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Merge branch 'master' of https://github.com/OpenDroneMap/OpenDroneMap into update-vtk 

# Conflicts:
#	opendm/tasks.py
#	scripts/resize.py
pull/644/head
Dakota Benjamin 2017-08-24 15:28:23 -04:00
rodzic 47498f3b90 b3903a848c
commit dda829f74e
23 zmienionych plików z 516 dodań i 140 usunięć
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3
.gitmodules vendored
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@ -1,3 +0,0 @@
[submodule "src/bundler"]
path = src/bundler
url = https://github.com/chris-cooper/bundler_sfm

1
CNAME 100644
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@ -0,0 +1 @@
opendronemap.org

5
Dockerfile
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@ -14,12 +14,12 @@ RUN apt-get install --no-install-recommends -y git cmake python-pip build-essent
libgtk2.0-dev libavcodec-dev libavformat-dev libswscale-dev python-dev python-numpy libtbb2 libtbb-dev libjpeg-dev libpng-dev libtiff-dev libjasper-dev libflann-dev \
libproj-dev libxext-dev liblapack-dev libeigen3-dev libvtk5-dev python-networkx libgoogle-glog-dev libsuitesparse-dev libboost-filesystem-dev libboost-iostreams-dev \
libboost-regex-dev libboost-python-dev libboost-date-time-dev libboost-thread-dev python-pyproj python-empy python-nose python-pyside python-pyexiv2 python-scipy \
jhead liblas-bin python-matplotlib libatlas-base-dev libgmp-dev libmpfr-dev
jhead liblas-bin python-matplotlib libatlas-base-dev libgmp-dev libmpfr-dev swig2.0 python-wheel libboost-log-dev libjsoncpp-dev
RUN apt-get remove libdc1394-22-dev
RUN pip install --upgrade pip
RUN pip install setuptools
RUN pip install -U PyYAML exifread gpxpy xmltodict catkin-pkg appsettings
RUN pip install -U PyYAML exifread gpxpy xmltodict catkin-pkg appsettings https://github.com/OpenDroneMap/gippy/archive/v0.3.9.tar.gz
ENV PYTHONPATH="$PYTHONPATH:/code/SuperBuild/install/lib/python2.7/dist-packages"
ENV PYTHONPATH="$PYTHONPATH:/code/SuperBuild/src/opensfm"
@ -44,6 +44,7 @@ COPY /SuperBuild/CMakeLists.txt /code/SuperBuild/CMakeLists.txt
COPY docker.settings.yaml /code/settings.yaml
COPY VERSION /code/VERSION
#Compile code in SuperBuild and root directories
RUN cd SuperBuild && mkdir build && cd build && cmake .. && make -j$(nproc) && cd ../.. && mkdir build && cd build && cmake .. && make -j$(nproc)

25
README.md
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@ -20,15 +20,22 @@ In a word, OpenDroneMap is a toolchain for processing raw civilian UAS imagery t
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.
## QUICKSTART
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.
### Docker (All platforms)
*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.*
The easiest way to run ODM is through Docker. If you don't have it installed,
see the [Docker Ubuntu installation tutorial](https://docs.docker.com/engine/installation/linux/ubuntulinux/) and follow the
instructions through "Create a Docker group". The Docker image workflow
has equivalent procedures for Mac OS X and Windows found at [docs.docker.com](docs.docker.com). Then run the following command which will build a pre-built image and run on images found in `$(pwd)/images` (you can change this if you need to, see the [wiki](https://github.com/OpenDroneMap/OpenDroneMap/wiki/Docker) for more detailed instructions.
**[Download the latest release here](https://github.com/OpenDroneMap/OpenDroneMap/releases)**
```
docker run -it --rm -v $(pwd)/images:/code/images -v $(pwd)/odm_orthophoto:/code/odm_orthophoto -v $(pwd)/odm_texturing:/code/odm_texturing opendronemap/opendronemap
```
### Native Install (Ubuntu 14.04 or later)
**[Download the latest release here](https://github.com/OpenDroneMap/OpenDroneMap/releases)**
Current version: 0.3.1 (this software is in beta)
1. Extract and enter the OpenDroneMap directory
@ -37,11 +44,13 @@ Current version: 0.3.1 (this software is in beta)
3. Download a sample dataset from [here](https://github.com/OpenDroneMap/odm_data_aukerman/archive/master.zip) (about 550MB) and extract it as a subdirectory in your project directory.
4. Run `./run.sh odm_data_aukerman`
5. Enter dataset directory to view results:
- orthophoto: odm_orthophoto/odm_orthophoto.tif
- textured mesh model: odm_texturing/odm_textured_model_geo.obj
- point cloud (georeferenced): odm_georeferencing/odm_georeferenced_model.ply
- orthophoto: odm_orthophoto/odm_orthophoto.tif
- textured mesh model: odm_texturing/odm_textured_model_geo.obj
- point cloud (georeferenced): odm_georeferencing/odm_georeferenced_model.ply
See [here](https://github.com/OpenDroneMap/OpenDroneMap/blob/3964f21377e27c261c305b30537f699853ac2004/README.md#installation) for more detailed installation instructions.
See below for more detailed installation instructions.
## Diving Deeper
### Installation

