Inside the InsightToolkit directory there is the Examples/Segmentation/ConnectedThresholdImageFilter.xx file.
Now, I want to make it operate on a three dimensional image. In this case, will the changes that I have to do bee applied to those lines of code (lines 102-110):
int main( int argc, char *argv[])
{
if( argc < 7 )
{
std::cerr << "Missing Parameters " << std::endl;
std::cerr << "Usage: " << argv[0];
std::cerr << " inputImage outputImage seedX seedY lowerThreshold upperThreshold" << std::endl;
return 1;
}
}
And, in order to do that, should I add the following seedZ to:
std::cerr << " inputImage outputImage seedX seedY lowerThreshold upperThreshold" << std::endl;
And, what change should I perform to the arguments in this case?
You need to add a z parameter like you mentioned in your post.
Then in the example, you need to make sure that the inputImage and the outputImage are set to be 3D. I don't have the code for the example but somewhere along the lines of:
typedef itk::Image< PixelType, 3 > InputImageType;
Hope this helps
Related
I want to use the Efficient Ransac implementation of CGAL, but whenever I try to set my own parameters, the algorithm doesn't detect any shape anymore.
This work is related to the Polyfit implementation in CGAL. I want to fine tune the plane detection to see the influence it has on the algorithm. When I use the standard call to ransac.detect(), it works perfectly. However, when I want to set my own parameters it just doesn't find any plane, even if I set them manually to the default values.
Here is my code, strongly related to this example
#include <CGAL/Exact_predicates_inexact_constructions_kernel.h>
#include <CGAL/IO/read_xyz_points.h>
#include <CGAL/IO/Writer_OFF.h>
#include <CGAL/property_map.h>
#include <CGAL/Surface_mesh.h>
#include <CGAL/Shape_detection/Efficient_RANSAC.h>
#include <CGAL/Polygonal_surface_reconstruction.h>
#ifdef CGAL_USE_SCIP
#include <CGAL/SCIP_mixed_integer_program_traits.h>
typedef CGAL::SCIP_mixed_integer_program_traits<double> MIP_Solver;
#elif defined(CGAL_USE_GLPK)
#include <CGAL/GLPK_mixed_integer_program_traits.h>
typedef CGAL::GLPK_mixed_integer_program_traits<double> MIP_Solver;
#endif
#if defined(CGAL_USE_GLPK) || defined(CGAL_USE_SCIP)
#include <CGAL/Timer.h>
#include <fstream>
typedef CGAL::Exact_predicates_inexact_constructions_kernel Kernel;
typedef Kernel::Point_3 Point;
typedef Kernel::Vector_3 Vector;
// Point with normal, and plane index
typedef boost::tuple<Point, Vector, int> PNI;
typedef std::vector<PNI> Point_vector;
typedef CGAL::Nth_of_tuple_property_map<0, PNI> Point_map;
typedef CGAL::Nth_of_tuple_property_map<1, PNI> Normal_map;
typedef CGAL::Nth_of_tuple_property_map<2, PNI> Plane_index_map;
typedef CGAL::Shape_detection::Efficient_RANSAC_traits<Kernel, Point_vector, Point_map, Normal_map> Traits;
typedef CGAL::Shape_detection::Efficient_RANSAC<Traits> Efficient_ransac;
typedef CGAL::Shape_detection::Plane<Traits> Plane;
typedef CGAL::Shape_detection::Point_to_shape_index_map<Traits> Point_to_shape_index_map;
typedef CGAL::Polygonal_surface_reconstruction<Kernel> Polygonal_surface_reconstruction;
typedef CGAL::Surface_mesh<Point> Surface_mesh;
int main(int argc, char ** argv)
{
Point_vector points;
// Loads point set from a file.
const std::string &input_file = argv[1];
//const std::string input_file(input);
std::ifstream input_stream(input_file.c_str());
if (input_stream.fail()) {
std::cerr << "failed open file \'" <<input_file << "\'" << std::endl;
return EXIT_FAILURE;
}
std::cout << "Loading point cloud: " << input_file << "...";
CGAL::Timer t;
t.start();
if (!input_stream ||
!CGAL::read_xyz_points(input_stream,
std::back_inserter(points),
CGAL::parameters::point_map(Point_map()).normal_map(Normal_map())))
{
std::cerr << "Error: cannot read file " << input_file << std::endl;
return EXIT_FAILURE;
}
else
std::cout << " Done. " << points.size() << " points. Time: " << t.time() << " sec." << std::endl;
// Shape detection
Efficient_ransac ransac;
ransac.set_input(points);
ransac.add_shape_factory<Plane>();
std::cout << "Extracting planes...";
t.reset();
// Set parameters for shape detection.
