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Merge pull request #20065 from dbudniko:dbudniko/G-API_mtcnn_demo_PR_hotfix2
G-API MTCNN demo hotfix to align overall pipeline accuracy with the reference Python code output. * MTCNN G-API demo aligned with Python from OMZ * clean up * more comments from Maxim are addressed. * address comment from Dmitry
This commit is contained in:
Dmitry Budnikov
GitHub
parent
a604d44d06
commit
4753206783
@ -56,45 +56,45 @@ namespace {
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#define NUM_PTS 5
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struct BBox {
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double x1;
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double y1;
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double x2;
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double y2;
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int x1;
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int y1;
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int x2;
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int y2;
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cv::Rect getRect() const { return cv::Rect(static_cast<int>(x1),
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static_cast<int>(y1),
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static_cast<int>(x2 - x1),
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static_cast<int>(y2 - y1)); }
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cv::Rect getRect() const { return cv::Rect(x1,
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y1,
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x2 - x1,
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y2 - y1); }
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BBox getSquare() const {
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BBox bbox;
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double bboxWidth = x2 - x1;
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double bboxHeight = y2 - y1;
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double side = std::max(bboxWidth, bboxHeight);
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bbox.x1 = static_cast<double>(x1) + (bboxWidth - side) * 0.5;
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bbox.y1 = static_cast<double>(y1) + (bboxHeight - side) * 0.5;
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bbox.x2 = bbox.x1 + side;
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bbox.y2 = bbox.y1 + side;
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float bboxWidth = static_cast<float>(x2 - x1);
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float bboxHeight = static_cast<float>(y2 - y1);
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float side = std::max(bboxWidth, bboxHeight);
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bbox.x1 = static_cast<int>(static_cast<float>(x1) + (bboxWidth - side) * 0.5f);
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bbox.y1 = static_cast<int>(static_cast<float>(y1) + (bboxHeight - side) * 0.5f);
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bbox.x2 = static_cast<int>(static_cast<float>(bbox.x1) + side);
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bbox.y2 = static_cast<int>(static_cast<float>(bbox.y1) + side);
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return bbox;
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}
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};
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struct Face {
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BBox bbox;
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double score;
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std::array<double, NUM_REGRESSIONS> regression;
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double ptsCoords[2 * NUM_PTS];
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float score;
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std::array<float, NUM_REGRESSIONS> regression;
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std::array<float, 2 * NUM_PTS> ptsCoords;
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static void applyRegression(std::vector<Face>& faces, bool addOne = false) {
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for (auto& face : faces) {
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double bboxWidth =
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face.bbox.x2 - face.bbox.x1 + static_cast<double>(addOne);
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double bboxHeight =
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face.bbox.y2 - face.bbox.y1 + static_cast<double>(addOne);
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face.bbox.x1 = face.bbox.x1 + static_cast<double>(face.regression[1]) * bboxWidth;
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face.bbox.y1 = face.bbox.y1 + static_cast<double>(face.regression[0]) * bboxHeight;
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face.bbox.x2 = face.bbox.x2 + static_cast<double>(face.regression[3]) * bboxWidth;
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face.bbox.y2 = face.bbox.y2 + static_cast<double>(face.regression[2]) * bboxHeight;
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float bboxWidth =
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face.bbox.x2 - face.bbox.x1 + static_cast<float>(addOne);
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float bboxHeight =
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face.bbox.y2 - face.bbox.y1 + static_cast<float>(addOne);
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face.bbox.x1 = static_cast<int>(static_cast<float>(face.bbox.x1) + (face.regression[1] * bboxWidth));
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face.bbox.y1 = static_cast<int>(static_cast<float>(face.bbox.y1) + (face.regression[0] * bboxHeight));
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face.bbox.x2 = static_cast<int>(static_cast<float>(face.bbox.x2) + (face.regression[3] * bboxWidth));
