--- /dev/null
+#include <algorithm>
+#include <cctype>
+#include <cmath>
+#include <iostream>
+#include <limits>
+#include <numeric>
+#include <stdexcept>
+#include <string>
+#include <vector>
+
+#include <opencv2/gapi.hpp>
+#include <opencv2/gapi/core.hpp>
+#include <opencv2/gapi/imgproc.hpp>
+#include <opencv2/gapi/cpu/gcpukernel.hpp>
+#include <opencv2/gapi/infer.hpp>
+#include <opencv2/gapi/infer/ie.hpp>
+#include <opencv2/gapi/streaming/cap.hpp>
+#include <opencv2/gapi/gopaque.hpp>
+#include <opencv2/highgui.hpp>
+
+const std::string about =
+"This is an OpenCV-based version of OMZ MTCNN Face Detection example";
+const std::string keys =
+"{ h help | | Print this help message }"
+"{ input | | Path to the input video file }"
+"{ mtcnnpm | mtcnn-p.xml | Path to OpenVINO MTCNN P (Proposal) detection model (.xml)}"
+"{ mtcnnpd | CPU | Target device for the MTCNN P (e.g. CPU, GPU, VPU, ...) }"
+"{ mtcnnrm | mtcnn-r.xml | Path to OpenVINO MTCNN R (Refinement) detection model (.xml)}"
+"{ mtcnnrd | CPU | Target device for the MTCNN R (e.g. CPU, GPU, VPU, ...) }"
+"{ mtcnnom | mtcnn-o.xml | Path to OpenVINO MTCNN O (Output) detection model (.xml)}"
+"{ mtcnnod | CPU | Target device for the MTCNN O (e.g. CPU, GPU, VPU, ...) }"
+"{ thrp | 0.6 | MTCNN P confidence threshold}"
+"{ thrr | 0.7 | MTCNN R confidence threshold}"
+"{ thro | 0.7 | MTCNN O confidence threshold}"
+"{ half_scale | false | MTCNN P use half scale pyramid}"
+;
+
+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);
+
+ const auto ext = model_path.substr(sz - EXT_LEN);
+ CV_Assert(cv::toLowerCase(ext) == ".xml");
+ return model_path.substr(0u, sz - EXT_LEN) + ".bin";
+}
+//////////////////////////////////////////////////////////////////////
+} // anonymous namespace
+
+namespace custom {
+namespace {
+
+// Define custom structures and operations
+#define NUM_REGRESSIONS 4
+#define NUM_PTS 5
+
+struct BBox {
+ double x1;
+ double y1;
+ double x2;
+ double y2;
+
+ cv::Rect getRect() const { return cv::Rect(static_cast<int>(x1),
+ static_cast<int>(y1),
+ static_cast<int>(x2 - x1),
+ static_cast<int>(y2 - y1)); }
+
+ BBox getSquare() const {
+ BBox bbox;
+ double bboxWidth = x2 - x1;
+ double bboxHeight = y2 - y1;
+ double side = std::max(bboxWidth, bboxHeight);
+ bbox.x1 = static_cast<double>(x1) + (bboxWidth - side) * 0.5;
+ bbox.y1 = static_cast<double>(y1) + (bboxHeight - side) * 0.5;
+ bbox.x2 = bbox.x1 + side;
+ bbox.y2 = bbox.y1 + side;
+ return bbox;
+ }
+};
+
+struct Face {
+ BBox bbox;
+ double score;
+ std::array<double, NUM_REGRESSIONS> regression;
+ double ptsCoords[2 * NUM_PTS];
+
+ static void applyRegression(std::vector<Face>& faces, bool addOne = false) {
+ for (auto& face : faces) {
+ double bboxWidth =
+ face.bbox.x2 - face.bbox.x1 + static_cast<double>(addOne);
+ double bboxHeight =
+ face.bbox.y2 - face.bbox.y1 + static_cast<double>(addOne);
+ face.bbox.x1 = face.bbox.x1 + static_cast<double>(face.regression[1]) * bboxWidth;
+ face.bbox.y1 = face.bbox.y1 + static_cast<double>(face.regression[0]) * bboxHeight;
+ face.bbox.x2 = face.bbox.x2 + static_cast<double>(face.regression[3]) * bboxWidth;
+ face.bbox.y2 = face.bbox.y2 + static_cast<double>(face.regression[2]) * bboxHeight;
+ }
+ }
+
+ static void bboxes2Squares(std::vector<Face>& faces) {
