Merge pull request #23595 from TolyaTalamanov:at/implement-openvino-backend

[G-API] Implement OpenVINO 2.0 backend #23595

### Pull Request Readiness Checklist

Implemented basic functionality for `OpenVINO` 2.0 G-API backend.

#### Overview
- [x] Implement `Infer` kernel with some of essential configurable parameters + IR/Blob models format support.
- [ ] Implement the rest of kernels: `InferList`, `InferROI`, `Infer2` + other configurable params (e.g reshape)
- [x] Asyncrhonous execution support
- [ ] Remote context support

See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request

- [x] I agree to contribute to the project under Apache 2 License.
- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [ ] The PR is proposed to the proper branch
- [ ] There is a reference to the original bug report and related work
- [x] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
      Patch to opencv_extra has the same branch name.
- [x] The feature is well documented and sample code can be built with the project CMake

modules/gapi/CMakeLists.txt

@@ -156,6 +156,10 @@ set(gapi_srcs
     src/backends/ie/giebackend.cpp
     src/backends/ie/giebackend/giewrapper.cpp
 
+    # OV Backend. FIXME: should be included by CMake
+    # if and only if OV support is enabled
+    src/backends/ov/govbackend.cpp
+
     # ONNX backend
     src/backends/onnx/gonnxbackend.cpp
 
@@ -182,6 +186,7 @@ set(gapi_srcs
     # Python bridge
     src/backends/ie/bindings_ie.cpp
     src/backends/onnx/bindings_onnx.cpp
+    src/backends/ov/bindings_ov.cpp
     src/backends/python/gpythonbackend.cpp
 
     # OpenVPL Streaming source

modules/gapi/include/opencv2/gapi/infer/bindings_ov.hpp

@@ -0,0 +1,128 @@
+// 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) 2023 Intel Corporation
+
+#ifndef OPENCV_GAPI_INFER_BINDINGS_OV_HPP
+#define OPENCV_GAPI_INFER_BINDINGS_OV_HPP
+
+#include <opencv2/gapi/util/any.hpp>
+#include "opencv2/gapi/own/exports.hpp" // GAPI_EXPORTS
+#include <opencv2/gapi/gkernel.hpp>     // GKernelPackage
+#include <opencv2/gapi/infer/ov.hpp>    // Params
+
+#include <string>
+
+namespace cv {
+namespace gapi {
+namespace ov {
+
+// NB: Used by python wrapper
+// This class can be marked as SIMPLE, because it's implemented as pimpl
+class GAPI_EXPORTS_W_SIMPLE PyParams {
+public:
+    GAPI_WRAP
+    PyParams() = default;
+
+    GAPI_WRAP
+    PyParams(const std::string &tag,
+             const std::string &model_path,
+             const std::string &bin_path,
+             const std::string &device);
+
+    GAPI_WRAP
+    PyParams(const std::string &tag,
+             const std::string &blob_path,
+             const std::string &device);
+
+    GAPI_WRAP
+    PyParams& cfgPluginConfig(
+            const std::map<std::string, std::string> &config);
+
+    GAPI_WRAP
+    PyParams& cfgInputTensorLayout(std::string tensor_layout);
+
+    GAPI_WRAP
+    PyParams& cfgInputTensorLayout(
+            std::map<std::string, std::string> layout_map);
+
+    GAPI_WRAP
+    PyParams& cfgInputModelLayout(std::string tensor_layout);
+
+    GAPI_WRAP
+    PyParams& cfgInputModelLayout(
+            std::map<std::string, std::string> layout_map);
+
+    GAPI_WRAP
+    PyParams& cfgOutputTensorLayout(std::string tensor_layout);
+
+    GAPI_WRAP
+    PyParams& cfgOutputTensorLayout(
+            std::map<std::string, std::string> layout_map);
+
+    GAPI_WRAP
+    PyParams& cfgOutputModelLayout(std::string tensor_layout);
+
+    GAPI_WRAP
+    PyParams& cfgOutputModelLayout(
+            std::map<std::string, std::string> layout_map);
+
+    GAPI_WRAP
+    PyParams& cfgOutputTensorPrecision(int precision);
+
+    GAPI_WRAP
+    PyParams& cfgOutputTensorPrecision(
+            std::map<std::string, int> precision_map);
+
+    GAPI_WRAP
+    PyParams& cfgReshape(std::vector<size_t> new_shape);
+
+    GAPI_WRAP
+    PyParams& cfgReshape(
+            std::map<std::string, std::vector<size_t>> new_shape_map);
+
+    GAPI_WRAP
+    PyParams& cfgNumRequests(const size_t nireq);
+
+    GAPI_WRAP
+    PyParams& cfgMean(std::vector<float> mean_values);
+
+    GAPI_WRAP
+    PyParams& cfgMean(
+            std::map<std::string, std::vector<float>> mean_map);
+
+    GAPI_WRAP
+    PyParams& cfgScale(std::vector<float> scale_values);
+
+    GAPI_WRAP
+    PyParams& cfgScale(
+            std::map<std::string, std::vector<float>> scale_map);
+
+    GAPI_WRAP
+    PyParams& cfgResize(int interpolation);
+
+    GAPI_WRAP
+    PyParams& cfgResize(std::map<std::string, int> interpolation);
+
+    GBackend      backend() const;
+    std::string   tag()     const;
+    cv::util::any params()  const;
+
+private:
+    std::shared_ptr<Params<cv::gapi::Generic>> m_priv;
+};
+
+GAPI_EXPORTS_W PyParams params(const std::string &tag,
+                               const std::string &model_path,
+                               const std::string &weights,
+                               const std::string &device);
+
+GAPI_EXPORTS_W PyParams params(const std::string &tag,
+                               const std::string &bin_path,
+                               const std::string &device);
+} // namespace ov
+} // namespace gapi
+} // namespace cv
+
+#endif // OPENCV_GAPI_INFER_BINDINGS_OV_HPP

