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demo.cpp
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demo.cpp
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#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 = 0.3, float scoreThresh = 0.5, int tok=5000 , 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();
}
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;
}
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 open 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,
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