239 lines
7.7 KiB
C++
239 lines
7.7 KiB
C++
/*********************************************************************
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* Software License Agreement (BSD License)
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*
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* Copyright (c) 2008, Willow Garage, Inc.
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* All rights reserved.
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*
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* Redistribution and use in source and binary forms, with or without
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* modification, are permitted provided that the following conditions
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* are met:
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*
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* * Redistributions of source code must retain the above copyright
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* notice, this list of conditions and the following disclaimer.
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* * Redistributions in binary form must reproduce the above
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* copyright notice, this list of conditions and the following
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* disclaimer in the documentation and/or other materials provided
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* with the distribution.
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* * Neither the name of the Willow Garage nor the names of its
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* contributors may be used to endorse or promote products derived
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* from this software without specific prior written permission.
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*
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* THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
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* "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
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* LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS
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* FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE
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* COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT,
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* INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING,
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* BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
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* LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
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* CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT
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* LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN
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* ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
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* POSSIBILITY OF SUCH DAMAGE.
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*********************************************************************/
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#ifndef DEPTH_IMAGE_PROC_DEPTH_CONVERSIONS
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#define DEPTH_IMAGE_PROC_DEPTH_CONVERSIONS
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#include <robot_sensor_msgs/Image.h>
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#include <robot_sensor_msgs/CameraInfo.h>
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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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typedef robot_sensor_msgs::PointCloud2 PointCloud;
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// Handles float or uint16 depths
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template<typename T>
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void convert(
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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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double range_max = 0.0)
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{
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// Use correct principal point from calibration
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float center_x = model.cx();
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float center_y = model.cy();
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// Combine unit conversion (if necessary) with scaling by focal length for computing (X,Y)
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double unit_scaling = DepthTraits<T>::toMeters( T(1) );
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float constant_x = unit_scaling / model.fx();
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float constant_y = unit_scaling / model.fy();
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float bad_point = std::numeric_limits<float>::quiet_NaN();
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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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const T* depth_row = reinterpret_cast<const T*>(&depth_msg.data[0]);
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int row_step = depth_msg.step / sizeof(T);
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for (int v = 0; v < (int)cloud_msg.height; ++v, depth_row += row_step)
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{
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for (int u = 0; u < (int)cloud_msg.width; ++u, ++iter_x, ++iter_y, ++iter_z)
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{
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T depth = depth_row[u];
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// Missing points denoted by NaNs
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if (!DepthTraits<T>::valid(depth))
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{
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if (range_max != 0.0)
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{
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depth = DepthTraits<T>::fromMeters(range_max);
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}
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else
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{
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*iter_x = *iter_y = *iter_z = bad_point;
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continue;
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}
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}
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// Fill in XYZ
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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 = DepthTraits<T>::toMeters(depth);
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}
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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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