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Merge pull request #15753 from dmatveev:dm/ng-5000-security_barrier-interactive_face

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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
This commit is contained in:
Dmitry Matveev
2019-11-27 17:54:17 +03:00
committed by Alexander Alekhin
parent d9efb55d29
commit fb5e7964b3
4 changed files with 809 additions and 18 deletions
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modules/gapi/src/backends/ie/giebackend.cpp
+85 -15
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@@ -12,7 +12,7 @@
#ifdef HAVE_INF_ENGINE
#if INF_ENGINE_RELEASE <= 2018050000
#if INF_ENGINE_RELEASE <= 2019010000
# error G-API IE module supports only OpenVINO IE >= 2019 R1
#endif
@@ -26,11 +26,13 @@
#include <ade/util/chain_range.hpp>
#include <ade/typed_graph.hpp>
#include <opencv2/core/utility.hpp>
#include <opencv2/core/utils/logger.hpp>
#include <opencv2/gapi/gcommon.hpp>
#include <opencv2/gapi/garray.hpp>
#include <opencv2/gapi/util/any.hpp>
#include <opencv2/gapi/gtype_traits.hpp>
#include <opencv2/gapi/infer.hpp>
#include "compiler/gobjref.hpp"
@@ -66,6 +68,21 @@ inline std::vector<int> toCV(const IE::SizeVector &vsz) {
return result;
}
inline IE::Layout toIELayout(const std::size_t ndims) {
static const IE::Layout lts[] = {
IE::Layout::SCALAR,
IE::Layout::C,
IE::Layout::NC,
IE::Layout::CHW,
IE::Layout::NCHW,
IE::Layout::NCDHW,
};
// FIXME: This is not really a good conversion,
// since it may also stand for NHWC/HW/CN/NDHWC data
CV_Assert(ndims < sizeof(lts) / sizeof(lts[0]));
return lts[ndims];
}
inline IE::Precision toIE(int depth) {
switch (depth) {
case CV_8U: return IE::Precision::U8;
@@ -83,13 +100,16 @@ inline int toCV(IE::Precision prec) {
return -1;
}
inline IE::TensorDesc toIE(const cv::Mat &mat) {
inline IE::TensorDesc toIE(const cv::Mat &mat, cv::gapi::ie::TraitAs hint) {
const auto &sz = mat.size;
// NB: For some reason RGB image is 2D image
// (since channel component is not counted here).
if (sz.dims() == 2) {
// Note: regular 2D vectors also fall into this category
if (sz.dims() == 2 && hint == cv::gapi::ie::TraitAs::IMAGE)
{
// NB: This logic is mainly taken from IE samples
const size_t pixsz = CV_ELEM_SIZE1(mat.type());
const size_t channels = mat.channels();
const size_t height = mat.size().height;
const size_t width = mat.size().width;
@@ -98,8 +118,8 @@ inline IE::TensorDesc toIE(const cv::Mat &mat) {
const size_t strideW = mat.step.buf[1];
const bool is_dense =
strideW == channels &&
strideH == channels * width;
strideW == pixsz * channels &&
strideH == strideW * width;
if (!is_dense)
cv::util::throw_error(std::logic_error("Doesn't support conversion"
@@ -110,12 +130,11 @@ inline IE::TensorDesc toIE(const cv::Mat &mat) {
IE::Layout::NHWC);
}
GAPI_Assert(sz.dims() == 4); // NB: Will relax when needed (to known use)
return IE::TensorDesc(toIE(mat.depth()), toIE(sz), IE::Layout::NCHW);
return IE::TensorDesc(toIE(mat.depth()), toIE(sz), toIELayout(sz.dims()));
}
inline IE::Blob::Ptr wrapIE(const cv::Mat &mat) {
const auto tDesc = toIE(mat);
inline IE::Blob::Ptr wrapIE(const cv::Mat &mat, cv::gapi::ie::TraitAs hint) {
const auto tDesc = toIE(mat, hint);
switch (mat.depth()) {
// NB: Seems there's no way to create an untyped (T-less) Blob::Ptr
// in IE given only precision via TensorDesc. So we have to do this:
@@ -187,15 +206,62 @@ struct IEUnit {
}
// This method is [supposed to be] called at Island compilation stage
// TODO: Move to a new OpenVINO Core API!
