Merge pull request #21095 from No-Plane-Cannot-Be-Detected:next_SIMD
Accelerated 3D point cloud Farthest Point Sampling calculation using SIMD. * Add several 3D point cloud sampling functions: Random, VoxelGrid, FarthestPoint. * Made some code detail changes and exposed the random number generator parameters at the interface. * Add simple tests for sampling. * Modify interface output parameters. * Modify interface return value. * The sampling test is modified for the new changes of function interface. * Improved test of VoxelGridFilterSampling * Improved test of VoxelGridFilterSampling and FPS. * Add test for the dist_lower_limit arguments of FPS function. * Optimization function _getMatFromInputArray. * Optimize the code style and some details according to the suggestions. * Clear prefix cv: in the source code. * Change the initialization of Mat in the sampling test. * 1. Unified code style 2. Optimize randomSampling method * 1. Optimize code comments. 2. Remove unused local variables. * Rebuild the structure of the test, make the test case more reliable, and change the code style. * Update test_sampling.cpp Fix a warning. * Use SIMD to optimize the farthest point sampling. * Optimize the farthest point sampling SIMD code. * 1. remove `\n` from the ptcloud.hpp comment. 2. updated the default value of the argument arrangement_of_points in the _getMatFromInputArray function in ptcloud_utils.hpp from 0 to 1, since the latter is more commonly used (such arrangement is easier for SIMD acceleration). 3. removed two functions in ptcloud_utils.hpp that were not used. * Remove the <br> in the comment. * Fix whitespace issues.pull/21201/head
Ruan
4 years ago
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GitHub
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3 changed files with 196 additions and 94 deletions
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// This file is part of OpenCV project.
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// It is subject to the license terms in the LICENSE file found in the top-level directory
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// of this distribution and at http://opencv.org/license.html.
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#ifndef OPENCV_3D_PTCLOUD_UTILS_HPP |
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#define OPENCV_3D_PTCLOUD_UTILS_HPP |
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namespace cv { |
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/**
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* @brief Get cv::Mat with Nx3 or 3xN type CV_32FC1 from cv::InputArray. |
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* |
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* @param input_pts Point cloud xyz data. |
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* @param[out] mat Point cloud xyz data in cv::Mat with Nx3 or 3xN type CV_32FC1. |
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* @param arrangement_of_points The arrangement of point data in the matrix, |
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* 0 by row (Nx3, [x1, y1, z1, ..., xn, yn, zn]), |
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* 1 by column (3xN, [x1, ..., xn, y1, ..., yn, z1, ..., zn]). |
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* @param clone_data Flag to specify whether data cloning is mandatory. |
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* |
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* @note The following cases will clone data even if flag clone_data is false: |
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* 1. Data is discontinuous in memory |
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* 2. Data type is not float |
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* 3. The original arrangement of data is not the same as the expected new arrangement. |
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* For example, transforming from |
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* Nx3(x1, y1, z1, ..., xn, yn, zn) to 3xN(x1, ..., xn, y1, ..., yn, z1, ..., zn) |
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* |
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*/ |
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inline void _getMatFromInputArray(InputArray input_pts, Mat &mat, |
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int arrangement_of_points = 1, bool clone_data = false) |
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{ |
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CV_Check(input_pts.dims(), input_pts.dims() < 3, |
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"Only support data with dimension less than 3."); |
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// Guaranteed data can construct N×3 point clouds
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int rows = input_pts.rows(), cols = input_pts.cols(), channels = input_pts.channels(); |
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size_t total = rows * cols * channels; |
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CV_Check(total, total % 3 == 0, |
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"total = input_pts.rows() * input_pts.cols() * input_pts.channels() must be an integer multiple of 3"); |
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/**
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Layout of point cloud data in memory space. |
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arrangement 0 : x1, y1, z1, ..., xn, yn, zn |
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For example, the input is std::vector<Point3d>, or std::vector<int>, |
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or cv::Mat with type N×1 CV_32FC3 |
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arrangement 1 : x1, ..., xn, y1, ..., yn, z1, ..., zn |
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For example, the input is cv::Mat with type 3×N CV_32FC1 |
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*/ |
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int ori_arrangement = (channels == 1 && rows == 3 && cols != 3) ? 1 : 0; |
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// Convert to single channel without copying the data.
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mat = ori_arrangement == 0 ? input_pts.getMat().reshape(1, (int) (total / 3)) |
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: input_pts.getMat(); |
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if (ori_arrangement != arrangement_of_points) |
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{ |
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Mat tmp; |
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transpose(mat, tmp); |
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swap(mat, tmp); |
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} |
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if (mat.type() != CV_32F) |
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{ |
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Mat tmp; |
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mat.convertTo(tmp, CV_32F); // Use float to store data
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swap(mat, tmp); |
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} |
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if (clone_data || (!mat.isContinuous())) |
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{ |
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mat = mat.clone(); |
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} |
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} |
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} |
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#endif //OPENCV_3D_PTCLOUD_UTILS_HPP
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