Health/Assets/OpenCVForUnity/org/opencv/dnn/Dnn.cs

#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 Dnn


    public class Dnn
    {

        // C++: enum cv.dnn.Backend
        public const int DNN_BACKEND_DEFAULT = 0;
        public const int DNN_BACKEND_HALIDE = 0 + 1;
        public const int DNN_BACKEND_INFERENCE_ENGINE = 0 + 2;
        public const int DNN_BACKEND_OPENCV = 0 + 3;
        public const int DNN_BACKEND_VKCOM = 0 + 4;
        public const int DNN_BACKEND_CUDA = 0 + 5;
        public const int DNN_BACKEND_WEBNN = 0 + 6;
        public const int DNN_BACKEND_TIMVX = 0 + 7;
        public const int DNN_BACKEND_CANN = 0 + 8;
        // C++: enum cv.dnn.DataLayout
        public const int DNN_LAYOUT_UNKNOWN = 0;
        public const int DNN_LAYOUT_ND = 1;
        public const int DNN_LAYOUT_NCHW = 2;
        public const int DNN_LAYOUT_NCDHW = 3;
        public const int DNN_LAYOUT_NHWC = 4;
        public const int DNN_LAYOUT_NDHWC = 5;
        public const int DNN_LAYOUT_PLANAR = 6;
        // C++: enum cv.dnn.ImagePaddingMode
        public const int DNN_PMODE_NULL = 0;
        public const int DNN_PMODE_CROP_CENTER = 1;
        public const int DNN_PMODE_LETTERBOX = 2;
        // C++: enum cv.dnn.SoftNMSMethod
        public const int SoftNMSMethod_SOFTNMS_LINEAR = 1;
        public const int SoftNMSMethod_SOFTNMS_GAUSSIAN = 2;
        // C++: enum cv.dnn.Target
        public const int DNN_TARGET_CPU = 0;
        public const int DNN_TARGET_OPENCL = 0 + 1;
        public const int DNN_TARGET_OPENCL_FP16 = 0 + 2;
        public const int DNN_TARGET_MYRIAD = 0 + 3;
        public const int DNN_TARGET_VULKAN = 0 + 4;
        public const int DNN_TARGET_FPGA = 0 + 5;
        public const int DNN_TARGET_CUDA = 0 + 6;
        public const int DNN_TARGET_CUDA_FP16 = 0 + 7;
        public const int DNN_TARGET_HDDL = 0 + 8;
        public const int DNN_TARGET_NPU = 0 + 9;
        public const int DNN_TARGET_CPU_FP16 = 0 + 10;
        //
        // C++:  vector_Target cv::dnn::getAvailableTargets(dnn_Backend be)
        //

        public static List<int> getAvailableTargets(int be)
        {

            List<int> retVal = new List<int>();
            Mat retValMat = new Mat(DisposableObject.ThrowIfNullIntPtr(dnn_Dnn_getAvailableTargets_10(be)));
            Converters.Mat_to_vector_Target(retValMat, retVal);
            return retVal;
        }


        //
        // C++:  Net cv::dnn::readNetFromDarknet(String cfgFile, String darknetModel = String())
        //

        /**
         * Reads a network model stored in &lt;a href="https://pjreddie.com/darknet/"&gt;Darknet&lt;/a&gt; model files.
         * param cfgFile      path to the .cfg file with text description of the network architecture.
         * param darknetModel path to the .weights file with learned network.
         * return Network object that ready to do forward, throw an exception in failure cases.
         */
        public static Net readNetFromDarknet(string cfgFile, string darknetModel)
        {


            return new Net(DisposableObject.ThrowIfNullIntPtr(dnn_Dnn_readNetFromDarknet_10(cfgFile, darknetModel)));


        }

        /**
         * Reads a network model stored in &lt;a href="https://pjreddie.com/darknet/"&gt;Darknet&lt;/a&gt; model files.
         * param cfgFile      path to the .cfg file with text description of the network architecture.
         * return Network object that ready to do forward, throw an exception in failure cases.
         */
        public static Net readNetFromDarknet(string cfgFile)
        {


            return new Net(DisposableObject.ThrowIfNullIntPtr(dnn_Dnn_readNetFromDarknet_11(cfgFile)));


        }


        //
        // C++:  Net cv::dnn::readNetFromDarknet(vector_uchar bufferCfg, vector_uchar bufferModel = std::vector<uchar>())
        //

        /**
         * Reads a network model stored in &lt;a href="https://pjreddie.com/darknet/"&gt;Darknet&lt;/a&gt; model files.
         * param bufferCfg   A buffer contains a content of .cfg file with text description of the network architecture.
         * param bufferModel A buffer contains a content of .weights file with learned network.
         * return Net object.
         */
        public static Net readNetFromDarknet(MatOfByte bufferCfg, MatOfByte bufferModel)
        {
            if (bufferCfg != null) bufferCfg.ThrowIfDisposed();
            if (bufferModel != null) bufferModel.ThrowIfDisposed();
            Mat bufferCfg_mat = bufferCfg;
            Mat bufferModel_mat = bufferModel;
            return new Net(DisposableObject.ThrowIfNullIntPtr(dnn_Dnn_readNetFromDarknet_12(bufferCfg_mat.nativeObj, bufferModel_mat.nativeObj)));


        }

        /**
         * Reads a network model stored in &lt;a href="https://pjreddie.com/darknet/"&gt;Darknet&lt;/a&gt; model files.
         * param bufferCfg   A buffer contains a content of .cfg file with text description of the network architecture.
         * return Net object.
         */
        public static Net readNetFromDarknet(MatOfByte bufferCfg)
        {
            if (bufferCfg != null) bufferCfg.ThrowIfDisposed();
            Mat bufferCfg_mat = bufferCfg;
            return new Net(DisposableObject.ThrowIfNullIntPtr(dnn_Dnn_readNetFromDarknet_13(bufferCfg_mat.nativeObj)));


        }


        //
        // C++:  Net cv::dnn::readNetFromCaffe(String prototxt, String caffeModel = String())
        //

        /**
         * Reads a network model stored in &lt;a href="http://caffe.berkeleyvision.org"&gt;Caffe&lt;/a&gt; framework's format.
         * param prototxt   path to the .prototxt file with text description of the network architecture.
         * param caffeModel path to the .caffemodel file with learned network.
         * return Net object.
         */
        public static Net readNetFromCaffe(string prototxt, string caffeModel)
        {


            return new Net(DisposableObject.ThrowIfNullIntPtr(dnn_Dnn_readNetFromCaffe_10(prototxt, caffeModel)));


        }

        /**
         * Reads a network model stored in &lt;a href="http://caffe.berkeleyvision.org"&gt;Caffe&lt;/a&gt; framework's format.
         * param prototxt   path to the .prototxt file with text description of the network architecture.
         * return Net object.
         */
        public static Net readNetFromCaffe(string prototxt)
        {


            return new Net(DisposableObject.ThrowIfNullIntPtr(dnn_Dnn_readNetFromCaffe_11(prototxt)));


        }


        //
        // C++:  Net cv::dnn::readNetFromCaffe(vector_uchar bufferProto, vector_uchar bufferModel = std::vector<uchar>())
        //

        /**
         * Reads a network model stored in Caffe model in memory.
         * param bufferProto buffer containing the content of the .prototxt file
         * param bufferModel buffer containing the content of the .caffemodel file
         * return Net object.
         */
        public static Net readNetFromCaffe(MatOfByte bufferProto, MatOfByte bufferModel)
        {
            if (bufferProto != null) bufferProto.ThrowIfDisposed();
            if (bufferModel != null) bufferModel.ThrowIfDisposed();
            Mat bufferProto_mat = bufferProto;
            Mat bufferModel_mat = bufferModel;
            return new Net(DisposableObject.ThrowIfNullIntPtr(dnn_Dnn_readNetFromCaffe_12(bufferProto_mat.nativeObj, bufferModel_mat.nativeObj)));


        }

        /**
         * Reads a network model stored in Caffe model in memory.
         * param bufferProto buffer containing the content of the .prototxt file
         * return Net object.
         */
        public static Net readNetFromCaffe(MatOfByte bufferProto)
        {
            if (bufferProto != null) bufferProto.ThrowIfDisposed();
            Mat bufferProto_mat = bufferProto;
            return new Net(DisposableObject.ThrowIfNullIntPtr(dnn_Dnn_readNetFromCaffe_13(bufferProto_mat.nativeObj)));


        }


        //
        // C++:  Net cv::dnn::readNetFromTensorflow(String model, String config = String())
        //

        /**
         * Reads a network model stored in &lt;a href="https://www.tensorflow.org/"&gt;TensorFlow&lt;/a&gt; framework's format.
         * param model  path to the .pb file with binary protobuf description of the network architecture
         * param config path to the .pbtxt file that contains text graph definition in protobuf format.
         * Resulting Net object is built by text graph using weights from a binary one that
         * let us make it more flexible.
         * return Net object.
         */
        public static Net readNetFromTensorflow(string model, string config)
        {


            return new Net(DisposableObject.ThrowIfNullIntPtr(dnn_Dnn_readNetFromTensorflow_10(model, config)));


        }

        /**
         * Reads a network model stored in &lt;a href="https://www.tensorflow.org/"&gt;TensorFlow&lt;/a&gt; framework's format.
         * param model  path to the .pb file with binary protobuf description of the network architecture
         * Resulting Net object is built by text graph using weights from a binary one that
         * let us make it more flexible.
         * return Net object.
         */
        public static Net readNetFromTensorflow(string model)
        {


            return new Net(DisposableObject.ThrowIfNullIntPtr(dnn_Dnn_readNetFromTensorflow_11(model)));


        }


        //
        // C++:  Net cv::dnn::readNetFromTensorflow(vector_uchar bufferModel, vector_uchar bufferConfig = std::vector<uchar>())
        //

        /**
         * Reads a network model stored in &lt;a href="https://www.tensorflow.org/"&gt;TensorFlow&lt;/a&gt; framework's format.
         * param bufferModel buffer containing the content of the pb file
         * param bufferConfig buffer containing the content of the pbtxt file
         * return Net object.
         */
        public static Net readNetFromTensorflow(MatOfByte bufferModel, MatOfByte bufferConfig)
        {
            if (bufferModel != null) bufferModel.ThrowIfDisposed();
            if (bufferConfig != null) bufferConfig.ThrowIfDisposed();
            Mat bufferModel_mat = bufferModel;
            Mat bufferConfig_mat = bufferConfig;
            return new Net(DisposableObject.ThrowIfNullIntPtr(dnn_Dnn_readNetFromTensorflow_12(bufferModel_mat.nativeObj, bufferConfig_mat.nativeObj)));


        }

        /**
         * Reads a network model stored in &lt;a href="https://www.tensorflow.org/"&gt;TensorFlow&lt;/a&gt; framework's format.
         * param bufferModel buffer containing the content of the pb file
         * return Net object.
         */
        public static Net readNetFromTensorflow(MatOfByte bufferModel)
        {
            if (bufferModel != null) bufferModel.ThrowIfDisposed();
            Mat bufferModel_mat = bufferModel;
            return new Net(DisposableObject.ThrowIfNullIntPtr(dnn_Dnn_readNetFromTensorflow_13(bufferModel_mat.nativeObj)));


        }


        //
        // C++:  Net cv::dnn::readNetFromTFLite(String model)
        //

        /**
         * Reads a network model stored in &lt;a href="https://www.tensorflow.org/lite"&gt;TFLite&lt;/a&gt; framework's format.
         * param model  path to the .tflite file with binary flatbuffers description of the network architecture
         * return Net object.
         */
        public static Net readNetFromTFLite(string model)
        {


            return new Net(DisposableObject.ThrowIfNullIntPtr(dnn_Dnn_readNetFromTFLite_10(model)));