modules/gapi/include/opencv2/gapi/infer/ov.hpp

@@ -0,0 +1,685 @@
+// 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) 2023 Intel Corporation
+
+#ifndef OPENCV_GAPI_INFER_OV_HPP
+#define OPENCV_GAPI_INFER_OV_HPP
+
+#include <string>
+
+#include <opencv2/gapi/util/any.hpp>
+#include <opencv2/gapi/own/exports.hpp> // GAPI_EXPORTS
+#include <opencv2/gapi/gkernel.hpp>     // GKernelType[M], GBackend
+#include <opencv2/gapi/infer.hpp>       // Generic
+
+#include <map>
+
+namespace cv {
+namespace gapi {
+
+/**
+ * @brief This namespace contains G-API OpenVINO 2.0 backend functions,
+ * structures, and symbols.
+ */
+namespace ov {
+
+GAPI_EXPORTS cv::gapi::GBackend backend();
+
+namespace detail {
+
+template <typename T>
+using AttrMap = std::map<std::string, T>;
+// NB: This type is supposed to be used to hold in/out layers
+// attributes such as precision, layout, shape etc.
+//
+// User can provide attributes either:
+// 1. cv::util::monostate - No value specified explicitly.
+// 2. Attr - value specified explicitly that should be broadcasted to all layers.
+// 3. AttrMap[str->T] - map specifies value for particular layer.
+template <typename Attr>
+using LayerVariantAttr = cv::util::variant< cv::util::monostate
+                                          , AttrMap<Attr>
+                                          , Attr>;
+
+struct ParamDesc {
+    struct Model {
+
+        Model(const std::string &model_path_,
+              const std::string &bin_path_)
+            : model_path(model_path_), bin_path(bin_path_) {
+        }
+
+        std::string model_path;
+        std::string bin_path;
+
+        LayerVariantAttr<std::string> input_tensor_layout;
+        LayerVariantAttr<std::string> input_model_layout;
+        LayerVariantAttr<std::string> output_tensor_layout;
+        LayerVariantAttr<std::string> output_model_layout;
+        LayerVariantAttr<int>         output_tensor_precision;
+
+        LayerVariantAttr<std::vector<size_t>> new_shapes;
+
+        LayerVariantAttr<std::vector<float>> mean_values;
+        LayerVariantAttr<std::vector<float>> scale_values;
+
+        LayerVariantAttr<int> interpolation;
+    };
+
+    struct CompiledModel {
+        std::string blob_path;
+    };
+
+    using Kind = cv::util::variant<Model, CompiledModel>;
+
+    ParamDesc(Kind              &&kind_,
+              const std::string &device_,
+              const bool        is_generic_,
+              const size_t      num_in_,
+              const size_t      num_out_)
+        : kind(std::move(kind_)), device(device_),
+          is_generic(is_generic_),
+          num_in(num_in_), num_out(num_out_) {
+    }
+
+    Kind kind;
+
+    std::string device;
+    bool is_generic;
+
+    std::size_t num_in;
+    std::size_t num_out;
+
+    std::vector<std::string> input_names;
+    std::vector<std::string> output_names;
+
+    using PluginConfigT = std::map<std::string, std::string>;
+    PluginConfigT config;
+
+    size_t nireq = 1;
+};
+
+// NB: Just helper to avoid code duplication.
+static detail::ParamDesc::Model&
+getModelToSetAttrOrThrow(detail::ParamDesc::Kind  &kind,
+                         const std::string        &attr_name) {
+    if (cv::util::holds_alternative<detail::ParamDesc::CompiledModel>(kind)) {
+        cv::util::throw_error(
+                std::logic_error("Specifying " + attr_name + " isn't"
+                                 " possible for compiled model."));
+    }
+    GAPI_Assert(cv::util::holds_alternative<detail::ParamDesc::Model>(kind));
+    return cv::util::get<detail::ParamDesc::Model>(kind);
+}
+
+} // namespace detail
+
+/**
+ * @brief This structure provides functions
+ * that fill inference parameters for "OpenVINO Toolkit" model.
+ */
+template<typename Net> struct Params {
+public:
+    /** @brief Class constructor.
+
+    Constructs Params based on model information and specifies default values for other
+    inference description parameters. Model is loaded and compiled using "OpenVINO Toolkit".
+
+    @param model_path Path to a model.
+    @param bin_path Path to a data file.
+    For IR format (*.bin):
+    If path is empty, will try to read a bin file with the same name as xml.
+    If the bin file with the same name is not found, will load IR without weights.
+    For PDPD (*.pdmodel) and ONNX (*.onnx) formats bin_path isn't used.
+    @param device target device to use.
+    */
+    Params(const std::string &model_path,
+           const std::string &bin_path,
+           const std::string &device)
+        : m_desc( detail::ParamDesc::Kind{detail::ParamDesc::Model{model_path, bin_path}}
+                 , device
+                 , false /* is generic */
+                 , std::tuple_size<typename Net::InArgs>::value
+                 , std::tuple_size<typename Net::OutArgs>::value) {
+    }
+
+    /** @overload
+    Use this constructor to work with pre-compiled network.
+    Model is imported from a pre-compiled blob.
+
+    @param blob_path path to the compiled model (*.blob).
+    @param device target device to use.
+    */
+    Params(const std::string &blob_path,
+           const std::string &device)
+        : m_desc( detail::ParamDesc::Kind{detail::ParamDesc::CompiledModel{blob_path}}
+                 , device
+                 , false /* is generic */
+                 , std::tuple_size<typename Net::InArgs>::value
+                 , std::tuple_size<typename Net::OutArgs>::value) {
+    }
+
+    /** @brief Specifies sequence of network input layers names for inference.
+
+    The function is used to associate cv::gapi::infer<> inputs with the model inputs.
+    Number of names has to match the number of network inputs as defined in G_API_NET().
+    In case a network has only single input layer, there is no need to specify name manually.
+
+    @param layer_names std::array<std::string, N> where N is the number of inputs
+    as defined in the @ref G_API_NET. Contains names of input layers.
+    @return reference to this parameter structure.
+    */
+    Params<Net>& cfgInputLayers(const std::vector<std::string> &layer_names) {
+        m_desc.input_names = layer_names;
+        return *this;
+    }
+
+    /** @brief Specifies sequence of network output layers names for inference.
+
+    The function is used to associate cv::gapi::infer<> outputs with the model outputs.
+    Number of names has to match the number of network outputs as defined in G_API_NET().
+    In case a network has only single output layer, there is no need to specify name manually.
+
+    @param layer_names std::array<std::string, N> where N is the number of outputs
+    as defined in the @ref G_API_NET. Contains names of output layers.
+    @return reference to this parameter structure.
+    */
+    Params<Net>& cfgOutputLayers(const std::vector<std::string> &layer_names) {
+        m_desc.output_names = layer_names;
+        return *this;
+    }
+
+    /** @brief Specifies OpenVINO plugin configuration.
+
+    The function is used to set configuration for OpenVINO plugin. Some parameters
+    can be different for each plugin. Please follow https://docs.openvinotoolkit.org/latest/index.html
+    to check information about specific plugin.
+
+    @param config Map of pairs: (config parameter name, config parameter value).
+    @return reference to this parameter structure.
+    */
+    Params<Net>& cfgPluginConfig(const detail::ParamDesc::PluginConfigT &config) {
+        m_desc.config = config;
+        return *this;
+    }
+
+    /** @brief Specifies tensor layout for an input layer.
+
+    The function is used to set tensor layout for an input layer.
+
+    @param layout Tensor layout ("NCHW", "NWHC", etc)
+    will be applied to all input layers.
+    @return reference to this parameter structure.
+    */
+    Params<Net>& cfgInputTensorLayout(std::string layout) {
+        detail::getModelToSetAttrOrThrow(m_desc.kind, "input tensor layout")
+            .input_tensor_layout = std::move(layout);
+        return *this;
+    }
+
+    /** @overload
+    @param layout_map Map of pairs: name of corresponding input layer
+    and its tensor layout represented in std::string ("NCHW", "NHWC", etc)
+    @return reference to this parameter structure.
+    */
+    Params<Net>&
+    cfgInputTensorLayout(detail::AttrMap<std::string> layout_map) {
+        detail::getModelToSetAttrOrThrow(m_desc.kind, "input tensor layout")
+            .input_tensor_layout = std::move(layout_map);
+        return *this;
+    }
+
+    /** @brief Specifies model layout for an input layer.
+
+    The function is used to set model layout for an input layer.
+
+    @param layout Model layout ("NCHW", "NHWC", etc)
+    will be applied to all input layers.
+    @return reference to this parameter structure.
+    */
+    Params<Net>& cfgInputModelLayout(std::string layout) {
+        detail::getModelToSetAttrOrThrow(m_desc.kind, "input model layout")
+            .input_model_layout = std::move(layout);
+        return *this;
+    }
+
+    /** @overload
+    @param layout_map Map of pairs: name of corresponding input layer
+    and its model layout ("NCHW", "NHWC", etc)
+    @return reference to this parameter structure.
+    */
+    Params<Net>&
+    cfgInputModelLayout(detail::AttrMap<std::string> layout_map) {
+        detail::getModelToSetAttrOrThrow(m_desc.kind, "input model layout")
+            .input_model_layout = std::move(layout_map);
+        return *this;
+    }
+
+    /** @brief Specifies tensor layout for an output layer.
+
+    The function is used to set tensor layout for an output layer.
+
+    @param layout Tensor layout ("NCHW", "NWHC", etc)
+    will be applied to all output layers.
+    @return reference to this parameter structure.
+    */
+    Params<Net>& cfgOutputTensorLayout(std::string layout) {
+        detail::getModelToSetAttrOrThrow(m_desc.kind, "output tensor layout")
+            .output_tensor_layout = std::move(layout);
+        return *this;
+    }
+
+    /** @overload
+    @param layout_map Map of pairs: name of corresponding output layer
+    and its tensor layout represented in std::string ("NCHW", "NHWC", etc)
+    @return reference to this parameter structure.
+    */
+    Params<Net>&
+    cfgOutputTensorLayout(detail::AttrMap<std::string> layout_map) {
+        detail::getModelToSetAttrOrThrow(m_desc.kind, "output tensor layout")
+            .output_tensor_layout = std::move(layout_map);
+        return *this;
+    }
+
+    /** @brief Specifies model layout for an output layer.
+
+    The function is used to set model layout for an output layer.
+
+    @param layout Model layout ("NCHW", "NHWC", etc)
+    will be applied to all output layers.
+    @return reference to this parameter structure.
+    */
+    Params<Net>& cfgOutputModelLayout(std::string layout) {
+        detail::getModelToSetAttrOrThrow(m_desc.kind, "output model layout")
+            .output_model_layout = std::move(layout);
+        return *this;
+    }
+
+    /** @overload
+    @param layout_map Map of pairs: name of corresponding output layer
+    and its model layout ("NCHW", "NHWC", etc)
+    @return reference to this parameter structure.
+    */
+    Params<Net>&
+    cfgOutputModelLayout(detail::AttrMap<std::string> layout_map) {
+        detail::getModelToSetAttrOrThrow(m_desc.kind, "output model layout")
+            .output_model_layout = std::move(layout_map);
+        return *this;
+    }
+
+    /** @brief Specifies tensor precision for an output layer.
+
+    The function is used to set tensor precision for an output layer..
+
+    @param precision Precision in OpenCV format (CV_8U, CV_32F, ...)
+    will be applied to all output layers.
+    @return reference to this parameter structure.
+    */
+    Params<Net>& cfgOutputTensorPrecision(int precision) {
+        detail::getModelToSetAttrOrThrow(m_desc.kind, "output tensor precision")
+            .output_tensor_precision = precision;
+        return *this;
+    }
+
+    /** @overload
+
+    @param precision_map Map of pairs: name of corresponding output layer
+    and its precision in OpenCV format (CV_8U, CV_32F, ...)
+    @return reference to this parameter structure.
+    */
+    Params<Net>&
+    cfgOutputTensorPrecision(detail::AttrMap<int> precision_map) {
+        detail::getModelToSetAttrOrThrow(m_desc.kind, "output tensor precision")
+            .output_tensor_precision = std::move(precision_map);
+        return *this;
+    }
+
+    /** @brief Specifies the new shape for input layers.
+
+    The function is used to set new shape for input layers.
+
+    @param new_shape New shape will be applied to all input layers.
+    @return reference to this parameter structure.
+    */
+    Params<Net>&
+    cfgReshape(std::vector<size_t> new_shape) {
+        detail::getModelToSetAttrOrThrow(m_desc.kind, "reshape")
+            .new_shapes = std::move(new_shape);
+        return *this;
+    }
+
+    /** @overload
+
+    @param new_shape_map Map of pairs: name of corresponding output layer
+    and its new shape.
+    @return reference to this parameter structure.
+    */
+    Params<Net>&
+    cfgReshape(detail::AttrMap<std::vector<size_t>> new_shape_map) {
+        detail::getModelToSetAttrOrThrow(m_desc.kind, "reshape")
+            .new_shapes = std::move(new_shape_map);
+        return *this;
+    }
+
+    /** @brief Specifies number of asynchronous inference requests.
+
+    @param nireq Number of inference asynchronous requests.
+    @return reference to this parameter structure.
+    */
+    Params<Net>& cfgNumRequests(const size_t nireq) {
+        if (nireq == 0) {
+            cv::util::throw_error(
+                    std::logic_error("Number of inference requests"
+                                     " must be greater than zero."));
+        }
+        m_desc.nireq = nireq;
+        return *this;
+    }
+
+    /** @brief Specifies mean values for preprocessing.
+     *
+    The function is used to set mean values for input layer preprocessing.
+
+    @param mean_values Float vector contains mean values
+    @return reference to this parameter structure.
+    */
+    Params<Net>& cfgMean(std::vector<float> mean_values) {
+        detail::getModelToSetAttrOrThrow(m_desc.kind, "mean values")
+            .mean_values = std::move(mean_values);
+        return *this;
+    }
+
+    /** @overload
+
+    @param mean_map Map of pairs: name of corresponding input layer
+    and its mean values.
+    @return reference to this parameter structure.
+    */
+    Params<Net>& cfgMean(detail::AttrMap<std::vector<float>> mean_map) {
+        detail::getModelToSetAttrOrThrow(m_desc.kind, "mean values")
+            .mean_values = std::move(mean_map);
+        return *this;
+    }
+
+    /** @brief Specifies scale values for preprocessing.
+     *
+    The function is used to set scale values for input layer preprocessing.
+
+    @param scale_values Float vector contains scale values
+    @return reference to this parameter structure.
+    */
+    Params<Net>& cfgScale(std::vector<float> scale_values) {
+        detail::getModelToSetAttrOrThrow(m_desc.kind, "scale values")
+            .scale_values = std::move(scale_values);
+        return *this;
+    }
+
+    /** @overload
+
+    @param scale_map Map of pairs: name of corresponding input layer
+    and its mean values.
+    @return reference to this parameter structure.
+    */
+    Params<Net>& cfgScale(detail::AttrMap<std::vector<float>> scale_map) {
+        detail::getModelToSetAttrOrThrow(m_desc.kind, "scale values")
+            .scale_values = std::move(scale_map);
+        return *this;
+    }
+
+    /** @brief Specifies resize interpolation algorithm.
+     *
+    The function is used to configure resize preprocessing for input layer.
+
+    @param interpolation Resize interpolation algorithm.
+    Supported algorithms: #INTER_NEAREST, #INTER_LINEAR, #INTER_CUBIC.
+    @return reference to this parameter structure.
+    */
+    Params<Net>& cfgResize(int interpolation) {
+        detail::getModelToSetAttrOrThrow(m_desc.kind, "resize preprocessing")
+            .interpolation = std::move(interpolation);
+        return *this;
+    }
+
+    /** @overload
+
+    @param interpolation Map of pairs: name of corresponding input layer
+    and its resize algorithm.
+    @return reference to this parameter structure.
+    */
+    Params<Net>& cfgResize(detail::AttrMap<int> interpolation) {
+        detail::getModelToSetAttrOrThrow(m_desc.kind, "resize preprocessing")
+            .interpolation = std::move(interpolation);
+        return *this;
+    }
+
+    // BEGIN(G-API's network parametrization API)
+    GBackend      backend() const { return cv::gapi::ov::backend(); }
+    std::string   tag()     const { return Net::tag(); }
+    cv::util::any params()  const { return { m_desc }; }
+    // END(G-API's network parametrization API)
+
+protected:
+    detail::ParamDesc m_desc;
+};
+
+/*
+* @brief This structure provides functions for generic network type that
+* fill inference parameters.
+* @see struct Generic
+*/
+template<>
+class Params<cv::gapi::Generic> {
+public:
+    /** @brief Class constructor.
+
+    Constructs Params based on model information and specifies default values for other
+    inference description parameters. Model is loaded and compiled using "OpenVINO Toolkit".
+
+    @param tag string tag of the network for which these parameters are intended.
+    @param model_path Path to a model.
+    @param bin_path Path to a data file.
+    For IR format (*.bin):
+    If path is empty, will try to read a bin file with the same name as xml.
+    If the bin file with the same name is not found, will load IR without weights.
+    For PDPD (*.pdmodel) and ONNX (*.onnx) formats bin_path isn't used.
+    @param device target device to use.
+    */
+    Params(const std::string &tag,
+           const std::string &model_path,
+           const std::string &bin_path,
+           const std::string &device)
+        : m_tag(tag),
+          m_desc( detail::ParamDesc::Kind{detail::ParamDesc::Model{model_path, bin_path}}
+                , device
+                , true /* is generic */
+                , 0u
+                , 0u) {
+    }
+
+    /** @overload
+
+    This constructor for pre-compiled networks. Model is imported from pre-compiled
+    blob.
+
+    @param tag string tag of the network for which these parameters are intended.
+    @param blob_path path to the compiled model (*.blob).
+    @param device target device to use.
+    */
+    Params(const std::string &tag,
+           const std::string &blob_path,
+           const std::string &device)
+        : m_tag(tag),
+          m_desc( detail::ParamDesc::Kind{detail::ParamDesc::CompiledModel{blob_path}}
+                , device
+                , true /* is generic */
+                , 0u
+                , 0u) {
+    }
+
+    /** @see ov::Params::cfgPluginConfig. */
+    Params& cfgPluginConfig(const detail::ParamDesc::PluginConfigT &config) {
+        m_desc.config = config;
+        return *this;
+    }
+
+    /** @see ov::Params::cfgInputTensorLayout. */
+    Params& cfgInputTensorLayout(std::string layout) {
+        detail::getModelToSetAttrOrThrow(m_desc.kind, "input tensor layout")
+            .input_tensor_layout = std::move(layout);
+        return *this;
+    }
+
+    /** @overload */
+    Params&
+    cfgInputTensorLayout(detail::AttrMap<std::string> layout_map) {
+        detail::getModelToSetAttrOrThrow(m_desc.kind, "input tensor layout")
+            .input_tensor_layout = std::move(layout_map);
+        return *this;
+    }
+
+    /** @see ov::Params::cfgInputModelLayout. */
+    Params& cfgInputModelLayout(std::string layout) {
+        detail::getModelToSetAttrOrThrow(m_desc.kind, "input model layout")
+            .input_model_layout = std::move(layout);
+        return *this;
+    }
+
+    /** @overload */
+    Params&
+    cfgInputModelLayout(detail::AttrMap<std::string> layout_map) {
+        detail::getModelToSetAttrOrThrow(m_desc.kind, "input model layout")
+            .input_model_layout = std::move(layout_map);
+        return *this;
+    }
+
+    /** @see ov::Params::cfgOutputTensorLayout. */
+    Params& cfgOutputTensorLayout(std::string layout) {
+        detail::getModelToSetAttrOrThrow(m_desc.kind, "output tensor layout")
+            .output_tensor_layout = std::move(layout);
+        return *this;
+    }
+
+    /** @overload */
+    Params&
+    cfgOutputTensorLayout(detail::AttrMap<std::string> layout_map) {
+        detail::getModelToSetAttrOrThrow(m_desc.kind, "output tensor layout")
+            .output_tensor_layout = std::move(layout_map);
+        return *this;
+    }
+
+    /** @see ov::Params::cfgOutputModelLayout. */
+    Params& cfgOutputModelLayout(std::string layout) {
+        detail::getModelToSetAttrOrThrow(m_desc.kind, "output model layout")
+            .output_model_layout = std::move(layout);
+        return *this;
+    }
+
+    /** @overload */
+    Params&
+    cfgOutputModelLayout(detail::AttrMap<std::string> layout_map) {
+        detail::getModelToSetAttrOrThrow(m_desc.kind, "output model layout")
+            .output_model_layout = std::move(layout_map);
+        return *this;
+    }
+
+    /** @see ov::Params::cfgOutputTensorPrecision. */
+    Params& cfgOutputTensorPrecision(int precision) {
+        detail::getModelToSetAttrOrThrow(m_desc.kind, "output tensor precision")
+            .output_tensor_precision = precision;
+        return *this;
+    }
+
+    /** @overload */
+    Params&
+    cfgOutputTensorPrecision(detail::AttrMap<int> precision_map) {
+        detail::getModelToSetAttrOrThrow(m_desc.kind, "output tensor precision")
+            .output_tensor_precision = std::move(precision_map);
+        return *this;
+    }
+
+    /** @see ov::Params::cfgReshape. */
+    Params& cfgReshape(std::vector<size_t> new_shape) {
+        detail::getModelToSetAttrOrThrow(m_desc.kind, "reshape")
+            .new_shapes = std::move(new_shape);
+        return *this;
+    }
+
+    /** @overload */
+    Params&
+    cfgReshape(detail::AttrMap<std::vector<size_t>> new_shape_map) {
+        detail::getModelToSetAttrOrThrow(m_desc.kind, "reshape")
+            .new_shapes = std::move(new_shape_map);
+        return *this;
+    }
+
+    /** @see ov::Params::cfgNumRequests. */
+    Params& cfgNumRequests(const size_t nireq) {
+        if (nireq == 0) {
+            cv::util::throw_error(
+                    std::logic_error("Number of inference requests"
+                                     " must be greater than zero."));
+        }
+        m_desc.nireq = nireq;
+        return *this;
+    }
+
+    /** @see ov::Params::cfgMean. */
+    Params& cfgMean(std::vector<float> mean_values) {
+        detail::getModelToSetAttrOrThrow(m_desc.kind, "mean values")
+            .mean_values = std::move(mean_values);
+        return *this;
+    }
+
+    /** @overload */
+    Params& cfgMean(detail::AttrMap<std::vector<float>> mean_map) {
+        detail::getModelToSetAttrOrThrow(m_desc.kind, "mean values")
+            .mean_values = std::move(mean_map);
+        return *this;
+    }
+
+    /** @see ov::Params::cfgScale. */
+    Params& cfgScale(std::vector<float> scale_values) {
+        detail::getModelToSetAttrOrThrow(m_desc.kind, "scale values")
+            .scale_values = std::move(scale_values);
+        return *this;
+    }
+
+    /** @overload */
+    Params& cfgScale(detail::AttrMap<std::vector<float>> scale_map) {
+        detail::getModelToSetAttrOrThrow(m_desc.kind, "scale values")
+            .scale_values = std::move(scale_map);
+        return *this;
+    }
+
+    /** @see ov::Params::cfgResize. */
+    Params& cfgResize(int interpolation) {
+        detail::getModelToSetAttrOrThrow(m_desc.kind, "resize preprocessing")
+            .interpolation = std::move(interpolation);
+        return *this;
+    }
+
+    /** @overload */
+    Params& cfgResize(detail::AttrMap<int> interpolation) {
+        detail::getModelToSetAttrOrThrow(m_desc.kind, "resize preprocessing")
+            .interpolation = std::move(interpolation);
+        return *this;
+    }
+
+    // BEGIN(G-API's network parametrization API)
+    GBackend      backend() const { return cv::gapi::ov::backend(); }
+    std::string   tag()     const { return m_tag; }
+    cv::util::any params()  const { return { m_desc }; }
+    // END(G-API's network parametrization API)
+
+protected:
+    std::string m_tag;
+    detail::ParamDesc m_desc;
+};
+
+} // namespace ov
+} // namespace gapi
+} // namespace cv
+
+#endif // OPENCV_GAPI_INFER_OV_HPP

