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#include <algorithm>
#include <iostream>
#include <sstream>
#include <opencv2/imgproc.hpp>
#include <opencv2/imgcodecs.hpp>
#include <opencv2/gapi.hpp>
#include <opencv2/gapi/core.hpp>
#include <opencv2/gapi/imgproc.hpp>
#include <opencv2/gapi/infer.hpp>
#include <opencv2/gapi/render.hpp>
#include <opencv2/gapi/infer/ie.hpp>
#include <opencv2/gapi/cpu/gcpukernel.hpp>
#include <opencv2/gapi/streaming/cap.hpp>
#include <opencv2/highgui.hpp>
#include <opencv2/gapi/infer/parsers.hpp>
const std::string keys =
"{ h help | | Print this help message }"
"{ input | | Path to the input video file }"
"{ facem | face-detection-adas-0001.xml | Path to OpenVINO IE face detection model (.xml) }"
"{ faced | CPU | Target device for face detection model (e.g. CPU, GPU, VPU, ...) }"
"{ r roi | -1,-1,-1,-1 | Region of interest (ROI) to use for inference. Identified automatically when not set }";
namespace {
std::string weights_path(const std::string &model_path) {
const auto EXT_LEN = 4u;
const auto sz = model_path.size();
CV_Assert(sz > EXT_LEN);
auto ext = model_path.substr(sz - EXT_LEN);
std::transform(ext.begin(), ext.end(), ext.begin(), [](unsigned char c){
return static_cast<unsigned char>(std::tolower(c));
});
CV_Assert(ext == ".xml");
return model_path.substr(0u, sz - EXT_LEN) + ".bin";
}
cv::util::optional<cv::Rect> parse_roi(const std::string &rc) {
cv::Rect rv;
char delim[3];
std::stringstream is(rc);
is >> rv.x >> delim[0] >> rv.y >> delim[1] >> rv.width >> delim[2] >> rv.height;
if (is.bad()) {
return cv::util::optional<cv::Rect>(); // empty value
}
const auto is_delim = [](char c) {
return c == ',';
};
if (!std::all_of(std::begin(delim), std::end(delim), is_delim)) {
return cv::util::optional<cv::Rect>(); // empty value
}
if (rv.x < 0 || rv.y < 0 || rv.width <= 0 || rv.height <= 0) {
return cv::util::optional<cv::Rect>(); // empty value
}
return cv::util::make_optional(std::move(rv));
}
} // namespace
namespace custom {
G_API_NET(FaceDetector, <cv::GMat(cv::GMat)>, "face-detector");
using GDetections = cv::GArray<cv::Rect>;
using GRect = cv::GOpaque<cv::Rect>;
using GSize = cv::GOpaque<cv::Size>;
using GPrims = cv::GArray<cv::gapi::wip::draw::Prim>;
G_API_OP(LocateROI, <GRect(cv::GMat)>, "sample.custom.locate-roi") {
static cv::GOpaqueDesc outMeta(const cv::GMatDesc &) {
return cv::empty_gopaque_desc();
}
};
G_API_OP(BBoxes, <GPrims(GDetections, GRect)>, "sample.custom.b-boxes") {
static cv::GArrayDesc outMeta(const cv::GArrayDesc &, const cv::GOpaqueDesc &) {
return cv::empty_array_desc();
}
};
GAPI_OCV_KERNEL(OCVLocateROI, LocateROI) {
// This is the place where we can run extra analytics
// on the input image frame and select the ROI (region
// of interest) where we want to detect our objects (or
// run any other inference).
