Projector-Camera Calibration - the "easy" way

First let me open by saying projector-camera calibration is NOT EASY. But it's technically not complicated too.

It is however, an amalgamation of optimizations that accrue and accumulate error with each step, so that the end product is not far from a random guess.
So 3D reconstructions I was able to get from my calibrated pro-cam were just a distorted mess of points.

Nevertheless, here come the deets.
Continue reading "Projector-Camera Calibration - the "easy" way"

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Revisiting graph-cut segmentation with SLIC and color histograms [w/Python]

As part of the computer vision class I'm teaching at SBU I asked students to implement a segmentation method based on SLIC superpixels. Here is my boilerplate implementation.

This follows the work I've done a very long time ago (2010) on the same subject.

For graph-cut I've used PyMaxflow: https://github.com/pmneila/PyMaxflow, which is very easily installed by just pip install PyMaxflow

The method is simple:

  • Calculate SLIC superpixels (the SKImage implementation)
  • Use markings to determine the foreground and background color histograms (from the superpixels under the markings)
  • Setup a graph with a straightforward energy model: Smoothness term = K-L-Div between superpix histogram and neighbor superpix histogram, and Match term = inf if marked as BG or FG, or K-L-Div between SuperPix histogram and FG and BG.
  • To find neighbors I've used Delaunay tessellation (from scipy.spatial), for simplicity. But a full neighbor finding could be implemented by looking at all the neighbors on the superpix's boundary.
  • Color histograms are 2D over H-S (from the HSV)

Result

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OMG CMake/OpenCV3 can you be more difficult? Linking order problems with OpenNI2...

So I just spent 1.5 hours figuring this out.
Compiling an example on Ubuntu 16.04 with OpenCV built from scratch with OpenNI2 support.
(OpenNI2 is from Orbbec, but that doesn't make any difference: https://orbbec3d.com/develop/)

When using this straightforward CMake script for compilation - it doesn't work:

cmake_minimum_required(VERSION 3.2)
project(MyApp)

find_package(OpenCV 3 REQUIRED)

set(OPENNI2_LIBS "OpenNI2")
link_directories("/home/user/Downloads/2-Linux/OpenNI-Linux-x64-2.3/Redist")

add_executable(myapp main.cpp)
target_link_libraries(myapp ${OpenCV_LIBS} ${OPENNI2_LIBS})

Complains of undefined references:

/usr/bin/c++   -g   CMakeFiles/myapp.dir/main.cpp.o  -o myapp  -L/home/user/Downloads/2-Linux/OpenNI-Linux-x64-2.3/Redist -rdynamic -lOpenNI2 /usr/local/lib/libopencv_shape.so.3.2.0 /usr/local/lib/libopencv_stitching.so.3.2.0 /usr/local/lib/libopencv_superres.so.3.2.0 /usr/local/lib/libopencv_videostab.so.3.2.0 /usr/local/lib/libopencv_objdetect.so.3.2.0 /usr/local/lib/libopencv_calib3d.so.3.2.0 /usr/local/lib/libopencv_features2d.so.3.2.0 /usr/local/lib/libopencv_flann.so.3.2.0 /usr/local/lib/libopencv_highgui.so.3.2.0 /usr/local/lib/libopencv_ml.so.3.2.0 /usr/local/lib/libopencv_photo.so.3.2.0 /usr/local/lib/libopencv_video.so.3.2.0 /usr/local/lib/libopencv_videoio.so.3.2.0 /usr/local/lib/libopencv_imgcodecs.so.3.2.0 /usr/local/lib/libopencv_imgproc.so.3.2.0 /usr/local/lib/libopencv_core.so.3.2.0 -Wl,-rpath,/home/user/Downloads/2-Linux/OpenNI-Linux-x64-2.3/Redist:/usr/local/lib 
/usr/local/lib/libopencv_videoio.so.3.2.0: undefined reference to `oniStreamGetProperty'
/usr/local/lib/libopencv_videoio.so.3.2.0: undefined reference to `oniRecorderDestroy'
/usr/local/lib/libopencv_videoio.so.3.2.0: undefined reference to `oniDeviceIsCommandSupported'
/usr/local/lib/libopencv_videoio.so.3.2.0: undefined reference to `oniDeviceSetProperty'

You'll notice that -lOpenNI2 does indeed appear for correct linking.
The linker doesn't complain that lib was not found - it just misses the references.
This lead me to understand it's a linking order problem (after ~45 minutes of banging my head vs. the keyboard and swearing profusely).