+ for (auto& face : faces) {
+ face.bbox = face.bbox.getSquare();
+ }
+ }
+
+ static std::vector<Face> runNMS(std::vector<Face>& faces, const double threshold,
+ const bool useMin = false) {
+ std::vector<Face> facesNMS;
+ if (faces.empty()) {
+ return facesNMS;
+ }
+
+ std::sort(faces.begin(), faces.end(), [](const Face& f1, const Face& f2) {
+ return f1.score > f2.score;
+ });
+
+ std::vector<int> indices(faces.size());
+ std::iota(indices.begin(), indices.end(), 0);
+
+ while (indices.size() > 0) {
+ const int idx = indices[0];
+ facesNMS.push_back(faces[idx]);
+ std::vector<int> tmpIndices = indices;
+ indices.clear();
+ const double area1 = (faces[idx].bbox.x2 - faces[idx].bbox.x1 + 1) *
+ (faces[idx].bbox.y2 - faces[idx].bbox.y1 + 1);
+ for (size_t i = 1; i < tmpIndices.size(); ++i) {
+ int tmpIdx = tmpIndices[i];
+ const double interX1 = std::max(faces[idx].bbox.x1, faces[tmpIdx].bbox.x1);
+ const double interY1 = std::max(faces[idx].bbox.y1, faces[tmpIdx].bbox.y1);
+ const double interX2 = std::min(faces[idx].bbox.x2, faces[tmpIdx].bbox.x2);
+ const double interY2 = std::min(faces[idx].bbox.y2, faces[tmpIdx].bbox.y2);
+
+ const double bboxWidth = std::max(0.0, (interX2 - interX1 + 1));
+ const double bboxHeight = std::max(0.0, (interY2 - interY1 + 1));
+
+ const double interArea = bboxWidth * bboxHeight;
+ const double area2 = (faces[tmpIdx].bbox.x2 - faces[tmpIdx].bbox.x1 + 1) *
+ (faces[tmpIdx].bbox.y2 - faces[tmpIdx].bbox.y1 + 1);
+ double overlap = 0.0;
+ if (useMin) {
+ overlap = interArea / std::min(area1, area2);
+ } else {
+ overlap = interArea / (area1 + area2 - interArea);
+ }
+ if (overlap <= threshold) {
+ indices.push_back(tmpIdx);
+ }
+ }
+ }
+ return facesNMS;
+ }
+};
+
+const double P_NET_WINDOW_SIZE = 12.0;
+const double P_NET_STRIDE = 2.0;
+
+std::vector<Face> buildFaces(const cv::Mat& scores,
+ const cv::Mat& regressions,
+ const double scaleFactor,
+ const double threshold) {
+
+ auto w = scores.size[3];
+ auto h = scores.size[2];
+ auto size = w * h;
+
+ const float* scores_data = scores.ptr<float>();
+ scores_data += size;
+
+ const float* reg_data = regressions.ptr<float>();
+
+ std::vector<Face> boxes;
+
+ for (int i = 0; i < size; i++) {
+ if (scores_data[i] >= (threshold)) {
+ int y = i / w;
+ int x = i - w * y;
+
+ Face faceInfo;
+ BBox& faceBox = faceInfo.bbox;
+
+ faceBox.x1 = (static_cast<double>(x) * P_NET_STRIDE) / scaleFactor;
+ faceBox.y1 = (static_cast<double>(y) * P_NET_STRIDE) / scaleFactor;
+ faceBox.x2 = (static_cast<double>(x) * P_NET_STRIDE + P_NET_WINDOW_SIZE - 1.f) / scaleFactor;
+ faceBox.y2 = (static_cast<double>(y) * P_NET_STRIDE + P_NET_WINDOW_SIZE - 1.f) / scaleFactor;
+ faceInfo.regression[0] = reg_data[i];
+ faceInfo.regression[1] = reg_data[i + size];
+ faceInfo.regression[2] = reg_data[i + 2 * size];
+ faceInfo.regression[3] = reg_data[i + 3 * size];
+ faceInfo.score = scores_data[i];
+ boxes.push_back(faceInfo);
+ }
+ }
+
+ return boxes;
+}
+
+// Define networks for this sample
+using GMat2 = std::tuple<cv::GMat, cv::GMat>;
+using GMat3 = std::tuple<cv::GMat, cv::GMat, cv::GMat>;
+using GMats = cv::GArray<cv::GMat>;
+using GRects = cv::GArray<cv::Rect>;
+using GSize = cv::GOpaque<cv::Size>;
+
+G_API_NET(MTCNNRefinement,
+ <GMat2(cv::GMat)>,
+ "sample.custom.mtcnn_refinement");
+
