294 lines
11 KiB
C++
294 lines
11 KiB
C++
|
#include <algorithm>
|
||
|
|
#include <fstream>
|
||
|
|
#include <iostream>
|
||
|
|
#include <cctype>
|
||
|
|
|
||
|
|
#include <opencv2/imgproc.hpp>
|
||
|
|
#include <opencv2/gapi.hpp>
|
||
|
|
#include <opencv2/gapi/core.hpp>
|
||
|
|
#include <opencv2/gapi/cpu/gcpukernel.hpp>
|
||
|
|
#include <opencv2/gapi/infer/ie.hpp>
|
||
|
|
#include <opencv2/gapi/render.hpp>
|
||
|
|
#include <opencv2/gapi/streaming/onevpl/source.hpp>
|
||
|
|
#include <opencv2/highgui.hpp> // CommandLineParser
|
||
|
|
|
||
|
|
const std::string about =
|
||
|
|
"This is an OpenCV-based version of oneVPLSource decoder example";
|
||
|
|
const std::string keys =
|
||
|
|
"{ h help | | Print this help message }"
|
||
|
|
"{ input | | Path to the input demultiplexed video file }"
|
||
|
|
"{ output | | Path to the output RAW video file. Use .avi extension }"
|
||
|
|
"{ facem | face-detection-adas-0001.xml | Path to OpenVINO IE face detection model (.xml) }"
|
||
|
|
"{ cfg_params | <prop name>:<value>;<prop name>:<value> | Semicolon separated list of oneVPL mfxVariants which is used for configuring source (see `MFXSetConfigFilterProperty` by https://spec.oneapi.io/versions/latest/elements/oneVPL/source/index.html) }";
|
||
|
|
|
||
|
|
|
||
|
|
namespace {
|
||
|
|
std::string get_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";
|
||
|
|
}
|
||
|
|
} // anonymous 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(GSize)>, "sample.custom.locate-roi") {
|
||
|
|
static cv::GOpaqueDesc outMeta(const cv::GOpaqueDesc &) {
|
||
|
|
return cv::empty_gopaque_desc();
|
||
|
|
}
|
||
|
|
};
|
||
|
|
|
||
|
|
G_API_OP(ParseSSD, <GDetections(cv::GMat, GRect, GSize)>, "sample.custom.parse-ssd") {
|
||
|
|
static cv::GArrayDesc outMeta(const cv::GMatDesc &, const cv::GOpaqueDesc &, const cv::GOpaqueDesc &) {
|
||
|
|
return cv::empty_array_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::Size& in_size, cv::Rect &out_rect) {
|
||
|
|
|
||
|
|
// Identify the central point & square size (- some padding)
|
||
|
|
const auto center = cv::Point{in_size.width/2, in_size.height/2};
|
||
|
|
auto sqside = std::min(in_size.width, in_size.height);
|
||
|
|
|
||
|
|
// Now build the central square ROI
|
||
|
|
out_rect = cv::Rect{ center.x - sqside/2
|
||
|
|
, center.y - sqside/2
|
||
|
|
, sqside
|
||
|
|
, sqside
|
||
|
|
};
|
||
|
|
}
|
||
|
|
};
|
||
|
|
|
||
|
|
GAPI_OCV_KERNEL(OCVParseSSD, ParseSSD) {
|
||
|
|
static void run(const cv::Mat &in_ssd_result,
|
||
|
|
const cv::Rect &in_roi,
|
||
|
|
const cv::Size &in_parent_size,
|
||
|
|
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 up_roi = in_roi.size();
|
||
|
|
const cv::Rect surface({0,0}, in_parent_size);
|
||
|
|
|
||
|
|
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];
|
||
|
|
(void) label; // unused
|
||
|
|
|
||
|
|
if (image_id < 0.f) {
|
||
|
|
break; // marks end-of-detections
|
||
|
|
}