Efficient_ransac::Parameters parameters;
// Set probability to miss the largest primitive at each iteration.
parameters.probability = 0.05;
// Detect shapes with at least 500 points.
parameters.min_points = 100;
// Set maximum Euclidean distance between a point and a shape.
parameters.epsilon = 0.01;
// Set maximum Euclidean distance between points to be clustered.
parameters.cluster_epsilon = 0.01;
// Set maximum normal deviation.
// 0.9 < dot(surface_normal, point_normal);
parameters.normal_threshold = 0.9;
// Detect shapes.
ransac.detect(parameters);
//ransac.detect();
Efficient_ransac::Plane_range planes = ransac.planes();
std::size_t num_planes = planes.size();
std::cout << " Done. " << num_planes << " planes extracted. Time: " << t.time() << " sec." << std::endl;
// Stores the plane index of each point as the third element of the tuple.
Point_to_shape_index_map shape_index_map(points, planes);
for (std::size_t i = 0; i < points.size(); ++i) {
// Uses the get function from the property map that accesses the 3rd element of the tuple.
int plane_index = get(shape_index_map, i);
points[i].get<2>() = plane_index;
}
//////////////////////////////////////////////////////////////////////////
std::cout << "Generating candidate faces...";
t.reset();
Polygonal_surface_reconstruction algo(
points,
Point_map(),
Normal_map(),
Plane_index_map()
);
std::cout << " Done. Time: " << t.time() << " sec." << std::endl;
//////////////////////////////////////////////////////////////////////////
Surface_mesh model;
std::cout << "Reconstructing...";
t.reset();
if (!algo.reconstruct<MIP_Solver>(model)) {
std::cerr << " Failed: " << algo.error_message() << std::endl;
return EXIT_FAILURE;
}
const std::string& output_file(input_file+"_result.off");
std::ofstream output_stream(output_file.c_str());
if (output_stream && CGAL::write_off(output_stream, model))
std::cout << " Done. Saved to " << output_file << ". Time: " << t.time() << " sec." << std::endl;
else {
std::cerr << " Failed saving file." << std::endl;
return EXIT_FAILURE;
}
//////////////////////////////////////////////////////////////////////////
// Also stores the candidate faces as a surface mesh to a file
Surface_mesh candidate_faces;
algo.output_candidate_faces(candidate_faces);
const std::string& candidate_faces_file(input_file+"_candidate_faces.off");
std::ofstream candidate_stream(candidate_faces_file.c_str());
if (candidate_stream && CGAL::write_off(candidate_stream, candidate_faces))
std::cout << "Candidate faces saved to " << candidate_faces_file << "." << std::endl;
return EXIT_SUCCESS;
}
#else
int main(int, char**)
{
std::cerr << "This test requires either GLPK or SCIP.\n";
return EXIT_SUCCESS;
}
#endif // defined(CGAL_USE_GLPK) || defined(CGAL_USE_SCIP)
When launched, I have the following message:
Loading point cloud: Scene1/test.xyz... Done. 169064 points. Time: 0.428 sec.
Extracting planes... Done. 0 planes extracted. Time: 8.328 sec.
Generating candidate faces... Done. Time: 0.028 sec.
Reconstructing... Failed: at least 4 planes required to reconstruct a closed surface mesh (only 1 provided)
While I have this when launching the code the ransac detection function without parameters:
Loading point cloud: Scene1/test.xyz... Done. 169064 points. Time: 0.448 sec.
Extracting planes... Done. 18 planes extracted. Time: 3.088 sec.
Generating candidate faces... Done. Time: 94.536 sec.