5
SuperBuild/CMakeLists.txt
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@ -98,6 +98,10 @@ option(ODM_BUILD_CGAL "Force to build CGAL library" OFF)
SETUP_EXTERNAL_PROJECT(CGAL ${ODM_CGAL_Version} ${ODM_BUILD_CGAL})
# ---------------------------------------------------------------------------------------------
# Hexer
#
SETUP_EXTERNAL_PROJECT(Hexer 1.4 ON)
# ---------------------------------------------------------------------------------------------
# Open Geometric Vision (OpenGV)
@ -114,6 +118,7 @@ set(custom_libs OpenGV
Ecto
PDAL
MvsTexturing
Lidar2dems
)
# Dependencies of the SLAM module

27
SuperBuild/cmake/External-Hexer.cmake 100644
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@ -0,0 +1,27 @@
set(_proj_name hexer)
set(_SB_BINARY_DIR "${SB_BINARY_DIR}/${_proj_name}")
ExternalProject_Add(${_proj_name}
DEPENDS
PREFIX ${_SB_BINARY_DIR}
TMP_DIR ${_SB_BINARY_DIR}/tmp
STAMP_DIR ${_SB_BINARY_DIR}/stamp
#--Download step--------------
DOWNLOAD_DIR ${SB_DOWNLOAD_DIR}
URL https://github.com/hobu/hexer/archive/2898b96b1105991e151696391b9111610276258f.tar.gz
URL_MD5 e8f2788332ad212cf78efa81a82e95dd
#--Update/Patch step----------
UPDATE_COMMAND ""
#--Configure step-------------
SOURCE_DIR ${SB_SOURCE_DIR}/${_proj_name}
CMAKE_ARGS
-DCMAKE_INSTALL_PREFIX:PATH=${SB_INSTALL_DIR}
#--Build step-----------------
BINARY_DIR ${_SB_BINARY_DIR}
#--Install step---------------
INSTALL_DIR ${SB_INSTALL_DIR}
#--Output logging-------------
LOG_DOWNLOAD OFF
LOG_CONFIGURE OFF
LOG_BUILD OFF
)

24
SuperBuild/cmake/External-Lidar2dems.cmake 100644
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@ -0,0 +1,24 @@
set(_proj_name lidar2dems)
set(_SB_BINARY_DIR "${SB_BINARY_DIR}/${_proj_name}")
ExternalProject_Add(${_proj_name}
PREFIX ${_SB_BINARY_DIR}
TMP_DIR ${_SB_BINARY_DIR}/tmp
STAMP_DIR ${_SB_BINARY_DIR}/stamp
#--Download step--------------
DOWNLOAD_DIR ${SB_DOWNLOAD_DIR}/${_proj_name}
URL https://github.com/OpenDroneMap/lidar2dems/archive/master.zip
#--Update/Patch step----------
UPDATE_COMMAND ""
#--Configure step-------------
SOURCE_DIR ${SB_SOURCE_DIR}/${_proj_name}
CONFIGURE_COMMAND ""
#--Build step-----------------
BUILD_COMMAND ""
#--Install step---------------
INSTALL_COMMAND "${SB_SOURCE_DIR}/${_proj_name}/install.sh" "${SB_INSTALL_DIR}"
#--Output logging-------------
LOG_DOWNLOAD OFF
LOG_CONFIGURE OFF
LOG_BUILD OFF
)

7
SuperBuild/cmake/External-PDAL.cmake
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@ -2,13 +2,14 @@ set(_proj_name pdal)
set(_SB_BINARY_DIR "${SB_BINARY_DIR}/${_proj_name}")
ExternalProject_Add(${_proj_name}
DEPENDS hexer
PREFIX ${_SB_BINARY_DIR}
TMP_DIR ${_SB_BINARY_DIR}/tmp
STAMP_DIR ${_SB_BINARY_DIR}/stamp
#--Download step--------------
DOWNLOAD_DIR ${SB_DOWNLOAD_DIR}
URL https://github.com/PDAL/PDAL/archive/aea5bb0cacc64b91d626eff491fbdbb5668c06d7.tar.gz
URL_MD5 726933f63f661e11e13775d6ce4e5ed0
URL https://github.com/PDAL/PDAL/archive/e881b581e3b91a928105d67db44c567f3b6d1afe.tar.gz
URL_MD5 438acbb736ba01fbe8f9ca7cdbf113bf
#--Update/Patch step----------
UPDATE_COMMAND ""
#--Configure step-------------
@ -19,7 +20,7 @@ ExternalProject_Add(${_proj_name}
-BUILD_PLUGIN_PGPOINTCLOUD=ON
-DBUILD_PLUGIN_CPD=OFF
-DBUILD_PLUGIN_GREYHOUND=OFF
-DBUILD_PLUGIN_HEXBIN=OFF
-DBUILD_PLUGIN_HEXBIN=ON
-DBUILD_PLUGIN_ICEBRIDGE=OFF
-DBUILD_PLUGIN_MRSID=OFF
-DBUILD_PLUGIN_NITF=OFF