+G_API_NET(MTCNNOutput,
+ <GMat3(cv::GMat)>,
+ "sample.custom.mtcnn_output");
+
+using GFaces = cv::GArray<Face>;
+G_API_OP(BuildFaces,
+ <GFaces(cv::GMat, cv::GMat, double, double)>,
+ "sample.custom.mtcnn.build_faces") {
+ static cv::GArrayDesc outMeta(const cv::GMatDesc&,
+ const cv::GMatDesc&,
+ const double,
+ const double) {
+ return cv::empty_array_desc();
+ }
+};
+
+G_API_OP(RunNMS,
+ <GFaces(GFaces, double, bool)>,
+ "sample.custom.mtcnn.run_nms") {
+ static cv::GArrayDesc outMeta(const cv::GArrayDesc&,
+ const double, const bool) {
+ return cv::empty_array_desc();
+ }
+};
+
+G_API_OP(AccumulatePyramidOutputs,
+ <GFaces(GFaces, GFaces)>,
+ "sample.custom.mtcnn.accumulate_pyramid_outputs") {
+ static cv::GArrayDesc outMeta(const cv::GArrayDesc&,
+ const cv::GArrayDesc&) {
+ return cv::empty_array_desc();
+ }
+};
+
+G_API_OP(ApplyRegression,
+ <GFaces(GFaces, bool)>,
+ "sample.custom.mtcnn.apply_regression") {
+ static cv::GArrayDesc outMeta(const cv::GArrayDesc&, const bool) {
+ return cv::empty_array_desc();
+ }
+};
+
+G_API_OP(BBoxesToSquares,
+ <GFaces(GFaces)>,
+ "sample.custom.mtcnn.bboxes_to_squares") {
+ static cv::GArrayDesc outMeta(const cv::GArrayDesc&) {
+ return cv::empty_array_desc();
+ }
+};
+
+G_API_OP(R_O_NetPreProcGetROIs,
+ <GRects(GFaces, GSize)>,
+ "sample.custom.mtcnn.bboxes_r_o_net_preproc_get_rois") {
+ static cv::GArrayDesc outMeta(const cv::GArrayDesc&, const cv::GOpaqueDesc&) {
+ return cv::empty_array_desc();
+ }
+};
+
+
+G_API_OP(RNetPostProc,
+ <GFaces(GFaces, GMats, GMats, double)>,
+ "sample.custom.mtcnn.rnet_postproc") {
+ static cv::GArrayDesc outMeta(const cv::GArrayDesc&,
+ const cv::GArrayDesc&,
+ const cv::GArrayDesc&,
+ const double) {
+ return cv::empty_array_desc();
+ }
+};
+
+G_API_OP(ONetPostProc,
+ <GFaces(GFaces, GMats, GMats, GMats, double)>,
+ "sample.custom.mtcnn.onet_postproc") {
+ static cv::GArrayDesc outMeta(const cv::GArrayDesc&,
+ const cv::GArrayDesc&,
+ const cv::GArrayDesc&,
+ const cv::GArrayDesc&,
+ const double) {
+ return cv::empty_array_desc();
+ }
+};
+
+G_API_OP(SwapFaces,
+ <GFaces(GFaces)>,
+ "sample.custom.mtcnn.swap_faces") {
+ static cv::GArrayDesc outMeta(const cv::GArrayDesc&) {
+ return cv::empty_array_desc();
+ }
+};
+
+G_API_OP(Transpose,
+ <cv::GMat(cv::GMat)>,
+ "sample.custom.mtcnn.transpose") {
+ static cv::GMatDesc outMeta(const cv::GMatDesc in) {
+ return in.withSize(cv::Size(in.size.height, in.size.width));
+ }
+};
+
+//Custom kernels implementation
+GAPI_OCV_KERNEL(OCVBuildFaces, BuildFaces) {
+ static void run(const cv::Mat & in_scores,
+ const cv::Mat & in_regresssions,
+ const double scaleFactor,
+ const double threshold,
+ std::vector<Face> &out_faces) {
+ out_faces = buildFaces(in_scores, in_regresssions, scaleFactor, threshold);
+ }
+};// GAPI_OCV_KERNEL(BuildFaces)
+
+GAPI_OCV_KERNEL(OCVRunNMS, RunNMS) {
+ static void run(const std::vector<Face> &in_faces,
+ const double threshold,
+ const bool useMin,
+ std::vector<Face> &out_faces) {
+ std::vector<Face> in_faces_copy = in_faces;
+ out_faces = Face::runNMS(in_faces_copy, threshold, useMin);
+ }
+};// GAPI_OCV_KERNEL(RunNMS)
+
+GAPI_OCV_KERNEL(OCVAccumulatePyramidOutputs, AccumulatePyramidOutputs) {
+ static void run(const std::vector<Face> &total_faces,
+ const std::vector<Face> &in_faces,
