I get an error as pointed in the title on execution of the following line:
from robotlocomotion import image_array_t
Is robotlocomotion no longer a part of drake?
I have installed drake on ubuntu 20.04 using pip and I am running the python file from within a virtual environment as mentioned here. The example code works fine.
Drake's build and install of the robotlocomotion lcmtypes was deprecated in v0.29.0 (April 2021), and removed as of v0.33.0 (August 2021).
Nothing within Drake itself should be importing those types. If you have your own uses for those types, you'll need to build them within your own project.
Drake offers drake.lcmt_image_array as a likely drop-in replacement.
Related
I have OpenCV 3.4 installed in Ubuntu 18. I also have installed ROS Melodic according to the website instructions. However, I keep on getting an error that libopencv_core.so.3.2 is required.
I already set my CMakeLists files to point to OpenCV 3.4.
However, I found out that in the file:
/ros/melodic/share/cv_bridge/cmake/cv_bridgeConfig.cmake
there is the following line hardcoded in opencv3.2:
set(libraries "cv_bridge;/usr/lib/x86_64-linux-gnu/libopencv_core.so.3.2.0;/usr/lib/x86_64-linux-gnu/libopencv_imgproc.so.3.2.0;/usr/lib/x86_64-linux-gnu/libopencv_imgcodecs.so.3.2.0").
I tried to change it to 3.4 but I can not rebuild it.
The error I am getting is:
/opt/ros/melodic/lib/image_view/image_view: error while loading shared libraries: libopencv_core.so.3.2: cannot open shared object file: No such file or directory
Why is OpenCV 3.2 hardcoded in cv_bridge and how can I rebuild it with OpenCV 3.4?
Update:
I eventually installed OpenCV 3.2 and it worked properly.
Because opencv development speed is much faster than ROS individual module. And a lot of ROS modules went depreciated after someone left the job.
But that's by no means the end of the day( maybe end of the day for noobs). You can build it directly with any version of opencv core function(besides imshow kind of function) others should perform just fine.
The easiest way is to do is: in the console before executing catkin_make try to execute the following
export CMAKE_PREFIX_PATH=/usr/local:$CMAKE_PREFIX_PATH
export LD_LIBRARY_PATH=/usr/local/lib:$LD_LIBRARY_PATH
This should give preference to your custom OpenCV installation when doing the find_package(OpenCV 3.X.0 REQUIRED). Then compile and use the function of that version.
Well if you do have to use 3.4 then I think you have to build ros version of opencv and image transport and cvbridge to the 3.4 if that's what you are targeting.
You can find the link here https://github.com/ros-gbp/opencv3-release The highest they provide seems to be 3.3
I'm interested in incorporating TensorFlow into a C++ server application built in Visual Studio on Windows 10 and I need to know if that's possible.
Google recently announced Windows support for TensorFlow: https://developers.googleblog.com/2016/11/tensorflow-0-12-adds-support-for-windows.html
but from what I can tell this is just a pip install for the more commonly used Python package, and to use the C++ API you need to build the repo from source yourself: How to build and use Google TensorFlow C++ api
I tried building the project myself using bazel, but ran into issues trying to configure the build.
Is there a way to get TensorFlow C++ to work in native Windows (not using Docker or the new Windows 10 Linux subsystem, as I've seen others post about)?
Thanks,
Ian
It is certainly possible to use TensorFlow's C++ API on Windows, but it is not currently very easy. Right now, the easiest way to build against the C++ API on Windows would be to build with CMake, and adapt the CMake rules for the tf_tutorials_example_trainer project (see the source code here). Building with CMake will give you a Visual Studio project in which you can implement your C++ TensorFlow program.
Note that the tf_tutorials_example_trainer project builds a Console Application that statically links all of the TensorFlow runtime into your program. At present we have not written the necessary rules to create a reusable TensorFlow DLL, although this would be technially possible: for example, the Python extension is a DLL that includes the runtime, but does not export the necessary symbols to use TensorFlow's C or C++ APIs directly.
