mirror of
https://github.com/opencv/opencv.git
synced 2024-12-12 23:49:36 +08:00
be63ce723f
updated links in cheatsheet renamed directory for Mat tutorial changed links from willow docs to opencv.itseez.com, from Trac to current Redmine
149 lines
4.9 KiB
ReStructuredText
149 lines
4.9 KiB
ReStructuredText
.. _feature_homography:
|
|
|
|
Features2D + Homography to find a known object
|
|
**********************************************
|
|
|
|
Goal
|
|
=====
|
|
|
|
In this tutorial you will learn how to:
|
|
|
|
.. container:: enumeratevisibleitemswithsquare
|
|
|
|
* Use the function :find_homography:`findHomography<>` to find the transform between matched keypoints.
|
|
* Use the function :perspective_transform:`perspectiveTransform<>` to map the points.
|
|
|
|
|
|
Theory
|
|
======
|
|
|
|
Code
|
|
====
|
|
|
|
This tutorial code's is shown lines below. You can also download it from `here <http://code.opencv.org/svn/opencv/trunk/opencv/samples/cpp/tutorial_code/features2D/SURF_Homography.cpp>`_
|
|
|
|
.. code-block:: cpp
|
|
|
|
#include <stdio.h>
|
|
#include <iostream>
|
|
#include "opencv2/core/core.hpp"
|
|
#include "opencv2/features2d/features2d.hpp"
|
|
#include "opencv2/highgui/highgui.hpp"
|
|
#include "opencv2/calib3d/calib3d.hpp"
|
|
|
|
using namespace cv;
|
|
|
|
void readme();
|
|
|
|
/** @function main */
|
|
int main( int argc, char** argv )
|
|
{
|
|
if( argc != 3 )
|
|
{ readme(); return -1; }
|
|
|
|
Mat img_object = imread( argv[1], CV_LOAD_IMAGE_GRAYSCALE );
|
|
Mat img_scene = imread( argv[2], CV_LOAD_IMAGE_GRAYSCALE );
|
|
|
|
if( !img_object.data || !img_scene.data )
|
|
{ std::cout<< " --(!) Error reading images " << std::endl; return -1; }
|
|
|
|
//-- Step 1: Detect the keypoints using SURF Detector
|
|
int minHessian = 400;
|
|
|
|
SurfFeatureDetector detector( minHessian );
|
|
|
|
std::vector<KeyPoint> keypoints_object, keypoints_scene;
|
|
|
|
detector.detect( img_object, keypoints_object );
|
|
detector.detect( img_scene, keypoints_scene );
|
|
|
|
//-- Step 2: Calculate descriptors (feature vectors)
|
|
SurfDescriptorExtractor extractor;
|
|
|
|
Mat descriptors_object, descriptors_scene;
|
|
|
|
extractor.compute( img_object, keypoints_object, descriptors_object );
|
|
extractor.compute( img_scene, keypoints_scene, descriptors_scene );
|
|
|
|
//-- Step 3: Matching descriptor vectors using FLANN matcher
|
|
FlannBasedMatcher matcher;
|
|
std::vector< DMatch > matches;
|
|
matcher.match( descriptors_object, descriptors_scene, matches );
|
|
|
|
double max_dist = 0; double min_dist = 100;
|
|
|
|
//-- Quick calculation of max and min distances between keypoints
|
|
for( int i = 0; i < descriptors_object.rows; i++ )
|
|
{ double dist = matches[i].distance;
|
|
if( dist < min_dist ) min_dist = dist;
|
|
if( dist > max_dist ) max_dist = dist;
|
|
}
|
|
|
|
printf("-- Max dist : %f \n", max_dist );
|
|
printf("-- Min dist : %f \n", min_dist );
|
|
|
|
//-- Draw only "good" matches (i.e. whose distance is less than 3*min_dist )
|
|
std::vector< DMatch > good_matches;
|
|
|
|
for( int i = 0; i < descriptors_object.rows; i++ )
|
|
{ if( matches[i].distance < 3*min_dist )
|
|
{ good_matches.push_back( matches[i]); }
|
|
}
|
|
|
|
Mat img_matches;
|
|
drawMatches( img_object, keypoints_object, img_scene, keypoints_scene,
|
|
good_matches, img_matches, Scalar::all(-1), Scalar::all(-1),
|
|
vector<char>(), DrawMatchesFlags::NOT_DRAW_SINGLE_POINTS );
|
|
|
|
//-- Localize the object
|
|
std::vector<Point2f> obj;
|
|
std::vector<Point2f> scene;
|
|
|
|
for( int i = 0; i < good_matches.size(); i++ )
|
|
{
|
|
//-- Get the keypoints from the good matches
|
|
obj.push_back( keypoints_object[ good_matches[i].queryIdx ].pt );
|
|
scene.push_back( keypoints_scene[ good_matches[i].trainIdx ].pt );
|
|
}
|
|
|
|
Mat H = findHomography( obj, scene, CV_RANSAC );
|
|
|
|
//-- Get the corners from the image_1 ( the object to be "detected" )
|
|
std::vector<Point2f> obj_corners(4);
|
|
obj_corners[0] = cvPoint(0,0); obj_corners[1] = cvPoint( img_object.cols, 0 );
|
|
obj_corners[2] = cvPoint( img_object.cols, img_object.rows ); obj_corners[3] = cvPoint( 0, img_object.rows );
|
|
std::vector<Point2f> scene_corners(4);
|
|
|
|
perspectiveTransform( obj_corners, scene_corners, H);
|
|
|
|
//-- Draw lines between the corners (the mapped object in the scene - image_2 )
|
|
line( img_matches, scene_corners[0] + Point2f( img_object.cols, 0), scene_corners[1] + Point2f( img_object.cols, 0), Scalar(0, 255, 0), 4 );
|
|
line( img_matches, scene_corners[1] + Point2f( img_object.cols, 0), scene_corners[2] + Point2f( img_object.cols, 0), Scalar( 0, 255, 0), 4 );
|
|
line( img_matches, scene_corners[2] + Point2f( img_object.cols, 0), scene_corners[3] + Point2f( img_object.cols, 0), Scalar( 0, 255, 0), 4 );
|
|
line( img_matches, scene_corners[3] + Point2f( img_object.cols, 0), scene_corners[0] + Point2f( img_object.cols, 0), Scalar( 0, 255, 0), 4 );
|
|
|
|
//-- Show detected matches
|
|
imshow( "Good Matches & Object detection", img_matches );
|
|
|
|
waitKey(0);
|
|
return 0;
|
|
}
|
|
|
|
/** @function readme */
|
|
void readme()
|
|
{ std::cout << " Usage: ./SURF_descriptor <img1> <img2>" << std::endl; }
|
|
|
|
Explanation
|
|
============
|
|
|
|
Result
|
|
======
|
|
|
|
|
|
#. And here is the result for the detected object (highlighted in green)
|
|
|
|
.. image:: images/Feature_Homography_Result.jpg
|
|
:align: center
|
|
:height: 200pt
|
|
|