+ std::vector<Face> &out_faces) {
+ out_faces = total_faces;
+ out_faces.insert(out_faces.end(), in_faces.begin(), in_faces.end());
+ }
+};// GAPI_OCV_KERNEL(AccumulatePyramidOutputs)
+
+GAPI_OCV_KERNEL(OCVApplyRegression, ApplyRegression) {
+ static void run(const std::vector<Face> &in_faces,
+ const bool addOne,
+ std::vector<Face> &out_faces) {
+ std::vector<Face> in_faces_copy = in_faces;
+ Face::applyRegression(in_faces_copy, addOne);
+ out_faces.clear();
+ out_faces.insert(out_faces.end(), in_faces_copy.begin(), in_faces_copy.end());
+ }
+};// GAPI_OCV_KERNEL(ApplyRegression)
+
+GAPI_OCV_KERNEL(OCVBBoxesToSquares, BBoxesToSquares) {
+ static void run(const std::vector<Face> &in_faces,
+ std::vector<Face> &out_faces) {
+ std::vector<Face> in_faces_copy = in_faces;
+ Face::bboxes2Squares(in_faces_copy);
+ out_faces.clear();
+ out_faces.insert(out_faces.end(), in_faces_copy.begin(), in_faces_copy.end());
+ }
+};// GAPI_OCV_KERNEL(BBoxesToSquares)
+
+GAPI_OCV_KERNEL(OCVR_O_NetPreProcGetROIs, R_O_NetPreProcGetROIs) {
+ static void run(const std::vector<Face> &in_faces,
+ const cv::Size & in_image_size,
+ std::vector<cv::Rect> &outs) {
+ outs.clear();
+ for (const auto& face : in_faces) {
+ cv::Rect tmp_rect = face.bbox.getRect();
+ //Compare to transposed sizes width<->height
+ tmp_rect &= cv::Rect(tmp_rect.x, tmp_rect.y, in_image_size.height - tmp_rect.x - 4, in_image_size.width - tmp_rect.y - 4);
+ outs.push_back(tmp_rect);
+ }
+ }
+};// GAPI_OCV_KERNEL(R_O_NetPreProcGetROIs)
+
+
+GAPI_OCV_KERNEL(OCVRNetPostProc, RNetPostProc) {
+ static void run(const std::vector<Face> &in_faces,
+ const std::vector<cv::Mat> &in_scores,
+ const std::vector<cv::Mat> &in_regresssions,
+ const double threshold,
+ std::vector<Face> &out_faces) {
+ out_faces.clear();
+ for (unsigned int k = 0; k < in_faces.size(); ++k) {
+ const float* scores_data = in_scores[k].ptr<float>();
+ const float* reg_data = in_regresssions[k].ptr<float>();
+ if (scores_data[1] >= threshold) {
+ Face info = in_faces[k];
+ info.score = scores_data[1];
+ std::copy_n(reg_data, NUM_REGRESSIONS, info.regression.begin());
+ out_faces.push_back(info);
+ }
+ }
+ }
+};// GAPI_OCV_KERNEL(RNetPostProc)
+
+GAPI_OCV_KERNEL(OCVONetPostProc, ONetPostProc) {
+ static void run(const std::vector<Face> &in_faces,
+ const std::vector<cv::Mat> &in_scores,
+ const std::vector<cv::Mat> &in_regresssions,
+ const std::vector<cv::Mat> &in_landmarks,
+ const double threshold,
+ std::vector<Face> &out_faces) {
+ out_faces.clear();
+ for (unsigned int k = 0; k < in_faces.size(); ++k) {
+ const float* scores_data = in_scores[k].ptr<float>();
+ const float* reg_data = in_regresssions[k].ptr<float>();
+ const float* landmark_data = in_landmarks[k].ptr<float>();
+ if (scores_data[1] >= threshold) {
+ Face info = in_faces[k];
+ info.score = scores_data[1];
+ for (int i = 0; i < 4; ++i) {
+ info.regression[i] = reg_data[i];
+ }
+ double w = info.bbox.x2 - info.bbox.x1 + 1.0;
+ double h = info.bbox.y2 - info.bbox.y1 + 1.0;
+
+ for (int p = 0; p < NUM_PTS; ++p) {
+ info.ptsCoords[2 * p] =
+ info.bbox.x1 + static_cast<double>(landmark_data[NUM_PTS + p]) * w - 1;
+ info.ptsCoords[2 * p + 1] = info.bbox.y1 + static_cast<double>(landmark_data[p]) * h - 1;
+ }
+
+ out_faces.push_back(info);
+ }
+ }
+ }
