kopia lustrzana https://github.com/OpenDroneMap/ODM
Resolved conflicts, added missing 2.5D meshing files, change skip-25dmesh to use-25dmesh
Piero Toffanin
commit
ce0ce5a655
11
README.md
11
README.md
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@ -14,22 +14,22 @@ In a word, OpenDroneMap is a toolchain for processing raw civilian UAS imagery t
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2. Digital Surface Models
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3. Textured Digital Surface Models
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4. Orthorectified Imagery
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5. Classified Point Clouds
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5. Classified Point Clouds (coming soon)
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6. Digital Elevation Models
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7. etc.
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So far, it does Point Clouds, Digital Surface Models, Textured Digital Surface Models, and Orthorectified Imagery. Open Drone Map now includes state-of-the-art 3D reconstruction work by Michael Waechter, Nils Moehrle, and Michael Goesele. See their publication at http://www.gcc.tu-darmstadt.de/media/gcc/papers/Waechter-2014-LTB.pdf.
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Open Drone Map now includes state-of-the-art 3D reconstruction work by Michael Waechter, Nils Moehrle, and Michael Goesele. See their publication at http://www.gcc.tu-darmstadt.de/media/gcc/papers/Waechter-2014-LTB.pdf.
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## QUICKSTART
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OpenDroneMap can run natively on Ubuntu 14.04 or later, see [Build and Run Using Docker](#build-and-run-using-docker) for running on Windows / MacOS. A Vagrant VM is also available: https://github.com/OpenDroneMap/odm_vagrant.
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OpenDroneMap can run natively on Ubuntu 14.04 or later, see [Build and Run Using Docker](#build-and-run-using-docker) for running on Windows / MacOS. A [Vagrant VM](https://github.com/OpenDroneMap/odm_vagrant) is also available.
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*Support for Ubuntu 12.04 is currently BROKEN with the addition of OpenSfM and Ceres-Solver. It is likely to remain broken unless a champion is found to fix it.*
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**[Download the latest release here](https://github.com/OpenDroneMap/OpenDroneMap/releases)**
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Current version: 0.2 (this software is in beta)
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Current version: 0.3 (this software is in beta)
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1. Extract and enter the OpenDroneMap directory
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2. Run `bash configure.sh install`
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@ -74,7 +74,7 @@ Note that using `run.sh` sets these temporarily in the shell.
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First you need a set of images, taken from a drone or otherwise. Example data can be obtained from https://github.com/OpenDroneMap/odm_data
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Next, you need to copy over the settings file `default.settings.yaml` and edit it. The only setting you must edit is the `project-path` key. Set this to an empty directory within projects will be saved. There are many options for tuning your project. See the [wiki](https://github.com/OpenDroneMap/OpenDroneMap/wiki/Run-Time-Parameters) or run `python run.py -h`
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Next, you need to edit the `settings.yaml` file. The only setting you must edit is the `project-path` key. Set this to an empty directory within projects will be saved. There are many options for tuning your project. See the [wiki](https://github.com/OpenDroneMap/OpenDroneMap/wiki/Run-Time-Parameters) or run `python run.py -h`
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Then run:
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@ -146,7 +146,6 @@ instructions through "Create a Docker group". Once Docker is installed, the fast
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If you want to build your own Docker image from sources, type:
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docker build -t packages -f packages.Dockerfile .
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docker build -t my_odm_image .
