mirror of
https://github.com/opencv/opencv.git
synced 2024-12-16 18:39:12 +08:00
d58cd9851f
Conflicts: CMakeLists.txt cmake/OpenCVDetectCUDA.cmake doc/tutorials/features2d/feature_flann_matcher/feature_flann_matcher.rst modules/core/src/cmdparser.cpp modules/gpu/CMakeLists.txt modules/gpu/doc/introduction.rst modules/gpu/perf/perf_video.cpp modules/highgui/doc/reading_and_writing_images_and_video.rst modules/ocl/src/cl_context.cpp modules/video/include/opencv2/video/background_segm.hpp samples/cpp/image_sequence.cpp samples/cpp/tutorial_code/ImgTrans/HoughCircle_Demo.cpp samples/python/chessboard.py samples/python/cvutils.py samples/python/demhist.py samples/python/dft.py samples/python/distrans.py samples/python/edge.py samples/python/ffilldemo.py samples/python/fitellipse.py samples/python/houghlines.py samples/python/inpaint.py samples/python/logpolar.py samples/python/morphology.py samples/python/numpy_array.py samples/python/watershed.py
103 lines
2.7 KiB
ReStructuredText
103 lines
2.7 KiB
ReStructuredText
.. _feature_description:
|
|
|
|
Feature Description
|
|
*******************
|
|
|
|
Goal
|
|
=====
|
|
|
|
In this tutorial you will learn how to:
|
|
|
|
.. container:: enumeratevisibleitemswithsquare
|
|
|
|
* Use the :descriptor_extractor:`DescriptorExtractor<>` interface in order to find the feature vector correspondent to the keypoints. Specifically:
|
|
|
|
* Use :surf_descriptor_extractor:`SurfDescriptorExtractor<>` and its function :descriptor_extractor:`compute<>` to perform the required calculations.
|
|
* Use a :brute_force_matcher:`BFMatcher<>` to match the features vector
|
|
* Use the function :draw_matches:`drawMatches<>` to draw the detected matches.
|
|
|
|
|
|
Theory
|
|
======
|
|
|
|
Code
|
|
====
|
|
|
|
This tutorial code's is shown lines below. You can also download it from `here <https://github.com/Itseez/opencv/tree/master/samples/cpp/tutorial_code/features2D/SURF_descriptor.cpp>`_
|
|
|
|
.. code-block:: cpp
|
|
|
|
#include <stdio.h>
|
|
#include <iostream>
|
|
#include "opencv2/core.hpp"
|
|
#include "opencv2/features2d.hpp"
|
|
#include "opencv2/highgui.hpp"
|
|
#include "opencv2/nonfree.hpp"
|
|
|
|
using namespace cv;
|
|
|
|
void readme();
|
|
|
|
/** @function main */
|
|
int main( int argc, char** argv )
|
|
{
|
|
if( argc != 3 )
|
|
{ return -1; }
|
|
|
|
Mat img_1 = imread( argv[1], CV_LOAD_IMAGE_GRAYSCALE );
|
|
Mat img_2 = imread( argv[2], CV_LOAD_IMAGE_GRAYSCALE );
|
|
|
|
if( !img_1.data || !img_2.data )
|
|
{ return -1; }
|
|
|
|
//-- Step 1: Detect the keypoints using SURF Detector
|
|
int minHessian = 400;
|
|
|
|
SurfFeatureDetector detector( minHessian );
|
|
|
|
std::vector<KeyPoint> keypoints_1, keypoints_2;
|
|
|
|
detector.detect( img_1, keypoints_1 );
|
|
detector.detect( img_2, keypoints_2 );
|
|
|
|
//-- Step 2: Calculate descriptors (feature vectors)
|
|
SurfDescriptorExtractor extractor;
|
|
|
|
Mat descriptors_1, descriptors_2;
|
|
|
|
extractor.compute( img_1, keypoints_1, descriptors_1 );
|
|
extractor.compute( img_2, keypoints_2, descriptors_2 );
|
|
|
|
//-- Step 3: Matching descriptor vectors with a brute force matcher
|
|
BFMatcher matcher(NORM_L2);
|
|
std::vector< DMatch > matches;
|
|
matcher.match( descriptors_1, descriptors_2, matches );
|
|
|
|
//-- Draw matches
|
|
Mat img_matches;
|
|
drawMatches( img_1, keypoints_1, img_2, keypoints_2, matches, img_matches );
|
|
|
|
//-- Show detected matches
|
|
imshow("Matches", img_matches );
|
|
|
|
waitKey(0);
|
|
|
|
return 0;
|
|
}
|
|
|
|
/** @function readme */
|
|
void readme()
|
|
{ std::cout << " Usage: ./SURF_descriptor <img1> <img2>" << std::endl; }
|
|
|
|
Explanation
|
|
============
|
|
|
|
Result
|
|
======
|
|
|
|
#. Here is the result after applying the BruteForce matcher between the two original images:
|
|
|
|
.. image:: images/Feature_Description_BruteForce_Result.jpg
|
|
:align: center
|
|
:height: 200pt
|