|
||
|
|
if (confidence < 0.5f) {
|
||
|
|
continue; // skip objects with low confidence
|
||
|
|
}
|
||
|
|
|
||
|
|
// map relative coordinates to the original image scale
|
||
|
|
// taking the ROI into account
|
||
|
|
cv::Rect rc;
|
||
|
|
rc.x = static_cast<int>(rc_left * up_roi.width);
|
||
|
|
rc.y = static_cast<int>(rc_top * up_roi.height);
|
||
|
|
rc.width = static_cast<int>(rc_right * up_roi.width) - rc.x;
|
||
|
|
rc.height = static_cast<int>(rc_bottom * up_roi.height) - rc.y;
|
||
|
|
rc.x += in_roi.x;
|
||
|
|
rc.y += in_roi.y;
|
||
|
|
out_objects.emplace_back(rc & surface);
|
||
|
|
}
|
||
|
|
}
|
||
|
|
};
|
||
|
|
|
||
|
|
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
|
||
|
|
|
||
|
|
namespace cfg {
|
||
|
|
typename cv::gapi::wip::onevpl::CfgParam create_from_string(const std::string &line);
|
||
|
|
}
|
||
|
|
|
||
|
|
int main(int argc, char *argv[]) {
|
||
|
|
|
||
|
|
cv::CommandLineParser cmd(argc, argv, keys);
|
||
|
|
cmd.about(about);
|
||
|
|
if (cmd.has("help")) {
|
||
|
|
cmd.printMessage();
|
||
|
|
return 0;
|
||
|
|
}
|
||
|
|
|
||
|
|
// get file name
|
||
|
|
std::string file_path = cmd.get<std::string>("input");
|
||
|
|
const std::string output = cmd.get<std::string>("output");
|
||
|
|
const auto face_model_path = cmd.get<std::string>("facem");
|
||
|
|
|
||
|
|
// check ouput file extension
|
||
|
|
if (!output.empty()) {
|
||
|
|
auto ext = output.find_last_of(".");
|
||
|
|
if (ext == std::string::npos || (output.substr(ext + 1) != "avi")) {
|
||
|
|
std::cerr << "Output file should have *.avi extension for output video" << std::endl;
|
||
|
|
return -1;
|
||
|
|
}
|
||
|
|
}
|
||
|
|
|
||
|
|
// get oneVPL cfg params from cmd
|
||
|
|
std::stringstream params_list(cmd.get<std::string>("cfg_params"));
|
||
|
|
std::vector<cv::gapi::wip::onevpl::CfgParam> source_cfgs;
|
||
|
|
try {
|
||
|
|
std::string line;
|
||
|
|
while (std::getline(params_list, line, ';')) {
|
||
|
|
source_cfgs.push_back(cfg::create_from_string(line));
|
||
|
|
}
|
||
|
|
} catch (const std::exception& ex) {
|
||
|
|
std::cerr << "Invalid cfg parameter: " << ex.what() << std::endl;
|
||
|
|
return -1;
|
||
|
|
}
|
||
|
|
|
||
|
|
auto face_net = cv::gapi::ie::Params<custom::FaceDetector> {
|
||
|
|
face_model_path, // path to topology IR
|
||
|
|
get_weights_path(face_model_path) // path to weights
|
||
|
|
};
|
||
|
|
auto kernels = cv::gapi::kernels
|
||
|
|
< custom::OCVLocateROI
|
||
|
|
, custom::OCVParseSSD
|
||
|
|
, custom::OCVBBoxes>();
|
||
|
|
auto networks = cv::gapi::networks(face_net);
|
||
|
|
|
||
|
|
// Create source
|
||
|
|
cv::Ptr<cv::gapi::wip::IStreamSource> cap;
|
||
|
|
try {
|
||
|
|
cap = cv::gapi::wip::make_onevpl_src(file_path, source_cfgs);
|
||
|
|
std::cout << "oneVPL source desription: " << cap->descr_of() << std::endl;
|
||
|
|
} catch (const std::exception& ex) {
|
||
|
|
std::cerr << "Cannot create source: " << ex.what() << std::endl;