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docker run -it --rm -v $(pwd)/images:/code/images -v $(pwd)/odm_orthophoto:/code/odm_orthophoto -v $(pwd)/odm_texturing:/code/odm_texturing my_odm_image
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2
VERSION
2
VERSION
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@ -1 +1 @@
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0.3-development
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0.3
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@ -42,6 +42,10 @@ project_path: '/' #DO NOT CHANGE THIS OR DOCKER WILL NOT WORK. It should be '/'
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#texturing_keep_unseen_faces: False
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#texturing_tone_mapping: 'none'
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#gcp: !!null # YAML tag for None
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#dem: False
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#dem_sample_radius: 1.0
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#dem_resolution: 2
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#dem_radius: 0.5
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#use_exif: False # Set to True if you have a GCP file (it auto-detects) and want to use EXIF
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#orthophoto_resolution: 20.0 # Pixels/meter
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#orthophoto_target_srs: !!null # Currently does nothing
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@ -0,0 +1,38 @@
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#pragma once
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#include <CGAL/Exact_predicates_inexact_constructions_kernel.h>
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#include <CGAL/Exact_predicates_inexact_constructions_kernel.h>
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#include <CGAL/Constrained_Delaunay_triangulation_2.h>
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#include <CGAL/Triangulation_vertex_base_with_info_2.h>
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#include <CGAL/Constrained_Delaunay_triangulation_2.h>
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#include <CGAL/Delaunay_mesh_face_base_2.h>
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#include <CGAL/Triangulation_2.h>
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#include <CGAL/Polyhedron_3.h>
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#include <CGAL/Surface_mesh_simplification/edge_collapse.h>
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typedef CGAL::Exact_predicates_inexact_constructions_kernel Kernel;
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typedef Kernel::FT FT;
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typedef Kernel::Point_3 Point3;
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typedef Kernel::Vector_3 Vector3;
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//We define a vertex_base with info. The "info" (size_t) allow us to keep track of the original point index.
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typedef CGAL::Triangulation_vertex_base_with_info_2<size_t, Kernel> Vb;
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typedef CGAL::Constrained_triangulation_face_base_2<Kernel> Fb;
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typedef CGAL::Triangulation_data_structure_2<Vb, Fb> Tds;
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typedef CGAL::Constrained_Delaunay_triangulation_2<Kernel, Tds> CDT;
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typedef CGAL::Polyhedron_3<Kernel> Polyhedron;
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typedef Polyhedron::HalfedgeDS HalfedgeDS;
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namespace SMS = CGAL::Surface_mesh_simplification;
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// Concurrency
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#ifdef CGAL_LINKED_WITH_TBB
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typedef CGAL::Parallel_tag Concurrency_tag;
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#else
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typedef CGAL::Sequential_tag Concurrency_tag;
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#endif
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//typedef CGAL::First_of_pair_property_map<Pwn> Point_map;
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//typedef CGAL::Second_of_pair_property_map<Pwn> Normal_map;
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@ -204,7 +204,7 @@ void Odm25dMeshing::buildMesh(){
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pointCount = gridPoints.size();
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log << "sampled " << (pointCountBeforeGridSampling - pointCount) << " points\n";
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const double RETAIN_PERCENTAGE = std::min<double>(100., 100. * static_cast<double>(maxVertexCount) / static_cast<double>(pointCount)); // percentage of points to retain.
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const double RETAIN_PERCENTAGE = std::min<double>(100., 100. * static_cast<double>(maxVertexCount / 4) / static_cast<double>(pointCount)); // percentage of points to retain.
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std::vector<Point3> simplifiedPoints;
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log << "Performing weighted locally optimal projection simplification and regularization (retain: " << RETAIN_PERCENTAGE << "%, iterate: " << wlopIterations << ")" << "\n";
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@ -0,0 +1,2 @@
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#include "PolyhedronBuilder.hpp"
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@ -0,0 +1,43 @@
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#include <vector>
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#include <algorithm>
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#include<CGAL/Polyhedron_incremental_builder_3.h>
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#include<CGAL/IO/Polyhedron_iostream.h>
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#include "CGAL.hpp"
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// A modifier creating a triangle with the incremental builder.
