将pcl :: pointcloud转换为二进制图像.C

transform a pcl::pointcloud to binary image. c++

本文关键字：二进制 图像 转换 pcl pointcloud      更新时间：2023-10-16

我正在尝试将pcl :: pointCloud的平面部分转换为二进制图像。我找到了一个名为Savepngfile的课程，但与我的程序不佳。

到现在为止，我做了一个ROI选择器和一个强度过滤器来获取我想要的点。

void regionOfInterest(VPointCloud::Ptr cloud_in, double x1, double x2, 
double y1, double y2, double z)
{
  for (VPoint& point: cloud_in->points)
    if ((z > point.z) && (y1 > point.y) && (y2 < point.y) && (x1 > point.x) 
    &&(x2 < point.x))
      cloud_out->points.push_back(point);
}

（vpointcloud是我需要使用数据的那种点云）我知道我在那里显示的代码也许没有相关的代码，但是它可以或多或少地向您显示我正在使用的类型。

有人知道如何将此点云导出到二进制图像中？在此步骤之后，我将与OpenCV一起工作。

谢谢

此方法应适用于有组织或无组织的数据。但是，您可能需要旋转输入点云平面，以使其平行于两个正交尺寸，并且您知道要删除的尺寸。STEPIZE1和STEPIZE2是设置点云的大小变为新图像中的像素的参数。这可以根据点密度计算灰度图像，但是很容易将其修改以显示深度或其他信息。简单的阈值也可以用来使图像二进制。

cv::Mat makeImageFromPointCloud(pcl::PointCloud<pcl::PointXYZ>::Ptr cloud, std::string dimensionToRemove, float stepSize1, float stepSize2)
{
    pcl::PointXYZI cloudMin, cloudMax;
    pcl::getMinMax3D(*cloud, cloudMin, cloudMax);
    std::string dimen1, dimen2;
    float dimen1Max, dimen1Min, dimen2Min, dimen2Max;
    if (dimensionToRemove == "x")
    {
        dimen1 = "y";
        dimen2 = "z";
        dimen1Min = cloudMin.y;
        dimen1Max = cloudMax.y;
        dimen2Min = cloudMin.z;
        dimen2Max = cloudMax.z;
    }
    else if (dimensionToRemove == "y")
    {
        dimen1 = "x";
        dimen2 = "z";
        dimen1Min = cloudMin.x;
        dimen1Max = cloudMax.x;
        dimen2Min = cloudMin.z;
        dimen2Max = cloudMax.z;
    }
    else if (dimensionToRemove == "z")
    {
        dimen1 = "x";
        dimen2 = "y";
        dimen1Min = cloudMin.x;
        dimen1Max = cloudMax.x;
        dimen2Min = cloudMin.y;
        dimen2Max = cloudMax.y;
    }
    std::vector<std::vector<int>> pointCountGrid;
    int maxPoints = 0;
    std::vector<pcl::PointCloud<pcl::PointXYZ>::Ptr> grid;
    for (float i = dimen1Min; i < dimen1Max; i += stepSize1)
    {
        pcl::PointCloud<pcl::PointXYZ>::Ptr slice = passThroughFilter1D(cloud, dimen1, i, i + stepSize1);
        grid.push_back(slice);
        std::vector<int> slicePointCount;
        for (float j = dimen2Min; j < dimen2Max; j += stepSize2)
        {
            pcl::PointCloud<pcl::PointXYZ>::Ptr grid_cell = passThroughFilter1D(slice, dimen2, j, j + stepSize2);
            int gridSize = grid_cell->size();
            slicePointCount.push_back(gridSize);
            if (gridSize > maxPoints)
            {
                maxPoints = gridSize;
            }
        }
        pointCountGrid.push_back(slicePointCount);
    }
    cv::Mat mat(static_cast<int>(pointCountGrid.size()), static_cast<int>(pointCountGrid.at(0).size()), CV_8UC1);
    mat = cv::Scalar(0);
    for (int i = 0; i < mat.rows; ++i)
    {
        for (int j = 0; j < mat.cols; ++j)
        {
            int pointCount = pointCountGrid.at(i).at(j);
            float percentOfMax = (pointCount + 0.0) / (maxPoints + 0.0);
            int intensity = percentOfMax * 255;
            mat.at<uchar>(i, j) = intensity;
        }
    }
    return mat;
}

pcl::PointCloud<pcl::PointXYZ>::Ptr passThroughFilter1D(const pcl::PointCloud<pcl::PointXYZ>::Ptr cloud, const std::string field, const double low, const double high, const bool remove_inside)
{
    if (low > high)
    {
        std::cout << "Warning! Min is greater than max!n";
    }
    pcl::PointCloud<pcl::PointXYZ>::Ptr cloud_filtered(new pcl::PointCloud<pcl::PointXYZI>);
    pcl::PassThrough<pcl::PointXYZI> pass;
    pass.setInputCloud(cloud);
    pass.setFilterFieldName(field);
    pass.setFilterLimits(low, high);
    pass.setFilterLimitsNegative(remove_inside);
    pass.filter(*cloud_filtered);
    return cloud_filtered;
}
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