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Add surface_matching module.

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modules/surface_matching/CMakeLists.txt 0 → 100644
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set(the_description "3D point features")
ocv_define_module(surface_matching opencv_core opencv_flann)
modules/surface_matching/doc/surface_matching.rst 0 → 100644
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.. _surfacematching:
Introduction to Surface Matching
**********************************************************
Surface Matching
================
Cameras and similar devices with the capability of sensation of 3D structure are getting more common everyday. Thus, using depth and intensity information for matching 3D objects (or parts) are of crucial importance for computer vision. Applications range from industrial control to guiding everyday actions for visually impaired people. The task in recognition and pose estimation in range images aims to identify and localize a queried 3D free-form object by matching it to the acquired database.
From an industrial perspective, enabling robots to automatically locate and pick up randomly placed and oriented objects from a bin is an important challenge in factory automation, replacing tedious and heavy manual labor. A system should be able to recognize and locate objects with a predefined shape and estimate the position with the precision necessary for a gripping robot to pick it up. This is where vision guided robotics takes the stage. Similar tools are also capable of guiding robots (and even people) through unstructured environments, leading to automated navigation. These properties make 3D matching from point clouds a ubiquitous necessity. Within this context, I will now describe OpenCV implementation of a 3D object recognition and pose estimation algorithm using 3D features.
Surface Matching Algorithm Through 3D Features
==============================================
The state of the algorithms in order to achieve the task 3D matching heavily based on [drost2010]_, which is one of the first and main practical methods presented in this area. The approach is composed of extracting 3D feature points randomly from depth images or generic point clouds, indexing them and later in runtime querying them efficiently. Only the 3D structure is considered, and a trivial hash table is used for feature queries.
While being fully aware that utilization of the nice CAD model structure in order to achieve a smart point sampling, I will be leaving that aside now in order to respect the generalizability of the methods (Typically for such algorithms training on a CAD model is not needed, and a point cloud would be sufficient). Below is the outline of the entire algorithm:
.. image:: surface_matching/pics/outline.jpg
:scale: 75 %
:align: center
:alt: Outline of the Algorithm
As explained, the algorithm relies on the extraction and indexing of point pair features, which are defined as follows:
.. math:: \bf{{F}}(\bf{{m1}}, \bf{{m2}}) = (||\bf{{d}}||_2, <(\bf{{n1}},\bf{{d}}), <(\bf{{n2}},\bf{{d}}), <(\bf{{n1}},\bf{{n2}}))
where :math:`\bf{{m1}}` and :math:`\bf{{m2}}` are feature two selected
points on the model (or scene), :math:`\bf{{d}}` is the difference
vector, :math:`\bf{{n1}}` and :math:`\bf{{n2}}` are the normals at
:math:`\bf{{m1}}` and :math:`\bf{m2}`. During the training stage, this
vector is quantized, indexed. In the test stage, same features are
extracted from the scene and compared to the database. With a few tricks
like separation of the rotational components, the pose estimation part
can also be made efficient (check the reference for more details). A
Hough-like voting and clustering is employed to estimate the object
pose. To cluster the poses, the raw pose hypotheses are sorted in decreasing order
of the number of votes. From the highest vote, a
new cluster is created. If the next pose hypothesis is close to one of
the existing clusters, the hypothesis is added to the cluster
and the cluster center is updated as the average of the pose
hypotheses within the cluster. If the next hypothesis is not
close to any of the clusters, it creates a new cluster. The
proximity testing is done with fixed thresholds in translation
and rotation. Distance computation and averaging for translation are performed in the 3D Euclidean space, while those
for rotation are performed using quaternion representation.
After clustering, the clusters are sorted in decreasing order
of the total number of votes which determines confidence of
the estimated poses.
This pose is further refined using :math:`ICP` in order to obtain
the final pose.
PPF presented above depends largely on robust computation of angles
between 3D vectors. Even though not reported in the paper, the naive way
of doing this (:math:`\theta = cos^{-1}({\bf{a}}\cdot{\bf{b}})` remains
numerically unstable. A better way to do this is then use inverse
tangents, like:
.. math::
<(\bf{n1},\bf{n2})=tan^{-1}(||{\bf{n1} \wedge \bf{n2}}||_2, \bf{n1} \cdot \bf{n2})
Rough Computation of Object Pose Given PPF
============================================
Let me summarize the following notation:
- :math:`p^i_m`: :math:`i^{th}` point of the model (:math:`p^j_m`
accordingly)
- :math:`n^i_m`: Normal of the :math:`i^{th}` point of the model
(:math:`n^j_m` accordingly)
- :math:`p^i_s`: :math:`i^{th}` point of the scene (:math:`p^j_s`
accordingly)
- :math:`n^i_s`: Normal of the :math:`i^{th}` point of the scene
(:math:`n^j_s` accordingly)
- :math:`T_{m\rightarrow g}`: The transformation required to translate
:math:`p^i_m` to the origin and rotate its normal :math:`n^i_m` onto
the :math:`x`-axis.
- :math:`R_{m\rightarrow g}`: Rotational component of
:math:`T_{m\rightarrow g}`.
- :math:`t_{m\rightarrow g}`: Translational component of
:math:`T_{m\rightarrow g}`.
- :math:`(p^i_m)^{'}`: :math:`i^{th}` point of the model transformed by
:math:`T_{m\rightarrow g}`. (:math:`(p^j_m)^{'}` accordingly).
- :math:`{\bf{R_{m\rightarrow g}}}`: Axis angle representation of
rotation :math:`R_{m\rightarrow g}`.
- :math:`\theta_{m\rightarrow g}`: The angular component of the axis
angle representation :math:`{\bf{R_{m\rightarrow g}}}`.
The transformation in a point pair feature is computed by first finding the transformation :math:`T_{m\rightarrow g}` from the first point, and applying the same transformation to the second one. Transforming each point, together with the normal, to the ground plane leaves us with an angle to find out, during a comparison with a new point pair.
We could now simply start writing
.. math::
(p^i_m)^{'} = T_{m\rightarrow g} p^i_m
where
.. math::
T_{m\rightarrow g} = -t_{m\rightarrow g}R_{m\rightarrow g}
Note that this is nothing but a stacked transformation. The translational component :math:`t_{m\rightarrow g}` reads
.. math::
t_{m\rightarrow g} = -R_{m\rightarrow g}p^i_m
and the rotational being
.. math::
\theta_{m\rightarrow g} = \cos^{-1}(n^i_m \cdot {\bf{x}})\\
{\bf{R_{m\rightarrow g}}} = n^i_m \wedge {\bf{x}}
in axis angle format. Note that bold refers to the vector form. After this transformation, the feature vectors of the model are registered onto the ground plane X and the angle with respect to :math:`x=0` is called :math:`\alpha_m`. Similarly, for the scene, it is called :math:`\alpha_s`.
Hough-like Voting Scheme
------------------------
As shown in the outline, PPF (point pair features) are extracted from the model, quantized, stored in the hashtable and indexed, during the training stage. During the runtime however, the similar operation is perfomed on the input scene with the exception that this time a similarity lookup over the hashtable is performed, instead of an insertion. This lookup also allows us to compute a transformation to the ground plane for the scene pairs. After this point, computing the rotational component of the pose reduces to computation of the difference :math:`\alpha=\alpha_m-\alpha_s`. This component carries
the cue about the object pose. A Hough-like voting scheme is performed over the local model coordinate vector and :math:`\alpha`. The highest poses achieved for every scene point lets us recover the object pose.
Source Code for PPF Matching
----------------------------
.. code-block:: cpp
// pc is the loaded point cloud of the model
// (Nx6) and pcTest is a loaded point cloud of
// the scene (Mx6)
ppf_match_3d::PPF3DDetector detector(0.03, 0.05);
detector.trainModel(pc);
vector < Pose3D* > results;
detector.match(pcTest, results, 1.0/10.0, 0.05);
cout << "Poses: " << endl;
// print the poses
for (size_t i=0; i<results.size(); i++)
{
Pose3D* pose = results[i];
cout << "Pose Result " << i << endl;
pose->printPose();
}
Pose Registration via ICP
=========================
The matching process terminates with the attainment of the pose.
However, due to the multiple matching points, erroneous hypothesis, pose
averaging and etc. such pose is very open to noise and many times is far
from being perfect. Although the visual results obtained in that stage
are pleasing, the quantitative evaluation shows :math:`~10` degrees
variation (error), which is an acceptable level of matching. Many times, the requirement
might be set well beyond this margin and it is desired to refine the
computed pose.
Furthermore, in typical RGBD scenes and point clouds, 3D structure can capture only
less than half of the model due to the visibility in the scene.
Therefore, a robust pose refinement algorithm, which can register
occluded and partially visible shapes quickly and correctly is not an
unrealistic wish.
At this point, a trivial option would be to use the well known iterative
closest point algorithm . However, utilization of the basic ICP leads to
slow convergence, bad registration, outlier sensitivity and failure to
register partial shapes. Thus, it is definitely not suited to the
problem. For this reason, many variants have been proposed . Different
variants contribute to different stages of the pose estimation process.
ICP is composed of :math:`6` stages and the improvements I propose for
each stage is summarized below.
Sampling
--------
To improve convergence speed and computation time, it is common to use
less points than the model actually has. However, sampling the correct
points to register is an issue in itself. The naive way would be to
sample uniformly and hope to get a reasonable subset. More smarter ways
try to identify the critical points, which are found to highly
contribute to the registration process. Gelfand et. al. exploit the
covariance matrix in order to constrain the eigenspace, so that a set of
points which affect both translation and rotation are used. This is a
clever way of subsampling, which I will optionally be using in the
implementation.
Correspondence Search
---------------------
As the name implies, this step is actually the assignment of the points
in the data and the model in a closest point fashion. Correct
assignments will lead to a correct pose, where wrong assignments
strongly degrade the result. In general, KD-trees are used in the search
of nearest neighbors, to increase the speed. However this is not an
optimality guarantee and many times causes wrong points to be matched.
Luckily the assignments are corrected over iterations.
To overcome some of the limitations, Picky ICP [pickyicp]_ and BC-ICP (ICP using
bi-unique correspondences) are two well-known methods. Picky ICP first
finds the correspondences in the old-fashioned way and then among the
resulting corresponding pairs, if more than one scene point :math:`p_i`
is assigned to the same model point :math:`m_j`, it selects :math:`p_i`
that corresponds to the minimum distance. BC-ICP on the other hand,
allows multiple correspondences first and then resolves the assignments
by establishing bi-unique correspondences. It also defines a novel
no-correspondence outlier, which intrinsically eases the process of
identifying outliers.
For reference, both methods are used. Because P-ICP is a bit faster,
with not-so-significant performance drawback, it will be the method of
choice in refinment of correspondences.
Weighting of Pairs
------------------
In my implementation, I currently do not use a weighting scheme. But the
common approaches involve *normal compatibility*
(:math:`w_i=n^1_i\cdot n^2_j`) or assigning lower weights to point pairs
with greater distances
(:math:`w=1-\frac{||dist(m_i,s_i)||_2}{dist_{max}}`).
Rejection of Pairs
------------------
The rejections are done using a dynamic thresholding based on a robust
estimate of the standard deviation. In other words, in each iteration, I
find the MAD estimate of the Std. Dev. I denote this as :math:`mad_i`. I
reject the pairs with distances :math:`d_i>\tau mad_i`. Here
:math:`\tau` is the threshold of rejection and by default set to
:math:`3`. The weighting is applied prior to Picky refinement, explained
in the previous stage.
Error Metric
------------
As described in , a linearization of point to plane as in [koklimlow]_ error metric is
used. This both speeds up the registration process and improves convergence.
Minimization
------------
Even though many non-linear optimizers (such as Levenberg Mardquardt)
are proposed, due to the linearization in the previous step, pose
estimation reduces to solving a linear system of equations. This is what
I do exactly using cv::solve with DECOMP_SVD option.
ICP Algorithm
-------------
Having described the steps above, here I summarize the layout of the ICP
algorithm.
Efficient ICP Through Point Cloud Pyramids
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
While the up-to-now-proposed variants deal well with some outliers and
bad initializations, they require significant number of iterations. Yet,
multi-resolution scheme can help reducing the number of iterations by
allowing the registration to start from a coarse level and propagate to
the lower and finer levels. Such approach both improves the performances
and enhances the runtime.
The search is done through multiple levels, in a hierarchical fashion.
The registration starts with a very coarse set of samples of the model.
Iteratively, the points are densified and sought. After each iteration
the previously estimated pose is used as an initial pose and refined
with the ICP.
Visual Results
^^^^^^^^^^^^^^
Results on Synthetic Data
~~~~~~~~~~~~~~~~~~~~~~~~~
In all of the results, the pose is initiated by PPF and the rest is left as:
:math:`[\theta_x, \theta_y, \theta_z, t_x, t_y, t_z]=[0]`
Source Code for Pose Refinement Using ICP
-----------------------------------------
.. code-block:: cpp
ICP icp(200, 0.001f, 2.5f, 8);
// Using the previously declared pc and pcTest
// This will perform registration for every pose
// contained in results
icp.registerModelToScene(pc, pcTest, results);
// results now contain the refined poses
Results
=======
This section is dedicated to the results of surface matching (point-pair-feature matching
and a following ICP refinement):
.. image:: surface_matching/pics/gsoc_forg_matches.jpg
:scale: 65 %
:align: center
:alt: Several matches of a single frog model using ppf + icp
Matches of different models for Mian dataset is presented below:
.. image:: surface_matching/pics/snapshot27.jpg
:scale: 50 %
:align: center
:alt: Matches of different models for Mian dataset
You might checkout the video on `youTube here <http://www.youtube.com/watch?v=uFnqLFznuZU>`_.
.. raw:: html
<div align="center">
<iframe width="775" height="436" src="http://www.youtube.com/embed/uFnqLFznuZU?list=UUMSqZYDAmbiaAhyvLPJGhsg" frameborder="0" allowfullscreen></iframe>
</div>
A Complete Sample
=================
.. literalinclude:: surface_matching/src/ppf_load_match.cpp
:language: cpp
:linenos:
:tab-width: 4
Parameter Tuning
----------------
Surface matching module treats its parameters relative to the model diameter (diameter of the axis parallel bounding box), whenever it can. This makes the parameters independent from the model size. This is why, both model and scene cloud were subsampled such that all points have a minimum distance of :math:`RelativeSamplingStep*DimensionRange`, where :math:`DimensionRange` is the distance along a given dimension. All three dimensions are sampled in similar manner. For example, if :math:`RelativeSamplingStep` is set to 0.05 and the diameter of model is 1m (1000mm), the points sampled from the object's surface will be approximately 50 mm apart. From another point of view, if the sampling RelativeSamplingStep is set to 0.05, at most :math:`20x20x20 = 8000` model points are generated (depending on how the model fills in the volume). Consequently this results in at most 8000x8000 pairs. In practice, because the models are not uniformly distributed over a rectangular prism, much less points are to be expected. Decreasing this value, results in more model points and thus a more accurate representation. However, note that number of point pair features to be computed is now quadratically increased as the complexity is O(N^2). This is especially a concern for 32 bit systems, where large models can easily overshoot the available memory. Typically, values in the range of 0.025 - 0.05 seem adequate for most of the applications, where the default value is 0.03. (Note that there is a difference in this paremeter with the one presented in [drost2010]_. In [drost2010]_ a uniform cuboid is used for quantization and model diameter is used for reference of sampling. In my implementation, the cuboid is a rectangular prism, and each dimension is quantized independently. I do not take reference from the diameter but along the individual dimensions.
It would very wise to remove the outliers from the model and prepare an ideal model initially. This is because, the outliers directly affect the relative computations and degrade the matching accuracy.
During runtime stage, the scene is again sampled by :math:`RelativeSamplingStep`, as described above. However this time, only a portion of the scene points are used as reference. This portion is controlled by the parameter :math:`RelativeSceneSampleStep`, where :math:`SceneSampleStep = (int)(1.0/RelativeSceneSampleStep)`. In other words, if the :math:`RelativeSceneSampleStep = 1.0/5.0`, the subsampled scene will once again be uniformly sampled to 1/5 of the number of points. Maximum value of this parameter is 1 and increasing this parameter also increases the stability, but decreases the speed. Again, because of the initial scene-independent relative sampling, fine tuning this parameter is not a big concern. This would only be an issue when the model shape occupies a volume uniformly, or when the model shape is condensed in a tiny place within the quantization volume (e.g. The octree representation would have too much empty cells).
:math:`RelativeDistanceStep` acts as a step of discretization over the hash table. The point pair features are quantized to be mapped to the buckets of the hashtable. This discretization involves a multiplication and a casting to the integer. Adjusting RelativeDistanceStep in theory controls the collision rate. Note that, more collisions on the hashtable results in less accurate estimations. Reducing this parameter increases the affect of quantization but starts to assign non-similar point pairs to the same bins. Increasing it however, wanes the ability to group the similar pairs. Generally, because during the sampling stage, the training model points are selected uniformly with a distance controlled by RelativeSamplingStep, RelativeDistanceStep is expected to equate to this value. Yet again, values in the range of 0.025-0.05 are sensible. This time however, when the model is dense, it is not advised to decrease this value. For noisy scenes, the value can be increased to improve the robustness of the matching against noisy points.
References
==========
.. [drost2010] B. Drost, S. Ilic 3D Object Detection and Localization Using Multimodal Point Pair Features Second Joint 3DIM/3DPVT Conference: 3D Imaging, Modeling, Processing, Visualization & Transmission (3DIMPVT), Zurich, Switzerland, October 2012
.. [pickyicp] Zinsser, Timo and Schmidt, Jochen and Niemann, Heinrich A refined ICP algorithm for robust 3-D correspondence estimation Image Processing, 2003. ICIP 2003. Proceedings. 2003 International Conference on Image Processing, IEEE.
.. [koklimlow] Kok Lim Low, Linear Least-Squares Optimization for Point-to-Plane ICP Surface Registration Technical Report TR04-004, Department of Computer Science, University of North Carolina at Chapel Hill, February 2004
modules/surface_matching/doc/surface_matching/src/ppf_load_match.cpp 0 → 100644
View file @ 607f2bfc
//
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
// By downloading, copying, installing or using the software you agree to this license.
// If you do not agree to this license, do not download, install,
// copy or use the software.
//
//
// License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2014, OpenCV Foundation, all rights reserved.
// Third party copyrights are property of their respective owners.
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
//
// * Redistribution's in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
//
// * The name of the copyright holders may not be used to endorse or promote products
// derived from this software without specific prior written permission.
//
// This software is provided by the copyright holders and contributors "as is" and
// any express or implied warranties, including, but not limited to, the implied
// warranties of merchantability and fitness for a particular purpose are disclaimed.
// In no event shall the Intel Corporation or contributors be liable for any direct,
// indirect, incidental, special, exemplary, or consequential damages
// (including, but not limited to, procurement of substitute goods or services;
// loss of use, data, or profits; or business interruption) however caused
// and on any theory of liability, whether in contract, strict liability,
// or tort (including negligence or otherwise) arising in any way out of
// the use of this software, even if advised of the possibility of such damage.
//
// Author: Tolga Birdal <tbirdal AT gmail.com>
#include "opencv2/ppf_match_3d.hpp"
#include <iostream>
#include "opencv2/icp.hpp"
#include "opencv2/surface_matching/ppf_helpers.hpp"
#include "opencv2/core/utility.hpp"
using namespace std;
using namespace cv;
using namespace ppf_match_3d;
static void help(std::string errorMessage)
{
std::cout<<"Program init error : "<<errorMessage<<std::endl;
std::cout<<"\nUsage : ppf_matching [input model file] [input scene file]"<<std::endl;
std::cout<<"\nPlease start again with new parameters"<<std::endl;
}
int main(int argc, char** argv)
{
// welcome message
std::cout<< "****************************************************"<<std::endl;
std::cout<< "* Surface Matching demonstration : demonstrates the use of surface matching"
" using point pair features."<<std::endl;
std::cout<< "* The sample loads a model and a scene, where the model lies in a different"
" pose than the training. It then "<<std::endl;
std::cout<< "****************************************************"<<std::endl;
if (argc < 3)
{
help("Not enough input arguments");
exit(1);
}
string modelFileName = (string)argv[1];
string sceneFileName = (string)argv[2];
Mat pc = loadPLYSimple(modelFileName.c_str(), 1);
// Now train the model
cout << "Training..." << endl;
int64 tick1 = cv::getTickCount();
ppf_match_3d::PPF3DDetector detector(0.03, 0.05);
detector.trainModel(pc);
int64 tick2 = cv::getTickCount();
cout << endl << "Training complete in "
<< (double)(tick2-tick1)/ cv::getTickFrequency()
<< " ms" << endl << "Loading model..." << endl;
// Read the scene
Mat pcTest = loadPLYSimple(sceneFileName.c_str(), 1);
// Match the model to the scene and get the pose
cout << endl << "Starting matching..." << endl;
vector < Pose3D* > results;
tick1 = cv::getTickCount();
detector.match(pcTest, results, 1.0/10.0, 0.05);
tick2 = cv::getTickCount();
cout << endl << "PPF Elapsed Time " <<
(tick2-tick1)/cv::getTickFrequency() << " ms" << endl;
// Get only first N results
int N = 2;
vector<Pose3D*>::const_iterator first = results.begin();
vector<Pose3D*>::const_iterator last = results.begin() + N;
vector<Pose3D*> resultsSub(first, last);
// Create an instance of ICP
ICP icp(200, 0.001f, 2.5f, 8);
int64 t1 = cv::getTickCount();
// Register for all selected poses
cout << endl << "Performing ICP on " << N << " poses..." << endl;
icp.registerModelToScene(pc, pcTest, resultsSub);
int64 t2 = cv::getTickCount();
cout << endl << "ICP Elapsed Time " <<
(t2-t1)/cv::getTickFrequency() << " sec" << endl;
cout << "Poses: " << endl;
// debug first five poses
for (size_t i=0; i<resultsSub.size(); i++)
{
Pose3D* pose = resultsSub[i];
cout << "Pose Result " << i << endl;
pose->printPose();
}
return 0;
}
modules/surface_matching/include/opencv2/icp.hpp 0 → 100644
View file @ 607f2bfc
//
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
// By downloading, copying, installing or using the software you agree to this license.
