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use std explicitly for isnan, comment unused variables, reorder initi… #20

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76 changes: 38 additions & 38 deletions CMT.cpp
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Original file line number Diff line number Diff line change
Expand Up @@ -5,19 +5,19 @@

#if __cplusplus < 201103L //test if c++11

#include <limits>
#include <limits>

#ifndef NAN
//may not be correct on all compilator, DON'T USE the flag FFAST-MATH
#ifndef NAN
//may not be correct on all compilator, DON'T USE the flag FFAST-MATH

#define NAN std::numeric_limits<float>::quiet_NaN()
#define NAN std::numeric_limits<float>::quiet_NaN()

template <typename T>
bool isnan(T d)
{
return d != d;
}
#endif
template <typename T>
bool isnan(T d)
{
return d != d;
}
#endif

#endif

Expand All @@ -37,7 +37,7 @@ void track(cv::Mat im_prev, cv::Mat im_gray, const std::vector<std::pair<cv::Key
//Status of tracked keypoint - True means successfully tracked
status = std::vector<unsigned char>();
//for(int i = 0; i < keypointsIN.size(); i++)
// status.push_back(false);
// status.push_back(false);
//If at least one keypoint is active
if(keypointsIN.size() > 0)
{
Expand Down Expand Up @@ -90,20 +90,20 @@ cv::Point2f rotate(cv::Point2f p, float rad)
return cv::Point2f(c*p.x-s*p.y,s*p.x+c*p.y);
}

CMT::CMT()
{
detectorType = "Feature2D.BRISK";
descriptorType = "Feature2D.BRISK";
matcherType = "BruteForce-Hamming";
thrOutlier = 20;
thrConf = 0.75;
thrRatio = 0.8;
descriptorLength = 512;
estimateScale = true;
estimateRotation = true;
nbInitialKeypoints = 0;
isInitialized = false;
}
CMT::CMT() :
detectorType("Feature2D.BRISK"),
descriptorType( "Feature2D.BRISK"),
matcherType("BruteForce-Hamming"),
descriptorLength(512),
thrOutlier(20),
thrConf(0.75),
thrRatio(0.8),
estimateScale(true),
estimateRotation(true),
hasResult(false),
isInitialized(false),
nbInitialKeypoints(0)
{}

void CMT::initialise(cv::Mat im_gray0, cv::Point2f topleft, cv::Point2f bottomright)
{
Expand Down Expand Up @@ -395,10 +395,10 @@ std::vector<int> argSortInt(const std::vector<int>& list)
{
std::vector<int> result(list.size());
std::vector<std::pair<int, int> > pairList(list.size());
for(int i = 0; i < list.size(); i++)
for(size_t i = 0; i < list.size(); i++)
pairList[i] = std::make_pair(i, list[i]);
std::sort(&pairList[0], &pairList[0]+pairList.size(), comparatorPairSecond);
for(int i = 0; i < list.size(); i++)
for(size_t i = 0; i < list.size(); i++)
result[i] = pairList[i].first;
return result;
}
Expand Down Expand Up @@ -530,9 +530,9 @@ void CMT::processFrame(cv::Mat im_gray)
{
if (!isInitialized)
{
std::cout << "ERROR: Tracking window has been not initialized yet. ";
std::cout << "Please call CMT::initialise first." << std::endl;
return;
std::cout << "ERROR: Tracking window has been not initialized yet. ";
std::cout << "Please call CMT::initialise first." << std::endl;
return;
}

trackedKeypoints = std::vector<std::pair<cv::KeyPoint, int> >();
Expand Down Expand Up @@ -564,11 +564,11 @@ void CMT::processFrame(cv::Mat im_gray)
descriptorMatcher->knnMatch(features, featuresDatabase, matchesAll, 2);

//Get all matches for selected features
if(!isnan(center.x) && !isnan(center.y))
if(!std::isnan(center.x) && ! std::isnan(center.y))
descriptorMatcher->knnMatch(features, selectedFeatures, selectedMatchesAll, selectedFeatures.rows);

std::vector<cv::Point2f> transformedSprings(springs.size());
for(int i = 0; i < springs.size(); i++)
for(size_t i = 0; i < springs.size(); i++)
transformedSprings[i] = scaleEstimate * rotate(springs[i], -rotationEstimate);

//For each keypoint and its descriptor
Expand All @@ -589,7 +589,7 @@ void CMT::processFrame(cv::Mat im_gray)

//Get best and second best index
int bestInd = matches[0].trainIdx;
int secondBestInd = matches[1].trainIdx;
// int secondBestInd = matches[1].trainIdx;

//Compute distance ratio according to Lowe
float ratio = (1-combined[0]) / (1-combined[1]);
Expand All @@ -603,20 +603,20 @@ void CMT::processFrame(cv::Mat im_gray)

//In a second step, try to match difficult keypoints
//If structural constraints are applicable
if(!(isnan(center.x) | isnan(center.y)))
if(!(std::isnan(center.x) | std::isnan(center.y)))
{
//Compute distances to initial descriptors
std::vector<cv::DMatch> matches = selectedMatchesAll[i];
std::vector<float> distances(matches.size()), distancesTmp(matches.size());
std::vector<int> trainIndex(matches.size());
for(int j = 0; j < matches.size(); j++)
for(size_t j = 0; j < matches.size(); j++)
{
distancesTmp[j] = matches[j].distance;
trainIndex[j] = matches[j].trainIdx;
}
//Re-order the distances based on indexing
std::vector<int> idxs = argSortInt(trainIndex);
for(int j = 0; j < idxs.size(); j++)
for(size_t j = 0; j < idxs.size(); j++)
distances[j] = distancesTmp[idxs[j]];

//Convert distances to confidences
Expand Down Expand Up @@ -692,7 +692,7 @@ void CMT::processFrame(cv::Mat im_gray)
}

//Update object state estimate
std::vector<std::pair<cv::KeyPoint, int> > activeKeypointsBefore = activeKeypoints;
// std::vector<std::pair<cv::KeyPoint, int> > activeKeypointsBefore = activeKeypoints;
im_prev = im_gray;
topLeft = cv::Point2f(NAN,NAN);
topRight = cv::Point2f(NAN,NAN);
Expand All @@ -701,7 +701,7 @@ void CMT::processFrame(cv::Mat im_gray)

boundingbox = cv::Rect_<float>(NAN,NAN,NAN,NAN);
hasResult = false;
if(!(isnan(center.x) | isnan(center.y)) && activeKeypoints.size() > nbInitialKeypoints / 10)
if(!(std::isnan(center.x) | std::isnan(center.y)) && activeKeypoints.size() > nbInitialKeypoints / 10)
{
hasResult = true;

Expand Down
1 change: 0 additions & 1 deletion CMT.h
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Original file line number Diff line number Diff line change
Expand Up @@ -21,7 +21,6 @@ class CMT
int thrOutlier;
float thrConf;
float thrRatio;

bool estimateScale;
bool estimateRotation;

Expand Down