        }


        //
        // C++:  Net cv::dnn::readNetFromTFLite(vector_uchar bufferModel)
        //

        /**
         * Reads a network model stored in &lt;a href="https://www.tensorflow.org/lite"&gt;TFLite&lt;/a&gt; framework's format.
         * param bufferModel buffer containing the content of the tflite file
         * return Net object.
         */
        public static Net readNetFromTFLite(MatOfByte bufferModel)
        {
            if (bufferModel != null) bufferModel.ThrowIfDisposed();
            Mat bufferModel_mat = bufferModel;
            return new Net(DisposableObject.ThrowIfNullIntPtr(dnn_Dnn_readNetFromTFLite_11(bufferModel_mat.nativeObj)));


        }


        //
        // C++:  Net cv::dnn::readNetFromTorch(String model, bool isBinary = true, bool evaluate = true)
        //

        /**
         * Reads a network model stored in &lt;a href="http://torch.ch"&gt;Torch7&lt;/a&gt; framework's format.
         * param model    path to the file, dumped from Torch by using torch.save() function.
         * param isBinary specifies whether the network was serialized in ascii mode or binary.
         * param evaluate specifies testing phase of network. If true, it's similar to evaluate() method in Torch.
         * return Net object.
         *
         * <b>Note:</b> Ascii mode of Torch serializer is more preferable, because binary mode extensively use {code long} type of C language,
         * which has various bit-length on different systems.
         *
         * The loading file must contain serialized &lt;a href="https://github.com/torch/nn/blob/master/doc/module.md"&gt;nn.Module&lt;/a&gt; object
         * with importing network. Try to eliminate a custom objects from serialazing data to avoid importing errors.
         *
         * List of supported layers (i.e. object instances derived from Torch nn.Module class):
         * - nn.Sequential
         * - nn.Parallel
         * - nn.Concat
         * - nn.Linear
         * - nn.SpatialConvolution
         * - nn.SpatialMaxPooling, nn.SpatialAveragePooling
         * - nn.ReLU, nn.TanH, nn.Sigmoid
         * - nn.Reshape
         * - nn.SoftMax, nn.LogSoftMax
         *
         * Also some equivalents of these classes from cunn, cudnn, and fbcunn may be successfully imported.
         */
        public static Net readNetFromTorch(string model, bool isBinary, bool evaluate)
        {


            return new Net(DisposableObject.ThrowIfNullIntPtr(dnn_Dnn_readNetFromTorch_10(model, isBinary, evaluate)));


        }

        /**
         * Reads a network model stored in &lt;a href="http://torch.ch"&gt;Torch7&lt;/a&gt; framework's format.
         * param model    path to the file, dumped from Torch by using torch.save() function.
         * param isBinary specifies whether the network was serialized in ascii mode or binary.
         * return Net object.
         *
         * <b>Note:</b> Ascii mode of Torch serializer is more preferable, because binary mode extensively use {code long} type of C language,
         * which has various bit-length on different systems.
         *
         * The loading file must contain serialized &lt;a href="https://github.com/torch/nn/blob/master/doc/module.md"&gt;nn.Module&lt;/a&gt; object
         * with importing network. Try to eliminate a custom objects from serialazing data to avoid importing errors.
         *
         * List of supported layers (i.e. object instances derived from Torch nn.Module class):
         * - nn.Sequential
         * - nn.Parallel
         * - nn.Concat
         * - nn.Linear
         * - nn.SpatialConvolution
         * - nn.SpatialMaxPooling, nn.SpatialAveragePooling
         * - nn.ReLU, nn.TanH, nn.Sigmoid
         * - nn.Reshape
         * - nn.SoftMax, nn.LogSoftMax
         *
         * Also some equivalents of these classes from cunn, cudnn, and fbcunn may be successfully imported.
         */
        public static Net readNetFromTorch(string model, bool isBinary)
        {


            return new Net(DisposableObject.ThrowIfNullIntPtr(dnn_Dnn_readNetFromTorch_11(model, isBinary)));


        }

        /**
         * Reads a network model stored in &lt;a href="http://torch.ch"&gt;Torch7&lt;/a&gt; framework's format.
         * param model    path to the file, dumped from Torch by using torch.save() function.
         * return Net object.
         *
         * <b>Note:</b> Ascii mode of Torch serializer is more preferable, because binary mode extensively use {code long} type of C language,
         * which has various bit-length on different systems.
         *
         * The loading file must contain serialized &lt;a href="https://github.com/torch/nn/blob/master/doc/module.md"&gt;nn.Module&lt;/a&gt; object
         * with importing network. Try to eliminate a custom objects from serialazing data to avoid importing errors.
         *
         * List of supported layers (i.e. object instances derived from Torch nn.Module class):
         * - nn.Sequential
         * - nn.Parallel
         * - nn.Concat
         * - nn.Linear
         * - nn.SpatialConvolution
         * - nn.SpatialMaxPooling, nn.SpatialAveragePooling
         * - nn.ReLU, nn.TanH, nn.Sigmoid
         * - nn.Reshape
         * - nn.SoftMax, nn.LogSoftMax
         *
         * Also some equivalents of these classes from cunn, cudnn, and fbcunn may be successfully imported.
         */
        public static Net readNetFromTorch(string model)
        {


            return new Net(DisposableObject.ThrowIfNullIntPtr(dnn_Dnn_readNetFromTorch_12(model)));


        }


        //
        // C++:  Net cv::dnn::readNet(String model, String config = "", String framework = "")
        //

        /**
         * Read deep learning network represented in one of the supported formats.
         * param model Binary file contains trained weights. The following file
         * extensions are expected for models from different frameworks:
         * * {code *.caffemodel} (Caffe, http://caffe.berkeleyvision.org/)
         * * {code *.pb} (TensorFlow, https://www.tensorflow.org/)
         * * {code *.t7} | {code *.net} (Torch, http://torch.ch/)
         * * {code *.weights} (Darknet, https://pjreddie.com/darknet/)
         * * {code *.bin} (DLDT, https://software.intel.com/openvino-toolkit)
         * * {code *.onnx} (ONNX, https://onnx.ai/)
         * param config Text file contains network configuration. It could be a
         * file with the following extensions:
         * * {code *.prototxt} (Caffe, http://caffe.berkeleyvision.org/)
         * * {code *.pbtxt} (TensorFlow, https://www.tensorflow.org/)
         * * {code *.cfg} (Darknet, https://pjreddie.com/darknet/)
         * * {code *.xml} (DLDT, https://software.intel.com/openvino-toolkit)
         * param framework Explicit framework name tag to determine a format.
         * return Net object.
         *
         * This function automatically detects an origin framework of trained model
         * and calls an appropriate function such REF: readNetFromCaffe, REF: readNetFromTensorflow,
         * REF: readNetFromTorch or REF: readNetFromDarknet. An order of {code model} and {code config}
         * arguments does not matter.
         */
        public static Net readNet(string model, string config, string framework)
        {


            return new Net(DisposableObject.ThrowIfNullIntPtr(dnn_Dnn_readNet_10(model, config, framework)));


        }

        /**
         * Read deep learning network represented in one of the supported formats.
         * param model Binary file contains trained weights. The following file
         * extensions are expected for models from different frameworks:
         * * {code *.caffemodel} (Caffe, http://caffe.berkeleyvision.org/)
         * * {code *.pb} (TensorFlow, https://www.tensorflow.org/)
         * * {code *.t7} | {code *.net} (Torch, http://torch.ch/)
         * * {code *.weights} (Darknet, https://pjreddie.com/darknet/)
         * * {code *.bin} (DLDT, https://software.intel.com/openvino-toolkit)
         * * {code *.onnx} (ONNX, https://onnx.ai/)
         * param config Text file contains network configuration. It could be a
         * file with the following extensions:
         * * {code *.prototxt} (Caffe, http://caffe.berkeleyvision.org/)
         * * {code *.pbtxt} (TensorFlow, https://www.tensorflow.org/)
         * * {code *.cfg} (Darknet, https://pjreddie.com/darknet/)
         * * {code *.xml} (DLDT, https://software.intel.com/openvino-toolkit)
         * return Net object.
         *
         * This function automatically detects an origin framework of trained model
         * and calls an appropriate function such REF: readNetFromCaffe, REF: readNetFromTensorflow,
         * REF: readNetFromTorch or REF: readNetFromDarknet. An order of {code model} and {code config}
         * arguments does not matter.
         */
        public static Net readNet(string model, string config)
        {


            return new Net(DisposableObject.ThrowIfNullIntPtr(dnn_Dnn_readNet_11(model, config)));


        }

        /**
         * Read deep learning network represented in one of the supported formats.
         * param model Binary file contains trained weights. The following file
         * extensions are expected for models from different frameworks:
         * * {code *.caffemodel} (Caffe, http://caffe.berkeleyvision.org/)
         * * {code *.pb} (TensorFlow, https://www.tensorflow.org/)
         * * {code *.t7} | {code *.net} (Torch, http://torch.ch/)
         * * {code *.weights} (Darknet, https://pjreddie.com/darknet/)
         * * {code *.bin} (DLDT, https://software.intel.com/openvino-toolkit)
         * * {code *.onnx} (ONNX, https://onnx.ai/)
         * file with the following extensions:
         * * {code *.prototxt} (Caffe, http://caffe.berkeleyvision.org/)
         * * {code *.pbtxt} (TensorFlow, https://www.tensorflow.org/)
         * * {code *.cfg} (Darknet, https://pjreddie.com/darknet/)
         * * {code *.xml} (DLDT, https://software.intel.com/openvino-toolkit)
         * return Net object.
         *
         * This function automatically detects an origin framework of trained model
         * and calls an appropriate function such REF: readNetFromCaffe, REF: readNetFromTensorflow,
         * REF: readNetFromTorch or REF: readNetFromDarknet. An order of {code model} and {code config}
         * arguments does not matter.
         */
        public static Net readNet(string model)
        {


            return new Net(DisposableObject.ThrowIfNullIntPtr(dnn_Dnn_readNet_12(model)));


        }


        //
        // C++:  Net cv::dnn::readNet(String framework, vector_uchar bufferModel, vector_uchar bufferConfig = std::vector<uchar>())
        //

        /**
         * Read deep learning network represented in one of the supported formats.
         * This is an overloaded member function, provided for convenience.
         * It differs from the above function only in what argument(s) it accepts.
         * param framework    Name of origin framework.
         * param bufferModel  A buffer with a content of binary file with weights
         * param bufferConfig A buffer with a content of text file contains network configuration.
         * return Net object.
         */
        public static Net readNet(string framework, MatOfByte bufferModel, MatOfByte bufferConfig)
        {
            if (bufferModel != null) bufferModel.ThrowIfDisposed();
            if (bufferConfig != null) bufferConfig.ThrowIfDisposed();
            Mat bufferModel_mat = bufferModel;
            Mat bufferConfig_mat = bufferConfig;
            return new Net(DisposableObject.ThrowIfNullIntPtr(dnn_Dnn_readNet_13(framework, bufferModel_mat.nativeObj, bufferConfig_mat.nativeObj)));