+};// GAPI_OCV_KERNEL(ONetPostProc)
+
+GAPI_OCV_KERNEL(OCVSwapFaces, SwapFaces) {
+ static void run(const std::vector<Face> &in_faces,
+ std::vector<Face> &out_faces) {
+ std::vector<Face> in_faces_copy = in_faces;
+ out_faces.clear();
+ if (!in_faces_copy.empty()) {
+ for (size_t i = 0; i < in_faces_copy.size(); ++i) {
+ std::swap(in_faces_copy[i].bbox.x1, in_faces_copy[i].bbox.y1);
+ std::swap(in_faces_copy[i].bbox.x2, in_faces_copy[i].bbox.y2);
+ for (int p = 0; p < NUM_PTS; ++p) {
+ std::swap(in_faces_copy[i].ptsCoords[2 * p], in_faces_copy[i].ptsCoords[2 * p + 1]);
+ }
+ }
+ out_faces = in_faces_copy;
+ }
+ }
+};// GAPI_OCV_KERNEL(SwapFaces)
+
+GAPI_OCV_KERNEL(OCVTranspose, Transpose) {
+ static void run(const cv::Mat &in_mat,
+ cv::Mat &out_mat) {
+ cv::transpose(in_mat, out_mat);
+ }
+};// GAPI_OCV_KERNEL(Transpose)
+} // anonymous namespace
+} // namespace custom
+
+namespace vis {
+namespace {
+void bbox(const cv::Mat& m, const cv::Rect& rc) {
+ cv::rectangle(m, rc, cv::Scalar{ 0,255,0 }, 2, cv::LINE_8, 0);
+};
+
+using rectPoints = std::pair<cv::Rect, std::vector<cv::Point>>;
+
+static cv::Mat drawRectsAndPoints(const cv::Mat& img,
+ const std::vector<rectPoints> data) {
+ cv::Mat outImg;
+ img.copyTo(outImg);
+
+ for (const auto& el : data) {
+ vis::bbox(outImg, el.first);
+ auto pts = el.second;
+ for (size_t i = 0; i < pts.size(); ++i) {
+ cv::circle(outImg, pts[i], 3, cv::Scalar(0, 255, 255), 1);
+ }
+ }
+ return outImg;
+}
+} // anonymous namespace
+} // namespace vis
+
+
+//Infer helper function
+namespace {
+static inline std::tuple<cv::GMat, cv::GMat> run_mtcnn_p(cv::GMat &in, const std::string &id) {
+ cv::GInferInputs inputs;
+ inputs["data"] = in;
+ auto outputs = cv::gapi::infer<cv::gapi::Generic>(id, inputs);
+ auto regressions = outputs.at("conv4-2");
+ auto scores = outputs.at("prob1");
+ return std::make_tuple(regressions, scores);
+}
+
+//Operator fot PNet network package creation in the loop
+inline cv::gapi::GNetPackage& operator += (cv::gapi::GNetPackage& lhs, const cv::gapi::GNetPackage& rhs) {
+ lhs.networks.reserve(lhs.networks.size() + rhs.networks.size());
+ lhs.networks.insert(lhs.networks.end(), rhs.networks.begin(), rhs.networks.end());
+ return lhs;
+}
+
+static inline std::string get_pnet_level_name(const cv::Size &in_size) {
+ return "MTCNNProposal_" + std::to_string(in_size.width) + "x" + std::to_string(in_size.height);
+}
+
+int calculate_scales(const cv::Size &input_size, std::vector<double> &out_scales, std::vector<cv::Size> &out_sizes ) {
+ //calculate multi - scale and limit the maxinum side to 1000
+ //pr_scale: limit the maxinum side to 1000, < 1.0
+ double pr_scale = 1.0;
+ double h = static_cast<double>(input_size.height);
+ double w = static_cast<double>(input_size.width);
+ if (std::min(w, h) > 1000)
+ {
+ pr_scale = 1000.0 / std::min(h, w);
+ w = w * pr_scale;
+ h = h * pr_scale;
+ }
+ else if (std::max(w, h) < 1000)
+ {
+ w = w * pr_scale;
+ h = h * pr_scale;
+ }
+ //multi - scale
+ out_scales.clear();
+ out_sizes.clear();
+ const double factor = 0.709;
+ int factor_count = 0;
+ double minl = std::min(h, w);
+ while (minl >= 12)
+ {
+ const double current_scale = pr_scale * std::pow(factor, factor_count);
+ cv::Size current_size(static_cast<int>(static_cast<double>(input_size.width) * current_scale),