2
code_of_conduct.md
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@ -55,7 +55,7 @@ further defined and clarified by project maintainers.
## Enforcement
Instances of abusive, harassing, or otherwise unacceptable behavior may be
reported by contacting the project team at [INSERT EMAIL ADDRESS]. All
reported by contacting the project team at `svm at clevelandmetroparks dot com`. All
complaints will be reviewed and investigated and will result in a response that
is deemed necessary and appropriate to the circumstances. The project team is
obligated to maintain confidentiality with regard to the reporter of an incident.

14
configure.sh
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@ -21,7 +21,8 @@ install() {
libgdal-dev \
gdal-bin \
libgeotiff-dev \
pkg-config
pkg-config \
libjsoncpp-dev
echo "Getting CMake 3.1 for MVS-Texturing"
sudo apt-get install -y software-properties-common python-software-properties
@ -72,7 +73,7 @@ install() {
appsettings
echo "Installing CGAL dependencies"
sudo apt-get install libgmp-dev libmpfr-dev
sudo apt-get install -y -qq libgmp-dev libmpfr-dev
echo "Installing Ecto Dependencies"
sudo pip install -U catkin-pkg
@ -86,6 +87,13 @@ install() {
jhead \
liblas-bin
echo "Installing lidar2dems Dependencies"
sudo apt-get install -y -qq swig2.0 \
python-wheel \
libboost-log-dev
sudo pip install -U https://github.com/OpenDroneMap/gippy/archive/v0.3.9.tar.gz
echo "Compiling SuperBuild"
cd ${RUNPATH}/SuperBuild
mkdir -p build && cd build
@ -134,4 +142,4 @@ else
echo "Invalid instructions." >&2
usage
exit 1
fi
fi

31
contrib/visveg/readme.md 100644
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@ -0,0 +1,31 @@
# Visible Vegetation Indexes
This script produces a Vegetation Index raster from a RGB orthophoto (odm_orthophoto.tif in your project)
## Requirements
* rasterio (pip install rasterio)
* numpy python package (included in ODM build)
## Usage
```
vegind.py <orthophoto.tif> index
positional arguments:
<orthophoto.tif> The RGB orthophoto. Must be a GeoTiff.
index Index identifier. Allowed values: ngrdi, tgi, vari
```
Output will be generated with index suffix in the same directory as input.
## Examples
`python vegind.py /path/to/odm_orthophoto.tif tgi`
Orthophoto photo of Koniaków grass field and forest in QGIS: ![](http://imgur.com/K6x3nB2.jpg)
The Triangular Greenness Index output in QGIS (with a spectral pseudocolor): ![](http://i.imgur.com/f9TzISU.jpg)
Visible Atmospheric Resistant Index: ![](http://imgur.com/Y7BHzLs.jpg)
Normalized green-red difference index: ![](http://imgur.com/v8cmaPS.jpg)
## Bibliography
1. Hunt, E. Raymond, et al. "A Visible Band Index for Remote Sensing Leaf Chlorophyll Content At the Canopy Scale." ITC journal 21(2013): 103-112. doi: 10.1016/j.jag.2012.07.020
(https://doi.org/10.1016/j.jag.2012.07.020)