modules/gapi/misc/python/pyopencv_gapi.hpp

@@ -15,6 +15,7 @@ using gapi_GKernelPackage           = cv::GKernelPackage;
 using gapi_GNetPackage              = cv::gapi::GNetPackage;
 using gapi_ie_PyParams              = cv::gapi::ie::PyParams;
 using gapi_onnx_PyParams            = cv::gapi::onnx::PyParams;
+using gapi_ov_PyParams              = cv::gapi::ov::PyParams;
 using gapi_wip_IStreamSource_Ptr    = cv::Ptr<cv::gapi::wip::IStreamSource>;
 using detail_ExtractArgsCallback    = cv::detail::ExtractArgsCallback;
 using detail_ExtractMetaCallback    = cv::detail::ExtractMetaCallback;
@@ -22,6 +23,11 @@ using vector_GNetParam              = std::vector<cv::gapi::GNetParam>;
 using vector_GMat                   = std::vector<cv::GMat>;
 using gapi_streaming_queue_capacity = cv::gapi::streaming::queue_capacity;
 using GStreamerSource_OutputType    = cv::gapi::wip::GStreamerSource::OutputType;
+using map_string_and_int            = std::map<std::string, int>;
+using map_string_and_string         = std::map<std::string, std::string>;
+using map_string_and_string         = std::map<std::string, std::string>;
+using map_string_and_vector_size_t  = std::map<std::string, std::vector<size_t>>;
+using map_string_and_vector_float   = std::map<std::string, std::vector<float>>;
 
