generic-library/opencv
1
0
Fork 0
Code Issues Pull Requests Releases Wiki Activity

svn repository web references are replaced with links to git

Browse Source
This commit is contained in:
Andrey Kamaev
2012-08-07 13:29:43 +04:00
parent a3527fc4d8
commit 5100ca7508
66 changed files with 1180 additions and 1305 deletions
Download Patch File Download Diff File Expand all files Collapse all files

26
doc/tutorials/features2d/feature_homography/feature_homography.rst
View File

@@ -12,7 +12,7 @@ In this tutorial you will learn how to:
* Use the function :find_homography:`findHomography<>` to find the transform between matched keypoints.
* Use the function :perspective_transform:`perspectiveTransform<>` to map the points.
Theory
======
@@ -20,9 +20,9 @@ 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>`_
This tutorial code's is shown lines below. You can also download it from `here <http://code.opencv.org/projects/opencv/repository/revisions/master/raw/samples/cpp/tutorial_code/features2D/SURF_Homography.cpp>`_
.. code-block:: cpp
.. code-block:: cpp
#include <stdio.h>
#include <iostream>
@@ -43,7 +43,7 @@ This tutorial code's is shown lines below. You can also download it from `here <
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; }
@@ -81,21 +81,21 @@ This tutorial code's is shown lines below. You can also download it from `here <
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 );
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
//-- Localize the object
std::vector<Point2f> obj;
std::vector<Point2f> scene;
@@ -103,7 +103,7 @@ This tutorial code's is shown lines below. You can also download it from `here <
{
//-- 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 );
scene.push_back( keypoints_scene[ good_matches[i].trainIdx ].pt );
}
Mat H = findHomography( obj, scene, CV_RANSAC );
@@ -143,6 +143,6 @@ Result
#. And here is the result for the detected object (highlighted in green)
.. image:: images/Feature_Homography_Result.jpg
:align: center
:height: 200pt
:align: center
:height: 200pt