Merge pull request #15753 from dmatveev:dm/ng-5000-security_barrier-interactive_face
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
committed by
Alexander Alekhin
Alexander Alekhin
parent
d9efb55d29
commit
fb5e7964b3
+352
@@ -0,0 +1,352 @@
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#include "opencv2/opencv_modules.hpp"
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#if defined(HAVE_OPENCV_GAPI)
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#include <chrono>
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#include <iomanip>
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#include "opencv2/imgproc.hpp"
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#include "opencv2/highgui.hpp"
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#include "opencv2/gapi.hpp"
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#include "opencv2/gapi/core.hpp"
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#include "opencv2/gapi/imgproc.hpp"
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#include "opencv2/gapi/infer.hpp"
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#include "opencv2/gapi/infer/ie.hpp"
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#include "opencv2/gapi/cpu/gcpukernel.hpp"
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#include "opencv2/gapi/streaming/cap.hpp"
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namespace {
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const std::string about =
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"This is an OpenCV-based version of Security Barrier Camera example";
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const std::string keys =
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"{ h help | | print this help message }"
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"{ input | | Path to an input video file }"
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"{ fdm | | IE face detection model IR }"
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"{ fdw | | IE face detection model weights }"
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"{ fdd | | IE face detection device }"
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"{ agem | | IE age/gender recognition model IR }"
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"{ agew | | IE age/gender recognition model weights }"
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"{ aged | | IE age/gender recognition model device }"
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"{ emom | | IE emotions recognition model IR }"
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"{ emow | | IE emotions recognition model weights }"
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"{ emod | | IE emotions recognition model device }"
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"{ pure | | When set, no output is displayed. Useful for benchmarking }";
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struct Avg {
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struct Elapsed {
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explicit Elapsed(double ms) : ss(ms/1000.), mm(static_cast<int>(ss)/60) {}
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const double ss;
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const int mm;
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};
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using MS = std::chrono::duration<double, std::ratio<1, 1000>>;
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using TS = std::chrono::time_point<std::chrono::high_resolution_clock>;
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TS started;
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void start() { started = now(); }
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TS now() const { return std::chrono::high_resolution_clock::now(); }
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double tick() const { return std::chrono::duration_cast<MS>(now() - started).count(); }
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Elapsed elapsed() const { return Elapsed{tick()}; }
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double fps(std::size_t n) const { return static_cast<double>(n) / (tick() / 1000.); }
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};
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std::ostream& operator<<(std::ostream &os, const Avg::Elapsed &e) {
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os << e.mm << ':' << (e.ss - 60*e.mm);
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return os;
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}
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} // namespace
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namespace custom {
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// Describe networks we use in our program.
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// In G-API, topologies act like "operations". Here we define our
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// topologies as operations which have inputs and outputs.
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// Every network requires three parameters to define:
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// 1) Network's TYPE name - this TYPE is then used as a template
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// parameter to generic functions like cv::gapi::infer<>(),
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// and is used to define network's configuration (per-backend).
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// 2) Network's SIGNATURE - a std::function<>-like record which defines
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// networks' input and output parameters (its API)
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// 3) Network's IDENTIFIER - a string defining what the network is.
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// Must be unique within the pipeline.
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// Note: these definitions are neutral to _how_ the networks are
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// executed. The _how_ is defined at graph compilation stage (via parameters),
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// not on the graph construction stage.
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// Face detector: takes one Mat, returns another Mat
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G_API_NET(Faces, <cv::GMat(cv::GMat)>, "face-detector");
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// Age/Gender recognition - takes one Mat, returns two:
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// one for Age and one for Gender. In G-API, multiple-return-value operations
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// are defined using std::tuple<>.
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using AGInfo = std::tuple<cv::GMat, cv::GMat>;
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G_API_NET(AgeGender, <AGInfo(cv::GMat)>, "age-gender-recoginition");
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// Emotion recognition - takes one Mat, returns another.
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G_API_NET(Emotions, <cv::GMat(cv::GMat)>, "emotions-recognition");
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// SSD Post-processing function - this is not a network but a kernel.
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// The kernel body is declared separately, this is just an interface.
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// This operation takes two Mats (detections and the source image),
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// and returns a vector of ROI (filtered by a default threshold).
