diff --git "a/models/pose_estimation_mediapipe/demo.cpp" "b/models/pose_estimation_mediapipe/demo.cpp"
new file mode 100644--- /dev/null
+++ "b/models/pose_estimation_mediapipe/demo.cpp"
@@ -0,0 +1,2866 @@
+#include <vector>
+#include <string>
+#include <utility>
+#include <cmath>
+
+#include <opencv2/opencv.hpp>
+
+const long double _M_PI = 3.141592653589793238L;
+using namespace std;
+using namespace cv;
+using namespace dnn;
+
+vector< pair<dnn::Backend, dnn::Target> > backendTargetPairs = {
+        std::make_pair<dnn::Backend, dnn::Target>(dnn::DNN_BACKEND_OPENCV, dnn::DNN_TARGET_CPU),
+        std::make_pair<dnn::Backend, dnn::Target>(dnn::DNN_BACKEND_CUDA, dnn::DNN_TARGET_CUDA),
+        std::make_pair<dnn::Backend, dnn::Target>(dnn::DNN_BACKEND_CUDA, dnn::DNN_TARGET_CUDA_FP16),
+        std::make_pair<dnn::Backend, dnn::Target>(dnn::DNN_BACKEND_TIMVX, dnn::DNN_TARGET_NPU),
+        std::make_pair<dnn::Backend, dnn::Target>(dnn::DNN_BACKEND_CANN, dnn::DNN_TARGET_NPU) };
+
+
+Mat getMediapipeAnchor();
+
+class MPPersonDet {
+private:
+    Net net;
+    string modelPath;
+    Size inputSize;
+    float scoreThreshold;
+    float nmsThreshold;
+    dnn::Backend backendId;
+    dnn::Target targetId;
+    int topK;
+    Mat anchors;
+
+public:
+    MPPersonDet(string modPath, float nmsThresh = 0.5, float scoreThresh = 0.3, int tok=1 , dnn::Backend bId = DNN_BACKEND_DEFAULT, dnn::Target tId = DNN_TARGET_CPU) :
+        modelPath(modPath), nmsThreshold(nmsThresh),
+        scoreThreshold(scoreThresh), topK(tok),
+        backendId(bId), targetId(tId)
+    {
+        this->inputSize = Size(224, 224);
+        this->net = readNet(this->modelPath);
+        this->net.setPreferableBackend(this->backendId);
+        this->net.setPreferableTarget(this->targetId);
+        this->anchors = getMediapipeAnchor();
+    }
+
+    void setBackendAndTarget(dnn::Backend bId, dnn::Target tId)
+    {
+        this->backendId = bId;
+        this->targetId = tId;
+        this->net.setPreferableBackend(this->backendId);
+        this->net.setPreferableTarget(this->targetId);
+    }
+
+    pair<Mat, Size> preprocess(Mat img)
+    {
+        Mat blob;
+        Image2BlobParams paramMediapipe;
+        paramMediapipe.datalayout = DNN_LAYOUT_NCHW;
+        paramMediapipe.ddepth = CV_32F;
+        paramMediapipe.mean = Scalar::all(127.5);
+        paramMediapipe.scalefactor = Scalar::all(1/127.5);
+        paramMediapipe.size = this->inputSize;
+        paramMediapipe.swapRB = true;
+        paramMediapipe.paddingmode = DNN_PMODE_LETTERBOX;
+
+        double ratio = min(this->inputSize.height / double(img.rows), this->inputSize.width / double(img.cols));
+        Size padBias(0, 0);
+        if (img.rows != this->inputSize.height || img.cols != this->inputSize.width)
+        {
+            // keep aspect ratio when resize
+            Size ratioSize(int(img.cols * ratio), int(img.rows* ratio));
+            int padH = this->inputSize.height - ratioSize.height;
+            int padW = this->inputSize.width - ratioSize.width;
+            padBias.width = padW / 2;
+            padBias.height = padH / 2;
+        }
+        blob = blobFromImageWithParams(img, paramMediapipe);
+        padBias = Size(int(padBias.width / ratio), int(padBias.height / ratio));
+        return pair<Mat, Size>(blob, padBias);
+    }
+
+   Mat infer(Mat srcimg)
+    {
+        pair<Mat, Size> w = this->preprocess(srcimg);
+        Mat inputBlob = get<0>(w);
+        Size padBias = get<1>(w);
+        this->net.setInput(inputBlob);
+        vector<Mat> outs;
+        this->net.forward(outs, this->net.getUnconnectedOutLayersNames());
+        Mat predictions = this->postprocess(outs, Size(srcimg.cols, srcimg.rows), padBias);
+        return predictions;
+    }
+
+    Mat postprocess(vector<Mat> outputs, Size orgSize, Size padBias)
+    {
+        Mat score = outputs[1].reshape(0, outputs[1].size[0]);
+        Mat boxLandDelta = outputs[0].reshape(outputs[0].size[0], outputs[0].size[1]);
+        Mat boxDelta = boxLandDelta.colRange(0, 4);
+        Mat landmarkDelta = boxLandDelta.colRange(4, boxLandDelta.cols);
+        float scale = float(max(orgSize.height, orgSize.width));
+        Mat mask = score < -100;
+        score.setTo(-100, mask);
+        mask = score > 100;
+        score.setTo(100, mask);
+        Mat deno;
+        exp(-score, deno);
+        divide(1.0, 1+deno, score);
+        boxDelta.colRange(0, 1) = boxDelta.colRange(0, 1) / this->inputSize.width;
+        boxDelta.colRange(1, 2) = boxDelta.colRange(1, 2) / this->inputSize.height;
+        boxDelta.colRange(2, 3) = boxDelta.colRange(2, 3) / this->inputSize.width;
+        boxDelta.colRange(3, 4) = boxDelta.colRange(3, 4) / this->inputSize.height;
+        Mat xy1 = (boxDelta.colRange(0, 2) - boxDelta.colRange(2, 4) / 2 + this->anchors) * scale;
+        Mat xy2 = (boxDelta.colRange(0, 2) + boxDelta.colRange(2, 4) / 2 + this->anchors) * scale;
+        Mat boxes;
+        hconcat(xy1, xy2, boxes);
+        vector< Rect2d > rotBoxes(boxes.rows);
+        boxes.colRange(0, 1) = boxes.colRange(0, 1) - padBias.width;
+        boxes.colRange(1, 2) = boxes.colRange(1, 2) - padBias.height;
+        boxes.colRange(2, 3) = boxes.colRange(2, 3) - padBias.width;
+        boxes.colRange(3, 4) = boxes.colRange(3, 4) - padBias.height;
+        for (int i = 0; i < boxes.rows; i++)
+        {
+            rotBoxes[i] = Rect2d(Point2d(boxes.at<float>(i, 0), boxes.at<float>(i, 1)), Point2d(boxes.at<float>(i, 2), boxes.at<float>(i, 3)));
+        }
+        vector< int > keep;
+        NMSBoxes(rotBoxes, score, this->scoreThreshold, this->nmsThreshold, keep, 1.0f, this->topK);
