#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}"
"{ queue_capacity | 1 | Streaming executor queue capacity. Calculated automatically if 0}"
;
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 {
int x1;
int y1;
int x2;
int y2;
cv::Rect getRect() const { return cv::Rect(x1,
y1,
x2 - x1,
y2 - y1); }
BBox getSquare() const {
BBox bbox;
float bboxWidth = static_cast<float>(x2 - x1);
float bboxHeight = static_cast<float>(y2 - y1);
float side = std::max(bboxWidth, bboxHeight);
bbox.x1 = static_cast<int>(static_cast<float>(x1) + (bboxWidth - side) * 0.5f);
bbox.y1 = static_cast<int>(static_cast<float>(y1) + (bboxHeight - side) * 0.5f);
bbox.x2 = static_cast<int>(static_cast<float>(bbox.x1) + side);
bbox.y2 = static_cast<int>(static_cast<float>(bbox.y1) + side);
return bbox;
}
};
struct Face {
BBox bbox;
float score;
std::array<float, NUM_REGRESSIONS> regression;
std::array<float, 2 * NUM_PTS> ptsCoords;
static void applyRegression(std::vector<Face>& faces, bool addOne = false) {
for (auto& face : faces) {
float bboxWidth =
face.bbox.x2 - face.bbox.x1 + static_cast<float>(addOne);
float bboxHeight =
face.bbox.y2 - face.bbox.y1 + static_cast<float>(addOne);
face.bbox.x1 = static_cast<int>(static_cast<float>(face.bbox.x1) + (face.regression[1] * bboxWidth));
face.bbox.y1 = static_cast<int>(static_cast<float>(face.bbox.y1) + (face.regression[0] * bboxHeight));
face.bbox.x2 = static_cast<int>(static_cast<float>(face.bbox.x2) + (face.regression[3] * bboxWidth));
face.bbox.y2 = static_cast<int>(static_cast<float>(face.bbox.y2) + (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 float 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 float area1 = static_cast<float>(faces[idx].bbox.x2 - faces[idx].bbox.x1 + 1) *
static_cast<float>(faces[idx].bbox.y2 - faces[idx].bbox.y1 + 1);
for (size_t i = 1; i < tmpIndices.size(); ++i) {
int tmpIdx = tmpIndices[i];
const float interX1 = static_cast<float>(std::max(faces[idx].bbox.x1, faces[tmpIdx].bbox.x1));
const float interY1 = static_cast<float>(std::max(faces[idx].bbox.y1, faces[tmpIdx].bbox.y1));
const float interX2 = static_cast<float>(std::min(faces[idx].bbox.x2, faces[tmpIdx].bbox.x2));
const float interY2 = static_cast<float>(std::min(faces[idx].bbox.y2, faces[tmpIdx].bbox.y2));
const float bboxWidth = std::max(0.0f, (interX2 - interX1 + 1));
const float bboxHeight = std::max(0.0f, (interY2 - interY1 + 1));
const float interArea = bboxWidth * bboxHeight;
const float area2 = static_cast<float>(faces[tmpIdx].bbox.x2 - faces[tmpIdx].bbox.x1 + 1) *
static_cast<float>(faces[tmpIdx].bbox.y2 - faces[tmpIdx].bbox.y1 + 1);
float 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 float P_NET_WINDOW_SIZE = 12.0f;
std::vector<Face> buildFaces(const cv::Mat& scores,
const cv::Mat& regressions,
const float scaleFactor,
const float 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>();
auto out_side = std::max(h, w);
auto in_side = 2 * out_side + 11;
float stride = 0.0f;
if (out_side != 1)
{
stride = static_cast<float>(in_side - P_NET_WINDOW_SIZE) / static_cast<float>(out_side - 1);
}
std::vector<Face> boxes;
for (int i = 0; i < size; i++) {
if (scores_data[i] >= (threshold)) {
float y = static_cast<float>(i / w);
float x = static_cast<float>(i - w * y);
Face faceInfo;
BBox& faceBox = faceInfo.bbox;
faceBox.x1 = std::max(0, static_cast<int>((x * stride) / scaleFactor));
faceBox.y1 = std::max(0, static_cast<int>((y * stride) / scaleFactor));
faceBox.x2 = static_cast<int>((x * stride + P_NET_WINDOW_SIZE - 1.0f) / scaleFactor);
faceBox.y2 = static_cast<int>((y * stride + P_NET_WINDOW_SIZE - 1.0f) / 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, float, float)>,
"sample.custom.mtcnn.build_faces") {
static cv::GArrayDesc outMeta(const cv::GMatDesc&,
const cv::GMatDesc&,
const float,
const float) {
return cv::empty_array_desc();
}
};
G_API_OP(RunNMS,
<GFaces(GFaces, float, bool)>,
"sample.custom.mtcnn.run_nms") {
static cv::GArrayDesc outMeta(const cv::GArrayDesc&,
const float, 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, float)>,
"sample.custom.mtcnn.rnet_postproc") {
static cv::GArrayDesc outMeta(const cv::GArrayDesc&,
const cv::GArrayDesc&,
const cv::GArrayDesc&,
const float) {
return cv::empty_array_desc();
}
};
G_API_OP(ONetPostProc,
<GFaces(GFaces, GMats, GMats, GMats, float)>,