 // NB: Python wrapper generate T_U for T<U>
 // This behavior is only observed for inputs

modules/gapi/misc/python/shadow_gapi.hpp

@@ -80,5 +80,6 @@ namespace detail
 {
     gapi::GNetParam GAPI_EXPORTS_W strip(gapi::ie::PyParams params);
     gapi::GNetParam GAPI_EXPORTS_W strip(gapi::onnx::PyParams params);
+    gapi::GNetParam GAPI_EXPORTS_W strip(gapi::ov::PyParams params);
 } // namespace detail
 } // namespace cv

modules/gapi/misc/python/test/test_gapi_infer_ov.py

@@ -0,0 +1,238 @@
+#!/usr/bin/env python
+
+import numpy as np
+import cv2 as cv
+import os
+import sys
+import unittest
+
+from tests_common import NewOpenCVTests
+
+
+try:
+
+    if sys.version_info[:2] < (3, 0):
+        raise unittest.SkipTest('Python 2.x is not supported')
+
+
+    openvino_is_available = True
+    try:
+        from openvino.runtime import Core, Type, Layout, PartialShape
+        from openvino.preprocess import ResizeAlgorithm, PrePostProcessor
+    except ImportError:
+        openvino_is_available = False
+
+
+    def skip_if_openvino_not_available():
+        if not openvino_is_available:
+            raise unittest.SkipTest("OpenVINO isn't available from python.")
+
+
+    class AgeGenderOV:
+        def __init__(self, model_path, bin_path, device):
+            self.device = device
+            self.core = Core()
+            self.model = self.core.read_model(model_path, bin_path)
+
+
+        def reshape(self, new_shape):
+            self.model.reshape(new_shape)
+
+
+        def cfgPrePostProcessing(self, pp_callback):
+            ppp = PrePostProcessor(self.model)
+            pp_callback(ppp)
+            self.model = ppp.build()
+
+
+        def apply(self, in_data):
+           compiled_model = self.core.compile_model(self.model, self.device)
+           infer_request = compiled_model.create_infer_request()
+           results = infer_request.infer(in_data)
+           ov_age = results['age_conv3'].squeeze()
+           ov_gender = results['prob'].squeeze()
+           return ov_age, ov_gender
+
+
+    class AgeGenderGAPI:
+        tag = 'age-gender-net'
+
+        def __init__(self, model_path, bin_path, device):
+            g_in   = cv.GMat()
+            inputs = cv.GInferInputs()
+            inputs.setInput('data', g_in)
+            # TODO: It'd be nice to pass dict instead.
+            # E.g cv.gapi.infer("net", {'data': g_in})
+            outputs = cv.gapi.infer(AgeGenderGAPI.tag, inputs)
+            age_g = outputs.at("age_conv3")
+            gender_g = outputs.at("prob")
+
+            self.comp = cv.GComputation(cv.GIn(g_in), cv.GOut(age_g, gender_g))
+            self.pp = cv.gapi.ov.params(AgeGenderGAPI.tag, \
+                                        model_path, bin_path, device)
+
+
+        def apply(self, in_data):
+           compile_args = cv.gapi.compile_args(cv.gapi.networks(self.pp))
+           gapi_age, gapi_gender = self.comp.apply(cv.gin(in_data), compile_args)
+           gapi_gender = gapi_gender.squeeze()
+           gapi_age = gapi_age.squeeze()
+           return gapi_age, gapi_gender
+
+
+    class test_gapi_infer_ov(NewOpenCVTests):
+
+        def test_age_gender_infer_image(self):
+            skip_if_openvino_not_available()
+
+            root_path  = '/omz_intel_models/intel/age-gender-recognition-retail-0013/FP32/age-gender-recognition-retail-0013'
+            model_path = self.find_file(root_path + '.xml',   [os.environ.get('OPENCV_DNN_TEST_DATA_PATH')])
+            bin_path   = self.find_file(root_path + '.bin',   [os.environ.get('OPENCV_DNN_TEST_DATA_PATH')])
+            device_id  = 'CPU'
+
+            img_path = self.find_file('cv/face/david2.jpg', [os.environ.get('OPENCV_TEST_DATA_PATH')])
+            img = cv.imread(img_path)
+
+            # OpenVINO
+            def preproc(ppp):
+                ppp.input().model().set_layout(Layout("NCHW"))
+                ppp.input().tensor().set_element_type(Type.u8)                            \
+                                    .set_spatial_static_shape(img.shape[0], img.shape[1]) \
+                                    .set_layout(Layout("NHWC"))
+                ppp.input().preprocess().resize(ResizeAlgorithm.RESIZE_LINEAR)
+
+
+            ref = AgeGenderOV(model_path, bin_path, device_id)
+            ref.cfgPrePostProcessing(preproc)
+            ov_age, ov_gender = ref.apply(np.expand_dims(img, 0))
+
+            # OpenCV G-API (No preproc required)
+            comp = AgeGenderGAPI(model_path, bin_path, device_id)
+            gapi_age, gapi_gender = comp.apply(img)
+
+            # Check
+            self.assertEqual(0.0, cv.norm(ov_gender, gapi_gender, cv.NORM_INF))
+            self.assertEqual(0.0, cv.norm(ov_age, gapi_age, cv.NORM_INF))
+
+
+        def test_age_gender_infer_tensor(self):
+            skip_if_openvino_not_available()
+
+            root_path  = '/omz_intel_models/intel/age-gender-recognition-retail-0013/FP32/age-gender-recognition-retail-0013'
+            model_path = self.find_file(root_path + '.xml',   [os.environ.get('OPENCV_DNN_TEST_DATA_PATH')])
+            bin_path   = self.find_file(root_path + '.bin',   [os.environ.get('OPENCV_DNN_TEST_DATA_PATH')])
+            device_id  = 'CPU'
+
+            img_path = self.find_file('cv/face/david2.jpg', [os.environ.get('OPENCV_TEST_DATA_PATH')])
+            img = cv.imread(img_path)
+
+            # Prepare data manually
+            tensor = cv.resize(img, (62, 62)).astype(np.float32)
+            tensor = np.transpose(tensor, (2, 0, 1))
+            tensor = np.expand_dims(tensor, 0)
+
+            # OpenVINO (No preproce required)
+            ref = AgeGenderOV(model_path, bin_path, device_id)
+            ov_age, ov_gender = ref.apply(tensor)
+
+            # OpenCV G-API (No preproc required)
+            comp = AgeGenderGAPI(model_path, bin_path, device_id)
+            gapi_age, gapi_gender = comp.apply(tensor)
+
+            # Check
+            self.assertEqual(0.0, cv.norm(ov_gender, gapi_gender, cv.NORM_INF))
+            self.assertEqual(0.0, cv.norm(ov_age, gapi_age, cv.NORM_INF))
+
+
+        def test_age_gender_infer_batch(self):
+            skip_if_openvino_not_available()
+
+            root_path  = '/omz_intel_models/intel/age-gender-recognition-retail-0013/FP32/age-gender-recognition-retail-0013'
+            model_path = self.find_file(root_path + '.xml',   [os.environ.get('OPENCV_DNN_TEST_DATA_PATH')])
+            bin_path   = self.find_file(root_path + '.bin',   [os.environ.get('OPENCV_DNN_TEST_DATA_PATH')])
+            device_id  = 'CPU'
+
+            img_path1 = self.find_file('cv/face/david1.jpg', [os.environ.get('OPENCV_TEST_DATA_PATH')])
+            img_path2 = self.find_file('cv/face/david2.jpg', [os.environ.get('OPENCV_TEST_DATA_PATH')])
+            img1 = cv.imread(img_path1)
+            img2 = cv.imread(img_path2)
+            # img1 and img2 have the same size
+            batch_img = np.array([img1, img2])
+
+            # OpenVINO
+            def preproc(ppp):
+                ppp.input().model().set_layout(Layout("NCHW"))
+                ppp.input().tensor().set_element_type(Type.u8)                              \
+                                    .set_spatial_static_shape(img1.shape[0], img2.shape[1]) \
+                                    .set_layout(Layout("NHWC"))
+                ppp.input().preprocess().resize(ResizeAlgorithm.RESIZE_LINEAR)
+
+
+            ref = AgeGenderOV(model_path, bin_path, device_id)
+            ref.reshape(PartialShape([2, 3, 62, 62]))
+            ref.cfgPrePostProcessing(preproc)
+            ov_age, ov_gender = ref.apply(batch_img)
+
+            # OpenCV G-API
+            comp = AgeGenderGAPI(model_path, bin_path, device_id)
+            comp.pp.cfgReshape([2, 3, 62, 62])   \
+                   .cfgInputModelLayout("NCHW")  \
+                   .cfgInputTensorLayout("NHWC") \
+                   .cfgResize(cv.INTER_LINEAR)
+            gapi_age, gapi_gender = comp.apply(batch_img)
+
+            # Check
+            self.assertEqual(0.0, cv.norm(ov_gender, gapi_gender, cv.NORM_INF))
+            self.assertEqual(0.0, cv.norm(ov_age, gapi_age, cv.NORM_INF))
+
+
+        def test_age_gender_infer_planar(self):
+            skip_if_openvino_not_available()
+
+            root_path  = '/omz_intel_models/intel/age-gender-recognition-retail-0013/FP32/age-gender-recognition-retail-0013'
+            model_path = self.find_file(root_path + '.xml',   [os.environ.get('OPENCV_DNN_TEST_DATA_PATH')])
+            bin_path   = self.find_file(root_path + '.bin',   [os.environ.get('OPENCV_DNN_TEST_DATA_PATH')])
+            device_id  = 'CPU'
+
+            img_path = self.find_file('cv/face/david2.jpg', [os.environ.get('OPENCV_TEST_DATA_PATH')])
+            img = cv.imread(img_path)
+            planar_img = np.transpose(img, (2, 0, 1))
+            planar_img = np.expand_dims(planar_img, 0)
+
+            # OpenVINO
+            def preproc(ppp):
+                ppp.input().tensor().set_element_type(Type.u8)                            \
+                                    .set_spatial_static_shape(img.shape[0], img.shape[1])
+                ppp.input().preprocess().resize(ResizeAlgorithm.RESIZE_LINEAR)
+
+
+            ref = AgeGenderOV(model_path, bin_path, device_id)
+            ref.cfgPrePostProcessing(preproc)
+            ov_age, ov_gender = ref.apply(planar_img)
+
+            # OpenCV G-API
+            comp = AgeGenderGAPI(model_path, bin_path, device_id)
+            comp.pp.cfgResize(cv.INTER_LINEAR)
+            gapi_age, gapi_gender = comp.apply(planar_img)
+
+            # Check
+            self.assertEqual(0.0, cv.norm(ov_gender, gapi_gender, cv.NORM_INF))
+            self.assertEqual(0.0, cv.norm(ov_age, gapi_age, cv.NORM_INF))
+
+
+except unittest.SkipTest as e:
+
+    message = str(e)
+
+    class TestSkip(unittest.TestCase):
+        def setUp(self):
+            self.skipTest('Skip tests: ' + message)
+
+        def test_skip():
+            pass
+
+    pass
+
+
+if __name__ == '__main__':
+    NewOpenCVTests.bootstrap()