+        if (keep.size() == 0)
+            return Mat();
+        int nbCols = landmarkDelta.cols + boxes.cols + 1;
+        Mat candidates(int(keep.size()), nbCols, CV_32FC1);
+        int row = 0;
+        for (auto idx : keep)
+        {
+            candidates.at<float>(row, nbCols - 1) = score.at<float>(idx);
+            boxes.row(idx).copyTo(candidates.row(row).colRange(0, 4));
+            candidates.at<float>(row, 4) = (landmarkDelta.at<float>(idx, 0) / this->inputSize.width + this->anchors.at<float>(idx,0)) * scale - padBias.width;
+            candidates.at<float>(row, 5) = (landmarkDelta.at<float>(idx, 1) / this->inputSize.height + this->anchors.at<float>(idx, 1))* scale - padBias.height;
+            candidates.at<float>(row, 6) = (landmarkDelta.at<float>(idx, 2) / this->inputSize.width + this->anchors.at<float>(idx, 0))* scale - padBias.width;
+            candidates.at<float>(row, 7) = (landmarkDelta.at<float>(idx, 3) / this->inputSize.height + this->anchors.at<float>(idx, 1))* scale - padBias.height;
+            candidates.at<float>(row, 8) = (landmarkDelta.at<float>(idx, 4) / this->inputSize.width + this->anchors.at<float>(idx, 0))* scale - padBias.width;
+            candidates.at<float>(row, 9) = (landmarkDelta.at<float>(idx, 5) / this->inputSize.height + this->anchors.at<float>(idx, 1))* scale - padBias.height;
+            candidates.at<float>(row, 10) = (landmarkDelta.at<float>(idx, 6) / this->inputSize.width + this->anchors.at<float>(idx, 0))* scale - padBias.width;
+            candidates.at<float>(row, 11) = (landmarkDelta.at<float>(idx, 7) / this->inputSize.height + this->anchors.at<float>(idx, 1))* scale - padBias.height;
+            row++;
+        }
+        return candidates;
+       
+    }
+
+
+};
+
+class MPPose {
+private:
+    Net net;
+    string modelPath;
+    Size inputSize;
+    float confThreshold;
+    dnn::Backend backendId;
+    dnn::Target targetId;
+    float personBoxPreEnlargeFactor;
+    float personBoxEnlargeFactor;
+    Mat anchors;
+
+public:
+    MPPose(string modPath, float confThresh = 0.5, dnn::Backend bId = DNN_BACKEND_DEFAULT, dnn::Target tId = DNN_TARGET_CPU) :
+        modelPath(modPath), confThreshold(confThresh),
+        backendId(bId), targetId(tId)
+    {
+        this->inputSize = Size(256, 256);
+        this->net = readNet(this->modelPath);
+        this->net.setPreferableBackend(this->backendId);
+        this->net.setPreferableTarget(this->targetId);
+        this->anchors = getMediapipeAnchor();
+        // RoI will be larger so the performance will be better, but preprocess will be slower.Default to 1.
+        this->personBoxPreEnlargeFactor = 1;
+        this->personBoxEnlargeFactor = 1.25;
+    }
+
+    void setBackendAndTarget(dnn::Backend bId, dnn::Target tId)
+    {
+        this->backendId = bId;
+        this->targetId = tId;
+        this->net.setPreferableBackend(this->backendId);
+        this->net.setPreferableTarget(this->targetId);
+    }
+
+    tuple<Mat, Mat, float, Mat, Size> preprocess(Mat image, Mat person)
+    {
+        /***
+                Rotate input for inference.
+                Parameters:
+                  image - input image of BGR channel order
+                  face_bbox - human face bounding box found in image of format [[x1, y1], [x2, y2]] (top-left and bottom-right points)
+                  person_landmarks - 4 landmarks (2 full body points, 2 upper body points) of shape [4, 2]
+                Returns:
+                  rotated_person - rotated person image for inference
+                  rotate_person_bbox - person box of interest range
+                  angle - rotate angle for person
+                  rotation_matrix - matrix for rotation and de-rotation
+                  pad_bias - pad pixels of interest range
+        */
+        //  crop and pad image to interest range
+        Size padBias(0, 0); // left, top
+        Mat personKeypoints = person.colRange(4, 12).reshape(0, 4);
+        Point2f midHipPoint = Point2f(personKeypoints.row(0));
+        Point2f fullBodyPoint = Point2f(personKeypoints.row(1));
+        // # get RoI
+        double fullDist = norm(midHipPoint - fullBodyPoint);
+        Mat fullBoxf,fullBox;
+        Mat v1 = Mat(midHipPoint) - fullDist, v2 = Mat(midHipPoint);
+        vector<Mat> vmat = { Mat(midHipPoint) - fullDist, Mat(midHipPoint) + fullDist };
+        hconcat(vmat, fullBoxf);
+        // enlarge to make sure full body can be cover
+        Mat cBox, centerBox, whBox;
+        reduce(fullBoxf, centerBox, 1, REDUCE_AVG, CV_32F);
+        whBox = fullBoxf.col(1) - fullBoxf.col(0);
+        Mat newHalfSize = whBox * this->personBoxPreEnlargeFactor / 2;
+        vmat[0] = centerBox - newHalfSize;
+        vmat[1] = centerBox + newHalfSize;
+        hconcat(vmat, fullBox);
+        Mat personBox;
+        fullBox.convertTo(personBox, CV_32S);
+        // refine person bbox
+        Mat idx = personBox.row(0) < 0;
+        personBox.row(0).setTo(0, idx);
+        idx = personBox.row(0) >= image.cols;
+        personBox.row(0).setTo(image.cols , idx);
+        idx = personBox.row(1) < 0;
+        personBox.row(1).setTo(0, idx);
+        idx = personBox.row(1) >= image.rows;
+        personBox.row(1).setTo(image.rows, idx);        // crop to the size of interest
+
+        image = image(Rect(personBox.at<int>(0, 0), personBox.at<int>(1, 0), personBox.at<int>(0, 1) - personBox.at<int>(0, 0), personBox.at<int>(1, 1) - personBox.at<int>(1, 0)));
+        // pad to square
+        int top = int(personBox.at<int>(1, 0) - fullBox.at<float>(1, 0));