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template<class HDS>
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class PolyhedronBuilder : public CGAL::Modifier_base<HDS> {
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public:
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std::vector<float> &vertices;
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std::vector<int> &vertexIndices;
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PolyhedronBuilder( std::vector<float> &vertices, std::vector<int> &vertexIndices )
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: vertices(vertices), vertexIndices(vertexIndices) {}
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void operator()( HDS& hds) {
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typedef typename HDS::Vertex Vertex;
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typedef typename Vertex::Point Point;
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CGAL::Polyhedron_incremental_builder_3<HDS> builder( hds, true);
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builder.begin_surface( vertices.size() / 3, vertexIndices.size() / 3 );
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for(size_t i = 0; i < vertices.size(); i+=3 ){
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builder.add_vertex(Point(vertices[i+0], vertices[i+1], vertices[i+2]));
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}
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for(size_t i = 0; i < vertexIndices.size(); i+=3){
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builder.begin_facet();
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builder.add_vertex_to_facet(vertexIndices[i+0]);
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builder.add_vertex_to_facet(vertexIndices[i+1]);
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builder.add_vertex_to_facet(vertexIndices[i+2]);
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builder.end_facet();
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}
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// finish up the surface
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builder.end_surface();
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}
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};
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@ -153,10 +153,10 @@ def config():
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help=('The maximum number of processes to use in dense '
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'reconstruction. Default: %(default)s'))
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parser.add_argument('--skip-25dmesh',
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parser.add_argument('--use-25dmesh',
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action='store_true',
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default=False,
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help='Do not build a 2.5D mesh and use the poisson mesh to compute the orthophoto')
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help='Use a 2.5D mesh to compute the orthophoto. This option tends to provide better results for planar surfaces. Experimental.')
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parser.add_argument('--use-pmvs',
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action='store_true',
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@ -336,6 +336,34 @@ def config():
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help=('Use this tag if you have a gcp_list.txt but '
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'want to use the exif geotags instead'))
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parser.add_argument('--dem',
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action='store_true',
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default=False,
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help='Use this tag to build a DEM using a progressive '
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'morphological filter in PDAL.')
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parser.add_argument('--dem-sample-radius',
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metavar='<float>',
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default=1.0,
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type=float,
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help='Minimum distance between samples for DEM '
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'generation.\nDefault=%(default)s')
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parser.add_argument('--dem-resolution',
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metavar='<float>',
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type=float,
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default=2,
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help='Length of raster cell edges in X/Y units.'
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'\nDefault: %(default)s')
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parser.add_argument('--dem-radius',
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metavar='<float>',
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type=float,
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default=0.5,
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help='Radius about cell center bounding points to '
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'use to calculate a cell value.\nDefault: '
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'%(default)s')
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parser.add_argument('--orthophoto-resolution',
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metavar='<float > 0.0>',
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default=20.0,
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113
opendm/types.py
113
opendm/types.py
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@ -158,7 +158,7 @@ class ODM_GeoRef(object):
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output = str(deg) + '/1 ' + str(minute) + '/1 ' + str(sec_numerator) + '/' + str(sec_denominator)
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return output, latRef
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def convert_to_las(self, _file, pdalXML):
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def convert_to_las(self, _file, _file_out, json_file):
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if not self.epsg:
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log.ODM_ERROR('Empty EPSG: Could not convert to LAS')
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@ -166,50 +166,82 @@ class ODM_GeoRef(object):
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kwargs = {'bin': context.pdal_path,
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'f_in': _file,
