OpenCV/opencv_contrib/modules/line_descriptor/tutorials/tutorial.markdown

Line Features Tutorial {#tutorial_line_descriptor_main}

In this tutorial it will be shown how to:

• Use the BinaryDescriptor interface to extract the lines and store them in KeyLine objects
• Use the same interface to compute descriptors for every extracted line
• Use the BynaryDescriptorMatcher to determine matches among descriptors obtained from different images

Lines extraction and descriptors computation

In the following snippet of code, it is shown how to detect lines from an image. The LSD extractor is initialized with LSD_REFINE_ADV option; remaining parameters are left to their default values. A mask of ones is used in order to accept all extracted lines, which, at the end, are displayed using random colors for octave 0.

@includelineno line_descriptor/samples/lsd_lines_extraction.cpp

This is the result obtained from the famous cameraman image:

Another way to extract lines is using LSDDetector class; such class uses the LSD extractor to compute lines. To obtain this result, it is sufficient to use the snippet code seen above, just modifying it by the rows

@code{.cpp} // create a pointer to an LSDDetector object Ptr lsd = LSDDetector::createLSDDetector();

// compute lines std::vector keylines; lsd->detect( imageMat, keylines, mask ); @endcode

Here's the result returned by LSD detector again on cameraman picture:

Once keylines have been detected, it is possible to compute their descriptors as shown in the following:

@includelineno line_descriptor/samples/compute_descriptors.cpp

Matching among descriptors

If we have extracted descriptors from two different images, it is possible to search for matches among them. One way of doing it is matching exactly a descriptor to each input query descriptor, choosing the one at closest distance:

@includelineno line_descriptor/samples/matching.cpp

Sometimes, we could be interested in searching for the closest k descriptors, given an input one. This requires modifying previous code slightly:

@code{.cpp} // prepare a structure to host matches std::vectorstd::vector<DMatch > matches;

// require knn match bdm->knnMatch( descr1, descr2, matches, 6 ); @endcode

In the above example, the closest 6 descriptors are returned for every query. In some cases, we could have a search radius and look for all descriptors distant at the most r from input query. Previous code must be modified like:

@code{.cpp} // prepare a structure to host matches std::vectorstd::vector<DMatch > matches;

// compute matches bdm->radiusMatch( queries, matches, 30 ); @endcode

Here's an example of matching among descriptors extracted from original cameraman image and its downsampled (and blurred) version:

Querying internal database

The BynaryDescriptorMatcher class owns an internal database that can be populated with descriptors extracted from different images and queried using one of the modalities described in the previous section. Population of internal dataset can be done using the add function; such function doesn't directly add new data to the database, but it just stores it them locally. The real update happens when the function train is invoked or when any querying function is executed, since each of them invokes train before querying. When queried, internal database not only returns required descriptors, but for every returned match, it is able to tell which image matched descriptor was extracted from. An example of internal dataset usage is described in the following code; after adding locally new descriptors, a radius search is invoked. This provokes local data to be transferred to dataset which in turn, is then queried.

@includelineno line_descriptor/samples/radius_matching.cpp