modules/gapi/src/api/gmat.cpp

@@ -153,10 +153,18 @@ std::ostream& operator<<(std::ostream& os, const cv::GMatDesc &desc)
         break;
     }
 
-    os << "C" << desc.chan;
-    if (desc.planar) os << "p";
-    os << " ";
-    os << desc.size.width << "x" << desc.size.height;
+    if (desc.isND()) {
+        os << " [";
+        for (size_t i = 0; i < desc.dims.size() - 1; ++i) {
+            os << desc.dims[i] << "x";
+        }
+        os << desc.dims.back() << "]";
+    } else {
+        os << "C" << desc.chan;
+        if (desc.planar) os << "p";
+        os << " ";
+        os << desc.size.width << "x" << desc.size.height;
+    }
 
     return os;
 }

modules/gapi/src/backends/common/gbackend.hpp

@@ -227,6 +227,12 @@ inline void convertInt64ToInt32(const int64_t* src, int* dst, size_t size)
                    [](int64_t el) { return static_cast<int>(el); });
 }
 
+inline void convertInt32ToInt64(const int* src, int64_t* dst, size_t size)
+{
+    std::transform(src, src + size, dst,
+                   [](int el) { return static_cast<int64_t>(el); });
+}
+
 }} // cv::gimpl
 
 #endif // OPENCV_GAPI_GBACKEND_HPP

modules/gapi/src/backends/ov/bindings_ov.cpp

@@ -0,0 +1,168 @@
+#include <opencv2/gapi/infer/bindings_ov.hpp>
+
+cv::gapi::ov::PyParams::PyParams(const std::string &tag,
+                                 const std::string &model_path,
+                                 const std::string &bin_path,
+                                 const std::string &device)
+    : m_priv(std::make_shared<Params<cv::gapi::Generic>>(tag, model_path, bin_path, device)) {
+}
+
+cv::gapi::ov::PyParams::PyParams(const std::string &tag,
+                                 const std::string &blob_path,
+                                 const std::string &device)
+    : m_priv(std::make_shared<Params<cv::gapi::Generic>>(tag, blob_path, device)) {
+}
+
+cv::gapi::GBackend cv::gapi::ov::PyParams::backend() const {
+    return m_priv->backend();
+}
+
+std::string cv::gapi::ov::PyParams::tag() const {
+    return m_priv->tag();
+}
+
+cv::util::any cv::gapi::ov::PyParams::params() const {
+    return m_priv->params();
+}
+
+cv::gapi::ov::PyParams&
+cv::gapi::ov::PyParams::cfgPluginConfig(
+        const std::map<std::string, std::string> &config) {
+    m_priv->cfgPluginConfig(config);
+    return *this;
+}
+
+cv::gapi::ov::PyParams&
+cv::gapi::ov::PyParams::cfgInputTensorLayout(std::string tensor_layout) {
+    m_priv->cfgInputTensorLayout(std::move(tensor_layout));
+    return *this;
+}
+
+cv::gapi::ov::PyParams&
+cv::gapi::ov::PyParams::cfgInputTensorLayout(
+        std::map<std::string, std::string> layout_map) {
+    m_priv->cfgInputTensorLayout(std::move(layout_map));
+    return *this;
+}
+
+cv::gapi::ov::PyParams&
+cv::gapi::ov::PyParams::cfgInputModelLayout(std::string tensor_layout) {
+    m_priv->cfgInputModelLayout(std::move(tensor_layout));
+    return *this;
+}
+
+cv::gapi::ov::PyParams&
+cv::gapi::ov::PyParams::cfgInputModelLayout(
+        std::map<std::string, std::string> layout_map) {
+    m_priv->cfgInputModelLayout(std::move(layout_map));
+    return *this;
+}
+
+cv::gapi::ov::PyParams&
+cv::gapi::ov::PyParams::cfgOutputTensorLayout(std::string tensor_layout) {
+    m_priv->cfgOutputTensorLayout(std::move(tensor_layout));
+    return *this;
+}
+
+cv::gapi::ov::PyParams&
+cv::gapi::ov::PyParams::cfgOutputTensorLayout(
+        std::map<std::string, std::string> layout_map) {
+    m_priv->cfgOutputTensorLayout(std::move(layout_map));
+    return *this;
+}
+
+cv::gapi::ov::PyParams&
+cv::gapi::ov::PyParams::cfgOutputModelLayout(std::string tensor_layout) {
+    m_priv->cfgOutputModelLayout(std::move(tensor_layout));
+    return *this;
+}
+
+cv::gapi::ov::PyParams&
+cv::gapi::ov::PyParams::cfgOutputModelLayout(
+        std::map<std::string, std::string> layout_map) {
+    m_priv->cfgOutputModelLayout(std::move(layout_map));
+    return *this;
+}
+
+cv::gapi::ov::PyParams&
+cv::gapi::ov::PyParams::cfgOutputTensorPrecision(int precision) {
+    m_priv->cfgOutputTensorPrecision(precision);
+    return *this;
+}
+
+cv::gapi::ov::PyParams&
+cv::gapi::ov::PyParams::cfgOutputTensorPrecision(
+        std::map<std::string, int> precision_map) {
+    m_priv->cfgOutputTensorPrecision(precision_map);
+    return *this;
+}
+
+cv::gapi::ov::PyParams&
+cv::gapi::ov::PyParams::cfgReshape(std::vector<size_t> new_shape) {
+    m_priv->cfgReshape(std::move(new_shape));
+    return *this;
+}
+
+cv::gapi::ov::PyParams&
+cv::gapi::ov::PyParams::cfgReshape(
+        std::map<std::string, std::vector<size_t>> new_shape_map) {
+    m_priv->cfgReshape(std::move(new_shape_map));
+    return *this;
+}
+
+cv::gapi::ov::PyParams&
+cv::gapi::ov::PyParams::cfgNumRequests(const size_t nireq) {
+    m_priv->cfgNumRequests(nireq);
+    return *this;
+}
+
+cv::gapi::ov::PyParams&
+cv::gapi::ov::PyParams::cfgMean(std::vector<float> mean_values) {
+    m_priv->cfgMean(std::move(mean_values));
+    return *this;
+}
+
+cv::gapi::ov::PyParams&
+cv::gapi::ov::PyParams::cfgMean(
+        std::map<std::string, std::vector<float>> mean_map) {
+    m_priv->cfgMean(std::move(mean_map));
+    return *this;
+}
+
+cv::gapi::ov::PyParams&
+cv::gapi::ov::PyParams::cfgScale(std::vector<float> scale_values) {
+    m_priv->cfgScale(std::move(scale_values));
+    return *this;
+}
+
+cv::gapi::ov::PyParams&
+cv::gapi::ov::PyParams::cfgScale(
+        std::map<std::string, std::vector<float>> scale_map) {
+    m_priv->cfgScale(std::move(scale_map));
+    return *this;
+}
+
+cv::gapi::ov::PyParams&
+cv::gapi::ov::PyParams::cfgResize(int interpolation) {
+    m_priv->cfgResize(interpolation);
+    return *this;
+}
+
+cv::gapi::ov::PyParams&
+cv::gapi::ov::PyParams::cfgResize(std::map<std::string, int> interpolation) {
+    m_priv->cfgResize(std::move(interpolation));
+    return *this;
+}
+
+cv::gapi::ov::PyParams cv::gapi::ov::params(const std::string &tag,
+                                            const std::string &model_path,
+                                            const std::string &weights,
+                                            const std::string &device) {
+    return {tag, model_path, weights, device};
+}
+
+cv::gapi::ov::PyParams cv::gapi::ov::params(const std::string &tag,
+                                            const std::string &blob_path,
+                                            const std::string &device) {
+    return {tag, blob_path, device};
+}