95
contrib/visveg/vegind.py 100644
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@ -0,0 +1,95 @@
#!/usr/bin/python
# -*- coding: utf-8 -*-
import rasterio, os, sys
import numpy as np
class bcolors:
OKBLUE = '\033[94m'
OKGREEN = '\033[92m'
WARNING = '\033[93m'
FAIL = '\033[91m'
ENDC = '\033[0m'
BOLD = '\033[1m'
UNDERLINE = '\033[4m'
try:
file = sys.argv[1]
typ = sys.argv[2]
(fileRoot, fileExt) = os.path.splitext(file)
outFileName = fileRoot + "_" + typ + fileExt
if typ not in ['vari', 'tgi', 'ngrdi']:
raise IndexError
except (TypeError, IndexError, NameError):
print bcolors.FAIL + 'Arguments messed up. Check arguments order and index name' + bcolors.ENDC
print 'Usage: ./vegind.py orto index'
print ' orto - filepath to RGB orthophoto'
print ' index - Vegetation Index'
print bcolors.OKGREEN + 'Available indexes: vari, ngrdi, tgi' + bcolors.ENDC
sys.exit()
def calcNgrdi(red, green):
"""
Normalized green red difference index
Tucker,C.J.,1979.
Red and photographic infrared linear combinations for monitoring vegetation.
Remote Sensing of Environment 8, 127–150
:param red: red visible channel
:param green: green visible channel
:return: ngrdi index array
"""
mask = np.not_equal(np.add(red,green), 0.0)
return np.choose(mask, (-9999.0, np.true_divide(
np.subtract(green,red),
np.add(red,green))))
def calcVari(red,green,blue):
"""
Calculates Visible Atmospheric Resistant Index
Gitelson, A.A., Kaufman, Y.J., Stark, R., Rundquist, D., 2002.
Novel algorithms for remote estimation of vegetation fraction.
Remote Sensing of Environment 80, 76–87.
:param red: red visible channel
:param green: green visible channel
:param blue: blue visible channel
:return: vari index array, that will be saved to tiff
"""
mask = np.not_equal(np.subtract(np.add(green,red),blue), 0.0)
return np.choose(mask, (-9999.0, np.true_divide(np.subtract(green,red),np.subtract(np.add(green,red),blue))))
def calcTgi(red,green,blue):
"""
Calculates Triangular Greenness Index
Hunt, E. Raymond Jr.; Doraiswamy, Paul C.; McMurtrey, James E.; Daughtry, Craig S.T.; Perry, Eileen M.; and Akhmedov, Bakhyt,
A visible band index for remote sensing leaf chlorophyll content at the canopy scale (2013).
Publications from USDA-ARS / UNL Faculty. Paper 1156.
http://digitalcommons.unl.edu/usdaarsfacpub/1156
:param red: red channel
:param green: green channel
:param blue: blue channel
:return: tgi index array, that will be saved to tiff
"""
mask = np.not_equal(green-red+blue-255.0, 0.0)
return np.choose(mask, (-9999.0, np.subtract(green, np.multiply(0.39,red), np.multiply(0.61, blue))))
try:
with rasterio.Env():
ds = rasterio.open(file)
profile = ds.profile
profile.update(dtype=rasterio.float32, count=1, nodata=-9999)
red = np.float32(ds.read(1))
green = np.float32(ds.read(2))
blue = np.float32(ds.read(3))
np.seterr(divide='ignore', invalid='ignore')
if typ == 'ngrdi':
indeks = calcNgrdi(red,green)
elif typ == 'vari':
indeks = calcVari(red, green, blue)
elif typ == 'tgi':
indeks = calcTgi(red, green, blue)
with rasterio.open(outFileName, 'w', **profile) as dst:
dst.write(indeks.astype(rasterio.float32), 1)
except rasterio.errors.RasterioIOError:
print bcolors.FAIL + 'Orthophoto file not found or access denied' + bcolors.ENDC
sys.exit()

4
core2.Dockerfile
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@ -14,12 +14,12 @@ RUN apt-get install --no-install-recommends -y git cmake python-pip build-essent
libgtk2.0-dev libavcodec-dev libavformat-dev libswscale-dev python-dev python-numpy libtbb2 libtbb-dev libjpeg-dev libpng-dev libtiff-dev libjasper-dev libflann-dev \
libproj-dev libxext-dev liblapack-dev libeigen3-dev libvtk5-dev python-networkx libgoogle-glog-dev libsuitesparse-dev libboost-filesystem-dev libboost-iostreams-dev \
libboost-regex-dev libboost-python-dev libboost-date-time-dev libboost-thread-dev python-pyproj python-empy python-nose python-pyside python-pyexiv2 python-scipy \
jhead liblas-bin python-matplotlib libatlas-base-dev libgmp-dev libmpfr-dev
jhead liblas-bin python-matplotlib libatlas-base-dev libgmp-dev libmpfr-dev swig2.0 python-wheel libboost-log-dev libjsoncpp-dev
RUN apt-get remove libdc1394-22-dev
RUN pip install --upgrade pip
RUN pip install setuptools
RUN pip install -U PyYAML exifread gpxpy xmltodict catkin-pkg appsettings
RUN pip install -U PyYAML exifread gpxpy xmltodict catkin-pkg appsettings https://github.com/OpenDroneMap/gippy/archive/v0.3.9.tar.gz
ENV PYTHONPATH="$PYTHONPATH:/code/SuperBuild/install/lib/python2.7/dist-packages"
ENV PYTHONPATH="$PYTHONPATH:/code/SuperBuild/src/opensfm"