There is a detailed guide by Joe Antognini and a similar TensorFlow ReadMe at GitHub explaining the building of TensorFlow source via CMake. You also need to have SWIG installed on your machine which allows connecting C/C++ source with the Python scripting language. I did use Visual CMAKE (cmake-gui) with the screen capture shown below.
In the CMake configuration, I used Visual Studio 15 2017 compiler. Once this stage successfully completes, you can click on the Generate button to go ahead with the actual build process.
However, on Visual Studio 2015, when I attempted building via the "ALL_BUILD" project, the setup gave me "build tools for v141 cannot be found" error. This did not go away even when I attempted to retarget my solution. Finally, the solution got built successfully with Visual Studio 2017. You also need to manually set the SWIG_EXECUTABLE path in CMake before it successfully configures.
As indicated in the Antognini link, for me the build took about half an hour on a 16GB RAM, Core i7 machine. Once done, you might want to validate your build by attempting to run the tf_tutorials_example_trainer.exe file.
Hope this helps!
For our latest work on building TensorFlow C++ API on Windows, please look at this github page. This works on Windows 10, currently without CUDA support (only CPU).
PS:
Only the bazel build method works, because CMake is not supported and not maintained anymore, resulting in CMake configuration errors.
I had to use a downgraded version of my Visual Studio 2017 (from 15.7.5 to 15.4) by adding "VC++ 2017 version 15.4 v14.11 toolset" through the installer (Individual Components tab).
The cmake command which worked for me was:
cmake .. -A x64 -DCMAKE_BUILD_TYPE=Release ^
-T "v141,version=14.11" ^
-DSWIG_EXECUTABLE="C:/Program Files/swigwin-3.0.12/swig.exe" ^
-DPYTHON_EXECUTABLE="C:/Program Files/Python/python.exe" ^
-DPYTHON_LIBRARIES="C:/Program Files/Python/libs/python27.lib" ^
-Dtensorflow_ENABLE_GPU=ON ^
-DCUDNN_HOME="C:/Program Files/cudnn-9.2-windows10-x64-v7.1/cuda" ^
-DCUDA_TOOLKIT_ROOT_DIR="C:/Program Files/NVIDIA GPU Computing Toolkit/CUDA/v9.0"
After the build, open tensorflow.sln in Visual Studio and build ALL_BUILD.
If you want to enable GPU computation, do check your Graphics Card here (Compute Capability > 3.5). Do remember to install all the packages (Cuda Toolkit 9.0, cuDNN, Python 3.7, SWIG, Git, CMake...) and add the paths to the environment variable in the beginning.
I made a README detailing how to I built the Tensorflow dll and .lib file for the C++ API on Windows with GPU support building from source with Bazel. Tensorflow version 1.14
The tutorial is step by step and starts at the very beginning, so you may have to scroll down past steps you have already done, like checking your hardware, installing Bazel etc.
Here is the url: https://github.com/sitting-duck/stuff/tree/master/ai/tensorflow/build_tensorflow_1.14_source_for_Windows
Probably you will want to scroll all the way down to this part:
https://github.com/sitting-duck/stuff/tree/master/ai/tensorflow/build_tensorflow_1.14_source_for_Windows#step-7-build-the-dll
It shows how to pass command to create .lib and .dll.
Then to test your .lib you should link it into your c++ project,
Then it will show you how to identify and fix the missing symbols using the TF_EXPORT macro
I am actively working on making this tutorial better so feel free to leave comments on this answer if you are having problems.