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// Threshold (or a class to select) may become a parameter, but since
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// this kernel is custom, it doesn't make a lot of sense.
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G_API_OP(PostProc, <cv::GArray<cv::Rect>(cv::GMat, cv::GMat)>, "custom.fd_postproc") {
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static cv::GArrayDesc outMeta(const cv::GMatDesc &, const cv::GMatDesc &) {
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// This function is required for G-API engine to figure out
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// what the output format is, given the input parameters.
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// Since the output is an array (with a specific type),
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// there's nothing to describe.
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return cv::empty_array_desc();
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}
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};
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GAPI_OCV_KERNEL(OCVPostProc, PostProc) {
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static void run(const cv::Mat &in_ssd_result,
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const cv::Mat &in_frame,
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std::vector<cv::Rect> &out_faces) {
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const int MAX_PROPOSALS = 200;
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const int OBJECT_SIZE = 7;
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const cv::Size upscale = in_frame.size();
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const cv::Rect surface({0,0}, upscale);
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out_faces.clear();
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const float *data = in_ssd_result.ptr<float>();
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for (int i = 0; i < MAX_PROPOSALS; i++) {
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const float image_id = data[i * OBJECT_SIZE + 0]; // batch id
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const float confidence = data[i * OBJECT_SIZE + 2];
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const float rc_left = data[i * OBJECT_SIZE + 3];
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const float rc_top = data[i * OBJECT_SIZE + 4];
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const float rc_right = data[i * OBJECT_SIZE + 5];
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const float rc_bottom = data[i * OBJECT_SIZE + 6];
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if (image_id < 0.f) { // indicates end of detections
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break;
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}
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if (confidence < 0.5f) { // fixme: hard-coded snapshot
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continue;
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}
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cv::Rect rc;
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rc.x = static_cast<int>(rc_left * upscale.width);
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rc.y = static_cast<int>(rc_top * upscale.height);
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rc.width = static_cast<int>(rc_right * upscale.width) - rc.x;
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rc.height = static_cast<int>(rc_bottom * upscale.height) - rc.y;
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out_faces.push_back(rc & surface);
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}
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}
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};
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} // namespace custom
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namespace labels {
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const std::string genders[] = {
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"Female", "Male"
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};
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const std::string emotions[] = {
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"neutral", "happy", "sad", "surprise", "anger"
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};
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namespace {
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void DrawResults(cv::Mat &frame,
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const std::vector<cv::Rect> &faces,
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const std::vector<cv::Mat> &out_ages,
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const std::vector<cv::Mat> &out_genders,
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const std::vector<cv::Mat> &out_emotions) {
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CV_Assert(faces.size() == out_ages.size());
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CV_Assert(faces.size() == out_genders.size());
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CV_Assert(faces.size() == out_emotions.size());
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for (auto it = faces.begin(); it != faces.end(); ++it) {
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const auto idx = std::distance(faces.begin(), it);
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const auto &rc = *it;
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const float *ages_data = out_ages[idx].ptr<float>();
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const float *genders_data = out_genders[idx].ptr<float>();
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const float *emotions_data = out_emotions[idx].ptr<float>();
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const auto gen_id = std::max_element(genders_data, genders_data + 2) - genders_data;
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const auto emo_id = std::max_element(emotions_data, emotions_data + 5) - emotions_data;
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std::stringstream ss;
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ss << static_cast<int>(ages_data[0]*100)
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<< ' '
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<< genders[gen_id]
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<< ' '
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<< emotions[emo_id];
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const int ATTRIB_OFFSET = 15;
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cv::rectangle(frame, rc, {0, 255, 0}, 4);
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cv::putText(frame, ss.str(),
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cv::Point(rc.x, rc.y - ATTRIB_OFFSET),
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cv::FONT_HERSHEY_COMPLEX_SMALL,
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1,
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cv::Scalar(0, 0, 255));
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}
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}
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void DrawFPS(cv::Mat &frame, std::size_t n, double fps) {
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std::ostringstream out;
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out << "FRAME " << n << ": "
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<< std::fixed << std::setprecision(2) << fps
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<< " FPS (AVG)";
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cv::putText(frame, out.str(),
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cv::Point(0, frame.rows),
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cv::FONT_HERSHEY_SIMPLEX,
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1,
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cv::Scalar(0, 255, 0),
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2);
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}
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} // anonymous namespace
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} // namespace labels
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int main(int argc, char *argv[])
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{
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cv::CommandLineParser cmd(argc, argv, keys);
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cmd.about(about);
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if (cmd.has("help")) {
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cmd.printMessage();
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return 0;
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}
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const std::string input = cmd.get<std::string>("input");
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const bool no_show = cmd.get<bool>("pure");
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// Express our processing pipeline. Lambda-based constructor
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// is used to keep all temporary objects in a dedicated scope.