13
docker.settings.yaml
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@ -43,10 +43,15 @@ project_path: '/' #DO NOT CHANGE THIS OR DOCKER WILL NOT WORK. It should be '/'
#texturing_keep_unseen_faces: False
#texturing_tone_mapping: 'none'
#gcp: !!null # YAML tag for None
#dem: False
#dem_sample_radius: 1.0
#dem_resolution: 2
#dem_radius: 0.5
#dtm: False # Use this tag to build a DTM (Digital Terrain Model
#dsm: False # Use this tag to build a DSM (Digital Surface Model
#dem-gapfill-steps: 4
#dem-resolution: 0.1
#dem-maxangle:20
#dem-maxsd: 2.5
#dem-approximate: False
#dem-decimation: 1
#dem-terrain-type: ComplexForest
#use_exif: False # Set to True if you have a GCP file (it auto-detects) and want to use EXIF
#orthophoto_resolution: 20.0 # Pixels/meter
#orthophoto_target_srs: !!null # Currently does nothing

102
opendm/config.py
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@ -10,7 +10,7 @@ import sys
# parse arguments
processopts = ['resize', 'opensfm', 'slam', 'cmvs', 'pmvs',
'odm_meshing', 'odm_25dmeshing', 'mvs_texturing', 'odm_georeferencing',
'odm_orthophoto']
'odm_dem', 'odm_orthophoto']
with open(io.join_paths(context.root_path, 'VERSION')) as version_file:
__version__ = version_file.read().strip()
@ -111,21 +111,6 @@ def config():
'More features leads to better results but slower '
'execution. Default: %(default)s'))
parser.add_argument('--matcher-threshold',
metavar='<percent>',
default=2.0,
type=float,
help=('Ignore matched keypoints if the two images share '
'less than <float> percent of keypoints. Default:'
' %(default)s'))
parser.add_argument('--matcher-ratio',
metavar='<float>',
default=0.6,
type=float,
help=('Ratio of the distance to the next best matched '
'keypoint. Default: %(default)s'))
parser.add_argument('--matcher-neighbors',
type=int,
metavar='<integer>',
@ -342,34 +327,87 @@ def config():
help=('Use this tag if you have a gcp_list.txt but '
'want to use the exif geotags instead'))
parser.add_argument('--dem',
parser.add_argument('--dtm',
action='store_true',
default=False,
help='Use this tag to build a DEM using a progressive '
'morphological filter in PDAL.')
help='Use this tag to build a DTM (Digital Terrain Model, ground only) using a progressive '
'morphological filter. Check the --dem* parameters for fine tuning.')
parser.add_argument('--dsm',
action='store_true',
default=False,
help='Use this tag to build a DSM (Digital Surface Model, ground + objects) using a progressive '
'morphological filter. Check the --dem* parameters for fine tuning.')
parser.add_argument('--dem-sample-radius',
metavar='<float>',
default=1.0,
type=float,
help='Minimum distance between samples for DEM '
'generation.\nDefault=%(default)s')
parser.add_argument('--dem-gapfill-steps',
metavar='<positive integer>',
default=4,
type=int,
help='Number of steps used to fill areas with gaps. Set to 0 to disable gap filling. '
'Starting with a radius equal to the output resolution, N different DEMs are generated with '
'progressively bigger radius using the inverse distance weighted (IDW) algorithm '
'and merged together. Remaining gaps are then merged using nearest neighbor interpolation. '
'\nDefault=%(default)s')
parser.add_argument('--dem-resolution',
metavar='<float>',
type=float,
default=2,
help='Length of raster cell edges in X/Y units.'
default=0.1,
help='Length of raster cell edges in meters.'
'\nDefault: %(default)s')
parser.add_argument('--dem-radius',
metavar='<float>',
parser.add_argument('--dem-maxangle',
metavar='<positive float>',
type=float,
default=0.5,
help='Radius about cell center bounding points to '
'use to calculate a cell value.\nDefault: '
default=20,
help='Points that are more than maxangle degrees off-nadir are discarded. '
'\nDefault: '
'%(default)s')
parser.add_argument('--dem-maxsd',
metavar='<positive float>',
type=float,
default=2.5,
help='Points that deviate more than maxsd standard deviations from the local mean '
'are discarded. \nDefault: '
'%(default)s')
parser.add_argument('--dem-initial-distance',
metavar='<positive float>',
type=float,
default=0.15,
help='Used to classify ground vs non-ground points. Set this value to account for Z noise in meters. '
'If you have an uncertainty of around 15 cm, set this value large enough to not exclude these points. '
'Too small of a value will exclude valid ground points, while too large of a value will misclassify non-ground points for ground ones. '
'\nDefault: '
'%(default)s')
parser.add_argument('--dem-approximate',
action='store_true',
default=False,
help='Use this tag use the approximate progressive '
'morphological filter, which computes DEMs faster '
'but is not as accurate.')
parser.add_argument('--dem-decimation',
metavar='<positive integer>',
default=1,
type=int,
help='Decimate the points before generating the DEM. 1 is no decimation (full quality). '
'100 decimates ~99%% of the points. Useful for speeding up '
'generation.\nDefault=%(default)s')
parser.add_argument('--dem-terrain-type',
metavar='<string>',
choices=['FlatNonForest', 'FlatForest', 'ComplexNonForest', 'ComplexForest'],
default='ComplexForest',
help='One of: %(choices)s. Specifies the type of terrain. This mainly helps reduce processing time. '
'\nFlatNonForest: Relatively flat region with little to no vegetation'
'\nFlatForest: Relatively flat region that is forested'
'\nComplexNonForest: Varied terrain with little to no vegetation'
'\nComplexForest: Varied terrain that is forested'
'\nDefault=%(default)s')
parser.add_argument('--orthophoto-resolution',
metavar='<float > 0.0>',
default=20.0,

