在C ++中使用OpenCV平滑手形轮廓
我有这个代码，但我的手轮廓不顺畅。我该如何解决？
void Hand::analyzeHand(cv::Mat xyzMap)
{
cv::Mat normalizedDepthMap;
cv::Mat channel[3];
cv::split(xyzMap, channel);
cv::normalize(channel[2], normalizedDepthMap, 0, 255, cv::NORM_MINMAX, CV_8UC1);
// Resize input
cv::Mat input;
cv::pyrUp(normalizedDepthMap, input, cv::Size(normalizedDepthMap.cols * 2, normalizedDepthMap.rows * 2));
cv::pyrUp(input, input, cv::Size(input.cols * 2, input.rows * 2));
cv::Mat threshold_output;
std::vector< std::vector<cv::Point> > contours;
std::vector<cv::Vec4i> hierarchy;
// Find contours
cv::threshold(input, threshold_output, 100, 255, cv::THRESH_BINARY);
cv::findContours(threshold_output, contours, hierarchy, CV_RETR_TREE, CV_CHAIN_APPROX_SIMPLE, cv::Point(0, 0));
// Find contour polygon
std::vector< std::vector< cv::Point> > contours_poly(contours.size());
for (auto i = 0; i < contours.size(); i++)
{
cv::approxPolyDP(cv::Mat(contours[i]), contours_poly[i], 3, true);
}
// Find largest contour
auto contour = Hand::findComplexContour(contours);
// Find approximated convex hull
std::vector<cv::Point> hull;
std::vector<cv::Point> completeHull;
std::vector<int> indexHull;
if (contour.size() > 1)
{
cv::convexHull(contour, completeHull, false, true);
cv::convexHull(contour, indexHull, false, false);
hull = Hand::clusterConvexHull(completeHull, Hand::CLUSTER_THRESHOLD);
}
// Find convexityDefects
std::vector<cv::Vec4i> defects;
if (indexHull.size() > 3)
{
cv::convexityDefects(contour, indexHull, defects);
}
// Find max and min distances
double minVal, maxVal;
cv::Point minLoc, maxLoc;
cv::minMaxLoc(channel[2], &minVal, &maxVal, &minLoc, &maxLoc);
// Find center of contour
auto center = Hand::findCenter(contour);
centroid_xyz = xyzMap.at<cv::Vec3f>(center.y / 4, center.x / 4);
centroid_ij = cv::Point2i(center.x, center.y); // SCALING
// Generate visual
cv::Mat img = cv::Mat::zeros(input.rows, input.cols, CV_8UC3);
auto color = cv::Scalar(0, 255, 0);
// Draw contours
cv::circle(img, center, 5, cv::Scalar(255, 0, 0), 2);
for (auto i = 0; i < contours.size(); i++)
{
cv::drawContours(img, contours_poly, i, color, 1, 8, std::vector<cv::Vec4i>(), 0, cv::Point());
}
// Draw hull
cv::Point index;
cv::Point index_right;
cv::Point index_left;
double farthest = 0;
if (hull.size() > 1)
{
for (auto i = 0; i < hull.size(); i++)
{
auto p1 = hull[i];
auto p2 = hull[(i + 1) % hull.size()];
//cv::line(img, p1, p2, cv::Scalar(255, 0, 0), 1);
if (p1.y < centroid_ij.y && Util::euclideanDistance2D(p1, centroid_ij) > farthest)
{
farthest = Util::euclideanDistance2D(p1, centroid_ij);
index = p1;
index_right = hull[(i + 1) % hull.size()];
index_left = hull[(i - 1) % hull.size()];
}
}
}
// Draw defects (filter)
std::vector<cv::Point> endpoints;
std::vector<cv::Point> fingerDefects;
cv::Point lastStart;
auto found = -1;
for (auto i = 0; i < defects.size(); i++)
{
auto defect = defects[i];
auto start = contour[defect[0]];
auto end = contour[defect[1]];
auto farPt = contour[defect[2]];
// Depth from edge of contour
// std::cout << "Depth: " << depth << "\tThreshold: " << cv::norm(maxLoc - center) << "\t";
// Defect conditions: depth is sufficient, inside contour, y value is above center
auto depth = defect[3];
// maxLoc largest depth
// first condition replace with meters distance from the edge
// second test if inside the hull (no change)
// above the center (no change)