+        int left = int(personBox.at<int>(0, 0) - fullBox.at<float>(0, 0));
+        int bottom = int(fullBox.at<float>(1, 1) - personBox.at<int>(1, 1));
+        int right = int(fullBox.at<float>(0, 1) - personBox.at<int>(0, 1));
+        copyMakeBorder(image, image, top, bottom, left, right, BORDER_CONSTANT, Scalar(0, 0, 0));
+        padBias = Point(padBias) + Point(personBox.col(0)) - Point(left, top);
+        // compute rotation
+        midHipPoint -= Point2f(padBias);
+        fullBodyPoint -= Point2f(padBias);
+        float radians = float(_M_PI / 2 - atan2(-(fullBodyPoint.y - midHipPoint.y), fullBodyPoint.x - midHipPoint.x));
+        radians = radians - 2 * float(_M_PI) * int((radians + _M_PI) / (2 * _M_PI));
+        float angle = (radians * 180 / float(_M_PI));
+        //  get rotation matrix*
+        Mat rotationMatrix = getRotationMatrix2D(midHipPoint, angle, 1.0);
+        //  get rotated image
+        Mat rotatedImage;
+        warpAffine(image, rotatedImage, rotationMatrix, Size(image.cols, image.rows));
+        //  get landmark bounding box
+        Mat blob;
+        Image2BlobParams paramPoseMediapipe;
+        paramPoseMediapipe.datalayout = DNN_LAYOUT_NHWC;
+        paramPoseMediapipe.ddepth = CV_32F;
+        paramPoseMediapipe.mean = Scalar::all(0);
+        paramPoseMediapipe.scalefactor = Scalar::all(1 / 255.);
+        paramPoseMediapipe.size = this->inputSize;
+        paramPoseMediapipe.swapRB = true;
+        paramPoseMediapipe.paddingmode = DNN_PMODE_NULL;
+        blob = blobFromImageWithParams(rotatedImage, paramPoseMediapipe); // resize INTER_AREA becomes INTER_LINEAR in blobFromImage
+        Mat rotatedPersonBox = (Mat_<float>(2, 2) << 0, 0, image.cols, image.rows);
+
+        return tuple<Mat, Mat, float, Mat, Size>(blob, rotatedPersonBox, angle, rotationMatrix, padBias);
+    }
+
+    tuple<Mat, Mat, Mat, Mat, Mat, float> infer(Mat image, Mat person)
+    {
+        int h = image.rows;
+        int w = image.cols;
+        // Preprocess
+        tuple<Mat, Mat, float, Mat, Size> tw;
+        tw = this->preprocess(image, person);
+        Mat inputBlob = get<0>(tw);
+        Mat rotatedPersonBbox = get<1>(tw);
+        float  angle = get<2>(tw);
+        Mat rotationMatrix = get<3>(tw);
+        Size padBias = get<4>(tw);
+
+        // Forward
+        this->net.setInput(inputBlob);
+        vector<Mat> outputBlob;
+        this->net.forward(outputBlob, this->net.getUnconnectedOutLayersNames());
+
+        // Postprocess
+        tuple<Mat, Mat, Mat, Mat, Mat, float> results;
+        results = this->postprocess(outputBlob, rotatedPersonBbox, angle, rotationMatrix, padBias, Size(w, h));
+        return results;// # [bbox_coords, landmarks_coords, conf]
+    }
+
+    tuple<Mat, Mat, Mat, Mat, Mat, float> postprocess(vector<Mat> blob, Mat rotatedPersonBox, float angle, Mat rotationMatrix, Size padBias, Size imgSize)
+    {
+        float valConf = blob[1].at<float>(0);
+        if (valConf < this->confThreshold)
+            return tuple<Mat, Mat, Mat, Mat, Mat, float>(Mat(), Mat(), Mat(), Mat(), Mat(), valConf);
+        Mat landmarks = blob[0].reshape(0, 39);
+        Mat mask = blob[2];
+        Mat heatmap = blob[3];
+        Mat landmarksWorld = blob[4].reshape(0, 39);
+
+        Mat deno;
+        // recover sigmoid score
+        exp(-landmarks.colRange(3, landmarks.cols), deno);
+        divide(1.0, 1 + deno, landmarks.colRange(3, landmarks.cols));
+        // TODO: refine landmarks with heatmap. reference: https://github.com/tensorflow/tfjs-models/blob/master/pose-detection/src/blazepose_tfjs/detector.ts#L577-L582
+        heatmap = heatmap.reshape(0, heatmap.size[0]);
+        // transform coords back to the input coords
+        Mat whRotatedPersonPbox = rotatedPersonBox.row(1) - rotatedPersonBox.row(0);
+        Mat scaleFactor = whRotatedPersonPbox.clone();
+        scaleFactor.col(0) /= this->inputSize.width;
+        scaleFactor.col(1) /= this->inputSize.height;
+        landmarks.col(0) = (landmarks.col(0) - this->inputSize.width / 2) * scaleFactor.at<float>(0);
+        landmarks.col(1) = (landmarks.col(1) - this->inputSize.height / 2) * scaleFactor.at<float>(1);
+        landmarks.col(2) = landmarks.col(2) * max(scaleFactor.at<float>(1), scaleFactor.at<float>(0));
+        Mat coordsRotationMatrix;
+        getRotationMatrix2D(Point(0, 0), angle, 1.0).convertTo(coordsRotationMatrix, CV_32F);
+        Mat rotatedLandmarks = landmarks.colRange(0, 2) * coordsRotationMatrix.colRange(0, 2);
+        hconcat(rotatedLandmarks, landmarks.colRange(2, landmarks.cols), rotatedLandmarks);
+        Mat rotatedLandmarksWorld = landmarksWorld.colRange(0, 2) * coordsRotationMatrix.colRange(0, 2);
+        hconcat(rotatedLandmarksWorld, landmarksWorld.col(2), rotatedLandmarksWorld);
+        // invert rotation
+        Mat rotationComponent  = (Mat_<double>(2, 2) <<rotationMatrix.at<double>(0,0), rotationMatrix.at<double>(1, 0), rotationMatrix.at<double>(0, 1), rotationMatrix.at<double>(1, 1));
+        Mat translationComponent = rotationMatrix(Rect(2, 0, 1, 2)).clone();
+        Mat invertedTranslation = -rotationComponent * translationComponent;
+        Mat inverseRotationMatrix;
+        hconcat(rotationComponent, invertedTranslation, inverseRotationMatrix);
+        Mat center, rc;
+        reduce(rotatedPersonBox, rc, 0, REDUCE_AVG, CV_64F);
+        hconcat(rc, Mat(1, 1, CV_64FC1, 1) , center);
+        //  get box center
+        Mat originalCenter(2, 1, CV_64FC1);
+        originalCenter.at<double>(0) = center.dot(inverseRotationMatrix.row(0));
+        originalCenter.at<double>(1) = center.dot(inverseRotationMatrix.row(1));