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'f_out': _file + '.las',
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'f_out': _file_out,
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'east': self.utm_east_offset,
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'north': self.utm_north_offset,
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'epsg': self.epsg,
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'xml': pdalXML}
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'json': json_file}
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# call txt2las
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# system.run('{bin}/txt2las -i {f_in} -o {f_out} -skip 30 -parse xyzRGBssss ' \
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# '-set_scale 0.01 0.01 0.01 -set_offset {east} {north} 0 ' \
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# '-translate_xyz 0 -epsg {epsg}'.format(**kwargs))
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#
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# create pipeline file transform.xml to enable transformation
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pipelineXml = '<?xml version=\"1.0\" encoding=\"utf-8\"?>'
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pipelineXml += '<Pipeline version=\"1.0\">'
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pipelineXml += ' <Writer type=\"writers.las\">'
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pipelineXml += ' <Option name=\"filename\">'
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pipelineXml += ' transformed.las'
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pipelineXml += ' </Option>'
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pipelineXml += ' <Option name=\"a_srs\">'
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pipelineXml += ' EPSG:{epsg}'.format(**kwargs)
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pipelineXml += ' </Option>'
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pipelineXml += ' <Filter type=\"filters.transformation\">'
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pipelineXml += ' <Option name=\"matrix\">'
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pipelineXml += ' 1 0 0 {east}'.format(**kwargs)
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pipelineXml += ' 0 1 0 {north}'.format(**kwargs)
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pipelineXml += ' 0 0 1 0'
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pipelineXml += ' 0 0 0 1'
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pipelineXml += ' </Option>'
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pipelineXml += ' <Reader type=\"readers.ply\">'
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pipelineXml += ' <Option name=\"filename\">'
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pipelineXml += ' untransformed.ply'
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pipelineXml += ' </Option>'
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pipelineXml += ' </Reader>'
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pipelineXml += ' </Filter>'
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pipelineXml += ' </Writer>'
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pipelineXml += '</Pipeline>'
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pipeline = '{{' \
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' "pipeline":[' \
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' "untransformed.ply",' \
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' {{' \
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' "type":"filters.transformation",' \
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' "matrix":"1 0 0 {east} 0 1 0 {north} 0 0 1 0 0 0 0 1"' \
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' }},' \
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' {{' \
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' "a_srs":"EPSG:{epsg}",' \
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' "forward":"scale",' \
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' "filename":"transformed.las"' \
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' }}' \
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' ]' \
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'}}'.format(**kwargs)
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with open(pdalXML, 'w') as f:
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f.write(pipelineXml)
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with open(json_file, 'w') as f:
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f.write(pipeline)
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# call pdal
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system.run('{bin}/pdal pipeline -i {xml} --readers.ply.filename={f_in} '
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system.run('{bin}/pdal pipeline -i {json} --readers.ply.filename={f_in} '
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'--writers.las.filename={f_out}'.format(**kwargs))
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def convert_to_dem(self, _file, _file_out, pdalJSON, sample_radius, gdal_res, gdal_radius):
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# Check if exists f_in
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if not io.file_exists(_file):
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log.ODM_ERROR('LAS file does not exist')
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return False
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kwargs = {
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'bin': context.pdal_path,
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'f_in': _file,
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'sample_radius': sample_radius,
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'gdal_res': gdal_res,
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'gdal_radius': gdal_radius,
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'f_out': _file_out,
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'json': pdalJSON
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}
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pipelineJSON = '{{' \
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' "pipeline":[' \
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' "input.las",' \
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' {{' \
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' "type":"filters.sample",' \
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' "radius":"{sample_radius}"' \
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' }},' \
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' {{' \
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' "type":"filters.pmf",' \
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' "extract":"true"' \
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' }},' \
