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