+        for (int idxRow = 0; idxRow < rotatedLandmarks.rows; idxRow++)
+        {
+            landmarks.at<float>(idxRow, 0) = float(rotatedLandmarks.at<float>(idxRow, 0) + originalCenter.at<double>(0) + padBias.width); // 
+            landmarks.at<float>(idxRow, 1) = float(rotatedLandmarks.at<float>(idxRow, 1) + originalCenter.at<double>(1) + padBias.height); // 
+        }
+        // get bounding box from rotated_landmarks
+        double vmin0, vmin1, vmax0, vmax1;
+        minMaxLoc(landmarks.col(0), &vmin0, &vmax0);
+        minMaxLoc(landmarks.col(1), &vmin1, &vmax1);
+        Mat bbox = (Mat_<float>(2, 2) << vmin0, vmin1, vmax0, vmax1);
+        Mat centerBox;
+        reduce(bbox, centerBox, 0, REDUCE_AVG, CV_32F);
+        Mat whBox = bbox.row(1) - bbox.row(0);
+        Mat newHalfSize = whBox * this->personBoxEnlargeFactor / 2;
+        vector<Mat> vmat(2);
+        vmat[0] = centerBox - newHalfSize;
+        vmat[1] = centerBox + newHalfSize;
+        vconcat(vmat, bbox);
+        // invert rotation for mask
+        mask = mask.reshape(1, 256);
+        Mat invertRotationMatrix = getRotationMatrix2D(Point(mask.cols / 2, mask.rows / 2), -angle, 1.0);
+        Mat invertRotationMask;
+        warpAffine(mask, invertRotationMask, invertRotationMatrix, Size(mask.cols, mask.rows));
+        // enlarge mask
+        resize(invertRotationMask, invertRotationMask, Size(int(whRotatedPersonPbox.at<float>(0)), int(whRotatedPersonPbox.at<float>(1))));
+        // crop and pad mask
+        int minW = -min(padBias.width, 0);
+        int minH= -min(padBias.height, 0);
+        int left = max(padBias.width, 0);
+        int top = max(padBias.height, 0);
+        Size padOver = imgSize - Size(invertRotationMask.cols, invertRotationMask.rows) - padBias;
+        int maxW = min(padOver.width, 0) + invertRotationMask.cols;
+        int maxH = min(padOver.height, 0) + invertRotationMask.rows;
+        int right = max(padOver.width, 0);
+        int bottom = max(padOver.height, 0);
+        invertRotationMask = invertRotationMask(Rect(minW, minH, maxW - minW, maxH - minH)).clone();
+        copyMakeBorder(invertRotationMask, invertRotationMask, top, bottom, left, right, BORDER_CONSTANT, Scalar::all(0));
+        // binarize mask
+        threshold(invertRotationMask, invertRotationMask, 1, 255, THRESH_BINARY);
+
+        /* 2*2 person bbox: [[x1, y1], [x2, y2]]
+        # 39*5 screen landmarks: 33 keypoints and 6 auxiliary points with [x, y, z, visibility, presence], z value is relative to HIP
+        # Visibility is probability that a keypoint is located within the frame and not occluded by another bigger body part or another object
+        # Presence is probability that a keypoint is located within the frame
+        # 39*3 world landmarks: 33 keypoints and 6 auxiliary points with [x, y, z] 3D metric x, y, z coordinate
+        # img_height*img_width mask: gray mask, where 255 indicates the full body of a person and 0 means background
+        # 64*64*39 heatmap: currently only used for refining landmarks, requires sigmod processing before use
+        # conf: confidence of prediction*/
+        return tuple<Mat , Mat, Mat, Mat, Mat, float>(bbox, landmarks, rotatedLandmarksWorld, invertRotationMask, heatmap, valConf);
+    }
+};
+
+std::string keys =
+"{ help  h          |                                               | Print help message. }"
+"{ model m          | pose_estimation_mediapipe_2023mar.onnx        | Usage: Path to the model, defaults to person_detection_mediapipe_2023mar.onnx  }"
+"{ input i          |                                               | Path to input image or video file. Skip this argument to capture frames from a camera.}"
+"{ conf_threshold   | 0.5                                           | Usage: Filter out hands of confidence < conf_threshold. }"
+"{ top_k            | 1                                             | Usage: Keep top_k bounding boxes before NMS. }"
+"{ save s           | true                                          | Usage: Specify to save file with results (i.e. bounding box, confidence level). Invalid in case of camera input. }"
+"{ vis v            | true                                          | Usage: Specify to open a new window to show results. Invalid in case of camera input. }"
+"{ backend bt       | 0                                             | Choose one of computation backends: "
+"0: (default) OpenCV implementation + CPU, "
+"1: CUDA + GPU (CUDA), "
+"2: CUDA + GPU (CUDA FP16), "
+"3: TIM-VX + NPU, "
+"4: CANN + NPU}";
+
+
+void drawLines(Mat image, Mat landmarks, Mat keeplandmarks, bool isDrawPoint = true, int thickness = 2)
+{
+    
+    vector<pair<int, int>> segment = {
+        make_pair(0, 1), make_pair(1, 2), make_pair(2, 3), make_pair(3, 7),
+        make_pair(0, 4), make_pair(4, 5), make_pair(5, 6), make_pair(6, 8),
+        make_pair(9, 10),
+        make_pair(12, 14), make_pair(14, 16), make_pair(16, 22), make_pair(16, 18), make_pair(16, 20), make_pair(18, 20),
+        make_pair(11, 13), make_pair(13, 15), make_pair(15, 21), make_pair(15, 19), make_pair(15, 17), make_pair(17, 19),
+        make_pair(11, 12), make_pair(11, 23), make_pair(23, 24), make_pair(24, 12),
+        make_pair(24, 26), make_pair(26, 28), make_pair(28, 30), make_pair(28, 32), make_pair(30, 32),
+        make_pair(23, 25), make_pair(25, 27),make_pair(27, 31), make_pair(27, 29), make_pair(29, 31) };
+    for (auto p : segment)
+        if (keeplandmarks.at<uchar>(p.first) && keeplandmarks.at<uchar>(p.second))