1
opendm/context.py
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@ -8,6 +8,7 @@ scripts_path = os.path.abspath(os.path.dirname(__file__))
root_path, _ = os.path.split(scripts_path)
superbuild_path = os.path.join(root_path, 'SuperBuild')
superbuild_bin_path = os.path.join(superbuild_path, 'install', 'bin')
tests_path = os.path.join(root_path, 'tests')
tests_data_path = os.path.join(root_path, 'tests/test_data')

10
opendm/system.py
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@ -17,10 +17,16 @@ def get_ccd_widths():
return dict(zip(map(string.lower, sensor_data.keys()), sensor_data.values()))
def run(cmd):
def run(cmd, env_paths=[]):
"""Run a system command"""
log.ODM_DEBUG('running %s' % cmd)
retcode = subprocess.call(cmd, shell=True)
env = None
if len(env_paths) > 0:
env = os.environ.copy()
env["PATH"] = env["PATH"] + ":" + ":".join(env_paths)
retcode = subprocess.call(cmd, shell=True, env=env)
if retcode < 0:
raise Exception("Child was terminated by signal {}".format(-retcode))

5
opendm/tasks.py
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@ -13,8 +13,9 @@ tasks_dict = {'1': 'opensfm',
'4': 'odm_meshing',
'5': 'mvs_texturing',
'6': 'odm_georeferencing',
'7': 'odm_orthophoto',
'8': 'zip_results'}
'7': 'odm_dem',
'8': 'odm_orthophoto',
'9': 'zip_results'}
class ODMTaskManager(object):

55
opendm/types.py
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@ -221,56 +221,6 @@ class ODM_GeoRef(object):
system.run('{bin}/pdal pipeline -i {json} --readers.ply.filename={f_in} '
'--writers.las.filename={f_out}'.format(**kwargs))
def convert_to_dem(self, _file, _file_out, pdalJSON, sample_radius, gdal_res, gdal_radius):
# Check if exists f_in
if not io.file_exists(_file):
log.ODM_ERROR('LAS file does not exist')
return False
kwargs = {
'bin': context.pdal_path,
'f_in': _file,
'sample_radius': sample_radius,
'gdal_res': gdal_res,
'gdal_radius': gdal_radius,
'f_out': _file_out,
'json': pdalJSON
}
pipelineJSON = '{{' \
' "pipeline":[' \
' "input.las",' \
' {{' \
' "type":"filters.sample",' \
' "radius":"{sample_radius}"' \
' }},' \
' {{' \
' "type":"filters.pmf"' \
' }},' \
' {{' \
' "type":"filters.range",' \
' "limits":"Classification[2:2]"' \
' }},' \
' {{' \
' "resolution": {gdal_res},' \
' "radius": {gdal_radius},' \
' "output_type":"idw",' \
' "filename":"outputfile.tif"' \
' }}' \
' ]' \
'}}'.format(**kwargs)
with open(pdalJSON, 'w') as f:
f.write(pipelineJSON)
system.run('{bin}/pdal pipeline {json} --readers.las.filename={f_in} '
'--writers.gdal.filename={f_out}'.format(**kwargs))
if io.file_exists(kwargs['f_out']):
return True
else:
return False
def utm_to_latlon(self, _file, _photo, idx):
gcp = self.gcps[idx]
@ -478,8 +428,6 @@ class ODM_Tree(object):
self.odm_georeferencing, 'odm_georeferenced_model.las')
self.odm_georeferencing_dem = io.join_paths(
self.odm_georeferencing, 'odm_georeferencing_model_dem.tif')
self.odm_georeferencing_dem_json = io.join_paths(
self.odm_georeferencing, 'dem.json')
# odm_orthophoto
self.odm_orthophoto_file = io.join_paths(self.odm_orthophoto, 'odm_orthophoto.png')
@ -488,3 +436,6 @@ class ODM_Tree(object):
self.odm_orthophoto_log = io.join_paths(self.odm_orthophoto, 'odm_orthophoto_log.txt')
self.odm_orthophoto_tif_log = io.join_paths(self.odm_orthophoto, 'gdal_translate_log.txt')
self.odm_orthophoto_gdaladdo_log = io.join_paths(self.odm_orthophoto, 'gdaladdo_log.txt')
def path(self, *args):
return io.join_paths(self.root_path, *args)