I'm trying to use luagraph, a binding to the graphviz library:
http://luagraph.luaforge.net/index.html
To install, I'm using luarocks in Mac OS X. The following command is executed in bash:
luarocks install luagraph
The output is the following:
Installing https://luarocks.org/luagraph-1.0.4-1.src.rock... Using
https://luarocks.org/luagraph-1.0.4-1.src.rock... switching to 'build'
mode
Error: Could not find expected file graphviz/graph.h, or
graphviz/graph.h for GRAPHVIZ -- you may have to install GRAPHVIZ in
your system and/or pass GRAPHVIZ_DIR or GRAPHVIZ_INCDIR to the
luarocks command. Example: luarocks install luagraph
GRAPHVIZ_DIR=/usr/local
I have been installed graphviz using homebrew, but I can't figure out how to pass GRAPHVIZ_DIR or GRAPHVIZ_INCDIR properly.
How can I install luagraph?
I've updated LuaGRAPH a couple of weeks ago. It now supports the newest version of Graphviz based on the cgraph library instead of the old graph library.
There is one drawback: I couldn't get luagraph to run on Windows using mingw because of some runtime library issues (compiler and dll compatibility probably). Please look at the README file for more details.
I personally never produced a rockspec for this module. This was created by someone else based on a fork of my luagraph library.
Installation without Luarocks is simple. Download from
https://github.com/hleuwer/luagraph
and follow the instruction in the documentation and the README file. You need adopt a simple config file which is included by make.
Herbert
Well, luaGRAPH is still the top result when searching for lua and graph. So the question is still standing.
And, unfortunately, the answer is: luagraph is OLD, the last update happened before the ubuntu 14.04 was released. And there seem to be some notable changes in the system itself, the flags the error message show do not seem to work. On top of that, graphviz is now about 20 releases newer than the luarock recommends.
There now is a bare bone alternative lua package: graphviz
It is extremely basic, and documentation in not at all informative, but at least it works.
update: Luagraph may be working again, but not through rocks. See the other answer.
I already have part of a program running in Python 3 but I need OpenCV (or SimpleCV), for a robotic vehicle, but I haven't found any install commands that seem to work, other than for Python 2.7.
If it is compatible could you please include instructions (/links to) for installation of the module?
I am using Ubuntu 14.
Maybe a little late to answer, but it's actually supported on OpenCV version 3 (in alpha state nowadays). I have successfully managed to install it, on MacOS, but I guess it would be similar on Ubuntu.
Now you have separated options for python2 and python3 when using Cmake. So you'll have to set those to make it work. That's all I needed to set:
BUILD_opencv_python3
PYTHON3_LIBRARY
PYTHON3_INCLUDE_DIR
PYTHON3_INCLUDE_DIR2
PYTHON3_NUMPY_INCLUDE_DIRS
...
Here you can find more detailed description: Link
Luigolas is correct that OpenCV 3.0 supports Python 3.x bindings. It was in release candidate status since April and the production version was released on 4 June 2015. Unfortunately for some reason the downloadable installation program on the OpenCV site does not contain a Python 3.x-compatible cv2.pyd file.
OP asked about Ubuntu but for those requiring a Windows installer, use Christoph Gohlke's site, which maintains Windows binaries for many Python packages, including OpenCV 3.0 with Python 3.x bindings. Visit:
http://www.lfd.uci.edu/~gohlke/pythonlibs/#opencv
To install, just download the 64-bit or 32-bit .whl file appropriate for your system, then run pip install [filename]. Then the instruction import cv2 should work in your Python 3.x interpreter.
I think i don't have parse library. So, Where can i get it?
As beginner, I need your help.
I wrote from lxml.html import parse , Computer say name 'parse' is not defined
You actually probably need the lxml package, which was in the comment by cel. It seems to me like your question is concerning how to actually install the package. Everything at PyPi (the Python package index) can be installed in several ways. For me, the easiest is using pip, which is a tool for installing Python packages. Here are some links for how to get pip on your machine for different operating systems: Windows, Mac OSX, or in general.
Once you have pip installed, you go to your command line and type
pip install ['package_name']
where, in your case, package_name would be lxml. Once you do that, your import should work just fine. The full pip documentation is here.
If you prefer to manually install the package, you can download the source and install it according to the Python docs for Python 2.x or 3.x.