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https://github.com/opencv/opencv.git
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fb5e7964b3
G-API: Introduced Security Barrier & Interactive Face Detection samples * G-API-NG/Samples: Added samples & relevant changes - Security barrier camera sample - Age/Gender/Emotions recognition sample - GIEBackend now loads CPU extension libraries - A couple of API-level workarounds added to deal with cv::Mat/Blob conversions * G-API-NG/Samples: removed HAVE_INF_ENGINE remnants
124 lines
3.9 KiB
C++
124 lines
3.9 KiB
C++
// This file is part of OpenCV project.
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// It is subject to the license terms in the LICENSE file found in the top-level directory
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// of this distribution and at http://opencv.org/license.html.
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//
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// Copyright (C) 2019 Intel Corporation
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#ifndef OPENCV_GAPI_INFER_IE_HPP
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#define OPENCV_GAPI_INFER_IE_HPP
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#include <unordered_map>
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#include <string>
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#include <array>
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#include <tuple> // tuple, tuple_size
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#include <opencv2/gapi/opencv_includes.hpp>
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#include <opencv2/gapi/util/any.hpp>
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#include <opencv2/core/cvdef.h> // GAPI_EXPORTS
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#include <opencv2/gapi/gkernel.hpp> // GKernelPackage
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namespace cv {
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namespace gapi {
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// FIXME: introduce a new sub-namespace for NN?
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namespace ie {
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GAPI_EXPORTS cv::gapi::GBackend backend();
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/**
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* Specify how G-API and IE should trait input data
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*
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* In OpenCV, the same cv::Mat is used to represent both
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* image and tensor data. Sometimes those are hardly distinguishable,
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* so this extra parameter is used to give G-API a hint.
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*
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* This hint controls how G-API reinterprets the data when converting
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* it to IE Blob format (and which layout/etc is assigned to this data).
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*/
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enum class TraitAs: int
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{
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TENSOR, //!< G-API traits an associated cv::Mat as a raw tensor and passes dimensions as-is
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IMAGE //!< G-API traits an associated cv::Mat as an image so creates an "image" blob (NCHW/NHWC, etc)
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};
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namespace detail {
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struct ParamDesc {
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std::string model_path;
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std::string weights_path;
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std::string device_id;
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// NB: Here order follows the `Net` API
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std::vector<std::string> input_names;
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std::vector<std::string> output_names;
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using ConstInput = std::pair<cv::Mat, TraitAs>;
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std::unordered_map<std::string, ConstInput> const_inputs;
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// NB: nun_* may differ from topology's real input/output port numbers
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// (e.g. topology's partial execution)
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std::size_t num_in; // How many inputs are defined in the operation
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std::size_t num_out; // How many outputs are defined in the operation
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};
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} // namespace detail
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// FIXME: this is probably a shared (reusable) thing
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template<typename Net>
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struct PortCfg {
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using In = std::array
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< std::string
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, std::tuple_size<typename Net::InArgs>::value >;
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using Out = std::array
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< std::string
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, std::tuple_size<typename Net::OutArgs>::value >;
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};
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template<typename Net> class Params {
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public:
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Params(const std::string &model,
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const std::string &weights,
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const std::string &device)
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: desc{ model, weights, device, {}, {}, {}
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, std::tuple_size<typename Net::InArgs>::value
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, std::tuple_size<typename Net::OutArgs>::value
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} {
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};
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Params<Net>& cfgInputLayers(const typename PortCfg<Net>::In &ll) {
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desc.input_names.clear();
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desc.input_names.reserve(ll.size());
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std::copy(ll.begin(), ll.end(),
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std::back_inserter(desc.input_names));
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return *this;
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}
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Params<Net>& cfgOutputLayers(const typename PortCfg<Net>::Out &ll) {
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desc.output_names.clear();
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desc.output_names.reserve(ll.size());
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std::copy(ll.begin(), ll.end(),
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std::back_inserter(desc.output_names));
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return *this;
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}
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Params<Net>& constInput(const std::string &layer_name,
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const cv::Mat &data,
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TraitAs hint = TraitAs::TENSOR) {
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desc.const_inputs[layer_name] = {data, hint};
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return *this;
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}
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// BEGIN(G-API's network parametrization API)
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GBackend backend() const { return cv::gapi::ie::backend(); }
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std::string tag() const { return Net::tag(); }
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cv::util::any params() const { return { desc }; }
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// END(G-API's network parametrization API)
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protected:
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detail::ParamDesc desc;
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};
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} // namespace ie
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} // namespace gapi
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} // namespace cv
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#endif // OPENCV_GAPI_INFER_HPP
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