13
scripts/odm_app.py
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@ -15,6 +15,7 @@ from odm_meshing import ODMeshingCell
from mvstex import ODMMvsTexCell
from odm_georeferencing import ODMGeoreferencingCell
from odm_orthophoto import ODMOrthoPhotoCell
from odm_dem import ODMDEMCell
class ODMApp(ecto.BlackBox):
@ -71,11 +72,8 @@ class ODMApp(ecto.BlackBox):
'georeferencing': ODMGeoreferencingCell(img_size=p.args.resize_to,
gcp_file=p.args.gcp,
use_exif=p.args.use_exif,
dem=p.args.dem,
sample_radius=p.args.dem_sample_radius,
gdal_res=p.args.dem_resolution,
gdal_radius=p.args.dem_radius,
verbose=p.args.verbose),
'dem': ODMDEMCell(verbose=p.args.verbose),
'orthophoto': ODMOrthoPhotoCell(resolution=p.args.orthophoto_resolution,
t_srs=p.args.orthophoto_target_srs,
no_tiled=p.args.orthophoto_no_tiled,
@ -148,7 +146,12 @@ class ODMApp(ecto.BlackBox):
self.args[:] >> self.georeferencing['args'],
self.dataset['photos'] >> self.georeferencing['photos'],
self.texturing['reconstruction'] >> self.georeferencing['reconstruction']]
# create odm dem
connections += [self.tree[:] >> self.dem['tree'],
self.args[:] >> self.dem['args'],
self.georeferencing['reconstruction'] >> self.dem['reconstruction']]
# create odm orthophoto
connections += [self.tree[:] >> self.orthophoto['tree'],
self.args[:] >> self.orthophoto['args'],