modules/gapi/src/backends/ov/govbackend.hpp

@@ -0,0 +1,66 @@
+// 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) 2023 Intel Corporation
+
+#ifndef OPENCV_GAPI_GOVBACKEND_HPP
+#define OPENCV_GAPI_GOVBACKEND_HPP
+
+// Include anyway - cv::gapi::ov::backend() still needs to be defined
+#include "opencv2/gapi/infer/ov.hpp"
+
+#ifdef HAVE_INF_ENGINE
+
+#include <openvino/openvino.hpp>
+
+#include "backends/common/gbackend.hpp"
+
+namespace cv {
+namespace gimpl {
+namespace ov {
+
+struct OVCompiled {
+    ::ov::CompiledModel compiled_model;
+};
+
+class RequestPool;
+
+class GOVExecutable final: public GIslandExecutable
+{
+    const ade::Graph &m_g;
+    GModel::ConstGraph m_gm;
+
+    // The only executable stuff in this graph
+    // (assuming it is always single-op)
+    ade::NodeHandle this_nh;
+    OVCompiled compiled;
+
+    // List of all resources in graph (both internal and external)
+    std::vector<ade::NodeHandle> m_dataNodes;
+
+    // To manage multiple async requests
+    std::unique_ptr<RequestPool> m_reqPool;
+
+public:
+    GOVExecutable(const ade::Graph                   &graph,
+                  const std::vector<ade::NodeHandle> &nodes);
+
+    virtual inline bool canReshape() const override { return false; }
+    virtual inline void reshape(ade::Graph&, const GCompileArgs&) override {
+        GAPI_Error("InternalError"); // Not implemented yet
+    }
+
+    virtual void run(std::vector<InObj>  &&,
+                     std::vector<OutObj> &&) override {
+        GAPI_Error("Not implemented");
+    }
+
+    virtual void run(GIslandExecutable::IInput  &in,
+                     GIslandExecutable::IOutput &out) override;
+};
+
+}}}
+
+#endif // HAVE_INF_ENGINE
+#endif // OPENCV_GAPI_GOVBACKEND_HPP

modules/gapi/src/backends/ov/util.hpp

@@ -0,0 +1,35 @@
+// 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) 2023 Intel Corporation
+
+#ifndef OPENCV_GAPI_INFER_OV_UTIL_HPP
+#define OPENCV_GAPI_INFER_OV_UTIL_HPP
+
+#ifdef HAVE_INF_ENGINE
+
+// NOTE: This file is not included by default in infer/ov.hpp
+// and won't be. infer/ov.hpp doesn't depend on OV headers itself.
+// This file does -- so needs to be included separately by those who care.
+
+#include <openvino/openvino.hpp>
+
+#include <opencv2/core/cvdef.h>     // GAPI_EXPORTS
+#include <opencv2/gapi/gkernel.hpp> // GKernelPackage
+
+namespace cv {
+namespace gapi {
+namespace ov {
+namespace util {
+
+// NB: These functions are EXPORTed to make them accessible by the
+// test suite only.
+GAPI_EXPORTS std::vector<int> to_ocv(const ::ov::Shape &shape);
+GAPI_EXPORTS int to_ocv(const ::ov::element::Type &type);
+
+}}}}
+
+#endif // HAVE_INF_ENGINE
+
+#endif // OPENCV_GAPI_INFER_OV_UTIL_HPP

