Visualisation of Machine Learning Algorithms(mldemos.epfl.ch)
mldemos.epfl.ch
Visualisation of Machine Learning Algorithms
http://mldemos.epfl.ch/
11 comments
Even after applying your patch I had a little trouble getting everything to link properly on x86_64 Fedora (14). I ended up manually tweaking my make files after running qmake.
Changed the LIBS line (in MLDemos/Makefile, _AlgorithmPlugins/<n>/Makefile.<n>, _IOPlugins/<n>/Makefile.<n>) to:
LIBS = $(SUBLIBS) -L/usr/lib64 -L/usr/local/lib -lhighgui -lcv -lcxcore -lQtGui -lQtCore -lpthread
I suspect there's an easier way, but this at least allowed me to build and everything seems to work properly.
Changed the LIBS line (in MLDemos/Makefile, _AlgorithmPlugins/<n>/Makefile.<n>, _IOPlugins/<n>/Makefile.<n>) to:
LIBS = $(SUBLIBS) -L/usr/lib64 -L/usr/local/lib -lhighgui -lcv -lcxcore -lQtGui -lQtCore -lpthread
I suspect there's an easier way, but this at least allowed me to build and everything seems to work properly.
I'm glad it built.
I think the issue we're all having stems from the structural redesign of opencv 2.2. Everything has been split up into smaller headers, and the functionality is similarly separated into different shared libraries. It's a more logical code structure, but it means that we need to have different makefile rules for pre-2.2 and 2.2+ code.
As far as -lhighgui -lcv -lcxcore is concerned, those libraries don't exist on my machine anymore. "highgui" is "/usr/lib/libopencv_highgui.so", and so on. That would explain why my 'fix' didn't work for you.
I think the issue we're all having stems from the structural redesign of opencv 2.2. Everything has been split up into smaller headers, and the functionality is similarly separated into different shared libraries. It's a more logical code structure, but it means that we need to have different makefile rules for pre-2.2 and 2.2+ code.
As far as -lhighgui -lcv -lcxcore is concerned, those libraries don't exist on my machine anymore. "highgui" is "/usr/lib/libopencv_highgui.so", and so on. That would explain why my 'fix' didn't work for you.
Here's a patch that lets you compile with opencv2: https://gist.github.com/999061.
edit: I'm still getting a Make error for cvSumPixels (unless I comment that check out);
sampleManager.cpp:(.text+0x1a06): undefined reference to `cvSumPixels'
edit: I'm still getting a Make error for cvSumPixels (unless I comment that check out);
sampleManager.cpp:(.text+0x1a06): undefined reference to `cvSumPixels'
you might need to add
#include <opencv2/legacy/legacy.hpp>
#include <opencv2/legacy/compat.hpp>
to get those
I haven't gotten around to porting it to opencv2.2 so the patch is very welcome!
Good call. I added those headers and linked to their related libraries. It's working perfectly on my Arch install.
Here are my modifications; https://gist.github.com/999427
Here are my modifications; https://gist.github.com/999427
Yeah, the install(_script).sh are to allow deployment on the Mac (which wants absolute paths to libraries). They will need to be changed to direct to the local libraries in case you need them... (hopefully a new version of MacDeployQt will appear at some point to take care of all that)
Wow. This could prove an invaluable tool for teaching. Great UI (love the way you can build the dataset with a paintbrush), top-quality visualization, wide choice of algorithms... Much better than R scripts if you want to show basic algorithms at work to a bunch of students. Many thanks
Here's another one, a simple Applet that lets you apply various classification algorithms on 2D data: http://www.cs.technion.ac.il/~rani/LocBoost/
Thank you! This is very, very useful. Not only to beginners, but also to those of us who want to roughly compare the behavior of several algorithms. You can estimate things like sensitivity to parameters and quickly visualize exactly how they behave.
It's a very useful tool.
It's a very useful tool.
Patch through a black and white video into this tool and watch how the algorithm tries to find patterns in a 2d reprsentation of a 3d space. Maybe it can predict where the table ends without full view of the table.
Neat stuff, it is the beginning of machine notion of object permanence. Filling in the gaps and making educated guesses about what kind of function controls where a sequence originated from.
Neat stuff, it is the beginning of machine notion of object permanence. Filling in the gaps and making educated guesses about what kind of function controls where a sequence originated from.
edit: Yep. Clone the git repo, then run qmake, followed by make.
edit #2: Here is a patch I wrote to make the project compile under OpenCV 2.2, and Linux: https://gist.github.com/999061