184
scripts/odm_dem.py 100644
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@ -0,0 +1,184 @@
import ecto, os, json
from shutil import copyfile
from opendm import io
from opendm import log
from opendm import system
from opendm import context
from opendm import types
class ODMDEMCell(ecto.Cell):
def declare_params(self, params):
params.declare("verbose", 'print additional messages to console', False)
def declare_io(self, params, inputs, outputs):
inputs.declare("tree", "Struct with paths", [])
inputs.declare("args", "The application arguments.", {})
inputs.declare("reconstruction", "list of ODMReconstructions", [])
def process(self, inputs, outputs):
# Benchmarking
start_time = system.now_raw()
log.ODM_INFO('Running ODM DEM Cell')
# get inputs
args = self.inputs.args
tree = self.inputs.tree
las_model_found = io.file_exists(tree.odm_georeferencing_model_las)
env_paths = [context.superbuild_bin_path]
# Just to make sure
l2d_module_installed = True
try:
system.run('l2d_classify --help > /dev/null', env_paths)
except:
log.ODM_WARNING('lidar2dems is not installed properly')
l2d_module_installed = False
log.ODM_INFO('Create DSM: ' + str(args.dsm))
log.ODM_INFO('Create DTM: ' + str(args.dtm))
log.ODM_INFO('DEM input file {0} found: {1}'.format(tree.odm_georeferencing_model_las, str(las_model_found)))
# Do we need to process anything here?
if (args.dsm or args.dtm) and las_model_found and l2d_module_installed:
# define paths and create working directories
odm_dem_root = tree.path('odm_dem')
system.mkdir_p(odm_dem_root)
dsm_output_filename = os.path.join(odm_dem_root, 'dsm.tif')
dtm_output_filename = os.path.join(odm_dem_root, 'dtm.tif')
# check if we rerun cell or not
rerun_cell = (args.rerun is not None and
args.rerun == 'odm_dem') or \
(args.rerun_all) or \
(args.rerun_from is not None and
'odm_dem' in args.rerun_from)
if (args.dtm and not io.file_exists(dtm_output_filename)) or \
(args.dsm and not io.file_exists(dsm_output_filename)) or \
rerun_cell:
# Extract boundaries and srs of point cloud
summary_file_path = os.path.join(odm_dem_root, 'odm_georeferenced_model.summary.json')
boundary_file_path = os.path.join(odm_dem_root, 'odm_georeferenced_model.boundary.json')
system.run('pdal info --summary {0} > {1}'.format(tree.odm_georeferencing_model_las, summary_file_path), env_paths)
system.run('pdal info --boundary {0} > {1}'.format(tree.odm_georeferencing_model_las, boundary_file_path), env_paths)
pc_proj4 = ""
pc_geojson_bounds_feature = None
with open(summary_file_path, 'r') as f:
json_f = json.loads(f.read())
pc_proj4 = json_f['summary']['srs']['proj4']
with open(boundary_file_path, 'r') as f:
json_f = json.loads(f.read())
pc_geojson_boundary_feature = json_f['boundary']['boundary_json']
# Write bounds to GeoJSON
bounds_geojson_path = os.path.join(odm_dem_root, 'odm_georeferenced_model.bounds.geojson')
with open(bounds_geojson_path, "w") as f:
f.write(json.dumps({
"type": "FeatureCollection",
"features": [{
"type": "Feature",
"geometry": pc_geojson_boundary_feature
}]
}))
bounds_shapefile_path = os.path.join(odm_dem_root, 'bounds.shp')
# Convert bounds to Shapefile
kwargs = {
'input': bounds_geojson_path,
'output': bounds_shapefile_path,
'proj4': pc_proj4
}
system.run('ogr2ogr -overwrite -a_srs "{proj4}" {output} {input}'.format(**kwargs))
# Process with lidar2dems
terrain_params_map = {
'flatnonforest': (1, 3),
'flatforest': (1, 2),
'complexnonforest': (5, 2),
'complexforest': (10, 2)
}
terrain_params = terrain_params_map[args.dem_terrain_type.lower()]
kwargs = {
'verbose': '-v' if self.params.verbose else '',
'slope': terrain_params[0],
'cellsize': terrain_params[1],
'outdir': odm_dem_root,
'site': bounds_shapefile_path
}
l2d_params = '--slope {slope} --cellsize {cellsize} ' \
'{verbose} ' \
'-o -s {site} ' \
'--outdir {outdir}'.format(**kwargs)
approximate = '--approximate' if args.dem_approximate else ''
# Classify only if we need a DTM
run_classification = args.dtm
if run_classification:
system.run('l2d_classify {0} --decimation {1} '
'{2} --initialDistance {3} {4}'.format(
l2d_params, args.dem_decimation, approximate,
args.dem_initial_distance, tree.odm_georeferencing), env_paths)
else:
log.ODM_INFO("Will skip classification, only DSM is needed")
copyfile(tree.odm_georeferencing_model_las, os.path.join(odm_dem_root, 'bounds-0_l2d_s{slope}c{cellsize}.las'.format(**kwargs)))
products = []
if args.dsm: products.append('dsm')
if args.dtm: products.append('dtm')
radius_steps = [args.dem_resolution]
for _ in range(args.dem_gapfill_steps - 1):
radius_steps.append(radius_steps[-1] * 3) # 3 is arbitrary, maybe there's a better value?
for product in products:
demargs = {
'product': product,
'indir': odm_dem_root,
'l2d_params': l2d_params,
'maxsd': args.dem_maxsd,
'maxangle': args.dem_maxangle,
'resolution': args.dem_resolution,
'radius_steps': ' '.join(map(str, radius_steps)),
'gapfill': '--gapfill' if args.dem_gapfill_steps > 0 else '',
# If we didn't run a classification, we should pass the decimate parameter here
'decimation': '--decimation {0}'.format(args.dem_decimation) if not run_classification else ''
}
system.run('l2d_dems {product} {indir} {l2d_params} '
'--maxsd {maxsd} --maxangle {maxangle} '
'--resolution {resolution} --radius {radius_steps} '
'{decimation} '
'{gapfill} '.format(**demargs), env_paths)
# Rename final output
if product == 'dsm':
os.rename(os.path.join(odm_dem_root, 'bounds-0_dsm.idw.tif'), dsm_output_filename)
elif product == 'dtm':
os.rename(os.path.join(odm_dem_root, 'bounds-0_dtm.idw.tif'), dtm_output_filename)
else:
log.ODM_WARNING('Found existing outputs in: %s' % odm_dem_root)
else:
log.ODM_WARNING('DEM will not be generated')
if args.time:
system.benchmark(start_time, tree.benchmarking, 'Dem')
log.ODM_INFO('Running ODM DEM Cell - Finished')
return ecto.OK if args.end_with != 'odm_dem' else ecto.QUIT

17
scripts/odm_georeferencing.py
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@ -17,10 +17,6 @@ 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):
@ -175,19 +171,6 @@ class ODMGeoreferencingCell(ecto.Cell):
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)")
with open(tree.odm_georeferencing_xyz_file, "wb") as csvfile:

13
settings.yaml
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@ -44,10 +44,15 @@ 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
#dtm: False # Use this tag to build a DTM (Digital Terrain Model
#dsm: False # Use this tag to build a DSM (Digital Surface Model
#dem-gapfill-steps: 4
#dem-resolution: 0.1
#dem-maxangle:20
#dem-maxsd: 2.5
#dem-approximate: False
#dem-decimation: 1
#dem-terrain-type: ComplexForest
#orthophoto_resolution: 20.0 # Pixels/meter
#orthophoto_target_srs: !!null # Currently does nothing
#orthophoto_no_tiled: False