Repository for code and documents relating to our 6.S078 project.

Google Drive folder

We are writing the code in a mostly functional style. This means we should try and treat objects as immutable, and we should write functions without side-effects. When we get to the point of optimization, we'll break this paradigm as needed, but we will not break it lightly.

For the python source code, class names will be in UpperCamelCase, and other variable names will be underscore_separated. Clarity in variable names is more important than brevity.

In our code, the shape of image arrays is (width, height), so that pixels can be accessed with the semantics image[x, y].

IMPORTANT: OpenCV uses the opposite convention for some reason. This means when using OpenCV functions it may be important to transpose the data. The functions scan.data_capture.image.to_OpenCV and scan.data_capture.image.from_OpenCV can be used to accomplish this transpose in their respective directions.

NaturalDocs compiles correctly formatted documentation of the code in software into an easily readable and searchable html page.

The only convention we have on top of those for NaturalDocs is to include the type of a parameter or return for python documentation in bold. For example, if we had a parameter for the pixel data of an image, it would be in this form

data - *ndarray* The array of pixel values for an image

To compile the documentation simply run make-docs.sh in ./software. Any arguments passed to this script will be passed on to NaturalDocs. -r forces recompilation of all documentation, and so might be helpful.

make-docs uses a script called dos2unix (can be installed with homebrew) to convert file endings before and after documentation compilation. NaturalDocs, otherwise, leaves CRLFs everywhere... ugh.

Compiled documentation will be placed in ./software/docs.

The general packages you need on your system are:

• dos2unix
• python >= 2.7
• opencv >= 2.4
• pcl

If you are running OS X, it will be easiest to install these dependencies using Homebrew.

Unfortunately, the PCL installation can be quite involved. You could either download a precompiled binary, or you could install from source. The python-pcl library is being developed for 1.7.0 (the source version of PCL), but should work with 1.6.0 (the precompilied version of PCL). Instructions for OS X with Homebrew follow.

• Install OpenNI. I haven't found a good source version to use (it's patched), so install the precompiled version.
• Add the science packages to homebrew:

$ brew tap homebrew/science

• Copy the Homebrew Formula software/lib/Formula/pcl.rb to /usr/local/Library/Formula/pcl.rb, and run

$ brew install pcl

• The python-pcl bindings are in software/lib/python-pcl. Note this is a git submodule--there are some things to be careful about with submodules (you can read more about it on the linked page). Running make inside that directory will build the python bindings. I recommend not installing the bindings, as they will be changing a lot during development, but just adding the directory to the python path. You can do this for your current shell by running

$ source software/setup-env.sh

You can add the location to the python path for your entire session by modifying your .bash_profile, or whatever you use.

As Python is being used for the core software library, it is recommended to use pip to install the necessary Python packages. Install using pip by running

$ pip install <package>

Below are the packages that are required:

• numpy
• scipy
• nose

We use Nose for unit testing. We have a soft goal for 100% code coverage: all code that can be tested with a unit test should be tested, but if you think that a unit test is really unreasonable in a certain situation, we'll trust you. Tests should go in the software/python/scan/test directory. To run all the tests, run software/run-tests.sh.

Looking at tests can be a good way of getting an idea of how to use the different functions. Additionally, if you are writing code to test how something is working, you should be writing that code as a test!

To set a breakpoint in a test, add the following line:

from nose.tools import set_trace; set_trace()

When writing tests, in general write setup code in the setUp method for a subclass of unittest.TestCase, but if the setup is very expensive, write it as a class method with the setUpClass method.

If there is a very slow test or test class, mark it with the following decorator:

@nose.plugins.attrib.attr('slow')

This way, if you want to run all tests except the slow ones, you can pass -a '!slow' to the test executable. Similarly, you could pass -a 'slow' to run only the slow tests.

If you only want to run a single test module, instead of all the tests, just type in the module name of the test after the script name, e.g.:

$ software/run-tests scan.tests.test_math

More details for what can be passed can be found in the nose documentation. All arguments passed to the script will be passed to nose