|
||
|
|
return -1;
|
||
|
|
}
|
||
|
|
|
||
|
|
cv::GMetaArg descr = cap->descr_of();
|
||
|
|
auto frame_descr = cv::util::get<cv::GFrameDesc>(descr);
|
||
|
|
|
||
|
|
// Now build the graph
|
||
|
|
cv::GFrame in;
|
||
|
|
auto size = cv::gapi::streaming::size(in);
|
||
|
|
auto roi = custom::LocateROI::on(size);
|
||
|
|
auto blob = cv::gapi::infer<custom::FaceDetector>(roi, in);
|
||
|
|
auto rcs = custom::ParseSSD::on(blob, roi, size);
|
||
|
|
auto out_frame = cv::gapi::wip::draw::renderFrame(in, custom::BBoxes::on(rcs, roi));
|
||
|
|
auto out = cv::gapi::streaming::BGR(out_frame);
|
||
|
|
|
||
|
|
cv::GStreamingCompiled pipeline;
|
||
|
|
try {
|
||
|
|
pipeline = cv::GComputation(cv::GIn(in), cv::GOut(out))
|
||
|
|
.compileStreaming(cv::compile_args(kernels, networks));
|
||
|
|
} catch (const std::exception& ex) {
|
||
|
|
std::cerr << "Exception occured during pipeline construction: " << ex.what() << std::endl;
|
||
|
|
return -1;
|
||
|
|
}
|
||
|
|
// The execution part
|
||
|
|
|
||
|
|
// TODO USE may set pool size from outside and set queue_capacity size,
|
||
|
|
// compile arg: cv::gapi::streaming::queue_capacity
|
||
|
|
pipeline.setSource(std::move(cap));
|
||
|
|
pipeline.start();
|
||
|
|
|
||
|
|
int framesCount = 0;
|
||
|
|
cv::TickMeter t;
|
||
|
|
cv::VideoWriter writer;
|
||
|
|
if (!output.empty() && !writer.isOpened()) {
|
||
|
|
const auto sz = cv::Size{frame_descr.size.width, frame_descr.size.height};
|
||
|
|
writer.open(output, cv::VideoWriter::fourcc('M','J','P','G'), 25.0, sz);
|
||
|
|
CV_Assert(writer.isOpened());
|
||
|
|
}
|
||
|
|
|
||
|
|
cv::Mat outMat;
|
||
|
|
t.start();
|
||
|
|
while (pipeline.pull(cv::gout(outMat))) {
|
||
|
|
cv::imshow("Out", outMat);
|
||
|
|
cv::waitKey(1);
|
||
|
|
if (!output.empty()) {
|
||
|
|
writer << outMat;
|
||
|
|
}
|
||
|
|
framesCount++;
|
||
|
|
}
|
||
|
|
t.stop();
|
||
|
|
std::cout << "Elapsed time: " << t.getTimeSec() << std::endl;
|
||
|
|
std::cout << "FPS: " << framesCount / t.getTimeSec() << std::endl;
|
||
|
|
std::cout << "framesCount: " << framesCount << std::endl;
|
||
|
|
|
||
|
|
return 0;
|
||
|
|
}
|
||
|
|
|
||
|
|
|
||
|
|
namespace cfg {
|
||
|
|
typename cv::gapi::wip::onevpl::CfgParam create_from_string(const std::string &line) {
|
||
|
|
using namespace cv::gapi::wip;
|
||
|
|
|
||
|
|
if (line.empty()) {
|
||
|
|
throw std::runtime_error("Cannot parse CfgParam from emply line");
|
||
|
|
}
|
||
|
|
|
||
|
|
std::string::size_type name_endline_pos = line.find(':');
|
||
|
|
if (name_endline_pos == std::string::npos) {
|
||
|
|
throw std::runtime_error("Cannot parse CfgParam from: " + line +
|
||
|
|
"\nExpected separator \":\"");
|
||
|
|
}
|
||
|
|
|
||
|
|
std::string name = line.substr(0, name_endline_pos);
|
||
|
|
std::string value = line.substr(name_endline_pos + 1);
|
||
|
|
|
||
|
|
return cv::gapi::wip::onevpl::CfgParam::create(name, value);
|
||
|
|
}
|
||
|
|
}
|