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cv::GComputation pp([]() {
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// Declare an empty GMat - the beginning of the pipeline.
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cv::GMat in;
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// Run face detection on the input frame. Result is a single GMat,
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// internally representing an 1x1x200x7 SSD output.
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// This is a single-patch version of infer:
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// - Inference is running on the whole input image;
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// - Image is converted and resized to the network's expected format
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// automatically.
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cv::GMat detections = cv::gapi::infer<custom::Faces>(in);
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// Parse SSD output to a list of ROI (rectangles) using
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// a custom kernel. Note: parsing SSD may become a "standard" kernel.
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cv::GArray<cv::Rect> faces = custom::PostProc::on(detections, in);
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// Now run Age/Gender model on every detected face. This model has two
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// outputs (for age and gender respectively).
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// A special ROI-list-oriented form of infer<>() is used here:
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// - First input argument is the list of rectangles to process,
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// - Second one is the image where to take ROI from;
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// - Crop/Resize/Layout conversion happens automatically for every image patch
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// from the list
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// - Inference results are also returned in form of list (GArray<>)
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// - Since there're two outputs, infer<> return two arrays (via std::tuple).
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cv::GArray<cv::GMat> ages;
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cv::GArray<cv::GMat> genders;
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std::tie(ages, genders) = cv::gapi::infer<custom::AgeGender>(faces, in);
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// Recognize emotions on every face.
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// ROI-list-oriented infer<>() is used here as well.
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// Since custom::Emotions network produce a single output, only one
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// GArray<> is returned here.
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cv::GArray<cv::GMat> emotions = cv::gapi::infer<custom::Emotions>(faces, in);
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// Return the decoded frame as a result as well.
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// Input matrix can't be specified as output one, so use copy() here
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// (this copy will be optimized out in the future).
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cv::GMat frame = cv::gapi::copy(in);
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// Now specify the computation's boundaries - our pipeline consumes
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// one images and produces five outputs.
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return cv::GComputation(cv::GIn(in),
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cv::GOut(frame, faces, ages, genders, emotions));
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});
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// Note: it might be very useful to have dimensions loaded at this point!
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// After our computation is defined, specify how it should be executed.
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// Execution is defined by inference backends and kernel backends we use to
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// compile the pipeline (it is a different step).
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// Declare IE parameters for FaceDetection network. Note here custom::Face
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// is the type name we specified in GAPI_NETWORK() previously.
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// cv::gapi::ie::Params<> is a generic configuration description which is
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// specialized to every particular network we use.
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//
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// OpenCV DNN backend will have its own parmater structure with settings
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// relevant to OpenCV DNN module. Same applies to other possible inference
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// backends, like cuDNN, etc (:-))
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auto det_net = cv::gapi::ie::Params<custom::Faces> {
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cmd.get<std::string>("fdm"), // read cmd args: path to topology IR
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cmd.get<std::string>("fdw"), // read cmd args: path to weights
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cmd.get<std::string>("fdd"), // read cmd args: device specifier
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};
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auto age_net = cv::gapi::ie::Params<custom::AgeGender> {
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cmd.get<std::string>("agem"), // read cmd args: path to topology IR
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cmd.get<std::string>("agew"), // read cmd args: path to weights
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cmd.get<std::string>("aged"), // read cmd args: device specifier
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}.cfgOutputLayers({ "age_conv3", "prob" });
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auto emo_net = cv::gapi::ie::Params<custom::Emotions> {
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cmd.get<std::string>("emom"), // read cmd args: path to topology IR
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cmd.get<std::string>("emow"), // read cmd args: path to weights
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cmd.get<std::string>("emod"), // read cmd args: device specifier
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};
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// Form a kernel package (with a single OpenCV-based implementation of our
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// post-processing) and a network package (holding our three networks).x
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auto kernels = cv::gapi::kernels<custom::OCVPostProc>();
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auto networks = cv::gapi::networks(det_net, age_net, emo_net);
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// Compile our pipeline for a specific input image format (TBD - can be relaxed)
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// and pass our kernels & networks as parameters.