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face.bbox.y2 = static_cast<int>(static_cast<float>(face.bbox.y2) + (face.regression[2] * bboxHeight));
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}
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}
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@ -104,7 +104,7 @@ struct Face {
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}
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}
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static std::vector<Face> runNMS(std::vector<Face>& faces, const double threshold,
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static std::vector<Face> runNMS(std::vector<Face>& faces, const float threshold,
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const bool useMin = false) {
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std::vector<Face> facesNMS;
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if (faces.empty()) {
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@ -123,22 +123,22 @@ struct Face {
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facesNMS.push_back(faces[idx]);
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std::vector<int> tmpIndices = indices;
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indices.clear();
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const double area1 = (faces[idx].bbox.x2 - faces[idx].bbox.x1 + 1) *
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(faces[idx].bbox.y2 - faces[idx].bbox.y1 + 1);
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const float area1 = static_cast<float>(faces[idx].bbox.x2 - faces[idx].bbox.x1 + 1) *
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static_cast<float>(faces[idx].bbox.y2 - faces[idx].bbox.y1 + 1);
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for (size_t i = 1; i < tmpIndices.size(); ++i) {
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int tmpIdx = tmpIndices[i];
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const double interX1 = std::max(faces[idx].bbox.x1, faces[tmpIdx].bbox.x1);
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const double interY1 = std::max(faces[idx].bbox.y1, faces[tmpIdx].bbox.y1);
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const double interX2 = std::min(faces[idx].bbox.x2, faces[tmpIdx].bbox.x2);
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const double interY2 = std::min(faces[idx].bbox.y2, faces[tmpIdx].bbox.y2);
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const float interX1 = static_cast<float>(std::max(faces[idx].bbox.x1, faces[tmpIdx].bbox.x1));
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const float interY1 = static_cast<float>(std::max(faces[idx].bbox.y1, faces[tmpIdx].bbox.y1));
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const float interX2 = static_cast<float>(std::min(faces[idx].bbox.x2, faces[tmpIdx].bbox.x2));
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const float interY2 = static_cast<float>(std::min(faces[idx].bbox.y2, faces[tmpIdx].bbox.y2));
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const double bboxWidth = std::max(0.0, (interX2 - interX1 + 1));
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const double bboxHeight = std::max(0.0, (interY2 - interY1 + 1));
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const float bboxWidth = std::max(0.0f, (interX2 - interX1 + 1));
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const float bboxHeight = std::max(0.0f, (interY2 - interY1 + 1));
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const double interArea = bboxWidth * bboxHeight;
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const double area2 = (faces[tmpIdx].bbox.x2 - faces[tmpIdx].bbox.x1 + 1) *
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(faces[tmpIdx].bbox.y2 - faces[tmpIdx].bbox.y1 + 1);
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double overlap = 0.0;
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const float interArea = bboxWidth * bboxHeight;
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const float area2 = static_cast<float>(faces[tmpIdx].bbox.x2 - faces[tmpIdx].bbox.x1 + 1) *
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static_cast<float>(faces[tmpIdx].bbox.y2 - faces[tmpIdx].bbox.y1 + 1);
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float overlap = 0.0;
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if (useMin) {
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overlap = interArea / std::min(area1, area2);
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} else {
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@ -153,13 +153,12 @@ struct Face {
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}
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};
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const double P_NET_WINDOW_SIZE = 12.0;
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const double P_NET_STRIDE = 2.0;
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const float P_NET_WINDOW_SIZE = 12.0f;
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std::vector<Face> buildFaces(const cv::Mat& scores,
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const cv::Mat& regressions,
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const double scaleFactor,
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const double threshold) {
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const float scaleFactor,
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const float threshold) {
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auto w = scores.size[3];
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auto h = scores.size[2];
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@ -170,20 +169,28 @@ std::vector<Face> buildFaces(const cv::Mat& scores,
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const float* reg_data = regressions.ptr<float>();
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auto out_side = std::max(h, w);
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auto in_side = 2 * out_side + 11;
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float stride = 0.0f;
