn_Model_setInputParams_14(IntPtr nativeObj, double scale); [DllImport(LIBNAME)] private static extern void dnn_Model_setInputParams_15(IntPtr nativeObj); // C++: void cv::dnn::Model::predict(Mat frame, vector_Mat& outs) [DllImport(LIBNAME)] private static extern void dnn_Model_predict_10(IntPtr nativeObj, IntPtr frame_nativeObj, IntPtr outs_mat_nativeObj); // C++: Model cv::dnn::Model::setPreferableBackend(dnn_Backend backendId) [DllImport(LIBNAME)] private static extern IntPtr dnn_Model_setPreferableBackend_10(IntPtr nativeObj, int backendId); // C++: Model cv::dnn::Model::setPreferableTarget(dnn_Target targetId) [DllImport(LIBNAME)] private static extern IntPtr dnn_Model_setPreferableTarget_10(IntPtr nativeObj, int targetId); // native support for java finalize() [DllImport(LIBNAME)] private static extern void dnn_Model_delete(IntPtr nativeObj); } } #endif