yashiang1986 / numpy-opencv-converter

OpenCV <=> NumPy Converter using Boost::Python

NUMPY <=> OpenCV's cv::Mat converter

Sudeep Pillai ([e-mail]) Sep 14, 2014

A convenience library that registers a boost::python converter to implicitly convert between OpenCV's popular cv::Mat datatype and NumPy's popular np.array() datatype. This allows a developer to go back and forth between their OpenCV C++ API and Python API written using NumPy with relative ease, avoiding the need to write additional wrappers that handle PyObjects being passed around or returned.

This work was mostly inspired by https://github.com/yati-sagade/opencv-ndarray-conversion.

BSD License

A simple example

Imagine writing a C++ API using OpenCV as so:

cv::Mat process_mat(const cv::Mat& in) { 
   // process matrix, or just plain-simple cloning!
   cv::Mat out = in.clone();
   return out;
}

Wrap it using Boost::Python in say, cv_module.cpp:

boost::python::def("process_mat", &process_mat);

Call it from Python:

import numpy as np
from cv_module import process_mat
A = np.random.random(shape=(4,3))
B = process_mat(A)

As simple as that! Hope you find it useful!

Build and Install

See dependencies below before building.

git clone [e-mail]:spillai/numpy-opencv-converter
mkdir build
cd build && cmake ..
make

Usage

Make sure the built np_opencv_converter.so and np_opencv_module.so (currently in numpy-opencv-converter/build/) are in the $LD_LIBRARY_PATH environment variable, before running the following in python/ipython.

In [1]: import np_opencv_module as npcv
PYTHON TYPE CONVERTERS exported

On succesful import, the cv::Mat <=> np.ndarray converters are exported.

We then test a simple function that takes in a cv::Mat (appropriately converted from np.ndarray A) as an argument and returns another cv::Mat (again appropriately converted back to np.ndarray B).

In [2]: import numpy as np
In [3]: A = np.random.random((4,3))
In [4]: B = npcv.test_np_mat(A)
in: [0.2793205402416998, 0.466896711918419, 0.3834843006535923;
  0.5374426625812107, 0.3737008026047054, 0.3685794034255524;
  0.993469313797578, 0.2619403678989528, 0.5700175530375297;
  0.5711496315041438, 0.3286727439294438, 0.1250325059375548]
sz: [3 x 4]
 
In [5]: print A.dtype, B.dtype
float64 float64

With default args

In [6]: npcv.test_with_args(A, var1=0, var2=20.0, name='test_name2')
in: [0.27932054, 0.46689671, 0.3834843;
  0.53744268, 0.3737008, 0.36857942;
    0.9934693, 0.26194036, 0.57001758;
	  0.57114965, 0.32867274, 0.1250325]
sz: [3 x 4]
Returning transpose

Out[6]: 
array([[ 0.27932054,  0.53744268,  0.9934693 ,  0.57114965],
 	  [ 0.46689671,  0.3737008 ,  0.26194036,  0.32867274],
	  [ 0.3834843 ,  0.36857942,  0.57001758,  0.1250325 ]],
	  dtype=float32)

Finally, testing a wrapper class

In [7]: gw = npcv.GenericWrapper(var_int=1, var_float=2.0, var_double=3.0, var_string='string')
In [8]: gw.process(A)

in: [0.2793205402416998, 0.466896711918419, 0.3834843006535923;
  0.5374426625812107, 0.3737008026047054, 0.3685794034255524;
    0.993469313797578, 0.2619403678989528, 0.5700175530375297;
	  0.5711496315041438, 0.3286727439294438, 0.1250325059375548]
sz: [3 x 4]
Returning transpose

Out[8]: 
array([[ 0.27932054,  0.53744266,  0.99346931,  0.57114963],
      [ 0.46689671,  0.3737008 ,  0.26194037,  0.32867274],
      [ 0.3834843 ,  0.3685794 ,  0.57001755,  0.12503251]])

Dependencies

Currently only Linux is supported, although the code is pretty barebones that should allow you to port it to Windows, or Mac OSX with relative ease. I personally wouldn't recommend installing opencv as indicated below, but I'll assume you know how to deal with pkg-config and opencv to make any modifications to the CMakeLists.txt file.

$ sudo apt-get install libboost-python-dev libopencv-dev