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if (out_side != 1)
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{
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stride = static_cast<float>(in_side - P_NET_WINDOW_SIZE) / static_cast<float>(out_side - 1);
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}
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std::vector<Face> boxes;
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for (int i = 0; i < size; i++) {
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if (scores_data[i] >= (threshold)) {
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int y = i / w;
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int x = i - w * y;
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float y = static_cast<float>(i / w);
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float x = static_cast<float>(i - w * y);
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Face faceInfo;
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BBox& faceBox = faceInfo.bbox;
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faceBox.x1 = (static_cast<double>(x) * P_NET_STRIDE) / scaleFactor;
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faceBox.y1 = (static_cast<double>(y) * P_NET_STRIDE) / scaleFactor;
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faceBox.x2 = (static_cast<double>(x) * P_NET_STRIDE + P_NET_WINDOW_SIZE - 1.f) / scaleFactor;
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faceBox.y2 = (static_cast<double>(y) * P_NET_STRIDE + P_NET_WINDOW_SIZE - 1.f) / scaleFactor;
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faceBox.x1 = std::max(0, static_cast<int>((x * stride) / scaleFactor));
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faceBox.y1 = std::max(0, static_cast<int>((y * stride) / scaleFactor));
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faceBox.x2 = static_cast<int>((x * stride + P_NET_WINDOW_SIZE - 1.0f) / scaleFactor);
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faceBox.y2 = static_cast<int>((y * stride + P_NET_WINDOW_SIZE - 1.0f) / scaleFactor);
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faceInfo.regression[0] = reg_data[i];
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faceInfo.regression[1] = reg_data[i + size];
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faceInfo.regression[2] = reg_data[i + 2 * size];
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@ -213,21 +220,21 @@ G_API_NET(MTCNNOutput,
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using GFaces = cv::GArray<Face>;
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G_API_OP(BuildFaces,
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<GFaces(cv::GMat, cv::GMat, double, double)>,
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<GFaces(cv::GMat, cv::GMat, float, float)>,
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"sample.custom.mtcnn.build_faces") {
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static cv::GArrayDesc outMeta(const cv::GMatDesc&,
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const cv::GMatDesc&,
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const double,
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const double) {
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const float,
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const float) {
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return cv::empty_array_desc();
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}
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};
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G_API_OP(RunNMS,
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<GFaces(GFaces, double, bool)>,
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<GFaces(GFaces, float, bool)>,
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"sample.custom.mtcnn.run_nms") {
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static cv::GArrayDesc outMeta(const cv::GArrayDesc&,
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const double, const bool) {
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const float, const bool) {
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return cv::empty_array_desc();
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}
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};
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@ -267,24 +274,24 @@ G_API_OP(R_O_NetPreProcGetROIs,
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G_API_OP(RNetPostProc,
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<GFaces(GFaces, GMats, GMats, double)>,
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<GFaces(GFaces, GMats, GMats, float)>,
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"sample.custom.mtcnn.rnet_postproc") {
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static cv::GArrayDesc outMeta(const cv::GArrayDesc&,
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const cv::GArrayDesc&,
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const cv::GArrayDesc&,
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const double) {
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const float) {
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return cv::empty_array_desc();
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}
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};
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G_API_OP(ONetPostProc,
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<GFaces(GFaces, GMats, GMats, GMats, double)>,
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<GFaces(GFaces, GMats, GMats, GMats, float)>,
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"sample.custom.mtcnn.onet_postproc") {
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static cv::GArrayDesc outMeta(const cv::GArrayDesc&,
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const cv::GArrayDesc&,
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const cv::GArrayDesc&,
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const cv::GArrayDesc&,
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const double) {