"sample.custom.mtcnn.onet_postproc") {
static cv::GArrayDesc outMeta(const cv::GArrayDesc&,
const cv::GArrayDesc&,
const cv::GArrayDesc&,
const cv::GArrayDesc&,
const float) {
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();
}
};
//Custom kernels implementation
GAPI_OCV_KERNEL(OCVBuildFaces, BuildFaces) {
static void run(const cv::Mat & in_scores,
const cv::Mat & in_regresssions,
const float scaleFactor,
const float 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 float 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, in_image_size.width - tmp_rect.y) &
cv::Rect(0, 0, in_image_size.height, in_image_size.width);
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 float 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 float 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 (size_t i = 0; i < 4; ++i) {
info.regression[i] = reg_data[i];
}
float w = info.bbox.x2 - info.bbox.x1 + 1.0f;
float h = info.bbox.y2 - info.bbox.y1 + 1.0f;
for (size_t p = 0; p < NUM_PTS; ++p) {
info.ptsCoords[2 * p] =
info.bbox.x1 + static_cast<float>(landmark_data[NUM_PTS + p]) * w - 1;
info.ptsCoords[2 * p + 1] = info.bbox.y1 + static_cast<float>(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 (size_t 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)
} // 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);
}
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 maximum side to 1000
//pr_scale: limit the maximum 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<float>("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<float>("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<float>("thro");
const auto use_half_scale = cmd.get<bool>("half_scale");
const auto streaming_queue_capacity = cmd.get<unsigned int>("queue_capacity");
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::GMat in_transposedRGB = cv::gapi::transpose(in_originalRGB);
cv::GOpaque<cv::Size> in_sz = cv::gapi::streaming::size(in_original);
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];
//The very first PNet pyramid layer to init total_faces[0]
std::tie(regressions[0], scores[0]) = run_mtcnn_p(in_transposedRGB, get_pnet_level_name(level_size[0]));
cv::GArray<custom::Face> faces0 = custom::BuildFaces::on(scores[0], regressions[0], static_cast<float>(scales[0]), conf_thresh_p);
cv::GArray<custom::Face> final_p_faces_for_bb2squares = custom::ApplyRegression::on(faces0, true);
cv::GArray<custom::Face> final_faces_pnet0 = custom::BBoxesToSquares::on(final_p_faces_for_bb2squares);
total_faces[0] = custom::RunNMS::on(final_faces_pnet0, 0.5f, false);
//The rest PNet pyramid layers to accumulate all layers result in total_faces[PYRAMID_LEVELS - 1]]
for (int i = 1; i < pyramid_levels; ++i)
{
std::tie(regressions[i], scores[i]) = run_mtcnn_p(in_transposedRGB, get_pnet_level_name(level_size[i]));
cv::GArray<custom::Face> faces = custom::BuildFaces::on(scores[i], regressions[i], static_cast<float>(scales[i]), conf_thresh_p);
cv::GArray<custom::Face> final_p_faces_for_bb2squares_i = custom::ApplyRegression::on(faces, true);
cv::GArray<custom::Face> final_faces_pnet_i = custom::BBoxesToSquares::on(final_p_faces_for_bb2squares_i);
nms_p_faces[i] = custom::RunNMS::on(final_faces_pnet_i, 0.5f, false);
total_faces[i] = custom::AccumulatePyramidOutputs::on(total_faces[i - 1], nms_p_faces[i]);
}
//Proposal post-processing
cv::GArray<custom::Face> final_faces_pnet = custom::RunNMS::on(total_faces[pyramid_levels - 1], 0.7f, true);
//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;
std::tie(regressionsRNet, scoresRNet) = cv::gapi::infer<custom::MTCNNRefinement>(faces_roi_pnet, in_transposedRGB);
//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, 0.7f, 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_transposedRGB);
//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, 0.7f, 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
>();
auto mtcnn_args = cv::compile_args(networks_mtcnn, kernels_mtcnn);
if (streaming_queue_capacity != 0)
mtcnn_args += cv::compile_args(cv::gapi::streaming::queue_capacity{ streaming_queue_capacity });
auto pipeline_mtcnn = graph_mtcnn.compileStreaming(std::move(mtcnn_args));
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 (size_t 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;
}