// If you do not agree to this license, do not download, install,
// copy or use the software.
//
//
// License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2014, OpenCV Foundation, all rights reserved.
// Third party copyrights are property of their respective owners.
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
//
// * Redistribution's in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
//
// * The name of the copyright holders may not be used to endorse or promote products
// derived from this software without specific prior written permission.
//
// This software is provided by the copyright holders and contributors "as is" and
// any express or implied warranties, including, but not limited to, the implied
// warranties of merchantability and fitness for a particular purpose are disclaimed.
// In no event shall the Intel Corporation or contributors be liable for any direct,
// indirect, incidental, special, exemplary, or consequential damages
// (including, but not limited to, procurement of substitute goods or services;
// loss of use, data, or profits; or business interruption) however caused
// and on any theory of liability, whether in contract, strict liability,
// or tort (including negligence or otherwise) arising in any way out of
// the use of this software, even if advised of the possibility of such damage.
//
// Author: Tolga Birdal <tbirdal AT gmail.com>
/**
* @file icp.hpp
*
* @brief Implementation of ICP (Iterative Closest Point) Algorithm
* @author Tolga Birdal
*/
#ifndef __OPENCV_ICP_HPP__
#define __OPENCV_ICP_HPP__
#include <opencv2/core.hpp>
#include "surface_matching/pose_3d.hpp"
#include <vector>
namespace cv
{
namespace ppf_match_3d
{
/**
* @class ICP
* @brief This class implements a very efficient and robust variant of the iterative closest point (ICP) algorithm.
* The task is to register a 3D model (or point cloud) against a set of noisy target data. The variants are put together
* by myself after certain tests. The task is to be able to match partial, noisy point clouds in cluttered scenes, quickly.
* You will find that my emphasis is on the performance, while retaining the accuracy.
* This implementation is based on Tolga Birdal's MATLAB implementation in here:
* http://www.mathworks.com/matlabcentral/fileexchange/47152-icp-registration-using-efficient-variants-and-multi-resolution-scheme
The main contributions come from:
1. Picky ICP:
http://www5.informatik.uni-erlangen.de/Forschung/Publikationen/2003/Zinsser03-ARI.pdf
2. Efficient variants of the ICP Algorithm:
http://docs.happycoders.org/orgadoc/graphics/imaging/fasticp_paper.pdf
3. Geometrically Stable Sampling for the ICP Algorithm: https://graphics.stanford.edu/papers/stabicp/stabicp.pdf
4. Multi-resolution registration:
http://www.cvl.iis.u-tokyo.ac.jp/~oishi/Papers/Alignment/Jost_MultiResolutionICP_3DIM03.pdf
5. Linearization of Point-to-Plane metric by Kok Lim Low:
https://www.comp.nus.edu.sg/~lowkl/publications/lowk_point-to-plane_icp_techrep.pdf
*/
class CV_EXPORTS ICP
{
public:
enum ICP_SAMPLING_TYPE
{
ICP_SAMPLING_TYPE_UNIFORM,
ICP_SAMPLING_TYPE_GELFAND
};
ICP()
{
m_tolerence = 0.005f;
m_rejectionScale = 2.5f;
m_maxItereations = 250;
m_numLevels = 6;
m_sampleType = ICP_SAMPLING_TYPE_UNIFORM;
m_numNeighborsCorr = 1;
}
virtual ~ICP() { }
/**
* \brief ICP constructor with default arguments.
* @param [in] tolerence Controls the accuracy of registration at each iteration of ICP.
* @param [in] rejectionScale Robust outlier rejection is applied for robustness. This value actually corresponds to the standard deviation coefficient. Points with rejectionScale * \sigma are ignored during registration.
* @param [in] numLevels Number of pyramid levels to proceed. Deep pyramids increase speed but decrease accuracy. Too coarse pyramids might have computational overhead on top of the inaccurate registrtaion. This parameter should be chosen to optimize a balance. Typical values range from 4 to 10.
* @param [in] sampleType Currently this parameter is ignored and only uniform sampling is applied. Leave it as 0.
* @param [in] numMaxCorr Currently this parameter is ignored and only PickyICP is applied. Leave it as 1.
* \return
*
* \details Constructor
*/
ICP(const int iterations, const float tolerence=0.05, const float rejectionScale=2.5, const int numLevels=6, const ICP_SAMPLING_TYPE sampleType = ICP_SAMPLING_TYPE_UNIFORM, const int numMaxCorr=1)
{
m_tolerence = tolerence;
m_numNeighborsCorr = numMaxCorr;
m_rejectionScale = rejectionScale;
m_maxItereations = iterations;
m_numLevels = numLevels;
m_sampleType = sampleType;
};
/**
* \brief Perform registration
*
* @param [in] srcPC The input point cloud for the model. Expected to have the normals (Nx6). Currently,
* CV_32F is the only supported data type.
* @param [in] dstPC The input point cloud for the scene. It is assumed that the model is registered on the scene. Scene remains static. Expected to have the normals (Nx6). Currently, CV_32F is the only supported data type.
* @param [out] residual The output registration error.
* \return On successful termination, the function returns 0.
*
* \details It is assumed that the model is registered on the scene. Scene remains static, while the model transforms. The output poses transform the models onto the scene. Because of the point to plane minimization, the scene is expected to have the normals available. Expected to have the normals (Nx6).
*/
int registerModelToScene(const Mat& srcPC, const Mat& dstPC, double& residual, double pose[16]);
/**
* \brief Perform registration with multiple initial poses
*
* @param [in] srcPC The input point cloud for the model. Expected to have the normals (Nx6). Currently,
* CV_32F is the only supported data type.
* @param [in] dstPC The input point cloud for the scene. Currently, CV_32F is the only supported data type.
* @param [out] poses List output of poses. For more detailed information check out Pose3D.
* \return On successful termination, the function returns 0.
*
* \details It is assumed that the model is registered on the scene. Scene remains static, while the model transforms. The output poses transform the models onto the scene. Because of the point to plane minimization, the scene is expected to have the normals available. Expected to have the normals (Nx6).
*/
int registerModelToScene(const Mat& srcPC, const Mat& dstPC, std::vector<Pose3D*>& poses);
private:
float m_tolerence;
int m_maxItereations;
float m_rejectionScale;
int m_numNeighborsCorr;
int m_numLevels;
int m_sampleType;
};
} // namespace ppf_match_3d
} // namespace cv
#endif
modules/surface_matching/include/opencv2/ppf_match_3d.hpp 0 → 100644
View file @ 607f2bfc
//
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
// By downloading, copying, installing or using the software you agree to this license.
// If you do not agree to this license, do not download, install,
// copy or use the software.
//
//
// License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2014, OpenCV Foundation, all rights reserved.
// Third party copyrights are property of their respective owners.
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
//
// * Redistribution's in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
//
// * The name of the copyright holders may not be used to endorse or promote products
// derived from this software without specific prior written permission.
//
// This software is provided by the copyright holders and contributors "as is" and
// any express or implied warranties, including, but not limited to, the implied
// warranties of merchantability and fitness for a particular purpose are disclaimed.
// In no event shall the Intel Corporation or contributors be liable for any direct,
// indirect, incidental, special, exemplary, or consequential damages
// (including, but not limited to, procurement of substitute goods or services;
// loss of use, data, or profits; or business interruption) however caused
// and on any theory of liability, whether in contract, strict liability,
// or tort (including negligence or otherwise) arising in any way out of
// the use of this software, even if advised of the possibility of such damage.
//
/**
** ppf_match_3d : Interfaces for matching 3d surfaces in 3d scenes. This module implements the algorithm from Bertram Drost and Slobodan Ilic.
** Use: Read a 3D model, load a 3D scene and match the model to the scene
**
**
** Creation - 2014
** Author: Tolga Birdal (tbirdal@gmail.com)
**
** Refer to the following research paper for more information:
** B. Drost, Markus Ulrich, N. Navab, S. Ilic
Model Globally, Match Locally: Efficient and Robust 3D Object Recognition
IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), San Francisco, California (USA), June 2010.
***/
// Author: Tolga Birdal <tbirdal AT gmail.com>
#ifndef __OPENCV_OBJDETECT_PPF_3D_HPP__
#define __OPENCV_OBJDETECT_PPF_3D_HPP__
#include <opencv2/core.hpp>
#include <vector>
#include "surface_matching/pose_3d.hpp"
#include "surface_matching/t_hash_int.hpp"
namespace cv
{
namespace ppf_match_3d
{
#define T_PPF_LENGTH 5
/**
* @struct THash
* @brief Struct, holding a node in the hashtable
*/
typedef struct THash
{
int id;
int i, ppfInd;
} THash;
/**
* @class PPF3DDetector
* @brief Class, allowing the load and matching 3D models.
* Typical Use:
*
* // Train a model
* ppf_match_3d::PPF3DDetector detector(0.05, 0.05);
* detector.trainModel(pc);
* // Search the model in a given scene
* vector < Pose3D* > results;
* detector.match(pcTest, results, 1.0/5.0,0.05);
*
*/
class CV_EXPORTS PPF3DDetector
{
public:
/**
* \brief Empty constructor. Sets default arguments
*/
PPF3DDetector();
/**
* Constructor with arguments
* @param [in] relativeSamplingStep Sampling distance relative to the object's diameter. Models are first sampled uniformly in order to improve efficiency. Decreasing this value leads to a denser model, and a more accurate pose estimation but the larger the model, the slower the training. Increasing the value leads to a less accurate pose computation but a smaller model and faster model generation and matching. Beware of the memory consumption when using small values.
* @param [in] relativeDistanceStep The discretization distance of the point pair distance relative to the model's diameter. This value has a direct impact on the hashtable. Using small values would lead to too fine discretization, and thus ambiguity in the bins of hashtable. Too large values would lead to no discrimination over the feature vectors and different point pair features would be assigned to the same bin. This argument defaults to the value of RelativeSamplingStep. For noisy scenes, the value can be increased to improve the robustness of the matching against noisy points.
* @param [in] numAngles Set the discretization of the point pair orientation as the number of subdivisions of the angle. This value is the equivalent of RelativeDistanceStep for the orientations. Increasing the value increases the precision of the matching but decreases the robustness against incorrect normal directions. Decreasing the value decreases the precision of the matching but increases the robustness against incorrect normal directions. For very noisy scenes where the normal directions can not be computed accurately, the value can be set to 25 or 20.
*/
PPF3DDetector(const double relativeSamplingStep, const double relativeDistanceStep=0.05, const double numAngles=30);
virtual ~PPF3DDetector();
/**
* Set the parameters for the search
* @param [in] numPoses The maximum number of poses to return
* @param [in] positionThreshold Position threshold controlling the similarity of translations. Depends on the units of calibration/model.
* @param [in] rotationThreshold Position threshold controlling the similarity of rotations. This parameter can be perceived as a threshold over the difference of angles
* @param [in] minMatchScore Not used at the moment
* @param [in] useWeightedClustering The algorithm by default clusters the poses without weighting. A non-zero value would indicate that the pose clustering should take into account the number of votes as the weights and perform a weighted averaging instead of a simple one.
*/
void setSearchParams(const int numPoses=5, const double positionThreshold=-1, const double rotationThreshold=-1, const double minMatchScore=0.5, const bool useWeightedClustering=false);
/**
* \brief Trains a new model.
*
* @param [in] Model The input point cloud with normals (Nx6)
*
* \details Uses the parameters set in the constructor to downsample and learn a new model. When the model is learnt, the instance gets ready for calling "match".
*/
void trainModel(const Mat& Model);
/**
* \brief Matches a trained model across a provided scene.
*
* @param [in] scene Point cloud for the scene
* @param [out] results List of output poses
* @param [in] relativeSceneSampleStep The ratio of scene points to be used for the matching after sampling with relativeSceneDistance. For example, if this value is set to 1.0/5.0, every 5th point from the scene is used for pose estimation. This parameter allows an easy trade-off between speed and accuracy of the matching. Increasing the value leads to less points being used and in turn to a faster but less accurate pose computation. Decreasing the value has the inverse effect.
* @param [in] relativeSceneDistance Set the distance threshold relative to the diameter of the model. This parameter is equivalent to relativeSamplingStep in the training stage. This parameter acts like a prior sampling with the relativeSceneSampleStep parameter.
*/
void match(const Mat& scene, std::vector<Pose3D*>& results, const double relativeSceneSampleStep=1.0/5.0, const double relativeSceneDistance=0.03);
void read(const FileNode& fn);
void write(FileStorage& fs) const;
protected:
double maxDist, angle_step, angleStepRadians, distance_step;
double samplingStepRelative, angleStepRelative, distanceStepRelative;
Mat inputPC, sampledPC, PPF;
int num_ref_points, sampled_step, ppf_step;
hashtable_int* hash_table;
THash* hash_nodes;
int NumPoses;
double PositionThreshold, RotationThreshold, MinMatchScore;
bool UseWeightedAvg;
float sampleStepSearch;
int SceneSampleStep;
void clearTrainingModels();
private:
void computePPFFeatures( const double p1[4], const double n1[4],
const double p2[4], const double n2[4],
double f[4]);
bool matchPose(const Pose3D& sourcePose, const Pose3D& targetPose);
int clusterPoses(Pose3D** poseList, int numPoses, std::vector<Pose3D*>& finalPoses);
bool trained;
};
} // namespace ppf_match_3d
} // namespace cv
#endif
modules/surface_matching/include/opencv2/surface_matching.hpp 0 → 100644
View file @ 607f2bfc
//
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
// By downloading, copying, installing or using the software you agree to this license.
// If you do not agree to this license, do not download, install,
// copy or use the software.
//
//
// License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2014, OpenCV Foundation, all rights reserved.
// Third party copyrights are property of their respective owners.
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
//
// * Redistribution's in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
//
// * The name of the copyright holders may not be used to endorse or promote products
// derived from this software without specific prior written permission.
//
// This software is provided by the copyright holders and contributors "as is" and
// any express or implied warranties, including, but not limited to, the implied
// warranties of merchantability and fitness for a particular purpose are disclaimed.
// In no event shall the Intel Corporation or contributors be liable for any direct,
// indirect, incidental, special, exemplary, or consequential damages
// (including, but not limited to, procurement of substitute goods or services;
// loss of use, data, or profits; or business interruption) however caused
// and on any theory of liability, whether in contract, strict liability,
// or tort (including negligence or otherwise) arising in any way out of
// the use of this software, even if advised of the possibility of such damage.
#ifndef __OPENCV_SURFACE_MATCHING_HPP__
#define __OPENCV_SURFACE_MATCHING_HPP__
#include "ppf_match_3d.hpp"
#include "icp.hpp"
#endif
modules/surface_matching/include/opencv2/surface_matching/pose_3d.hpp 0 → 100644
View file @ 607f2bfc
//
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
// By downloading, copying, installing or using the software you agree to this license.
// If you do not agree to this license, do not download, install,
// copy or use the software.
//
//
// License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2014, OpenCV Foundation, all rights reserved.
// Third party copyrights are property of their respective owners.
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
//
// * Redistribution's in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
//
// * The name of the copyright holders may not be used to endorse or promote products
// derived from this software without specific prior written permission.
//
// This software is provided by the copyright holders and contributors "as is" and
// any express or implied warranties, including, but not limited to, the implied
// warranties of merchantability and fitness for a particular purpose are disclaimed.
// In no event shall the Intel Corporation or contributors be liable for any direct,
// indirect, incidental, special, exemplary, or consequential damages
// (including, but not limited to, procurement of substitute goods or services;
// loss of use, data, or profits; or business interruption) however caused
// and on any theory of liability, whether in contract, strict liability,
// or tort (including negligence or otherwise) arising in any way out of
// the use of this software, even if advised of the possibility of such damage.
//
// Author: Tolga Birdal <tbirdal AT gmail.com>
#ifndef _OPENCV_POSE3D_HPP_
#define _OPENCV_POSE3D_HPP_
#include <vector>
#include <string>
namespace cv
{
namespace ppf_match_3d
{
/**
* @class Pose3D
* @brief Class, allowing the storage of a pose. The data structure stores both
* the quaternions and the matrix forms. It supports IO functionality together with
* various helper methods to work with poses
*
*/
class CV_EXPORTS Pose3D
{
public:
Pose3D()
{
alpha=0;
modelIndex=0;
numVotes=0;
residual = 0;
for (int i=0; i<16; i++)
Pose[i]=0;
};
Pose3D(double Alpha, unsigned int ModelIndex=0, unsigned int NumVotes=0)
{
alpha = Alpha;
modelIndex = ModelIndex;
numVotes = NumVotes;
residual=0;
for (int i=0; i<16; i++)
Pose[i]=0;
};
/**
* \brief Updates the pose with the new one
* \param [in] NewPose New pose to overwrite
*/
void updatePose(double NewPose[16]);
/**
* \brief Updates the pose with the new one
* \param [in] NewPose New pose to overwrite
*/
void updatePose(double NewR[9], double NewT[3]);
/**
* \brief Updates the pose with the new one, but this time using quaternions to represent rotation
* \param [in] NewPose New pose to overwrite
*/
void updatePoseQuat(double Q[4], double NewT[3]);
/**
* \brief Left multiplies the existing pose in order to update the transformation
* \param [in] IncrementalPose New pose to apply
*/
void appendPose(double IncrementalPose[16]);
void printPose();
Pose3D* clone();
int writePose(FILE* f);
int readPose(FILE* f);
int writePose(const std::string& FileName);
int readPose(const std::string& FileName);
virtual ~Pose3D() {};
double alpha, residual;
unsigned int modelIndex;
unsigned int numVotes;
double Pose[16], angle, t[3], q[4];
};
/**
* @class PoseCluster3D
* @brief When multiple poses (see Pose3D) are grouped together (contribute to the same transformation)
* pose clusters occur. This class is a general container for such groups of poses. It is possible to store,
* load and perform IO on these poses.
*/
class CV_EXPORTS PoseCluster3D
{
public:
PoseCluster3D()
{
//poseList.clear();
numVotes=0;
id=0;
};
PoseCluster3D(Pose3D* newPose)
{
//poseList.clear();
poseList.push_back(newPose);
numVotes=newPose->numVotes;
id=0;
};
PoseCluster3D(Pose3D* newPose, int newId)
{
//poseList.clear();
poseList.push_back(newPose);
this->numVotes = newPose->numVotes;
this->id = newId;
};
virtual ~PoseCluster3D()
{
numVotes=0;
id=0;
//poseList.clear();
};
/**
* \brief Adds a new pose to the cluster. The pose should be "close" to the mean poses
* in order to preserve the consistency
* \param [in] newPose Pose to add to the cluster
*/
void addPose(Pose3D* newPose) ;
int writePoseCluster(FILE* f);
int readPoseCluster(FILE* f);
int writePoseCluster(const std::string& FileName);
int readPoseCluster(const std::string& FileName);
std::vector < Pose3D* > poseList;
int numVotes;
int id;
};
} // namespace ppf_match_3d
} // namespace cv
#endif
modules/surface_matching/include/opencv2/surface_matching/ppf_helpers.hpp 0 → 100644
View file @ 607f2bfc
//
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
// By downloading, copying, installing or using the software you agree to this license.
// If you do not agree to this license, do not download, install,
// copy or use the software.
//
//
// License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2014, OpenCV Foundation, all rights reserved.
// Third party copyrights are property of their respective owners.
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
//
// * Redistribution's in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
//
// * The name of the copyright holders may not be used to endorse or promote products
// derived from this software without specific prior written permission.
//
// This software is provided by the copyright holders and contributors "as is" and
// any express or implied warranties, including, but not limited to, the implied
// warranties of merchantability and fitness for a particular purpose are disclaimed.
// In no event shall the Intel Corporation or contributors be liable for any direct,
// indirect, incidental, special, exemplary, or consequential damages
// (including, but not limited to, procurement of substitute goods or services;
// loss of use, data, or profits; or business interruption) however caused
// and on any theory of liability, whether in contract, strict liability,
// or tort (including negligence or otherwise) arising in any way out of
// the use of this software, even if advised of the possibility of such damage.
//
// Author: Tolga Birdal <tbirdal AT gmail.com>
#ifndef __OPENCV_PPF_3D_HELPERS_HPP__
#define __OPENCV_PPF_3D_HELPERS_HPP__
#include <opencv2/core.hpp>
namespace cv
{
namespace ppf_match_3d
{
/**
* \brief Load a PLY file
*
* \param [in] fileName The PLY model to read
* \param [in] withNormals Flag wheather the input PLY contains normal information,
* and whether it should be loaded or not
* \return Returns the matrix on successfull load
*/
CV_EXPORTS cv::Mat loadPLYSimple(const char* fileName, int withNormals);
/**
* \brief Write a point cloud to PLY file
* \param [in] fileName The PLY model file to write
*/
CV_EXPORTS void writePLY(cv::Mat PC, const char* fileName);
cv::Mat samplePCUniform(cv::Mat PC, int sampleStep);
cv::Mat samplePCUniformInd(cv::Mat PC, int sampleStep, std::vector<int>& indices);
/**
* Sample a point cloud using uniform steps
* @param [in] xrange X components (min and max) of the bounding box of the model
* @param [in] yrange Y components (min and max) of the bounding box of the model
* @param [in] zrange Z components (min and max) of the bounding box of the model
* @param [in] sample_step_relative The point cloud is sampled such that all points
* have a certain minimum distance. This minimum distance is determined relatively using
* the parameter sample_step_relative.
* @param [in] weightByCenter The contribution of the quantized data points can be weighted
* by the distance to the origin. This parameter enables/disables the use of weighting.
* \return Sampled point cloud
*/
CV_EXPORTS cv::Mat samplePCByQuantization(cv::Mat pc, float xrange[2], float yrange[2], float zrange[2], float sample_step_relative, int weightByCenter=0);
void computeBboxStd(cv::Mat pc, float xRange[2], float yRange[2], float zRange[2]);
void* indexPCFlann(cv::Mat pc);
void destroyFlann(void* flannIndex);
void queryPCFlann(void* flannIndex, cv::Mat& pc, cv::Mat& indices, cv::Mat& distances);
/**
* Mostly for visualization purposes. Normalizes the point cloud in a Hartley-Zissermann
* fashion. In other words, the point cloud is centered, and scaled such that the largest
* distance from the origin is sqrt(2). Finally a rescaling is applied.
