Version: 1.5.0 Date: 11-12-2014
You can get the latest version of the code from github:
https://github.com/pablofdezalc/akaze
You can try the CUDA A-KAZE implementation from Niklas Bergström:
https://github.com/nbergst/akaze
There is also a very fast CPU A-KAZE version from Hideaki Suzuki
https://github.com/h2suzuki/fast_akaze
Version: 1.5.0 Changes:
- The code has been changed for compatibility with OpenCV 3.0
Version: 1.4.0 Changes:
- Maximum number of OpenMP threads can be set with OMP_MAX_THREADS definition in AKAZEConfig.h. By default is set to 16. This avoid problems with some systems that have many cores. Thanks to Thomas Fraenz
- Namespace libAKAZE created to avoid conflict with AKAZE class in OpenCV 3.0
- Speed-up in detection in description thanks to the improvements done in the Google Summer of Code 2014 program. Most of this improvements are thanks to Fedor Mozorov, Vadim Pisarevsky and Gary Bradsky
- akaze_match and akaze_compare now automatically perform the image matching estimating a planar homography using RANSAC if the homography txt file is not provided as input argument
Version: 1.3.0 Changes:
- More efficient memory usage
- Now the smoothing is performed after the FED process
Version: 1.2.0 Changes:
- Header file config.h replaced by AKAZEConfig.h
- Header files akaze_compare.h, akaze_features.h and akaze_match.h have been removed
- Matlab interface added by Zohar Bar-Yehuda
Version: 1.1.0 Changes:
- Code style has been changed substantially to match portability with other libraries
- Small bug has been corrected in generateDescriptorSubsample for the random bit selection
- Several modifications proposed from Jesus Nuevo, Pierre Moulon and David Ok have been integrated
- Descriptor modes have changed. No longer use of use_upright flag
- Python interface added by David Ok
This file explains how to make use of source code for computing A-KAZE features and two practical image matching applications
The code is mainly based on the OpenCV library using the C++ interface
In order to compile the code, the following libraries to be installed on your system:
- OpenCV version 2.4.0 or higher
- Cmake version 2.6 or higher
If you want to use OpenMP parallelization you will need to install OpenMP in your system In Linux you can do this by installing the gomp library
You will also need doxygen in case you need to generate the documentation
Tested compilers:
- GCC 4.2-4.7
- MSVC 11 x64
Tested systems:
- Ubuntu 11.10, 12.04, 12.10
- Kubuntu 10.04
- Windows 8
Compiling:
$ mkdir build$ cd build>$ cmake ..$ make
Additionally you can also install the library in /usr/local/akaze/lib by typing:
$ sudo make install
If the compilation is successful you should see three executables in the folder bin:
akaze_featuresakaze_matchakaze_compare
Additionally, the library libAKAZE[.a, .lib] will be created in the folder lib.
If there is any error in the compilation, perhaps some libraries are missing. Please check the Library dependencies section.
Examples: To see how the code works, examine the three examples provided.
A mex interface for computing AKAZE features is supplied, in the file mex/akaze.cpp.
To be able to use it, first compile the library as explained above. Then, you will need to compile the mex from Matlab.
The following is an example for compiling the mex on Windows 64 bit, Visual Studio 10 and OpenCV 2.4.8. from the mex folder, type in MATLAB:
mex akaze.cpp -I'..\src\lib\' -L'..\build\lib\Release\' -I'<path_to_opencv>\build\include' -L'<path_to_opencv>\build\x64\vc10\lib' -lopencv_calib3d248 -lopencv_contrib248 -lopencv_core248 -lopencv_highgui248 -lopencv_imgproc248 -lAKAZE
The following is an example for compiling the mex on Linux, Ubuntu 12.10, 64 bit, gcc 4.6.4 and OpenCV 2.4.8. from the mex folder, type in MATLAB:
For other platforms / compilers / OpenCV versions, change the above line accordingly.
mex akaze.cpp -I'../src/lib/' -L'../build/lib/' -lAKAZE -L'/usr/local/lib/' -lopencv_imgproc -lopencv_core -lopencv_calib3d -lopencv_highgui -lgomp
On Windows, you'll need to make sure that the corresponding OpenCV bin folder is added to your system path before staring MATLAB. e.g.:
PATH=<path_to_opencv>\build\x64\vc10\bin
Once the mex file is compiled successfully, type:
akaze
to display function help.
