add fillter test cam intel
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@@ -39,7 +39,10 @@
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#include <robot_sensor_msgs/point_cloud2_iterator.h>
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#include <robot_image_geometry/pinhole_camera_model.h>
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#include <robot_depth_image_proc/depth_traits.h>
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#include <robot_depth_image_proc/point_cloud_xyz.h>
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#include <algorithm>
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#include <cmath>
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#include <limits>
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namespace depth_image_proc {
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@@ -97,6 +100,139 @@ void convert(
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}
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}
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// True when at least `min_neighbors` of the 8-connected neighbors (full
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// resolution) have a depth within `max_delta` meters of `depth_m`. Isolated
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// "flying pixels" at object edges fail this test.
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template<typename T>
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inline bool hasConsistentNeighbors(
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const T* depth_data,
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int row_step,
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int width,
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int height,
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int u,
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int v,
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float depth_m,
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float max_delta,
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int min_neighbors)
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{
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int consistent = 0;
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for (int dv = -1; dv <= 1; ++dv)
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{
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const int nv = v + dv;
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if (nv < 0 || nv >= height)
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{
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continue;
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}
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const T* neighbor_row = depth_data + static_cast<size_t>(nv) * row_step;
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for (int du = -1; du <= 1; ++du)
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{
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if (du == 0 && dv == 0)
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{
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continue;
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}
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const int nu = u + du;
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if (nu < 0 || nu >= width)
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{
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continue;
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}
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const T neighbor = neighbor_row[nu];
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if (!DepthTraits<T>::valid(neighbor))
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{
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continue;
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}
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if (std::abs(DepthTraits<T>::toMeters(neighbor) - depth_m) <= max_delta)
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{
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if (++consistent >= min_neighbors)
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{
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return true;
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}
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}
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}
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}
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return false;
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}
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// Converts with range clipping, NxN decimation and speckle removal.
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// Produces an unorganized dense cloud (height = 1, no NaN points).
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// cloud_msg must already have its xyz fields set by the caller.
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template<typename T>
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void convertFiltered(
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const robot_sensor_msgs::Image& depth_msg,
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PointCloud& cloud_msg,
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const image_geometry::PinholeCameraModel& model,
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const DepthFilterConfig& config)
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{
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const float center_x = model.cx();
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const float center_y = model.cy();
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const double unit_scaling = DepthTraits<T>::toMeters( T(1) );
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const float constant_x = unit_scaling / model.fx();
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const float constant_y = unit_scaling / model.fy();
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const int width = static_cast<int>(depth_msg.width);
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const int height = static_cast<int>(depth_msg.height);
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const int decimation = std::max(1, config.decimation);
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const int row_step = depth_msg.step / sizeof(T);
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const T* depth_data = reinterpret_cast<const T*>(&depth_msg.data[0]);
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const float range_min = static_cast<float>(config.range_min);
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const float range_max = static_cast<float>(config.range_max);
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const float speckle_delta = static_cast<float>(config.speckle_max_delta);
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const bool use_speckle = config.speckle_min_neighbors > 0;
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const size_t max_points =
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static_cast<size_t>((height + decimation - 1) / decimation) *
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static_cast<size_t>((width + decimation - 1) / decimation);
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cloud_msg.height = 1;
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cloud_msg.is_dense = true;
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robot_sensor_msgs::PointCloud2Modifier pcd_modifier(cloud_msg);
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pcd_modifier.resize(max_points);
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robot_sensor_msgs::PointCloud2Iterator<float> iter_x(cloud_msg, "x");
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robot_sensor_msgs::PointCloud2Iterator<float> iter_y(cloud_msg, "y");
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robot_sensor_msgs::PointCloud2Iterator<float> iter_z(cloud_msg, "z");
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size_t valid_points = 0;
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for (int v = 0; v < height; v += decimation)
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{
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const T* depth_row = depth_data + static_cast<size_t>(v) * row_step;
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for (int u = 0; u < width; u += decimation)
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{
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const T depth = depth_row[u];
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if (!DepthTraits<T>::valid(depth))
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{
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continue;
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}
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const float z = DepthTraits<T>::toMeters(depth);
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if (z < range_min || z > range_max)
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{
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continue;
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}
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if (use_speckle &&
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!hasConsistentNeighbors<T>(
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depth_data, row_step, width, height, u, v, z, speckle_delta,
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config.speckle_min_neighbors))
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{
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continue;
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}
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*iter_x = (u - center_x) * depth * constant_x;
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*iter_y = (v - center_y) * depth * constant_y;
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*iter_z = z;
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++iter_x;
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++iter_y;
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++iter_z;
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++valid_points;
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}
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}
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pcd_modifier.resize(valid_points);
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}
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} // namespace depth_image_proc
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#endif
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@@ -8,11 +8,47 @@
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namespace depth_image_proc
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{
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/**
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* Filtering applied while converting a depth image to a point cloud.
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* Filtering at the depth-image level is much cheaper than filtering the
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* generated cloud, and the output stays small enough for costmap_2d.
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*/
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struct DepthFilterConfig
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{
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/// Keep 1 pixel out of every (decimation x decimation) block. >= 1.
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int decimation = 4;
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/// Drop points closer than this depth [m] (sensor near-range noise).
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double range_min = 0.3;
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/// Drop points farther than this depth [m].
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double range_max = 4.0;
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/// Neighbor depth difference [m] below which a neighbor counts as consistent.
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double speckle_max_delta = 0.08;
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/// Minimum consistent 8-connected neighbors to keep a point. 0 disables
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/// the speckle ("flying pixel") filter.
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int speckle_min_neighbors = 3;
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bool valid() const
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{
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return decimation >= 1 && range_min >= 0.0 && range_max > range_min &&
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speckle_max_delta > 0.0 && speckle_min_neighbors >= 0 &&
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speckle_min_neighbors <= 8;
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}
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};
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/// Dense organized cloud, one point per pixel (invalid pixels become NaN).
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robot_sensor_msgs::PointCloud2 convertDepthToPointCloud(
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const robot_sensor_msgs::Image& depth_msg,
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const robot_sensor_msgs::CameraInfo& info_msg,
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double range_max = 4.0);
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/// Filtered unorganized cloud (height = 1, is_dense = true): range clip,
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/// NxN decimation and speckle removal. Suitable as costmap_2d observation
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/// source input.
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robot_sensor_msgs::PointCloud2 convertDepthToPointCloudFiltered(
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const robot_sensor_msgs::Image& depth_msg,
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const robot_sensor_msgs::CameraInfo& info_msg,
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const DepthFilterConfig& config);
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} // namespace depth_image_proc
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#endif
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