* @param [in] pc Input point cloud (CV_32F family). Point clouds with 3 or 6 elements per
* row are expected.
* @param [in] scale The scale after normalization. Default to 1.
* \return Normalized point cloud
*/
CV_EXPORTS cv::Mat normalize_pc(cv::Mat pc, float scale);
cv::Mat normalizePCCoeff(cv::Mat pc, float scale, float* Cx, float* Cy, float* Cz, float* MinVal, float* MaxVal);
cv::Mat transPCCoeff(cv::Mat pc, float scale, float Cx, float Cy, float Cz, float MinVal, float MaxVal);
/**
* Transforms the point cloud with a given a homogeneous 4x4 pose matrix (in double precision)
* @param [in] pc Input point cloud (CV_32F family). Point clouds with 3 or 6 elements per
* row are expected. In the case where the normals are provided, they are also rotated to be
* compatible with the entire transformation
* @param [in] Pose 4x4 pose matrix, but linearized in row-major form.
* \return Transformed point cloud
*/
CV_EXPORTS cv::Mat transformPCPose(cv::Mat pc, double Pose[16]);
/**
* Generate a random 4x4 pose matrix
* @param [out] Pose The random pose
*/
CV_EXPORTS void getRandomPose(double Pose[16]);
/**
* Adds a uniform noise in the given scale to the input point cloud
* @param [in] pc Input point cloud (CV_32F family).
* @param [in] scale Input scale of the noise. The larger the scale, the more
* noisy the output
*/
CV_EXPORTS cv::Mat addNoisePC(cv::Mat pc, double scale);
/**
* \brief Compute the normals of an arbitrary point cloud
*
* @param [in] PC Input point cloud to compute the normals for.
* @param [in] PCNormals Output point cloud
* @param [in] NumNeighbors Number of neighbors to take into account in a local region
* @param [in] FlipViewpoint Should normals be flipped to a viewing direction?
* \return Returns 0 on success
*
* \details computeNormalsPC3d uses a plane fitting approach to smoothly compute
* local normals. Normals are obtained through the eigenvector of the covariance
* matrix, corresponding to the smallest eigen value.
* If PCNormals is provided to be an Nx6 matrix, then no new allocation
* is made, instead the existing memory is overwritten.
*/
CV_EXPORTS int computeNormalsPC3d(const cv::Mat& PC, cv::Mat& PCNormals, const int NumNeighbors, const bool FlipViewpoint, const double viewpoint[3]);
} // namespace ppf_match_3d
} // namespace cv
#endif
modules/surface_matching/include/opencv2/surface_matching/t_hash_int.hpp 0 → 100644
View file @ 607f2bfc
//
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
// By downloading, copying, installing or using the software you agree to this license.
// If you do not agree to this license, do not download, install,
// copy or use the software.
//
//
// License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2014, OpenCV Foundation, all rights reserved.
// Third party copyrights are property of their respective owners.
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
//
// * Redistribution's in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
//
// * The name of the copyright holders may not be used to endorse or promote products
// derived from this software without specific prior written permission.
//
// This software is provided by the copyright holders and contributors "as is" and
// any express or implied warranties, including, but not limited to, the implied
// warranties of merchantability and fitness for a particular purpose are disclaimed.
// In no event shall the Intel Corporation or contributors be liable for any direct,
// indirect, incidental, special, exemplary, or consequential damages
// (including, but not limited to, procurement of substitute goods or services;
// loss of use, data, or profits; or business interruption) however caused
// and on any theory of liability, whether in contract, strict liability,
// or tort (including negligence or otherwise) arising in any way out of
// the use of this software, even if advised of the possibility of such damage.
//
// Author: Tolga Birdal <tbirdal AT gmail.com>
#ifndef __OPENCV_T_HASH_INT_HPP__
#define __OPENCV_T_HASH_INT_HPP__
#include <stdio.h>
#include <stdlib.h>
namespace cv
{
namespace ppf_match_3d
{
typedef unsigned int KeyType;
typedef struct hashnode_i
{
KeyType key;
void *data;
struct hashnode_i *next;
} hashnode_i ;
typedef struct HSHTBL_i
{
size_t size;
struct hashnode_i **nodes;
size_t (*hashfunc)(unsigned int);
} hashtable_int;
__inline static unsigned int next_power_of_two(unsigned int value)
{
/* Round up to the next highest power of 2 */
/* from http://www-graphics.stanford.edu/~seander/bithacks.html */
--value;
value |= value >> 1;
value |= value >> 2;
value |= value >> 4;
value |= value >> 8;
value |= value >> 16;
++value;
return value;
}
hashtable_int *hashtableCreate(size_t size, size_t (*hashfunc)(unsigned int));
void hashtableDestroy(hashtable_int *hashtbl);
int hashtableInsert(hashtable_int *hashtbl, KeyType key, void *data);
int hashtableInsertHashed(hashtable_int *hashtbl, KeyType key, void *data);
int hashtableRemove(hashtable_int *hashtbl, KeyType key);
void *hashtableGet(hashtable_int *hashtbl, KeyType key);
hashnode_i* hashtableGetBucketHashed(hashtable_int *hashtbl, KeyType key);
int hashtableResize(hashtable_int *hashtbl, size_t size);
hashtable_int *hashtable_int_clone(hashtable_int *hashtbl);
hashtable_int *hashtableRead(FILE* f);
int hashtableWrite(const hashtable_int * hashtbl, const size_t dataSize, FILE* f);
void hashtablePrint(hashtable_int *hashtbl);
} // namespace ppf_match_3d
} // namespace cv
#endif
modules/surface_matching/samples/data/parasaurolophus_6700.ply 0 → 100644
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modules/surface_matching/samples/data/parasaurolophus_low_normals2.ply 0 → 100644
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modules/surface_matching/samples/data/rs1_normals.ply 0 → 100644
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modules/surface_matching/samples/ppf_load_match.cpp 0 → 100644
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//
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
// By downloading, copying, installing or using the software you agree to this license.
// If you do not agree to this license, do not download, install,
// copy or use the software.
//
//
// License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2014, OpenCV Foundation, all rights reserved.
// Third party copyrights are property of their respective owners.
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
//
// * Redistribution's in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
//
// * The name of the copyright holders may not be used to endorse or promote products
// derived from this software without specific prior written permission.
//
// This software is provided by the copyright holders and contributors "as is" and
// any express or implied warranties, including, but not limited to, the implied
// warranties of merchantability and fitness for a particular purpose are disclaimed.
// In no event shall the Intel Corporation or contributors be liable for any direct,
// indirect, incidental, special, exemplary, or consequential damages
// (including, but not limited to, procurement of substitute goods or services;
// loss of use, data, or profits; or business interruption) however caused
// and on any theory of liability, whether in contract, strict liability,
// or tort (including negligence or otherwise) arising in any way out of
// the use of this software, even if advised of the possibility of such damage.
//
// Author: Tolga Birdal <tbirdal AT gmail.com>
#include "opencv2/ppf_match_3d.hpp"
#include <iostream>
#include "opencv2/icp.hpp"
#include "opencv2/surface_matching/ppf_helpers.hpp"
#include "opencv2/core/utility.hpp"
using namespace std;
using namespace cv;
using namespace ppf_match_3d;
static void help(std::string errorMessage)
{
std::cout<<"Program init error : "<<errorMessage<<std::endl;
std::cout<<"\nUsage : ppf_matching [input model file] [input scene file]"<<std::endl;
std::cout<<"\nPlease start again with new parameters"<<std::endl;
}
int main(int argc, char** argv)
{
// welcome message
std::cout<< "****************************************************"<<std::endl;
std::cout<< "* Surface Matching demonstration : demonstrates the use of surface matching"
" using point pair features."<<std::endl;
std::cout<< "* The sample loads a model and a scene, where the model lies in a different"
" pose than the training.\n* It then trains the model and searches for it in the"
" input scene. The detected poses are further refined by ICP\n* and printed to the "
" standard output."<<std::endl;
std::cout<< "****************************************************"<<std::endl;
if (argc < 3)
{
help("Not enough input arguments");
exit(1);
}
#if (defined __x86_64__ || defined _M_X64)
std::cout << "Running on 64 bits" << std::endl;
#else
std::cout << "Running on 32 bits" << std::endl;
#endif
#ifdef _OPENMP
std::cout << "Running with OpenMP" << std::endl;
#else
std::cout << "Running without OpenMP and without TBB" << std::endl;
#endif
string modelFileName = (string)argv[1];
string sceneFileName = (string)argv[2];
Mat pc = loadPLYSimple(modelFileName.c_str(), 1);
// Now train the model
cout << "Training..." << endl;
int64 tick1 = cv::getTickCount();
ppf_match_3d::PPF3DDetector detector(0.025, 0.05);
detector.trainModel(pc);
int64 tick2 = cv::getTickCount();
cout << endl << "Training complete in "
<< (double)(tick2-tick1)/ cv::getTickFrequency()
<< " sec" << endl << "Loading model..." << endl;
// Read the scene
Mat pcTest = loadPLYSimple(sceneFileName.c_str(), 1);
// Match the model to the scene and get the pose
cout << endl << "Starting matching..." << endl;
vector < Pose3D* > results;
tick1 = cv::getTickCount();
detector.match(pcTest, results, 1.0/40.0, 0.05);
tick2 = cv::getTickCount();
cout << endl << "PPF Elapsed Time " <<
(tick2-tick1)/cv::getTickFrequency() << " sec" << endl;
// Get only first N results
int N = 2;
vector<Pose3D*>::const_iterator first = results.begin();
vector<Pose3D*>::const_iterator last = results.begin() + N;
vector<Pose3D*> resultsSub(first, last);
// Create an instance of ICP
ICP icp(100, 0.005f, 2.5f, 8);
int64 t1 = cv::getTickCount();
// Register for all selected poses
cout << endl << "Performing ICP on " << N << " poses..." << endl;
icp.registerModelToScene(pc, pcTest, resultsSub);
int64 t2 = cv::getTickCount();
cout << endl << "ICP Elapsed Time " <<
(t2-t1)/cv::getTickFrequency() << " sec" << endl;
cout << "Poses: " << endl;
// debug first five poses
for (size_t i=0; i<resultsSub.size(); i++)
{
Pose3D* pose = resultsSub[i];
cout << "Pose Result " << i << endl;
pose->printPose();
if (i==0)
{
Mat pct = transformPCPose(pc, pose->Pose);
writePLY(pct, "para6700PCTrans.ply");
}
}
return 0;
}
modules/surface_matching/src/c_utils.hpp 0 → 100644
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//
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
// By downloading, copying, installing or using the software you agree to this license.
// If you do not agree to this license, do not download, install,
// copy or use the software.
//
//
// License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2014, OpenCV Foundation, all rights reserved.
// Third party copyrights are property of their respective owners.
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
//
// * Redistribution's in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
//
// * The name of the copyright holders may not be used to endorse or promote products
// derived from this software without specific prior written permission.
//
// This software is provided by the copyright holders and contributors "as is" and
// any express or implied warranties, including, but not limited to, the implied
// warranties of merchantability and fitness for a particular purpose are disclaimed.
// In no event shall the Intel Corporation or contributors be liable for any direct,
// indirect, incidental, special, exemplary, or consequential damages
// (including, but not limited to, procurement of substitute goods or services;
// loss of use, data, or profits; or business interruption) however caused
// and on any theory of liability, whether in contract, strict liability,
// or tort (including negligence or otherwise) arising in any way out of
// the use of this software, even if advised of the possibility of such damage.
//
// Author: Tolga Birdal <tbirdal AT gmail.com>
#ifndef __OPENCV_PPF_UTILS_HPP_
#define __OPENCV_PPF_UTILS_HPP_
#include <cmath>
#include <cstdio>
namespace cv
{
namespace ppf_match_3d
{
const float EPS = 1.192092896e-07F; /* smallest such that 1.0+FLT_EPSILON != 1.0 */
#ifndef M_PI
#define M_PI 3.1415926535897932384626433832795
#endif
static inline double TNorm3(const double v[])
{
return (sqrt(v[0]*v[0]+v[1]*v[1]+v[2]*v[2]));
}
static inline void TNormalize3(double v[])
{
double normTemp=TNorm3(v);
if (normTemp>0)
{
v[0]/=normTemp;
v[1]/=normTemp;
v[2]/=normTemp;
}
}
static inline double TDot3(const double a[3], const double b[3])
{
return ((a[0])*(b[0])+(a[1])*(b[1])+(a[2])*(b[2]));
}
static inline void TCross(const double a[], const double b[], double c[])
{
c[0] = (a[1])*(b[2])-(a[2])*(b[1]);
c[1] = (a[2])*(b[0])-(a[0])*(b[2]);
c[2] = (a[0])*(b[1])-(a[1])*(b[0]);
}
static inline double TAngle3(const double a[3], const double b[3])
{
double c[3];
TCross(a,b,c);
return (atan2(TNorm3(c), TDot3(a, b)));
}
static inline void matrixProduct33(double *A, double *B, double *R)
{
R[0] = A[0] * B[0] + A[1] * B[3] + A[2] * B[6];
R[1] = A[0] * B[1] + A[1] * B[4] + A[2] * B[7];
R[2] = A[0] * B[2] + A[1] * B[5] + A[2] * B[8];
R[3] = A[3] * B[0] + A[4] * B[3] + A[5] * B[6];
R[4] = A[3] * B[1] + A[4] * B[4] + A[5] * B[7];
R[5] = A[3] * B[2] + A[4] * B[5] + A[5] * B[8];
R[6] = A[6] * B[0] + A[7] * B[3] + A[8] * B[6];
R[7] = A[6] * B[1] + A[7] * B[4] + A[8] * B[7];
R[8] = A[6] * B[2] + A[7] * B[5] + A[8] * B[8];
}
// A is a vector
static inline void matrixProduct133(double *A, double *B, double *R)
{
R[0] = A[0] * B[0] + A[1] * B[3] + A[2] * B[6];
R[1] = A[0] * B[1] + A[1] * B[4] + A[2] * B[7];
R[2] = A[0] * B[2] + A[1] * B[5] + A[2] * B[8];
}
static inline void matrixProduct331(const double A[9], const double b[3], double r[3])
{
r[0] = A[0] * b[0] + A[1] * b[1] + A[2] * b[2];
r[1] = A[3] * b[0] + A[4] * b[1] + A[5] * b[2];
r[2] = A[6] * b[0] + A[7] * b[1] + A[8] * b[2];
}
static inline void matrixTranspose33(double *A, double *At)
{
At[0] = A[0];
At[4] = A[4];
At[8] = A[8];
At[1] = A[3];
At[2] = A[6];
At[3] = A[1];
At[5] = A[7];
At[6] = A[2];
At[7] = A[5];
}
static inline void matrixProduct44(const double A[16], const double B[16], double R[16])
{
R[0] = A[0] * B[0] + A[1] * B[4] + A[2] * B[8] + A[3] * B[12];
R[1] = A[0] * B[1] + A[1] * B[5] + A[2] * B[9] + A[3] * B[13];
R[2] = A[0] * B[2] + A[1] * B[6] + A[2] * B[10] + A[3] * B[14];
R[3] = A[0] * B[3] + A[1] * B[7] + A[2] * B[11] + A[3] * B[15];
R[4] = A[4] * B[0] + A[5] * B[4] + A[6] * B[8] + A[7] * B[12];
R[5] = A[4] * B[1] + A[5] * B[5] + A[6] * B[9] + A[7] * B[13];
R[6] = A[4] * B[2] + A[5] * B[6] + A[6] * B[10] + A[7] * B[14];
R[7] = A[4] * B[3] + A[5] * B[7] + A[6] * B[11] + A[7] * B[15];
R[8] = A[8] * B[0] + A[9] * B[4] + A[10] * B[8] + A[11] * B[12];
R[9] = A[8] * B[1] + A[9] * B[5] + A[10] * B[9] + A[11] * B[13];
R[10] = A[8] * B[2] + A[9] * B[6] + A[10] * B[10] + A[11] * B[14];
R[11] = A[8] * B[3] + A[9] * B[7] + A[10] * B[11] + A[11] * B[15];
R[12] = A[12] * B[0] + A[13] * B[4] + A[14] * B[8] + A[15] * B[12];
R[13] = A[12] * B[1] + A[13] * B[5] + A[14] * B[9] + A[15] * B[13];
R[14] = A[12] * B[2] + A[13] * B[6] + A[14] * B[10] + A[15] * B[14];
R[15] = A[12] * B[3] + A[13] * B[7] + A[14] * B[11] + A[15] * B[15];
}
static inline void matrixProduct441(const double A[16], const double B[4], double R[4])
{
R[0] = A[0] * B[0] + A[1] * B[1] + A[2] * B[2] + A[3] * B[3];
R[1] = A[4] * B[0] + A[5] * B[1] + A[6] * B[2] + A[7] * B[3];
R[2] = A[8] * B[0] + A[9] * B[1] + A[10] * B[2] + A[11] * B[3];
R[3] = A[12] * B[0] + A[13] * B[1] + A[14] * B[2] + A[15] * B[3];
}
static inline void matrixPrint(double *A, int m, int n)
{
int i, j;
for (i = 0; i < m; i++)
{
printf(" ");
for (j = 0; j < n; j++)
{
printf(" %0.6f ", A[i * n + j]);
}
printf("\n");
}
}
static inline void matrixIdentity(int n, double *A)
{
int i;
for (i = 0; i < n*n; i++)
{
A[i] = 0.0;
}
for (i = 0; i < n; i++)
{
A[i * n + i] = 1.0;
}
}
static inline void rtToPose(const double R[9], const double t[3], double Pose[16])
{
Pose[0]=R[0];
Pose[1]=R[1];
Pose[2]=R[2];
Pose[4]=R[3];
Pose[5]=R[4];
Pose[6]=R[5];
Pose[8]=R[6];
Pose[9]=R[7];
Pose[10]=R[8];
Pose[3]=t[0];
Pose[7]=t[1];
Pose[11]=t[2];
Pose[15] = 1;
}
static inline void poseToRT(const double Pose[16], double R[9], double t[3])
{
R[0] = Pose[0];
R[1] = Pose[1];
R[2] = Pose[2];
R[3] = Pose[4];
R[4] = Pose[5];
R[5] = Pose[6];
R[6] = Pose[8];
R[7] = Pose[9];
R[8] = Pose[10];
t[0]=Pose[3];
t[1]=Pose[7];
t[2]=Pose[11];
}
static inline void poseToR(const double Pose[16], double R[9])
{
R[0] = Pose[0];
R[1] = Pose[1];
R[2] = Pose[2];
R[3] = Pose[4];
R[4] = Pose[5];
R[5] = Pose[6];
R[6] = Pose[8];
R[7] = Pose[9];
R[8] = Pose[10];
}
/**
* \brief Axis angle to rotation but only compute y and z components
*/
static inline void aaToRyz(double angle, const double r[3], double row2[3], double row3[3])
{
const double sinA=sin(angle);
const double cosA=cos(angle);
const double cos1A=(1-cosA);
row2[0] = 0.f;
row2[1] = cosA;
row2[2] = 0.f;
row3[0] = 0.f;
row3[1] = 0.f;
row3[2] = cosA;
row2[0] += r[2] * sinA;
row2[2] += -r[0] * sinA;
row3[0] += -r[1] * sinA;
row3[1] += r[0] * sinA;
row2[0] += r[1] * r[0] * cos1A;
row2[1] += r[1] * r[1] * cos1A;
row2[2] += r[1] * r[2] * cos1A;
row3[0] += r[2] * r[0] * cos1A;
row3[1] += r[2] * r[1] * cos1A;
row3[2] += r[2] * r[2] * cos1A;
}
/**
* \brief Axis angle to rotation
*/
static inline void aaToR(double angle, const double r[3], double R[9])
{
const double sinA=sin(angle);
const double cosA=cos(angle);