        }

        /**
         * Read deep learning network represented in one of the supported formats.
         * This is an overloaded member function, provided for convenience.
         * It differs from the above function only in what argument(s) it accepts.
         * param framework    Name of origin framework.
         * param bufferModel  A buffer with a content of binary file with weights
         * return Net object.
         */
        public static Net readNet(string framework, MatOfByte bufferModel)
        {
            if (bufferModel != null) bufferModel.ThrowIfDisposed();
            Mat bufferModel_mat = bufferModel;
            return new Net(DisposableObject.ThrowIfNullIntPtr(dnn_Dnn_readNet_14(framework, bufferModel_mat.nativeObj)));


        }


        //
        // C++:  Mat cv::dnn::readTorchBlob(String filename, bool isBinary = true)
        //

        /**
         * Loads blob which was serialized as torch.Tensor object of Torch7 framework.
         * WARNING: This function has the same limitations as readNetFromTorch().
         * param filename automatically generated
         * param isBinary automatically generated
         * return automatically generated
         */
        public static Mat readTorchBlob(string filename, bool isBinary)
        {


            return new Mat(DisposableObject.ThrowIfNullIntPtr(dnn_Dnn_readTorchBlob_10(filename, isBinary)));


        }

        /**
         * Loads blob which was serialized as torch.Tensor object of Torch7 framework.
         * WARNING: This function has the same limitations as readNetFromTorch().
         * param filename automatically generated
         * return automatically generated
         */
        public static Mat readTorchBlob(string filename)
        {


            return new Mat(DisposableObject.ThrowIfNullIntPtr(dnn_Dnn_readTorchBlob_11(filename)));


        }


        //
        // C++:  Net cv::dnn::readNetFromModelOptimizer(String xml, String bin)
        //

        /**
         * Load a network from Intel's Model Optimizer intermediate representation.
         * param xml XML configuration file with network's topology.
         * param bin Binary file with trained weights.
         * return Net object.
         * Networks imported from Intel's Model Optimizer are launched in Intel's Inference Engine
         * backend.
         */
        public static Net readNetFromModelOptimizer(string xml, string bin)
        {


            return new Net(DisposableObject.ThrowIfNullIntPtr(dnn_Dnn_readNetFromModelOptimizer_10(xml, bin)));


        }


        //
        // C++:  Net cv::dnn::readNetFromModelOptimizer(vector_uchar bufferModelConfig, vector_uchar bufferWeights)
        //

        /**
         * Load a network from Intel's Model Optimizer intermediate representation.
         * param bufferModelConfig Buffer contains XML configuration with network's topology.
         * param bufferWeights Buffer contains binary data with trained weights.
         * return Net object.
         * Networks imported from Intel's Model Optimizer are launched in Intel's Inference Engine
         * backend.
         */
        public static Net readNetFromModelOptimizer(MatOfByte bufferModelConfig, MatOfByte bufferWeights)
        {
            if (bufferModelConfig != null) bufferModelConfig.ThrowIfDisposed();
            if (bufferWeights != null) bufferWeights.ThrowIfDisposed();
            Mat bufferModelConfig_mat = bufferModelConfig;
            Mat bufferWeights_mat = bufferWeights;
            return new Net(DisposableObject.ThrowIfNullIntPtr(dnn_Dnn_readNetFromModelOptimizer_11(bufferModelConfig_mat.nativeObj, bufferWeights_mat.nativeObj)));


        }


        //
        // C++:  Net cv::dnn::readNetFromONNX(String onnxFile)
        //

        /**
         * Reads a network model &lt;a href="https://onnx.ai/"&gt;ONNX&lt;/a&gt;.
         * param onnxFile path to the .onnx file with text description of the network architecture.
         * return Network object that ready to do forward, throw an exception in failure cases.
         */
        public static Net readNetFromONNX(string onnxFile)
        {


            return new Net(DisposableObject.ThrowIfNullIntPtr(dnn_Dnn_readNetFromONNX_10(onnxFile)));


        }


        //
        // C++:  Net cv::dnn::readNetFromONNX(vector_uchar buffer)
        //

        /**
         * Reads a network model from &lt;a href="https://onnx.ai/"&gt;ONNX&lt;/a&gt;
         * in-memory buffer.
         * param buffer in-memory buffer that stores the ONNX model bytes.
         * return Network object that ready to do forward, throw an exception
         * in failure cases.
         */
        public static Net readNetFromONNX(MatOfByte buffer)
        {
            if (buffer != null) buffer.ThrowIfDisposed();
            Mat buffer_mat = buffer;
            return new Net(DisposableObject.ThrowIfNullIntPtr(dnn_Dnn_readNetFromONNX_11(buffer_mat.nativeObj)));


        }


        //
        // C++:  Mat cv::dnn::readTensorFromONNX(String path)
        //

        /**
         * Creates blob from .pb file.
         * param path to the .pb file with input tensor.
         * return Mat.
         */
        public static Mat readTensorFromONNX(string path)
        {


            return new Mat(DisposableObject.ThrowIfNullIntPtr(dnn_Dnn_readTensorFromONNX_10(path)));


        }


        //
        // C++:  Mat cv::dnn::blobFromImage(Mat image, double scalefactor = 1.0, Size size = Size(), Scalar mean = Scalar(), bool swapRB = false, bool crop = false, int ddepth = CV_32F)
        //

        /**
         * Creates 4-dimensional blob from image. Optionally resizes and crops {code image} from center,
         * subtract {code mean} values, scales values by {code scalefactor}, swap Blue and Red channels.
         * param image input image (with 1-, 3- or 4-channels).
         * param scalefactor multiplier for {code images} values.
         * param size spatial size for output image
         * param mean scalar with mean values which are subtracted from channels. Values are intended
         * to be in (mean-R, mean-G, mean-B) order if {code image} has BGR ordering and {code swapRB} is true.
         * param swapRB flag which indicates that swap first and last channels
         * in 3-channel image is necessary.
         * param crop flag which indicates whether image will be cropped after resize or not
         * param ddepth Depth of output blob. Choose CV_32F or CV_8U.
         * if {code crop} is true, input image is resized so one side after resize is equal to corresponding
         * dimension in {code size} and another one is equal or larger. Then, crop from the center is performed.
         * If {code crop} is false, direct resize without cropping and preserving aspect ratio is performed.
         * return 4-dimensional Mat with NCHW dimensions order.
         *
         * <b>Note:</b>
         * The order and usage of {code scalefactor} and {code mean} are (input - mean) * scalefactor.
         */
        public static Mat blobFromImage(Mat image, double scalefactor, Size size, Scalar mean, bool swapRB, bool crop, int ddepth)
        {
            if (image != null) image.ThrowIfDisposed();

            return new Mat(DisposableObject.ThrowIfNullIntPtr(dnn_Dnn_blobFromImage_10(image.nativeObj, scalefactor, size.width, size.height, mean.val[0], mean.val[1], mean.val[2], mean.val[3], swapRB, crop, ddepth)));


        }

        /**
         * Creates 4-dimensional blob from image. Optionally resizes and crops {code image} from center,
         * subtract {code mean} values, scales values by {code scalefactor}, swap Blue and Red channels.
         * param image input image (with 1-, 3- or 4-channels).
         * param scalefactor multiplier for {code images} values.
         * param size spatial size for output image
         * param mean scalar with mean values which are subtracted from channels. Values are intended
         * to be in (mean-R, mean-G, mean-B) order if {code image} has BGR ordering and {code swapRB} is true.
         * param swapRB flag which indicates that swap first and last channels
         * in 3-channel image is necessary.
         * param crop flag which indicates whether image will be cropped after resize or not
         * if {code crop} is true, input image is resized so one side after resize is equal to corresponding
         * dimension in {code size} and another one is equal or larger. Then, crop from the center is performed.
         * If {code crop} is false, direct resize without cropping and preserving aspect ratio is performed.
         * return 4-dimensional Mat with NCHW dimensions order.
         *
         * <b>Note:</b>
         * The order and usage of {code scalefactor} and {code mean} are (input - mean) * scalefactor.
         */
        public static Mat blobFromImage(Mat image, double scalefactor, Size size, Scalar mean, bool swapRB, bool crop)
        {
            if (image != null) image.ThrowIfDisposed();

            return new Mat(DisposableObject.ThrowIfNullIntPtr(dnn_Dnn_blobFromImage_11(image.nativeObj, scalefactor, size.width, size.height, mean.val[0], mean.val[1], mean.val[2], mean.val[3], swapRB, crop)));


        }

        /**
         * Creates 4-dimensional blob from image. Optionally resizes and crops {code image} from center,
         * subtract {code mean} values, scales values by {code scalefactor}, swap Blue and Red channels.
         * param image input image (with 1-, 3- or 4-channels).
         * param scalefactor multiplier for {code images} values.
         * param size spatial size for output image
         * param mean scalar with mean values which are subtracted from channels. Values are intended
         * to be in (mean-R, mean-G, mean-B) order if {code image} has BGR ordering and {code swapRB} is true.
         * param swapRB flag which indicates that swap first and last channels
         * in 3-channel image is necessary.
         * if {code crop} is true, input image is resized so one side after resize is equal to corresponding
         * dimension in {code size} and another one is equal or larger. Then, crop from the center is performed.
         * If {code crop} is false, direct resize without cropping and preserving aspect ratio is performed.
         * return 4-dimensional Mat with NCHW dimensions order.
         *
         * <b>Note:</b>
         * The order and usage of {code scalefactor} and {code mean} are (input - mean) * scalefactor.
         */
        public static Mat blobFromImage(Mat image, double scalefactor, Size size, Scalar mean, bool swapRB)
        {
            if (image != null) image.ThrowIfDisposed();

            return new Mat(DisposableObject.ThrowIfNullIntPtr(dnn_Dnn_blobFromImage_12(image.nativeObj, scalefactor, size.width, size.height, mean.val[0], mean.val[1], mean.val[2], mean.val[3], swapRB)));


        }

        /**
         * Creates 4-dimensional blob from image. Optionally resizes and crops {code image} from center,
         * subtract {code mean} values, scales values by {code scalefactor}, swap Blue and Red channels.
         * param image input image (with 1-, 3- or 4-channels).
         * param scalefactor multiplier for {code images} values.
         * param size spatial size for output image
         * param mean scalar with mean values which are subtracted from channels. Values are intended
         * to be in (mean-R, mean-G, mean-B) order if {code image} has BGR ordering and {code swapRB} is true.
         * in 3-channel image is necessary.
         * if {code crop} is true, input image is resized so one side after resize is equal to corresponding
         * dimension in {code size} and another one is equal or larger. Then, crop from the center is performed.
         * If {code crop} is false, direct resize without cropping and preserving aspect ratio is performed.
         * return 4-dimensional Mat with NCHW dimensions order.
         *
         * <b>Note:</b>
         * The order and usage of {code scalefactor} and {code mean} are (input - mean) * scalefactor.
         */
        public static Mat blobFromImage(Mat image, double scalefactor, Size size, Scalar mean)
        {
            if (image != null) image.ThrowIfDisposed();

            return new Mat(DisposableObject.ThrowIfNullIntPtr(dnn_Dnn_blobFromImage_13(image.nativeObj, scalefactor, size.width, size.height, mean.val[0], mean.val[1], mean.val[2], mean.val[3])));