Some more swearing and head banging got me to understand that CMake is messing around with the link order.
So even if try:

target_link_libraries(myapp ${OpenCV_LIBS} ${OPENNI2_LIBS} ${OpenCV_LIBS} ${OPENNI2_LIBS})

i.e. making the order effectively meaningless -- it still doesn't work!

More swearing and head banging, another ~40 minutes passed, and I figured out a solution.
The real solution is to slap someone in CMake in the face with a trout, but here's a solution to my problem:

find_package(OpenCV 3 REQUIRED core highgui videoio) # ORDER MATTERS!!! videoio must be last!
set(OpenCV_LIBS "${OpenCV_LIBS};OpenNI2") #add openni2 at the end (although cmake doesn't keep order anyway)
target_link_libraries(myapp ${OpenCV_LIBS})

Now it compiles.

And look at the make VERBOSE=1:

/usr/bin/c++   -g   CMakeFiles/myapp.dir/main.cpp.o  -o myapp  -L/home/user/Downloads/2-Linux/OpenNI-Linux-x64-2.3/Redist -rdynamic /usr/local/lib/libopencv_highgui.so.3.2.0 /usr/local/lib/libopencv_videoio.so.3.2.0 -lOpenNI2 /usr/local/lib/libopencv_core.so.3.2.0 -Wl,-rpath,/home/user/Downloads/2-Linux/OpenNI-Linux-x64-2.3/Redist:/usr/local/lib -Wl,-rpath-link,/usr/local/lib 

Can you see how highgui and videoio are before OpenNI2, and core is after?
Why? Whhhhhhy?

The key is to get OpenNI to be linked in order after videoio.

OMG CMake, OMG OpenCV, OMG you gaiz, W-T-F?

Update:
This method breaks down as soon as more OpenCV components are added. The order goes haywire again, and OpenNI2 comes before videoio, which breaks the link.
As of now the way I can compile it is like so:

set(LINK_LIBS /usr/local/lib/libopencv_core.so.3.2
              /usr/local/lib/libopencv_highgui.so.3.2
              /usr/local/lib/libopencv_videoio.so.3.2
              /usr/local/lib/libopencv_imgproc.so.3.2
              /usr/local/lib/libopencv_calib3d.so.3.2
              OpenNI2)
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New edition to the Mastering OpenCV book - now with OpenCV3!

Mastering OpenCV 3
I'm happy to announce that the new edition of Mastering OpenCV is out!
You can get it on Amazon: Mastering OpenCV 3

It brings up most of the older OpenCV2 book projects to OpenCV3, including my Toy-SfM (or "Exploring SfM") project.

A lot has happened in the OpenCV3 APIs with respect to Structure from Motion.
It got much easier!
The book chapter on SfM is a gentle introduction to the subject, that focuses on coding and the core concepts, while abstracting on the math.

Thanks for listening!
Roy

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Using Hidden Markov Models for staff line removal (in OMR) [w/code]

So lately I'm into Optical Music Recognition (OMR), and a central part of that is doing staff line removal. That is when you get rid of the staff lines that obscure the musical symbols to make recognition much easier. There are a lot of ways to do it, but I'm going to share with you how I did it (fairly easily) with Hidden Markov Models (HMMs), which will also teach us a good lesson on this wonderfully useful approach.

OMR has been around for ages, and if you're interested in learning about it [Fornes 2014] and [Rebelo 2012] are good summary articles.
The matter of Staff Line Removal has occupied dozens of researchers for as long as OMR exists; [Dalitz 2008] give a good overview. Basically the goal is to remove the staff lines that obscure the musical symbols, so they would be easier to recognize.
Screen Shot 2015-01-24 at 10.11.00 PM
But, the staff lines are connected to the symbols, so simply removing them will cut up the symbols and make them hardly recognizable.
So let's see how we could do this with HMMs.
Continue reading "Using Hidden Markov Models for staff line removal (in OMR) [w/code]"

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