const double cos1A=(1-cosA);
double *row1 = &R[0];
double *row2 = &R[3];
double *row3 = &R[6];
row1[0] = cosA;
row1[1] = 0.0f;
row1[2] = 0.f;
row2[0] = 0.f;
row2[1] = cosA;
row2[2] = 0.f;
row3[0] = 0.f;
row3[1] = 0.f;
row3[2] = cosA;
row1[1] += -r[2] * sinA;
row1[2] += r[1] * sinA;
row2[0] += r[2] * sinA;
row2[2] += -r[0] * sinA;
row3[0] += -r[1] * sinA;
row3[1] += r[0] * sinA;
row1[0] += r[0] * r[0] * cos1A;
row1[1] += r[0] * r[1] * cos1A;
row1[2] += r[0] * r[2] * cos1A;
row2[0] += r[1] * r[0] * cos1A;
row2[1] += r[1] * r[1] * cos1A;
row2[2] += r[1] * r[2] * cos1A;
row3[0] += r[2] * r[0] * cos1A;
row3[1] += r[2] * r[1] * cos1A;
row3[2] += r[2] * r[2] * cos1A;
}
/**
* \brief Compute a rotation in order to rotate around X direction
*/
static inline void getUnitXRotation(double angle, double R[9])
{
const double sinA=sin(angle);
const double cosA=cos(angle);
double *row1 = &R[0];
double *row2 = &R[3];
double *row3 = &R[6];
row1[0] = 1;
row1[1] = 0.0f;
row1[2] = 0.f;
row2[0] = 0.f;
row2[1] = cosA;
row2[2] = -sinA;
row3[0] = 0.f;
row3[1] = sinA;
row3[2] = cosA;
}
/**
* \brief Compute a transformation in order to rotate around X direction
*/
static inline void getUnitXRotation_44(double angle, double T[16])
{
const double sinA=sin(angle);
const double cosA=cos(angle);
double *row1 = &T[0];
double *row2 = &T[4];
double *row3 = &T[8];
row1[0] = 1;
row1[1] = 0.0f;
row1[2] = 0.f;
row2[0] = 0.f;
row2[1] = cosA;
row2[2] = -sinA;
row3[0] = 0.f;
row3[1] = sinA;
row3[2] = cosA;
row1[3]=0;
row2[3]=0;
row3[3]=0;
T[3]=0;
T[7]=0;
T[11]=0;
T[15] = 1;
}
/**
* \brief Compute the yz components of the transformation needed to rotate n1 onto x axis and p1 to origin
*/
static inline void computeTransformRTyz(const double p1[4], const double n1[4], double row2[3], double row3[3], double t[3])
{
// dot product with x axis
double angle=acos( n1[0] );
// cross product with x axis
double axis[3]={0, n1[2], -n1[1]};
double axisNorm;
// we try to project on the ground plane but it's already parallel
if (n1[1]==0 && n1[2]==0)
{
axis[1]=1;
axis[2]=0;
}
else
{
axisNorm=sqrt(axis[2]*axis[2]+axis[1]*axis[1]);
if (axisNorm>EPS)
{
axis[1]/=axisNorm;
axis[2]/=axisNorm;
}
}
aaToRyz(angle, axis, row2, row3);
t[1] = row2[0] * (-p1[0]) + row2[1] * (-p1[1]) + row2[2] * (-p1[2]);
t[2] = row3[0] * (-p1[0]) + row3[1] * (-p1[1]) + row3[2] * (-p1[2]);
}
/**
* \brief Compute the transformation needed to rotate n1 onto x axis and p1 to origin
*/
static inline void computeTransformRT(const double p1[4], const double n1[4], double R[9], double t[3])
{
// dot product with x axis
double angle=acos( n1[0] );
// cross product with x axis
double axis[3]={0, n1[2], -n1[1]};
double axisNorm;
double *row1, *row2, *row3;
// we try to project on the ground plane but it's already parallel
if (n1[1]==0 && n1[2]==0)
{
axis[1]=1;
axis[2]=0;
}
else
{
axisNorm=sqrt(axis[2]*axis[2]+axis[1]*axis[1]);
if (axisNorm>EPS)
{
axis[1]/=axisNorm;
axis[2]/=axisNorm;
}
}
aaToR(angle, axis, R);
row1 = &R[0];
row2 = &R[3];
row3 = &R[6];
t[0] = row1[0] * (-p1[0]) + row1[1] * (-p1[1]) + row1[2] * (-p1[2]);
t[1] = row2[0] * (-p1[0]) + row2[1] * (-p1[1]) + row2[2] * (-p1[2]);
t[2] = row3[0] * (-p1[0]) + row3[1] * (-p1[1]) + row3[2] * (-p1[2]);
}
/**
* \brief Flip a normal to the viewing direction
*
* \param [in] point Scene point
* \param [in] vp_x X component of view direction
* \param [in] vp_y Y component of view direction
* \param [in] vp_z Z component of view direction
* \param [in] nx X component of normal
* \param [in] ny Y component of normal
* \param [in] nz Z component of normal
*/
static inline void flipNormalViewpoint(const float* point, double vp_x, double vp_y, double vp_z, double *nx, double *ny, double *nz)
{
double cos_theta;
// See if we need to flip any plane normals
vp_x -= (double)point[0];
vp_y -= (double)point[1];
vp_z -= (double)point[2];
// Dot product between the (viewpoint - point) and the plane normal
cos_theta = (vp_x * (*nx) + vp_y * (*ny) + vp_z * (*nz));
// Flip the plane normal
if (cos_theta < 0)
{
(*nx) *= -1;
(*ny) *= -1;
(*nz) *= -1;
}
}
/**
* \brief Flip a normal to the viewing direction
*
* \param [in] point Scene point
* \param [in] vp_x X component of view direction
* \param [in] vp_y Y component of view direction
* \param [in] vp_z Z component of view direction
* \param [in] nx X component of normal
* \param [in] ny Y component of normal
* \param [in] nz Z component of normal
*/
static inline void flipNormalViewpoint_32f(const float* point, float vp_x, float vp_y, float vp_z, float *nx, float *ny, float *nz)
{
float cos_theta;
// See if we need to flip any plane normals
vp_x -= (float)point[0];
vp_y -= (float)point[1];
vp_z -= (float)point[2];
// Dot product between the (viewpoint - point) and the plane normal
cos_theta = (vp_x * (*nx) + vp_y * (*ny) + vp_z * (*nz));
// Flip the plane normal
if (cos_theta < 0)
{
(*nx) *= -1;
(*ny) *= -1;
(*nz) *= -1;
}
}
/**
* \brief Convert a rotation matrix to axis angle representation
*
* \param [in] R Rotation matrix
* \param [in] axis Axis vector
* \param [in] angle Angle in radians
*/
static inline void dcmToAA(double *R, double *axis, double *angle)
{
double d1 = R[7] - R[5];
double d2 = R[2] - R[6];
double d3 = R[3] - R[1];
double norm = sqrt(d1 * d1 + d2 * d2 + d3 * d3);
double x = (R[7] - R[5]) / norm;
double y = (R[2] - R[6]) / norm;
double z = (R[3] - R[1]) / norm;
*angle = acos((R[0] + R[4] + R[8] - 1.0) * 0.5);
axis[0] = x;
axis[1] = y;
axis[2] = z;
}
/**
* \brief Convert axis angle representation to rotation matrix
*
* \param [in] axis Axis Vector
* \param [in] angle Angle (In radians)
* \param [in] R 3x3 Rotation matrix
*/
static inline void aaToDCM(double *axis, double angle, double *R)
{
double ident[9]={1,0,0,0,1,0,0,0,1};
double n[9] = { 0.0, -axis[2], axis[1],
axis[2], 0.0, -axis[0],
-axis[1], axis[0], 0.0
};
double nsq[9];
double c, s;
int i;
//c = 1-cos(angle);
c = cos(angle);
s = sin(angle);
matrixProduct33(n, n, nsq);
for (i = 0; i < 9; i++)
{
const double sni = n[i]*s;
const double cnsqi = nsq[i]*(c);
R[i]=ident[i]+sni+cnsqi;
}
// The below code is the matrix based implemntation of the above
// double nsq[9], sn[9], cnsq[9], tmp[9];
//matrix_scale(3, 3, n, s, sn);
//matrix_scale(3, 3, nsq, (1 - c), cnsq);
//matrix_sum(3, 3, 3, 3, ident, sn, tmp);
//matrix_sum(3, 3, 3, 3, tmp, cnsq, R);
}
/**
* \brief Convert a discrete cosine matrix to quaternion
*
* \param [in] R Rotation Matrix
* \param [in] q Quaternion
*/
static inline void dcmToQuat(double *R, double *q)
{
double n4; // the norm of quaternion multiplied by 4
double tr = R[0] + R[4] + R[8]; // trace of martix
double factor;
if (tr > 0.0)
{
q[1] = R[5] - R[7];
q[2] = R[6] - R[2];
q[3] = R[1] - R[3];
q[0] = tr + 1.0;
n4 = q[0];
}
else
if ((R[0] > R[4]) && (R[0] > R[8]))
{
q[1] = 1.0 + R[0] - R[4] - R[8];
q[2] = R[3] + R[1];
q[3] = R[6] + R[2];
q[0] = R[5] - R[7];
n4 = q[1];
}
else
if (R[4] > R[8])
{
q[1] = R[3] + R[1];
q[2] = 1.0 + R[4] - R[0] - R[8];
q[3] = R[7] + R[5];
q[0] = R[6] - R[2];
n4 = q[2];
}
else
{
q[1] = R[6] + R[2];
q[2] = R[7] + R[5];
q[3] = 1.0 + R[8] - R[0] - R[4];
q[0] = R[1] - R[3];
n4 = q[3];
}
factor = 0.5 / sqrt(n4);
q[0] *= factor;
q[1] *= factor;
q[2] *= factor;
q[3] *= factor;
}
/**
* \brief Convert quaternion to a discrete cosine matrix
*
* \param [in] q Quaternion (w is at first element)
* \param [in] R Rotation Matrix
*
*/
static inline void quatToDCM(double *q, double *R)
{
double sqw = q[0] * q[0];
double sqx = q[1] * q[1];
double sqy = q[2] * q[2];
double sqz = q[3] * q[3];
double tmp1, tmp2;
R[0] = sqx - sqy - sqz + sqw; // since sqw + sqx + sqy + sqz = 1
R[4] = -sqx + sqy - sqz + sqw;
R[8] = -sqx - sqy + sqz + sqw;
tmp1 = q[1] * q[2];
tmp2 = q[3] * q[0];
R[1] = 2.0 * (tmp1 + tmp2);
R[3] = 2.0 * (tmp1 - tmp2);
tmp1 = q[1] * q[3];
tmp2 = q[2] * q[0];
R[2] = 2.0 * (tmp1 - tmp2);
R[6] = 2.0 * (tmp1 + tmp2);
tmp1 = q[2] * q[3];
tmp2 = q[1] * q[0];
R[5] = 2.0 * (tmp1 + tmp2);
R[7] = 2.0 * (tmp1 - tmp2);
}
/**
* @brief Analytical solution to find the eigenvector corresponding to the smallest
* eigenvalue of a 3x3 matrix. As this implements the analytical solution, it's not
* really the most robust way. Whenever possible, this implementation can be replaced
* via a robust numerical scheme.
* @param [in] C The matrix
* @param [in] A The eigenvector corresponding to the lowest eigenvalue
* @author Tolga Birdal
*/
static inline void eigenLowest33(const double C[3][3], double A[3])
{
const double a = C[0][0];
const double b = C[0][1];
const double c = C[0][2];
const double d = C[1][1];
const double e = C[1][2];
const double f = C[2][2];
const double t2 = c*c;
const double t3 = e*e;
const double t4 = b*t2;
const double t5 = c*d*e;
const double t34 = b*t3;
const double t35 = a*c*e;
const double t6 = t4+t5-t34-t35;
const double t7 = 1.0/t6;
const double t8 = a+d+f;
const double t9 = b*b;
const double t23 = a*d;
const double t24 = a*f;
const double t25 = d*f;
const double t10 = t2+t3+t9-t23-t24-t25;
const double t11 = t8*t10*(1.0/6.0);
const double t12 = t8*t8;
const double t20 = t8*t12*(1.0/2.7E1);
const double t21 = b*c*e;
const double t22 = a*d*f*(1.0/2.0);
const double t26 = a*t3*(1.0/2.0);
const double t27 = d*t2*(1.0/2.0);
const double t28 = f*t9*(1.0/2.0);
const double t14 = t9*(1.0/3.0);
const double t15 = t2*(1.0/3.0);
const double t16 = t3*(1.0/3.0);
const double t17 = t12*(1.0/9.0);
const double t30 = a*d*(1.0/3.0);
const double t31 = a*f*(1.0/3.0);
const double t32 = d*f*(1.0/3.0);
const double t18 = t14+t15+t16+t17-t30-t31-t32;
const double t19 = t18*t18;
const double t36 = a*(1.0/3.0);
const double t37 = d*(1.0/3.0);
const double t38 = f*(1.0/3.0);
const double t39 = t8*(t2+t3+t9-t23-t24-t25)*(1.0/6.0);
const double t41 = t18*t19;
const double t43 = e*t2;
const double t60 = b*c*f;
const double t61 = d*e*f;
const double t44 = t43-t60-t61+e*t3;
const double t45 = t7*t44;
const double t57 = sqrt(3.0);
const double t51 = b*c;
const double t52 = d*e;
const double t53 = e*f;
const double t54 = t51+t52+t53;
const double t62 = t11+t20+t21+t22-t26-t27-t28;
const double t63 = t11+t20+t21+t22-t26-t27-t28;
const double t64 = t11+t20+t21+t22-t26-t27-t28;
const double t65 = t11+t20+t21+t22-t26-t27-t28;
const double t66 = t36+t37+t38-t18*1.0/pow(t20+t21+t22-t26-t27-t28+t39+sqrt(-t41+t62*t62),1.0/3.0)*(1.0/2.0)+t57*(t18*1.0/pow(t20+t21+t22-t26-t27-t28+t39+sqrt(-t41+t64*t64),1.0/3.0)-pow(t20+t21+t22-t26-t27-t28+t39+sqrt(-t41+t65*t65),1.0/3.0))*5.0E-1*sqrt(-1.0)-pow(t20+t21+t22-t26-t27-t28+t39+sqrt(-t41+t63*t63),1.0/3.0)*(1.0/2.0);
const double t67 = t11+t20+t21+t22-t26-t27-t28;
const double t68 = t11+t20+t21+t22-t26-t27-t28;
const double t69 = t11+t20+t21+t22-t26-t27-t28;
const double t70 = t11+t20+t21+t22-t26-t27-t28;
const double t76 = c*t3;
const double t91 = a*c*f;
const double t92 = b*e*f;
const double t77 = t76-t91-t92+c*t2;
const double t83 = a*c;
const double t84 = b*e;
const double t85 = c*f;
const double t86 = t83+t84+t85;
const double t93 = t11+t20+t21+t22-t26-t27-t28;
const double t94 = t11+t20+t21+t22-t26-t27-t28;
const double t95 = t11+t20+t21+t22-t26-t27-t28;
const double t96 = t11+t20+t21+t22-t26-t27-t28;
const double t97 = t36+t37+t38-t18*1.0/pow(t20+t21+t22-t26-t27-t28+t39+sqrt(-t41+t93*t93),1.0/3.0)*(1.0/2.0)+t57*(t18*1.0/pow(t20+t21+t22-t26-t27-t28+t39+sqrt(-t41+t95*t95),1.0/3.0)-pow(t20+t21+t22-t26-t27-t28+t39+sqrt(-t41+t96*t96),1.0/3.0))*5.0E-1*sqrt(-1.0)-pow(t20+t21+t22-t26-t27-t28+t39+sqrt(-t41+t94*t94),1.0/3.0)*(1.0/2.0);
const double t98 = t11+t20+t21+t22-t26-t27-t28;
const double t99 = t11+t20+t21+t22-t26-t27-t28;
const double t100 = t11+t20+t21+t22-t26-t27-t28;
const double t101 = t11+t20+t21+t22-t26-t27-t28;
A[0] = t45-e*t7*(t66*t66)+t7*t54*(t36+t37+t38-t18*1.0/pow(t20+t21+t22-t26-t27-t28+t39+sqrt(-t41+t67*t67),1.0/3.0)*(1.0/2.0)+t57*(t18*1.0/pow(t20+t21+t22-t26-t27-t28+t39+sqrt(-t41+t69*t69),1.0/3.0)-pow(t20+t21+t22-t26-t27-t28+t39+sqrt(-t41+t70*t70),1.0/3.0))*5.0E-1*sqrt(-1.0)-pow(t20+t21+t22-t26-t27-t28+t39+sqrt(-t41+t68*t68),1.0/3.0)*(1.0/2.0));
A[1] = -t7*t77+c*t7*(t97*t97)-t7*t86*(t36+t37+t38-t18*1.0/pow(t20+t21+t22-t26-t27-t28+t39+sqrt(-t41+t98*t98),1.0/3.0)*(1.0/2.0)+t57*(t18*1.0/pow(t20+t21+t22-t26-t27-t28+t39+sqrt(-t41+t100*t100),1.0/3.0)-pow(t20+t21+t22-t26-t27-t28+t39+sqrt(-t41+t101*t101),1.0/3.0))*5.0E-1*sqrt(-1.0)-pow(t20+t21+t22-t26-t27-t28+t39+sqrt(-t41+t99*t99),1.0/3.0)*(1.0/2.0));
A[2] = 1.0;
}
} // namespace ppf_match_3d
} // namespace cv
#endif
modules/surface_matching/src/hash_murmur.hpp 0 → 100644
View file @ 607f2bfc
//
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
// By downloading, copying, installing or using the software you agree to this license.
// If you do not agree to this license, do not download, install,
// copy or use the software.
//
//
// License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2014, OpenCV Foundation, all rights reserved.
// Third party copyrights are property of their respective owners.
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
//
// * Redistribution's in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
//
// * The name of the copyright holders may not be used to endorse or promote products
// derived from this software without specific prior written permission.
//
// This software is provided by the copyright holders and contributors "as is" and
// any express or implied warranties, including, but not limited to, the implied
// warranties of merchantability and fitness for a particular purpose are disclaimed.
// In no event shall the Intel Corporation or contributors be liable for any direct,
// indirect, incidental, special, exemplary, or consequential damages
// (including, but not limited to, procurement of substitute goods or services;
// loss of use, data, or profits; or business interruption) however caused
// and on any theory of liability, whether in contract, strict liability,
// or tort (including negligence or otherwise) arising in any way out of
// the use of this software, even if advised of the possibility of such damage.
//
// Author: Tolga Birdal <tbirdal AT gmail.com>
#ifndef __OPENCV_HASH_MURMUR_HPP_
#define __OPENCV_HASH_MURMUR_HPP_
namespace cv
{
namespace ppf_match_3d
{
#if defined(_MSC_VER)
#define FORCE_INLINE inline static
#include <stdlib.h>
#define ROTL32(x,y) _rotl(x,y)
#define ROTL64(x,y) _rotl64(x,y)
#else
//#define FORCE_INLINE __attribute__((always_inline))
#define FORCE_INLINE inline static
/* gcc recognises this code and generates a rotate instruction for CPUs with one */
#define ROTL32(x,r) (((uint32_t)x << r) | ((uint32_t)x >> (32 - r)))
inline static long long ROTL64 ( long long x, int8_t r )
{
return (x << r) | (x >> (64 - r));
}
#endif // !defined(_MSC_VER)
#if (defined __x86_64__ || defined _M_X64)
#include "hash_murmur64.hpp"
#define murmurHash hashMurmurx64
#else
#include "hash_murmur86.hpp"
#define murmurHash hashMurmurx86
#endif
}
}
#endif
\ No newline at end of file
modules/surface_matching/src/hash_murmur64.hpp 0 → 100644
View file @ 607f2bfc
#ifndef __OPENCV_HASH_MURMUR64_HPP_
#define __OPENCV_HASH_MURMUR64_HPP_
//-----------------------------------------------------------------------------
// Block read - if your platform needs to do endian-swapping or can only
// handle aligned reads, do the conversion here
FORCE_INLINE unsigned int getblock ( const unsigned int * p, int i )
{
return p[i];
}
//----------
// Finalization mix - force all bits of a hash block to avalanche
// avalanches all bits to within 0.25% bias
FORCE_INLINE unsigned int fmix32 ( unsigned int h )
{
h ^= h >> 16;
h *= 0x85ebca6b;
h ^= h >> 13;
h *= 0xc2b2ae35;
h ^= h >> 16;
return h;
}
//-----------------------------------------------------------------------------
FORCE_INLINE void bmix32 ( unsigned int & h1, unsigned int & k1, unsigned int & c1, unsigned int & c2 )
{
k1 *= c1;
k1 = ROTL32(k1,11);
k1 *= c2;
h1 ^= k1;
h1 = h1*3+0x52dce729;
c1 = c1*5+0x7b7d159c;
c2 = c2*5+0x6bce6396;
}
//-----------------------------------------------------------------------------
FORCE_INLINE void bmix32 ( unsigned int & h1, unsigned int & h2, unsigned int & k1, unsigned int & k2, unsigned int & c1, unsigned int & c2 )
{
k1 *= c1;
k1 = ROTL32(k1,11);
k1 *= c2;
h1 ^= k1;
h1 += h2;
h2 = ROTL32(h2,17);
k2 *= c2;
k2 = ROTL32(k2,11);
k2 *= c1;
h2 ^= k2;
h2 += h1;
h1 = h1*3+0x52dce729;
h2 = h2*3+0x38495ab5;
c1 = c1*5+0x7b7d159c;
c2 = c2*5+0x6bce6396;
}
//----------
FORCE_INLINE void hashMurmurx64 ( const void * key, const int len, const unsigned int seed, void * out )
{
const unsigned char * data = (const unsigned char*)key;
const int nblocks = len / 8;
unsigned int h1 = 0x8de1c3ac ^ seed;
unsigned int h2 = 0xbab98226 ^ seed;
unsigned int c1 = 0x95543787;
unsigned int c2 = 0x2ad7eb25;
//----------
// body
const unsigned int * blocks = (const unsigned int *)(data + nblocks*8);
for (int i = -nblocks; i; i++)
{
unsigned int k1 = getblock(blocks,i*2+0);
unsigned int k2 = getblock(blocks,i*2+1);
bmix32(h1,h2,k1,k2,c1,c2);
}
//----------
// tail
const unsigned char * tail = (const unsigned char*)(data + nblocks*8);
unsigned int k1 = 0;
unsigned int k2 = 0;
switch (len & 7)
{
case 7:
k2 ^= tail[6] << 16;
case 6:
k2 ^= tail[5] << 8;
case 5:
k2 ^= tail[4] << 0;
case 4:
k1 ^= tail[3] << 24;
case 3:
k1 ^= tail[2] << 16;
case 2:
k1 ^= tail[1] << 8;
case 1:
k1 ^= tail[0] << 0;
bmix32(h1,h2,k1,k2,c1,c2);
};
//----------
// finalization
h2 ^= len;
h1 += h2;
h2 += h1;
h1 = fmix32(h1);
h2 = fmix32(h2);
h1 += h2;
h2 += h1;
((unsigned int*)out)[0] = h1;
((unsigned int*)out)[1] = h2;
}
#endif
\ No newline at end of file
modules/surface_matching/src/hash_murmur86.hpp 0 → 100644
View file @ 607f2bfc
/*-----------------------------------------------------------------------------
* MurmurHash3 was written by Austin Appleby, and is placed in the public
* domain.
*
* This implementation was written by Shane Day, and is also public domain.
*
* This is a portable ANSI C implementation of MurmurHash3_x86_32 (Murmur3A)
* with support for progressive processing.