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const float) {
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return cv::empty_array_desc();
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}
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};
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@ -309,8 +316,8 @@ G_API_OP(Transpose,
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GAPI_OCV_KERNEL(OCVBuildFaces, BuildFaces) {
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static void run(const cv::Mat & in_scores,
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const cv::Mat & in_regresssions,
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const double scaleFactor,
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const double threshold,
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const float scaleFactor,
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const float threshold,
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std::vector<Face> &out_faces) {
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out_faces = buildFaces(in_scores, in_regresssions, scaleFactor, threshold);
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}
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@ -318,7 +325,7 @@ GAPI_OCV_KERNEL(OCVBuildFaces, BuildFaces) {
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GAPI_OCV_KERNEL(OCVRunNMS, RunNMS) {
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static void run(const std::vector<Face> &in_faces,
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const double threshold,
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const float threshold,
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const bool useMin,
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std::vector<Face> &out_faces) {
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std::vector<Face> in_faces_copy = in_faces;
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@ -375,7 +382,7 @@ GAPI_OCV_KERNEL(OCVRNetPostProc, RNetPostProc) {
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static void run(const std::vector<Face> &in_faces,
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const std::vector<cv::Mat> &in_scores,
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const std::vector<cv::Mat> &in_regresssions,
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const double threshold,
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const float threshold,
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std::vector<Face> &out_faces) {
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out_faces.clear();
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for (unsigned int k = 0; k < in_faces.size(); ++k) {
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@ -396,7 +403,7 @@ GAPI_OCV_KERNEL(OCVONetPostProc, ONetPostProc) {
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const std::vector<cv::Mat> &in_scores,
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const std::vector<cv::Mat> &in_regresssions,
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const std::vector<cv::Mat> &in_landmarks,
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const double threshold,
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const float threshold,
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std::vector<Face> &out_faces) {
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out_faces.clear();
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for (unsigned int k = 0; k < in_faces.size(); ++k) {
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@ -406,16 +413,16 @@ GAPI_OCV_KERNEL(OCVONetPostProc, ONetPostProc) {
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if (scores_data[1] >= threshold) {
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Face info = in_faces[k];
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info.score = scores_data[1];
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for (int i = 0; i < 4; ++i) {
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for (size_t i = 0; i < 4; ++i) {
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info.regression[i] = reg_data[i];
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}
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double w = info.bbox.x2 - info.bbox.x1 + 1.0;
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double h = info.bbox.y2 - info.bbox.y1 + 1.0;
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float w = info.bbox.x2 - info.bbox.x1 + 1.0f;
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float h = info.bbox.y2 - info.bbox.y1 + 1.0f;
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for (int p = 0; p < NUM_PTS; ++p) {
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for (size_t p = 0; p < NUM_PTS; ++p) {
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info.ptsCoords[2 * p] =
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info.bbox.x1 + static_cast<double>(landmark_data[NUM_PTS + p]) * w - 1;
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info.ptsCoords[2 * p + 1] = info.bbox.y1 + static_cast<double>(landmark_data[p]) * h - 1;
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info.bbox.x1 + static_cast<float>(landmark_data[NUM_PTS + p]) * w - 1;
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info.ptsCoords[2 * p + 1] = info.bbox.y1 + static_cast<float>(landmark_data[p]) * h - 1;
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}
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out_faces.push_back(info);
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@ -433,7 +440,7 @@ GAPI_OCV_KERNEL(OCVSwapFaces, SwapFaces) {
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for (size_t i = 0; i < in_faces_copy.size(); ++i) {
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std::swap(in_faces_copy[i].bbox.x1, in_faces_copy[i].bbox.y1);
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std::swap(in_faces_copy[i].bbox.x2, in_faces_copy[i].bbox.y2);
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for (int p = 0; p < NUM_PTS; ++p) {