        }

        /**
         * Creates 4-dimensional blob from image. Optionally resizes and crops {code image} from center,
         * subtract {code mean} values, scales values by {code scalefactor}, swap Blue and Red channels.
         * param image input image (with 1-, 3- or 4-channels).
         * param scalefactor multiplier for {code images} values.
         * param size spatial size for output image
         * to be in (mean-R, mean-G, mean-B) order if {code image} has BGR ordering and {code swapRB} is true.
         * in 3-channel image is necessary.
         * if {code crop} is true, input image is resized so one side after resize is equal to corresponding
         * dimension in {code size} and another one is equal or larger. Then, crop from the center is performed.
         * If {code crop} is false, direct resize without cropping and preserving aspect ratio is performed.
         * return 4-dimensional Mat with NCHW dimensions order.
         *
         * <b>Note:</b>
         * The order and usage of {code scalefactor} and {code mean} are (input - mean) * scalefactor.
         */
        public static Mat blobFromImage(Mat image, double scalefactor, Size size)
        {
            if (image != null) image.ThrowIfDisposed();

            return new Mat(DisposableObject.ThrowIfNullIntPtr(dnn_Dnn_blobFromImage_14(image.nativeObj, scalefactor, size.width, size.height)));


        }

        /**
         * Creates 4-dimensional blob from image. Optionally resizes and crops {code image} from center,
         * subtract {code mean} values, scales values by {code scalefactor}, swap Blue and Red channels.
         * param image input image (with 1-, 3- or 4-channels).
         * param scalefactor multiplier for {code images} values.
         * to be in (mean-R, mean-G, mean-B) order if {code image} has BGR ordering and {code swapRB} is true.
         * in 3-channel image is necessary.
         * if {code crop} is true, input image is resized so one side after resize is equal to corresponding
         * dimension in {code size} and another one is equal or larger. Then, crop from the center is performed.
         * If {code crop} is false, direct resize without cropping and preserving aspect ratio is performed.
         * return 4-dimensional Mat with NCHW dimensions order.
         *
         * <b>Note:</b>
         * The order and usage of {code scalefactor} and {code mean} are (input - mean) * scalefactor.
         */
        public static Mat blobFromImage(Mat image, double scalefactor)
        {
            if (image != null) image.ThrowIfDisposed();

            return new Mat(DisposableObject.ThrowIfNullIntPtr(dnn_Dnn_blobFromImage_15(image.nativeObj, scalefactor)));


        }

        /**
         * Creates 4-dimensional blob from image. Optionally resizes and crops {code image} from center,
         * subtract {code mean} values, scales values by {code scalefactor}, swap Blue and Red channels.
         * param image input image (with 1-, 3- or 4-channels).
         * to be in (mean-R, mean-G, mean-B) order if {code image} has BGR ordering and {code swapRB} is true.
         * in 3-channel image is necessary.
         * if {code crop} is true, input image is resized so one side after resize is equal to corresponding
         * dimension in {code size} and another one is equal or larger. Then, crop from the center is performed.
         * If {code crop} is false, direct resize without cropping and preserving aspect ratio is performed.
         * return 4-dimensional Mat with NCHW dimensions order.
         *
         * <b>Note:</b>
         * The order and usage of {code scalefactor} and {code mean} are (input - mean) * scalefactor.
         */
        public static Mat blobFromImage(Mat image)
        {
            if (image != null) image.ThrowIfDisposed();

            return new Mat(DisposableObject.ThrowIfNullIntPtr(dnn_Dnn_blobFromImage_16(image.nativeObj)));


        }


        //
        // C++:  Mat cv::dnn::blobFromImages(vector_Mat images, double scalefactor = 1.0, Size size = Size(), Scalar mean = Scalar(), bool swapRB = false, bool crop = false, int ddepth = CV_32F)
        //

        /**
         * Creates 4-dimensional blob from series of images. Optionally resizes and
         * crops {code images} from center, subtract {code mean} values, scales values by {code scalefactor},
         * swap Blue and Red channels.
         * param images input images (all with 1-, 3- or 4-channels).
         * param size spatial size for output image
         * param mean scalar with mean values which are subtracted from channels. Values are intended
         * to be in (mean-R, mean-G, mean-B) order if {code image} has BGR ordering and {code swapRB} is true.
         * param scalefactor multiplier for {code images} values.
         * param swapRB flag which indicates that swap first and last channels
         * in 3-channel image is necessary.
         * param crop flag which indicates whether image will be cropped after resize or not
         * param ddepth Depth of output blob. Choose CV_32F or CV_8U.
         * if {code crop} is true, input image is resized so one side after resize is equal to corresponding
         * dimension in {code size} and another one is equal or larger. Then, crop from the center is performed.
         * If {code crop} is false, direct resize without cropping and preserving aspect ratio is performed.
         * return 4-dimensional Mat with NCHW dimensions order.
         *
         * <b>Note:</b>
         * The order and usage of {code scalefactor} and {code mean} are (input - mean) * scalefactor.
         */
        public static Mat blobFromImages(List<Mat> images, double scalefactor, Size size, Scalar mean, bool swapRB, bool crop, int ddepth)
        {

            Mat images_mat = Converters.vector_Mat_to_Mat(images);
            return new Mat(DisposableObject.ThrowIfNullIntPtr(dnn_Dnn_blobFromImages_10(images_mat.nativeObj, scalefactor, size.width, size.height, mean.val[0], mean.val[1], mean.val[2], mean.val[3], swapRB, crop, ddepth)));


        }

        /**
         * Creates 4-dimensional blob from series of images. Optionally resizes and
         * crops {code images} from center, subtract {code mean} values, scales values by {code scalefactor},
         * swap Blue and Red channels.
         * param images input images (all with 1-, 3- or 4-channels).
         * param size spatial size for output image
         * param mean scalar with mean values which are subtracted from channels. Values are intended
         * to be in (mean-R, mean-G, mean-B) order if {code image} has BGR ordering and {code swapRB} is true.
         * param scalefactor multiplier for {code images} values.
         * param swapRB flag which indicates that swap first and last channels
         * in 3-channel image is necessary.
         * param crop flag which indicates whether image will be cropped after resize or not
         * if {code crop} is true, input image is resized so one side after resize is equal to corresponding
         * dimension in {code size} and another one is equal or larger. Then, crop from the center is performed.
         * If {code crop} is false, direct resize without cropping and preserving aspect ratio is performed.
         * return 4-dimensional Mat with NCHW dimensions order.
         *
         * <b>Note:</b>
         * The order and usage of {code scalefactor} and {code mean} are (input - mean) * scalefactor.
         */
        public static Mat blobFromImages(List<Mat> images, double scalefactor, Size size, Scalar mean, bool swapRB, bool crop)
        {

            Mat images_mat = Converters.vector_Mat_to_Mat(images);
            return new Mat(DisposableObject.ThrowIfNullIntPtr(dnn_Dnn_blobFromImages_11(images_mat.nativeObj, scalefactor, size.width, size.height, mean.val[0], mean.val[1], mean.val[2], mean.val[3], swapRB, crop)));


        }

        /**
         * Creates 4-dimensional blob from series of images. Optionally resizes and
         * crops {code images} from center, subtract {code mean} values, scales values by {code scalefactor},
         * swap Blue and Red channels.
         * param images input images (all with 1-, 3- or 4-channels).
         * param size spatial size for output image
         * param mean scalar with mean values which are subtracted from channels. Values are intended
         * to be in (mean-R, mean-G, mean-B) order if {code image} has BGR ordering and {code swapRB} is true.
         * param scalefactor multiplier for {code images} values.
         * param swapRB flag which indicates that swap first and last channels
         * in 3-channel image is necessary.
         * if {code crop} is true, input image is resized so one side after resize is equal to corresponding
         * dimension in {code size} and another one is equal or larger. Then, crop from the center is performed.
         * If {code crop} is false, direct resize without cropping and preserving aspect ratio is performed.
         * return 4-dimensional Mat with NCHW dimensions order.
         *
         * <b>Note:</b>
         * The order and usage of {code scalefactor} and {code mean} are (input - mean) * scalefactor.
         */
        public static Mat blobFromImages(List<Mat> images, double scalefactor, Size size, Scalar mean, bool swapRB)
        {

            Mat images_mat = Converters.vector_Mat_to_Mat(images);
            return new Mat(DisposableObject.ThrowIfNullIntPtr(dnn_Dnn_blobFromImages_12(images_mat.nativeObj, scalefactor, size.width, size.height, mean.val[0], mean.val[1], mean.val[2], mean.val[3], swapRB)));


        }

        /**
         * Creates 4-dimensional blob from series of images. Optionally resizes and
         * crops {code images} from center, subtract {code mean} values, scales values by {code scalefactor},
         * swap Blue and Red channels.
         * param images input images (all with 1-, 3- or 4-channels).
         * param size spatial size for output image
         * param mean scalar with mean values which are subtracted from channels. Values are intended
         * to be in (mean-R, mean-G, mean-B) order if {code image} has BGR ordering and {code swapRB} is true.
         * param scalefactor multiplier for {code images} values.
         * in 3-channel image is necessary.
         * if {code crop} is true, input image is resized so one side after resize is equal to corresponding
         * dimension in {code size} and another one is equal or larger. Then, crop from the center is performed.
         * If {code crop} is false, direct resize without cropping and preserving aspect ratio is performed.
         * return 4-dimensional Mat with NCHW dimensions order.
         *
         * <b>Note:</b>
         * The order and usage of {code scalefactor} and {code mean} are (input - mean) * scalefactor.
         */
        public static Mat blobFromImages(List<Mat> images, double scalefactor, Size size, Scalar mean)
        {

            Mat images_mat = Converters.vector_Mat_to_Mat(images);
            return new Mat(DisposableObject.ThrowIfNullIntPtr(dnn_Dnn_blobFromImages_13(images_mat.nativeObj, scalefactor, size.width, size.height, mean.val[0], mean.val[1], mean.val[2], mean.val[3])));


        }

        /**
         * Creates 4-dimensional blob from series of images. Optionally resizes and
         * crops {code images} from center, subtract {code mean} values, scales values by {code scalefactor},
         * swap Blue and Red channels.
         * param images input images (all with 1-, 3- or 4-channels).
         * param size spatial size for output image
         * to be in (mean-R, mean-G, mean-B) order if {code image} has BGR ordering and {code swapRB} is true.
         * param scalefactor multiplier for {code images} values.
         * in 3-channel image is necessary.
         * if {code crop} is true, input image is resized so one side after resize is equal to corresponding
         * dimension in {code size} and another one is equal or larger. Then, crop from the center is performed.
         * If {code crop} is false, direct resize without cropping and preserving aspect ratio is performed.
         * return 4-dimensional Mat with NCHW dimensions order.
         *
         * <b>Note:</b>
         * The order and usage of {code scalefactor} and {code mean} are (input - mean) * scalefactor.
         */
        public static Mat blobFromImages(List<Mat> images, double scalefactor, Size size)
        {

            Mat images_mat = Converters.vector_Mat_to_Mat(images);
            return new Mat(DisposableObject.ThrowIfNullIntPtr(dnn_Dnn_blobFromImages_14(images_mat.nativeObj, scalefactor, size.width, size.height)));


        }

        /**
         * Creates 4-dimensional blob from series of images. Optionally resizes and
         * crops {code images} from center, subtract {code mean} values, scales values by {code scalefactor},
         * swap Blue and Red channels.
         * param images input images (all with 1-, 3- or 4-channels).
         * to be in (mean-R, mean-G, mean-B) order if {code image} has BGR ordering and {code swapRB} is true.
         * param scalefactor multiplier for {code images} values.
         * in 3-channel image is necessary.
         * if {code crop} is true, input image is resized so one side after resize is equal to corresponding
         * dimension in {code size} and another one is equal or larger. Then, crop from the center is performed.
         * If {code crop} is false, direct resize without cropping and preserving aspect ratio is performed.
         * return 4-dimensional Mat with NCHW dimensions order.
         *
         * <b>Note:</b>
         * The order and usage of {code scalefactor} and {code mean} are (input - mean) * scalefactor.
         */
        public static Mat blobFromImages(List<Mat> images, double scalefactor)
        {