+            line(image, Point(landmarks.row(p.first)), Point(landmarks.row(p.second)), Scalar(255, 255, 255), thickness);
+    if (isDrawPoint)
+        for (int idxRow = 0; idxRow < landmarks.rows; idxRow++)
+            if (keeplandmarks.at<uchar>(idxRow))
+                circle(image, Point(landmarks.row(idxRow)), thickness, Scalar(0, 0, 255), -1);
+}
+
+
+pair<Mat, Mat> visualize(Mat image, vector<tuple<Mat, Mat, Mat, Mat, Mat, float>> poses, float fps=-1)
+{
+    Mat displayScreen = image.clone();
+    Mat display3d(400, 400, CV_8UC3, Scalar::all(0));
+    line(display3d, Point(200, 0), Point(200, 400), Scalar(255, 255, 255), 2);
+    line(display3d, Point(0, 200), Point(400, 200), Scalar(255, 255, 255), 2);
+    putText(display3d, "Main View", Point(0, 12), FONT_HERSHEY_DUPLEX, 0.5, Scalar(0, 0, 255));
+    putText(display3d, "Top View", Point(200, 12), FONT_HERSHEY_DUPLEX, 0.5, Scalar(0, 0, 255));
+    putText(display3d, "Left View", Point(0, 212), FONT_HERSHEY_DUPLEX, 0.5, Scalar(0, 0, 255));
+    putText(display3d, "Right View", Point(200, 212), FONT_HERSHEY_DUPLEX, 0.5, Scalar(0, 0, 255));
+    bool isDraw = false;  // ensure only one person is drawn
+
+    for (auto pose : poses)
+    {
+        Mat bbox = get<0>(pose);
+        if (!bbox.empty())
+        {
+            Mat landmarksScreen = get<1>(pose);
+            Mat landmarksWord = get<2>(pose);
+            Mat mask;
+            get<3>(pose).convertTo(mask, CV_8U);
+            Mat heatmap = get<4>(pose);
+            float conf = get<5>(pose);
+            Mat edges;
+            Canny(mask, edges, 100, 200);
+            Mat kernel(2, 2, CV_8UC1, Scalar::all(1)); // expansion edge to 2 pixels
+            dilate(edges, edges, kernel);
+            Mat edgesBGR;
+            cvtColor(edges, edgesBGR, COLOR_GRAY2BGR);
+            Mat idxSelec = edges == 255;
+            edgesBGR.setTo(Scalar(0, 255, 0), idxSelec);
+
+            add(edgesBGR, displayScreen, displayScreen);
+            // draw box
+            Mat box;
+            bbox.convertTo(box, CV_32S);
+
+            rectangle(displayScreen, Point(box.row(0)), Point(box.row(1)), Scalar(0, 255, 0), 2);
+            putText(displayScreen, format("Conf = %4f", conf), Point(0, 35), FONT_HERSHEY_DUPLEX, 0.7,Scalar(0, 0, 255), 2);
+            if (fps > 0)
+                putText(displayScreen, format("FPS = %.2f", fps), Point(0, 55), FONT_HERSHEY_SIMPLEX, 0.7, Scalar(0, 0, 255), 2);
+            // Draw line between each key points
+            landmarksScreen = landmarksScreen.rowRange(0, landmarksScreen.rows - 6);
+            landmarksWord = landmarksWord.rowRange(0, landmarksWord.rows - 6);
+
+            Mat keepLandmarks = landmarksScreen.col(4) > 0.8; // only show visible keypoints which presence bigger than 0.8
+
+            Mat landmarksXY;
+            landmarksScreen.colRange(0, 2).convertTo(landmarksXY, CV_32S);
+            drawLines(displayScreen, landmarksXY, keepLandmarks, false);
+
+            // z value is relative to HIP, but we use constant to instead
+            for (int idxRow = 0; idxRow < landmarksScreen.rows; idxRow++)
+            {
+                Mat landmark;// p in enumerate(landmarks_screen[:, 0 : 3].astype(np.int32))
+                landmarksScreen.row(idxRow).convertTo(landmark, CV_32S);
+                if (keepLandmarks.at<uchar>(idxRow))
+                    circle(displayScreen, Point(landmark.at<int>(0), landmark.at<int>(1)), 2, Scalar(0, 0, 255), -1);
+            }
+
+            if (!isDraw)
+            {
+                isDraw = true;
+                // Main view
+                Mat landmarksXY = landmarksWord.colRange(0, 2).clone();
+                Mat x = landmarksXY * 100 + 100;
+                x.convertTo(landmarksXY, CV_32S);
+                drawLines(display3d, landmarksXY, keepLandmarks, true, 2);
+
+                // Top view
+                Mat landmarksXZ;
+                hconcat(landmarksWord.col(0), landmarksWord.col(2), landmarksXZ);
+                landmarksXZ.col(1) = -landmarksXZ.col(1);
+                x = landmarksXZ * 100;
+                x.col(0) += 300;
+                x.col(1) += 100;
+                x.convertTo(landmarksXZ, CV_32S);
+                drawLines(display3d, landmarksXZ, keepLandmarks, true, 2);
+
+                // Left view
+                Mat landmarksYZ;
+                hconcat(landmarksWord.col(2), landmarksWord.col(1), landmarksYZ);
+                landmarksYZ.col(0) = -landmarksYZ.col(0);
+                x = landmarksYZ * 100;
+                x.col(0) += 100;
+                x.col(1) += 300;
+                x.convertTo(landmarksYZ, CV_32S);
+                drawLines(display3d, landmarksYZ, keepLandmarks, true, 2);
+
+                // Right view
+                Mat landmarksZY;
+                hconcat(landmarksWord.col(2), landmarksWord.col(1), landmarksZY);
+                x = landmarksZY * 100;
+                x.col(0) += 300;
+                x.col(1) += 300;
+                x.convertTo(landmarksZY, CV_32S);
+                drawLines(display3d, landmarksZY, keepLandmarks, true, 2);
+            }
+        }
+    }
+    return pair<Mat, Mat>(displayScreen, display3d);
+}
+
+
+
+int main(int argc, char** argv)
+{
+    CommandLineParser parser(argc, argv, keys);
+ 
+    parser.about("Person Detector from MediaPipe");
+    if (parser.has("help"))
+    {
+        parser.printMessage();
+        return 0;
+    }
+
+    string model = parser.get<String>("model");
+    float confThreshold = parser.get<float>("conf_threshold");
+    float scoreThreshold = 0.5f;
+    float nmsThreshold = 0.3f;
+    int topK = 5000;
+    bool vis = parser.get<bool>("vis");
