OpenCV how to use UsacParams and specify USAC method - opencv

Question

I am using openCV 4.5 and I'm using USAC to find the fundamental matrix. This works fine for specifying a USAC method:
import cv2
a,b = cv2.FindFundamentalMat(pts1, pts2, cv2.USAC_FAST)
and this works fine for specifying USAC parameters:
params = cv2.UsacParams()
params.maxIterations=700
a,b = cv2.FindFundamentalMat(pts1, pts2, params)
But I can't figure out how to specify both the method and the settings. Or does the method determine the settings?

Answer 1

Looking into the C++ source code, we can see that the two different ways of calling the function (with or without the UsacParams) lead to different calls to setParameters.
In conclusion, you're right that the choice of method (e.g. USAC_FAST) determines the settings (at least in C++ code).
From https://github.com/opencv/opencv/blob/4.x/modules/calib3d/src/usac/ransac_solvers.cpp
If you pass UsacParams:
void setParameters (Ptr<Model> &params, EstimationMethod estimator, const UsacParams &usac_params, bool mask_needed) {
params = Model::create(usac_params.threshold, estimator, usac_params.sampler, usac_params.confidence, usac_params.maxIterations, usac_params.score);
params->setLocalOptimization(usac_params.loMethod);
params->setLOSampleSize(usac_params.loSampleSize);
params->setLOIterations(usac_params.loIterations);
params->setParallel(usac_params.isParallel);
params->setNeighborsType(usac_params.neighborsSearch);
params->setRandomGeneratorState(usac_params.randomGeneratorState);
params->maskRequired(mask_needed);
}
If you pass a method instead of UsacParams:
void setParameters (int flag, Ptr<Model> &params, EstimationMethod estimator, double thr,
int max_iters, double conf, bool mask_needed) {
switch (flag) {
case USAC_DEFAULT:
params = Model::create(thr, estimator, SamplingMethod::SAMPLING_UNIFORM, conf, max_iters, ScoreMethod::SCORE_METHOD_MSAC);
params->setLocalOptimization(LocalOptimMethod ::LOCAL_OPTIM_INNER_AND_ITER_LO);
break;
case USAC_MAGSAC:
params = Model::create(thr, estimator, SamplingMethod::SAMPLING_UNIFORM, conf, max_iters, ScoreMethod::SCORE_METHOD_MAGSAC);
params->setLocalOptimization(LocalOptimMethod ::LOCAL_OPTIM_SIGMA);
params->setLOSampleSize(params->isHomography() ? 75 : 50);
params->setLOIterations(params->isHomography() ? 15 : 10);
break;
case USAC_PARALLEL:
params = Model::create(thr, estimator, SamplingMethod::SAMPLING_UNIFORM, conf, max_iters, ScoreMethod::SCORE_METHOD_MSAC);
params->setParallel(true);
params->setLocalOptimization(LocalOptimMethod ::LOCAL_OPTIM_INNER_LO);
break;
case USAC_ACCURATE:
params = Model::create(thr, estimator, SamplingMethod::SAMPLING_UNIFORM, conf, max_iters, ScoreMethod::SCORE_METHOD_MSAC);
params->setLocalOptimization(LocalOptimMethod ::LOCAL_OPTIM_GC);
params->setLOSampleSize(20);
params->setLOIterations(25);
break;
case USAC_FAST:
params = Model::create(thr, estimator, SamplingMethod::SAMPLING_UNIFORM, conf, max_iters, ScoreMethod::SCORE_METHOD_MSAC);
params->setLocalOptimization(LocalOptimMethod ::LOCAL_OPTIM_INNER_AND_ITER_LO);
params->setLOIterations(5);
params->setLOIterativeIters(3);
break;
case USAC_PROSAC:
params = Model::create(thr, estimator, SamplingMethod::SAMPLING_PROSAC, conf, max_iters, ScoreMethod::SCORE_METHOD_MSAC);
params->setLocalOptimization(LocalOptimMethod ::LOCAL_OPTIM_INNER_LO);
break;
case USAC_FM_8PTS:
params = Model::create(thr, EstimationMethod::Fundamental8,SamplingMethod::SAMPLING_UNIFORM, conf, max_iters,ScoreMethod::SCORE_METHOD_MSAC);
params->setLocalOptimization(LocalOptimMethod ::LOCAL_OPTIM_INNER_LO);
break;
default: CV_Error(cv::Error::StsBadFlag, "Incorrect flag for USAC!");
}
// do not do too many iterations for PnP
if (estimator == EstimationMethod::P3P) {
if (params->getLOInnerMaxIters() > 15)
params->setLOIterations(15);
params->setLOIterativeIters(0);
}
params->maskRequired(mask_needed);
}
For more information, check out the descriptions of the different parameters here: https://docs.opencv.org/4.5.0/d1/df1/md__build_master-contrib_docs-lin64_opencv_doc_tutorials_calib3d_usac.html