+ static_cast<int>(static_cast<double>(input_size.height) * current_scale));
+ out_scales.push_back(current_scale);
+ out_sizes.push_back(current_size);
+ minl *= factor;
+ factor_count += 1;
+ }
+ return factor_count;
+}
+
+int calculate_half_scales(const cv::Size &input_size, std::vector<double>& out_scales, std::vector<cv::Size>& out_sizes) {
+ double pr_scale = 0.5;
+ const double h = static_cast<double>(input_size.height);
+ const double w = static_cast<double>(input_size.width);
+ //multi - scale
+ out_scales.clear();
+ out_sizes.clear();
+ const double factor = 0.5;
+ int factor_count = 0;
+ double minl = std::min(h, w);
+ while (minl >= 12.0*2.0)
+ {
+ const double current_scale = pr_scale;
+ cv::Size current_size(static_cast<int>(static_cast<double>(input_size.width) * current_scale),
+ static_cast<int>(static_cast<double>(input_size.height) * current_scale));
+ out_scales.push_back(current_scale);
+ out_sizes.push_back(current_size);
+ minl *= factor;
+ factor_count += 1;
+ pr_scale *= 0.5;
+ }
+ return factor_count;
+}
+
+const int MAX_PYRAMID_LEVELS = 13;
+//////////////////////////////////////////////////////////////////////
+} // anonymous namespace
+
+int main(int argc, char* argv[]) {
+ cv::CommandLineParser cmd(argc, argv, keys);
+ cmd.about(about);
+ if (cmd.has("help")) {
+ cmd.printMessage();
+ return 0;
+ }
+ const auto input_file_name = cmd.get<std::string>("input");
+ const auto model_path_p = cmd.get<std::string>("mtcnnpm");
+ const auto target_dev_p = cmd.get<std::string>("mtcnnpd");
+ const auto conf_thresh_p = cmd.get<double>("thrp");
+ const auto model_path_r = cmd.get<std::string>("mtcnnrm");
+ const auto target_dev_r = cmd.get<std::string>("mtcnnrd");
+ const auto conf_thresh_r = cmd.get<double>("thrr");
+ const auto model_path_o = cmd.get<std::string>("mtcnnom");
+ const auto target_dev_o = cmd.get<std::string>("mtcnnod");
+ const auto conf_thresh_o = cmd.get<double>("thro");
+ const auto use_half_scale = cmd.get<bool>("half_scale");
+
+ std::vector<cv::Size> level_size;
+ std::vector<double> scales;
+ //MTCNN input size
+ cv::VideoCapture cap;
+ cap.open(input_file_name);
+ if (!cap.isOpened())
+ CV_Assert(false);
+ auto in_rsz = cv::Size{ static_cast<int>(cap.get(cv::CAP_PROP_FRAME_WIDTH)),
+ static_cast<int>(cap.get(cv::CAP_PROP_FRAME_HEIGHT)) };
+ //Calculate scales, number of pyramid levels and sizes for PNet pyramid
+ auto pyramid_levels = use_half_scale ? calculate_half_scales(in_rsz, scales, level_size) :
+ calculate_scales(in_rsz, scales, level_size);
+ CV_Assert(pyramid_levels <= MAX_PYRAMID_LEVELS);
+
+ //Proposal part of MTCNN graph
+ //Preprocessing BGR2RGB + transpose (NCWH is expected instead of NCHW)
+ cv::GMat in_original;
+ cv::GMat in_originalRGB = cv::gapi::BGR2RGB(in_original);
+ cv::GOpaque<cv::Size> in_sz = cv::gapi::streaming::size(in_original);
+ cv::GMat in_resized[MAX_PYRAMID_LEVELS];
+ cv::GMat in_transposed[MAX_PYRAMID_LEVELS];
+ cv::GMat regressions[MAX_PYRAMID_LEVELS];
+ cv::GMat scores[MAX_PYRAMID_LEVELS];
+ cv::GArray<custom::Face> nms_p_faces[MAX_PYRAMID_LEVELS];
+ cv::GArray<custom::Face> total_faces[MAX_PYRAMID_LEVELS];
+ cv::GArray<custom::Face> faces_init(std::vector<custom::Face>{});
+
+ //The very first PNet pyramid layer to init total_faces[0]