            Mat images_mat = Converters.vector_Mat_to_Mat(images);
            return new Mat(DisposableObject.ThrowIfNullIntPtr(dnn_Dnn_blobFromImages_15(images_mat.nativeObj, scalefactor)));


        }

        /**
         * Creates 4-dimensional blob from series of images. Optionally resizes and
         * crops {code images} from center, subtract {code mean} values, scales values by {code scalefactor},
         * swap Blue and Red channels.
         * param images input images (all with 1-, 3- or 4-channels).
         * to be in (mean-R, mean-G, mean-B) order if {code image} has BGR ordering and {code swapRB} is true.
         * in 3-channel image is necessary.
         * if {code crop} is true, input image is resized so one side after resize is equal to corresponding
         * dimension in {code size} and another one is equal or larger. Then, crop from the center is performed.
         * If {code crop} is false, direct resize without cropping and preserving aspect ratio is performed.
         * return 4-dimensional Mat with NCHW dimensions order.
         *
         * <b>Note:</b>
         * The order and usage of {code scalefactor} and {code mean} are (input - mean) * scalefactor.
         */
        public static Mat blobFromImages(List<Mat> images)
        {

            Mat images_mat = Converters.vector_Mat_to_Mat(images);
            return new Mat(DisposableObject.ThrowIfNullIntPtr(dnn_Dnn_blobFromImages_16(images_mat.nativeObj)));


        }


        //
        // C++:  Mat cv::dnn::blobFromImageWithParams(Mat image, Image2BlobParams param = Image2BlobParams())
        //

        /**
         * Creates 4-dimensional blob from image with given params.
         *
         * This function is an extension of REF: blobFromImage to meet more image preprocess needs.
         * Given input image and preprocessing parameters, and function outputs the blob.
         *
         * param image input image (all with 1-, 3- or 4-channels).
         * param param struct of Image2BlobParams, contains all parameters needed by processing of image to blob.
         * return 4-dimensional Mat.
         */
        public static Mat blobFromImageWithParams(Mat image, Image2BlobParams param)
        {
            if (image != null) image.ThrowIfDisposed();
            if (param != null) param.ThrowIfDisposed();

            return new Mat(DisposableObject.ThrowIfNullIntPtr(dnn_Dnn_blobFromImageWithParams_10(image.nativeObj, param.nativeObj)));


        }

        /**
         * Creates 4-dimensional blob from image with given params.
         *
         * This function is an extension of REF: blobFromImage to meet more image preprocess needs.
         * Given input image and preprocessing parameters, and function outputs the blob.
         *
         * param image input image (all with 1-, 3- or 4-channels).
         * return 4-dimensional Mat.
         */
        public static Mat blobFromImageWithParams(Mat image)
        {
            if (image != null) image.ThrowIfDisposed();

            return new Mat(DisposableObject.ThrowIfNullIntPtr(dnn_Dnn_blobFromImageWithParams_11(image.nativeObj)));


        }


        //
        // C++:  void cv::dnn::blobFromImageWithParams(Mat image, Mat& blob, Image2BlobParams param = Image2BlobParams())
        //

        public static void blobFromImageWithParams(Mat image, Mat blob, Image2BlobParams param)
        {
            if (image != null) image.ThrowIfDisposed();
            if (blob != null) blob.ThrowIfDisposed();
            if (param != null) param.ThrowIfDisposed();

            dnn_Dnn_blobFromImageWithParams_12(image.nativeObj, blob.nativeObj, param.nativeObj);


        }

        public static void blobFromImageWithParams(Mat image, Mat blob)
        {
            if (image != null) image.ThrowIfDisposed();
            if (blob != null) blob.ThrowIfDisposed();

            dnn_Dnn_blobFromImageWithParams_13(image.nativeObj, blob.nativeObj);


        }


        //
        // C++:  Mat cv::dnn::blobFromImagesWithParams(vector_Mat images, Image2BlobParams param = Image2BlobParams())
        //

        /**
         * Creates 4-dimensional blob from series of images with given params.
         *
         * This function is an extension of REF: blobFromImages to meet more image preprocess needs.
         * Given input image and preprocessing parameters, and function outputs the blob.
         *
         * param images input image (all with 1-, 3- or 4-channels).
         * param param struct of Image2BlobParams, contains all parameters needed by processing of image to blob.
         * return 4-dimensional Mat.
         */
        public static Mat blobFromImagesWithParams(List<Mat> images, Image2BlobParams param)
        {
            if (param != null) param.ThrowIfDisposed();
            Mat images_mat = Converters.vector_Mat_to_Mat(images);
            return new Mat(DisposableObject.ThrowIfNullIntPtr(dnn_Dnn_blobFromImagesWithParams_10(images_mat.nativeObj, param.nativeObj)));


        }

        /**
         * Creates 4-dimensional blob from series of images with given params.
         *
         * This function is an extension of REF: blobFromImages to meet more image preprocess needs.
         * Given input image and preprocessing parameters, and function outputs the blob.
         *
         * param images input image (all with 1-, 3- or 4-channels).
         * return 4-dimensional Mat.
         */
        public static Mat blobFromImagesWithParams(List<Mat> images)
        {

            Mat images_mat = Converters.vector_Mat_to_Mat(images);
            return new Mat(DisposableObject.ThrowIfNullIntPtr(dnn_Dnn_blobFromImagesWithParams_11(images_mat.nativeObj)));


        }


        //
        // C++:  void cv::dnn::blobFromImagesWithParams(vector_Mat images, Mat& blob, Image2BlobParams param = Image2BlobParams())
        //

        public static void blobFromImagesWithParams(List<Mat> images, Mat blob, Image2BlobParams param)
        {
            if (blob != null) blob.ThrowIfDisposed();
            if (param != null) param.ThrowIfDisposed();
            Mat images_mat = Converters.vector_Mat_to_Mat(images);
            dnn_Dnn_blobFromImagesWithParams_12(images_mat.nativeObj, blob.nativeObj, param.nativeObj);


        }

        public static void blobFromImagesWithParams(List<Mat> images, Mat blob)
        {
            if (blob != null) blob.ThrowIfDisposed();
            Mat images_mat = Converters.vector_Mat_to_Mat(images);
            dnn_Dnn_blobFromImagesWithParams_13(images_mat.nativeObj, blob.nativeObj);


        }


        //
        // C++:  void cv::dnn::imagesFromBlob(Mat blob_, vector_Mat& images_)
        //

        /**
         * Parse a 4D blob and output the images it contains as 2D arrays through a simpler data structure
         * (std::vector&lt;cv::Mat&gt;).
         * param blob_ 4 dimensional array (images, channels, height, width) in floating point precision (CV_32F) from
         * which you would like to extract the images.
         * param images_ array of 2D Mat containing the images extracted from the blob in floating point precision
         * (CV_32F). They are non normalized neither mean added. The number of returned images equals the first dimension
         * of the blob (batch size). Every image has a number of channels equals to the second dimension of the blob (depth).
         */
        public static void imagesFromBlob(Mat blob_, List<Mat> images_)
        {
            if (blob_ != null) blob_.ThrowIfDisposed();
            Mat images__mat = new Mat();
            dnn_Dnn_imagesFromBlob_10(blob_.nativeObj, images__mat.nativeObj);
            Converters.Mat_to_vector_Mat(images__mat, images_);
            images__mat.release();

        }


        //
        // C++:  void cv::dnn::shrinkCaffeModel(String src, String dst, vector_String layersTypes = std::vector<String>())
        //

        /**
         * Convert all weights of Caffe network to half precision floating point.
         * param src Path to origin model from Caffe framework contains single
         * precision floating point weights (usually has {code .caffemodel} extension).
         * param dst Path to destination model with updated weights.
         * param layersTypes Set of layers types which parameters will be converted.
         * By default, converts only Convolutional and Fully-Connected layers'
         * weights.
         *
         * <b>Note:</b> Shrinked model has no origin float32 weights so it can't be used
         * in origin Caffe framework anymore. However the structure of data
         * is taken from NVidia's Caffe fork: https://github.com/NVIDIA/caffe.
         * So the resulting model may be used there.
         */
        public static void shrinkCaffeModel(string src, string dst, List<string> layersTypes)
        {

            Mat layersTypes_mat = Converters.vector_String_to_Mat(layersTypes);
            dnn_Dnn_shrinkCaffeModel_10(src, dst, layersTypes_mat.nativeObj);


        }

        /**
         * Convert all weights of Caffe network to half precision floating point.
         * param src Path to origin model from Caffe framework contains single
         * precision floating point weights (usually has {code .caffemodel} extension).
         * param dst Path to destination model with updated weights.
         * By default, converts only Convolutional and Fully-Connected layers'
         * weights.
         *
         * <b>Note:</b> Shrinked model has no origin float32 weights so it can't be used
         * in origin Caffe framework anymore. However the structure of data
         * is taken from NVidia's Caffe fork: https://github.com/NVIDIA/caffe.
         * So the resulting model may be used there.
         */
        public static void shrinkCaffeModel(string src, string dst)
        {


            dnn_Dnn_shrinkCaffeModel_11(src, dst);


        }


        //
        // C++:  void cv::dnn::writeTextGraph(String model, String output)
        //

        /**
         * Create a text representation for a binary network stored in protocol buffer format.
         * param model  A path to binary network.
         * param output A path to output text file to be created.
         *
         * <b>Note:</b> To reduce output file size, trained weights are not included.
         */
        public static void writeTextGraph(string model, string output)
        {


            dnn_Dnn_writeTextGraph_10(model, output);


        }


        //
        // C++:  void cv::dnn::NMSBoxes(vector_Rect2d bboxes, vector_float scores, float score_threshold, float nms_threshold, vector_int& indices, float eta = 1.f, int top_k = 0)
        //

        /**
         * Performs non maximum suppression given boxes and corresponding scores.
         *
         * param bboxes a set of bounding boxes to apply NMS.
         * param scores a set of corresponding confidences.
         * param score_threshold a threshold used to filter boxes by score.
         * param nms_threshold a threshold used in non maximum suppression.
         * param indices the kept indices of bboxes after NMS.
         * param eta a coefficient in adaptive threshold formula: \(nms\_threshold_{i+1}=eta\cdot nms\_threshold_i\).
         * param top_k if {code &gt;0}, keep at most {code top_k} picked indices.
         */
        public static void NMSBoxes(MatOfRect2d bboxes, MatOfFloat scores, float score_threshold, float nms_threshold, MatOfInt indices, float eta, int top_k)
        {
            if (bboxes != null) bboxes.ThrowIfDisposed();
            if (scores != null) scores.ThrowIfDisposed();
            if (indices != null) indices.ThrowIfDisposed();
            Mat bboxes_mat = bboxes;
            Mat scores_mat = scores;
            Mat indices_mat = indices;
            dnn_Dnn_NMSBoxes_10(bboxes_mat.nativeObj, scores_mat.nativeObj, score_threshold, nms_threshold, indices_mat.nativeObj, eta, top_k);


