feat: 切换后端至PaddleOCR-NCNN，切换工程为CMake

1.项目后端整体迁移至PaddleOCR-NCNN算法，已通过基本的兼容性测试
2.工程改为使用CMake组织，后续为了更好地兼容第三方库，不再提供QMake工程
3.重整权利声明文件，重整代码工程，确保最小化侵权风险

Log: 切换后端至PaddleOCR-NCNN，切换工程为CMake
Change-Id: I4d5d2c5d37505a4a24b389b1a4c5d12f17bfa38c

3rdparty/opencv-4.5.4/modules/dnn/include/opencv2/dnn.hpp

@@ -0,0 +1,78 @@
+/*M///////////////////////////////////////////////////////////////////////////////////////
+//
+//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
+//
+//  By downloading, copying, installing or using the software you agree to this license.
+//  If you do not agree to this license, do not download, install,
+//  copy or use the software.
+//
+//
+//                           License Agreement
+//                For Open Source Computer Vision Library
+//
+// Copyright (C) 2013, OpenCV Foundation, all rights reserved.
+// Third party copyrights are property of their respective owners.
+//
+// Redistribution and use in source and binary forms, with or without modification,
+// are permitted provided that the following conditions are met:
+//
+//   * Redistribution's of source code must retain the above copyright notice,
+//     this list of conditions and the following disclaimer.
+//
+//   * Redistribution's in binary form must reproduce the above copyright notice,
+//     this list of conditions and the following disclaimer in the documentation
+//     and/or other materials provided with the distribution.
+//
+//   * The name of the copyright holders may not be used to endorse or promote products
+//     derived from this software without specific prior written permission.
+//
+// This software is provided by the copyright holders and contributors "as is" and
+// any express or implied warranties, including, but not limited to, the implied
+// warranties of merchantability and fitness for a particular purpose are disclaimed.
+// In no event shall the Intel Corporation or contributors be liable for any direct,
+// indirect, incidental, special, exemplary, or consequential damages
+// (including, but not limited to, procurement of substitute goods or services;
+// loss of use, data, or profits; or business interruption) however caused
+// and on any theory of liability, whether in contract, strict liability,
+// or tort (including negligence or otherwise) arising in any way out of
+// the use of this software, even if advised of the possibility of such damage.
+//
+//M*/
+
+#ifndef OPENCV_DNN_HPP
+#define OPENCV_DNN_HPP
+
+// This is an umbrella header to include into you project.
+// We are free to change headers layout in dnn subfolder, so please include
+// this header for future compatibility
+
+
+/** @defgroup dnn Deep Neural Network module
+  @{
+    This module contains:
+        - API for new layers creation, layers are building bricks of neural networks;
+        - set of built-in most-useful Layers;
+        - API to construct and modify comprehensive neural networks from layers;
+        - functionality for loading serialized networks models from different frameworks.
+
+    Functionality of this module is designed only for forward pass computations (i.e. network testing).
+    A network training is in principle not supported.
+  @}
+*/
+/** @example samples/dnn/classification.cpp
+Check @ref tutorial_dnn_googlenet "the corresponding tutorial" for more details
+*/
+/** @example samples/dnn/colorization.cpp
+*/
+/** @example samples/dnn/object_detection.cpp
+Check @ref tutorial_dnn_yolo "the corresponding tutorial" for more details
+*/
+/** @example samples/dnn/openpose.cpp
+*/
+/** @example samples/dnn/segmentation.cpp
+*/
+/** @example samples/dnn/text_detection.cpp
+*/
+#include <opencv2/dnn/dnn.hpp>
+
+#endif /* OPENCV_DNN_HPP */

