forked from etola/libdaisy
-
Notifications
You must be signed in to change notification settings - Fork 4
DAISY: A Fast Local Descriptor for Dense Matching
License
TheFrenchLeaf/libdaisy
Folders and files
Name | Name | Last commit message | Last commit date | |
---|---|---|---|---|
Repository files navigation
-------------------------------------------------------------------------------- AUTHOR: Engin Tola -------------------------------------------------------------------------------- CONTACT: web : http://www.engintola.com email : engintola [email protected] -------------------------------------------------------------------------------- LICENCE: See licence.txt file. -------------------------------------------------------------------------------- CONTEXT DAISY is a local image descriptor designed for dense wide-baseline matching purposes. For more details about the descriptor please read the papers: @article{Tola10, author = "E. Tola and V. Lepetit and P. Fua", title = {{DAISY: An Efficient Dense Descriptor Applied to Wide Baseline Stereo}}, journal = "IEEE Transactions on Pattern Analysis and Machine Intelligence", year = 2010, month = "May", pages = "815--830", volume = "32", number = "5" } AND @inproceedings{Tola08, author = "E. Tola and V.Lepetit and P. Fua", title = {{A Fast Local Descriptor for Dense Matching}}, booktitle = "Proceedings of Computer Vision and Pattern Recognition", year = 2008, address = "Alaska, USA" } -------------------------------------------------------------------------------- SOFTWARE There is a main.cpp file under ./src/ directory as an example code. You can inspect it on how to use the daisy class. However, this software is intended to be used as a library and has additional operation modes in that form. 1. To build the example application code run make ./daisy (look at EXAMPLES section below) 2. To build the library, remove src/main.cpp from the 'sources' list in the makefile. Run 'make library' for a static library and 'make slib' for a shared library. This will generate the libdaisy.a (libdaisy.so for shared library) file under ./lib directory. To install this to your system, run make install-lib ( or make install-slib for shared library) This will install the library to 'installdir' in your makefile. This command also generates the daisy.pc file which is installed to '$(installdir)/lib/pkgconfig/'. The pkg-config[1] utility can be used to include daisy to your projects with CFLAGS =`pkg-config --cflags daisy` LDFLAGS =`pkg-config --libs daisy`. In your source file, include 'daisy/daisy.h' to use the library. 3. The code is documented as per Doxygen[3] standards and a Doxyfile is supplied in the library. To generate the documentation, run doxygen Doxyfile or make dox You can reach the html documentation under './doc/html/index.html' It is possible to use opencv[2] with daisy. Opencv implementation of the convolution is faster than the one used by default in the library. Apart from this, the library is free of opencv. Opencv is included/linked using the pkg-config utility and if you don't have pkg-config you should either install it or change the makefile accordingly. To enable opencv, uncomment the 'define_flags' and 'external_library' lines in the makefile. 4. To enable debugging for the library change the 'optimize=true' to 'optimize=false' in the makefile. 5. Library can be compiled with OpenMP[4] for parallel processing. To enable this, change 'parallelize = false' to 'parallelize = true' in the makefile and enable -DUSE_OPENMP flag for 'define_flags'. When the optimize flag is set to false then parallelization is disabled also. [1] pkg-config : http://pkg-config.freedesktop.org/wiki/ [2] opencv : http://sourceforge.net/projects/opencvlibrary/ [3] doxygen : http://www.stack.nl/~dimitri/doxygen/ [4] openmp : http://www.openmp.org/ -------------------------------------------------------------------------------- USAGE ---- The library has two operation modes: MODE 1. In the first case, you can precompute the descriptors at every point with daisy* desc = new daisy(); desc->set_image(im,h,w); desc->verbose( verbose_level ); // 0,1,2,3 -> how much output do you want while running desc->set_parameters(rad, radq, thq, histq); // default values are 15,3,8,8 desc->initialize_single_descriptor_mode(); desc->compute_descriptors(); // precompute all the descriptors (NOT NORMALIZED!) // the descriptors are not normalized yet desc->normalize_descriptors(); and get the specific descriptor at a point (y,x) with float* thor = NULL; desc->get_descriptor(y,x,thor); Here, you can get the descriptors at only integer y,x values. MODE 2. You can also precompute orientation layers with daisy* desc = new daisy(); desc->set_image(im,h,w); desc->verbose( verbose_level ); // 0,1,2,3 -> how much output do you want while running desc->set_parameters(rad, radq, thq, histq); // we use 15,3,8,8 for wide baseline stereo. desc->initialize_single_descriptor_mode(); a) get the descriptor at floating point locations with any orientation as float* thor = new thor[desc->descriptor_size()]; desc->get_descriptor(y,x,orientation,thor); // returns normalized descriptor or