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' {{' \
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' "resolution": {gdal_res},' \
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' "radius": {gdal_radius},' \
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' "output_type":"idw",' \
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' "filename":"outputfile.tif"' \
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' }}' \
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' ]' \
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'}}'.format(**kwargs)
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with open(pdalJSON, 'w') as f:
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f.write(pipelineJSON)
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system.run('{bin}/pdal pipeline {json} --readers.las.filename={f_in} '
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'--writers.gdal.filename={f_out}'.format(**kwargs))
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if io.file_exists(kwargs['f_out']):
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return True
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else:
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return False
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def utm_to_latlon(self, _file, _photo, idx):
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gcp = self.gcps[idx]
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@ -331,6 +363,7 @@ class ODM_GeoRef(object):
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x, y = xyz[:2]
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z = 0
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self.gcps.append(ODM_GCPoint(float(x), float(y), float(z)))
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# Write to json file
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class ODM_Tree(object):
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@ -410,8 +443,14 @@ class ODM_Tree(object):
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self.odm_georeferencing_model_obj_geo = 'odm_textured_model_geo.obj'
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self.odm_georeferencing_xyz_file = io.join_paths(
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self.odm_georeferencing, 'odm_georeferenced_model.csv')
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self.odm_georeferencing_pdal = io.join_paths(
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self.odm_georeferencing, 'pipeline.xml')
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self.odm_georeferencing_las_json = io.join_paths(
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self.odm_georeferencing, 'las.json')
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self.odm_georeferencing_model_las = io.join_paths(
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self.odm_georeferencing, 'odm_georeferenced_model.las')
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self.odm_georeferencing_dem = io.join_paths(
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self.odm_georeferencing, 'odm_georeferencing_model_dem.tif')
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self.odm_georeferencing_dem_json = io.join_paths(
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self.odm_georeferencing, 'dem.json')
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# odm_orthophoto
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self.odm_orthophoto_file = io.join_paths(self.odm_orthophoto, 'odm_orthophoto.png')
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@ -39,7 +39,7 @@ class ODMMvsTexCell(ecto.Cell):
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# define paths and create working directories
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system.mkdir_p(tree.odm_texturing)
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if not args.skip_25dmesh: system.mkdir_p(tree.odm_25dtexturing)
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if args.use_25dmesh: system.mkdir_p(tree.odm_25dtexturing)
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# check if we rerun cell or not
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rerun_cell = (args.rerun is not None and
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@ -53,7 +53,7 @@ class ODMMvsTexCell(ecto.Cell):
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'model': tree.odm_mesh
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}]
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if not args.skip_25dmesh:
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if args.use_25dmesh:
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runs += [{
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'out_dir': tree.odm_25dtexturing,
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'model': tree.odm_25dmesh
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@ -72,6 +72,10 @@ class ODMApp(ecto.BlackBox):
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'georeferencing': ODMGeoreferencingCell(img_size=p.args.resize_to,
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gcp_file=p.args.gcp,
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use_exif=p.args.use_exif,
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dem=p.args.dem,
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sample_radius=p.args.dem_sample_radius,
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gdal_res=p.args.dem_resolution,
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gdal_radius=p.args.dem_radius,
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verbose=p.args.verbose),
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'orthophoto': ODMOrthoPhotoCell(resolution=p.args.orthophoto_resolution,
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t_srs=p.args.orthophoto_target_srs,
|
||||
|
|
|
|||
|
|
@ -17,6 +17,10 @@ class ODMGeoreferencingCell(ecto.Cell):
|
|||
'northing height pixelrow pixelcol imagename', 'gcp_list.txt')
|
||||
params.declare("img_size", 'image size used in calibration', 2400)
|
||||
params.declare("use_exif", 'use exif', False)
|
||||
params.declare("dem", 'Generate a dem', False)
|
||||
params.declare("sample_radius", "Minimum distance between samples for DEM gen", 3)
|
||||
params.declare("gdal_res", "Length of raster cell edges in X/Y units ", 2)
|
||||
params.declare("gdal_radius", "Radius about cell center bounding points to use to calculate a cell value", 0.5)
|
||||
params.declare("verbose", 'print additional messages to console', False)
|
||||
|
||||
def declare_io(self, params, inputs, outputs):
|
||||
|
|
@ -48,7 +52,7 @@ class ODMGeoreferencingCell(ecto.Cell):
|
|||
|
||||
# define paths and create working directories
|
||||
system.mkdir_p(tree.odm_georeferencing)
|
||||
if not args.skip_25dmesh: system.mkdir_p(tree.odm_25dgeoreferencing)
|
||||
if args.use_25dmesh: system.mkdir_p(tree.odm_25dgeoreferencing)