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// This is the place where G-API learns which networks & kernels we're actually
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// operating with (the graph description itself known nothing about that).
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auto cc = pp.compileStreaming(cv::GMatDesc{CV_8U,3,cv::Size(1280,720)},
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cv::compile_args(kernels, networks));
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std::cout << "Reading " << input << std::endl;
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cc.setSource(cv::gapi::wip::make_src<cv::gapi::wip::GCaptureSource>(input));
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Avg avg;
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avg.start();
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cc.start();
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cv::Mat frame;
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std::vector<cv::Rect> faces;
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std::vector<cv::Mat> out_ages;
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std::vector<cv::Mat> out_genders;
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std::vector<cv::Mat> out_emotions;
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std::size_t frames = 0u;
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// Implement different execution policies depending on the display option
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// for the best performance.
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while (cc.running()) {
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auto out_vector = cv::gout(frame, faces, out_ages, out_genders, out_emotions);
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if (no_show) {
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// This is purely a video processing. No need to balance with UI rendering.
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// Use a blocking pull() to obtain data. Break the loop if the stream is over.
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if (!cc.pull(std::move(out_vector)))
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break;
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} else if (!cc.try_pull(std::move(out_vector))) {
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// Use a non-blocking try_pull() to obtain data.
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// If there's no data, let UI refresh (and handle keypress)
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if (cv::waitKey(1) >= 0) break;
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else continue;
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}
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// At this point we have data for sure (obtained in either blocking or non-blocking way).
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frames++;
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labels::DrawResults(frame, faces, out_ages, out_genders, out_emotions);
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labels::DrawFPS(frame, frames, avg.fps(frames));
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if (!no_show) cv::imshow("Out", frame);
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}
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cc.stop();
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std::cout << "Processed " << frames << " frames in " << avg.elapsed() << std::endl;
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return 0;
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}
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#else
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#include <iostream>
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int main()
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{
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std::cerr << "This tutorial code requires G-API module "
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"with Inference Engine backend to run"