desc->get_unnormalized_descriptor(y,x,orientation,thor); // unnormalized see NORMALIZATION section for details on normalization. orientation in [0 360) b) or get the descriptor with a warped grid float* thor = new thor[desc->descriptor_size()]; desc->get_descriptor(y,x,orientation, H, thor); here H (float H[9]) is a homography matrix used to warp the grid of daisy and (y,x) is on the unwarped image. You can use this function in the case where you have two images and you want to compare the descriptors of two corresponding points which are related with a planar transformation. The transformation is encoded with a Homography matrix. orientation in [0 360) -------------------------------------------------------------------------------- NORMALIZATION : -------------------------------------------------------------------------------- As of version 1.5, by default, descriptors are not normalized in MODE 1. You need to call normalize_descriptors() in order to apply normalization. For MODE 2, you have two options: (a) get_unnormalized_descriptor (b) get_descriptor function (a), as per its name, returns an unnormalized descriptor and function (b) returns the normalized descriptor. You can call set_normalization() function with NRM_PARTIAL, NRM_FULL and NRM_SIFT flags to change the applied normalization algoritm. For example, desc->set_normalization(NRM_SIFT); desc->get_descriptor(y,x,orientation,thor); is equivalent to desc->get_unnormalized_descriptor(y,x,orientation,thor); desc->normalize_descriptor(thor,NRM_SIFT); which is equivalent to desc->get_unnormalized_descriptor(y,x,orientation,thor); desc->set_normalization(NRM_SIFT); desc->normalize_descriptor(thor); set_normalization() function changes the used algorithm globally whereas you can apply individual normalizations using normalize_descriptor() function. The default algorith is NRM_PARTIAL. Possibilities are: NRM_PARTIAL: Each histogram is normalized independently so that their L2 norm is 1. NRM_FULL : The whole descriptor is normalized so that its L2 norm is 1. NRM_SIFT : The whole descriptor is normalized recursively so that its L2 norm is 1 and no individual value is bigger than m_descriptor_normalization_threshold = 0.154 as in SIFT. -------------------------------------------------------------------------------- ADVANCED DETAILS ON OPERATION: -------------------------------------------------------------------------------- Interpolation: -------------- While computing the descriptors, by default, interpolation is used. However, it is possible to disable the interpolation using the disable_interpolation() function. This will decrease the computation time at the expense of some minor performance loss. You can alleviate this by using the extra_sub_layers() and upscale_image() functions. I recommend to disable the interpolation in only cases where you run DAISY in MODE 2 and you have to compute the same descriptor at many different orientations many times ( i.e. in a dense stereo problem, we test many different depths at many different orientations due to epipole change. this causes the use of excessive interpolation and it starts to become a performance issue. ) -------------------------------------------------------------------------------- MEMORY MANAGEMENT: -------------------------------------------------------------------------------- It is possible to set the memory of the library from outside to let the user call the library in a loop without allocating/deallocating the necessary space. The functions for this are : void set_descriptor_memory(float *descriptor, long int d_size) void set_workspace_memory (float *workspace, long int w_size) you can use compute_workspace_memory() and compute_descriptor_memory() functions to get the required memory; i.e, wsz = desc->compute_workspace_memory(); float* workspace = new float[ wsz ]; desc->set_workspace_memory( workspace, wsz ); if you're not running the library in MODE_1 you don't need to set the descriptor memory. In fact, you should not set it cause it'll be a waste. YOU SHOULD CALL THESE FUNCTIONS BEFORE CALLING INITIALIZE AND AFTER SETTING PARAMETERS! -------------------------------------------------------------------------------- EXAMPLES ---- You can use the executable for 1.a. computing the descriptor of a single point y,x=(45,132) with orientation 35 ./daisy -i image.png -d 45 132 35 1.b. computing the descriptor of a single point y,x=(45,132) with orientation 35 without using interpolation ./daisy -i image.png -di -d 45 132 35 2. computing all of the descriptors and saving them in ascii ./daisy -i image.png -sa 3. computing all of the descriptors and saving in binary with NRM_FULL normalization ./daisy -i image.png -sb -nt 1 ps: you can use the load_binary function in kutility/fileio.h to load the saved descriptors like float* descriptors = 0; int h,w,nb; load_binary(file, descriptors, h, w, nb); 4. making a time run ./daisy -i image.png -tr ---- Have Fun! Tuesday, August 18, 2009 13:27:58 0200
About
DAISY: A Fast Local Descriptor for Dense Matching
Resources
License
Stars
Watchers
Forks
Releases
No releases published
Packages 0
No packages published
Languages
- C 65.1%
- C 34.9%