Reputation: 48101
Is there any available code to use MLESAC instead of RANSAC with OpenCV to find a known object?
MLESAC should be much more robust than RANSAC. An example of use is provided here:
http://docs.opencv.org/doc/tutorials/features2d/feature_homography/feature_homography.html
Upvotes: 3
Views: 2520
Reputation: 2850
You can use MLESAC (as well as PROSAC, etc.) from the PCL library. http://docs.pointclouds.org/trunk/a02954.html I don`t use PCL, so could not make an example.
I`m using LO-RANSAC from Karel Lebeda http://cmp.felk.cvut.cz/software/LO-RANSAC/index.xhtml
int do_lo = 1; //local optimization
unsigned int tent_size = tentatives.size();
double err_threshold = 2.0; //in pixels
double confidence = 0.99;
int max_samples = 100000;//max trials
int inl_limit = 0;//no limit
if (tent_size >= 5)
{
double H[3*3];
unsigned stats[3];
double *u2Ptr = new double[tent_size*6], *u2; u2=u2Ptr;
typedef unsigned char uchar;
unsigned char *inl2 = new uchar[tent_size];
for( int i = 0; i < tentatives.size(); i++ )
{
*u2Ptr = keypoints_object[ tentatives[i].queryIdx ].pt.x;
u2Ptr++;
*u2Ptr = keypoints_object[ tentatives[i].queryIdx ].pt.y;
u2Ptr++;
*u2Ptr = 1.;
u2Ptr++;
*u2Ptr = keypoints_scene[ tentatives[i].trainIdx ].pt.x;
u2Ptr++;
*u2Ptr = keypoints_scene[ tentatives[i].trainIdx ].pt.y;
u2Ptr++;
*u2Ptr = 1.;
u2Ptr++;
}
ransacH(u2, tent_size, err_threshold*err_threshold, confidence, max_samples, do_lo, , H, inl2,stats);
for(i=0; i < tent_size; i++)
if (inl2[i])
inliers.push_back(good_matches[i]);
delete[] u2; delete[] inl2;
Upvotes: 3