*/
/* ------------------------------------------------------------------------- */
/* Determine what native type to use for uint32_t */
/* We can't use the name 'uint32_t' here because it will conflict with
* any version provided by the system headers or application. */
/* First look for special cases */
#if defined(_MSC_VER)
#define MH_UINT32 unsigned long
#endif
/* If the compiler says it's C99 then take its word for it */
#if !defined(MH_UINT32) && ( \
defined(__STDC_VERSION__) && __STDC_VERSION__ >= 199901L )
#include <stdint.h>
#define MH_UINT32 uint32_t
#endif
/* Otherwise try testing against max value macros from limit.h */
#if !defined(MH_UINT32)
#include <limits.h>
#if (USHRT_MAX == 0xffffffffUL)
#define MH_UINT32 unsigned short
#elif (UINT_MAX == 0xffffffffUL)
#define MH_UINT32 unsigned int
#elif (ULONG_MAX == 0xffffffffUL)
#define MH_UINT32 unsigned long
#endif
#endif
#if !defined(MH_UINT32)
#error Unable to determine type name for unsigned 32-bit int
#endif
/* I'm yet to work on a platform where 'unsigned char' is not 8 bits */
#define MH_UINT8 unsigned char
void PMurHash32_Process(MH_UINT32 *ph1, MH_UINT32 *pcarry, const void *key, int len);
MH_UINT32 PMurHash32_Result(MH_UINT32 h1, MH_UINT32 carry, MH_UINT32 total_length);
MH_UINT32 PMurHash32(MH_UINT32 seed, const void *key, int len);
/* I used ugly type names in the header to avoid potential conflicts with
* application or system typedefs & defines. Since I'm not including any more
* headers below here I can rename these so that the code reads like C99 */
#undef uint32_t
#define uint32_t MH_UINT32
#undef uint8_t
#define uint8_t MH_UINT8
/* MSVC warnings we choose to ignore */
#if defined(_MSC_VER)
#pragma warning(disable: 4127) /* conditional expression is constant */
#endif
/*-----------------------------------------------------------------------------
* Endianess, misalignment capabilities and util macros
*
* The following 3 macros are defined in this section. The other macros defined
* are only needed to help derive these 3.
*
* READ_UINT32(x) Read a little endian unsigned 32-bit int
* UNALIGNED_SAFE Defined if READ_UINT32 works on non-word boundaries
* ROTL32(x,r) Rotate x left by r bits
*/
/* Convention is to define __BYTE_ORDER == to one of these values */
#if !defined(__BIG_ENDIAN)
#define __BIG_ENDIAN 4321
#endif
#if !defined(__LITTLE_ENDIAN)
#define __LITTLE_ENDIAN 1234
#endif
/* I386 */
#if defined(_M_IX86) || defined(__i386__) || defined(__i386) || defined(i386)
#define __BYTE_ORDER __LITTLE_ENDIAN
#define UNALIGNED_SAFE
#endif
/* gcc 'may' define __LITTLE_ENDIAN__ or __BIG_ENDIAN__ to 1 (Note the trailing __),
* or even _LITTLE_ENDIAN or _BIG_ENDIAN (Note the single _ prefix) */
#if !defined(__BYTE_ORDER)
#if defined(__LITTLE_ENDIAN__) && __LITTLE_ENDIAN__==1 || defined(_LITTLE_ENDIAN) && _LITTLE_ENDIAN==1
#define __BYTE_ORDER __LITTLE_ENDIAN
#elif defined(__BIG_ENDIAN__) && __BIG_ENDIAN__==1 || defined(_BIG_ENDIAN) && _BIG_ENDIAN==1
#define __BYTE_ORDER __BIG_ENDIAN
#endif
#endif
/* gcc (usually) defines xEL/EB macros for ARM and MIPS endianess */
#if !defined(__BYTE_ORDER)
#if defined(__ARMEL__) || defined(__MIPSEL__)
#define __BYTE_ORDER __LITTLE_ENDIAN
#endif
#if defined(__ARMEB__) || defined(__MIPSEB__)
#define __BYTE_ORDER __BIG_ENDIAN
#endif
#endif
/* Now find best way we can to READ_UINT32 */
#if __BYTE_ORDER==__LITTLE_ENDIAN
/* CPU endian matches murmurhash algorithm, so read 32-bit word directly */
#define READ_UINT32(ptr) (*((uint32_t*)(ptr)))
#elif __BYTE_ORDER==__BIG_ENDIAN
/* TODO: Add additional cases below where a compiler provided bswap32 is available */
#if defined(__GNUC__) && (__GNUC__>4 || (__GNUC__==4 && __GNUC_MINOR__>=3))
#define READ_UINT32(ptr) (__builtin_bswap32(*((uint32_t*)(ptr))))
#else
/* Without a known fast bswap32 we're just as well off doing this */
#define READ_UINT32(ptr) (ptr[0]|ptr[1]<<8|ptr[2]<<16|ptr[3]<<24)
#define UNALIGNED_SAFE
#endif
#else
/* Unknown endianess so last resort is to read individual bytes */
#define READ_UINT32(ptr) (ptr[0]|ptr[1]<<8|ptr[2]<<16|ptr[3]<<24)
/* Since we're not doing word-reads we can skip the messing about with realignment */
#define UNALIGNED_SAFE
#endif
/*-----------------------------------------------------------------------------
* Core murmurhash algorithm macros */
#define C1 (0xcc9e2d51)
#define C2 (0x1b873593)
/* This is the main processing body of the algorithm. It operates
* on each full 32-bits of input. */
#define DOBLOCK(h1, k1) do{ \
k1 *= C1; \
k1 = ROTL32(k1,15); \
k1 *= C2; \
\
h1 ^= k1; \
h1 = ROTL32(h1,13); \
h1 = h1*5+0xe6546b64; \
}while (0)
/* Append unaligned bytes to carry, forcing hash churn if we have 4 bytes */
/* cnt=bytes to process, h1=name of h1 var, c=carry, n=bytes in c, ptr/len=payload */
#define DOBYTES(cnt, h1, c, n, ptr, len) do{ \
int _i = cnt; \
while (_i--){ \
c = c>>8 | *ptr++<<24; \
n++; len--; \
if (n==4) { \
DOBLOCK(h1, c); \
n = 0; \
} \
}}while (0)
/*---------------------------------------------------------------------------*/
/* Main hashing function. Initialise carry to 0 and h1 to 0 or an initial seed
* if wanted. Both ph1 and pcarry are required arguments. */
void PMurHash32_Process(uint32_t *ph1, uint32_t *pcarry, const void *key, int len)
{
uint32_t h1 = *ph1;
uint32_t c = *pcarry;
const uint8_t *ptr = (uint8_t*) key;
const uint8_t *end;
/* Extract carry count from low 2 bits of c value */
int n = c & 3;
#if defined(UNALIGNED_SAFE)
/* This CPU handles unaligned word access */
/* Consume any carry bytes */
int i = (4-n) & 3;
if (i && i <= len)
{
DOBYTES(i, h1, c, n, ptr, len);
}
/* Process 32-bit chunks */
end = ptr + len/4*4;
for ( ; ptr < end ; ptr+=4)
{
uint32_t k1 = READ_UINT32(ptr);
DOBLOCK(h1, k1);
}
#else /*UNALIGNED_SAFE*/
/* This CPU does not handle unaligned word access */
/* Consume enough so that the next data byte is word aligned */
int i = -(long)ptr & 3;
if (i && i <= len)
{
DOBYTES(i, h1, c, n, ptr, len);
}
/* We're now aligned. Process in aligned blocks. Specialise for each possible carry count */
end = ptr + len/4*4;
switch (n)
{
/* how many bytes in c */
case 0: /* c=[----] w=[3210] b=[3210]=w c'=[----] */
for ( ; ptr < end ; ptr+=4)
{
uint32_t k1 = READ_UINT32(ptr);
DOBLOCK(h1, k1);
}
break;
case 1: /* c=[0---] w=[4321] b=[3210]=c>>24|w<<8 c'=[4---] */
for ( ; ptr < end ; ptr+=4)
{
uint32_t k1 = c>>24;
c = READ_UINT32(ptr);
k1 |= c<<8;
DOBLOCK(h1, k1);
}
break;
case 2: /* c=[10--] w=[5432] b=[3210]=c>>16|w<<16 c'=[54--] */
for ( ; ptr < end ; ptr+=4)
{
uint32_t k1 = c>>16;
c = READ_UINT32(ptr);
k1 |= c<<16;
DOBLOCK(h1, k1);
}
break;
case 3: /* c=[210-] w=[6543] b=[3210]=c>>8|w<<24 c'=[654-] */
for ( ; ptr < end ; ptr+=4)
{
uint32_t k1 = c>>8;
c = READ_UINT32(ptr);
k1 |= c<<24;
DOBLOCK(h1, k1);
}
}
#endif /*UNALIGNED_SAFE*/
/* Advance over whole 32-bit chunks, possibly leaving 1..3 bytes */
len -= len/4*4;
/* Append any remaining bytes into carry */
DOBYTES(len, h1, c, n, ptr, len);
/* Copy out new running hash and carry */
*ph1 = h1;
*pcarry = (c & ~0xff) | n;
}
/*---------------------------------------------------------------------------*/
/* Finalize a hash. To match the original Murmur3A the total_length must be provided */
uint32_t PMurHash32_Result(uint32_t h, uint32_t carry, uint32_t total_length)
{
uint32_t k1;
int n = carry & 3;
if (n)
{
k1 = carry >> (4-n)*8;
k1 *= C1;
k1 = ROTL32(k1,15);
k1 *= C2;
h ^= k1;
}
h ^= total_length;
/* fmix */
h ^= h >> 16;
h *= 0x85ebca6b;
h ^= h >> 13;
h *= 0xc2b2ae35;
h ^= h >> 16;
return h;
}
/*---------------------------------------------------------------------------*/
/* Murmur3A compatable all-at-once */
uint32_t PMurHash32(uint32_t seed, const void *key, int len)
{
uint32_t h1=seed, carry=0;
PMurHash32_Process(&h1, &carry, key, len);
return PMurHash32_Result(h1, carry, len);
}
void hashMurmurx86 ( const void * key, const int len, const unsigned int seed, void * out )
{
*(unsigned int*)out = PMurHash32 (seed, key, len);
}
modules/surface_matching/src/icp.cpp 0 → 100644
View file @ 607f2bfc
//
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
// By downloading, copying, installing or using the software you agree to this license.
// If you do not agree to this license, do not download, install,
// copy or use the software.
//
//
// License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2014, OpenCV Foundation, all rights reserved.
// Third party copyrights are property of their respective owners.
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
//
// * Redistribution's in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
//
// * The name of the copyright holders may not be used to endorse or promote products
// derived from this software without specific prior written permission.
//
// This software is provided by the copyright holders and contributors "as is" and
// any express or implied warranties, including, but not limited to, the implied
// warranties of merchantability and fitness for a particular purpose are disclaimed.
// In no event shall the Intel Corporation or contributors be liable for any direct,
// indirect, incidental, special, exemplary, or consequential damages
// (including, but not limited to, procurement of substitute goods or services;
// loss of use, data, or profits; or business interruption) however caused
// and on any theory of liability, whether in contract, strict liability,
// or tort (including negligence or otherwise) arising in any way out of
// the use of this software, even if advised of the possibility of such damage.
//
// Author: Tolga Birdal <tbirdal AT gmail.com>
#include "precomp.hpp"
namespace cv
{
namespace ppf_match_3d
{
static void subtractColumns(Mat srcPC, double mean[3])
{
int height = srcPC.rows;
for (int i=0; i<height; i++)
{
float *row = (float*)(&srcPC.data[i*srcPC.step]);
{
row[0]-=(float)mean[0];
row[1]-=(float)mean[1];
row[2]-=(float)mean[2];
}
}
}
// as in PCA
static void computeMeanCols(Mat srcPC, double mean[3])
{
int height = srcPC.rows;
double mean1=0, mean2 = 0, mean3 = 0;
for (int i=0; i<height; i++)
{
const float *row = (float*)(&srcPC.data[i*srcPC.step]);
{
mean1 += (double)row[0];
mean2 += (double)row[1];
mean3 += (double)row[2];
}
}
mean1/=(double)height;
mean2/=(double)height;
mean3/=(double)height;
mean[0] = mean1;
mean[1] = mean2;
mean[2] = mean3;
}
// as in PCA
/*static void subtractMeanFromColumns(Mat srcPC, double mean[3])
{
computeMeanCols(srcPC, mean);
subtractColumns(srcPC, mean);
}*/
// compute the average distance to the origin
static double computeDistToOrigin(Mat srcPC)
{
int height = srcPC.rows;
double dist = 0;
for (int i=0; i<height; i++)
{
const float *row = (float*)(&srcPC.data[i*srcPC.step]);
dist += sqrt(row[0]*row[0]+row[1]*row[1]+row[2]*row[2]);
}
return dist;
}
// From numerical receipes: Finds the median of an array
static float medianF(float arr[], int n)
{
int low, high ;
int median;
int middle, ll, hh;
low = 0 ;
high = n-1 ;
median = (low + high) >>1;
for (;;)
{
if (high <= low) /* One element only */
return arr[median] ;
if (high == low + 1)
{
/* Two elements only */
if (arr[low] > arr[high])
std::swap(arr[low], arr[high]) ;
return arr[median] ;
}
/* Find median of low, middle and high items; swap into position low */
middle = (low + high) >>1;
if (arr[middle] > arr[high])
std::swap(arr[middle], arr[high]) ;
if (arr[low] > arr[high])
std::swap(arr[low], arr[high]) ;
if (arr[middle] > arr[low])
std::swap(arr[middle], arr[low]) ;
/* Swap low item (now in position middle) into position (low+1) */
std::swap(arr[middle], arr[low+1]) ;
/* Nibble from each end towards middle, swapping items when stuck */
ll = low + 1;
hh = high;
for (;;)
{
do
ll++;
while (arr[low] > arr[ll]) ;
do
hh--;
while (arr[hh] > arr[low]) ;
if (hh < ll)
break;
std::swap(arr[ll], arr[hh]) ;
}
/* Swap middle item (in position low) back into correct position */
std::swap(arr[low], arr[hh]) ;
/* Re-set active partition */
if (hh <= median)
low = ll;
if (hh >= median)
high = hh - 1;
}
}
static float getRejectionThreshold(float* r, int m, float outlierScale)
{
float* t=(float*)calloc(m, sizeof(float));
int i=0;
float s=0, medR, threshold;
memcpy(t, r, m*sizeof(float));
medR=medianF(t, m);
for (i=0; i<m; i++)
t[i] = (float)fabs((double)r[i]-(double)medR);
s = 1.48257968f * medianF(t, m);
threshold = (outlierScale*s+medR);
free(t);
return threshold;
}
// Kok Lim Low's linearization
static void minimizePointToPlaneMetric(Mat Src, Mat Dst, Mat& X)
{
//Mat sub = Dst - Src;
Mat A=Mat(Src.rows, 6, CV_64F);
Mat b=Mat(Src.rows, 1, CV_64F);
#if defined _OPENMP
#pragma omp parallel for
#endif
for (int i=0; i<Src.rows; i++)
{
const double *srcPt = (double*)&Src.data[i*Src.step];
const double *dstPt = (double*)&Dst.data[i*Dst.step];
const double *normals = &dstPt[3];
double *bVal = (double*)&b.data[i*b.step];
double *aRow = (double*)&A.data[i*A.step];
const double sub[3]={dstPt[0]-srcPt[0], dstPt[1]-srcPt[1], dstPt[2]-srcPt[2]};
*bVal = TDot3(sub, normals);
TCross(srcPt, normals, aRow);
aRow[3] = normals[0];
aRow[4] = normals[1];
aRow[5] = normals[2];
}
cv::solve(A, b, X, DECOMP_SVD);
}
static void getTransformMat(Mat X, double Pose[16])
{
Mat DCM;
double *r1, *r2, *r3;
double* x = (double*)X.data;
const double sx = sin(x[0]);
const double cx = cos(x[0]);
const double sy = sin(x[1]);
const double cy = cos(x[1]);
const double sz = sin(x[2]);
const double cz = cos(x[2]);
Mat R1 = Mat::eye(3,3, CV_64F);
Mat R2 = Mat::eye(3,3, CV_64F);
Mat R3 = Mat::eye(3,3, CV_64F);
r1= (double*)R1.data;
r2= (double*)R2.data;
r3= (double*)R3.data;
r1[4]= cx;
r1[5]= -sx;
r1[7]= sx;
r1[8]= cx;
r2[0]= cy;
r2[2]= sy;
r2[6]= -sy;
r2[8]= cy;
r3[0]= cz;
r3[1]= -sz;
r3[3]= sz;
r3[4]= cz;
DCM = R1*(R2*R3);
Pose[0] = DCM.at<double>(0,0);
Pose[1] = DCM.at<double>(0,1);
Pose[2] = DCM.at<double>(0,2);
Pose[4] = DCM.at<double>(1,0);
Pose[5] = DCM.at<double>(1,1);
Pose[6] = DCM.at<double>(1,2);
Pose[8] = DCM.at<double>(2,0);
Pose[9] = DCM.at<double>(2,1);
Pose[10] = DCM.at<double>(2,2);
Pose[3]=x[3];
Pose[7]=x[4];
Pose[11]=x[5];
Pose[15]=1;
}
/* Fast way to look up the duplicates
duplicates is pre-allocated
make sure that the max element in array will not exceed maxElement
*/
static hashtable_int* getHashtable(int* data, size_t length, int numMaxElement)
{
hashtable_int* hashtable = hashtableCreate(static_cast<size_t>(numMaxElement*2), 0);
for (size_t i = 0; i < length; i++)
{
const KeyType key = (KeyType)data[i];
hashtableInsertHashed(hashtable, key+1, reinterpret_cast<void*>(i+1));
}
return hashtable;
}
// source point clouds are assumed to contain their normals
int ICP::registerModelToScene(const Mat& srcPC, const Mat& dstPC, double& Residual, double Pose[16])
{
int n = srcPC.rows;
//double PoseInit[16];
bool UseRobustReject = m_rejectionScale>0;
Mat srcTemp = srcPC.clone();
Mat dstTemp = dstPC.clone();
double meanSrc[3], meanDst[3];
computeMeanCols(srcTemp, meanSrc);
computeMeanCols(dstTemp, meanDst);
double meanAvg[3]={0.5*(meanSrc[0]+meanDst[0]), 0.5*(meanSrc[1]+meanDst[1]), 0.5*(meanSrc[2]+meanDst[2])};
subtractColumns(srcTemp, meanAvg);
subtractColumns(dstTemp, meanAvg);
double distSrc = computeDistToOrigin(srcTemp);
double distDst = computeDistToOrigin(dstTemp);
double scale = (double)n / ((distSrc + distDst)*0.5);
//srcTemp(cv::Range(0, srcTemp.rows), cv::Range(0,3)) = (srcTemp(cv::Range(0, srcTemp.rows), cv::Range(0,3)) ) * scale;
//dstTemp(cv::Range(0, dstTemp.rows), cv::Range(0,3)) = (dstTemp(cv::Range(0, dstTemp.rows), cv::Range(0,3)) ) * scale;
srcTemp(cv::Range(0, srcTemp.rows), cv::Range(0,3)) *= scale;
dstTemp(cv::Range(0, dstTemp.rows), cv::Range(0,3)) *= scale;
Mat srcPC0 = srcTemp;
Mat dstPC0 = dstTemp;
// initialize pose
matrixIdentity(4, Pose);
void* flann = indexPCFlann(dstPC0);
Mat M = Mat::eye(4,4,CV_64F);
double tempResidual = 0;
// walk the pyramid
for (int level = m_numLevels-1; level >=0; level--)
{
const double impact = 2;
double div = pow((double)impact, (double)level);
//double div2 = div*div;
const int numSamples = cvRound((double)(n/(div)));
const double TolP = m_tolerence*(double)(level+1)*(level+1);
const int MaxIterationsPyr = cvRound((double)m_maxItereations/(level+1));
// Obtain the sampled point clouds for this level: Also rotates the normals
Mat srcPCT = transformPCPose(srcPC0, Pose);
const int sampleStep = cvRound((double)n/(double)numSamples);
std::vector<int> srcSampleInd;
/*
Note by Tolga Birdal
Downsample the model point clouds. If more optimization is required,
one could also downsample the scene points, but I think this might
decrease the accuracy. That's why I won't be implementing it at this
moment.
Also note that you have to compute a KD-tree for each level.