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<< std::endl;
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return 1;
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}
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#endif // HAVE_OPECV_GAPI
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@@ -0,0 +1,351 @@
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#include "opencv2/opencv_modules.hpp"
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#include <iostream>
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#if defined(HAVE_OPENCV_GAPI)
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#include <chrono>
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#include <iomanip>
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#include "opencv2/imgproc.hpp"
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#include "opencv2/imgcodecs.hpp"
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#include "opencv2/gapi.hpp"
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#include "opencv2/gapi/core.hpp"
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#include "opencv2/gapi/imgproc.hpp"
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#include "opencv2/gapi/infer.hpp"
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#include "opencv2/gapi/infer/ie.hpp"
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#include "opencv2/gapi/cpu/gcpukernel.hpp"
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#include "opencv2/gapi/streaming/cap.hpp"
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#include "opencv2/highgui.hpp"
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const std::string about =
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"This is an OpenCV-based version of Security Barrier Camera example";
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const std::string keys =
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"{ h help | | print this help message }"
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"{ input | | Path to an input video file }"
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"{ detm | | IE vehicle/license plate detection model IR }"
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"{ detw | | IE vehicle/license plate detection model weights }"
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"{ detd | | IE vehicle/license plate detection model device }"
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"{ vehm | | IE vehicle attributes model IR }"
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"{ vehw | | IE vehicle attributes model weights }"
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"{ vehd | | IE vehicle attributes model device }"
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"{ lprm | | IE license plate recognition model IR }"
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"{ lprw | | IE license plate recognition model weights }"
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"{ lprd | | IE license plate recognition model device }"
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"{ pure | | When set, no output is displayed. Useful for benchmarking }"
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"{ ser | | When set, runs a regular (serial) pipeline }";
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namespace {
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struct Avg {
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struct Elapsed {
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explicit Elapsed(double ms) : ss(ms/1000.), mm(static_cast<int>(ss)/60) {}
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const double ss;
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const int mm;
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};
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using MS = std::chrono::duration<double, std::ratio<1, 1000>>;
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using TS = std::chrono::time_point<std::chrono::high_resolution_clock>;
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TS started;