+    bool save = parser.get<bool>("save");
+    int backendTargetid = parser.get<int>("backend");
+
+    if (model.empty())
+    {
+        CV_Error(Error::StsError, "Model file " + model + " not found");
+    }
+    VideoCapture cap;
+    if (parser.has("input"))
+        cap.open(samples::findFile(parser.get<String>("input")));
+    else
+        cap.open(0);
+    Mat frame;
+    // person detector
+    MPPersonDet modelNet("../person_detection_mediapipe/person_detection_mediapipe_2023mar.onnx", nmsThreshold, scoreThreshold, topK,
+        backendTargetPairs[backendTargetid].first, backendTargetPairs[backendTargetid].second);
+    // pose estimator
+    MPPose poseEstimator(model, confThreshold, backendTargetPairs[backendTargetid].first, backendTargetPairs[backendTargetid].second);
+    //! [Open a video file or an image file or a camera stream]
+    if (!cap.isOpened())
+        CV_Error(Error::StsError, "Cannot opend video or file");
+
+    static const std::string kWinName = "MPPose Demo";
+    while (waitKey(1) < 0)
+    {
+        cap >> frame;
+        if (frame.empty())
+        {
+            if (parser.has("input"))
+            {
+                cout << "Frame is empty" << endl;
+                break;
+            }
+            else
+                continue;
+        }
+        TickMeter tm;
+        tm.start();
+        Mat person = modelNet.infer(frame);
+        tm.stop();
+        vector<tuple<Mat, Mat, Mat, Mat, Mat, float>> pose;
+        for (int idxRow = 0; idxRow < person.rows; idxRow++)
+        {
+            tuple<Mat, Mat, Mat, Mat, Mat, float> re = poseEstimator.infer(frame, person.row(idxRow));
+            if (!get<0>(re).empty())
+                pose.push_back(re);
+        }
+        cout << "Inference time: " << tm.getTimeMilli() << " ms\n";
+        pair<Mat, Mat> duoimg = visualize(frame, pose, tm.getFPS());
+        if (vis)
+        {
+            imshow(kWinName, get<0>(duoimg));
+            imshow("3d", get<1>(duoimg));
+        }
+    }
+    return 0;
+}
+
+
+Mat getMediapipeAnchor()
+{
+    Mat anchor= (Mat_<float>(2254,2) << 0.017857142857142856, 0.017857142857142856,
+        0.017857142857142856, 0.017857142857142856,
+        0.05357142857142857, 0.017857142857142856,
+        0.05357142857142857, 0.017857142857142856,
+        0.08928571428571429, 0.017857142857142856,
+        0.08928571428571429, 0.017857142857142856,
+        0.125, 0.017857142857142856,
+        0.125, 0.017857142857142856,
+        0.16071428571428573, 0.017857142857142856,
+        0.16071428571428573, 0.017857142857142856,
+        0.19642857142857142, 0.017857142857142856,
+        0.19642857142857142, 0.017857142857142856,
+        0.23214285714285715, 0.017857142857142856,
+        0.23214285714285715, 0.017857142857142856,
+        0.26785714285714285, 0.017857142857142856,
+        0.26785714285714285, 0.017857142857142856,
+        0.30357142857142855, 0.017857142857142856,
+        0.30357142857142855, 0.017857142857142856,
+        0.3392857142857143, 0.017857142857142856,
+        0.3392857142857143, 0.017857142857142856,
+        0.375, 0.017857142857142856,
+        0.375, 0.017857142857142856,
+        0.4107142857142857, 0.017857142857142856,
+        0.4107142857142857, 0.017857142857142856,
+        0.44642857142857145, 0.017857142857142856,
+        0.44642857142857145, 0.017857142857142856,
+        0.48214285714285715, 0.017857142857142856,
+        0.48214285714285715, 0.017857142857142856,
+        0.5178571428571429, 0.017857142857142856,
+        0.5178571428571429, 0.017857142857142856,
+        0.5535714285714286, 0.017857142857142856,
+        0.5535714285714286, 0.017857142857142856,
+        0.5892857142857143, 0.017857142857142856,
+        0.5892857142857143, 0.017857142857142856,
+        0.625, 0.017857142857142856,
+        0.625, 0.017857142857142856,
+        0.6607142857142857, 0.017857142857142856,
+        0.6607142857142857, 0.017857142857142856,
+        0.6964285714285714, 0.017857142857142856,
+        0.6964285714285714, 0.017857142857142856,
+        0.7321428571428571, 0.017857142857142856,
+        0.7321428571428571, 0.017857142857142856,
+        0.7678571428571429, 0.017857142857142856,
+        0.7678571428571429, 0.017857142857142856,
+        0.8035714285714286, 0.017857142857142856,
+        0.8035714285714286, 0.017857142857142856,
+        0.8392857142857143, 0.017857142857142856,
+        0.8392857142857143, 0.017857142857142856,
+        0.875, 0.017857142857142856,
+        0.875, 0.017857142857142856,
+        0.9107142857142857, 0.017857142857142856,
+        0.9107142857142857, 0.017857142857142856,
+        0.9464285714285714, 0.017857142857142856,
+        0.9464285714285714, 0.017857142857142856,
+        0.9821428571428571, 0.017857142857142856,
+        0.9821428571428571, 0.017857142857142856,
+        0.017857142857142856, 0.05357142857142857,
+        0.017857142857142856, 0.05357142857142857,
+        0.05357142857142857, 0.05357142857142857,
+        0.05357142857142857, 0.05357142857142857,
+        0.08928571428571429, 0.05357142857142857,
+        0.08928571428571429, 0.05357142857142857,
+        0.125, 0.05357142857142857,
+        0.125, 0.05357142857142857,
+        0.16071428571428573, 0.05357142857142857,
+        0.16071428571428573, 0.05357142857142857,
+        0.19642857142857142, 0.05357142857142857,
+        0.19642857142857142, 0.05357142857142857,
+        0.23214285714285715, 0.05357142857142857,
+        0.23214285714285715, 0.05357142857142857,
+        0.26785714285714285, 0.05357142857142857,