//
// Currently it doesn't do anything intelligent,
// but only crops the input image to square (this is
// the most convenient aspect ratio for detectors to use)
static void run(const cv::Mat &in_mat, cv::Rect &out_rect) {
// Identify the central point & square size (- some padding)
const auto center = cv::Point{in_mat.cols/2, in_mat.rows/2};
auto sqside = std::min(in_mat.cols, in_mat.rows);
// Now build the central square ROI
out_rect = cv::Rect{ center.x - sqside/2
, center.y - sqside/2
, sqside
, sqside
};
}
};
GAPI_OCV_KERNEL(OCVBBoxes, BBoxes) {
// This kernel converts the rectangles into G-API's
// rendering primitives
static void run(const std::vector<cv::Rect> &in_face_rcs,
const cv::Rect &in_roi,
std::vector<cv::gapi::wip::draw::Prim> &out_prims) {
out_prims.clear();
const auto cvt = [](const cv::Rect &rc, const cv::Scalar &clr) {
return cv::gapi::wip::draw::Rect(rc, clr, 2);
};
out_prims.emplace_back(cvt(in_roi, CV_RGB(0,255,255))); // cyan
for (auto &&rc : in_face_rcs) {
out_prims.emplace_back(cvt(rc, CV_RGB(0,255,0))); // green
}
}
};
} // namespace custom
int main(int argc, char *argv[])
{
cv::CommandLineParser cmd(argc, argv, keys);
if (cmd.has("help")) {
cmd.printMessage();
return 0;
}
// Prepare parameters first
const std::string input = cmd.get<std::string>("input");
const auto opt_roi = parse_roi(cmd.get<std::string>("roi"));
const auto face_model_path = cmd.get<std::string>("facem");
auto face_net = cv::gapi::ie::Params<custom::FaceDetector> {
face_model_path, // path to topology IR
weights_path(face_model_path), // path to weights
cmd.get<std::string>("faced"), // device specifier
};
auto kernels = cv::gapi::kernels
<custom::OCVLocateROI
, custom::OCVBBoxes>();
auto networks = cv::gapi::networks(face_net);
// Now build the graph. The graph structure may vary
// passed on the input parameters
cv::GStreamingCompiled pipeline;
auto inputs = cv::gin(cv::gapi::wip::make_src<cv::gapi::wip::GCaptureSource>(input));
cv::GMat in;
cv::GOpaque<cv::Size> sz = cv::gapi::streaming::size(in);
if (opt_roi.has_value()) {
// Use the value provided by user
std::cout << "Will run inference for static region "
<< opt_roi.value()
<< " only"
<< std::endl;
cv::GOpaque<cv::Rect> in_roi;
auto blob = cv::gapi::infer<custom::FaceDetector>(in_roi, in);
cv::GArray<cv::Rect> rcs = cv::gapi::parseSSD(blob, sz, 0.5f, true, true);
auto out = cv::gapi::wip::draw::render3ch(in, custom::BBoxes::on(rcs, in_roi));
pipeline = cv::GComputation(cv::GIn(in, in_roi), cv::GOut(out))
.compileStreaming(cv::compile_args(kernels, networks));
// Since the ROI to detect is manual, make it part of the input vector
inputs.push_back(cv::gin(opt_roi.value())[0]);
} else {
// Automatically detect ROI to infer. Make it output parameter
std::cout << "ROI is not set or invalid. Locating it automatically"
<< std::endl;
cv::GOpaque<cv::Rect> roi = custom::LocateROI::on(in);
auto blob = cv::gapi::infer<custom::FaceDetector>(roi, in);
cv::GArray<cv::Rect> rcs = cv::gapi::parseSSD(blob, sz, 0.5f, true, true);
auto out = cv::gapi::wip::draw::render3ch(in, custom::BBoxes::on(rcs, roi));
pipeline = cv::GComputation(cv::GIn(in), cv::GOut(out))
.compileStreaming(cv::compile_args(kernels, networks));
}
// The execution part
pipeline.setSource(std::move(inputs));
pipeline.start();
cv::Mat out;
size_t frames = 0u;
cv::TickMeter tm;
tm.start();
while (pipeline.pull(cv::gout(out))) {
cv::imshow("Out", out);
cv::waitKey(1);
++frames;
}
tm.stop();
std::cout << "Processed " << frames << " frames" << " (" << frames / tm.getTimeSec() << " FPS)" << std::endl;
return 0;
}
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