cv::gimpl::ie::IECompiled compile() const {
auto this_plugin = IE::PluginDispatcher().getPluginByDevice(params.device_id);
// Load extensions (taken from DNN module)
if (params.device_id == "CPU" || params.device_id == "FPGA")
{
const std::string suffixes[] = { "_avx2", "_sse4", ""};
const bool haveFeature[] = {
cv::checkHardwareSupport(CPU_AVX2),
cv::checkHardwareSupport(CPU_SSE4_2),
true
};
std::vector<std::string> candidates;
for (auto &&it : ade::util::zip(ade::util::toRange(suffixes),
ade::util::toRange(haveFeature)))
{
std::string suffix;
bool available = false;
std::tie(suffix, available) = it;
if (!available) continue;
#ifdef _WIN32
candidates.push_back("cpu_extension" + suffix + ".dll");
#elif defined(__APPLE__)
candidates.push_back("libcpu_extension" + suffix + ".so"); // built as loadable module
candidates.push_back("libcpu_extension" + suffix + ".dylib"); // built as shared library
#else
candidates.push_back("libcpu_extension" + suffix + ".so");
#endif // _WIN32
}
for (auto &&extlib : candidates)
{
try
{
this_plugin.AddExtension(IE::make_so_pointer<IE::IExtension>(extlib));
CV_LOG_INFO(NULL, "DNN-IE: Loaded extension plugin: " << extlib);
break;
}
catch(...)
{
CV_LOG_WARNING(NULL, "Failed to load IE extension " << extlib);
}
}
}
auto this_network = this_plugin.LoadNetwork(net, {}); // FIXME: 2nd parameter to be
// configurable via the API
auto this_request = this_network.CreateInferRequest();
// Bind const data to infer request
for (auto &&p : params.const_inputs) {
this_request.SetBlob(p.first, wrapIE(p.second));
// FIXME: SetBlob is known to be inefficient,
// it is worth to make a customizable "initializer" and pass the
// cv::Mat-wrapped blob there to support IE's optimal "GetBlob idiom"
// Still, constant data is to set only once.
this_request.SetBlob(p.first, wrapIE(p.second.first, p.second.second));
}
return {this_plugin, this_network, this_request};
@@ -444,7 +510,9 @@ struct Infer: public cv::detail::KernelTag {
// (A memory dialog comes to the picture again)
const cv::Mat this_mat = to_ocv(ctx.inMat(i));
IE::Blob::Ptr this_blob = wrapIE(this_mat);
// FIXME: By default here we trait our inputs as images.
// May be we need to make some more intelligence here about it
IE::Blob::Ptr this_blob = wrapIE(this_mat, cv::gapi::ie::TraitAs::IMAGE);
iec.this_request.SetBlob(uu.params.input_names[i], this_blob);
}
iec.this_request.Infer();
@@ -514,7 +582,8 @@ struct InferList: public cv::detail::KernelTag {
const auto& in_roi_vec = ctx.inArg<cv::detail::VectorRef>(0u).rref<cv::Rect>();
const cv::Mat this_mat = to_ocv(ctx.inMat(1u));
IE::Blob::Ptr this_blob = wrapIE(this_mat);
// Since we do a ROI list inference, always assume our input buffer is image
IE::Blob::Ptr this_blob = wrapIE(this_mat, cv::gapi::ie::TraitAs::IMAGE);
// FIXME: This could be done ONCE at graph compile stage!
std::vector< std::vector<int> > cached_dims(uu.params.num_out);
@@ -601,10 +670,11 @@ std::vector<int> cv::gapi::ie::util::to_ocv(const InferenceEngine::SizeVector &d
}
InferenceEngine::Blob::Ptr cv::gapi::ie::util::to_ie(cv::Mat &blob) {
return wrapIE(blob);
return wrapIE(blob, cv::gapi::ie::TraitAs::IMAGE);
}
#else
#else // HAVE_INF_ENGINE
cv::gapi::GBackend cv::gapi::ie::backend() {
// Still provide this symbol to avoid linking issues
util::throw_error(std::runtime_error("G-API has been compiled without OpenVINO IE support"));