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void start() { started = now(); }
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TS now() const { return std::chrono::high_resolution_clock::now(); }
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double tick() const { return std::chrono::duration_cast<MS>(now() - started).count(); }
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Elapsed elapsed() const { return Elapsed{tick()}; }
|
||||
double fps(std::size_t n) const { return static_cast<double>(n) / (tick() / 1000.); }
|
||||
};
|
||||
std::ostream& operator<<(std::ostream &os, const Avg::Elapsed &e) {
|
||||
os << e.mm << ':' << (e.ss - 60*e.mm);
|
||||
return os;
|
||||
}
|
||||
} // namespace
|
||||
|
||||
|
||||
namespace custom {
|
||||
G_API_NET(VehicleLicenseDetector, <cv::GMat(cv::GMat)>, "vehicle-license-plate-detector");
|
||||
|
||||
using Attrs = std::tuple<cv::GMat, cv::GMat>;
|
||||
G_API_NET(VehicleAttributes, <Attrs(cv::GMat)>, "vehicle-attributes");
|
||||
G_API_NET(LPR, <cv::GMat(cv::GMat)>, "license-plate-recognition");
|
||||
|
||||
using GVehiclesPlates = std::tuple< cv::GArray<cv::Rect>
|
||||
, cv::GArray<cv::Rect> >;
|
||||
G_API_OP_M(ProcessDetections,
|
||||
<GVehiclesPlates(cv::GMat, cv::GMat)>,
|
||||
"custom.security_barrier.detector.postproc") {
|
||||
static std::tuple<cv::GArrayDesc,cv::GArrayDesc>
|
||||
outMeta(const cv::GMatDesc &, const cv::GMatDesc) {
|
||||
// FIXME: Need to get rid of this - literally there's nothing useful
|
||||
return std::make_tuple(cv::empty_array_desc(), cv::empty_array_desc());
|
||||
}
|
||||
};
|
||||
|
||||
GAPI_OCV_KERNEL(OCVProcessDetections, ProcessDetections) {
|
||||
static void run(const cv::Mat &in_ssd_result,
|
||||
const cv::Mat &in_frame,
|
||||
std::vector<cv::Rect> &out_vehicles,
|
||||
std::vector<cv::Rect> &out_plates) {
|
||||
const int MAX_PROPOSALS = 200;
|
||||
const int OBJECT_SIZE = 7;
|
||||
const cv::Size upscale = in_frame.size();
|
||||
const cv::Rect surface({0,0}, upscale);
|
||||
|
||||
out_vehicles.clear();
|
||||
out_plates.clear();
|
||||
|
||||
const float *data = in_ssd_result.ptr<float>();
|
||||
for (int i = 0; i < MAX_PROPOSALS; i++) {
|
||||
const float image_id = data[i * OBJECT_SIZE + 0]; // batch id
|
||||
const float label = data[i * OBJECT_SIZE + 1];
|
||||
const float confidence = data[i * OBJECT_SIZE + 2];
|
||||
const float rc_left = data[i * OBJECT_SIZE + 3];
|
||||
const float rc_top = data[i * OBJECT_SIZE + 4];
|
||||
const float rc_right = data[i * OBJECT_SIZE + 5];
|
||||
const float rc_bottom = data[i * OBJECT_SIZE + 6];
|
||||
|
||||
if (image_id < 0.f) { // indicates end of detections
|
||||
break;
|
||||
}
|
||||
if (confidence < 0.5f) { // fixme: hard-coded snapshot
|
||||
continue;
|
||||
}
|
||||
|
||||
cv::Rect rc;
|
||||
rc.x = static_cast<int>(rc_left * upscale.width);
|
||||
rc.y = static_cast<int>(rc_top * upscale.height);
|
||||
rc.width = static_cast<int>(rc_right * upscale.width) - rc.x;
|
||||
rc.height = static_cast<int>(rc_bottom * upscale.height) - rc.y;
|
||||
|
||||
using PT = cv::Point;
|
||||
using SZ = cv::Size;
|
||||
switch (static_cast<int>(label)) {
|
||||
case 1: out_vehicles.push_back(rc & surface); break;
|
||||
case 2: out_plates.emplace_back((rc-PT(15,15)+SZ(30,30)) & surface); break;
|
||||
default: CV_Assert(false && "Unknown object class");
|
||||
}
|
||||
}
|
||||
}
|
||||
};
|
||||
} // namespace custom
|
||||
|
||||
namespace labels {
|
||||
const std::string colors[] = {
|
||||
"white", "gray", "yellow", "red", "green", "blue", "black"
|
||||
};
|
||||
const std::string types[] = {
|
||||
"car", "van", "truck", "bus"
|
||||
};
|
||||
const std::vector<std::string> license_text = {
|
||||
"0", "1", "2", "3", "4", "5", "6", "7", "8", "9",
|
||||
"<Anhui>", "<Beijing>", "<Chongqing>", "<Fujian>",
|
||||
"<Gansu>", "<Guangdong>", "<Guangxi>", "<Guizhou>",
|
||||
"<Hainan>", "<Hebei>", "<Heilongjiang>", "<Henan>",
|
||||
"<HongKong>", "<Hubei>", "<Hunan>", "<InnerMongolia>",
|
||||
"<Jiangsu>", "<Jiangxi>", "<Jilin>", "<Liaoning>",
|
||||