+ in_resized[0] = cv::gapi::resize(in_originalRGB, level_size[0]);
+ in_transposed[0] = custom::Transpose::on(in_resized[0]);
+ std::tie(regressions[0], scores[0]) = run_mtcnn_p(in_transposed[0], get_pnet_level_name(level_size[0]));
+ cv::GArray<custom::Face> faces0 = custom::BuildFaces::on(scores[0], regressions[0], scales[0], conf_thresh_p);
+ nms_p_faces[0] = custom::RunNMS::on(faces0, 0.5, false);
+ total_faces[0] = custom::AccumulatePyramidOutputs::on(faces_init, nms_p_faces[0]);
+ //The rest PNet pyramid layers to accumlate all layers result in total_faces[PYRAMID_LEVELS - 1]]
+ for (int i = 1; i < pyramid_levels; ++i)
+ {
+ in_resized[i] = cv::gapi::resize(in_originalRGB, level_size[i]);
+ in_transposed[i] = custom::Transpose::on(in_resized[i]);
+ std::tie(regressions[i], scores[i]) = run_mtcnn_p(in_transposed[i], get_pnet_level_name(level_size[i]));
+ cv::GArray<custom::Face> faces = custom::BuildFaces::on(scores[i], regressions[i], scales[i], conf_thresh_p);
+ nms_p_faces[i] = custom::RunNMS::on(faces, 0.5, false);
+ total_faces[i] = custom::AccumulatePyramidOutputs::on(total_faces[i - 1], nms_p_faces[i]);
+ }
+
+ //Proposal post-processing
+ cv::GArray<custom::Face> nms07_p_faces_total = custom::RunNMS::on(total_faces[pyramid_levels - 1], 0.7, false);
+ cv::GArray<custom::Face> final_p_faces_for_bb2squares = custom::ApplyRegression::on(nms07_p_faces_total, false);
+ cv::GArray<custom::Face> final_faces_pnet = custom::BBoxesToSquares::on(final_p_faces_for_bb2squares);
+
+ //Refinement part of MTCNN graph
+ cv::GArray<cv::Rect> faces_roi_pnet = custom::R_O_NetPreProcGetROIs::on(final_faces_pnet, in_sz);
+ cv::GArray<cv::GMat> regressionsRNet, scoresRNet;
+ cv::GMat in_originalRGB_transposed = custom::Transpose::on(in_originalRGB);
+ std::tie(regressionsRNet, scoresRNet) = cv::gapi::infer<custom::MTCNNRefinement>(faces_roi_pnet, in_originalRGB_transposed);
+
+ //Refinement post-processing
+ cv::GArray<custom::Face> rnet_post_proc_faces = custom::RNetPostProc::on(final_faces_pnet, scoresRNet, regressionsRNet, conf_thresh_r);
+ cv::GArray<custom::Face> nms07_r_faces_total = custom::RunNMS::on(rnet_post_proc_faces, 0.7, false);
+ cv::GArray<custom::Face> final_r_faces_for_bb2squares = custom::ApplyRegression::on(nms07_r_faces_total, true);
+ cv::GArray<custom::Face> final_faces_rnet = custom::BBoxesToSquares::on(final_r_faces_for_bb2squares);
+
+ //Output part of MTCNN graph
+ cv::GArray<cv::Rect> faces_roi_rnet = custom::R_O_NetPreProcGetROIs::on(final_faces_rnet, in_sz);
+ cv::GArray<cv::GMat> regressionsONet, scoresONet, landmarksONet;
+ std::tie(regressionsONet, landmarksONet, scoresONet) = cv::gapi::infer<custom::MTCNNOutput>(faces_roi_rnet, in_originalRGB_transposed);
+
+ //Output post-processing
+ cv::GArray<custom::Face> onet_post_proc_faces = custom::ONetPostProc::on(final_faces_rnet, scoresONet, regressionsONet, landmarksONet, conf_thresh_o);
+ cv::GArray<custom::Face> final_o_faces_for_nms07 = custom::ApplyRegression::on(onet_post_proc_faces, true);
+ cv::GArray<custom::Face> nms07_o_faces_total = custom::RunNMS::on(final_o_faces_for_nms07, 0.7, true);
+ cv::GArray<custom::Face> final_faces_onet = custom::SwapFaces::on(nms07_o_faces_total);
+
+ cv::GComputation graph_mtcnn(cv::GIn(in_original), cv::GOut(cv::gapi::copy(in_original), final_faces_onet));