+        0.26785714285714285, 0.05357142857142857,
+        0.30357142857142855, 0.05357142857142857,
+        0.30357142857142855, 0.05357142857142857,
+        0.3392857142857143, 0.05357142857142857,
+        0.3392857142857143, 0.05357142857142857,
+        0.375, 0.05357142857142857,
+        0.375, 0.05357142857142857,
+        0.4107142857142857, 0.05357142857142857,
+        0.4107142857142857, 0.05357142857142857,
+        0.44642857142857145, 0.05357142857142857,
+        0.44642857142857145, 0.05357142857142857,
+        0.48214285714285715, 0.05357142857142857,
+        0.48214285714285715, 0.05357142857142857,
+        0.5178571428571429, 0.05357142857142857,
+        0.5178571428571429, 0.05357142857142857,
+        0.5535714285714286, 0.05357142857142857,
+        0.5535714285714286, 0.05357142857142857,
+        0.5892857142857143, 0.05357142857142857,
+        0.5892857142857143, 0.05357142857142857,
+        0.625, 0.05357142857142857,
+        0.625, 0.05357142857142857,
+        0.6607142857142857, 0.05357142857142857,
+        0.6607142857142857, 0.05357142857142857,
+        0.6964285714285714, 0.05357142857142857,
+        0.6964285714285714, 0.05357142857142857,
+        0.7321428571428571, 0.05357142857142857,
+        0.7321428571428571, 0.05357142857142857,
+        0.7678571428571429, 0.05357142857142857,
+        0.7678571428571429, 0.05357142857142857,
+        0.8035714285714286, 0.05357142857142857,
+        0.8035714285714286, 0.05357142857142857,
+        0.8392857142857143, 0.05357142857142857,
+        0.8392857142857143, 0.05357142857142857,
+        0.875, 0.05357142857142857,
+        0.875, 0.05357142857142857,
+        0.9107142857142857, 0.05357142857142857,
+        0.9107142857142857, 0.05357142857142857,
+        0.9464285714285714, 0.05357142857142857,
+        0.9464285714285714, 0.05357142857142857,
+        0.9821428571428571, 0.05357142857142857,
+        0.9821428571428571, 0.05357142857142857,
+        0.017857142857142856, 0.08928571428571429,
+        0.017857142857142856, 0.08928571428571429,
+        0.05357142857142857, 0.08928571428571429,
+        0.05357142857142857, 0.08928571428571429,
+        0.08928571428571429, 0.08928571428571429,
+        0.08928571428571429, 0.08928571428571429,
+        0.125, 0.08928571428571429,
+        0.125, 0.08928571428571429,
+        0.16071428571428573, 0.08928571428571429,
+        0.16071428571428573, 0.08928571428571429,
+        0.19642857142857142, 0.08928571428571429,
+        0.19642857142857142, 0.08928571428571429,
+        0.23214285714285715, 0.08928571428571429,
+        0.23214285714285715, 0.08928571428571429,
+        0.26785714285714285, 0.08928571428571429,
+        0.26785714285714285, 0.08928571428571429,
+        0.30357142857142855, 0.08928571428571429,
+        0.30357142857142855, 0.08928571428571429,
+        0.3392857142857143, 0.08928571428571429,
+        0.3392857142857143, 0.08928571428571429,
+        0.375, 0.08928571428571429,
+        0.375, 0.08928571428571429,
+        0.4107142857142857, 0.08928571428571429,
+        0.4107142857142857, 0.08928571428571429,
+        0.44642857142857145, 0.08928571428571429,
+        0.44642857142857145, 0.08928571428571429,
+        0.48214285714285715, 0.08928571428571429,
+        0.48214285714285715, 0.08928571428571429,
+        0.5178571428571429, 0.08928571428571429,
+        0.5178571428571429, 0.08928571428571429,
+        0.5535714285714286, 0.08928571428571429,
+        0.5535714285714286, 0.08928571428571429,
+        0.5892857142857143, 0.08928571428571429,
+        0.5892857142857143, 0.08928571428571429,
+        0.625, 0.08928571428571429,
+        0.625, 0.08928571428571429,
+        0.6607142857142857, 0.08928571428571429,
+        0.6607142857142857, 0.08928571428571429,
+        0.6964285714285714, 0.08928571428571429,
+        0.6964285714285714, 0.08928571428571429,
+        0.7321428571428571, 0.08928571428571429,
+        0.7321428571428571, 0.08928571428571429,
+        0.7678571428571429, 0.08928571428571429,
+        0.7678571428571429, 0.08928571428571429,
+        0.8035714285714286, 0.08928571428571429,
+        0.8035714285714286, 0.08928571428571429,
+        0.8392857142857143, 0.08928571428571429,
+        0.8392857142857143, 0.08928571428571429,
+        0.875, 0.08928571428571429,
+        0.875, 0.08928571428571429,
+        0.9107142857142857, 0.08928571428571429,
+        0.9107142857142857, 0.08928571428571429,
+        0.9464285714285714, 0.08928571428571429,
+        0.9464285714285714, 0.08928571428571429,
+        0.9821428571428571, 0.08928571428571429,
+        0.9821428571428571, 0.08928571428571429,
+        0.017857142857142856, 0.125,
+        0.017857142857142856, 0.125,
+        0.05357142857142857, 0.125,
+        0.05357142857142857, 0.125,
+        0.08928571428571429, 0.125,
+        0.08928571428571429, 0.125,
+        0.125, 0.125,
+        0.125, 0.125,
+        0.16071428571428573, 0.125,
+        0.16071428571428573, 0.125,
+        0.19642857142857142, 0.125,
+        0.19642857142857142, 0.125,
+        0.23214285714285715, 0.125,
+        0.23214285714285715, 0.125,
+        0.26785714285714285, 0.125,
+        0.26785714285714285, 0.125,
+        0.30357142857142855, 0.125,
+        0.30357142857142855, 0.125,
+        0.3392857142857143, 0.125,
+        0.3392857142857143, 0.125,
+        0.375, 0.125,
+        0.375, 0.125,
+        0.4107142857142857, 0.125,
+        0.4107142857142857, 0.125,
+        0.44642857142857145, 0.125,
+        0.44642857142857145, 0.125,
+        0.48214285714285715, 0.125,
+        0.48214285714285715, 0.125,