*/
srcPCT = samplePCUniformInd(srcPCT, sampleStep, srcSampleInd);
double fval_old=9999999999;
double fval_perc=0;
double fval_min=9999999999;
Mat Src_Moved = srcPCT.clone();
int i=0;
size_t numElSrc = (size_t)Src_Moved.rows;
int sizesResult[2] = {(int)numElSrc, 1};
float* distances = new float[numElSrc];
int* indices = new int[numElSrc];
Mat Indices(2, sizesResult, CV_32S, indices, 0);
Mat Distances(2, sizesResult, CV_32F, distances, 0);
// use robust weighting for outlier treatment
int* indicesModel = new int[numElSrc];
int* indicesScene = new int[numElSrc];
int* newI = new int[numElSrc];
int* newJ = new int[numElSrc];
double PoseX[16]={0};
matrixIdentity(4, PoseX);
while ( (!(fval_perc<(1+TolP) && fval_perc>(1-TolP))) && i<MaxIterationsPyr)
{
size_t di=0, selInd = 0;
queryPCFlann(flann, Src_Moved, Indices, Distances);
for (di=0; di<numElSrc; di++)
{
newI[di] = (int)di;
newJ[di] = indices[di];
}
if (UseRobustReject)
{
int numInliers = 0;
float threshold = getRejectionThreshold(distances, Distances.rows, m_rejectionScale);
Mat acceptInd = Distances<threshold;
uchar *accPtr = (uchar*)acceptInd.data;
for (int l=0; l<acceptInd.rows; l++)
{
if (accPtr[l])
{
newI[numInliers] = l;
newJ[numInliers] = indices[l];
numInliers++;
}
}
numElSrc=numInliers;
}
// Step 2: Picky ICP
// Among the resulting corresponding pairs, if more than one scene point p_i
// is assigned to the same model point m_j, then select p_i that corresponds
// to the minimum distance
hashtable_int* duplicateTable = getHashtable(newJ, numElSrc, dstPC0.rows);
for (di=0; di<duplicateTable->size; di++)
{
hashnode_i *node = duplicateTable->nodes[di];
if (node)
{
// select the first node
int idx = reinterpret_cast<long>(node->data)-1, dn=0;
int dup = (int)node->key-1;
int minIdxD = idx;
float minDist = distances[idx];
while ( node )
{
idx = reinterpret_cast<long>(node->data)-1;
if (distances[idx] < minDist)
{
minDist = distances[idx];
minIdxD = idx;
}
node = node->next;
dn++;
}
indicesModel[ selInd ] = newI[ minIdxD ];
indicesScene[ selInd ] = dup ;
selInd++;
}
}
hashtableDestroy(duplicateTable);
if (selInd)
{
Mat Src_Match = Mat((int)selInd, srcPCT.cols, CV_64F);
Mat Dst_Match = Mat((int)selInd, srcPCT.cols, CV_64F);
for (di=0; di<selInd; di++)
{
const int indModel = indicesModel[di];
const int indScene = indicesScene[di];
const float *srcPt = (float*)&srcPCT.data[indModel*srcPCT.step];
const float *dstPt = (float*)&dstPC0.data[indScene*dstPC0.step];
double *srcMatchPt = (double*)&Src_Match.data[di*Src_Match.step];
double *dstMatchPt = (double*)&Dst_Match.data[di*Dst_Match.step];
int ci=0;
for (ci=0; ci<srcPCT.cols; ci++)
{
srcMatchPt[ci] = (double)srcPt[ci];
dstMatchPt[ci] = (double)dstPt[ci];
}
}
Mat X;
minimizePointToPlaneMetric(Src_Match, Dst_Match, X);
getTransformMat(X, PoseX);
Src_Moved = transformPCPose(srcPCT, PoseX);
double fval = cv::norm(Src_Match, Dst_Match)/(double)(Src_Moved.rows);
// Calculate change in error between iterations
fval_perc=fval/fval_old;
// Store error value
fval_old=fval;
if (fval < fval_min)
fval_min = fval;
}
else
break;
i++;
}
double TempPose[16];
matrixProduct44(PoseX, Pose, TempPose);
// no need to copy the last 4 rows
for (int c=0; c<12; c++)
Pose[c] = TempPose[c];
Residual = tempResidual;
delete[] newI;
delete[] newJ;
delete[] indicesModel;
delete[] indicesScene;
delete[] distances;
delete[] indices;
tempResidual = fval_min;
}
// Pose(1:3, 4) = Pose(1:3, 4)./scale;
Pose[3] = Pose[3]/scale + meanAvg[0];
Pose[7] = Pose[7]/scale + meanAvg[1];
Pose[11] = Pose[11]/scale + meanAvg[2];
// In MATLAB this would be : Pose(1:3, 4) = Pose(1:3, 4)./scale + meanAvg' - Pose(1:3, 1:3)*meanAvg';
double Rpose[9], Cpose[3];
poseToR(Pose, Rpose);
matrixProduct331(Rpose, meanAvg, Cpose);
Pose[3] -= Cpose[0];
Pose[7] -= Cpose[1];
Pose[11] -= Cpose[2];
Residual = tempResidual;
destroyFlann(flann);
flann = 0;
return 0;
}
// source point clouds are assumed to contain their normals
int ICP::registerModelToScene(const Mat& srcPC, const Mat& dstPC, std::vector<Pose3D*>& poses)
{
for (size_t i=0; i<poses.size(); i++)
{
double PoseICP[16]={0};
Mat srcTemp = transformPCPose(srcPC, poses[i]->Pose);
registerModelToScene(srcTemp, dstPC, poses[i]->residual, PoseICP);
poses[i]->appendPose(PoseICP);
}
return 0;
}
} // namespace ppf_match_3d
} // namespace cv
modules/surface_matching/src/pose_3d.cpp 0 → 100644
View file @ 607f2bfc
//
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
// By downloading, copying, installing or using the software you agree to this license.
// If you do not agree to this license, do not download, install,
// copy or use the software.
//
//
// License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2014, OpenCV Foundation, all rights reserved.
// Third party copyrights are property of their respective owners.
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
//
// * Redistribution's in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
//
// * The name of the copyright holders may not be used to endorse or promote products
// derived from this software without specific prior written permission.
//
// This software is provided by the copyright holders and contributors "as is" and
// any express or implied warranties, including, but not limited to, the implied
// warranties of merchantability and fitness for a particular purpose are disclaimed.
// In no event shall the Intel Corporation or contributors be liable for any direct,
// indirect, incidental, special, exemplary, or consequential damages
// (including, but not limited to, procurement of substitute goods or services;
// loss of use, data, or profits; or business interruption) however caused
// and on any theory of liability, whether in contract, strict liability,
// or tort (including negligence or otherwise) arising in any way out of
// the use of this software, even if advised of the possibility of such damage.
//
// Author: Tolga Birdal <tbirdal AT gmail.com>
#include "precomp.hpp"
namespace cv
{
namespace ppf_match_3d
{
void Pose3D::updatePose(double NewPose[16])
{
double R[9];
for (int i=0; i<16; i++)
Pose[i]=NewPose[i];
R[0] = Pose[0];
R[1] = Pose[1];
R[2] = Pose[2];
R[3] = Pose[4];
R[4] = Pose[5];
R[5] = Pose[6];
R[6] = Pose[8];
R[7] = Pose[9];
R[8] = Pose[10];
t[0]=Pose[3];
t[1]=Pose[7];
t[2]=Pose[11];
// compute the angle
const double trace = R[0] + R[4] + R[8];
if (fabs(trace - 3) <= EPS)
{
angle = 0;
}
else
if (fabs(trace + 1) <= EPS)
{
angle = M_PI;
}
else
{
angle = ( acos((trace - 1)/2) );
}
// compute the quaternion
dcmToQuat(R, q);
}
void Pose3D::updatePose(double NewR[9], double NewT[3])
{
Pose[0]=NewR[0];
Pose[1]=NewR[1];
Pose[2]=NewR[2];
Pose[3]=NewT[0];
Pose[4]=NewR[3];
Pose[5]=NewR[4];
Pose[6]=NewR[5];
Pose[7]=NewT[1];
Pose[8]=NewR[6];
Pose[9]=NewR[7];
Pose[10]=NewR[8];
Pose[11]=NewT[2];
Pose[12]=0;
Pose[13]=0;
Pose[14]=0;
Pose[15]=1;
// compute the angle
const double trace = NewR[0] + NewR[4] + NewR[8];
if (fabs(trace - 3) <= EPS)
{
angle = 0;
}
else
if (fabs(trace + 1) <= EPS)
{
angle = M_PI;
}
else
{
angle = ( acos((trace - 1)/2) );
}
// compute the quaternion
dcmToQuat(NewR, q);
}
void Pose3D::updatePoseQuat(double Q[4], double NewT[3])
{
double NewR[9];
quatToDCM(Q, NewR);
q[0]=Q[0];
q[1]=Q[1];
q[2]=Q[2];
q[3]=Q[3];
Pose[0]=NewR[0];
Pose[1]=NewR[1];
Pose[2]=NewR[2];
Pose[3]=NewT[0];
Pose[4]=NewR[3];
Pose[5]=NewR[4];
Pose[6]=NewR[5];
Pose[7]=NewT[1];
Pose[8]=NewR[6];
Pose[9]=NewR[7];
Pose[10]=NewR[8];
Pose[11]=NewT[2];
Pose[12]=0;
Pose[13]=0;
Pose[14]=0;
Pose[15]=1;
// compute the angle
const double trace = NewR[0] + NewR[4] + NewR[8];
if (fabs(trace - 3) <= EPS)
{
angle = 0;
}
else
{
if (fabs(trace + 1) <= EPS)
{
angle = M_PI;
}
else
{
angle = ( acos((trace - 1)/2) );
}
}
}
void Pose3D::appendPose(double IncrementalPose[16])
{
double R[9], PoseFull[16]={0};
matrixProduct44(IncrementalPose, this->Pose, PoseFull);
R[0] = PoseFull[0];
R[1] = PoseFull[1];
R[2] = PoseFull[2];
R[3] = PoseFull[4];
R[4] = PoseFull[5];
R[5] = PoseFull[6];
R[6] = PoseFull[8];
R[7] = PoseFull[9];
R[8] = PoseFull[10];
t[0]=PoseFull[3];
t[1]=PoseFull[7];
t[2]=PoseFull[11];
// compute the angle
const double trace = R[0] + R[4] + R[8];
if (fabs(trace - 3) <= EPS)
{
angle = 0;
}
else
if (fabs(trace + 1) <= EPS)
{
angle = M_PI;
}
else
{
angle = ( acos((trace - 1)/2) );
}
// compute the quaternion
dcmToQuat(R, q);
for (int i=0; i<16; i++)
Pose[i]=PoseFull[i];
}
Pose3D* Pose3D::clone()
{
Pose3D* pose = new Pose3D(alpha, modelIndex, numVotes);
for (int i=0; i<16; i++)
pose->Pose[i]= Pose[i];
pose->q[0]=q[0];
pose->q[1]=q[1];
pose->q[2]=q[2];
pose->q[3]=q[3];
pose->t[0]=t[0];
pose->t[1]=t[1];
pose->t[2]=t[2];
pose->angle=angle;
return pose;
}
void Pose3D::printPose()
{
printf("\n-- Pose to Model Index %d: NumVotes = %d, Residual = %f\n", this->modelIndex, this->numVotes, this->residual);
for (int j=0; j<4; j++)
{
for (int k=0; k<4; k++)
{
printf("%f ", this->Pose[j*4+k]);
}
printf("\n");
}
printf("\n");
}
int Pose3D::writePose(FILE* f)
{
int POSE_MAGIC = 7673;
fwrite(&POSE_MAGIC, sizeof(int), 1, f);
fwrite(&angle, sizeof(double), 1, f);
fwrite(&numVotes, sizeof(int), 1, f);
fwrite(&modelIndex, sizeof(int), 1, f);
fwrite(Pose, sizeof(double)*16, 1, f);
fwrite(t, sizeof(double)*3, 1, f);
fwrite(q, sizeof(double)*4, 1, f);
fwrite(&residual, sizeof(double), 1, f);
return 0;
}
int Pose3D::readPose(FILE* f)
{
int POSE_MAGIC = 7673, magic;
size_t status = fread(&magic, sizeof(int), 1, f);
if (status && magic == POSE_MAGIC)
{
status = fread(&angle, sizeof(double), 1, f);
status = fread(&numVotes, sizeof(int), 1, f);
status = fread(&modelIndex, sizeof(int), 1, f);
status = fread(Pose, sizeof(double)*16, 1, f);
status = fread(t, sizeof(double)*3, 1, f);
status = fread(q, sizeof(double)*4, 1, f);
status = fread(&residual, sizeof(double), 1, f);
return 0;
}
return -1;
}
int Pose3D::writePose(const std::string& FileName)
{
FILE* f = fopen(FileName.c_str(), "wb");
if (!f)
return -1;
int status = writePose(f);
fclose(f);
return status;
}
int Pose3D::readPose(const std::string& FileName)
{
FILE* f = fopen(FileName.c_str(), "rb");
if (!f)
return -1;
int status = readPose(f);
fclose(f);
return status;
}
void PoseCluster3D::addPose(Pose3D* newPose)
{
poseList.push_back(newPose);
this->numVotes += newPose->numVotes;
};
int PoseCluster3D::writePoseCluster(FILE* f)
{
int POSE_CLUSTER_MAGIC_IO = 8462597;
fwrite(&POSE_CLUSTER_MAGIC_IO, sizeof(int), 1, f);
fwrite(&id, sizeof(int), 1, f);
fwrite(&numVotes, sizeof(int), 1, f);
int numPoses = (int)poseList.size();
fwrite(&numPoses, sizeof(int), 1, f);
for (int i=0; i<numPoses; i++)
poseList[i]->writePose(f);
return 0;
}
int PoseCluster3D::readPoseCluster(FILE* f)
{
// The magic values are only used to check the files
int POSE_CLUSTER_MAGIC_IO = 8462597;
int magic=0, numPoses=0;
size_t status;
status = fread(&magic, sizeof(int), 1, f);
if (!status || magic!=POSE_CLUSTER_MAGIC_IO)
return -1;
status = fread(&id, sizeof(int), 1, f);
status = fread(&numVotes, sizeof(int), 1, f);
status = fread(&numPoses, sizeof(int), 1, f);
fclose(f);
poseList.clear();
poseList.resize(numPoses);
for (size_t i=0; i<poseList.size(); i++)
{
poseList[i] = new Pose3D();
poseList[i]->readPose(f);
}
return 0;
}
int PoseCluster3D::writePoseCluster(const std::string& FileName)
{
FILE* f = fopen(FileName.c_str(), "wb");
if (!f)
return -1;
int status = writePoseCluster(f);
fclose(f);
return status;
}
int PoseCluster3D::readPoseCluster(const std::string& FileName)
{
FILE* f = fopen(FileName.c_str(), "rb");
if (!f)
return -1;
int status = readPoseCluster(f);
fclose(f);
return status;
}
} // namespace ppf_match_3d
} // namespace cv
modules/surface_matching/src/ppf_helpers.cpp 0 → 100644
View file @ 607f2bfc
//
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
// By downloading, copying, installing or using the software you agree to this license.
// If you do not agree to this license, do not download, install,
// copy or use the software.
//
//
// License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2014, OpenCV Foundation, all rights reserved.
// Third party copyrights are property of their respective owners.
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
//
// * Redistribution's in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
//
// * The name of the copyright holders may not be used to endorse or promote products
// derived from this software without specific prior written permission.
//
// This software is provided by the copyright holders and contributors "as is" and
// any express or implied warranties, including, but not limited to, the implied
// warranties of merchantability and fitness for a particular purpose are disclaimed.
// In no event shall the Intel Corporation or contributors be liable for any direct,
// indirect, incidental, special, exemplary, or consequential damages
// (including, but not limited to, procurement of substitute goods or services;
// loss of use, data, or profits; or business interruption) however caused
// and on any theory of liability, whether in contract, strict liability,
// or tort (including negligence or otherwise) arising in any way out of
// the use of this software, even if advised of the possibility of such damage.
//
// Author: Tolga Birdal <tbirdal AT gmail.com>
#include "precomp.hpp"
namespace cv
{
namespace ppf_match_3d
{
typedef cv::flann::L2<float> Distance_32F;
typedef cv::flann::GenericIndex< Distance_32F > FlannIndex;
void shuffle(int *array, size_t n);
Mat genRandomMat(int rows, int cols, double mean, double stddev, int type);
void getRandQuat(double q[4]);
void getRandomRotation(double R[9]);
void meanCovLocalPC(const float* pc, const int ws, const int point_count, double CovMat[3][3], double Mean[4]);
void meanCovLocalPCInd(const float* pc, const int* Indices, const int ws, const int point_count, double CovMat[3][3], double Mean[4]);
Mat loadPLYSimple(const char* fileName, int withNormals)
{
Mat cloud;
int numVertices=0;
std::ifstream ifs(fileName);
if (!ifs.is_open())
{
printf("Cannot open file...\n");
return Mat();
}
std::string str;
while (str.substr(0, 10) !="end_header")
{
std::string entry = str.substr(0, 14);
if (entry == "element vertex")
{
numVertices = atoi(str.substr(15, str.size()-15).c_str());
}
std::getline(ifs, str);
}
if (withNormals)
cloud=Mat(numVertices, 6, CV_32FC1);
else
cloud=Mat(numVertices, 3, CV_32FC1);
for (int i = 0; i < numVertices; i++)
{
float* data = (float*)(&cloud.data[i*cloud.step[0]]);
if (withNormals)
{
ifs >> data[0] >> data[1] >> data[2] >> data[3] >> data[4] >> data[5];
// normalize to unit norm
double norm = sqrt(data[3]*data[3] + data[4]*data[4] + data[5]*data[5]);
if (norm>0.00001)
{
data[3]/=(float)norm;
data[4]/=(float)norm;
data[5]/=(float)norm;
}
}
else
{
ifs >> data[0] >> data[1] >> data[2];
}
}
//cloud *= 5.0f;
return cloud;
}
void writePLY(Mat PC, const char* FileName)
{
std::ofstream outFile( FileName );
if ( !outFile )
{
//cerr << "Error opening output file: " << FileName << "!" << endl;
printf("Error opening output file: %s!\n", FileName);
exit( 1 );
}
////
// Header
////
const int pointNum = ( int ) PC.rows;
const int vertNum = ( int ) PC.cols;
outFile << "ply" << std::endl;
outFile << "format ascii 1.0" << std::endl;
outFile << "element vertex " << pointNum << std::endl;
outFile << "property float x" << std::endl;
outFile << "property float y" << std::endl;
outFile << "property float z" << std::endl;
if (vertNum==6)
{
outFile << "property float nx" << std::endl;
outFile << "property float ny" << std::endl;
outFile << "property float nz" << std::endl;
}
outFile << "end_header" << std::endl;
////
// Points
////
for ( int pi = 0; pi < pointNum; ++pi )
{
const float* point = (float*)(&PC.data[ pi*PC.step ]);
outFile << point[0] << " "<<point[1]<<" "<<point[2];
if (vertNum==6)
{
outFile<<" " << point[3] << " "<<point[4]<<" "<<point[5];
}
outFile << std::endl;
}
return;
}
Mat samplePCUniform(Mat PC, int sampleStep)
{
int numRows = PC.rows/sampleStep;
Mat sampledPC = Mat(numRows, PC.cols, PC.type());
int c=0;
for (int i=0; i<PC.rows && c<numRows; i+=sampleStep)
{
PC.row(i).copyTo(sampledPC.row(c++));
}
return sampledPC;
}
Mat samplePCUniformInd(Mat PC, int sampleStep, std::vector<int> &indices)
{
int numRows = cvRound((double)PC.rows/(double)sampleStep);
indices.resize(numRows);
Mat sampledPC = Mat(numRows, PC.cols, PC.type());
int c=0;
for (int i=0; i<PC.rows && c<numRows; i+=sampleStep)
{
indices[c] = i;
PC.row(i).copyTo(sampledPC.row(c++));
}
return sampledPC;
}
void* indexPCFlann(Mat pc)
{
Mat dest_32f;
pc.colRange(0,3).copyTo(dest_32f);
return new FlannIndex(dest_32f, cvflann::KDTreeSingleIndexParams(8));
}
void destroyFlann(void* flannIndex)
{
delete ((FlannIndex*)flannIndex);
}
// For speed purposes this function assumes that PC, Indices and Distances are created with continuous structures
void queryPCFlann(void* flannIndex, cv::Mat& pc, cv::Mat& indices, cv::Mat& distances)
{
Mat obj_32f;
pc.colRange(0,3).copyTo(obj_32f);
((FlannIndex*)flannIndex)->knnSearch(obj_32f, indices, distances, 1, cvflann::SearchParams(32) );
}
// uses a volume instead of an octree
// TODO: Right now normals are required.
// This is much faster than sample_pc_octree
Mat samplePCByQuantization(Mat pc, float xrange[2], float yrange[2], float zrange[2], float sampleStep, int weightByCenter)
{
std::vector< std::vector<int> > map;
int numSamplesDim = (int)(1.0/sampleStep);
float xr = xrange[1] - xrange[0];
float yr = yrange[1] - yrange[0];
float zr = zrange[1] - zrange[0];
int numPoints = 0;
map.resize((numSamplesDim+1)*(numSamplesDim+1)*(numSamplesDim+1));
// OpenMP might seem like a good idea, but it didn't speed this up for me
//#pragma omp parallel for
for (int i=0; i<pc.rows; i++)
{
const float* point = (float*)(&pc.data[i * pc.step]);
// quantize a point
const int xCell =(int) ((float)numSamplesDim*(point[0]-xrange[0])/xr);
const int yCell =(int) ((float)numSamplesDim*(point[1]-yrange[0])/yr);
const int zCell =(int) ((float)numSamplesDim*(point[2]-zrange[0])/zr);
const int index = xCell*numSamplesDim*numSamplesDim+yCell*numSamplesDim+zCell;
/*#pragma omp critical
{*/
map[index].push_back(i);
// }
}
for (unsigned int i=0; i<map.size(); i++)
{
numPoints += (map[i].size()>0);
}
Mat pcSampled = Mat(numPoints, pc.cols, CV_32F);
int c = 0;
for (unsigned int i=0; i<map.size(); i++)
{
double px=0, py=0, pz=0;
double nx=0, ny=0, nz=0;
std::vector<int> curCell = map[i];
int cn = (int)curCell.size();
if (cn>0)
{
if (weightByCenter)
{
int xCell, yCell, zCell;
double xc, yc, zc;
double weightSum = 0 ;
zCell = i % numSamplesDim;
yCell = ((i-zCell)/numSamplesDim) % numSamplesDim;
xCell = ((i-zCell-yCell*numSamplesDim)/(numSamplesDim*numSamplesDim));
xc = ((double)xCell+0.5) * (double)xr/numSamplesDim + (double)xrange[0];
yc = ((double)yCell+0.5) * (double)yr/numSamplesDim + (double)yrange[0];
zc = ((double)zCell+0.5) * (double)zr/numSamplesDim + (double)zrange[0];
for (int j=0; j<cn; j++)
{
const int ptInd = curCell[j];
float* point = (float*)(&pc.data[ptInd * pc.step]);
const double dx = point[0]-xc;
const double dy = point[1]-yc;
const double dz = point[2]-zc;
const double d = sqrt(dx*dx+dy*dy+dz*dz);
double w = 0;
if (d>EPS)
{
// it is possible to use different weighting schemes.