if (cv::norm(maxLoc - center) * 15 < depth && cv::pointPolygonTest(hull, farPt, false) > 0 && farPt.y < center.y)
{
auto pt1 = xyzMap.at<cv::Vec3f>(farPt.y / 4, farPt.x / 4);
if (Util::euclidianDistance3D(pt1, centroid_xyz) > 0.05)
{
endpoints.push_back(start);
endpoints.push_back(end);
fingerDefects.push_back(farPt);
}
}
}
// Cluster fingertip locations
endpoints = Hand::clusterConvexHull(endpoints, Hand::CLUSTER_THRESHOLD);
for (auto i = 0; i < endpoints.size(); i++)
{
auto endpoint = endpoints[i];
cv::Point closestDefect;
auto minDefectDistance = 1 << 29;
for (auto j = 0; j < fingerDefects.size(); j++)
{
if (cv::norm(endpoint - fingerDefects[j]) < minDefectDistance)
{
minDefectDistance = cv::norm(endpoint - fingerDefects[j]);
closestDefect = fingerDefects[j];
}
}
auto endPoint_xyz = Util::averageAroundPoint(xyzMap, cv::Point2i(endpoint.x / 4, endpoint.y / 4), 10);
auto closestDefect_xyz = Util::averageAroundPoint(xyzMap, cv::Point2i(closestDefect.x / 4, closestDefect.y / 4), 10);
auto finger_length = Util::euclidianDistance3D(endPoint_xyz, closestDefect_xyz);
if (finger_length < 0.08 && finger_length > 0.025 && endpoint.y < closestDefect.y)
{
fingers_xyz.push_back(endPoint_xyz);
fingers_ij.push_back(cv::Point2i(endpoint.x, endpoint.y)); // SCALING
defects_xyz.push_back(Util::averageAroundPoint(xyzMap, cv::Point2i(closestDefect.x / 4, closestDefect.y / 4), 5));
defects_ij.push_back(cv::Point2i(closestDefect.x, closestDefect.y)); // SCALING
}
}
if (static_cast<float>(cv::countNonZero(channel[2])) / (xyzMap.rows*xyzMap.cols) > 0.3)
{
return;
}
// If there is one or less visible fingers
if (fingers_xyz.size() <= 1)
{
fingers_xyz.clear();
fingers_ij.clear();
auto indexFinger = Util::averageAroundPoint(xyzMap, cv::Point2i(index.x / 4, index.y / 4), 10);
fingers_xyz.push_back(indexFinger);
fingers_ij.push_back(cv::Point2i(index.x, index.y)); // SCALING
auto angle = Util::TriangleAngleCalculation(index_left.x, index_left.y, index.x, index.y, index_right.x, index_right.y);
if (defects_ij.size() != 0)
{
for (auto i = 0; i < fingers_xyz.size(); i++)
{
cv::circle(img, fingers_ij[i], 5, cv::Scalar(0, 0, 255), 3);
cv::line(img, defects_ij[i], fingers_ij[i], cv::Scalar(255, 0, 255), 2);
cv::circle(img, defects_ij[i], 5, cv::Scalar(0, 255, 255), 2);
cv::line(img, defects_ij[i], centroid_ij, cv::Scalar(255, 0, 255), 2);
}
}
else if (angle > ANGLE_THRESHHOLD)
{
cv::circle(img, fingers_ij[0], 5, cv::Scalar(0, 0, 255), 3);
cv::line(img, fingers_ij[0], centroid_ij, cv::Scalar(255, 0, 255), 2);
}
}
else {
for (auto i = 0; i < fingers_xyz.size(); i++)
{
cv::circle(img, fingers_ij[i], 5, cv::Scalar(0, 0, 255), 3);
cv::line(img, defects_ij[i], fingers_ij[i], cv::Scalar(255, 0, 255), 2);
cv::circle(img, defects_ij[i], 3, cv::Scalar(0, 255, 255), 2);
cv::line(img, defects_ij[i], centroid_ij, cv::Scalar(255, 0, 255), 2);
}
}
if (camera_name == "sr300")
{
cv::Mat img_dst;
cv::resize(img, img_dst, cv::Size(640, 489), 0, 0, cv::INTER_AREA);
cv::namedWindow("Contours", CV_WINDOW_AUTOSIZE);
cv::imshow("Contours", img_dst);
}
else
{
cv::namedWindow("Contours", CV_WINDOW_AUTOSIZE);
cv::imshow("Contours", img);
}
}
这就是我得到的：
另外，我在a book中读到这对于epsilon使用以下公式是一个好主意但是根本不能正常工作
cv::approxPolyDP(cv::Mat(contours[i]), contours_poly[i], 0.01*cv::arcLength(cv::Mat(contours[i]), true), true);
0 个答案:
没有答案
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