In the working folder, type: doxygen
The documentation will be generated in the ./doc folder
For running the program you need to type in the command line the following arguments:
./akaze_features img.jpg [options]
The [options] are not mandatory. In case you do not specify additional options, default arguments will be
used. Here is a description of the additional options:
--verbose: if verbosity is required--help: for showing the command line options--soffset: the base scale offset (sigma units)--omax: the coarsest nonlinear scale space level (sigma units)--nsublevels: number of sublevels per octave--diffusivity: diffusivity function0-> Perona-Malik 1,1-> Perona-Malik 2,2-> Weickert--dthreshold: Feature detector threshold response for accepting points--descriptor: Descriptor Type, 0-> SURF_UPRIGHT, 1->SURF 2-> M-SURF_UPRIGHT, 3->M-SURF 4-> M-LDB_UPRIGHT, 5->M-LDB--descriptor_channels: Descriptor Channels for M-LDB. Valid values: 1, 2 (intensity+gradient magnitude), 3(intensity + X and Y gradients)--descriptor_size: Descriptor size for M-LDB in bits. 0 means the full length descriptor (486). Any other value will use a random bit selection--show_results:1in case we want to show detection results.0otherwise
- Check
config.hin case you would like to change the value of some default settings - The k constrast factor is computed as the 70% percentile of the gradient histogram of a smoothed version of the original image. Normally, this empirical value gives good results, but depending on the input image the diffusion will not be good enough. Therefore I highly recommend you to visualize the output images from save_scale_space and test with other k factors if the results are not satisfactory
The code contains one program to perform image matching between two images. If the ground truth transformation is not provided, the program estimates a fundamental matrix or a planar homography using RANSAC between the set of correspondences between the two images.
For running the program you need to type in the command line the following arguments:
./akaze_match img1.jpg img2.pgm homography.txt [options]
The [options] are not mandatory. In case you do not specify additional options, default arguments will be
used.
The datasets folder contains the Iguazu dataset described in the paper and additional datasets from Mikolajczyk et al. evaluation. The Iguazu dataset was generated by adding Gaussian noise of increasing standard deviation.
For example, with the default configuration parameters used in the current code version you should get the following results:
./akaze_match ../../datasets/iguazu/img1.pgm
../../datasets/iguazu/img4.pgm
../../datasets/iguazu/H1to4p
--descriptor 4
Number of Keypoints Image 1: 1823
Number of Keypoints Image 2: 2373
A-KAZE Features Extraction Time (ms): 411.231
Matching Descriptors Time (ms): 19.5631
Number of Matches: 1283
Number of Inliers: 1136
Number of Outliers: 147
Inliers Ratio: 88.5425
The code contains one program to perform image matching between two images, showing a comparison between A-KAZE features, ORB and BRISK. All these implementations are based on the OpenCV library.
The program assumes that the ground truth transformation is provided
For running the program you need to type in the command line the following arguments:
./akaze_compare img1.jpg img2.pgm homography.txt [options]
For example, running kaze_compare with the first and third images from the boat dataset you should get the following results:
./akaze_compare ../../datasets/boat/img1.pgm
../../datasets/boat/img3.pgm
../../datasets/boat/H1to3p
--dthreshold 0.004
ORB Results
**************************************
Number of Keypoints Image 1: 1511
Number of Keypoints Image 2: 1517
Number of Matches: 489
Number of Inliers: 469
Number of Outliers: 20
Inliers Ratio: 95.91
ORB Features Extraction Time (ms): 272.932
BRISK Results
**************************************
Number of Keypoints Image 1: 3449
Number of Keypoints Image 2: 3028
Number of Matches: 158
Number of Inliers: 116
Number of Outliers: 42
Inliers Ratio: 73.4177
BRISK Features Extraction Time (ms): 188.407
A-KAZE Results
**************************************
Number of Keypoints Image 1: 2129
Number of Keypoints Image 2: 1668
Number of Matches: 764
Number of Inliers: 688
Number of Outliers: 76
Inliers Ratio: 90.0524
A-KAZE Features Extraction Time (ms): 292.35
A-KAZE features is open source and you can use that freely even in commercial applications. The code is released under BSD license. While A-KAZE is a bit slower compared to ORB and BRISK, it provides much better performance. In addition, for images with small resolution such as 640x480 the algorithm can run in real-time. In the next future we plan to release a GPGPU implementation.
If you use this code as part of your work, please cite the following papers:
-
Fast Explicit Diffusion for Accelerated Features in Nonlinear Scale Spaces. Pablo F. Alcantarilla, J. Nuevo and Adrien Bartoli. In British Machine Vision Conference (BMVC), Bristol, UK, September 2013
-
KAZE Features. Pablo F. Alcantarilla, Adrien Bartoli and Andrew J. Davison. In European Conference on Computer Vision (ECCV), Fiorenze, Italy, October 2012
Important: If you work in a research institution, university, company or you are a freelance and you are using KAZE or A-KAZE in your work, please send me an email!! I would like to know the people that are using KAZE and A-KAZE around the world!!"
In case you have any question, find any bug in the code or want to share some improvements, please contact me:
Pablo F. Alcantarilla email: [email protected]