modules/gapi/test/infer/gapi_infer_ov_tests.cpp

@@ -0,0 +1,540 @@
+// 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) 2023 Intel Corporation
+
+#ifdef HAVE_INF_ENGINE
+
+#include "../test_precomp.hpp"
+
+#include "backends/ov/util.hpp"
+
+#include <opencv2/gapi/infer/ov.hpp>
+
+#include <openvino/openvino.hpp>
+
+namespace opencv_test
+{
+
+namespace {
+// FIXME: taken from DNN module
+void initDLDTDataPath()
+{
+#ifndef WINRT
+    static bool initialized = false;
+    if (!initialized)
+    {
+        const char* omzDataPath = getenv("OPENCV_OPEN_MODEL_ZOO_DATA_PATH");
+        if (omzDataPath)
+            cvtest::addDataSearchPath(omzDataPath);
+        const char* dnnDataPath = getenv("OPENCV_DNN_TEST_DATA_PATH");
+        if (dnnDataPath) {
+            // Add the dnnDataPath itself - G-API is using some images there directly
+            cvtest::addDataSearchPath(dnnDataPath);
+            cvtest::addDataSearchPath(dnnDataPath + std::string("/omz_intel_models"));
+        }
+        initialized = true;
+    }
+#endif // WINRT
+}
+
+static const std::string SUBDIR = "intel/age-gender-recognition-retail-0013/FP32/";
+
+void copyFromOV(ov::Tensor &tensor, cv::Mat &mat) {
+    GAPI_Assert(tensor.get_byte_size() == mat.total() * mat.elemSize());
+    std::copy_n(reinterpret_cast<uint8_t*>(tensor.data()),
+                tensor.get_byte_size(),
+                mat.ptr<uint8_t>());
+}
+
+void copyToOV(const cv::Mat &mat, ov::Tensor &tensor) {
+    GAPI_Assert(tensor.get_byte_size() == mat.total() * mat.elemSize());
+    std::copy_n(mat.ptr<uint8_t>(),
+                tensor.get_byte_size(),
+                reinterpret_cast<uint8_t*>(tensor.data()));
+}
+
+// FIXME: taken from the DNN module
+void normAssert(cv::InputArray ref, cv::InputArray test,
+                const char *comment /*= ""*/,
+                double l1 = 0.00001, double lInf = 0.0001) {
+    double normL1 = cvtest::norm(ref, test, cv::NORM_L1) / ref.getMat().total();
+    EXPECT_LE(normL1, l1) << comment;
+
+    double normInf = cvtest::norm(ref, test, cv::NORM_INF);
+    EXPECT_LE(normInf, lInf) << comment;
+}
+
+ov::Core getCore() {
+    static ov::Core core;
+    return core;
+}
+
+// TODO: AGNetGenComp, AGNetTypedComp, AGNetOVComp, AGNetOVCompiled
+// can be generalized to work with any model and used as parameters for tests.
+
+struct AGNetGenComp {
+    static constexpr const char* tag = "age-gender-generic";
+    using Params = cv::gapi::ov::Params<cv::gapi::Generic>;
+
+    static Params params(const std::string &xml,
+                         const std::string &bin,
+                         const std::string &device) {
+        return {tag, xml, bin, device};
+    }
+
+    static Params params(const std::string &blob_path,
+                         const std::string &device) {
+        return {tag, blob_path, device};
+    }
+
+    static cv::GComputation create() {
+        cv::GMat in;
+        GInferInputs inputs;
+        inputs["data"] = in;
+        auto outputs = cv::gapi::infer<cv::gapi::Generic>(tag, inputs);
+        auto age = outputs.at("age_conv3");
+        auto gender = outputs.at("prob");
+        return cv::GComputation{cv::GIn(in), cv::GOut(age, gender)};
+    }
+};
+
+struct AGNetTypedComp {
+    using AGInfo = std::tuple<cv::GMat, cv::GMat>;
+    G_API_NET(AgeGender, <AGInfo(cv::GMat)>, "typed-age-gender");
+    using Params = cv::gapi::ov::Params<AgeGender>;
+
+    static Params params(const std::string &xml_path,
+                         const std::string &bin_path,
+                         const std::string &device) {
+        return Params {
+            xml_path, bin_path, device
+        }.cfgOutputLayers({ "age_conv3", "prob" });
+    }
+
+    static cv::GComputation create() {
+        cv::GMat in;
+        cv::GMat age, gender;
+        std::tie(age, gender) = cv::gapi::infer<AgeGender>(in);
+        return cv::GComputation{cv::GIn(in), cv::GOut(age, gender)};
+    }
+};
+
+class AGNetOVCompiled {
+public:
+    AGNetOVCompiled(ov::CompiledModel &&compiled_model)
+        : m_compiled_model(std::move(compiled_model)) {
+    }
+
+    void operator()(const cv::Mat &in_mat,
+                          cv::Mat &age_mat,
+                          cv::Mat &gender_mat) {
+        auto infer_request = m_compiled_model.create_infer_request();
+        auto input_tensor   = infer_request.get_input_tensor();
+        copyToOV(in_mat, input_tensor);
+
+        infer_request.infer();
+
+        auto age_tensor = infer_request.get_tensor("age_conv3");
+        age_mat.create(cv::gapi::ov::util::to_ocv(age_tensor.get_shape()),
+                       cv::gapi::ov::util::to_ocv(age_tensor.get_element_type()));
+        copyFromOV(age_tensor, age_mat);
+
+        auto gender_tensor = infer_request.get_tensor("prob");
+        gender_mat.create(cv::gapi::ov::util::to_ocv(gender_tensor.get_shape()),
+                          cv::gapi::ov::util::to_ocv(gender_tensor.get_element_type()));
+        copyFromOV(gender_tensor, gender_mat);
+    }
+
+    void export_model(const std::string &outpath) {
+        std::ofstream file{outpath, std::ios::out | std::ios::binary};
+        GAPI_Assert(file.is_open());
+        m_compiled_model.export_model(file);
+    }
+
+private:
+    ov::CompiledModel m_compiled_model;
+};
+
+struct ImageInputPreproc {
+    void operator()(ov::preprocess::PrePostProcessor &ppp) {
+        ppp.input().tensor().set_layout(ov::Layout("NHWC"))
+                            .set_element_type(ov::element::u8)
+                            .set_shape({1, size.height, size.width, 3});
+        ppp.input().model().set_layout(ov::Layout("NCHW"));
+        ppp.input().preprocess().resize(::ov::preprocess::ResizeAlgorithm::RESIZE_LINEAR);
+    }
+
+    cv::Size size;
+};
+
+class AGNetOVComp {
+public:
+    AGNetOVComp(const std::string &xml_path,
+                const std::string &bin_path,
+                const std::string &device)
+        : m_device(device) {
+        m_model = getCore().read_model(xml_path, bin_path);
+    }
+
+    using PrePostProcessF = std::function<void(ov::preprocess::PrePostProcessor&)>;
+
+    void cfgPrePostProcessing(PrePostProcessF f) {
+        ov::preprocess::PrePostProcessor ppp(m_model);
+        f(ppp);
+        m_model = ppp.build();
+    }
+
+    AGNetOVCompiled compile() {
+        auto compiled_model = getCore().compile_model(m_model, m_device);
+        return {std::move(compiled_model)};
+    }
+
+    void apply(const cv::Mat &in_mat,
+                     cv::Mat &age_mat,
+                     cv::Mat &gender_mat) {
+        compile()(in_mat, age_mat, gender_mat);
+    }
+
+private:
+    std::string m_device;
+    std::shared_ptr<ov::Model> m_model;
+};
+
+} // anonymous namespace
+
+// TODO: Make all of tests below parmetrized to avoid code duplication
+TEST(TestAgeGenderOV, InferTypedTensor) {
+    initDLDTDataPath();
+    const std::string xml_path = findDataFile(SUBDIR + "age-gender-recognition-retail-0013.xml");
+    const std::string bin_path = findDataFile(SUBDIR + "age-gender-recognition-retail-0013.bin");
+    const std::string device   = "CPU";
+
+    cv::Mat in_mat({1, 3, 62, 62}, CV_32F);
+    cv::randu(in_mat, -1, 1);
+    cv::Mat ov_age, ov_gender, gapi_age, gapi_gender;
+
+    // OpenVINO
+    AGNetOVComp ref(xml_path, bin_path, device);
+    ref.apply(in_mat, ov_age, ov_gender);
+
+    // G-API
+    auto comp = AGNetTypedComp::create();
+    auto pp   = AGNetTypedComp::params(xml_path, bin_path, device);
+    comp.apply(cv::gin(in_mat), cv::gout(gapi_age, gapi_gender),
+               cv::compile_args(cv::gapi::networks(pp)));
+
+    // Assert
+    normAssert(ov_age,    gapi_age,    "Test age output"   );
+    normAssert(ov_gender, gapi_gender, "Test gender output");
+}
+
+TEST(TestAgeGenderOV, InferTypedImage) {
+    initDLDTDataPath();
+    const std::string xml_path = findDataFile(SUBDIR + "age-gender-recognition-retail-0013.xml");
+    const std::string bin_path = findDataFile(SUBDIR + "age-gender-recognition-retail-0013.bin");
+    const std::string device   = "CPU";
+
+    cv::Mat in_mat(300, 300, CV_8UC3);
+    cv::randu(in_mat, 0, 255);
+    cv::Mat ov_age, ov_gender, gapi_age, gapi_gender;
+
+    // OpenVINO
+    AGNetOVComp ref(xml_path, bin_path, device);
+    ref.cfgPrePostProcessing(ImageInputPreproc{in_mat.size()});
+    ref.apply(in_mat, ov_age, ov_gender);
+
+    // G-API
+    auto comp = AGNetTypedComp::create();
+    auto pp   = AGNetTypedComp::params(xml_path, bin_path, device);
+    comp.apply(cv::gin(in_mat), cv::gout(gapi_age, gapi_gender),
+               cv::compile_args(cv::gapi::networks(pp)));
+
+    // Assert
+    normAssert(ov_age,    gapi_age,    "Test age output"   );
+    normAssert(ov_gender, gapi_gender, "Test gender output");
+}
+
+TEST(TestAgeGenderOV, InferGenericTensor) {
+    initDLDTDataPath();
+    const std::string xml_path = findDataFile(SUBDIR + "age-gender-recognition-retail-0013.xml");
+    const std::string bin_path = findDataFile(SUBDIR + "age-gender-recognition-retail-0013.bin");
+    const std::string device   = "CPU";
+
+    cv::Mat in_mat({1, 3, 62, 62}, CV_32F);
+    cv::randu(in_mat, -1, 1);
+    cv::Mat ov_age, ov_gender, gapi_age, gapi_gender;
+
+    // OpenVINO
+    AGNetOVComp ref(xml_path, bin_path, device);
+    ref.apply(in_mat, ov_age, ov_gender);
+
+    // G-API
+    auto comp = AGNetGenComp::create();
+    auto pp   = AGNetGenComp::params(xml_path, bin_path, device);
+    comp.apply(cv::gin(in_mat), cv::gout(gapi_age, gapi_gender),
+               cv::compile_args(cv::gapi::networks(pp)));
+
+    // Assert
+    normAssert(ov_age,    gapi_age,    "Test age output"   );
+    normAssert(ov_gender, gapi_gender, "Test gender output");
+}
+
+TEST(TestAgeGenderOV, InferGenericImage) {
+    initDLDTDataPath();
+    const std::string xml_path = findDataFile(SUBDIR + "age-gender-recognition-retail-0013.xml");
+    const std::string bin_path = findDataFile(SUBDIR + "age-gender-recognition-retail-0013.bin");
+    const std::string device   = "CPU";
+
+    cv::Mat in_mat(300, 300, CV_8UC3);
+    cv::randu(in_mat, 0, 255);
+    cv::Mat ov_age, ov_gender, gapi_age, gapi_gender;
+
+    // OpenVINO
+    AGNetOVComp ref(xml_path, bin_path, device);
+    ref.cfgPrePostProcessing(ImageInputPreproc{in_mat.size()});
+    ref.apply(in_mat, ov_age, ov_gender);
+
+    // G-API
+    auto comp = AGNetGenComp::create();
+    auto pp   = AGNetGenComp::params(xml_path, bin_path, device);
+    comp.apply(cv::gin(in_mat), cv::gout(gapi_age, gapi_gender),
+               cv::compile_args(cv::gapi::networks(pp)));
+
+    // Assert
+    normAssert(ov_age,    gapi_age,    "Test age output"   );
+    normAssert(ov_gender, gapi_gender, "Test gender output");
+}
+
+TEST(TestAgeGenderOV, InferGenericImageBlob) {
+    initDLDTDataPath();
+    const std::string xml_path = findDataFile(SUBDIR + "age-gender-recognition-retail-0013.xml");
+    const std::string bin_path = findDataFile(SUBDIR + "age-gender-recognition-retail-0013.bin");
+    const std::string blob_path = "age-gender-recognition-retail-0013.blob";
+    const std::string device   = "CPU";
+
+    cv::Mat in_mat(300, 300, CV_8UC3);
+    cv::randu(in_mat, 0, 255);
+    cv::Mat ov_age, ov_gender, gapi_age, gapi_gender;
+
+    // OpenVINO
+    AGNetOVComp ref(xml_path, bin_path, device);
+    ref.cfgPrePostProcessing(ImageInputPreproc{in_mat.size()});
+    auto cc_ref = ref.compile();
+    // NB: Output blob will contain preprocessing inside.
+    cc_ref.export_model(blob_path);
+    cc_ref(in_mat, ov_age, ov_gender);
+
+    // G-API
+    auto comp = AGNetGenComp::create();
+    auto pp   = AGNetGenComp::params(blob_path, device);
+    comp.apply(cv::gin(in_mat), cv::gout(gapi_age, gapi_gender),
+               cv::compile_args(cv::gapi::networks(pp)));
+
+    // Assert
+    normAssert(ov_age,    gapi_age,    "Test age output"   );
+    normAssert(ov_gender, gapi_gender, "Test gender output");
+}
+
+TEST(TestAgeGenderOV, InferGenericTensorBlob) {
+    initDLDTDataPath();
+    const std::string xml_path = findDataFile(SUBDIR + "age-gender-recognition-retail-0013.xml");
+    const std::string bin_path = findDataFile(SUBDIR + "age-gender-recognition-retail-0013.bin");
+    const std::string blob_path = "age-gender-recognition-retail-0013.blob";
+    const std::string device   = "CPU";
+
+    cv::Mat in_mat({1, 3, 62, 62}, CV_32F);
+    cv::randu(in_mat, -1, 1);
+    cv::Mat ov_age, ov_gender, gapi_age, gapi_gender;
+
+    // OpenVINO
+    AGNetOVComp ref(xml_path, bin_path, device);
+    auto cc_ref = ref.compile();
+    cc_ref.export_model(blob_path);
+    cc_ref(in_mat, ov_age, ov_gender);
+
+    // G-API
+    auto comp = AGNetGenComp::create();
+    auto pp   = AGNetGenComp::params(blob_path, device);
+    comp.apply(cv::gin(in_mat), cv::gout(gapi_age, gapi_gender),
+               cv::compile_args(cv::gapi::networks(pp)));
+
+    // Assert
+    normAssert(ov_age,    gapi_age,    "Test age output"   );
+    normAssert(ov_gender, gapi_gender, "Test gender output");
+}
+
+TEST(TestAgeGenderOV, InferBothOutputsFP16) {
+    initDLDTDataPath();
+    const std::string xml_path = findDataFile(SUBDIR + "age-gender-recognition-retail-0013.xml");
+    const std::string bin_path = findDataFile(SUBDIR + "age-gender-recognition-retail-0013.bin");
+    const std::string device   = "CPU";
+
+    cv::Mat in_mat({1, 3, 62, 62}, CV_32F);
+    cv::randu(in_mat, -1, 1);
+    cv::Mat ov_age, ov_gender, gapi_age, gapi_gender;
+
+    // OpenVINO
+    AGNetOVComp ref(xml_path, bin_path, device);
+    ref.cfgPrePostProcessing([](ov::preprocess::PrePostProcessor &ppp){
+        ppp.output(0).tensor().set_element_type(ov::element::f16);
+        ppp.output(1).tensor().set_element_type(ov::element::f16);
+    });
+    ref.apply(in_mat, ov_age, ov_gender);
+
+    // G-API
+    auto comp = AGNetGenComp::create();
+    auto pp   = AGNetGenComp::params(xml_path, bin_path, device);
+    pp.cfgOutputTensorPrecision(CV_16F);
+
+    comp.apply(cv::gin(in_mat), cv::gout(gapi_age, gapi_gender),
+               cv::compile_args(cv::gapi::networks(pp)));
+
+    // Assert
+    normAssert(ov_age,    gapi_age,    "Test age output"   );
+    normAssert(ov_gender, gapi_gender, "Test gender output");
+}
+
+TEST(TestAgeGenderOV, InferOneOutputFP16) {
+    initDLDTDataPath();
+    const std::string xml_path = findDataFile(SUBDIR + "age-gender-recognition-retail-0013.xml");
+    const std::string bin_path = findDataFile(SUBDIR + "age-gender-recognition-retail-0013.bin");
+    const std::string device   = "CPU";
+
+    cv::Mat in_mat({1, 3, 62, 62}, CV_32F);
+    cv::randu(in_mat, -1, 1);
+    cv::Mat ov_age, ov_gender, gapi_age, gapi_gender;
+
+    // OpenVINO
+    const std::string fp16_output_name = "prob";
+    AGNetOVComp ref(xml_path, bin_path, device);
+    ref.cfgPrePostProcessing([&](ov::preprocess::PrePostProcessor &ppp){
+        ppp.output(fp16_output_name).tensor().set_element_type(ov::element::f16);
+    });
+    ref.apply(in_mat, ov_age, ov_gender);
+
+    // G-API
+    auto comp = AGNetGenComp::create();
+    auto pp   = AGNetGenComp::params(xml_path, bin_path, device);
+    pp.cfgOutputTensorPrecision({{fp16_output_name, CV_16F}});
+
+    comp.apply(cv::gin(in_mat), cv::gout(gapi_age, gapi_gender),
+               cv::compile_args(cv::gapi::networks(pp)));
+
+    // Assert
+    normAssert(ov_age,    gapi_age,    "Test age output"   );
+    normAssert(ov_gender, gapi_gender, "Test gender output");
+}
+
+TEST(TestAgeGenderOV, ThrowCfgOutputPrecForBlob) {
+    initDLDTDataPath();
+    const std::string xml_path = findDataFile(SUBDIR + "age-gender-recognition-retail-0013.xml");
+    const std::string bin_path = findDataFile(SUBDIR + "age-gender-recognition-retail-0013.bin");
+    const std::string blob_path = "age-gender-recognition-retail-0013.blob";
+    const std::string device   = "CPU";
+
+    // OpenVINO (Just for blob compilation)
+    AGNetOVComp ref(xml_path, bin_path, device);
+    auto cc_ref = ref.compile();
+    cc_ref.export_model(blob_path);
+
+    // G-API
+    auto comp = AGNetGenComp::create();
+    auto pp   = AGNetGenComp::params(blob_path, device);
+
+    EXPECT_ANY_THROW(pp.cfgOutputTensorPrecision(CV_16F));
+}
+
+TEST(TestAgeGenderOV, ThrowInvalidConfigIR) {
+    initDLDTDataPath();
+    const std::string xml_path = findDataFile(SUBDIR + "age-gender-recognition-retail-0013.xml");
+    const std::string bin_path = findDataFile(SUBDIR + "age-gender-recognition-retail-0013.bin");
+    const std::string device   = "CPU";
+
+    // G-API
+    auto comp = AGNetGenComp::create();
+    auto pp   = AGNetGenComp::params(xml_path, bin_path, device);
+    pp.cfgPluginConfig({{"some_key", "some_value"}});
+
+    EXPECT_ANY_THROW(comp.compile(cv::GMatDesc{CV_8U,3,cv::Size{320, 240}},
+                                  cv::compile_args(cv::gapi::networks(pp))));
+}
+
+TEST(TestAgeGenderOV, ThrowInvalidConfigBlob) {
+    initDLDTDataPath();
+    const std::string xml_path = findDataFile(SUBDIR + "age-gender-recognition-retail-0013.xml");
+    const std::string bin_path = findDataFile(SUBDIR + "age-gender-recognition-retail-0013.bin");
+    const std::string blob_path = "age-gender-recognition-retail-0013.blob";
+    const std::string device   = "CPU";
+
+    // OpenVINO (Just for blob compilation)
+    AGNetOVComp ref(xml_path, bin_path, device);
+    auto cc_ref = ref.compile();
+    cc_ref.export_model(blob_path);
+
+    // G-API
+    auto comp = AGNetGenComp::create();
+    auto pp   = AGNetGenComp::params(blob_path, device);
+    pp.cfgPluginConfig({{"some_key", "some_value"}});
+
+    EXPECT_ANY_THROW(comp.compile(cv::GMatDesc{CV_8U,3,cv::Size{320, 240}},
+                                  cv::compile_args(cv::gapi::networks(pp))));
+}
+
+TEST(TestAgeGenderOV, ThrowInvalidImageLayout) {
+    initDLDTDataPath();
+    const std::string xml_path = findDataFile(SUBDIR + "age-gender-recognition-retail-0013.xml");
+    const std::string bin_path = findDataFile(SUBDIR + "age-gender-recognition-retail-0013.bin");
+    const std::string device   = "CPU";
+
+    // NB: This mat may only have "NHWC" layout.
+    cv::Mat in_mat(300, 300, CV_8UC3);
+    cv::randu(in_mat, 0, 255);
+    cv::Mat gender, gapi_age, gapi_gender;
+    auto comp = AGNetTypedComp::create();
+    auto pp = AGNetTypedComp::params(xml_path, bin_path, device);
+
+    pp.cfgInputTensorLayout("NCHW");
+
+    EXPECT_ANY_THROW(comp.compile(cv::descr_of(in_mat),
+                     cv::compile_args(cv::gapi::networks(pp))));
+}
+
+TEST(TestAgeGenderOV, InferTensorWithPreproc) {
+    initDLDTDataPath();
+    const std::string xml_path = findDataFile(SUBDIR + "age-gender-recognition-retail-0013.xml");
+    const std::string bin_path = findDataFile(SUBDIR + "age-gender-recognition-retail-0013.bin");
+    const std::string device   = "CPU";
+
+    cv::Mat in_mat({1, 240, 320, 3}, CV_32F);
+    cv::randu(in_mat, -1, 1);
+    cv::Mat ov_age, ov_gender, gapi_age, gapi_gender;
+
+    // OpenVINO
+    AGNetOVComp ref(xml_path, bin_path, device);
+    ref.cfgPrePostProcessing([](ov::preprocess::PrePostProcessor &ppp) {
+        auto& input = ppp.input();
+        input.tensor().set_spatial_static_shape(240, 320)
+                      .set_layout("NHWC");
+        input.preprocess().resize(ov::preprocess::ResizeAlgorithm::RESIZE_LINEAR);
+    });
+    ref.apply(in_mat, ov_age, ov_gender);
+
+    // G-API
+    auto comp = AGNetTypedComp::create();
+    auto pp = AGNetTypedComp::params(xml_path, bin_path, device);
+    pp.cfgResize(cv::INTER_LINEAR)
+      .cfgInputTensorLayout("NHWC");
+
+    comp.apply(cv::gin(in_mat), cv::gout(gapi_age, gapi_gender),
+               cv::compile_args(cv::gapi::networks(pp)));
+
+    // Assert
+    normAssert(ov_age,    gapi_age,    "Test age output"   );
+    normAssert(ov_gender, gapi_gender, "Test gender output");
+}
+
+} // namespace opencv_test
+
+#endif //  HAVE_INF_ENGINE