3rdparty/opencv-4.5.4/modules/dnn/include/opencv2/dnn/all_layers.hpp

@@ -0,0 +1,832 @@
+/*M///////////////////////////////////////////////////////////////////////////////////////
+//
+//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
+//
+//  By downloading, copying, installing or using the software you agree to this license.
+//  If you do not agree to this license, do not download, install,
+//  copy or use the software.
+//
+//
+//                           License Agreement
+//                For Open Source Computer Vision Library
+//
+// Copyright (C) 2013, OpenCV Foundation, all rights reserved.
+// Third party copyrights are property of their respective owners.
+//
+// Redistribution and use in source and binary forms, with or without modification,
+// are permitted provided that the following conditions are met:
+//
+//   * Redistribution's of source code must retain the above copyright notice,
+//     this list of conditions and the following disclaimer.
+//
+//   * Redistribution's in binary form must reproduce the above copyright notice,
+//     this list of conditions and the following disclaimer in the documentation
+//     and/or other materials provided with the distribution.
+//
+//   * The name of the copyright holders may not be used to endorse or promote products
+//     derived from this software without specific prior written permission.
+//
+// This software is provided by the copyright holders and contributors "as is" and
+// any express or implied warranties, including, but not limited to, the implied
+// warranties of merchantability and fitness for a particular purpose are disclaimed.
+// In no event shall the Intel Corporation or contributors be liable for any direct,
+// indirect, incidental, special, exemplary, or consequential damages
+// (including, but not limited to, procurement of substitute goods or services;
+// loss of use, data, or profits; or business interruption) however caused
+// and on any theory of liability, whether in contract, strict liability,
+// or tort (including negligence or otherwise) arising in any way out of
+// the use of this software, even if advised of the possibility of such damage.
+//
+//M*/
+
+#ifndef OPENCV_DNN_DNN_ALL_LAYERS_HPP
+#define OPENCV_DNN_DNN_ALL_LAYERS_HPP
+#include <opencv2/dnn.hpp>
+
+namespace cv {
+namespace dnn {
+CV__DNN_INLINE_NS_BEGIN
+//! @addtogroup dnn
+//! @{
+
+/** @defgroup dnnLayerList Partial List of Implemented Layers
+  @{
+  This subsection of dnn module contains information about built-in layers and their descriptions.
+
+  Classes listed here, in fact, provides C++ API for creating instances of built-in layers.
+  In addition to this way of layers instantiation, there is a more common factory API (see @ref dnnLayerFactory), it allows to create layers dynamically (by name) and register new ones.
+  You can use both API, but factory API is less convenient for native C++ programming and basically designed for use inside importers (see @ref readNetFromCaffe(), @ref readNetFromTorch(), @ref readNetFromTensorflow()).
+
+  Built-in layers partially reproduce functionality of corresponding Caffe and Torch7 layers.
+  In particular, the following layers and Caffe importer were tested to reproduce <a href="http://caffe.berkeleyvision.org/tutorial/layers.html">Caffe</a> functionality:
+  - Convolution
+  - Deconvolution
+  - Pooling
+  - InnerProduct
+  - TanH, ReLU, Sigmoid, BNLL, Power, AbsVal
+  - Softmax
+  - Reshape, Flatten, Slice, Split
+  - LRN
+  - MVN
+  - Dropout (since it does nothing on forward pass -))
+*/
+
+    class CV_EXPORTS BlankLayer : public Layer
+    {
+    public:
+        static Ptr<Layer> create(const LayerParams &params);
+    };
+
+    /**
+     * Constant layer produces the same data blob at an every forward pass.
+     */
+    class CV_EXPORTS ConstLayer : public Layer
+    {
+    public:
+        static Ptr<Layer> create(const LayerParams &params);
+    };
+
+    //! LSTM recurrent layer
+    class CV_EXPORTS LSTMLayer : public Layer
+    {
+    public:
+        /** Creates instance of LSTM layer */
+        static Ptr<LSTMLayer> create(const LayerParams& params);
+
+        /** @deprecated Use LayerParams::blobs instead.
+        @brief Set trained weights for LSTM layer.
+
+        LSTM behavior on each step is defined by current input, previous output, previous cell state and learned weights.
+
+        Let @f$x_t@f$ be current input, @f$h_t@f$ be current output, @f$c_t@f$ be current state.
+        Than current output and current cell state is computed as follows:
+        @f{eqnarray*}{
+        h_t &= o_t \odot tanh(c_t),               \\
+        c_t &= f_t \odot c_{t-1} + i_t \odot g_t, \\
+        @f}
+        where @f$\odot@f$ is per-element multiply operation and @f$i_t, f_t, o_t, g_t@f$ is internal gates that are computed using learned weights.
+
+        Gates are computed as follows:
+        @f{eqnarray*}{
+        i_t &= sigmoid&(W_{xi} x_t + W_{hi} h_{t-1} + b_i), \\
+        f_t &= sigmoid&(W_{xf} x_t + W_{hf} h_{t-1} + b_f), \\
+        o_t &= sigmoid&(W_{xo} x_t + W_{ho} h_{t-1} + b_o), \\
+        g_t &= tanh   &(W_{xg} x_t + W_{hg} h_{t-1} + b_g), \\
+        @f}
+        where @f$W_{x?}@f$, @f$W_{h?}@f$ and @f$b_{?}@f$ are learned weights represented as matrices:
+        @f$W_{x?} \in R^{N_h \times N_x}@f$, @f$W_{h?} \in R^{N_h \times N_h}@f$, @f$b_? \in R^{N_h}@f$.
+
+        For simplicity and performance purposes we use @f$ W_x = [W_{xi}; W_{xf}; W_{xo}, W_{xg}] @f$
+        (i.e. @f$W_x@f$ is vertical concatenation of @f$ W_{x?} @f$), @f$ W_x \in R^{4N_h \times N_x} @f$.
+        The same for @f$ W_h = [W_{hi}; W_{hf}; W_{ho}, W_{hg}], W_h \in R^{4N_h \times N_h} @f$
+        and for @f$ b = [b_i; b_f, b_o, b_g]@f$, @f$b \in R^{4N_h} @f$.
+
+        @param Wh is matrix defining how previous output is transformed to internal gates (i.e. according to above mentioned notation is @f$ W_h @f$)
+        @param Wx is matrix defining how current input is transformed to internal gates (i.e. according to above mentioned notation is @f$ W_x @f$)
+        @param b  is bias vector (i.e. according to above mentioned notation is @f$ b @f$)
+        */
+        CV_DEPRECATED virtual void setWeights(const Mat &Wh, const Mat &Wx, const Mat &b) = 0;
+
+        /** @brief Specifies shape of output blob which will be [[`T`], `N`] + @p outTailShape.
+          * @details If this parameter is empty or unset then @p outTailShape = [`Wh`.size(0)] will be used,
+          * where `Wh` is parameter from setWeights().
+          */
+        virtual void setOutShape(const MatShape &outTailShape = MatShape()) = 0;
+
+        /** @deprecated Use flag `produce_cell_output` in LayerParams.
+          * @brief Specifies either interpret first dimension of input blob as timestamp dimension either as sample.
+          *
+          * If flag is set to true then shape of input blob will be interpreted as [`T`, `N`, `[data dims]`] where `T` specifies number of timestamps, `N` is number of independent streams.
+          * In this case each forward() call will iterate through `T` timestamps and update layer's state `T` times.
+          *
+          * If flag is set to false then shape of input blob will be interpreted as [`N`, `[data dims]`].
+          * In this case each forward() call will make one iteration and produce one timestamp with shape [`N`, `[out dims]`].
+          */
+        CV_DEPRECATED virtual void setUseTimstampsDim(bool use = true) = 0;
+
+        /** @deprecated Use flag `use_timestamp_dim` in LayerParams.
+         * @brief If this flag is set to true then layer will produce @f$ c_t @f$ as second output.
+         * @details Shape of the second output is the same as first output.
+         */
+        CV_DEPRECATED virtual void setProduceCellOutput(bool produce = false) = 0;
+
+        /* In common case it use single input with @f$x_t@f$ values to compute output(s) @f$h_t@f$ (and @f$c_t@f$).
+         * @param input should contain packed values @f$x_t@f$
+         * @param output contains computed outputs: @f$h_t@f$ (and @f$c_t@f$ if setProduceCellOutput() flag was set to true).
+         *
+         * If setUseTimstampsDim() is set to true then @p input[0] should has at least two dimensions with the following shape: [`T`, `N`, `[data dims]`],
+         * where `T` specifies number of timestamps, `N` is number of independent streams (i.e. @f$ x_{t_0 + t}^{stream} @f$ is stored inside @p input[0][t, stream, ...]).
+         *
+         * If setUseTimstampsDim() is set to false then @p input[0] should contain single timestamp, its shape should has form [`N`, `[data dims]`] with at least one dimension.
+         * (i.e. @f$ x_{t}^{stream} @f$ is stored inside @p input[0][stream, ...]).
+        */
+
+        int inputNameToIndex(String inputName) CV_OVERRIDE;
+        int outputNameToIndex(const String& outputName) CV_OVERRIDE;
+    };
+
+    /** @brief GRU recurrent one-layer
+     *
+     * Accepts input sequence and computes the final hidden state for each element in the batch.
+     *
+     * - input[0] containing the features of the input sequence.
+     * input[0] should have shape [`T`, `N`, `data_dims`] where `T` is sequence length, `N` is batch size, `data_dims` is input size
+     * - output would have shape [`T`, `N`, `D` * `hidden_size`] where `D = 2` if layer is bidirectional otherwise `D = 1`
+     *
+     * Depends on the following attributes:
+     * - hidden_size - Number of neurons in the hidden layer
+     * - direction - RNN could be bidirectional or forward
+     *
+     * The final hidden state @f$ h_t @f$ computes by the following formulas:
+     *
+     @f{eqnarray*}{
+     r_t = \sigma(W_{ir} x_t + b_{ir} + W_{hr} h_{(t-1)} + b_{hr}) \\
+     z_t = \sigma(W_{iz} x_t + b_{iz} + W_{hz} h_{(t-1)} + b_{hz}) \\
+     n_t = \tanh(W_{in} x_t + b_{in} + r_t \odot (W_{hn} h_{(t-1)}+ b_{hn})) \\
+     h_t = (1 - z_t) \odot n_t + z_t \odot h_{(t-1)} \\
+     @f}
+     * Where @f$x_t@f$ is current input, @f$h_{(t-1)}@f$ is previous or initial hidden state.