+        0.5178571428571429, 0.125,
+        0.5178571428571429, 0.125,
+        0.5535714285714286, 0.125,
+        0.5535714285714286, 0.125,
+        0.5892857142857143, 0.125,
+        0.5892857142857143, 0.125,
+        0.625, 0.125,
+        0.625, 0.125,
+        0.6607142857142857, 0.125,
+        0.6607142857142857, 0.125,
+        0.6964285714285714, 0.125,
+        0.6964285714285714, 0.125,
+        0.7321428571428571, 0.125,
+        0.7321428571428571, 0.125,
+        0.7678571428571429, 0.125,
+        0.7678571428571429, 0.125,
+        0.8035714285714286, 0.125,
+        0.8035714285714286, 0.125,
+        0.8392857142857143, 0.125,
+        0.8392857142857143, 0.125,
+        0.875, 0.125,
+        0.875, 0.125,
+        0.9107142857142857, 0.125,
+        0.9107142857142857, 0.125,
+        0.9464285714285714, 0.125,
+        0.9464285714285714, 0.125,
+        0.9821428571428571, 0.125,
+        0.9821428571428571, 0.125,
+        0.017857142857142856, 0.16071428571428573,
+        0.017857142857142856, 0.16071428571428573,
+        0.05357142857142857, 0.16071428571428573,
+        0.05357142857142857, 0.16071428571428573,
+        0.08928571428571429, 0.16071428571428573,
+        0.08928571428571429, 0.16071428571428573,
+        0.125, 0.16071428571428573,
+        0.125, 0.16071428571428573,
+        0.16071428571428573, 0.16071428571428573,
+        0.16071428571428573, 0.16071428571428573,
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+        0.6428571428571429, 0.6428571428571429,
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+        0.7857142857142857, 0.6428571428571429,
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+        0.07142857142857142, 0.7857142857142857,
+        0.07142857142857142, 0.7857142857142857,
+        0.07142857142857142, 0.7857142857142857,
+        0.07142857142857142, 0.7857142857142857,
+        0.07142857142857142, 0.7857142857142857,
+        0.07142857142857142, 0.7857142857142857,
+        0.21428571428571427, 0.7857142857142857,
+        0.21428571428571427, 0.7857142857142857,
+        0.21428571428571427, 0.7857142857142857,
+        0.21428571428571427, 0.7857142857142857,
+        0.21428571428571427, 0.7857142857142857,
+        0.21428571428571427, 0.7857142857142857,
+        0.35714285714285715, 0.7857142857142857,
+        0.35714285714285715, 0.7857142857142857,
+        0.35714285714285715, 0.7857142857142857,
+        0.35714285714285715, 0.7857142857142857,
+        0.35714285714285715, 0.7857142857142857,
+        0.35714285714285715, 0.7857142857142857,
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+        0.5, 0.7857142857142857,
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+        0.6428571428571429, 0.7857142857142857,
+        0.6428571428571429, 0.7857142857142857,
+        0.6428571428571429, 0.7857142857142857,
+        0.6428571428571429, 0.7857142857142857,
+        0.6428571428571429, 0.7857142857142857,
+        0.6428571428571429, 0.7857142857142857,
+        0.7857142857142857, 0.7857142857142857,
+        0.7857142857142857, 0.7857142857142857,
+        0.7857142857142857, 0.7857142857142857,
+        0.7857142857142857, 0.7857142857142857,
+        0.7857142857142857, 0.7857142857142857,
+        0.7857142857142857, 0.7857142857142857,
+        0.9285714285714286, 0.7857142857142857,
+        0.9285714285714286, 0.7857142857142857,
+        0.9285714285714286, 0.7857142857142857,
+        0.9285714285714286, 0.7857142857142857,
+        0.9285714285714286, 0.7857142857142857,
+        0.9285714285714286, 0.7857142857142857,
+        0.07142857142857142, 0.9285714285714286,
+        0.07142857142857142, 0.9285714285714286,
+        0.07142857142857142, 0.9285714285714286,
+        0.07142857142857142, 0.9285714285714286,
+        0.07142857142857142, 0.9285714285714286,
+        0.07142857142857142, 0.9285714285714286,
+        0.21428571428571427, 0.9285714285714286,
+        0.21428571428571427, 0.9285714285714286,
+        0.21428571428571427, 0.9285714285714286,
+        0.21428571428571427, 0.9285714285714286,
+        0.21428571428571427, 0.9285714285714286,
+        0.21428571428571427, 0.9285714285714286,
+        0.35714285714285715, 0.9285714285714286,
+        0.35714285714285715, 0.9285714285714286,
+        0.35714285714285715, 0.9285714285714286,
+        0.35714285714285715, 0.9285714285714286,
+        0.35714285714285715, 0.9285714285714286,
+        0.35714285714285715, 0.9285714285714286,
+        0.5, 0.9285714285714286,
+        0.5, 0.9285714285714286,
+        0.5, 0.9285714285714286,
+        0.5, 0.9285714285714286,
+        0.5, 0.9285714285714286,
+        0.5, 0.9285714285714286,
+        0.6428571428571429, 0.9285714285714286,
+        0.6428571428571429, 0.9285714285714286,
+        0.6428571428571429, 0.9285714285714286,
+        0.6428571428571429, 0.9285714285714286,
+        0.6428571428571429, 0.9285714285714286,
+        0.6428571428571429, 0.9285714285714286,
+        0.7857142857142857, 0.9285714285714286,
+        0.7857142857142857, 0.9285714285714286,
+        0.7857142857142857, 0.9285714285714286,
+        0.7857142857142857, 0.9285714285714286,
+        0.7857142857142857, 0.9285714285714286,
+        0.7857142857142857, 0.9285714285714286,
+        0.9285714285714286, 0.9285714285714286,
+        0.9285714285714286, 0.9285714285714286,
+        0.9285714285714286, 0.9285714285714286,
+        0.9285714285714286, 0.9285714285714286,
+        0.9285714285714286, 0.9285714285714286,
+        0.9285714285714286, 0.9285714285714286);
+        return anchor;
+}