modules/python/src2/cv2_convert.hpp

@@ -6,6 +6,8 @@
 #include "cv2_numpy.hpp"
 #include <vector>
 #include <string>
+#include <unordered_map>
+#include <map>
 #include <type_traits>  // std::enable_if
 
 extern PyTypeObject* pyopencv_Mat_TypePtr;
@@ -263,6 +265,38 @@ PyObject* pyopencv_from(const std::vector<Tp>& value)
     return pyopencvVecConverter<Tp>::from(value);
 }
 
+template<typename K, typename V>
+bool pyopencv_to(PyObject *obj, std::map<K,V> &map, const ArgInfo& info)
+{
+    PyObject* py_key = nullptr;
+    PyObject* py_value = nullptr;
+    Py_ssize_t pos = 0;
+
+    if (!PyDict_Check(obj)) {
+        failmsg("Can't parse '%s'. Input argument isn't dict or"
+                " an instance of subtype of the dict type", info.name);
+        return false;
+    }
+
+    while(PyDict_Next(obj, &pos, &py_key, &py_value))
+    {
+        K cpp_key;
+        if (!pyopencv_to(py_key, cpp_key, ArgInfo("key", false))) {
+            failmsg("Can't parse dict key. Key on position %lu has a wrong type", pos);
+            return false;
+        }
+
+        V cpp_value;
+        if (!pyopencv_to(py_value, cpp_value, ArgInfo("value", false))) {
+            failmsg("Can't parse dict value. Value on position %lu has a wrong type", pos);
+            return false;
+        }
+
+        map.emplace(cpp_key, cpp_value);
+    }
+    return true;
+}
+
 template <typename Tp>
 static bool pyopencv_to_generic_vec(PyObject* obj, std::vector<Tp>& value, const ArgInfo& info)
 {

modules/python/src2/typing_stubs_generation/predefined_types.py

@@ -190,6 +190,14 @@ _PREDEFINED_TYPES = (
                             PrimitiveTypeNode.float_(),
                             PrimitiveTypeNode.str_())
                         ), export_name="SearchParams"),
+    AliasTypeNode.dict_("map_string_and_string", PrimitiveTypeNode.str_("map_string_and_string::key"),
+                        PrimitiveTypeNode.str_("map_string_and_string::key::value")),
+    AliasTypeNode.dict_("map_string_and_int", PrimitiveTypeNode.str_("map_string_and_int::key"),
+                        PrimitiveTypeNode.int_("map_string_and_int::key::value")),
+    AliasTypeNode.dict_("map_string_and_vector_size_t", PrimitiveTypeNode.str_("map_string_and_vector_size_t::key"),
+                        SequenceTypeNode("map_string_and_vector_size_t::key::value", PrimitiveTypeNode.int_("size_t"))),
+    AliasTypeNode.dict_("map_string_and_vector_float", PrimitiveTypeNode.str_("map_string_and_vector_float::key"),
+                        SequenceTypeNode("map_string_and_vector_float::key::value", PrimitiveTypeNode.float_())),
 )
 
 PREDEFINED_TYPES = dict(zip((t.ctype_name for t in _PREDEFINED_TYPES), _PREDEFINED_TYPES))