#define NUM_PTS 5
struct BBox {
- double x1;
- double y1;
- double x2;
- double y2;
+ int x1;
+ int y1;
+ int x2;
+ int 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)); }
+ cv::Rect getRect() const { return cv::Rect(x1,
+ y1,
+ x2 - x1,
+ 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;
+ 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;
- double score;
- std::array<double, NUM_REGRESSIONS> regression;
- double ptsCoords[2 * NUM_PTS];
+ 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) {
- 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;
+ 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 std::vector<Face> runNMS(std::vector<Face>& faces, const double threshold,
+ static std::vector<Face> runNMS(std::vector<Face>& faces, const float threshold,
const bool useMin = false) {
std::vector<Face> facesNMS;
if (faces.empty()) {
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);
+ 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 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;
+ 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 {
}
};
-const double P_NET_WINDOW_SIZE = 12.0;
-const double P_NET_STRIDE = 2.0;
+const float P_NET_WINDOW_SIZE = 12.0f;
std::vector<Face> buildFaces(const cv::Mat& scores,
const cv::Mat& regressions,
- const double scaleFactor,
- const double threshold) {
+ const float scaleFactor,
+ const float threshold) {
auto w = scores.size[3];
auto h = scores.size[2];
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)) {
- int y = i / w;
- int x = i - w * y;
+ float y = static_cast<float>(i / w);
+ float x = static_cast<float>(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;
+ 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];
using GFaces = cv::GArray<Face>;
G_API_OP(BuildFaces,
- <GFaces(cv::GMat, cv::GMat, double, double)>,
+ <GFaces(cv::GMat, cv::GMat, float, float)>,
"sample.custom.mtcnn.build_faces") {
static cv::GArrayDesc outMeta(const cv::GMatDesc&,
const cv::GMatDesc&,
- const double,
- const double) {
+ const float,
+ const float) {
return cv::empty_array_desc();
}
};
G_API_OP(RunNMS,
- <GFaces(GFaces, double, bool)>,
+ <GFaces(GFaces, float, bool)>,
"sample.custom.mtcnn.run_nms") {
static cv::GArrayDesc outMeta(const cv::GArrayDesc&,
- const double, const bool) {
+ const float, const bool) {
return cv::empty_array_desc();
}
};
G_API_OP(RNetPostProc,
- <GFaces(GFaces, GMats, GMats, double)>,
+ <GFaces(GFaces, GMats, GMats, float)>,
"sample.custom.mtcnn.rnet_postproc") {
static cv::GArrayDesc outMeta(const cv::GArrayDesc&,
const cv::GArrayDesc&,
const cv::GArrayDesc&,
- const double) {
+ const float) {
return cv::empty_array_desc();
}
};
G_API_OP(ONetPostProc,
- <GFaces(GFaces, GMats, GMats, GMats, double)>,
+ <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 double) {
+ const float) {
return cv::empty_array_desc();
}
};
GAPI_OCV_KERNEL(OCVBuildFaces, BuildFaces) {
static void run(const cv::Mat & in_scores,
const cv::Mat & in_regresssions,
- const double scaleFactor,
- const double threshold,
+ const float scaleFactor,
+ const float threshold,
std::vector<Face> &out_faces) {
out_faces = buildFaces(in_scores, in_regresssions, scaleFactor, threshold);
}
GAPI_OCV_KERNEL(OCVRunNMS, RunNMS) {
static void run(const std::vector<Face> &in_faces,
- const double threshold,
+ const float threshold,
const bool useMin,
std::vector<Face> &out_faces) {
std::vector<Face> in_faces_copy = in_faces;
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,
+ const float threshold,
std::vector<Face> &out_faces) {
out_faces.clear();
for (unsigned int k = 0; k < in_faces.size(); ++k) {
const std::vector<cv::Mat> &in_scores,
const std::vector<cv::Mat> &in_regresssions,
const std::vector<cv::Mat> &in_landmarks,
- const double threshold,
+ const float threshold,
std::vector<Face> &out_faces) {
out_faces.clear();
for (unsigned int k = 0; k < in_faces.size(); ++k) {
if (scores_data[1] >= threshold) {
Face info = in_faces[k];
info.score = scores_data[1];
- for (int i = 0; i < 4; ++i) {
+ for (size_t 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;
+ float w = info.bbox.x2 - info.bbox.x1 + 1.0f;
+ float h = info.bbox.y2 - info.bbox.y1 + 1.0f;
- for (int p = 0; p < NUM_PTS; ++p) {
+ for (size_t 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;
+ 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);
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) {
+ 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]);
}
}
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 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<double>("thrr");
+ 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<double>("thro");
+ const auto conf_thresh_o = cmd.get<float>("thro");
const auto use_half_scale = cmd.get<bool>("half_scale");
std::vector<cv::Size> level_size;
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);
+ 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);
+ nms_p_faces[0] = custom::RunNMS::on(final_faces_pnet0, 0.5f, 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);
+ 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> 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);
+ 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);
//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> 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 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> 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));
// 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) {
+ 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])));
}
projects / platform / upstream / opencv.git / commitdiff