        }

        /**
         * Performs non maximum suppression given boxes and corresponding scores.
         *
         * param bboxes a set of bounding boxes to apply NMS.
         * param scores a set of corresponding confidences.
         * param score_threshold a threshold used to filter boxes by score.
         * param nms_threshold a threshold used in non maximum suppression.
         * param indices the kept indices of bboxes after NMS.
         * param eta a coefficient in adaptive threshold formula: \(nms\_threshold_{i+1}=eta\cdot nms\_threshold_i\).
         */
        public static void NMSBoxes(MatOfRect2d bboxes, MatOfFloat scores, float score_threshold, float nms_threshold, MatOfInt indices, float eta)
        {
            if (bboxes != null) bboxes.ThrowIfDisposed();
            if (scores != null) scores.ThrowIfDisposed();
            if (indices != null) indices.ThrowIfDisposed();
            Mat bboxes_mat = bboxes;
            Mat scores_mat = scores;
            Mat indices_mat = indices;
            dnn_Dnn_NMSBoxes_11(bboxes_mat.nativeObj, scores_mat.nativeObj, score_threshold, nms_threshold, indices_mat.nativeObj, eta);


        }

        /**
         * Performs non maximum suppression given boxes and corresponding scores.
         *
         * param bboxes a set of bounding boxes to apply NMS.
         * param scores a set of corresponding confidences.
         * param score_threshold a threshold used to filter boxes by score.
         * param nms_threshold a threshold used in non maximum suppression.
         * param indices the kept indices of bboxes after NMS.
         */
        public static void NMSBoxes(MatOfRect2d bboxes, MatOfFloat scores, float score_threshold, float nms_threshold, MatOfInt indices)
        {
            if (bboxes != null) bboxes.ThrowIfDisposed();
            if (scores != null) scores.ThrowIfDisposed();
            if (indices != null) indices.ThrowIfDisposed();
            Mat bboxes_mat = bboxes;
            Mat scores_mat = scores;
            Mat indices_mat = indices;
            dnn_Dnn_NMSBoxes_12(bboxes_mat.nativeObj, scores_mat.nativeObj, score_threshold, nms_threshold, indices_mat.nativeObj);


        }


        //
        // C++:  void cv::dnn::NMSBoxes(vector_RotatedRect bboxes, vector_float scores, float score_threshold, float nms_threshold, vector_int& indices, float eta = 1.f, int top_k = 0)
        //

        public static void NMSBoxesRotated(MatOfRotatedRect bboxes, MatOfFloat scores, float score_threshold, float nms_threshold, MatOfInt indices, float eta, int top_k)
        {
            if (bboxes != null) bboxes.ThrowIfDisposed();
            if (scores != null) scores.ThrowIfDisposed();
            if (indices != null) indices.ThrowIfDisposed();
            Mat bboxes_mat = bboxes;
            Mat scores_mat = scores;
            Mat indices_mat = indices;
            dnn_Dnn_NMSBoxesRotated_10(bboxes_mat.nativeObj, scores_mat.nativeObj, score_threshold, nms_threshold, indices_mat.nativeObj, eta, top_k);


        }

        public static void NMSBoxesRotated(MatOfRotatedRect bboxes, MatOfFloat scores, float score_threshold, float nms_threshold, MatOfInt indices, float eta)
        {
            if (bboxes != null) bboxes.ThrowIfDisposed();
            if (scores != null) scores.ThrowIfDisposed();
            if (indices != null) indices.ThrowIfDisposed();
            Mat bboxes_mat = bboxes;
            Mat scores_mat = scores;
            Mat indices_mat = indices;
            dnn_Dnn_NMSBoxesRotated_11(bboxes_mat.nativeObj, scores_mat.nativeObj, score_threshold, nms_threshold, indices_mat.nativeObj, eta);


        }

        public static void NMSBoxesRotated(MatOfRotatedRect bboxes, MatOfFloat scores, float score_threshold, float nms_threshold, MatOfInt indices)
        {
            if (bboxes != null) bboxes.ThrowIfDisposed();
            if (scores != null) scores.ThrowIfDisposed();
            if (indices != null) indices.ThrowIfDisposed();
            Mat bboxes_mat = bboxes;
            Mat scores_mat = scores;
            Mat indices_mat = indices;
            dnn_Dnn_NMSBoxesRotated_12(bboxes_mat.nativeObj, scores_mat.nativeObj, score_threshold, nms_threshold, indices_mat.nativeObj);


        }


        //
        // C++:  void cv::dnn::NMSBoxesBatched(vector_Rect2d bboxes, vector_float scores, vector_int class_ids, float score_threshold, float nms_threshold, vector_int& indices, float eta = 1.f, int top_k = 0)
        //

        /**
         * Performs batched non maximum suppression on given boxes and corresponding scores across different classes.
         *
         * param bboxes a set of bounding boxes to apply NMS.
         * param scores a set of corresponding confidences.
         * param class_ids a set of corresponding class ids. Ids are integer and usually start from 0.
         * param score_threshold a threshold used to filter boxes by score.
         * param nms_threshold a threshold used in non maximum suppression.
         * param indices the kept indices of bboxes after NMS.
         * param eta a coefficient in adaptive threshold formula: \(nms\_threshold_{i+1}=eta\cdot nms\_threshold_i\).
         * param top_k if {code &gt;0}, keep at most {code top_k} picked indices.
         */
        public static void NMSBoxesBatched(MatOfRect2d bboxes, MatOfFloat scores, MatOfInt class_ids, float score_threshold, float nms_threshold, MatOfInt indices, float eta, int top_k)
        {
            if (bboxes != null) bboxes.ThrowIfDisposed();
            if (scores != null) scores.ThrowIfDisposed();
            if (class_ids != null) class_ids.ThrowIfDisposed();
            if (indices != null) indices.ThrowIfDisposed();
            Mat bboxes_mat = bboxes;
            Mat scores_mat = scores;
            Mat class_ids_mat = class_ids;
            Mat indices_mat = indices;
            dnn_Dnn_NMSBoxesBatched_10(bboxes_mat.nativeObj, scores_mat.nativeObj, class_ids_mat.nativeObj, score_threshold, nms_threshold, indices_mat.nativeObj, eta, top_k);


        }

        /**
         * Performs batched non maximum suppression on given boxes and corresponding scores across different classes.
         *
         * param bboxes a set of bounding boxes to apply NMS.
         * param scores a set of corresponding confidences.
         * param class_ids a set of corresponding class ids. Ids are integer and usually start from 0.
         * param score_threshold a threshold used to filter boxes by score.
         * param nms_threshold a threshold used in non maximum suppression.
         * param indices the kept indices of bboxes after NMS.
         * param eta a coefficient in adaptive threshold formula: \(nms\_threshold_{i+1}=eta\cdot nms\_threshold_i\).
         */
        public static void NMSBoxesBatched(MatOfRect2d bboxes, MatOfFloat scores, MatOfInt class_ids, float score_threshold, float nms_threshold, MatOfInt indices, float eta)
        {
            if (bboxes != null) bboxes.ThrowIfDisposed();
            if (scores != null) scores.ThrowIfDisposed();
            if (class_ids != null) class_ids.ThrowIfDisposed();
            if (indices != null) indices.ThrowIfDisposed();
            Mat bboxes_mat = bboxes;
            Mat scores_mat = scores;
            Mat class_ids_mat = class_ids;
            Mat indices_mat = indices;
            dnn_Dnn_NMSBoxesBatched_11(bboxes_mat.nativeObj, scores_mat.nativeObj, class_ids_mat.nativeObj, score_threshold, nms_threshold, indices_mat.nativeObj, eta);


        }

        /**
         * Performs batched non maximum suppression on given boxes and corresponding scores across different classes.
         *
         * param bboxes a set of bounding boxes to apply NMS.
         * param scores a set of corresponding confidences.
         * param class_ids a set of corresponding class ids. Ids are integer and usually start from 0.
         * param score_threshold a threshold used to filter boxes by score.
         * param nms_threshold a threshold used in non maximum suppression.
         * param indices the kept indices of bboxes after NMS.
         */
        public static void NMSBoxesBatched(MatOfRect2d bboxes, MatOfFloat scores, MatOfInt class_ids, float score_threshold, float nms_threshold, MatOfInt indices)
        {
            if (bboxes != null) bboxes.ThrowIfDisposed();
            if (scores != null) scores.ThrowIfDisposed();
            if (class_ids != null) class_ids.ThrowIfDisposed();
            if (indices != null) indices.ThrowIfDisposed();
            Mat bboxes_mat = bboxes;
            Mat scores_mat = scores;
            Mat class_ids_mat = class_ids;
            Mat indices_mat = indices;
            dnn_Dnn_NMSBoxesBatched_12(bboxes_mat.nativeObj, scores_mat.nativeObj, class_ids_mat.nativeObj, score_threshold, nms_threshold, indices_mat.nativeObj);


        }


        //
        // C++:  void cv::dnn::softNMSBoxes(vector_Rect bboxes, vector_float scores, vector_float& updated_scores, float score_threshold, float nms_threshold, vector_int& indices, size_t top_k = 0, float sigma = 0.5, SoftNMSMethod method = SoftNMSMethod::SOFTNMS_GAUSSIAN)
        //

        /**
         * Performs soft non maximum suppression given boxes and corresponding scores.
         * Reference: https://arxiv.org/abs/1704.04503
         * param bboxes a set of bounding boxes to apply Soft NMS.
         * param scores a set of corresponding confidences.
         * param updated_scores a set of corresponding updated confidences.
         * param score_threshold a threshold used to filter boxes by score.
         * param nms_threshold a threshold used in non maximum suppression.
         * param indices the kept indices of bboxes after NMS.
         * param top_k keep at most {code top_k} picked indices.
         * param sigma parameter of Gaussian weighting.
         * SEE: SoftNMSMethod
         */
        public static void softNMSBoxes(MatOfRect bboxes, MatOfFloat scores, MatOfFloat updated_scores, float score_threshold, float nms_threshold, MatOfInt indices, long top_k, float sigma)
        {
            if (bboxes != null) bboxes.ThrowIfDisposed();
            if (scores != null) scores.ThrowIfDisposed();
            if (updated_scores != null) updated_scores.ThrowIfDisposed();
            if (indices != null) indices.ThrowIfDisposed();
            Mat bboxes_mat = bboxes;
            Mat scores_mat = scores;
            Mat updated_scores_mat = updated_scores;
            Mat indices_mat = indices;
            dnn_Dnn_softNMSBoxes_10(bboxes_mat.nativeObj, scores_mat.nativeObj, updated_scores_mat.nativeObj, score_threshold, nms_threshold, indices_mat.nativeObj, top_k, sigma);


        }

        /**
         * Performs soft non maximum suppression given boxes and corresponding scores.
         * Reference: https://arxiv.org/abs/1704.04503
         * param bboxes a set of bounding boxes to apply Soft NMS.
         * param scores a set of corresponding confidences.
         * param updated_scores a set of corresponding updated confidences.
         * param score_threshold a threshold used to filter boxes by score.
         * param nms_threshold a threshold used in non maximum suppression.
         * param indices the kept indices of bboxes after NMS.
         * param top_k keep at most {code top_k} picked indices.
         * SEE: SoftNMSMethod
         */
        public static void softNMSBoxes(MatOfRect bboxes, MatOfFloat scores, MatOfFloat updated_scores, float score_threshold, float nms_threshold, MatOfInt indices, long top_k)
        {
            if (bboxes != null) bboxes.ThrowIfDisposed();
            if (scores != null) scores.ThrowIfDisposed();
            if (updated_scores != null) updated_scores.ThrowIfDisposed();
            if (indices != null) indices.ThrowIfDisposed();
            Mat bboxes_mat = bboxes;
            Mat scores_mat = scores;
            Mat updated_scores_mat = updated_scores;
            Mat indices_mat = indices;
            dnn_Dnn_softNMSBoxes_12(bboxes_mat.nativeObj, scores_mat.nativeObj, updated_scores_mat.nativeObj, score_threshold, nms_threshold, indices_mat.nativeObj, top_k);