// inverse weigthing was just good for me
// exp( - (distance/h)**2 )
//const double w = exp(-d*d);
w = 1.0/d;
}
//float weights[3]={1,1,1};
px += w*(double)point[0];
py += w*(double)point[1];
pz += w*(double)point[2];
nx += w*(double)point[3];
ny += w*(double)point[4];
nz += w*(double)point[5];
weightSum+=w;
}
px/=(double)weightSum;
py/=(double)weightSum;
pz/=(double)weightSum;
nx/=(double)weightSum;
ny/=(double)weightSum;
nz/=(double)weightSum;
}
else
{
for (int j=0; j<cn; j++)
{
const int ptInd = curCell[j];
float* point = (float*)(&pc.data[ptInd * pc.step]);
px += (double)point[0];
py += (double)point[1];
pz += (double)point[2];
nx += (double)point[3];
ny += (double)point[4];
nz += (double)point[5];
}
px/=(double)cn;
py/=(double)cn;
pz/=(double)cn;
nx/=(double)cn;
ny/=(double)cn;
nz/=(double)cn;
}
float *pcData = (float*)(&pcSampled.data[c*pcSampled.step[0]]);
pcData[0]=(float)px;
pcData[1]=(float)py;
pcData[2]=(float)pz;
// normalize the normals
double norm = sqrt(nx*nx+ny*ny+nz*nz);
if (norm>EPS)
{
pcData[3]=(float)(nx/norm);
pcData[4]=(float)(ny/norm);
pcData[5]=(float)(nz/norm);
}
//#pragma omp atomic
c++;
curCell.clear();
}
}
map.clear();
return pcSampled;
}
void shuffle(int *array, size_t n)
{
size_t i;
for (i = 0; i < n - 1; i++)
{
size_t j = i + rand() / (RAND_MAX / (n - i) + 1);
int t = array[j];
array[j] = array[i];
array[i] = t;
}
}
// compute the standard bounding box
void computeBboxStd(Mat pc, float xRange[2], float yRange[2], float zRange[2])
{
Mat pcPts = pc.colRange(0, 3);
int num = pcPts.rows;
float* points = (float*)pcPts.data;
xRange[0] = points[0];
xRange[1] = points[0];
yRange[0] = points[1];
yRange[1] = points[1];
zRange[0] = points[2];
zRange[1] = points[2];
for ( int ind = 0; ind < num; ind++ )
{
const float* row = (float*)(pcPts.data + (ind * pcPts.step));
const float x = row[0];
const float y = row[1];
const float z = row[2];
if (x<xRange[0])
xRange[0]=x;
if (x>xRange[1])
xRange[1]=x;
if (y<yRange[0])
yRange[0]=y;
if (y>yRange[1])
yRange[1]=y;
if (z<zRange[0])
zRange[0]=z;
if (z>zRange[1])
zRange[1]=z;
}
}
Mat normalizePCCoeff(Mat pc, float scale, float* Cx, float* Cy, float* Cz, float* MinVal, float* MaxVal)
{
double minVal=0, maxVal=0;
Mat x,y,z, pcn;
pc.col(0).copyTo(x);
pc.col(1).copyTo(y);
pc.col(2).copyTo(z);
float cx = (float) cv::mean(x).val[0];
float cy = (float) cv::mean(y).val[0];
float cz = (float) cv::mean(z).val[0];
cv::minMaxIdx(pc, &minVal, &maxVal);
x=x-cx;
y=y-cy;
z=z-cz;
pcn.create(pc.rows, 3, CV_32FC1);
x.copyTo(pcn.col(0));
y.copyTo(pcn.col(1));
z.copyTo(pcn.col(2));
cv::minMaxIdx(pcn, &minVal, &maxVal);
pcn=(float)scale*(pcn)/((float)maxVal-(float)minVal);
*MinVal=(float)minVal;
*MaxVal=(float)maxVal;
*Cx=(float)cx;
*Cy=(float)cy;
*Cz=(float)cz;
return pcn;
}
Mat transPCCoeff(Mat pc, float scale, float Cx, float Cy, float Cz, float MinVal, float MaxVal)
{
Mat x,y,z, pcn;
pc.col(0).copyTo(x);
pc.col(1).copyTo(y);
pc.col(2).copyTo(z);
x=x-Cx;
y=y-Cy;
z=z-Cz;
pcn.create(pc.rows, 3, CV_32FC1);
x.copyTo(pcn.col(0));
y.copyTo(pcn.col(1));
z.copyTo(pcn.col(2));
pcn=(float)scale*(pcn)/((float)MaxVal-(float)MinVal);
return pcn;
}
Mat transformPCPose(Mat pc, double Pose[16])
{
Mat pct = Mat(pc.rows, pc.cols, CV_32F);
double R[9], t[3];
poseToRT(Pose, R, t);
#if defined _OPENMP
#pragma omp parallel for
#endif
for (int i=0; i<pc.rows; i++)
{
const float *pcData = (float*)(&pc.data[i*pc.step]);
float *pcDataT = (float*)(&pct.data[i*pct.step]);
const float *n1 = &pcData[3];
float *nT = &pcDataT[3];
double p[4] = {(double)pcData[0], (double)pcData[1], (double)pcData[2], 1};
double p2[4];
matrixProduct441(Pose, p, p2);
// p2[3] should normally be 1
if (fabs(p2[3])>EPS)
{
pcDataT[0] = (float)(p2[0]/p2[3]);
pcDataT[1] = (float)(p2[1]/p2[3]);
pcDataT[2] = (float)(p2[2]/p2[3]);
}
// Rotate the normals, too
double n[3] = {(double)n1[0], (double)n1[1], (double)n1[2]}, n2[3];
matrixProduct331(R, n, n2);
double nNorm = sqrt(n2[0]*n2[0]+n2[1]*n2[1]+n2[2]*n2[2]);
if (nNorm>EPS)
{
nT[0]=(float)(n2[0]/nNorm);
nT[1]=(float)(n2[1]/nNorm);
nT[2]=(float)(n2[2]/nNorm);
}
}
return pct;
}
Mat genRandomMat(int rows, int cols, double mean, double stddev, int type)
{
cv::Mat meanMat = mean*cv::Mat::ones(1,1,type);
cv::Mat sigmaMat= stddev*cv::Mat::ones(1,1,type);
cv::RNG rng(time(0));
cv::Mat matr(rows, cols,type);
rng.fill(matr, cv::RNG::NORMAL, meanMat, sigmaMat);
return matr;
}
void getRandQuat(double q[4])
{
q[0] = (float)rand()/(float)(RAND_MAX);
q[1] = (float)rand()/(float)(RAND_MAX);
q[2] = (float)rand()/(float)(RAND_MAX);
q[3] = (float)rand()/(float)(RAND_MAX);
double n = sqrt(q[0]*q[0]+q[1]*q[1]+q[2]*q[2]+q[3]*q[3]);
q[0]/=n;
q[1]/=n;
q[2]/=n;
q[3]/=n;
q[0]=fabs(q[0]);
}
void getRandomRotation(double R[9])
{
double q[4];
getRandQuat(q);
quatToDCM(q, R);
}
void getRandomPose(double Pose[16])
{
double R[9], t[3];
srand((unsigned int)time(0));
getRandomRotation(R);
t[0] = (float)rand()/(float)(RAND_MAX);
t[1] = (float)rand()/(float)(RAND_MAX);
t[2] = (float)rand()/(float)(RAND_MAX);
rtToPose(R,t,Pose);
}
Mat addNoisePC(Mat pc, double scale)
{
Mat randT = genRandomMat(pc.rows,pc.cols,0,scale,CV_32FC1);
return randT + pc;
}
//////////// COMPUTE NORMALS OF POINT CLOUD //////////////////////////////////
/*
The routines below use the eigenvectors of the local covariance matrix
to compute the normals of a point cloud.
The algorithm uses FLANN and Joachim Kopp's fast 3x3 eigenvector computations
to improve accuracy and increase speed
Also, view point flipping as in point cloud library is implemented
*/
void meanCovLocalPC(const float* pc, const int ws, const int point_count, double CovMat[3][3], double Mean[4])
{
int i;
double accu[16]={0};
// For each point in the cloud
for (i = 0; i < point_count; ++i)
{
const float* cloud = &pc[i*ws];
accu [0] += cloud[0] * cloud[0];
accu [1] += cloud[0] * cloud[1];
accu [2] += cloud[0] * cloud[2];
accu [3] += cloud[1] * cloud[1]; // 4
accu [4] += cloud[1] * cloud[2]; // 5
accu [5] += cloud[2] * cloud[2]; // 8
accu [6] += cloud[0];
accu [7] += cloud[1];
accu [8] += cloud[2];
}
for (i = 0; i < 9; ++i)
accu[i]/=(double)point_count;
Mean[0] = accu[6];
Mean[1] = accu[7];
Mean[2] = accu[8];
Mean[3] = 0;
CovMat[0][0] = accu [0] - accu [6] * accu [6];
CovMat[0][1] = accu [1] - accu [6] * accu [7];
CovMat[0][2] = accu [2] - accu [6] * accu [8];
CovMat[1][1] = accu [3] - accu [7] * accu [7];
CovMat[1][2] = accu [4] - accu [7] * accu [8];
CovMat[2][2] = accu [5] - accu [8] * accu [8];
CovMat[1][0] = CovMat[0][1];
CovMat[2][0] = CovMat[0][2];
CovMat[2][1] = CovMat[1][2];
}
void meanCovLocalPCInd(const float* pc, const int* Indices, const int ws, const int point_count, double CovMat[3][3], double Mean[4])
{
int i;
double accu[16]={0};
for (i = 0; i < point_count; ++i)
{
const float* cloud = &pc[ Indices[i] * ws ];
accu [0] += cloud[0] * cloud[0];
accu [1] += cloud[0] * cloud[1];
accu [2] += cloud[0] * cloud[2];
accu [3] += cloud[1] * cloud[1]; // 4
accu [4] += cloud[1] * cloud[2]; // 5
accu [5] += cloud[2] * cloud[2]; // 8
accu [6] += cloud[0];
accu [7] += cloud[1];
accu [8] += cloud[2];
}
for (i = 0; i < 9; ++i)
accu[i]/=(double)point_count;
Mean[0] = accu[6];
Mean[1] = accu[7];
Mean[2] = accu[8];
Mean[3] = 0;
CovMat[0][0] = accu [0] - accu [6] * accu [6];
CovMat[0][1] = accu [1] - accu [6] * accu [7];
CovMat[0][2] = accu [2] - accu [6] * accu [8];
CovMat[1][1] = accu [3] - accu [7] * accu [7];
CovMat[1][2] = accu [4] - accu [7] * accu [8];
CovMat[2][2] = accu [5] - accu [8] * accu [8];
CovMat[1][0] = CovMat[0][1];
CovMat[2][0] = CovMat[0][2];
CovMat[2][1] = CovMat[1][2];
}
CV_EXPORTS int computeNormalsPC3d(const Mat& PC, Mat& PCNormals, const int NumNeighbors, const bool FlipViewpoint, const double viewpoint[3])
{
int i;
if (PC.cols!=3 && PC.cols!=6) // 3d data is expected
{
//return -1;
CV_Error(cv::Error::BadImageSize, "PC should have 3 or 6 elements in its columns");
}
int sizes[2] = {PC.rows, 3};
int sizesResult[2] = {PC.rows, NumNeighbors};
float* dataset = new float[PC.rows*3];
float* distances = new float[PC.rows*NumNeighbors];
int* indices = new int[PC.rows*NumNeighbors];
for (i=0; i<PC.rows; i++)
{
const float* src = (float*)(&PC.data[i*PC.step]);
float* dst = (float*)(&dataset[i*3]);
dst[0] = src[0];
dst[1] = src[1];
dst[2] = src[2];
}
Mat PCInput(2, sizes, CV_32F, dataset, 0);
void* flannIndex = indexPCFlann(PCInput);
Mat Indices(2, sizesResult, CV_32S, indices, 0);
Mat Distances(2, sizesResult, CV_32F, distances, 0);
queryPCFlann(flannIndex, PCInput, Indices, Distances);
destroyFlann(flannIndex);
flannIndex = 0;
PCNormals = Mat(PC.rows, 6, CV_32F);
for (i=0; i<PC.rows; i++)
{
double C[3][3], mu[4];
const float* pci = &dataset[i*3];
float* pcr = (float*)(&PCNormals.data[i*PCNormals.step]);
double nr[3];
int* indLocal = &indices[i*NumNeighbors];
// compute covariance matrix
meanCovLocalPCInd(dataset, indLocal, 3, NumNeighbors, C, mu);
// eigenvectors of covariance matrix
eigenLowest33(C, nr);
pcr[0] = pci[0];
pcr[1] = pci[1];
pcr[2] = pci[2];
if (FlipViewpoint)
{
flipNormalViewpoint(pci, viewpoint[0], viewpoint[1], viewpoint[2], &nr[0], &nr[1], &nr[2]);
}
pcr[3] = (float)nr[0];
pcr[4] = (float)nr[1];
pcr[5] = (float)nr[2];
}
delete[] indices;
delete[] distances;
delete[] dataset;
return 1;
}
//////////////////////////////////////// END OF NORMAL COMPUTATIONS ///////////////////////////////////
} // namespace ppf_match_3d
} // namespace cv
modules/surface_matching/src/ppf_match_3d.cpp 0 → 100644
View file @ 607f2bfc
//
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
// By downloading, copying, installing or using the software you agree to this license.
// If you do not agree to this license, do not download, install,
// copy or use the software.
//
//
// License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2014, OpenCV Foundation, all rights reserved.
// Third party copyrights are property of their respective owners.
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
//
// * Redistribution's in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
//
// * The name of the copyright holders may not be used to endorse or promote products
// derived from this software without specific prior written permission.
//
// This software is provided by the copyright holders and contributors "as is" and
// any express or implied warranties, including, but not limited to, the implied
// warranties of merchantability and fitness for a particular purpose are disclaimed.
// In no event shall the Intel Corporation or contributors be liable for any direct,
// indirect, incidental, special, exemplary, or consequential damages
// (including, but not limited to, procurement of substitute goods or services;
// loss of use, data, or profits; or business interruption) however caused
// and on any theory of liability, whether in contract, strict liability,
// or tort (including negligence or otherwise) arising in any way out of
// the use of this software, even if advised of the possibility of such damage.
//
// Author: Tolga Birdal <tbirdal AT gmail.com>
#include "precomp.hpp"
#include "hash_murmur.hpp"
namespace cv
{
namespace ppf_match_3d
{
// routines for assisting sort
static int qsortPoseCmp (const void * a, const void * b)
{
Pose3D* pose1 = *(Pose3D**)a;
Pose3D* pose2 = *(Pose3D**)b;
return ( pose2->numVotes - pose1->numVotes );
}
static int sortPoseClusters (const PoseCluster3D* a, const PoseCluster3D* b)
{
return ( a->numVotes > b->numVotes );
}
// simple hashing
/*static int hashPPFSimple(const double f[4], const double AngleStep, const double DistanceStep)
{
const unsigned char d1 = (unsigned char) (floor ((double)f[0] / (double)AngleStep));
const unsigned char d2 = (unsigned char) (floor ((double)f[1] / (double)AngleStep));
const unsigned char d3 = (unsigned char) (floor ((double)f[2] / (double)AngleStep));
const unsigned char d4 = (unsigned char) (floor ((double)f[3] / (double)DistanceStep));
int hashKey = (d1 | (d2<<8) | (d3<<16) | (d4<<24));
return hashKey;
}*/
// quantize ppf and hash it for proper indexing
static KeyType hashPPF(const double f[4], const double AngleStep, const double DistanceStep)
{
const int d1 = (int) (floor ((double)f[0] / (double)AngleStep));
const int d2 = (int) (floor ((double)f[1] / (double)AngleStep));
const int d3 = (int) (floor ((double)f[2] / (double)AngleStep));
const int d4 = (int) (floor ((double)f[3] / (double)DistanceStep));
int key[4]={d1,d2,d3,d4};
KeyType hashKey=0;
//hashMurmurx86(key, 4*sizeof(int), 42, &hashKey);
murmurHash(key, 4*sizeof(int), 42, &hashKey);
return hashKey;
}
/*static size_t hashMurmur(unsigned int key)
{
size_t hashKey=0;
hashMurmurx86((void*)&key, 4, 42, &hashKey);
return hashKey;
}*/
static double computeAlpha(const double p1[4], const double n1[4], const double p2[4])
{
double Tmg[3], mpt[3], row2[3], row3[3], alpha;
computeTransformRTyz(p1, n1, row2, row3, Tmg);
// checked row2, row3: They are correct
mpt[1] = Tmg[1] + row2[0] * p2[0] + row2[1] * p2[1] + row2[2] * p2[2];
mpt[2] = Tmg[2] + row3[0] * p2[0] + row3[1] * p2[1] + row3[2] * p2[2];
alpha=atan2(-mpt[2], mpt[1]);
if ( alpha != alpha)
{
return 0;
}
if (sin(alpha)*mpt[2]<0.0)
alpha=-alpha;
return (-alpha);
}
PPF3DDetector::PPF3DDetector()
{
samplingStepRelative = 0.05;
distanceStepRelative = 0.05;
SceneSampleStep = (int)(1/0.04);
angleStepRelative = 30;
angleStepRadians = (360.0/angleStepRelative)*M_PI/180.0;
angle_step = angleStepRadians;
trained = false;
setSearchParams();
}
PPF3DDetector::PPF3DDetector(const double RelativeSamplingStep, const double RelativeDistanceStep, const double NumAngles)
{
samplingStepRelative = RelativeSamplingStep;
distanceStepRelative = RelativeDistanceStep;
angleStepRelative = NumAngles;
angleStepRadians = (360.0/angleStepRelative)*M_PI/180.0;
//SceneSampleStep = 1.0/RelativeSceneSampleStep;
angle_step = angleStepRadians;
trained = false;
setSearchParams();
}
void PPF3DDetector::setSearchParams(const int numPoses, const double positionThreshold, const double rotationThreshold, const double minMatchScore, const bool useWeightedClustering)
{
NumPoses=numPoses;
if (positionThreshold<0)
PositionThreshold = samplingStepRelative;
else
PositionThreshold = positionThreshold;
if (rotationThreshold<0)
RotationThreshold = ((360/angle_step) / 180.0 * M_PI);
else
RotationThreshold = rotationThreshold;
UseWeightedAvg = useWeightedClustering;
MinMatchScore = minMatchScore;
}
// compute per point PPF as in paper
void PPF3DDetector::computePPFFeatures( const double p1[4], const double n1[4],
const double p2[4], const double n2[4],
double f[4])
{
/*
Vectors will be defined as of length 4 instead of 3, because of:
- Further SIMD vectorization
- Cache alignment
*/
double d[4] = {p2[0] - p1[0], p2[1] - p1[1], p2[2] - p1[2], 0};
double norm = TNorm3(d);
f[3] = norm;
if (norm)
{
d[0] /= f[3];
d[1] /= f[3];
d[2] /= f[3];
}
else
{
// TODO: Handle this
f[0] = 0;
f[1] = 0;
f[2] = 0;
return ;
}
/*
Tolga Birdal's note:
Issues of numerical stability is of concern here.
Bertram's suggestion: atan2(a dot b, |axb|)
My correction :
I guess it should be: angle = atan2(norm(cross(a,b)), dot(a,b))
The macro is implemented accordingly.
TAngle3 actually outputs in range [0, pi] as
Bertram suggests
*/
f[0] = TAngle3(n1, d);
f[1] = TAngle3(n2, d);
f[2] = TAngle3(n1, n2);
}
void PPF3DDetector::clearTrainingModels()
{
if (this->hash_nodes)
{
free(this->hash_nodes);
this->hash_nodes=0;
}
if (this->hash_table)
{
hashtableDestroy(this->hash_table);
this->hash_table=0;
}
}
PPF3DDetector::~PPF3DDetector()
{
clearTrainingModels();
}
// TODO: Check all step sizes to be positive
void PPF3DDetector::trainModel(const Mat &PC)
{
CV_Assert(PC.type() == CV_32F || PC.type() == CV_32FC1);
// compute bbox
float xRange[2], yRange[2], zRange[2];
computeBboxStd(PC, xRange, yRange, zRange);
// compute sampling step from diameter of bbox
float dx = xRange[1] - xRange[0];
float dy = yRange[1] - yRange[0];
float dz = zRange[1] - zRange[0];
float diameter = sqrt ( dx * dx + dy * dy + dz * dz );
float distanceStep = (float)(diameter * samplingStepRelative);
Mat sampled = samplePCByQuantization(PC, xRange, yRange, zRange, (float)samplingStepRelative,0);
int size = sampled.rows*sampled.rows;
hashtable_int* hashTable = hashtableCreate(size, NULL);
int numPPF = sampled.rows*sampled.rows;
PPF = Mat(numPPF, T_PPF_LENGTH, CV_32FC1);
int ppfStep = (int)PPF.step;
int sampledStep = (int)sampled.step;
// TODO: Maybe I could sample 1/5th of them here. Check the performance later.
int numRefPoints = sampled.rows;
// pre-allocate the hash nodes
hash_nodes = (THash*)calloc(numRefPoints*numRefPoints, sizeof(THash));
// TODO : This can easily be parallelized. But we have to lock hashtable_insert.
