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217 lines
8.5 KiB
C++
217 lines
8.5 KiB
C++
#include <algorithm>
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#include <iostream>
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#include <cctype>
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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/render.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 Privacy Masking 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 the input video file }"
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"{ platm | vehicle-license-plate-detection-barrier-0106.xml | Path to OpenVINO IE vehicle/plate detection model (.xml) }"
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"{ platd | CPU | Target device for vehicle/plate detection model (e.g. CPU, GPU, VPU, ...) }"
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"{ facem | face-detection-retail-0005.xml | Path to OpenVINO IE face detection model (.xml) }"
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"{ faced | CPU | Target device for face detection model (e.g. CPU, GPU, VPU, ...) }"
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"{ trad | false | Run processing in a traditional (non-pipelined) way }"
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"{ noshow | false | Don't display UI (improves performance) }";
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namespace {
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std::string weights_path(const std::string &model_path) {
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const auto EXT_LEN = 4u;
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const auto sz = model_path.size();
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CV_Assert(sz > EXT_LEN);
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auto ext = model_path.substr(sz - EXT_LEN);
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std::transform(ext.begin(), ext.end(), ext.begin(), [](unsigned char c){ return static_cast<unsigned char>(std::tolower(c)); });
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CV_Assert(ext == ".xml");
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return model_path.substr(0u, sz - EXT_LEN) + ".bin";
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}
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} // namespace
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namespace custom {
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G_API_NET(VehLicDetector, <cv::GMat(cv::GMat)>, "vehicle-license-plate-detector");
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G_API_NET(FaceDetector, <cv::GMat(cv::GMat)>, "face-detector");
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using GDetections = cv::GArray<cv::Rect>;
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G_API_OP(ParseSSD, <GDetections(cv::GMat, cv::GMat, int)>, "custom.privacy_masking.postproc") {
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static cv::GArrayDesc outMeta(const cv::GMatDesc &, const cv::GMatDesc &, int) {
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return cv::empty_array_desc();
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}
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};
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using GPrims = cv::GArray<cv::gapi::wip::draw::Prim>;
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G_API_OP(ToMosaic, <GPrims(GDetections, GDetections)>, "custom.privacy_masking.to_mosaic") {
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static cv::GArrayDesc outMeta(const cv::GArrayDesc &, const cv::GArrayDesc &) {
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return cv::empty_array_desc();
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}
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};
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GAPI_OCV_KERNEL(OCVParseSSD, ParseSSD) {
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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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const int filter_label,
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std::vector<cv::Rect> &out_objects) {
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const auto &in_ssd_dims = in_ssd_result.size;
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CV_Assert(in_ssd_dims.dims() == 4u);
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const int MAX_PROPOSALS = in_ssd_dims[2];
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const int OBJECT_SIZE = in_ssd_dims[3];
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CV_Assert(OBJECT_SIZE == 7); // fixed SSD object size
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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_objects.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];
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const float label = data[i * OBJECT_SIZE + 1];
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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) {
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break; // marks end-of-detections
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}
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if (confidence < 0.5f) {
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continue; // skip objects with low confidence
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}
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if (filter_label != -1 && static_cast<int>(label) != filter_label) {
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continue; // filter out object classes if filter is specified
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}
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cv::Rect rc; // map relative coordinates to the original image scale
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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_objects.emplace_back(rc & surface);
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}
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}
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};
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GAPI_OCV_KERNEL(OCVToMosaic, ToMosaic) {
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static void run(const std::vector<cv::Rect> &in_plate_rcs,
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const std::vector<cv::Rect> &in_face_rcs,
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std::vector<cv::gapi::wip::draw::Prim> &out_prims) {
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out_prims.clear();
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const auto cvt = [](cv::Rect rc) {
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// Align the mosaic region to mosaic block size
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const int BLOCK_SIZE = 24;
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const int dw = BLOCK_SIZE - (rc.width % BLOCK_SIZE);
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const int dh = BLOCK_SIZE - (rc.height % BLOCK_SIZE);
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rc.width += dw;
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rc.height += dh;
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rc.x -= dw / 2;
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rc.y -= dh / 2;
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return cv::gapi::wip::draw::Mosaic{rc, BLOCK_SIZE, 0};
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};
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for (auto &&rc : in_plate_rcs) { out_prims.emplace_back(cvt(rc)); }
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for (auto &&rc : in_face_rcs) { out_prims.emplace_back(cvt(rc)); }
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}
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};
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} // namespace custom
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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>("noshow");
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const bool run_trad = cmd.get<bool>("trad");
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cv::GMat in;
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cv::GMat blob_plates = cv::gapi::infer<custom::VehLicDetector>(in);
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cv::GMat blob_faces = cv::gapi::infer<custom::FaceDetector>(in);
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// VehLicDetector from Open Model Zoo marks vehicles with label "1" and
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// license plates with label "2", filter out license plates only.
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cv::GArray<cv::Rect> rc_plates = custom::ParseSSD::on(blob_plates, in, 2);
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// Face detector produces faces only so there's no need to filter by label,
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// pass "-1".
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cv::GArray<cv::Rect> rc_faces = custom::ParseSSD::on(blob_faces, in, -1);
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cv::GMat out = cv::gapi::wip::draw::render3ch(in, custom::ToMosaic::on(rc_plates, rc_faces));
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cv::GComputation graph(in, out);
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const auto plate_model_path = cmd.get<std::string>("platm");
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auto plate_net = cv::gapi::ie::Params<custom::VehLicDetector> {
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plate_model_path, // path to topology IR
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weights_path(plate_model_path), // path to weights
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cmd.get<std::string>("platd"), // device specifier
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};
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const auto face_model_path = cmd.get<std::string>("facem");
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auto face_net = cv::gapi::ie::Params<custom::FaceDetector> {
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face_model_path, // path to topology IR
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weights_path(face_model_path), // path to weights
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cmd.get<std::string>("faced"), // device specifier
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};
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auto kernels = cv::gapi::kernels<custom::OCVParseSSD, custom::OCVToMosaic>();
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auto networks = cv::gapi::networks(plate_net, face_net);
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cv::TickMeter tm;
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cv::Mat out_frame;
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std::size_t frames = 0u;
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std::cout << "Reading " << input << std::endl;
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if (run_trad) {
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cv::Mat in_frame;
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cv::VideoCapture cap(input);
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cap >> in_frame;
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auto exec = graph.compile(cv::descr_of(in_frame), cv::compile_args(kernels, networks));
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tm.start();
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do {
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exec(in_frame, out_frame);
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if (!no_show) {
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cv::imshow("Out", out_frame);
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cv::waitKey(1);
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}
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frames++;
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} while (cap.read(in_frame));
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tm.stop();
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} else {
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auto pipeline = graph.compileStreaming(cv::compile_args(kernels, networks));
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pipeline.setSource(cv::gapi::wip::make_src<cv::gapi::wip::GCaptureSource>(input));
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pipeline.start();
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tm.start();
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while (pipeline.pull(cv::gout(out_frame))) {
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frames++;
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if (!no_show) {
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cv::imshow("Out", out_frame);
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cv::waitKey(1);
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}
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}
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tm.stop();
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}
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std::cout << "Processed " << frames << " frames"
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<< " (" << frames / tm.getTimeSec() << " FPS)" << std::endl;
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return 0;
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}
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