        }

        /**
         * Performs soft non maximum suppression given boxes and corresponding scores.
         * Reference: https://arxiv.org/abs/1704.04503
         * param bboxes a set of bounding boxes to apply Soft NMS.
         * param scores a set of corresponding confidences.
         * param updated_scores a set of corresponding updated confidences.
         * param score_threshold a threshold used to filter boxes by score.
         * param nms_threshold a threshold used in non maximum suppression.
         * param indices the kept indices of bboxes after NMS.
         * SEE: SoftNMSMethod
         */
        public static void softNMSBoxes(MatOfRect bboxes, MatOfFloat scores, MatOfFloat updated_scores, float score_threshold, float nms_threshold, MatOfInt indices)
        {
            if (bboxes != null) bboxes.ThrowIfDisposed();
            if (scores != null) scores.ThrowIfDisposed();
            if (updated_scores != null) updated_scores.ThrowIfDisposed();
            if (indices != null) indices.ThrowIfDisposed();
            Mat bboxes_mat = bboxes;
            Mat scores_mat = scores;
            Mat updated_scores_mat = updated_scores;
            Mat indices_mat = indices;
            dnn_Dnn_softNMSBoxes_13(bboxes_mat.nativeObj, scores_mat.nativeObj, updated_scores_mat.nativeObj, score_threshold, nms_threshold, indices_mat.nativeObj);


        }


        //
        // C++:  String cv::dnn::getInferenceEngineBackendType()
        //

        /**
         * Returns Inference Engine internal backend API.
         *
         * See values of {code CV_DNN_BACKEND_INFERENCE_ENGINE_*} macros.
         *
         * {code OPENCV_DNN_BACKEND_INFERENCE_ENGINE_TYPE} runtime parameter (environment variable) is ignored since 4.6.0.
         *
         * deprecated
         * return automatically generated
         */
        [Obsolete("This method is deprecated.")]
        public static string getInferenceEngineBackendType()
        {


            string retVal = Marshal.PtrToStringAnsi(DisposableObject.ThrowIfNullIntPtr(dnn_Dnn_getInferenceEngineBackendType_10()));

            return retVal;
        }


        //
        // C++:  String cv::dnn::setInferenceEngineBackendType(String newBackendType)
        //

        /**
         * Specify Inference Engine internal backend API.
         *
         * See values of {code CV_DNN_BACKEND_INFERENCE_ENGINE_*} macros.
         *
         * return previous value of internal backend API
         *
         * deprecated
         * param newBackendType automatically generated
         */
        [Obsolete("This method is deprecated.")]
        public static string setInferenceEngineBackendType(string newBackendType)
        {


            string retVal = Marshal.PtrToStringAnsi(DisposableObject.ThrowIfNullIntPtr(dnn_Dnn_setInferenceEngineBackendType_10(newBackendType)));

            return retVal;
        }


        //
        // C++:  void cv::dnn::resetMyriadDevice()
        //

        /**
         * Release a Myriad device (binded by OpenCV).
         *
         * Single Myriad device cannot be shared across multiple processes which uses
         * Inference Engine's Myriad plugin.
         */
        public static void resetMyriadDevice()
        {


            dnn_Dnn_resetMyriadDevice_10();


        }


        //
        // C++:  String cv::dnn::getInferenceEngineVPUType()
        //

        /**
         * Returns Inference Engine VPU type.
         *
         * See values of {code CV_DNN_INFERENCE_ENGINE_VPU_TYPE_*} macros.
         * return automatically generated
         */
        public static string getInferenceEngineVPUType()
        {


            string retVal = Marshal.PtrToStringAnsi(DisposableObject.ThrowIfNullIntPtr(dnn_Dnn_getInferenceEngineVPUType_10()));

            return retVal;
        }


        //
        // C++:  String cv::dnn::getInferenceEngineCPUType()
        //

        /**
         * Returns Inference Engine CPU type.
         *
         * Specify OpenVINO plugin: CPU or ARM.
         * return automatically generated
         */
        public static string getInferenceEngineCPUType()
        {


            string retVal = Marshal.PtrToStringAnsi(DisposableObject.ThrowIfNullIntPtr(dnn_Dnn_getInferenceEngineCPUType_10()));

            return retVal;
        }


        //
        // C++:  void cv::dnn::releaseHDDLPlugin()
        //

        /**
         * Release a HDDL plugin.
         */
        public static void releaseHDDLPlugin()
        {


            dnn_Dnn_releaseHDDLPlugin_10();


        }


#if (UNITY_IOS || UNITY_WEBGL) && !UNITY_EDITOR
        const string LIBNAME = "__Internal";
#else
        const string LIBNAME = "opencvforunity";
#endif



        // C++:  vector_Target cv::dnn::getAvailableTargets(dnn_Backend be)
        [DllImport(LIBNAME)]
        private static extern IntPtr dnn_Dnn_getAvailableTargets_10(int be);

        // C++:  Net cv::dnn::readNetFromDarknet(String cfgFile, String darknetModel = String())
        [DllImport(LIBNAME)]
        private static extern IntPtr dnn_Dnn_readNetFromDarknet_10(string cfgFile, string darknetModel);
        [DllImport(LIBNAME)]
        private static extern IntPtr dnn_Dnn_readNetFromDarknet_11(string cfgFile);

        // C++:  Net cv::dnn::readNetFromDarknet(vector_uchar bufferCfg, vector_uchar bufferModel = std::vector<uchar>())
        [DllImport(LIBNAME)]
        private static extern IntPtr dnn_Dnn_readNetFromDarknet_12(IntPtr bufferCfg_mat_nativeObj, IntPtr bufferModel_mat_nativeObj);
        [DllImport(LIBNAME)]
        private static extern IntPtr dnn_Dnn_readNetFromDarknet_13(IntPtr bufferCfg_mat_nativeObj);

        // C++:  Net cv::dnn::readNetFromCaffe(String prototxt, String caffeModel = String())
        [DllImport(LIBNAME)]
        private static extern IntPtr dnn_Dnn_readNetFromCaffe_10(string prototxt, string caffeModel);
        [DllImport(LIBNAME)]
        private static extern IntPtr dnn_Dnn_readNetFromCaffe_11(string prototxt);

        // C++:  Net cv::dnn::readNetFromCaffe(vector_uchar bufferProto, vector_uchar bufferModel = std::vector<uchar>())
        [DllImport(LIBNAME)]
        private static extern IntPtr dnn_Dnn_readNetFromCaffe_12(IntPtr bufferProto_mat_nativeObj, IntPtr bufferModel_mat_nativeObj);
        [DllImport(LIBNAME)]
        private static extern IntPtr dnn_Dnn_readNetFromCaffe_13(IntPtr bufferProto_mat_nativeObj);

        // C++:  Net cv::dnn::readNetFromTensorflow(String model, String config = String())
        [DllImport(LIBNAME)]
        private static extern IntPtr dnn_Dnn_readNetFromTensorflow_10(string model, string config);
        [DllImport(LIBNAME)]
        private static extern IntPtr dnn_Dnn_readNetFromTensorflow_11(string model);

        // C++:  Net cv::dnn::readNetFromTensorflow(vector_uchar bufferModel, vector_uchar bufferConfig = std::vector<uchar>())
        [DllImport(LIBNAME)]
        private static extern IntPtr dnn_Dnn_readNetFromTensorflow_12(IntPtr bufferModel_mat_nativeObj, IntPtr bufferConfig_mat_nativeObj);
        [DllImport(LIBNAME)]
        private static extern IntPtr dnn_Dnn_readNetFromTensorflow_13(IntPtr bufferModel_mat_nativeObj);

        // C++:  Net cv::dnn::readNetFromTFLite(String model)
        [DllImport(LIBNAME)]
        private static extern IntPtr dnn_Dnn_readNetFromTFLite_10(string model);

        // C++:  Net cv::dnn::readNetFromTFLite(vector_uchar bufferModel)
        [DllImport(LIBNAME)]
        private static extern IntPtr dnn_Dnn_readNetFromTFLite_11(IntPtr bufferModel_mat_nativeObj);

        // C++:  Net cv::dnn::readNetFromTorch(String model, bool isBinary = true, bool evaluate = true)
        [DllImport(LIBNAME)]
        private static extern IntPtr dnn_Dnn_readNetFromTorch_10(string model, [MarshalAs(UnmanagedType.U1)] bool isBinary, [MarshalAs(UnmanagedType.U1)] bool evaluate);
        [DllImport(LIBNAME)]
        private static extern IntPtr dnn_Dnn_readNetFromTorch_11(string model, [MarshalAs(UnmanagedType.U1)] bool isBinary);
        [DllImport(LIBNAME)]
        private static extern IntPtr dnn_Dnn_readNetFromTorch_12(string model);

        // C++:  Net cv::dnn::readNet(String model, String config = "", String framework = "")
        [DllImport(LIBNAME)]
        private static extern IntPtr dnn_Dnn_readNet_10(string model, string config, string framework);
        [DllImport(LIBNAME)]
        private static extern IntPtr dnn_Dnn_readNet_11(string model, string config);
        [DllImport(LIBNAME)]
        private static extern IntPtr dnn_Dnn_readNet_12(string model);

        // C++:  Net cv::dnn::readNet(String framework, vector_uchar bufferModel, vector_uchar bufferConfig = std::vector<uchar>())
        [DllImport(LIBNAME)]
        private static extern IntPtr dnn_Dnn_readNet_13(string framework, IntPtr bufferModel_mat_nativeObj, IntPtr bufferConfig_mat_nativeObj);
        [DllImport(LIBNAME)]
        private static extern IntPtr dnn_Dnn_readNet_14(string framework, IntPtr bufferModel_mat_nativeObj);

        // C++:  Mat cv::dnn::readTorchBlob(String filename, bool isBinary = true)
        [DllImport(LIBNAME)]
        private static extern IntPtr dnn_Dnn_readTorchBlob_10(string filename, [MarshalAs(UnmanagedType.U1)] bool isBinary);
        [DllImport(LIBNAME)]
        private static extern IntPtr dnn_Dnn_readTorchBlob_11(string filename);

        // C++:  Net cv::dnn::readNetFromModelOptimizer(String xml, String bin)
        [DllImport(LIBNAME)]
        private static extern IntPtr dnn_Dnn_readNetFromModelOptimizer_10(string xml, string bin);

        // C++:  Net cv::dnn::readNetFromModelOptimizer(vector_uchar bufferModelConfig, vector_uchar bufferWeights)
        [DllImport(LIBNAME)]
        private static extern IntPtr dnn_Dnn_readNetFromModelOptimizer_11(IntPtr bufferModelConfig_mat_nativeObj, IntPtr bufferWeights_mat_nativeObj);

        // C++:  Net cv::dnn::readNetFromONNX(String onnxFile)
        [DllImport(LIBNAME)]
        private static extern IntPtr dnn_Dnn_readNetFromONNX_10(string onnxFile);

        // C++:  Net cv::dnn::readNetFromONNX(vector_uchar buffer)
        [DllImport(LIBNAME)]
        private static extern IntPtr dnn_Dnn_readNetFromONNX_11(IntPtr buffer_mat_nativeObj);

        // C++:  Mat cv::dnn::readTensorFromONNX(String path)
        [DllImport(LIBNAME)]
        private static extern IntPtr dnn_Dnn_readTensorFromONNX_10(string path);