Reconstructing... Done. Saved to Scene1/test.xyz_result.off. Time: 30.28 sec.
Can someone help me setting my own parameters for the ransac shape detection?
However, when I want to set my own parameters it just doesn't find any
plane, even if I set them manually to the default values.
Just to be sure: "setting them manually to the default values" is not what you are doing in the code you shared.
Default values are documented as:
1% of the total number of points for min_points, which should be around 1700 points in your case, not 100
1% of the bounding box diagonal for epsilon and cluster_epsilon. For that obviously I don't know if that is what you used (0.01) as I don't have access to your point set, but if you want to reproduce default values, you should use the CGAL::Bbox_3 object at some point
If you use these values, there's no reason why it should behave differently than with no parameters given (if it does not work, then please let me know because there may be a bug).
I am a beginner to ITK and c++. I have the following code where I can get the height and width of an image. Instead of giving the input image in the console, I want to do it in the code itself. How do I directly give the input image to this code?
#include "itkImage.h"
#include "itkImageFileReader.h"
int main()
{
mat m("filename");
imshow("windowname", m);
}
// verify command line arguments
if( argc < 2 )
{
std::cout << "usage: " << std::endl;
std::cerr << argv[0] << " inputimagefile" << std::endl;
return exit_failure;
}
typedef itk::image<float, 2> imagetype;
typedef itk::imagefilereader<imagetype> readertype;
readertype::pointer reader = readertype::new();
reader->setfilename( argv[1] );
reader->update();
std::cout << reader->getoutput()->getlargestpossibleregion().getsize()[0] << " "
<< reader->getoutput()->getlargestpossibleregion().getsize()[1] << std::endl;
// an example image had w = 200 and h = 100 (it is wider than it is tall). the above output
// 200 100
// so w = getsize()[0]
// and h = getsize()[1]
// a pixel inside the region
itk::index<2> indexinside;
indexinside[0] = 150;
indexinside[1] = 50;
std::cout << reader->getoutput()-
>getlargestpossibleregion().isinside(indexinside) << std::endl;
// a pixel outside the region
itk::index<2> indexoutside;
indexoutside[0] = 50;
indexoutside[1] = 150;
std::cout << reader->getoutput()- >getlargestpossibleregion().isinside(indexoutside) << std::endl;
// this means that the [0] component of the index is referencing the left to right (column) index
// and the [1] component of index is referencing the top to bottom (row) index
return exit_success;
}
Change the line reader->setfilename( argv[1] ); by reader->setfilename( "C:/path/to/file.png" );
I assume that
mat m("filename");
imshow("windowname", m);
sneaked in from some unrelated code? Otherwise the example would not compile.
I am trying to load the pixel rgb/ga information of a png image into a matrix, using the library png++, to do some computations with them.
My Code (which does not work at the moment):
#include <iostream>
#include <png++/image.hpp>
#include <png++/rgb_pixel.hpp>
int main(int argc, const char * argv[]) {
const std::string path="img_03.png";
png::image< png::basic_rgb_pixel <unsigned char> > pic(path);
pixel=pic.get_pixel(0, 0);
pixelp = &pixel;
std::cout << "value=" << pic[10][10].red << std::endl; //output: '?'
std::cout << "value=" << pixel.red << std::endl; //nothing
std::cout << "pointer=" << pixelp << std::endl; //delivers adress
pic.read(path);
std::cout << "value=" << pic[10][10].red << std::endl; //nothing
pic.write("picOutput.png"); //same picture
return 0;
}
However, none of those methods work to get the rgb values of each pixel.
If there is another way to get rgb/ga information of each pixel, please mention it.
The line pic.write("picOutput.png"); delivers the same png i loaded in the line pic.read(path). This is a personal exercise for me to get more used to C++, criticise my code as much as you can.
Thanks!
Here comes the solution:
change line:
std::cout << "value=" << pic[10][10].red << std::endl; //nothing
with:
std::cout << "value=" << (int) pic[10][10].red << std::endl; //nothing
because std::cout can't output types of unsigned char.
Thanks to Alex!
For in-depth explanation, look here:
cout not printing unsigned char
I am trying to implement K-means clustering algorithm on a point cloud. I am not sure, however, how to import the data as input for the k-means member of pcl. The documentation has proven to be a little confusing.