"<Macau>", "<Ningxia>", "<Qinghai>", "<Shaanxi>",
|
||||
"<Shandong>", "<Shanghai>", "<Shanxi>", "<Sichuan>",
|
||||
"<Tianjin>", "<Tibet>", "<Xinjiang>", "<Yunnan>",
|
||||
"<Zhejiang>", "<police>",
|
||||
"A", "B", "C", "D", "E", "F", "G", "H", "I", "J",
|
||||
"K", "L", "M", "N", "O", "P", "Q", "R", "S", "T",
|
||||
"U", "V", "W", "X", "Y", "Z"
|
||||
};
|
||||
namespace {
|
||||
void DrawResults(cv::Mat &frame,
|
||||
const std::vector<cv::Rect> &vehicles,
|
||||
const std::vector<cv::Mat> &out_colors,
|
||||
const std::vector<cv::Mat> &out_types,
|
||||
const std::vector<cv::Rect> &plates,
|
||||
const std::vector<cv::Mat> &out_numbers) {
|
||||
CV_Assert(vehicles.size() == out_colors.size());
|
||||
CV_Assert(vehicles.size() == out_types.size());
|
||||
CV_Assert(plates.size() == out_numbers.size());
|
||||
|
||||
for (auto it = vehicles.begin(); it != vehicles.end(); ++it) {
|
||||
const auto idx = std::distance(vehicles.begin(), it);
|
||||
const auto &rc = *it;
|
||||
|
||||
const float *colors_data = out_colors[idx].ptr<float>();
|
||||
const float *types_data = out_types [idx].ptr<float>();
|
||||
const auto color_id = std::max_element(colors_data, colors_data + 7) - colors_data;
|
||||
const auto type_id = std::max_element(types_data, types_data + 4) - types_data;
|
||||
|
||||
const int ATTRIB_OFFSET = 25;
|
||||
cv::rectangle(frame, rc, {0, 255, 0}, 4);
|
||||
cv::putText(frame, labels::colors[color_id],
|
||||
cv::Point(rc.x + 5, rc.y + ATTRIB_OFFSET),
|
||||
cv::FONT_HERSHEY_COMPLEX_SMALL,
|
||||
1,
|
||||
cv::Scalar(255, 0, 0));
|
||||
cv::putText(frame, labels::types[type_id],
|
||||
cv::Point(rc.x + 5, rc.y + ATTRIB_OFFSET * 2),
|
||||
cv::FONT_HERSHEY_COMPLEX_SMALL,
|
||||
1,
|
||||
cv::Scalar(255, 0, 0));
|
||||
}
|
||||
|
||||
for (auto it = plates.begin(); it != plates.end(); ++it) {
|
||||
const int MAX_LICENSE = 88;
|
||||
const int LPR_OFFSET = 50;
|
||||
|
||||
const auto &rc = *it;
|
||||
const auto idx = std::distance(plates.begin(), it);
|
||||
|
||||
std::string result;
|
||||
const auto *lpr_data = out_numbers[idx].ptr<float>();
|
||||
for (int i = 0; i < MAX_LICENSE; i++) {
|
||||
if (lpr_data[i] == -1) break;
|
||||
result += labels::license_text[static_cast<size_t>(lpr_data[i])];
|
||||
}
|
||||
|
||||
const int y_pos = std::max(0, rc.y + rc.height - LPR_OFFSET);
|
||||
cv::rectangle(frame, rc, {0, 0, 255}, 4);
|
||||
cv::putText(frame, result,
|
||||
cv::Point(rc.x, y_pos),
|
||||
cv::FONT_HERSHEY_COMPLEX_SMALL,
|
||||
1,
|
||||
cv::Scalar(0, 0, 255));
|
||||
}
|
||||
}
|
||||
|
||||
void DrawFPS(cv::Mat &frame, std::size_t n, double fps) {
|
||||
std::ostringstream out;
|
||||
out << "FRAME " << n << ": "
|
||||
<< std::fixed << std::setprecision(2) << fps
|
||||
<< " FPS (AVG)";
|
||||
cv::putText(frame, out.str(),
|
||||
cv::Point(0, frame.rows),
|
||||
cv::FONT_HERSHEY_SIMPLEX,
|
||||
1,
|
||||
cv::Scalar(0, 0, 0),
|
||||
2);
|
||||
}
|
||||
} // anonymous namespace
|
||||
} // namespace labels
|
||||
|
||||
int main(int argc, char *argv[])
|
||||
{
|
||||
cv::CommandLineParser cmd(argc, argv, keys);
|
||||
cmd.about(about);
|
||||
if (cmd.has("help")) {
|
||||
cmd.printMessage();
|
||||
return 0;
|
||||
}
|
||||
const std::string input = cmd.get<std::string>("input");
|
||||
const bool no_show = cmd.get<bool>("pure");
|
||||
|
||||
cv::GComputation pp([]() {
|
||||
cv::GMat in;
|
||||
cv::GMat detections = cv::gapi::infer<custom::VehicleLicenseDetector>(in);
|
||||
cv::GArray<cv::Rect> vehicles;
|
||||
cv::GArray<cv::Rect> plates;
|
||||
std::tie(vehicles, plates) = custom::ProcessDetections::on(detections, in);
|
||||
cv::GArray<cv::GMat> colors;
|
||||
cv::GArray<cv::GMat> types;
|
||||
std::tie(colors, types) = cv::gapi::infer<custom::VehicleAttributes>(vehicles, in);
|
||||
cv::GArray<cv::GMat> numbers = cv::gapi::infer<custom::LPR>(plates, in);
|
||||
cv::GMat frame = cv::gapi::copy(in); // pass-through the input frame
|
||||
return cv::GComputation(cv::GIn(in),
|
||||
cv::GOut(frame, vehicles, colors, types, plates, numbers));
|
||||
});
|
||||
|
||||
// Note: it might be very useful to have dimensions loaded at this point!