|
||||
|
||||
# in case a gcp file it's not provided, let's try to generate it using
|
||||
# images metadata. Internally calls jhead.
|
||||
|
|
@ -93,7 +97,7 @@ class ODMGeoreferencingCell(ecto.Cell):
|
|||
'texturing_dir': tree.odm_texturing,
|
||||
'model': os.path.join(tree.odm_texturing, tree.odm_textured_model_obj)
|
||||
}]
|
||||
if not args.skip_25dmesh:
|
||||
if args.use_25dmesh:
|
||||
runs += [{
|
||||
'georeferencing_dir': tree.odm_25dgeoreferencing,
|
||||
'texturing_dir': tree.odm_25dtexturing,
|
||||
|
|
@ -166,10 +170,24 @@ class ODMGeoreferencingCell(ecto.Cell):
|
|||
|
||||
# convert ply model to LAS reference system
|
||||
geo_ref.convert_to_las(odm_georeferencing_model_ply_geo,
|
||||
tree.odm_georeferencing_pdal)
|
||||
tree.odm_georeferencing_model_las,
|
||||
tree.odm_georeferencing_las_json)
|
||||
|
||||
# If --dem, create a DEM
|
||||
if args.dem:
|
||||
demcreated = geo_ref.convert_to_dem(tree.odm_georeferencing_model_las,
|
||||
tree.odm_georeferencing_dem,
|
||||
tree.odm_georeferencing_dem_json,
|
||||
self.params.sample_radius,
|
||||
self.params.gdal_res,
|
||||
self.params.gdal_radius)
|
||||
if not demcreated:
|
||||
log.ODM_WARNING('Something went wrong. Check the logs in odm_georeferencing.')
|
||||
else:
|
||||
log.ODM_INFO('DEM created at {0}'.format(tree.odm_georeferencing_dem))
|
||||
|
||||
# XYZ point cloud output
|
||||
log.ODM_INFO("Creating geo-referenced CSV file (XYZ format, can be used with GRASS to create DEM)")
|
||||
log.ODM_INFO("Creating geo-referenced CSV file (XYZ format)")
|
||||
with open(tree.odm_georeferencing_xyz_file, "wb") as csvfile:
|
||||
csvfile_writer = csv.writer(csvfile, delimiter=",")
|
||||
reachedpoints = False
|
||||
|
|
|
|||
|
|
@ -85,7 +85,7 @@ class ODMeshingCell(ecto.Cell):
|
|||
tree.odm_mesh)
|
||||
|
||||
# Do we need to generate a 2.5D mesh also?
|
||||
if not args.skip_25dmesh:
|
||||
if args.use_25dmesh:
|
||||
if not io.file_exists(tree.odm_25dmesh) or rerun_cell:
|
||||
log.ODM_DEBUG('Writing ODM 2.5D Mesh file in: %s' % tree.odm_25dmesh)
|
||||
|
||||
|
|
|
|||
|
|
@ -58,12 +58,12 @@ class ODMOrthoPhotoCell(ecto.Cell):
|
|||
|
||||
# Have geo coordinates?
|
||||
if io.file_exists(tree.odm_georeferencing_coords):
|
||||
if not args.skip_25dmesh:
|
||||
if args.use_25dmesh:
|
||||
kwargs['model_geo'] = os.path.join(tree.odm_25dtexturing, tree.odm_georeferencing_model_obj_geo)
|
||||
else:
|
||||
kwargs['model_geo'] = os.path.join(tree.odm_texturing, tree.odm_georeferencing_model_obj_geo)
|
||||
else:
|
||||
if not args.skip_25dmesh:
|
||||
if args.use_25dmesh:
|
||||
kwargs['model_geo'] = os.path.join(tree.odm_25dtexturing, tree.odm_textured_model_obj)
|
||||
else:
|
||||
kwargs['model_geo'] = os.path.join(tree.odm_texturing, tree.odm_textured_model_obj)
|
||||
|
|
|
|||
|
|
@ -43,6 +43,10 @@ project_path: '' # Example: '/home/user/ODMProjects
|
|||
#texturing_tone_mapping: 'none'
|
||||
#gcp: !!null # YAML tag for None
|
||||
#use_exif: False # Set to True if you have a GCP file (it auto-detects) and want to use EXIF
|
||||
#dem: False
|
||||
#dem_sample_radius: 1.0
|
||||
#dem_resolution: 2
|
||||
#dem_radius: 0.5
|
||||
#orthophoto_resolution: 20.0 # Pixels/meter
|
||||
#orthophoto_target_srs: !!null # Currently does nothing
|
||||
#orthophoto_no_tiled: False
|
||||
|
|
|
|||
Ładowanie…
Reference in New Issue