So far I have imported the pcd into a point cloud and transferred it into a vector but I dont know how to proceed from here and initialize Kmeans directly.
int main (int argc, char** argv)
{
pcl::PointCloud<pcl::PointXYZ>::Ptr cloud_in(new pcl::PointCloud<pcl::PointXYZ>);
std::vector<pcl::PointXYZ> cloud;
pcl::io::loadPCDFile ("Scene02 - Cloud.pcd", *cloud_in);
for (int i = 0; i < cloud_in->size(); i++)
{
cloud[i] = cloud_in->points[i];
}
pcl::Kmeans real(300000, 3);
real.setInputData(cloud);
}
I realize that the syntax is wrong but I am not sure what the right one is either.
This function is very odd as compared to how pcl generally does things (centering around custom point types). Basically, the oddity is that you have to enter points via a specified dimension vector rather than a custom point type. Here is tested and functional sample code: (obviously you need to provide your own file name, and you will likely want to adjust cluster size)
int main(int argc, char** argv) {
std::string filePath = "../PointCloudFiles/beaconJR.pcd";
pcl::PointCloud<pcl::PointXYZ>::Ptr tempCloud(new pcl::PointCloud<pcl::PointXYZ>);
if (pcl::io::loadPCDFile(filePath, *tempCloud) == -1) //* load the file
{printf("failed file load!\n");}
else
{
pcl::Kmeans real(static_cast<int> (tempCloud->points.size()), 3);
real.setClusterSize(3); //it is important that you set this term appropriately for your application
for (size_t i = 0; i < tempCloud->points.size(); i++)
{
std::vector<float> data(3);
data[0] = tempCloud->points[i].x;
data[1] = tempCloud->points[i].y;
data[2] = tempCloud->points[i].z;
real.addDataPoint(data);
}
real.kMeans();
// get the cluster centroids
pcl::Kmeans::Centroids centroids = real.get_centroids();
std::cout << "points in total Cloud : " << tempCloud->points.size() << std::endl;
std::cout << "centroid count: " << centroids.size() << std::endl;
for (int i = 0; i<centroids.size(); i++)
{
std::cout << i << "_cent output: x: " << centroids[i][0] << " ,";
std::cout << "y: " << centroids[i][1] << " ,";
std::cout << "z: " << centroids[i][2] << std::endl;
}
}
std::cin.get();
std::cin.get();
}
Cheers!
--edit
As far as visualizing the clusters. I think (untested) that "pcl::Kmeans::PointsToClusters" is going to give you a vector with custer labels per point which you can use to index through the original cloud and separate them.
I notice that this simple ArrayFire program is causing loaded TIFF images to be heavily distorted:
#include <iostream>
#include <arrayfire.h>
int main( int argc, char** argv ) {
af::array img = af::loadImage( argv[1] );
double mn, mx;
unsigned idxn, idxx;
af::min( &mn, &idxn, img );
af::max( &mx, &idxx, img );
std::cout << "Image size = " << img.dims()[0] << ", " << img.dims()[1] << '\n';
std::cout << "Data type = " << img.type() << '\n';
std::cout << "Min = " << mn << " (at " << idxn << ")\n";
std::cout << "Max = " << mx << " (at " << idxx << ")\n";
af::saveImage( argv[2], img );
return 0;
}
I then compile and run on a simple (monochrome) image:
./a.out orig.tif out.tif
with the following output:
Image size = 256, 256
Data type = 0
Min = 0 (at 65535)
Max = 81.5025 (at 31356)
When I visualize these images I get the following result:
which of course is not what ArrayFire is expected to do; I would expect it to dump the exact same image out since I didn't make any changes to it. Unfortunately I don't know enough about the TIFF image format or the graphics backend of ArrayFire to understand what is going on. Am I doing something wrong while loading the image? (I followed the ArrayFire documentation for loadImage and saveImage).
I also tried using loadImageNative and saveImageNative alternatively, but the latter returns a 4-layer TIFF image while the original image is only a 1-layer TIFF.
Any help at all from ArrayFire experts would be great.
Thanks!