// I realized that performance drops when this loop is parallelized (unordered
// inserts into the hashtable
// But it is still there to be investigated. For now, I leave this unparallelized
// since this is just a training part.
for (int i=0; i<numRefPoints; i++)
{
float* f1 = (float*)(&sampled.data[i * sampledStep]);
const double p1[4] = {f1[0], f1[1], f1[2], 0};
const double n1[4] = {f1[3], f1[4], f1[5], 0};
//printf("///////////////////// NEW REFERENCE ////////////////////////\n");
for (int j=0; j<numRefPoints; j++)
{
// cannnot compute the ppf with myself
if (i!=j)
{
float* f2 = (float*)(&sampled.data[j * sampledStep]);
const double p2[4] = {f2[0], f2[1], f2[2], 0};
const double n2[4] = {f2[3], f2[4], f2[5], 0};
double f[4]={0};
computePPFFeatures(p1, n1, p2, n2, f);
KeyType hashValue = hashPPF(f, angleStepRadians, distanceStep);
double alpha = computeAlpha(p1, n1, p2);
unsigned int corrInd = i*numRefPoints+j;
unsigned int ppfInd = corrInd*ppfStep;
THash* hashNode = &hash_nodes[i*numRefPoints+j];
hashNode->id = hashValue;
hashNode->i = i;
hashNode->ppfInd = ppfInd;
hashtableInsertHashed(hashTable, hashValue, (void*)hashNode);
float* ppfRow = (float*)(&(PPF.data[ ppfInd ]));
ppfRow[0] = (float)f[0];
ppfRow[1] = (float)f[1];
ppfRow[2] = (float)f[2];
ppfRow[3] = (float)f[3];
ppfRow[4] = (float)alpha;
}
}
}
angle_step = angleStepRadians;
distance_step = distanceStep;
hash_table = hashTable;
sampled_step = sampledStep;
ppf_step = ppfStep;
num_ref_points = numRefPoints;
//samplingStepRelative = sampling_step_relative;
sampledPC = sampled;
trained = true;
}
///////////////////////// MATCHING ////////////////////////////////////////
bool PPF3DDetector::matchPose(const Pose3D& sourcePose, const Pose3D& targetPose)
{
// translational difference
double dv[3] = {targetPose.t[0]-sourcePose.t[0], targetPose.t[1]-sourcePose.t[1], targetPose.t[2]-sourcePose.t[2]};
double dNorm = sqrt(dv[0]*dv[0]+dv[1]*dv[1]+dv[2]*dv[2]);
const double phi = fabs ( sourcePose.angle - targetPose.angle );
return (phi<this->RotationThreshold && dNorm < this->PositionThreshold);
}
int PPF3DDetector::clusterPoses(Pose3D** poseList, int numPoses, std::vector<Pose3D*>& finalPoses)
{
std::vector<PoseCluster3D*> poseClusters;
poseClusters.clear();
finalPoses.clear();
// sort the poses for stability
qsort(poseList, numPoses, sizeof(Pose3D*), qsortPoseCmp);
for (int i=0; i<numPoses; i++)
{
Pose3D* pose = poseList[i];
bool assigned = false;
// search all clusters
for (size_t j=0; j<poseClusters.size() && !assigned; j++)
{
const Pose3D* poseCenter = poseClusters[j]->poseList[0];
if (matchPose(*pose, *poseCenter))
{
poseClusters[j]->addPose(pose);
assigned = true;
}
}
if (!assigned)
{
poseClusters.push_back ( new PoseCluster3D(pose));
}
}
// sort the clusters so that we could output multiple hypothesis
std::sort (poseClusters.begin(), poseClusters.end(), sortPoseClusters);
finalPoses.resize(poseClusters.size());
// TODO: Use MinMatchScore
if (UseWeightedAvg)
{
#if defined _OPENMP
#pragma omp parallel for
#endif
// uses weighting by the number of votes
for (size_t i=0; i<poseClusters.size(); i++)
{
// We could only average the quaternions. So I will make use of them here
double qAvg[4]={0}, tAvg[3]={0};
// Perform the final averaging
PoseCluster3D* curCluster = poseClusters[i];
std::vector<Pose3D*> curPoses = curCluster->poseList;
int curSize = (int)curPoses.size();
int numTotalVotes = 0;
for (int j=0; j<curSize; j++)
numTotalVotes += curPoses[j]->numVotes;
double wSum=0;
for (int j=0; j<curSize; j++)
{
const double w = (double)curPoses[j]->numVotes / (double)numTotalVotes;
qAvg[0]+= w*curPoses[j]->q[0];
qAvg[1]+= w*curPoses[j]->q[1];
qAvg[2]+= w*curPoses[j]->q[2];
qAvg[3]+= w*curPoses[j]->q[3];
tAvg[0]+= w*curPoses[j]->t[0];
tAvg[1]+= w*curPoses[j]->t[1];
tAvg[2]+= w*curPoses[j]->t[2];
wSum+=w;
}
tAvg[0]/=wSum;
tAvg[1]/=wSum;
tAvg[2]/=wSum;
qAvg[0]/=wSum;
qAvg[1]/=wSum;
qAvg[2]/=wSum;
qAvg[3]/=wSum;
curPoses[0]->updatePoseQuat(qAvg, tAvg);
curPoses[0]->numVotes=curCluster->numVotes;
finalPoses[i]=curPoses[0]->clone();
delete poseClusters[i];
}
}
else
{
#if defined _OPENMP
#pragma omp parallel for
#endif
for (size_t i=0; i<poseClusters.size(); i++)
{
// We could only average the quaternions. So I will make use of them here
double qAvg[4]={0}, tAvg[3]={0};
// Perform the final averaging
PoseCluster3D* curCluster = poseClusters[i];
std::vector<Pose3D*> curPoses = curCluster->poseList;
int curSize = (int)curPoses.size();
for (int j=0; j<curSize; j++)
{
qAvg[0]+= curPoses[j]->q[0];
qAvg[1]+= curPoses[j]->q[1];
qAvg[2]+= curPoses[j]->q[2];
qAvg[3]+= curPoses[j]->q[3];
tAvg[0]+= curPoses[j]->t[0];
tAvg[1]+= curPoses[j]->t[1];
tAvg[2]+= curPoses[j]->t[2];
}
tAvg[0]/=(double)curSize;
tAvg[1]/=(double)curSize;
tAvg[2]/=(double)curSize;
qAvg[0]/=(double)curSize;
qAvg[1]/=(double)curSize;
qAvg[2]/=(double)curSize;
qAvg[3]/=(double)curSize;
curPoses[0]->updatePoseQuat(qAvg, tAvg);
curPoses[0]->numVotes=curCluster->numVotes;
finalPoses[i]=curPoses[0]->clone();
// we won't need this
delete poseClusters[i];
}
}
poseClusters.clear();
return 0;
}
void PPF3DDetector::match(const Mat& pc, std::vector<Pose3D*>& results, const double RelativeSceneSampleStep, const double RelativeSceneDistance)
{
if (!trained)
{
throw cv::Exception(cv::Error::StsError, "The model is not trained. Cannot match without training", __FUNCTION__, __FILE__, __LINE__);
}
CV_Assert(pc.type() == CV_32F || pc.type() == CV_32FC1);
CV_Assert(RelativeSceneSampleStep<=1 && RelativeSceneSampleStep>0);
SceneSampleStep = (int)(1.0/RelativeSceneSampleStep);
//int numNeighbors = 10;
int numAngles = (int) (floor (2 * M_PI / angle_step));
float distanceStep = (float)distance_step;
unsigned int n = num_ref_points;
Pose3D** poseList;
int sceneSamplingStep = SceneSampleStep;
// compute bbox
float xRange[2], yRange[2], zRange[2];
computeBboxStd(pc, xRange, yRange, zRange);
// sample the point cloud
/*float dx = xRange[1] - xRange[0];
float dy = yRange[1] - yRange[0];
float dz = zRange[1] - zRange[0];
float diameter = sqrt ( dx * dx + dy * dy + dz * dz );
float distanceSampleStep = diameter * RelativeSceneDistance;*/
Mat sampled = samplePCByQuantization(pc, xRange, yRange, zRange, (float)RelativeSceneDistance, 0);
// allocate the accumulator : Moved this to the inside of the loop
/*#if !defined (_OPENMP)
unsigned int* accumulator = (unsigned int*)calloc(numAngles*n, sizeof(unsigned int));
#endif*/
poseList = (Pose3D**)calloc((sampled.rows/sceneSamplingStep)+4, sizeof(Pose3D*));
#if defined _OPENMP
#pragma omp parallel for
#endif
for (int i = 0; i < sampled.rows; i += sceneSamplingStep)
{
unsigned int refIndMax = 0, alphaIndMax = 0;
unsigned int maxVotes = 0;
float* f1 = (float*)(&sampled.data[i * sampled.step]);
const double p1[4] = {f1[0], f1[1], f1[2], 0};
const double n1[4] = {f1[3], f1[4], f1[5], 0};
double *row2, *row3, tsg[3]={0}, Rsg[9]={0}, RInv[9]={0};
unsigned int* accumulator = (unsigned int*)calloc(numAngles*n, sizeof(unsigned int));
computeTransformRT(p1, n1, Rsg, tsg);
row2=&Rsg[3];
row3=&Rsg[6];
// Tolga Birdal's notice:
// As a later update, we might want to look into a local neighborhood only
// To do this, simply search the local neighborhood by radius look up
// and collect the neighbors to compute the relative pose
for (int j = 0; j < sampled.rows; j ++)
{
if (i!=j)
{
float* f2 = (float*)(&sampled.data[j * sampled.step]);
const double p2[4] = {f2[0], f2[1], f2[2], 0};
const double n2[4] = {f2[3], f2[4], f2[5], 0};
double p2t[4], alpha_scene;
double f[4]={0};
computePPFFeatures(p1, n1, p2, n2, f);
KeyType hashValue = hashPPF(f, angle_step, distanceStep);
// we don't need to call this here, as we already estimate the tsg from scene reference point
// double alpha = computeAlpha(p1, n1, p2);
p2t[1] = tsg[1] + row2[0] * p2[0] + row2[1] * p2[1] + row2[2] * p2[2];
p2t[2] = tsg[2] + row3[0] * p2[0] + row3[1] * p2[1] + row3[2] * p2[2];
alpha_scene=atan2(-p2t[2], p2t[1]);
if ( alpha_scene != alpha_scene)
{
continue;
}
if (sin(alpha_scene)*p2t[2]<0.0)
alpha_scene=-alpha_scene;
alpha_scene=-alpha_scene;
hashnode_i* node = hashtableGetBucketHashed(hash_table, (hashValue));
while (node)
{
THash* tData = (THash*) node->data;
int corrI = (int)tData->i;
int ppfInd = (int)tData->ppfInd;
float* ppfCorrScene = (float*)(&PPF.data[ppfInd]);
double alpha_model = (double)ppfCorrScene[T_PPF_LENGTH-1];
double alpha = alpha_model - alpha_scene;
/* Tolga Birdal's note: Map alpha to the indices:
atan2 generates results in (-pi pi]
That's why alpha should be in range [-2pi 2pi]
So the quantization would be :
numAngles * (alpha+2pi)/(4pi)
*/
//printf("%f\n", alpha);
int alpha_index = (int)(numAngles*(alpha + 2*M_PI) / (4*M_PI));
unsigned int accIndex = corrI * numAngles + alpha_index;
accumulator[accIndex]++;
node = node->next;
}
}
}
// Maximize the accumulator
for (unsigned int k = 0; k < n; k++)
{
for (int j = 0; j < numAngles; j++)
{
const unsigned int accInd = k*numAngles + j;
const unsigned int accVal = accumulator[ accInd ];
if (accVal > maxVotes)
{
maxVotes = accVal;
refIndMax = k;
alphaIndMax = j;
}
#if !defined (_OPENMP)
accumulator[accInd ] = 0;
#endif
}
}
// invert Tsg : Luckily rotation is orthogonal: Inverse = Transpose.
// We are not required to invert.
double tInv[3], tmg[3], Rmg[9];
matrixTranspose33(Rsg, RInv);
matrixProduct331(RInv, tsg, tInv);
double TsgInv[16] = { RInv[0], RInv[1], RInv[2], -tInv[0],
RInv[3], RInv[4], RInv[5], -tInv[1],
RInv[6], RInv[7], RInv[8], -tInv[2],
0, 0, 0, 1
};
// TODO : Compute pose
const float* fMax = (float*)(&sampledPC.data[refIndMax * sampledPC.step]);
const double pMax[4] = {fMax[0], fMax[1], fMax[2], 1};
const double nMax[4] = {fMax[3], fMax[4], fMax[5], 1};
computeTransformRT(pMax, nMax, Rmg, tmg);
row2=&Rsg[3];
row3=&Rsg[6];
double Tmg[16] = { Rmg[0], Rmg[1], Rmg[2], tmg[0],
Rmg[3], Rmg[4], Rmg[5], tmg[1],
Rmg[6], Rmg[7], Rmg[8], tmg[2],
0, 0, 0, 1
};
// convert alpha_index to alpha
int alpha_index = alphaIndMax;
double alpha = (alpha_index*(4*M_PI))/numAngles-2*M_PI;
// Equation 2:
double Talpha[16]={0};
getUnitXRotation_44(alpha, Talpha);
double Temp[16]={0};
double Pose[16]={0};
matrixProduct44(Talpha, Tmg, Temp);
matrixProduct44(TsgInv, Temp, Pose);
Pose3D *ppf = new Pose3D(alpha, refIndMax, maxVotes);
ppf->updatePose(Pose);
poseList[i/sceneSamplingStep] = ppf;
#if defined (_OPENMP)
free(accumulator);
#endif
}
// TODO : Make the parameters relative if not arguments.
//double MinMatchScore = 0.5;
int numPosesAdded = sampled.rows/sceneSamplingStep;
clusterPoses(poseList, numPosesAdded, results);
// free up the used space
sampled.release();
for (int i=0; i<numPosesAdded; i++)
{
Pose3D* pose = poseList[i];
delete pose;
poseList[i]=0;
}
free(poseList);
/*#if !defined (_OPENMP)
free(accumulator);
#endif*/
}
} // namespace ppf_match_3d
} // namespace cv
modules/surface_matching/src/precomp.hpp 0 → 100644
View file @ 607f2bfc
/*
By downloading, copying, installing or using the software you agree to this
license. If you do not agree to this license, do not download, install,
copy or use the software.
License Agreement
For Open Source Computer Vision Library
(3-clause BSD License)
Copyright (C) 2014, OpenCV Foundation, all rights reserved.
Third party copyrights are property of their respective owners.
Redistribution and use in source and binary forms, with or without modification,
are permitted provided that the following conditions are met:
* Redistributions of source code must retain the above copyright notice,
this list of conditions and the following disclaimer.
* Redistributions in binary form must reproduce the above copyright notice,
this list of conditions and the following disclaimer in the documentation
and/or other materials provided with the distribution.
* Neither the names of the copyright holders nor the names of the contributors
may be used to endorse or promote products derived from this software
without specific prior written permission.
This software is provided by the copyright holders and contributors "as is" and
any express or implied warranties, including, but not limited to, the implied
warranties of merchantability and fitness for a particular purpose are
disclaimed. In no event shall copyright holders or contributors be liable for
any direct, indirect, incidental, special, exemplary, or consequential damages
(including, but not limited to, procurement of substitute goods or services;
loss of use, data, or profits; or business interruption) however caused
and on any theory of liability, whether in contract, strict liability,
or tort (including negligence or otherwise) arising in any way out of
the use of this software, even if advised of the possibility of such damage.
*/
#ifndef __OPENCV_SURFACE_MATCHING_PRECOMP_HPP__
#define __OPENCV_SURFACE_MATCHING_PRECOMP_HPP__
#include "opencv2/ppf_match_3d.hpp"
#include "opencv2/icp.hpp"
#include "opencv2/surface_matching/ppf_helpers.hpp"
#include <string>
#include <cstdio>
#include <cstdlib>
#include <math.h>
#include <ctime>
#include <fstream>
#include <iostream>
#if defined (_OPENMP)
#include<omp.h>
#endif
#include <sstream> // WTF bananas
#include "opencv2/flann.hpp"
#include "c_utils.hpp"
#endif /* __OPENCV_SURFACE_MATCHING_PRECOMP_HPP__ */
modules/surface_matching/src/t_hash_int.cpp 0 → 100644
View file @ 607f2bfc
//
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
// By downloading, copying, installing or using the software you agree to this license.
// If you do not agree to this license, do not download, install,
// copy or use the software.
//
//
// License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2014, OpenCV Foundation, all rights reserved.
// Third party copyrights are property of their respective owners.
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
//
// * Redistribution's in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
//
// * The name of the copyright holders may not be used to endorse or promote products
// derived from this software without specific prior written permission.
//
// This software is provided by the copyright holders and contributors "as is" and
// any express or implied warranties, including, but not limited to, the implied
// warranties of merchantability and fitness for a particular purpose are disclaimed.
// In no event shall the Intel Corporation or contributors be liable for any direct,
// indirect, incidental, special, exemplary, or consequential damages
// (including, but not limited to, procurement of substitute goods or services;
// loss of use, data, or profits; or business interruption) however caused
// and on any theory of liability, whether in contract, strict liability,
// or tort (including negligence or otherwise) arising in any way out of
// the use of this software, even if advised of the possibility of such damage.
//
// Author: Tolga Birdal <tbirdal AT gmail.com>
#include "precomp.hpp"
namespace cv
{
namespace ppf_match_3d
{
// This magic value is just
#define T_HASH_MAGIC 427462442
size_t hash( unsigned int a);
// default hash function
size_t hash( unsigned int a)
{
a = (a+0x7ed55d16) + (a<<12);
a = (a^0xc761c23c) ^ (a>>19);
a = (a+0x165667b1) + (a<<5);
a = (a+0xd3a2646c) ^ (a<<9);
a = (a+0xfd7046c5) + (a<<3);
a = (a^0xb55a4f09) ^ (a>>16);
return a;
}
hashtable_int *hashtableCreate(size_t size, size_t (*hashfunc)(unsigned int))
{
hashtable_int *hashtbl;
if (size < 16)
{
size = 16;
}
else
{
size = (size_t)next_power_of_two((unsigned int)size);
}
hashtbl=(hashtable_int*)malloc(sizeof(hashtable_int));
if (!hashtbl)
return NULL;
hashtbl->nodes=(hashnode_i**)calloc(size, sizeof(struct hashnode_i*));
if (!hashtbl->nodes)
{
free(hashtbl);
return NULL;
}
hashtbl->size=size;
if (hashfunc)
hashtbl->hashfunc=hashfunc;
else
hashtbl->hashfunc=hash;
return hashtbl;
}
void hashtableDestroy(hashtable_int *hashtbl)
{
size_t n;
struct hashnode_i *node, *oldnode;
for (n=0; n<hashtbl->size; ++n)
{
node=hashtbl->nodes[n];
while (node)
{
oldnode=node;
node=node->next;
free(oldnode);
}
}
free(hashtbl->nodes);
free(hashtbl);
}
int hashtableInsert(hashtable_int *hashtbl, KeyType key, void *data)
{
struct hashnode_i *node;
size_t hash=hashtbl->hashfunc(key)%hashtbl->size;
/* fpruintf(stderr, "hashtbl_insert() key=%s, hash=%d, data=%s\n", key, hash, (char*)data);*/
node=hashtbl->nodes[hash];
while (node)
{
if (node->key!= key)
{
node->data=data;
return 0;
}
node=node->next;
}
node=(hashnode_i*)malloc(sizeof(struct hashnode_i));
if (!node)
return -1;
node->key=key;
node->data=data;
node->next=hashtbl->nodes[hash];
hashtbl->nodes[hash]=node;
return 0;
}
int hashtableInsertHashed(hashtable_int *hashtbl, KeyType key, void *data)
{
struct hashnode_i *node;
size_t hash = key % hashtbl->size;
/* fpruintf(stderr, "hashtbl_insert() key=%s, hash=%d, data=%s\n", key, hash, (char*)data);*/
node=hashtbl->nodes[hash];
while (node)
{
if (node->key!= key)
{
node->data=data;
return 0;
}
node=node->next;
}
node=(hashnode_i*)malloc(sizeof(struct hashnode_i));
if (!node)
return -1;
node->key=key;
node->data=data;
node->next=hashtbl->nodes[hash];
hashtbl->nodes[hash]=node;
return 0;
}
int hashtableRemove(hashtable_int *hashtbl, KeyType key)
{
struct hashnode_i *node, *prevnode=NULL;
size_t hash=hashtbl->hashfunc(key)%hashtbl->size;
node=hashtbl->nodes[hash];
while (node)
{
if (node->key==key)
{
if (prevnode)
prevnode->next=node->next;
else
hashtbl->nodes[hash]=node->next;
free(node);
return 0;
}
prevnode=node;
node=node->next;
}
return -1;
}
void *hashtableGet(hashtable_int *hashtbl, KeyType key)
{
struct hashnode_i *node;
size_t hash=hashtbl->hashfunc(key)%hashtbl->size;
/* fprintf(stderr, "hashtbl_get() key=%s, hash=%d\n", key, hash);*/
node=hashtbl->nodes[hash];
while (node)
{
if (node->key==key)
return node->data;
node=node->next;
}
return NULL;
}
hashnode_i* hashtableGetBucketHashed(hashtable_int *hashtbl, KeyType key)
{
size_t hash = key % hashtbl->size;
return hashtbl->nodes[hash];
}
int hashtableResize(hashtable_int *hashtbl, size_t size)
{
hashtable_int newtbl;
size_t n;
struct hashnode_i *node,*next;
newtbl.size=size;
newtbl.hashfunc=hashtbl->hashfunc;
newtbl.nodes=(hashnode_i**)calloc(size, sizeof(struct hashnode_i*));
if (!newtbl.nodes)
return -1;
for (n=0; n<hashtbl->size; ++n)
{
for (node=hashtbl->nodes[n]; node; node=next)
{
next = node->next;
hashtableInsert(&newtbl, node->key, node->data);
hashtableRemove(hashtbl, node->key);
}
}
free(hashtbl->nodes);
hashtbl->size=newtbl.size;
hashtbl->nodes=newtbl.nodes;
return 0;
}
int hashtableWrite(const hashtable_int * hashtbl, const size_t dataSize, FILE* f)
{
size_t hashMagic=T_HASH_MAGIC;
size_t n=hashtbl->size;
size_t i;
fwrite(&hashMagic, sizeof(size_t),1, f);
fwrite(&n, sizeof(size_t),1, f);
fwrite(&dataSize, sizeof(size_t),1, f);
for (i=0; i<hashtbl->size; i++)
{
struct hashnode_i* node=hashtbl->nodes[i];
size_t noEl=0;
while (node)
{
noEl++;
node=node->next;
}
fwrite(&noEl, sizeof(size_t),1, f);
node=hashtbl->nodes[i];
while (node)
{
fwrite(&node->key, sizeof(KeyType), 1, f);
fwrite(&node->data, dataSize, 1, f);
node=node->next;
}
}
return 1;
}
void hashtablePrint(hashtable_int *hashtbl)
{
size_t n;
struct hashnode_i *node,*next;
for (n=0; n<hashtbl->size; ++n)
{
for (node=hashtbl->nodes[n]; node; node=next)
{
next = node->next;
std::cout<<"Key : "<<node->key<<", Data : "<<node->data<<std::endl;
}
}
}
hashtable_int *hashtableRead(FILE* f)
{
size_t hashMagic = 0;
size_t n = 0, status;
hashtable_int *hashtbl = 0;
status = fread(&hashMagic, sizeof(size_t),1, f);
if (status && hashMagic==T_HASH_MAGIC)
{
size_t i;
size_t dataSize;
status = fread(&n, sizeof(size_t),1, f);
status = fread(&dataSize, sizeof(size_t),1, f);
hashtbl=hashtableCreate(n, hash);
for (i=0; i<hashtbl->size; i++)
{
size_t j=0;
status = fread(&n, sizeof(size_t),1, f);
for (j=0; j<n; j++)
{
int key=0;
void* data=0;
status = fread(&key, sizeof(KeyType), 1, f);
if (dataSize>sizeof(void*))
{
data=malloc(dataSize);
if (!data)
return NULL;
status = fread(data, dataSize, 1, f);
}
else
status = fread(&data, dataSize, 1, f);
hashtableInsert(hashtbl, key, data);
//free(key);
}
}
}
else
return 0;
return hashtbl;
}
} // namespace ppf_match_3d
} // namespace cv
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