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196 lines
6.9 KiB
C++
196 lines
6.9 KiB
C++
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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/infer/onnx.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 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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"{ emom | | ONNX emotions recognition model }"
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"{ output | | (Optional) Path to an output video file }"
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;
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} // namespace
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namespace custom {
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G_API_NET(Faces, <cv::GMat(cv::GMat)>, "face-detector");
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G_API_NET(Emotions, <cv::GMat(cv::GMat)>, "emotions-recognition");
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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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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) {
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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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//! [Postproc]
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} // namespace custom
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namespace labels {
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// Labels as defined in
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// https://github.com/onnx/models/tree/master/vision/body_analysis/emotion_ferplus
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//
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const std::string emotions[] = {
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"neutral", "happiness", "surprise", "sadness", "anger", "disgust", "fear", "contempt"
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};
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namespace {
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template<typename Iter>
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std::vector<float> softmax(Iter begin, Iter end) {
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std::vector<float> prob(end - begin, 0.f);
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std::transform(begin, end, prob.begin(), [](float x) { return std::exp(x); });
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float sum = std::accumulate(prob.begin(), prob.end(), 0.0f);
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for (int i = 0; i < static_cast<int>(prob.size()); i++)
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prob[i] /= sum;
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return prob;
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}
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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_emotions) {
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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 *emotions_data = out_emotions[idx].ptr<float>();
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auto sm = softmax(emotions_data, emotions_data + 8);
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const auto emo_id = std::max_element(sm.begin(), sm.end()) - sm.begin();
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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, emotions[emo_id],
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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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std::cout << emotions[emo_id] << " at " << rc << std::endl;
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}
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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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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 std::string output = cmd.get<std::string>("output");
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// OpenVINO FD parameters here
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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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// ONNX Emotions parameters here
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auto emo_net = cv::gapi::onnx::Params<custom::Emotions> {
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cmd.get<std::string>("emom"), // read cmd args: path to the ONNX model
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}.cfgNormalize({false}); // model accepts 0..255 range in FP32
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auto kernels = cv::gapi::kernels<custom::OCVPostProc>();
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auto networks = cv::gapi::networks(det_net, emo_net);
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cv::GMat in;
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cv::GMat bgr = cv::gapi::copy(in);
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cv::GMat frame = cv::gapi::streaming::desync(bgr);
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cv::GMat detections = cv::gapi::infer<custom::Faces>(frame);
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cv::GArray<cv::Rect> faces = custom::PostProc::on(detections, frame);
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cv::GArray<cv::GMat> emotions = cv::gapi::infer<custom::Emotions>(faces, frame);
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auto pipeline = cv::GComputation(cv::GIn(in), cv::GOut(bgr, faces, emotions))
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.compileStreaming(cv::compile_args(kernels, networks));
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auto in_src = cv::gapi::wip::make_src<cv::gapi::wip::GCaptureSource>(input);
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pipeline.setSource(cv::gin(in_src));
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pipeline.start();
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cv::util::optional<cv::Mat> out_frame;
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cv::util::optional<std::vector<cv::Rect>> out_faces;
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cv::util::optional<std::vector<cv::Mat>> out_emotions;
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cv::Mat last_mat;
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std::vector<cv::Rect> last_faces;
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std::vector<cv::Mat> last_emotions;
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cv::VideoWriter writer;
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while (pipeline.pull(cv::gout(out_frame, out_faces, out_emotions))) {
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if (out_faces && out_emotions) {
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last_faces = *out_faces;
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last_emotions = *out_emotions;
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}
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if (out_frame) {
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last_mat = *out_frame;
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labels::DrawResults(last_mat, last_faces, last_emotions);
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if (!output.empty()) {
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if (!writer.isOpened()) {
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const auto sz = cv::Size{last_mat.cols, last_mat.rows};
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writer.open(output, cv::VideoWriter::fourcc('M','J','P','G'), 25.0, sz);
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CV_Assert(writer.isOpened());
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}
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writer << last_mat;
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}
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}
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if (!last_mat.empty()) {
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cv::imshow("Out", last_mat);
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cv::waitKey(1);
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}
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}
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return 0;
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}
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