|
||||
auto det_net = cv::gapi::ie::Params<custom::VehicleLicenseDetector> {
|
||||
cmd.get<std::string>("detm"), // path to topology IR
|
||||
cmd.get<std::string>("detw"), // path to weights
|
||||
cmd.get<std::string>("detd"), // device specifier
|
||||
};
|
||||
|
||||
auto attr_net = cv::gapi::ie::Params<custom::VehicleAttributes> {
|
||||
cmd.get<std::string>("vehm"), // path to topology IR
|
||||
cmd.get<std::string>("vehw"), // path to weights
|
||||
cmd.get<std::string>("vehd"), // device specifier
|
||||
}.cfgOutputLayers({ "color", "type" });
|
||||
|
||||
// Fill a special LPR input (seq_ind) with a predefined value
|
||||
// First element is 0.f, the rest 87 are 1.f
|
||||
const std::vector<int> lpr_seq_dims = {88,1};
|
||||
cv::Mat lpr_seq(lpr_seq_dims, CV_32F, cv::Scalar(1.f));
|
||||
lpr_seq.ptr<float>()[0] = 0.f;
|
||||
auto lpr_net = cv::gapi::ie::Params<custom::LPR> {
|
||||
cmd.get<std::string>("lprm"), // path to topology IR
|
||||
cmd.get<std::string>("lprw"), // path to weights
|
||||
cmd.get<std::string>("lprd"), // device specifier
|
||||
}.constInput("seq_ind", lpr_seq);
|
||||
|
||||
auto kernels = cv::gapi::kernels<custom::OCVProcessDetections>();
|
||||
auto networks = cv::gapi::networks(det_net, attr_net, lpr_net);
|
||||
|
||||
Avg avg;
|
||||
cv::Mat frame;
|
||||
std::vector<cv::Rect> vehicles, plates;
|
||||
std::vector<cv::Mat> out_colors;
|
||||
std::vector<cv::Mat> out_types;
|
||||
std::vector<cv::Mat> out_numbers;
|
||||
std::size_t frames = 0u;
|
||||
|
||||
std::cout << "Reading " << input << std::endl;
|
||||
|
||||
if (cmd.get<bool>("ser")) {
|
||||
std::cout << "Going serial..." << std::endl;
|
||||
cv::VideoCapture cap(input);
|
||||
|
||||
auto cc = pp.compile(cv::GMatDesc{CV_8U,3,cv::Size(1920,1080)},
|
||||
cv::compile_args(kernels, networks));
|
||||
|
||||
avg.start();
|
||||
while (cv::waitKey(1) < 0) {
|
||||
cap >> frame;
|
||||
if (frame.empty()) break;
|
||||
|
||||
cc(cv::gin(frame),
|
||||
cv::gout(frame, vehicles, out_colors, out_types, plates, out_numbers));
|
||||
frames++;
|
||||
labels::DrawResults(frame, vehicles, out_colors, out_types, plates, out_numbers);
|
||||
labels::DrawFPS(frame, frames, avg.fps(frames));
|
||||
if (!no_show) cv::imshow("Out", frame);
|
||||
}
|
||||
} else {
|
||||
std::cout << "Going pipelined..." << std::endl;
|
||||
|
||||
auto cc = pp.compileStreaming(cv::GMatDesc{CV_8U,3,cv::Size(1920,1080)},
|
||||
cv::compile_args(kernels, networks));
|
||||
|
||||
cc.setSource(cv::gapi::wip::make_src<cv::gapi::wip::GCaptureSource>(input));
|
||||
|
||||
avg.start();
|
||||
cc.start();
|
||||
|
||||
// Implement different execution policies depending on the display option
|
||||
// for the best performance.
|
||||
while (cc.running()) {
|
||||
auto out_vector = cv::gout(frame, vehicles, out_colors, out_types, plates, out_numbers);
|
||||
if (no_show) {
|
||||
// This is purely a video processing. No need to balance with UI rendering.
|
||||
// Use a blocking pull() to obtain data. Break the loop if the stream is over.
|
||||
if (!cc.pull(std::move(out_vector)))
|
||||
break;
|
||||
} else if (!cc.try_pull(std::move(out_vector))) {
|
||||
// Use a non-blocking try_pull() to obtain data.
|
||||
// If there's no data, let UI refresh (and handle keypress)
|
||||
if (cv::waitKey(1) >= 0) break;
|
||||
else continue;
|
||||
}
|
||||
// At this point we have data for sure (obtained in either blocking or non-blocking way).
|
||||
frames++;
|
||||
labels::DrawResults(frame, vehicles, out_colors, out_types, plates, out_numbers);
|
||||
labels::DrawFPS(frame, frames, avg.fps(frames));
|
||||
if (!no_show) cv::imshow("Out", frame);
|
||||
}
|
||||
cc.stop();
|
||||
}
|
||||
std::cout << "Processed " << frames << " frames in " << avg.elapsed() << std::endl;
|
||||
|
||||
return 0;
|
||||
}
|
||||
#else
|
||||
int main()
|
||||
{
|
||||
std::cerr << "This tutorial code requires G-API module "
|
||||
"with Inference Engine backend to run"
|
||||
<< std::endl;
|
||||
return 1;
|
||||
}
|
||||
#endif // HAVE_OPECV_GAPI
|
||||
Reference in New Issue
Block a user