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for (size_t p = 0; p < NUM_PTS; ++p) {
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std::swap(in_faces_copy[i].ptsCoords[2 * p], in_faces_copy[i].ptsCoords[2 * p + 1]);
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}
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}
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@ -573,13 +580,13 @@ int main(int argc, char* argv[]) {
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const auto input_file_name = cmd.get<std::string>("input");
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const auto model_path_p = cmd.get<std::string>("mtcnnpm");
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const auto target_dev_p = cmd.get<std::string>("mtcnnpd");
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const auto conf_thresh_p = cmd.get<double>("thrp");
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const auto conf_thresh_p = cmd.get<float>("thrp");
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const auto model_path_r = cmd.get<std::string>("mtcnnrm");
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const auto target_dev_r = cmd.get<std::string>("mtcnnrd");
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const auto conf_thresh_r = cmd.get<double>("thrr");
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const auto conf_thresh_r = cmd.get<float>("thrr");
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const auto model_path_o = cmd.get<std::string>("mtcnnom");
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const auto target_dev_o = cmd.get<std::string>("mtcnnod");
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const auto conf_thresh_o = cmd.get<double>("thro");
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const auto conf_thresh_o = cmd.get<float>("thro");
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const auto use_half_scale = cmd.get<bool>("half_scale");
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std::vector<cv::Size> level_size;
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@ -613,8 +620,10 @@ int main(int argc, char* argv[]) {
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in_resized[0] = cv::gapi::resize(in_originalRGB, level_size[0]);
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in_transposed[0] = custom::Transpose::on(in_resized[0]);
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std::tie(regressions[0], scores[0]) = run_mtcnn_p(in_transposed[0], get_pnet_level_name(level_size[0]));
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cv::GArray<custom::Face> faces0 = custom::BuildFaces::on(scores[0], regressions[0], scales[0], conf_thresh_p);
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nms_p_faces[0] = custom::RunNMS::on(faces0, 0.5, false);
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cv::GArray<custom::Face> faces0 = custom::BuildFaces::on(scores[0], regressions[0], static_cast<float>(scales[0]), conf_thresh_p);
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cv::GArray<custom::Face> final_p_faces_for_bb2squares = custom::ApplyRegression::on(faces0, true);
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cv::GArray<custom::Face> final_faces_pnet0 = custom::BBoxesToSquares::on(final_p_faces_for_bb2squares);
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nms_p_faces[0] = custom::RunNMS::on(final_faces_pnet0, 0.5f, false);
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total_faces[0] = custom::AccumulatePyramidOutputs::on(faces_init, nms_p_faces[0]);
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//The rest PNet pyramid layers to accumlate all layers result in total_faces[PYRAMID_LEVELS - 1]]
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for (int i = 1; i < pyramid_levels; ++i)
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@ -622,15 +631,15 @@ int main(int argc, char* argv[]) {
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in_resized[i] = cv::gapi::resize(in_originalRGB, level_size[i]);
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in_transposed[i] = custom::Transpose::on(in_resized[i]);
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std::tie(regressions[i], scores[i]) = run_mtcnn_p(in_transposed[i], get_pnet_level_name(level_size[i]));
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cv::GArray<custom::Face> faces = custom::BuildFaces::on(scores[i], regressions[i], scales[i], conf_thresh_p);
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nms_p_faces[i] = custom::RunNMS::on(faces, 0.5, false);
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cv::GArray<custom::Face> faces = custom::BuildFaces::on(scores[i], regressions[i], static_cast<float>(scales[i]), conf_thresh_p);
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cv::GArray<custom::Face> final_p_faces_for_bb2squares_i = custom::ApplyRegression::on(faces, true);
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cv::GArray<custom::Face> final_faces_pnet_i = custom::BBoxesToSquares::on(final_p_faces_for_bb2squares_i);
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nms_p_faces[i] = custom::RunNMS::on(final_faces_pnet_i, 0.5f, false);
|
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total_faces[i] = custom::AccumulatePyramidOutputs::on(total_faces[i - 1], nms_p_faces[i]);
|
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}
|
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|
||||
//Proposal post-processing
|
||||
cv::GArray<custom::Face> nms07_p_faces_total = custom::RunNMS::on(total_faces[pyramid_levels - 1], 0.7, false);
|
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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);
|
||||
@ -640,7 +649,7 @@ int main(int argc, char* argv[]) {
|
||||
|
||||
//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);
|
||||
|
||||
@ -652,7 +661,7 @@ int main(int argc, char* argv[]) {
|
||||
//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));
|
||||
@ -723,7 +732,7 @@ int main(int argc, char* argv[]) {
|
||||
// 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])));
|
||||
}
|
||||
|
||||
Loading…
Reference in New Issue
Block a user