+     *
+     * @f$W_{x?}@f$, @f$W_{h?}@f$ and @f$b_{?}@f$ are learned weights represented as matrices:
+     * @f$W_{x?} \in R^{N_h \times N_x}@f$, @f$W_{h?} \in R^{N_h \times N_h}@f$, @f$b_? \in R^{N_h}@f$.
+     *
+     * @f$\odot@f$ is per-element multiply operation.
+    */
+    class CV_EXPORTS GRULayer : public Layer
+    {
+    public:
+        /** Creates instance of GRU layer */
+        static Ptr<GRULayer> create(const LayerParams& params);
+    };
+
+    /** @brief Classical recurrent layer
+
+    Accepts two inputs @f$x_t@f$ and @f$h_{t-1}@f$ and compute two outputs @f$o_t@f$ and @f$h_t@f$.
+
+    - input: should contain packed input @f$x_t@f$.
+    - output: should contain output @f$o_t@f$ (and @f$h_t@f$ if setProduceHiddenOutput() is set to true).
+
+    input[0] should have shape [`T`, `N`, `data_dims`] where `T` and `N` is number of timestamps and number of independent samples of @f$x_t@f$ respectively.
+
+    output[0] will have shape [`T`, `N`, @f$N_o@f$], where @f$N_o@f$ is number of rows in @f$ W_{xo} @f$ matrix.
+
+    If setProduceHiddenOutput() is set to true then @p output[1] will contain a Mat with shape [`T`, `N`, @f$N_h@f$], where @f$N_h@f$ is number of rows in @f$ W_{hh} @f$ matrix.
+    */
+    class CV_EXPORTS RNNLayer : public Layer
+    {
+    public:
+        /** Creates instance of RNNLayer */
+        static Ptr<RNNLayer> create(const LayerParams& params);
+
+        /** Setups learned weights.
+
+        Recurrent-layer behavior on each step is defined by current input @f$ x_t @f$, previous state @f$ h_t @f$ and learned weights as follows:
+        @f{eqnarray*}{
+        h_t &= tanh&(W_{hh} h_{t-1} + W_{xh} x_t + b_h),  \\
+        o_t &= tanh&(W_{ho} h_t + b_o),
+        @f}
+
+        @param Wxh is @f$ W_{xh} @f$ matrix
+        @param bh  is @f$ b_{h}  @f$ vector
+        @param Whh is @f$ W_{hh} @f$ matrix
+        @param Who is @f$ W_{xo} @f$ matrix
+        @param bo  is @f$ b_{o}  @f$ vector
+        */
+        virtual void setWeights(const Mat &Wxh, const Mat &bh, const Mat &Whh, const Mat &Who, const Mat &bo) = 0;
+
+        /** @brief If this flag is set to true then layer will produce @f$ h_t @f$ as second output.
+         * @details Shape of the second output is the same as first output.
+         */
+        virtual void setProduceHiddenOutput(bool produce = false) = 0;
+
+    };
+
+    class CV_EXPORTS BaseConvolutionLayer : public Layer
+    {
+    public:
+        CV_DEPRECATED_EXTERNAL Size kernel, stride, pad, dilation, adjustPad;
+        std::vector<size_t> adjust_pads;
+        std::vector<size_t> kernel_size, strides, dilations;
+        std::vector<size_t> pads_begin, pads_end;
+        String padMode;
+        int numOutput;
+    };
+
+    class CV_EXPORTS ConvolutionLayer : public BaseConvolutionLayer
+    {
+    public:
+        static Ptr<BaseConvolutionLayer> create(const LayerParams& params);
+    };
+
+    class CV_EXPORTS ConvolutionLayerInt8 : public BaseConvolutionLayer
+    {
+    public:
+        int input_zp, output_zp;
+        float output_sc;
+        static Ptr<BaseConvolutionLayer> create(const LayerParams& params);
+    };
+
+    class CV_EXPORTS DeconvolutionLayer : public BaseConvolutionLayer
+    {
+    public:
+        static Ptr<BaseConvolutionLayer> create(const LayerParams& params);
+    };
+
+    class CV_EXPORTS LRNLayer : public Layer
+    {
+    public:
+        int type;
+
+        int size;
+        float alpha, beta, bias;
+        bool normBySize;
+
+        static Ptr<LRNLayer> create(const LayerParams& params);
+    };
+
+    class CV_EXPORTS PoolingLayer : public Layer
+    {
+    public:
+        int type;
+        std::vector<size_t> kernel_size, strides;
+        std::vector<size_t> pads_begin, pads_end;
+        bool globalPooling; //!< Flag is true if at least one of the axes is global pooled.
+        std::vector<bool> isGlobalPooling;
+        bool computeMaxIdx;
+        String padMode;
+        bool ceilMode;
+        // If true for average pooling with padding, divide an every output region
+        // by a whole kernel area. Otherwise exclude zero padded values and divide
+        // by number of real values.
+        bool avePoolPaddedArea;
+        // ROIPooling parameters.
+        Size pooledSize;
+        float spatialScale;
+        // PSROIPooling parameters.
+        int psRoiOutChannels;
+
+        static Ptr<PoolingLayer> create(const LayerParams& params);
+    };
+
+    class CV_EXPORTS PoolingLayerInt8 : public PoolingLayer
+    {
+    public:
+        int input_zp, output_zp;
+        static Ptr<PoolingLayerInt8> create(const LayerParams& params);
+    };
+
+    class CV_EXPORTS SoftmaxLayer : public Layer
+    {
+    public:
+        bool logSoftMax;
+
+        static Ptr<SoftmaxLayer> create(const LayerParams& params);
+    };
+
+    class CV_EXPORTS SoftmaxLayerInt8 : public SoftmaxLayer
+    {
+    public:
+        float output_sc;
+        int output_zp;
+        static Ptr<SoftmaxLayerInt8> create(const LayerParams& params);
+    };
+
+    class CV_EXPORTS InnerProductLayer : public Layer
+    {
+    public:
+        int axis;
+        static Ptr<InnerProductLayer> create(const LayerParams& params);
+    };
+
+    class CV_EXPORTS InnerProductLayerInt8 : public InnerProductLayer
+    {
+    public:
+        int output_zp;
+        static Ptr<InnerProductLayerInt8> create(const LayerParams& params);
+    };
+
+    class CV_EXPORTS MVNLayer : public Layer
+    {
+    public:
+        float eps;
+        bool normVariance, acrossChannels;
+
+        static Ptr<MVNLayer> create(const LayerParams& params);
+    };
+
+    /* Reshaping */
+
+    class CV_EXPORTS ReshapeLayer : public Layer
+    {
+    public:
+        MatShape newShapeDesc;
+        Range newShapeRange;
+
+        static Ptr<ReshapeLayer> create(const LayerParams& params);
+    };
+
+    class CV_EXPORTS FlattenLayer : public Layer
+    {
+    public:
+        static Ptr<FlattenLayer> create(const LayerParams &params);
+    };
+
+    class CV_EXPORTS QuantizeLayer : public Layer
+    {
+    public:
+        float scale;
+        int zeropoint;
+        static Ptr<QuantizeLayer> create(const LayerParams &params);
+    };
+
+    class CV_EXPORTS DequantizeLayer : public Layer
+    {
+    public:
+        float scale;
+        int zeropoint;
+        static Ptr<DequantizeLayer> create(const LayerParams &params);
+    };
+
+    class CV_EXPORTS RequantizeLayer : public Layer
+    {
+    public:
+        float scale, shift;
+        static Ptr<RequantizeLayer> create(const LayerParams &params);
+    };
+
+    class CV_EXPORTS ConcatLayer : public Layer
+    {
+    public:
+        int axis;
+        /**
+         * @brief Add zero padding in case of concatenation of blobs with different
+         * spatial sizes.
+         *
+         * Details: https://github.com/torch/nn/blob/master/doc/containers.md#depthconcat
+         */
+        bool padding;
+        int paddingValue;
+
+        static Ptr<ConcatLayer> create(const LayerParams &params);
+    };
+
+    class CV_EXPORTS SplitLayer : public Layer
+    {
+    public:
+        int outputsCount; //!< Number of copies that will be produced (is ignored when negative).
+
+        static Ptr<SplitLayer> create(const LayerParams &params);
+    };
+
+    /**
+     * Slice layer has several modes:
+     * 1. Caffe mode
+     * @param[in] axis Axis of split operation
+     * @param[in] slice_point Array of split points
+     *
+     * Number of output blobs equals to number of split points plus one. The
+     * first blob is a slice on input from 0 to @p slice_point[0] - 1 by @p axis,
+     * the second output blob is a slice of input from @p slice_point[0] to
+     * @p slice_point[1] - 1 by @p axis and the last output blob is a slice of
+     * input from @p slice_point[-1] up to the end of @p axis size.
+     *
+     * 2. TensorFlow mode
+     * @param begin Vector of start indices
+     * @param size Vector of sizes
+     *
+     * More convenient numpy-like slice. One and only output blob
+     * is a slice `input[begin[0]:begin[0]+size[0], begin[1]:begin[1]+size[1], ...]`
+     *
+     * 3. Torch mode
+     * @param axis Axis of split operation
+     *
+     * Split input blob on the equal parts by @p axis.
+     */
+    class CV_EXPORTS SliceLayer : public Layer
+    {
+    public:
+        /**
+         * @brief Vector of slice ranges.
+         *
+         * The first dimension equals number of output blobs.
+         * Inner vector has slice ranges for the first number of input dimensions.
+         */
+        std::vector<std::vector<Range> > sliceRanges;
+        std::vector<std::vector<int> > sliceSteps;
+        int axis;
+        int num_split;
+
+        static Ptr<SliceLayer> create(const LayerParams &params);
+    };
+
+    class CV_EXPORTS PermuteLayer : public Layer
+    {
+    public:
+        static Ptr<PermuteLayer> create(const LayerParams& params);
+    };
+
+    /**
+     * Permute channels of 4-dimensional input blob.
+     * @param group Number of groups to split input channels and pick in turns
+     *              into output blob.
+     *
+     * \f[ groupSize = \frac{number\ of\ channels}{group} \f]
+     * \f[ output(n, c, h, w) = input(n, groupSize \times (c \% group) + \lfloor \frac{c}{group} \rfloor, h, w) \f]
+     * Read more at https://arxiv.org/pdf/1707.01083.pdf
+     */
+    class CV_EXPORTS ShuffleChannelLayer : public Layer
+    {
+    public:
+        static Ptr<Layer> create(const LayerParams& params);
+
+        int group;
+    };
+
+    /**
+     * @brief Adds extra values for specific axes.
+     * @param paddings Vector of paddings in format
+     *                 @code
+     *                 [ pad_before, pad_after,  // [0]th dimension
+     *                   pad_before, pad_after,  // [1]st dimension
+     *                   ...
+     *                   pad_before, pad_after ] // [n]th dimension
+     *                 @endcode