        // C++:  Mat cv::dnn::blobFromImage(Mat image, double scalefactor = 1.0, Size size = Size(), Scalar mean = Scalar(), bool swapRB = false, bool crop = false, int ddepth = CV_32F)
        [DllImport(LIBNAME)]
        private static extern IntPtr dnn_Dnn_blobFromImage_10(IntPtr image_nativeObj, double scalefactor, 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, int ddepth);
        [DllImport(LIBNAME)]
        private static extern IntPtr dnn_Dnn_blobFromImage_11(IntPtr image_nativeObj, double scalefactor, 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 IntPtr dnn_Dnn_blobFromImage_12(IntPtr image_nativeObj, double scalefactor, 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 IntPtr dnn_Dnn_blobFromImage_13(IntPtr image_nativeObj, double scalefactor, double size_width, double size_height, double mean_val0, double mean_val1, double mean_val2, double mean_val3);
        [DllImport(LIBNAME)]
        private static extern IntPtr dnn_Dnn_blobFromImage_14(IntPtr image_nativeObj, double scalefactor, double size_width, double size_height);
        [DllImport(LIBNAME)]
        private static extern IntPtr dnn_Dnn_blobFromImage_15(IntPtr image_nativeObj, double scalefactor);
        [DllImport(LIBNAME)]
        private static extern IntPtr dnn_Dnn_blobFromImage_16(IntPtr image_nativeObj);

        // C++:  Mat cv::dnn::blobFromImages(vector_Mat images, double scalefactor = 1.0, Size size = Size(), Scalar mean = Scalar(), bool swapRB = false, bool crop = false, int ddepth = CV_32F)
        [DllImport(LIBNAME)]
        private static extern IntPtr dnn_Dnn_blobFromImages_10(IntPtr images_mat_nativeObj, double scalefactor, 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, int ddepth);
        [DllImport(LIBNAME)]
        private static extern IntPtr dnn_Dnn_blobFromImages_11(IntPtr images_mat_nativeObj, double scalefactor, 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 IntPtr dnn_Dnn_blobFromImages_12(IntPtr images_mat_nativeObj, double scalefactor, 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 IntPtr dnn_Dnn_blobFromImages_13(IntPtr images_mat_nativeObj, double scalefactor, double size_width, double size_height, double mean_val0, double mean_val1, double mean_val2, double mean_val3);
        [DllImport(LIBNAME)]
        private static extern IntPtr dnn_Dnn_blobFromImages_14(IntPtr images_mat_nativeObj, double scalefactor, double size_width, double size_height);
        [DllImport(LIBNAME)]
        private static extern IntPtr dnn_Dnn_blobFromImages_15(IntPtr images_mat_nativeObj, double scalefactor);
        [DllImport(LIBNAME)]
        private static extern IntPtr dnn_Dnn_blobFromImages_16(IntPtr images_mat_nativeObj);

        // C++:  Mat cv::dnn::blobFromImageWithParams(Mat image, Image2BlobParams param = Image2BlobParams())
        [DllImport(LIBNAME)]
        private static extern IntPtr dnn_Dnn_blobFromImageWithParams_10(IntPtr image_nativeObj, IntPtr param_nativeObj);
        [DllImport(LIBNAME)]
        private static extern IntPtr dnn_Dnn_blobFromImageWithParams_11(IntPtr image_nativeObj);

        // C++:  void cv::dnn::blobFromImageWithParams(Mat image, Mat& blob, Image2BlobParams param = Image2BlobParams())
        [DllImport(LIBNAME)]
        private static extern void dnn_Dnn_blobFromImageWithParams_12(IntPtr image_nativeObj, IntPtr blob_nativeObj, IntPtr param_nativeObj);
        [DllImport(LIBNAME)]
        private static extern void dnn_Dnn_blobFromImageWithParams_13(IntPtr image_nativeObj, IntPtr blob_nativeObj);

        // C++:  Mat cv::dnn::blobFromImagesWithParams(vector_Mat images, Image2BlobParams param = Image2BlobParams())
        [DllImport(LIBNAME)]
        private static extern IntPtr dnn_Dnn_blobFromImagesWithParams_10(IntPtr images_mat_nativeObj, IntPtr param_nativeObj);
        [DllImport(LIBNAME)]
        private static extern IntPtr dnn_Dnn_blobFromImagesWithParams_11(IntPtr images_mat_nativeObj);

        // C++:  void cv::dnn::blobFromImagesWithParams(vector_Mat images, Mat& blob, Image2BlobParams param = Image2BlobParams())
        [DllImport(LIBNAME)]
        private static extern void dnn_Dnn_blobFromImagesWithParams_12(IntPtr images_mat_nativeObj, IntPtr blob_nativeObj, IntPtr param_nativeObj);
        [DllImport(LIBNAME)]
        private static extern void dnn_Dnn_blobFromImagesWithParams_13(IntPtr images_mat_nativeObj, IntPtr blob_nativeObj);

        // C++:  void cv::dnn::imagesFromBlob(Mat blob_, vector_Mat& images_)
        [DllImport(LIBNAME)]
        private static extern void dnn_Dnn_imagesFromBlob_10(IntPtr blob__nativeObj, IntPtr images__mat_nativeObj);

        // C++:  void cv::dnn::shrinkCaffeModel(String src, String dst, vector_String layersTypes = std::vector<String>())
        [DllImport(LIBNAME)]
        private static extern void dnn_Dnn_shrinkCaffeModel_10(string src, string dst, IntPtr layersTypes_mat_nativeObj);
        [DllImport(LIBNAME)]
        private static extern void dnn_Dnn_shrinkCaffeModel_11(string src, string dst);

        // C++:  void cv::dnn::writeTextGraph(String model, String output)
        [DllImport(LIBNAME)]
        private static extern void dnn_Dnn_writeTextGraph_10(string model, string output);

        // C++:  void cv::dnn::NMSBoxes(vector_Rect2d bboxes, vector_float scores, float score_threshold, float nms_threshold, vector_int& indices, float eta = 1.f, int top_k = 0)
        [DllImport(LIBNAME)]
        private static extern void dnn_Dnn_NMSBoxes_10(IntPtr bboxes_mat_nativeObj, IntPtr scores_mat_nativeObj, float score_threshold, float nms_threshold, IntPtr indices_mat_nativeObj, float eta, int top_k);
        [DllImport(LIBNAME)]
        private static extern void dnn_Dnn_NMSBoxes_11(IntPtr bboxes_mat_nativeObj, IntPtr scores_mat_nativeObj, float score_threshold, float nms_threshold, IntPtr indices_mat_nativeObj, float eta);
        [DllImport(LIBNAME)]
        private static extern void dnn_Dnn_NMSBoxes_12(IntPtr bboxes_mat_nativeObj, IntPtr scores_mat_nativeObj, float score_threshold, float nms_threshold, IntPtr indices_mat_nativeObj);

        // C++:  void cv::dnn::NMSBoxes(vector_RotatedRect bboxes, vector_float scores, float score_threshold, float nms_threshold, vector_int& indices, float eta = 1.f, int top_k = 0)
        [DllImport(LIBNAME)]
        private static extern void dnn_Dnn_NMSBoxesRotated_10(IntPtr bboxes_mat_nativeObj, IntPtr scores_mat_nativeObj, float score_threshold, float nms_threshold, IntPtr indices_mat_nativeObj, float eta, int top_k);
        [DllImport(LIBNAME)]
        private static extern void dnn_Dnn_NMSBoxesRotated_11(IntPtr bboxes_mat_nativeObj, IntPtr scores_mat_nativeObj, float score_threshold, float nms_threshold, IntPtr indices_mat_nativeObj, float eta);
        [DllImport(LIBNAME)]
        private static extern void dnn_Dnn_NMSBoxesRotated_12(IntPtr bboxes_mat_nativeObj, IntPtr scores_mat_nativeObj, float score_threshold, float nms_threshold, IntPtr indices_mat_nativeObj);

        // C++:  void cv::dnn::NMSBoxesBatched(vector_Rect2d bboxes, vector_float scores, vector_int class_ids, float score_threshold, float nms_threshold, vector_int& indices, float eta = 1.f, int top_k = 0)
        [DllImport(LIBNAME)]
        private static extern void dnn_Dnn_NMSBoxesBatched_10(IntPtr bboxes_mat_nativeObj, IntPtr scores_mat_nativeObj, IntPtr class_ids_mat_nativeObj, float score_threshold, float nms_threshold, IntPtr indices_mat_nativeObj, float eta, int top_k);
        [DllImport(LIBNAME)]
        private static extern void dnn_Dnn_NMSBoxesBatched_11(IntPtr bboxes_mat_nativeObj, IntPtr scores_mat_nativeObj, IntPtr class_ids_mat_nativeObj, float score_threshold, float nms_threshold, IntPtr indices_mat_nativeObj, float eta);
        [DllImport(LIBNAME)]
        private static extern void dnn_Dnn_NMSBoxesBatched_12(IntPtr bboxes_mat_nativeObj, IntPtr scores_mat_nativeObj, IntPtr class_ids_mat_nativeObj, float score_threshold, float nms_threshold, IntPtr indices_mat_nativeObj);

        // C++:  void cv::dnn::softNMSBoxes(vector_Rect bboxes, vector_float scores, vector_float& updated_scores, float score_threshold, float nms_threshold, vector_int& indices, size_t top_k = 0, float sigma = 0.5, SoftNMSMethod method = SoftNMSMethod::SOFTNMS_GAUSSIAN)
        [DllImport(LIBNAME)]
        private static extern void dnn_Dnn_softNMSBoxes_10(IntPtr bboxes_mat_nativeObj, IntPtr scores_mat_nativeObj, IntPtr updated_scores_mat_nativeObj, float score_threshold, float nms_threshold, IntPtr indices_mat_nativeObj, long top_k, float sigma);
        [DllImport(LIBNAME)]
        private static extern void dnn_Dnn_softNMSBoxes_12(IntPtr bboxes_mat_nativeObj, IntPtr scores_mat_nativeObj, IntPtr updated_scores_mat_nativeObj, float score_threshold, float nms_threshold, IntPtr indices_mat_nativeObj, long top_k);
        [DllImport(LIBNAME)]
        private static extern void dnn_Dnn_softNMSBoxes_13(IntPtr bboxes_mat_nativeObj, IntPtr scores_mat_nativeObj, IntPtr updated_scores_mat_nativeObj, float score_threshold, float nms_threshold, IntPtr indices_mat_nativeObj);

        // C++:  String cv::dnn::getInferenceEngineBackendType()
        [DllImport(LIBNAME)]
        private static extern IntPtr dnn_Dnn_getInferenceEngineBackendType_10();

        // C++:  String cv::dnn::setInferenceEngineBackendType(String newBackendType)
        [DllImport(LIBNAME)]
        private static extern IntPtr dnn_Dnn_setInferenceEngineBackendType_10(string newBackendType);

        // C++:  void cv::dnn::resetMyriadDevice()
        [DllImport(LIBNAME)]
        private static extern void dnn_Dnn_resetMyriadDevice_10();

        // C++:  String cv::dnn::getInferenceEngineVPUType()
        [DllImport(LIBNAME)]
        private static extern IntPtr dnn_Dnn_getInferenceEngineVPUType_10();

        // C++:  String cv::dnn::getInferenceEngineCPUType()
        [DllImport(LIBNAME)]
        private static extern IntPtr dnn_Dnn_getInferenceEngineCPUType_10();

        // C++:  void cv::dnn::releaseHDDLPlugin()
        [DllImport(LIBNAME)]
        private static extern void dnn_Dnn_releaseHDDLPlugin_10();

    }
}

#endif