+
+ // MTCNN Refinement detection network
+ auto mtcnnr_net = cv::gapi::ie::Params<custom::MTCNNRefinement>{
+ model_path_r, // path to topology IR
+ weights_path(model_path_r), // path to weights
+ target_dev_r, // device specifier
+ }.cfgOutputLayers({ "conv5-2", "prob1" }).cfgInputLayers({ "data" });
+
+ // MTCNN Output detection network
+ auto mtcnno_net = cv::gapi::ie::Params<custom::MTCNNOutput>{
+ model_path_o, // path to topology IR
+ weights_path(model_path_o), // path to weights
+ target_dev_o, // device specifier
+ }.cfgOutputLayers({ "conv6-2", "conv6-3", "prob1" }).cfgInputLayers({ "data" });
+
+ auto networks_mtcnn = cv::gapi::networks(mtcnnr_net, mtcnno_net);
+
+ // MTCNN Proposal detection network
+ for (int i = 0; i < pyramid_levels; ++i)
+ {
+ std::string net_id = get_pnet_level_name(level_size[i]);
+ std::vector<size_t> reshape_dims = { 1, 3, (size_t)level_size[i].width, (size_t)level_size[i].height };
+ cv::gapi::ie::Params<cv::gapi::Generic> mtcnnp_net{
+ net_id, // tag
+ model_path_p, // path to topology IR
+ weights_path(model_path_p), // path to weights
+ target_dev_p, // device specifier
+ };
+ mtcnnp_net.cfgInputReshape({ {"data", reshape_dims} });
+ networks_mtcnn += cv::gapi::networks(mtcnnp_net);
+ }
+
+ auto kernels_mtcnn = cv::gapi::kernels< custom::OCVBuildFaces
+ , custom::OCVRunNMS
+ , custom::OCVAccumulatePyramidOutputs
+ , custom::OCVApplyRegression
+ , custom::OCVBBoxesToSquares
+ , custom::OCVR_O_NetPreProcGetROIs
+ , custom::OCVRNetPostProc
+ , custom::OCVONetPostProc
+ , custom::OCVSwapFaces
+ , custom::OCVTranspose
+ >();
+ auto pipeline_mtcnn = graph_mtcnn.compileStreaming(cv::compile_args(networks_mtcnn, kernels_mtcnn));
+
+ std::cout << "Reading " << input_file_name << std::endl;
+ // Input stream
+ auto in_src = cv::gapi::wip::make_src<cv::gapi::wip::GCaptureSource>(input_file_name);
+
+ // Set the pipeline source & start the pipeline
+ pipeline_mtcnn.setSource(cv::gin(in_src));
+ pipeline_mtcnn.start();
+
+ // Declare the output data & run the processing loop
+ cv::TickMeter tm;
+ cv::Mat image;
+ std::vector<custom::Face> out_faces;
+
+ tm.start();
+ int frames = 0;
+ while (pipeline_mtcnn.pull(cv::gout(image, out_faces))) {
+ frames++;
+ std::cout << "Final Faces Size " << out_faces.size() << std::endl;
+ std::vector<vis::rectPoints> data;
+ // show the image with faces in it
+ for (const auto& out_face : out_faces) {
+ std::vector<cv::Point> pts;
+ for (int p = 0; p < NUM_PTS; ++p) {
+ pts.push_back(
+ cv::Point(static_cast<int>(out_face.ptsCoords[2 * p]), static_cast<int>(out_face.ptsCoords[2 * p + 1])));
+ }
+ auto rect = out_face.bbox.getRect();
+ auto d = std::make_pair(rect, pts);
+ data.push_back(d);
+ }
+ // Visualize results on the frame
+ auto resultImg = vis::drawRectsAndPoints(image, data);
+ tm.stop();
+ const auto fps_str = std::to_string(frames / tm.getTimeSec()) + " FPS";
+ cv::putText(resultImg, fps_str, { 0,32 }, cv::FONT_HERSHEY_SIMPLEX, 1.0, { 0,255,0 }, 2);
+ cv::imshow("Out", resultImg);
+ cv::waitKey(1);
+ out_faces.clear();
+ tm.start();
+ }
+ tm.stop();
+ std::cout << "Processed " << frames << " frames"
+ << " (" << frames / tm.getTimeSec() << " FPS)" << std::endl;
+ return 0;
+}
projects / platform / upstream / opencv.git / commitdiff