+     *                 that represents number of padded values at every dimension
+     *                 starting from the first one. The rest of dimensions won't
+     *                 be padded.
+     * @param value Value to be padded. Defaults to zero.
+     * @param type Padding type: 'constant', 'reflect'
+     * @param input_dims Torch's parameter. If @p input_dims is not equal to the
+     *                   actual input dimensionality then the `[0]th` dimension
+     *                   is considered as a batch dimension and @p paddings are shifted
+     *                   to a one dimension. Defaults to `-1` that means padding
+     *                   corresponding to @p paddings.
+     */
+    class CV_EXPORTS PaddingLayer : public Layer
+    {
+    public:
+        static Ptr<PaddingLayer> create(const LayerParams& params);
+    };
+
+    /* Activations */
+    class CV_EXPORTS ActivationLayer : public Layer
+    {
+    public:
+        virtual void forwardSlice(const float* src, float* dst, int len,
+                                  size_t outPlaneSize, int cn0, int cn1) const {};
+        virtual void forwardSlice(const int* src, const int* lut, int* dst, int len,
+                                  size_t outPlaneSize, int cn0, int cn1) const {};
+        virtual void forwardSlice(const int8_t* src, const int8_t* lut, int8_t* dst, int len,
+                                  size_t outPlaneSize, int cn0, int cn1) const {};
+    };
+
+    class CV_EXPORTS ReLULayer : public ActivationLayer
+    {
+    public:
+        float negativeSlope;
+
+        static Ptr<ReLULayer> create(const LayerParams &params);
+    };
+
+    class CV_EXPORTS ReLU6Layer : public ActivationLayer
+    {
+    public:
+        float minValue, maxValue;
+
+        static Ptr<ReLU6Layer> create(const LayerParams &params);
+    };
+
+    class CV_EXPORTS ChannelsPReLULayer : public ActivationLayer
+    {
+    public:
+        static Ptr<Layer> create(const LayerParams& params);
+    };
+
+    class CV_EXPORTS ELULayer : public ActivationLayer
+    {
+    public:
+        static Ptr<ELULayer> create(const LayerParams &params);
+    };
+
+    class CV_EXPORTS TanHLayer : public ActivationLayer
+    {
+    public:
+        static Ptr<TanHLayer> create(const LayerParams &params);
+    };
+
+    class CV_EXPORTS SwishLayer : public ActivationLayer
+    {
+    public:
+        static Ptr<SwishLayer> create(const LayerParams &params);
+    };
+
+    class CV_EXPORTS MishLayer : public ActivationLayer
+    {
+    public:
+        static Ptr<MishLayer> create(const LayerParams &params);
+    };
+
+    class CV_EXPORTS SigmoidLayer : public ActivationLayer
+    {
+    public:
+        static Ptr<SigmoidLayer> create(const LayerParams &params);
+    };
+
+    class CV_EXPORTS BNLLLayer : public ActivationLayer
+    {
+    public:
+        static Ptr<BNLLLayer> create(const LayerParams &params);
+    };
+
+    class CV_EXPORTS AbsLayer : public ActivationLayer
+    {
+    public:
+        static Ptr<AbsLayer> create(const LayerParams &params);
+    };
+
+    class CV_EXPORTS PowerLayer : public ActivationLayer
+    {
+    public:
+        float power, scale, shift;
+
+        static Ptr<PowerLayer> create(const LayerParams &params);
+    };
+
+    class CV_EXPORTS ExpLayer : public ActivationLayer
+    {
+    public:
+        float base, scale, shift;
+
+        static Ptr<ExpLayer> create(const LayerParams &params);
+    };
+
+    class CV_EXPORTS ActivationLayerInt8 : public ActivationLayer
+    {
+    public:
+        static Ptr<ActivationLayerInt8> create(const LayerParams &params);
+    };
+
+    /* Layers used in semantic segmentation */
+
+    class CV_EXPORTS CropLayer : public Layer
+    {
+    public:
+        static Ptr<Layer> create(const LayerParams &params);
+    };
+
+    /** @brief Element wise operation on inputs
+
+    Extra optional parameters:
+    - "operation" as string. Values are "sum" (default), "prod", "max", "div", "min"
+    - "coeff" as float array. Specify weights of inputs for SUM operation
+    - "output_channels_mode" as string. Values are "same" (default, all input must have the same layout), "input_0", "input_0_truncate", "max_input_channels"
+    */
+    class CV_EXPORTS EltwiseLayer : public Layer
+    {
+    public:
+        static Ptr<EltwiseLayer> create(const LayerParams &params);
+    };
+
+    class CV_EXPORTS EltwiseLayerInt8 : public Layer
+    {
+    public:
+        static Ptr<EltwiseLayerInt8> create(const LayerParams &params);
+    };
+
+    class CV_EXPORTS BatchNormLayer : public ActivationLayer
+    {
+    public:
+        bool hasWeights, hasBias;
+        float epsilon;
+
+        static Ptr<BatchNormLayer> create(const LayerParams &params);
+    };
+
+    class CV_EXPORTS BatchNormLayerInt8 : public BatchNormLayer
+    {
+    public:
+        float input_sc, output_sc;
+        int input_zp, output_zp;
+        static Ptr<BatchNormLayerInt8> create(const LayerParams &params);
+    };
+
+    class CV_EXPORTS MaxUnpoolLayer : public Layer
+    {
+    public:
+        Size poolKernel;
+        Size poolPad;
+        Size poolStride;
+
+        static Ptr<MaxUnpoolLayer> create(const LayerParams &params);
+    };
+
+    class CV_EXPORTS ScaleLayer : public Layer
+    {
+    public:
+        bool hasBias;
+        int axis;
+
+        static Ptr<ScaleLayer> create(const LayerParams& params);
+    };
+
+    class CV_EXPORTS ScaleLayerInt8 : public ScaleLayer
+    {
+    public:
+        float output_sc;
+        int output_zp;
+        static Ptr<ScaleLayerInt8> create(const LayerParams &params);
+    };
+
+    class CV_EXPORTS ShiftLayer : public Layer
+    {
+    public:
+        static Ptr<Layer> create(const LayerParams& params);
+    };
+
+    class CV_EXPORTS ShiftLayerInt8 : public Layer
+    {
+    public:
+        static Ptr<Layer> create(const LayerParams& params);
+    };
+
+    class CV_EXPORTS DataAugmentationLayer : public Layer
+    {
+    public:
+        static Ptr<DataAugmentationLayer> create(const LayerParams& params);
+    };
+
+    class CV_EXPORTS CorrelationLayer : public Layer
+    {
+    public:
+        static Ptr<CorrelationLayer> create(const LayerParams& params);
+    };
+
+    class CV_EXPORTS AccumLayer : public Layer
+    {
+    public:
+        static Ptr<AccumLayer> create(const LayerParams& params);
+    };
+
+    class CV_EXPORTS FlowWarpLayer : public Layer
+    {
+    public:
+        static Ptr<FlowWarpLayer> create(const LayerParams& params);
+    };
+
+    class CV_EXPORTS PriorBoxLayer : public Layer
+    {
+    public:
+        static Ptr<PriorBoxLayer> create(const LayerParams& params);
+    };
+
+    class CV_EXPORTS ReorgLayer : public Layer
+    {
+    public:
+        static Ptr<ReorgLayer> create(const LayerParams& params);
+    };
+
+    class CV_EXPORTS RegionLayer : public Layer
+    {
+    public:
+        float nmsThreshold;
+
+        static Ptr<RegionLayer> create(const LayerParams& params);
+    };
+
+    /**
+     * @brief Detection output layer.
+     *
+     * The layer size is: @f$ (1 \times 1 \times N \times 7) @f$
+     *    where N is [keep_top_k] parameter multiplied by batch size. Each row is:
+     *    [image_id, label, confidence, xmin, ymin, xmax, ymax]
+     *    where image_id is the index of image input in the batch.
+     */
+    class CV_EXPORTS DetectionOutputLayer : public Layer
+    {
+    public:
+        static Ptr<DetectionOutputLayer> create(const LayerParams& params);
+    };
+
+    /**
+     * @brief \f$ L_p \f$ - normalization layer.
+     * @param p Normalization factor. The most common `p = 1` for \f$ L_1 \f$ -
+     *          normalization or `p = 2` for \f$ L_2 \f$ - normalization or a custom one.
+     * @param eps Parameter \f$ \epsilon \f$ to prevent a division by zero.
+     * @param across_spatial If true, normalize an input across all non-batch dimensions.
+     *                       Otherwise normalize an every channel separately.
+     *
+     * Across spatial:
+     * @f[
+     * norm = \sqrt[p]{\epsilon + \sum_{x, y, c} |src(x, y, c)|^p } \\
+     * dst(x, y, c) = \frac{ src(x, y, c) }{norm}
+     * @f]
+     *
+     * Channel wise normalization:
+     * @f[
+     * norm(c) = \sqrt[p]{\epsilon + \sum_{x, y} |src(x, y, c)|^p } \\
+     * dst(x, y, c) = \frac{ src(x, y, c) }{norm(c)}
+     * @f]
+     *
+     * Where `x, y` - spatial coordinates, `c` - channel.
+     *
+     * An every sample in the batch is normalized separately. Optionally,
+     * output is scaled by the trained parameters.
+     */
+    class CV_EXPORTS NormalizeBBoxLayer : public Layer
+    {
+    public:
+        float pnorm, epsilon;
+        CV_DEPRECATED_EXTERNAL bool acrossSpatial;
+
+        static Ptr<NormalizeBBoxLayer> create(const LayerParams& params);
+    };
+
+    /**
+     * @brief Resize input 4-dimensional blob by nearest neighbor or bilinear strategy.
+     *
+     * Layer is used to support TensorFlow's resize_nearest_neighbor and resize_bilinear ops.
+     */
+    class CV_EXPORTS ResizeLayer : public Layer
+    {
+    public:
+        static Ptr<ResizeLayer> create(const LayerParams& params);
+    };
+
+    /**
+     * @brief Bilinear resize layer from https://github.com/cdmh/deeplab-public-ver2
+     *
+     * It differs from @ref ResizeLayer in output shape and resize scales computations.
+     */
+    class CV_EXPORTS InterpLayer : public Layer
+    {
+    public:
+        static Ptr<Layer> create(const LayerParams& params);
+    };
+
+    class CV_EXPORTS ProposalLayer : public Layer
+    {
+    public:
+        static Ptr<ProposalLayer> create(const LayerParams& params);
+    };
+
+    class CV_EXPORTS CropAndResizeLayer : public Layer
+    {
+    public:
+        static Ptr<Layer> create(const LayerParams& params);
+    };
+
+    class CV_EXPORTS CumSumLayer : public Layer
+    {
+    public:
+        int exclusive;
+        int reverse;
+
+        static Ptr<CumSumLayer> create(const LayerParams& params);
+    };
+
+//! @}
+//! @}
+CV__DNN_INLINE_NS_END
+}
+}
+#endif

3rdparty/opencv-4.5.4/modules/dnn/include/opencv2/dnn/dict.hpp

@@ -0,0 +1,160 @@
+/*M///////////////////////////////////////////////////////////////////////////////////////
+//
+//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
+//
+//  By downloading, copying, installing or using the software you agree to this license.
+//  If you do not agree to this license, do not download, install,
+//  copy or use the software.
+//
+//
+//                           License Agreement
+//                For Open Source Computer Vision Library
+//
+// Copyright (C) 2013, OpenCV Foundation, all rights reserved.
+// Third party copyrights are property of their respective owners.
+//
+// Redistribution and use in source and binary forms, with or without modification,
+// are permitted provided that the following conditions are met:
+//
+//   * Redistribution's of source code must retain the above copyright notice,
+//     this list of conditions and the following disclaimer.
+//
+//   * Redistribution's in binary form must reproduce the above copyright notice,
+//     this list of conditions and the following disclaimer in the documentation
+//     and/or other materials provided with the distribution.
+//
+//   * The name of the copyright holders may not be used to endorse or promote products
+//     derived from this software without specific prior written permission.
+//
+// This software is provided by the copyright holders and contributors "as is" and
+// any express or implied warranties, including, but not limited to, the implied
+// warranties of merchantability and fitness for a particular purpose are disclaimed.
+// In no event shall the Intel Corporation or contributors be liable for any direct,
+// indirect, incidental, special, exemplary, or consequential damages
+// (including, but not limited to, procurement of substitute goods or services;
+// loss of use, data, or profits; or business interruption) however caused
+// and on any theory of liability, whether in contract, strict liability,
+// or tort (including negligence or otherwise) arising in any way out of
+// the use of this software, even if advised of the possibility of such damage.
+//
+//M*/
+
+#include <opencv2/core.hpp>
+#include <map>
+#include <ostream>
+
+#include <opencv2/dnn/dnn.hpp>
+
+#ifndef OPENCV_DNN_DNN_DICT_HPP
+#define OPENCV_DNN_DNN_DICT_HPP
+
+namespace cv {
+namespace dnn {
+CV__DNN_INLINE_NS_BEGIN
+//! @addtogroup dnn
+//! @{
+
+/** @brief This struct stores the scalar value (or array) of one of the following type: double, cv::String or int64.
+ *  @todo Maybe int64 is useless because double type exactly stores at least 2^52 integers.
+ */
+struct CV_EXPORTS_W DictValue
+{
+    DictValue(const DictValue &r);
+    DictValue(bool i)           : type(Param::INT), pi(new AutoBuffer<int64,1>) { (*pi)[0] = i ? 1 : 0; }       //!< Constructs integer scalar
+    DictValue(int64 i = 0)      : type(Param::INT), pi(new AutoBuffer<int64,1>) { (*pi)[0] = i; }       //!< Constructs integer scalar
+    CV_WRAP DictValue(int i)    : type(Param::INT), pi(new AutoBuffer<int64,1>) { (*pi)[0] = i; }       //!< Constructs integer scalar
+    DictValue(unsigned p)       : type(Param::INT), pi(new AutoBuffer<int64,1>) { (*pi)[0] = p; }       //!< Constructs integer scalar
+    CV_WRAP DictValue(double p)         : type(Param::REAL), pd(new AutoBuffer<double,1>) { (*pd)[0] = p; }     //!< Constructs floating point scalar
+    CV_WRAP DictValue(const String &s)  : type(Param::STRING), ps(new AutoBuffer<String,1>) { (*ps)[0] = s; }   //!< Constructs string scalar
+    DictValue(const char *s)            : type(Param::STRING), ps(new AutoBuffer<String,1>) { (*ps)[0] = s; }   //!< @overload
+
+    template<typename TypeIter>
+    static DictValue arrayInt(TypeIter begin, int size);    //!< Constructs integer array
+    template<typename TypeIter>
+    static DictValue arrayReal(TypeIter begin, int size);   //!< Constructs floating point array
+    template<typename TypeIter>
+    static DictValue arrayString(TypeIter begin, int size); //!< Constructs array of strings
+
+    template<typename T>
+    T get(int idx = -1) const; //!< Tries to convert array element with specified index to requested type and returns its.
+
+    int size() const;
+
+    CV_WRAP bool isInt() const;
+    CV_WRAP bool isString() const;
+    CV_WRAP bool isReal() const;
+
+    CV_WRAP int getIntValue(int idx = -1) const;
+    CV_WRAP double getRealValue(int idx = -1) const;
+    CV_WRAP String getStringValue(int idx = -1) const;
+
+    DictValue &operator=(const DictValue &r);
+
+    friend std::ostream &operator<<(std::ostream &stream, const DictValue &dictv);
+
+    ~DictValue();
+
+private:
+
+    Param type;
+
+    union
+    {
+        AutoBuffer<int64, 1> *pi;
+        AutoBuffer<double, 1> *pd;
+        AutoBuffer<String, 1> *ps;
+        void *pv;
+    };
+
+    DictValue(Param _type, void *_p) : type(_type), pv(_p) {}
+    void release();
+};
+
+/** @brief This class implements name-value dictionary, values are instances of DictValue. */
+class CV_EXPORTS Dict
+{
+    typedef std::map<String, DictValue> _Dict;
+    _Dict dict;
+
+public:
+
+    //! Checks a presence of the @p key in the dictionary.
+    bool has(const String &key) const;
+
+    //! If the @p key in the dictionary then returns pointer to its value, else returns NULL.
+    DictValue *ptr(const String &key);
+
+    /** @overload */
+    const DictValue *ptr(const String &key) const;
+
+    //! If the @p key in the dictionary then returns its value, else an error will be generated.
+    const DictValue &get(const String &key) const;
+
+    /** @overload */
+    template <typename T>
+    T get(const String &key) const;
+
+    //! If the @p key in the dictionary then returns its value, else returns @p defaultValue.
+    template <typename T>
+    T get(const String &key, const T &defaultValue) const;
+
+    //! Sets new @p value for the @p key, or adds new key-value pair into the dictionary.
+    template<typename T>
+    const T &set(const String &key, const T &value);
+
+    //! Erase @p key from the dictionary.
+    void erase(const String &key);
+
+    friend std::ostream &operator<<(std::ostream &stream, const Dict &dict);
+
+    std::map<String, DictValue>::const_iterator begin() const;
+
+    std::map<String, DictValue>::const_iterator end() const;
+};
+
+//! @}
+CV__DNN_INLINE_NS_END
+}
+}
+
+#endif

3rdparty/opencv-4.5.4/modules/dnn/include/opencv2/dnn/dnn.inl.hpp

@@ -0,0 +1,412 @@
+/*M///////////////////////////////////////////////////////////////////////////////////////
+//
+//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
+//
+//  By downloading, copying, installing or using the software you agree to this license.
+//  If you do not agree to this license, do not download, install,
+//  copy or use the software.
+//
+//
+//                           License Agreement
+//                For Open Source Computer Vision Library
+//
+// Copyright (C) 2013, OpenCV Foundation, all rights reserved.
+// Third party copyrights are property of their respective owners.
+//
+// Redistribution and use in source and binary forms, with or without modification,
+// are permitted provided that the following conditions are met:
+//
+//   * Redistribution's of source code must retain the above copyright notice,
+//     this list of conditions and the following disclaimer.
+//
+//   * Redistribution's in binary form must reproduce the above copyright notice,
+//     this list of conditions and the following disclaimer in the documentation
+//     and/or other materials provided with the distribution.
+//
+//   * The name of the copyright holders may not be used to endorse or promote products
+//     derived from this software without specific prior written permission.
+//
+// This software is provided by the copyright holders and contributors "as is" and
+// any express or implied warranties, including, but not limited to, the implied
+// warranties of merchantability and fitness for a particular purpose are disclaimed.
+// In no event shall the Intel Corporation or contributors be liable for any direct,
+// indirect, incidental, special, exemplary, or consequential damages
+// (including, but not limited to, procurement of substitute goods or services;
+// loss of use, data, or profits; or business interruption) however caused
+// and on any theory of liability, whether in contract, strict liability,
+// or tort (including negligence or otherwise) arising in any way out of
+// the use of this software, even if advised of the possibility of such damage.
+//
+//M*/
+
+#ifndef OPENCV_DNN_DNN_INL_HPP
+#define OPENCV_DNN_DNN_INL_HPP
+
+#include <opencv2/dnn.hpp>
+
+namespace cv {
+namespace dnn {
+CV__DNN_INLINE_NS_BEGIN
+
+template<typename TypeIter>
+DictValue DictValue::arrayInt(TypeIter begin, int size)
+{
+    DictValue res(Param::INT, new AutoBuffer<int64, 1>(size));
+    for (int j = 0; j < size; begin++, j++)
+        (*res.pi)[j] = *begin;
+    return res;
+}
+
+template<typename TypeIter>
+DictValue DictValue::arrayReal(TypeIter begin, int size)
+{
+    DictValue res(Param::REAL, new AutoBuffer<double, 1>(size));
+    for (int j = 0; j < size; begin++, j++)
+        (*res.pd)[j] = *begin;
+    return res;
+}
+
+template<typename TypeIter>
+DictValue DictValue::arrayString(TypeIter begin, int size)
+{
+    DictValue res(Param::STRING, new AutoBuffer<String, 1>(size));
+    for (int j = 0; j < size; begin++, j++)
+        (*res.ps)[j] = *begin;
+    return res;
+}
+
+template<>
+inline DictValue DictValue::get<DictValue>(int idx) const
+{
+    CV_Assert(idx == -1);
+    return *this;
+}
+
+template<>
+inline int64 DictValue::get<int64>(int idx) const
+{
+    CV_Assert((idx == -1 && size() == 1) || (idx >= 0 && idx < size()));
+    idx = (idx == -1) ? 0 : idx;
+
+    if (type == Param::INT)
+    {
+        return (*pi)[idx];
+    }
+    else if (type == Param::REAL)
+    {
+        double doubleValue = (*pd)[idx];
+
+        double fracpart, intpart;
+        fracpart = std::modf(doubleValue, &intpart);
+        CV_Assert(fracpart == 0.0);
+
+        return (int64)doubleValue;
+    }
+    else if (type == Param::STRING)
+    {
+        return std::atoi((*ps)[idx].c_str());
+    }
+    else
+    {
+        CV_Assert(isInt() || isReal() || isString());
+        return 0;
+    }
+}
+
+template<>
+inline int DictValue::get<int>(int idx) const
+{
+    return (int)get<int64>(idx);
+}
+
+inline int DictValue::getIntValue(int idx) const
+{
+    return (int)get<int64>(idx);
+}
+
+template<>
+inline unsigned DictValue::get<unsigned>(int idx) const
+{
+    return (unsigned)get<int64>(idx);
+}
+
+template<>
+inline bool DictValue::get<bool>(int idx) const
+{
+    return (get<int64>(idx) != 0);
+}
+
+template<>
+inline double DictValue::get<double>(int idx) const
+{
+    CV_Assert((idx == -1 && size() == 1) || (idx >= 0 && idx < size()));
+    idx = (idx == -1) ? 0 : idx;
+
+    if (type == Param::REAL)
+    {
+        return (*pd)[idx];
+    }
+    else if (type == Param::INT)
+    {
+        return (double)(*pi)[idx];
+    }
+    else if (type == Param::STRING)
+    {
+        return std::atof((*ps)[idx].c_str());
+    }
+    else
+    {
+        CV_Assert(isReal() || isInt() || isString());
+        return 0;
+    }
+}
+
+inline double DictValue::getRealValue(int idx) const
+{
+    return get<double>(idx);
+}
+
+template<>
+inline float DictValue::get<float>(int idx) const
+{
+    return (float)get<double>(idx);
+}
+
+template<>
+inline String DictValue::get<String>(int idx) const
+{
+    CV_Assert(isString());
+    CV_Assert((idx == -1 && ps->size() == 1) || (idx >= 0 && idx < (int)ps->size()));
+    return (*ps)[(idx == -1) ? 0 : idx];
+}
+
+
+inline String DictValue::getStringValue(int idx) const
+{
+    return get<String>(idx);
+}
+
+inline void DictValue::release()
+{
+    switch (type)
+    {
+    case Param::INT:
+        delete pi;
+        break;
+    case Param::STRING:
+        delete ps;
+        break;
+    case Param::REAL:
+        delete pd;
+        break;
+    case Param::BOOLEAN:
+    case Param::MAT:
+    case Param::MAT_VECTOR:
+    case Param::ALGORITHM:
+    case Param::FLOAT:
+    case Param::UNSIGNED_INT:
+    case Param::UINT64:
+    case Param::UCHAR:
+    case Param::SCALAR:
+        break; // unhandled
+    }
+}
+
+inline DictValue::~DictValue()
+{
+    release();
+}
+
+inline DictValue & DictValue::operator=(const DictValue &r)
+{
+    if (&r == this)
+        return *this;
+
+    if (r.type == Param::INT)
+    {
+        AutoBuffer<int64, 1> *tmp = new AutoBuffer<int64, 1>(*r.pi);
+        release();
+        pi = tmp;
+    }
+    else if (r.type == Param::STRING)
+    {
+        AutoBuffer<String, 1> *tmp = new AutoBuffer<String, 1>(*r.ps);
+        release();
+        ps = tmp;
+    }
+    else if (r.type == Param::REAL)
+    {
+        AutoBuffer<double, 1> *tmp = new AutoBuffer<double, 1>(*r.pd);
+        release();
+        pd = tmp;
+    }
+
+    type = r.type;
+
+    return *this;
+}
+
+inline DictValue::DictValue(const DictValue &r)
+    : pv(NULL)
+{
+    type = r.type;
+
+    if (r.type == Param::INT)
+        pi = new AutoBuffer<int64, 1>(*r.pi);
+    else if (r.type == Param::STRING)
+        ps = new AutoBuffer<String, 1>(*r.ps);
+    else if (r.type == Param::REAL)
+        pd = new AutoBuffer<double, 1>(*r.pd);
+}
+
+inline bool DictValue::isString() const
+{
+    return (type == Param::STRING);
+}
+
+inline bool DictValue::isInt() const
+{
+    return (type == Param::INT);
+}
+
+inline bool DictValue::isReal() const
+{
+    return (type == Param::REAL || type == Param::INT);
+}
+
+inline int DictValue::size() const
+{
+    switch (type)
+    {
+    case Param::INT:
+        return (int)pi->size();
+    case Param::STRING:
+        return (int)ps->size();
+    case Param::REAL:
+        return (int)pd->size();
+    case Param::BOOLEAN:
+    case Param::MAT:
+    case Param::MAT_VECTOR:
+    case Param::ALGORITHM:
+    case Param::FLOAT:
+    case Param::UNSIGNED_INT:
+    case Param::UINT64:
+    case Param::UCHAR:
+    case Param::SCALAR:
+        break; // unhandled
+    }
+    CV_Error_(Error::StsInternal, ("Unhandled type (%d)", static_cast<int>(type)));
+}
+
+inline std::ostream &operator<<(std::ostream &stream, const DictValue &dictv)
+{
+    int i;
+
+    if (dictv.isInt())
+    {
+        for (i = 0; i < dictv.size() - 1; i++)
+            stream << dictv.get<int64>(i) << ", ";
+        stream << dictv.get<int64>(i);
+    }
+    else if (dictv.isReal())
+    {
+        for (i = 0; i < dictv.size() - 1; i++)
+            stream << dictv.get<double>(i) << ", ";
+        stream << dictv.get<double>(i);
+    }
+    else if (dictv.isString())
+    {
+        for (i = 0; i < dictv.size() - 1; i++)
+            stream << "\"" << dictv.get<String>(i) << "\", ";
+        stream << dictv.get<String>(i);
+    }
+
+    return stream;
+}
+
+/////////////////////////////////////////////////////////////////
+
+inline bool Dict::has(const String &key) const
+{
+    return dict.count(key) != 0;
+}
+
+inline DictValue *Dict::ptr(const String &key)
+{
+    _Dict::iterator i = dict.find(key);
+    return (i == dict.end()) ? NULL : &i->second;
+}
+
+inline const DictValue *Dict::ptr(const String &key) const
+{
+    _Dict::const_iterator i = dict.find(key);
+    return (i == dict.end()) ? NULL : &i->second;
+}
+
+inline const DictValue &Dict::get(const String &key) const
+{
+    _Dict::const_iterator i = dict.find(key);
+    if (i == dict.end())
+        CV_Error(Error::StsObjectNotFound, "Required argument \"" + key + "\" not found into dictionary");
+    return i->second;
+}
+
+template <typename T>
+inline T Dict::get(const String &key) const
+{
+    return this->get(key).get<T>();
+}
+
+template <typename T>
+inline T Dict::get(const String &key, const T &defaultValue) const
+{
+    _Dict::const_iterator i = dict.find(key);
+
+    if (i != dict.end())
+        return i->second.get<T>();
+    else
+        return defaultValue;
+}
+
+template<typename T>
+inline const T &Dict::set(const String &key, const T &value)
+{
+    _Dict::iterator i = dict.find(key);
+
+    if (i != dict.end())
+        i->second = DictValue(value);
+    else
+        dict.insert(std::make_pair(key, DictValue(value)));
+
+    return value;
+}
+
+inline void Dict::erase(const String &key)
+{
+    dict.erase(key);
+}
+
+inline std::ostream &operator<<(std::ostream &stream, const Dict &dict)
+{
+    Dict::_Dict::const_iterator it;
+    for (it = dict.dict.begin(); it != dict.dict.end(); it++)
+        stream << it->first << " : " << it->second << "\n";
+
+    return stream;
+}
+
+inline std::map<String, DictValue>::const_iterator Dict::begin() const
+{
+    return dict.begin();
+}
+
+inline std::map<String, DictValue>::const_iterator Dict::end() const
+{
+    return dict.end();
+}
+
+CV__DNN_INLINE_NS_END
+}
+}
+
+#endif

3rdparty/opencv-4.5.4/modules/dnn/include/opencv2/dnn/layer.details.hpp

@@ -0,0 +1,78 @@
+// This file is part of OpenCV project.
+// It is subject to the license terms in the LICENSE file found in the top-level directory
+// of this distribution and at http://opencv.org/license.html.
+//
+#ifndef OPENCV_DNN_LAYER_DETAILS_HPP
+#define OPENCV_DNN_LAYER_DETAILS_HPP
+
+#include <opencv2/dnn/layer.hpp>
+
+namespace cv {
+namespace dnn {
+CV__DNN_INLINE_NS_BEGIN
+
+/** @brief Registers layer constructor in runtime.
+*   @param type string, containing type name of the layer.
+*   @param constructorFunc pointer to the function of type LayerRegister::Constructor, which creates the layer.
+*   @details This macros must be placed inside the function code.
+*/
+#define CV_DNN_REGISTER_LAYER_FUNC(type, constructorFunc) \
+    cv::dnn::LayerFactory::registerLayer(#type, constructorFunc);
+
+/** @brief Registers layer class in runtime.
+ *  @param type string, containing type name of the layer.
+ *  @param class C++ class, derived from Layer.
+ *  @details This macros must be placed inside the function code.
+ */
+#define CV_DNN_REGISTER_LAYER_CLASS(type, class) \
+    cv::dnn::LayerFactory::registerLayer(#type, cv::dnn::details::_layerDynamicRegisterer<class>);
+
+/** @brief Registers layer constructor on module load time.
+*   @param type string, containing type name of the layer.
+*   @param constructorFunc pointer to the function of type LayerRegister::Constructor, which creates the layer.
+*   @details This macros must be placed outside the function code.
+*/
+#define CV_DNN_REGISTER_LAYER_FUNC_STATIC(type, constructorFunc) \
+static cv::dnn::details::_LayerStaticRegisterer __LayerStaticRegisterer_##type(#type, constructorFunc);
+
+/** @brief Registers layer class on module load time.
+ *  @param type string, containing type name of the layer.
+ *  @param class C++ class, derived from Layer.
+ *  @details This macros must be placed outside the function code.
+ */
+#define CV_DNN_REGISTER_LAYER_CLASS_STATIC(type, class)                         \
+Ptr<Layer> __LayerStaticRegisterer_func_##type(LayerParams &params) \
+    { return Ptr<Layer>(new class(params)); }                       \
+static cv::dnn::details::_LayerStaticRegisterer __LayerStaticRegisterer_##type(#type, __LayerStaticRegisterer_func_##type);
+
+namespace details {
+
+template<typename LayerClass>
+Ptr<Layer> _layerDynamicRegisterer(LayerParams &params)
+{
+    return Ptr<Layer>(LayerClass::create(params));
+}
+
+//allows automatically register created layer on module load time
+class _LayerStaticRegisterer
+{
+    String type;
+public:
+
+    _LayerStaticRegisterer(const String &layerType, LayerFactory::Constructor layerConstructor)
+    {
+        this->type = layerType;
+        LayerFactory::registerLayer(layerType, layerConstructor);
+    }
+
+    ~_LayerStaticRegisterer()
+    {
+        LayerFactory::unregisterLayer(type);
+    }
+};
+
+} // namespace
+CV__DNN_INLINE_NS_END
+}} // namespace
+
+#endif

3rdparty/opencv-4.5.4/modules/dnn/include/opencv2/dnn/layer.hpp

@@ -0,0 +1,85 @@
+/*M///////////////////////////////////////////////////////////////////////////////////////
+//
+//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
+//
+//  By downloading, copying, installing or using the software you agree to this license.
+//  If you do not agree to this license, do not download, install,
+//  copy or use the software.
+//
+//
+//                           License Agreement
+//                For Open Source Computer Vision Library
+//
+// Copyright (C) 2013, OpenCV Foundation, all rights reserved.
+// Third party copyrights are property of their respective owners.
+//
+// Redistribution and use in source and binary forms, with or without modification,
+// are permitted provided that the following conditions are met:
+//
+//   * Redistribution's of source code must retain the above copyright notice,
+//     this list of conditions and the following disclaimer.
+//
+//   * Redistribution's in binary form must reproduce the above copyright notice,
+//     this list of conditions and the following disclaimer in the documentation
+//     and/or other materials provided with the distribution.
+//
+//   * The name of the copyright holders may not be used to endorse or promote products
+//     derived from this software without specific prior written permission.
+//
+// This software is provided by the copyright holders and contributors "as is" and
+// any express or implied warranties, including, but not limited to, the implied
+// warranties of merchantability and fitness for a particular purpose are disclaimed.
+// In no event shall the Intel Corporation or contributors be liable for any direct,
+// indirect, incidental, special, exemplary, or consequential damages
+// (including, but not limited to, procurement of substitute goods or services;
+// loss of use, data, or profits; or business interruption) however caused
+// and on any theory of liability, whether in contract, strict liability,
+// or tort (including negligence or otherwise) arising in any way out of
+// the use of this software, even if advised of the possibility of such damage.
+//
+//M*/
+
+#ifndef OPENCV_DNN_LAYER_HPP
+#define OPENCV_DNN_LAYER_HPP
+#include <opencv2/dnn.hpp>
+
+namespace cv {
+namespace dnn {
+CV__DNN_INLINE_NS_BEGIN
+//! @addtogroup dnn
+//! @{
+//!
+//! @defgroup dnnLayerFactory Utilities for New Layers Registration
+//! @{
+
+/** @brief %Layer factory allows to create instances of registered layers. */
+class CV_EXPORTS LayerFactory
+{
+public:
+
+    //! Each Layer class must provide this function to the factory
+    typedef Ptr<Layer>(*Constructor)(LayerParams &params);
+
+    //! Registers the layer class with typename @p type and specified @p constructor. Thread-safe.
+    static void registerLayer(const String &type, Constructor constructor);
+
+    //! Unregisters registered layer with specified type name. Thread-safe.
+    static void unregisterLayer(const String &type);
+
+    /** @brief Creates instance of registered layer.
+     *  @param type type name of creating layer.
+     *  @param params parameters which will be used for layer initialization.
+     *  @note Thread-safe.
+     */
+    static Ptr<Layer> createLayerInstance(const String &type, LayerParams& params);
+
+private:
+    LayerFactory();
+};
+
+//! @}
+//! @}
+CV__DNN_INLINE_NS_END
+}
+}
+#endif

3rdparty/opencv-4.5.4/modules/dnn/include/opencv2/dnn/layer_reg.private.hpp

@@ -0,0 +1,29 @@
+// This file is part of OpenCV project.
+// It is subject to the license terms in the LICENSE file found in the top-level directory
+// of this distribution and at http://opencv.org/license.html.
+
+#ifndef OPENCV_DNN_LAYER_REG_HPP
+#define OPENCV_DNN_LAYER_REG_HPP
+#include <opencv2/dnn.hpp>
+
+namespace cv {
+namespace dnn {
+CV__DNN_INLINE_NS_BEGIN
+//! @addtogroup dnn
+//! @{
+
+typedef std::map<std::string, std::vector<LayerFactory::Constructor> > LayerFactory_Impl;
+
+//! Register layer types of DNN model.
+//!
+//! @note In order to thread-safely access the factory, see getLayerFactoryMutex() function.
+LayerFactory_Impl& getLayerFactoryImpl();
+
+//! Get the mutex guarding @ref LayerFactory_Impl, see getLayerFactoryImpl() function.
+Mutex& getLayerFactoryMutex();
+
+//! @}
+CV__DNN_INLINE_NS_END
+}
+}
+#endif

3rdparty/opencv-4.5.4/modules/dnn/include/opencv2/dnn/shape_utils.hpp

@@ -0,0 +1,259 @@
+/*M///////////////////////////////////////////////////////////////////////////////////////
+//
+//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
+//
+//  By downloading, copying, installing or using the software you agree to this license.
+//  If you do not agree to this license, do not download, install,
+//  copy or use the software.
+//
+//
+//                           License Agreement
+//                For Open Source Computer Vision Library
+//
+// Copyright (C) 2013, OpenCV Foundation, all rights reserved.
+// Third party copyrights are property of their respective owners.
+//
+// Redistribution and use in source and binary forms, with or without modification,
+// are permitted provided that the following conditions are met:
+//
+//   * Redistribution's of source code must retain the above copyright notice,
+//     this list of conditions and the following disclaimer.
+//
+//   * Redistribution's in binary form must reproduce the above copyright notice,
+//     this list of conditions and the following disclaimer in the documentation
+//     and/or other materials provided with the distribution.
+//
+//   * The name of the copyright holders may not be used to endorse or promote products
+//     derived from this software without specific prior written permission.
+//
+// This software is provided by the copyright holders and contributors "as is" and
+// any express or implied warranties, including, but not limited to, the implied
+// warranties of merchantability and fitness for a particular purpose are disclaimed.
+// In no event shall the Intel Corporation or contributors be liable for any direct,
+// indirect, incidental, special, exemplary, or consequential damages
+// (including, but not limited to, procurement of substitute goods or services;
+// loss of use, data, or profits; or business interruption) however caused
+// and on any theory of liability, whether in contract, strict liability,
+// or tort (including negligence or otherwise) arising in any way out of
+// the use of this software, even if advised of the possibility of such damage.
+//
+//M*/
+
+#ifndef OPENCV_DNN_DNN_SHAPE_UTILS_HPP
+#define OPENCV_DNN_DNN_SHAPE_UTILS_HPP
+
+#include <opencv2/dnn/dnn.hpp>
+#include <opencv2/core/types_c.h>  // CV_MAX_DIM
+#include <iostream>
+#include <ostream>
+#include <sstream>
+
+namespace cv {
+namespace dnn {
+CV__DNN_INLINE_NS_BEGIN
+
+//Slicing
+
+struct _Range : public cv::Range
+{
+    _Range(const Range &r) : cv::Range(r) {}
+    _Range(int start_, int size_ = 1) : cv::Range(start_, start_ + size_) {}
+};
+
+static inline Mat slice(const Mat &m, const _Range &r0)
+{
+    Range ranges[CV_MAX_DIM];
+    for (int i = 1; i < m.dims; i++)
+        ranges[i] = Range::all();
+    ranges[0] = r0;
+    return m(&ranges[0]);
+}
+
+static inline Mat slice(const Mat &m, const _Range &r0, const _Range &r1)
+{
+    CV_Assert(m.dims >= 2);
+    Range ranges[CV_MAX_DIM];
+    for (int i = 2; i < m.dims; i++)
+        ranges[i] = Range::all();
+    ranges[0] = r0;
+    ranges[1] = r1;
+    return m(&ranges[0]);
+}
+
+static inline Mat slice(const Mat &m, const _Range &r0, const _Range &r1, const _Range &r2)
+{
+    CV_Assert(m.dims >= 3);
+    Range ranges[CV_MAX_DIM];
+    for (int i = 3; i < m.dims; i++)
+        ranges[i] = Range::all();
+    ranges[0] = r0;
+    ranges[1] = r1;
+    ranges[2] = r2;
+    return m(&ranges[0]);
+}
+
+static inline Mat slice(const Mat &m, const _Range &r0, const _Range &r1, const _Range &r2, const _Range &r3)
+{
+    CV_Assert(m.dims >= 4);
+    Range ranges[CV_MAX_DIM];
+    for (int i = 4; i < m.dims; i++)
+        ranges[i] = Range::all();
+    ranges[0] = r0;
+    ranges[1] = r1;
+    ranges[2] = r2;
+    ranges[3] = r3;
+    return m(&ranges[0]);
+}
+
+static inline Mat getPlane(const Mat &m, int n, int cn)
+{
+    CV_Assert(m.dims > 2);
+    int sz[CV_MAX_DIM];
+    for(int i = 2; i < m.dims; i++)
+    {
+        sz[i-2] = m.size.p[i];
+    }
+    return Mat(m.dims - 2, sz, m.type(), (void*)m.ptr<float>(n, cn));
+}
+
+static inline MatShape shape(const int* dims, const int n)
+{
+    MatShape shape;
+    shape.assign(dims, dims + n);
+    return shape;
+}
+
+static inline MatShape shape(const Mat& mat)
+{
+    return shape(mat.size.p, mat.dims);
+}
+
+static inline MatShape shape(const MatSize& sz)
+{
+    return shape(sz.p, sz.dims());
+}
+
+static inline MatShape shape(const UMat& mat)
+{
+    return shape(mat.size.p, mat.dims);
+}
+
+#if 0  // issues with MatExpr wrapped into InputArray
+static inline
+MatShape shape(InputArray input)
+{
+    int sz[CV_MAX_DIM];
+    int ndims = input.sizend(sz);
+    return shape(sz, ndims);
+}
+#endif
+
+namespace {inline bool is_neg(int i) { return i < 0; }}
+
+static inline MatShape shape(int a0, int a1=-1, int a2=-1, int a3=-1)
+{
+    int dims[] = {a0, a1, a2, a3};
+    MatShape s = shape(dims, 4);
+    s.erase(std::remove_if(s.begin(), s.end(), is_neg), s.end());
+    return s;
+}
+
+static inline int total(const MatShape& shape, int start = -1, int end = -1)
+{
+    if (start == -1) start = 0;
+    if (end == -1) end = (int)shape.size();
+
+    if (shape.empty())
+        return 0;
+
+    int elems = 1;
+    CV_Assert(start <= (int)shape.size() && end <= (int)shape.size() &&
+              start <= end);
+    for(int i = start; i < end; i++)
+    {
+        elems *= shape[i];
+    }
+    return elems;
+}
+
+static inline MatShape concat(const MatShape& a, const MatShape& b)
+{
+    MatShape c = a;
+    c.insert(c.end(), b.begin(), b.end());
+
+    return c;
+}
+
+static inline std::string toString(const MatShape& shape, const String& name = "")
+{
+    std::ostringstream ss;
+    if (!name.empty())
+        ss << name << ' ';
+    ss << '[';
+    for(size_t i = 0, n = shape.size(); i < n; ++i)
+        ss << ' ' << shape[i];
+    ss << " ]";
+    return ss.str();
+}
+static inline void print(const MatShape& shape, const String& name = "")
+{
+    std::cout << toString(shape, name) << std::endl;
+}
+static inline std::ostream& operator<<(std::ostream &out, const MatShape& shape)
+{
+    out << toString(shape);
+    return out;
+}
+
+/// @brief Converts axis from `[-dims; dims)` (similar to Python's slice notation) to `[0; dims)` range.
+static inline
+int normalize_axis(int axis, int dims)
+{
+    CV_Check(axis, axis >= -dims && axis < dims, "");
+    axis = (axis < 0) ? (dims + axis) : axis;
+    CV_DbgCheck(axis, axis >= 0 && axis < dims, "");
+    return axis;
+}
+
+static inline
+int normalize_axis(int axis, const MatShape& shape)
+{
+    return normalize_axis(axis, (int)shape.size());
+}
+
+static inline
+Range normalize_axis_range(const Range& r, int axisSize)
+{
+    if (r == Range::all())
+        return Range(0, axisSize);
+    CV_CheckGE(r.start, 0, "");
+    Range clamped(r.start,
+                  r.end > 0 ? std::min(r.end, axisSize) : axisSize + r.end + 1);
+    CV_DbgCheckGE(clamped.start, 0, "");
+    CV_CheckLT(clamped.start, clamped.end, "");
+    CV_CheckLE(clamped.end, axisSize, "");
+    return clamped;
+}
+
+static inline
+bool isAllOnes(const MatShape &inputShape, int startPos, int endPos)
+{
+    CV_Assert(!inputShape.empty());
+
+    CV_CheckGE((int) inputShape.size(), startPos, "");
+    CV_CheckGE(startPos, 0, "");
+    CV_CheckLE(startPos, endPos, "");
+    CV_CheckLE((size_t)endPos, inputShape.size(), "");
+
+    for (size_t i = startPos; i < endPos; i++)
+    {
+        if (inputShape[i] != 1)
+            return false;
+    }
+    return true;
+}
+
+CV__DNN_INLINE_NS_END
+}
+}
+#endif

3rdparty/opencv-4.5.4/modules/dnn/include/opencv2/dnn/utils/debug_utils.hpp

@@ -0,0 +1,24 @@
+// This file is part of OpenCV project.
+// It is subject to the license terms in the LICENSE file found in the top-level directory
+// of this distribution and at http://opencv.org/license.html.
+
+#ifndef OPENCV_DNN_UTILS_DEBUG_UTILS_HPP
+#define OPENCV_DNN_UTILS_DEBUG_UTILS_HPP
+
+#include "../dnn.hpp"
+
+namespace cv { namespace dnn {
+CV__DNN_INLINE_NS_BEGIN
+
+/**
+ * @brief Skip model import after diagnostic run in readNet() functions.
+ * @param[in] skip Indicates whether to skip the import.
+ *
+ * This is an internal OpenCV function not intended for users.
+ */
+CV_EXPORTS void skipModelImport(bool skip);
+
+CV__DNN_INLINE_NS_END
+}} // namespace
+
+#endif // OPENCV_DNN_UTILS_DEBUG_UTILS_HPP

3rdparty/opencv-4.5.4/modules/dnn/include/opencv2/dnn/utils/inference_engine.hpp

@@ -0,0 +1,76 @@
+// This file is part of OpenCV project.
+// It is subject to the license terms in the LICENSE file found in the top-level directory
+// of this distribution and at http://opencv.org/license.html.
+//
+// Copyright (C) 2018-2019, Intel Corporation, all rights reserved.
+// Third party copyrights are property of their respective owners.
+
+#ifndef OPENCV_DNN_UTILS_INF_ENGINE_HPP
+#define OPENCV_DNN_UTILS_INF_ENGINE_HPP
+
+#include "../dnn.hpp"
+
+namespace cv { namespace dnn {
+CV__DNN_INLINE_NS_BEGIN
+
+
+/* Values for 'OPENCV_DNN_BACKEND_INFERENCE_ENGINE_TYPE' parameter */
+#define CV_DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_API     "NN_BUILDER"
+#define CV_DNN_BACKEND_INFERENCE_ENGINE_NGRAPH             "NGRAPH"
+
+/** @brief Returns Inference Engine internal backend API.
+ *
+ * See values of `CV_DNN_BACKEND_INFERENCE_ENGINE_*` macros.
+ *
+ * Default value is controlled through `OPENCV_DNN_BACKEND_INFERENCE_ENGINE_TYPE` runtime parameter (environment variable).
+ */
+CV_EXPORTS_W cv::String getInferenceEngineBackendType();
+
+/** @brief Specify Inference Engine internal backend API.
+ *
+ * See values of `CV_DNN_BACKEND_INFERENCE_ENGINE_*` macros.
+ *
+ * @returns previous value of internal backend API
+ */
+CV_EXPORTS_W cv::String setInferenceEngineBackendType(const cv::String& newBackendType);
+
+
+/** @brief Release a Myriad device (binded by OpenCV).
+ *
+ * Single Myriad device cannot be shared across multiple processes which uses
+ * Inference Engine's Myriad plugin.
+ */
+CV_EXPORTS_W void resetMyriadDevice();
+
+
+/* Values for 'OPENCV_DNN_IE_VPU_TYPE' parameter */
+#define CV_DNN_INFERENCE_ENGINE_VPU_TYPE_UNSPECIFIED ""
+/// Intel(R) Movidius(TM) Neural Compute Stick, NCS (USB 03e7:2150), Myriad2 (https://software.intel.com/en-us/movidius-ncs)
+#define CV_DNN_INFERENCE_ENGINE_VPU_TYPE_MYRIAD_2 "Myriad2"
+/// Intel(R) Neural Compute Stick 2, NCS2 (USB 03e7:2485), MyriadX (https://software.intel.com/ru-ru/neural-compute-stick)
+#define CV_DNN_INFERENCE_ENGINE_VPU_TYPE_MYRIAD_X "MyriadX"
+#define CV_DNN_INFERENCE_ENGINE_CPU_TYPE_ARM_COMPUTE "ARM_COMPUTE"
+#define CV_DNN_INFERENCE_ENGINE_CPU_TYPE_X86         "X86"
+
+
+/** @brief Returns Inference Engine VPU type.
+ *
+ * See values of `CV_DNN_INFERENCE_ENGINE_VPU_TYPE_*` macros.
+ */
+CV_EXPORTS_W cv::String getInferenceEngineVPUType();
+
+/** @brief Returns Inference Engine CPU type.
+ *
+ * Specify OpenVINO plugin: CPU or ARM.
+ */
+CV_EXPORTS_W cv::String getInferenceEngineCPUType();
+
+/** @brief Release a HDDL plugin.
+ */
+CV_EXPORTS_W void releaseHDDLPlugin();
+
+
+CV__DNN_INLINE_NS_END
+}} // namespace
+
+#endif // OPENCV_DNN_UTILS_INF_ENGINE_HPP

3rdparty/opencv-4.5.4/modules/dnn/include/opencv2/dnn/version.hpp

@@ -0,0 +1,21 @@
+// This file is part of OpenCV project.
+// It is subject to the license terms in the LICENSE file found in the top-level directory
+// of this distribution and at http://opencv.org/license.html.
+
+#ifndef OPENCV_DNN_VERSION_HPP
+#define OPENCV_DNN_VERSION_HPP
+
+/// Use with major OpenCV version only.
+#define OPENCV_DNN_API_VERSION 20211004
+
+#if !defined CV_DOXYGEN && !defined CV_STATIC_ANALYSIS && !defined CV_DNN_DONT_ADD_INLINE_NS
+#define CV__DNN_INLINE_NS __CV_CAT(dnn4_v, OPENCV_DNN_API_VERSION)
+#define CV__DNN_INLINE_NS_BEGIN namespace CV__DNN_INLINE_NS {
+#define CV__DNN_INLINE_NS_END }
+namespace cv { namespace dnn { namespace CV__DNN_INLINE_NS { } using namespace CV__DNN_INLINE_NS; }}
+#else
+#define CV__DNN_INLINE_NS_BEGIN
+#define CV__DNN_INLINE_NS_END
+#endif
+
+#endif  // OPENCV_DNN_VERSION_HPP