How to replace an instance with another instance via pointer? - c++

Question

I'm doing online destructive clustering (clusters replace clustered objects) on a list of class instances (stl::list).
Background
My list of current percepUnits is: stl::list<percepUnit> units; and for each iteration I get a new list of input percepUnits stl::list<percepUnit> scratch; that need to be clustered with the units.
I want to maintain a fixed number of percepUnits (so units.size() is constant), so for each new scratch percepUnit I need to merge it with the nearest percepUnit in units. Following is a code snippet that builds a list (dists) of structures (percepUnitDist) that contain pointers to each pair of items in scratch and units percepDist.scratchUnit = &(*scratchUnit); and percepDist.unit = &(*unit); and their distance. Additionally, for each item in scratch I keep track of which item in units has the least distance minDists.
// For every scratch percepUnit:
for (scratchUnit = scratch.begin(); scratchUnit != scratch.end(); scratchUnit++) {
float minDist=2025.1172; // This is the max possible distance in unnormalized CIELuv, and much larger than the normalized dist.
// For every percepUnit:
for (unit = units.begin(); unit != units.end(); unit++) {
// compare pairs
float dist = featureDist(*scratchUnit, *unit, FGBG);
//cout << "distance: " << dist << endl;
// Put pairs in a structure that caches their distances
percepUnitDist percepDist;
percepDist.scratchUnit = &(*scratchUnit); // address of where scratchUnit points to.
percepDist.unit = &(*unit);
percepDist.dist = dist;
// Figure out the percepUnit that is closest to this scratchUnit.
if (dist < minDist)
minDist = dist;
dists.push_back(percepDist); // append dist struct
}
minDists.push_back(minDist); // append the min distance to the nearest percepUnit for this particular scratchUnit.
}
So now I just need to loop through the percepUnitDist items in dists and match the distances with the minimum distances to figure out which percepUnit in scratch should be merged with which percepUnit in units. The merging process mergePerceps() creates a new percepUnit which is a weighted average of the "parent" percepUnits in scratch and units.
Question
I want to replace the instance in the units list with the new percepUnit constructed by mergePerceps(), but I would like to do so in the context of looping through the percepUnitDists. This is my current code:
// Loop through dists and merge all the closest pairs.
// Loop through all dists
for (distIter = dists.begin(); distIter != dists.end(); distIter++) {
// Loop through all minDists for each scratchUnit.
for (minDistsIter = minDists.begin(); minDistsIter != minDists.end(); minDistsIter++) {
// if this is the closest cluster, and the closest cluster has not already been merged, and the scratch has not already been merged.
if (*minDistsIter == distIter->dist and not distIter->scratchUnit->remove) {
percepUnit newUnit;
mergePerceps(*(distIter->scratchUnit), *(distIter->unit), newUnit, FGBG);
*(distIter->unit) = newUnit; // replace the cluster with the new merged version.
distIter->scratchUnit->remove = true;
}
}
}
I thought that I could replace the instance in units via the percepUnitDist pointer with the new percepUnit instance using *(distIter->unit) = newUnit;, but that does not seem to be working as I'm seeing a memory leak, implying the instances in the units are not getting replaced.
How do I delete the percepUnit in the units list and replace it with a new percepUnit instance such that the new unit is located in the same location?
EDIT1
Here is the percepUnit class. Note the cv::Mat members. Following is the mergePerceps() function and the mergeImages() function on which it depends:
// Function to construct an accumulation.
void clustering::mergeImages(Mat &scratch, Mat &unit, cv::Mat &merged, const string maskOrImage, const string FGBG, const float scratchWeight, const float unitWeight) {
int width, height, type=CV_8UC3;
Mat scratchImagePad, unitImagePad, scratchImage, unitImage;
// use the resolution and aspect of the largest of the pair.
if (unit.cols > scratch.cols)
width = unit.cols;
else
width = scratch.cols;
if (unit.rows > scratch.rows)
height = unit.rows;
else
height = scratch.rows;
if (maskOrImage == "mask")
type = CV_8UC1; // single channel mask
else if (maskOrImage == "image")
type = CV_8UC3; // three channel image
else
cout << "maskOrImage is not 'mask' or 'image'\n";
merged = Mat(height, width, type, Scalar::all(0));
scratchImagePad = Mat(height, width, type, Scalar::all(0));
unitImagePad = Mat(height, width, type, Scalar::all(0));
// weight images before summation.
// because these pass by reference, they mess up the images in memory!
scratch *= scratchWeight;
unit *= unitWeight;
// copy images into padded images.
scratch.copyTo(scratchImagePad(Rect((scratchImagePad.cols-scratch.cols)/2,
(scratchImagePad.rows-scratch.rows)/2,
scratch.cols,
scratch.rows)));
unit.copyTo(unitImagePad(Rect((unitImagePad.cols-unit.cols)/2,
(unitImagePad.rows-unit.rows)/2,
unit.cols,
unit.rows)));
merged = scratchImagePad+unitImagePad;
}
// Merge two perceps and return a new percept to replace them.
void clustering::mergePerceps(percepUnit scratch, percepUnit unit, percepUnit &mergedUnit, const string FGBG) {
Mat accumulation;
Mat accumulationMask;
Mat meanColour;
int x, y, w, h, area;
float l,u,v;
int numMerges=0;
std::vector<float> featuresVar; // Normalized, Sum, Variance.
//float featuresVarMin, featuresVarMax; // min and max variance accross all features.
float scratchWeight, unitWeight;
if (FGBG == "FG") {
// foreground percepts don't get merged as much.
scratchWeight = 0.65;
unitWeight = 1-scratchWeight;
} else {
scratchWeight = 0.85;
unitWeight = 1-scratchWeight;
}
// Images TODO remove the meanColour if needbe.
mergeImages(scratch.image, unit.image, accumulation, "image", FGBG, scratchWeight, unitWeight);
mergeImages(scratch.mask, unit.mask, accumulationMask, "mask", FGBG, scratchWeight, unitWeight);
mergeImages(scratch.meanColour, unit.meanColour, meanColour, "image", "FG", scratchWeight, unitWeight); // merge images
// Position and size.
x = (scratch.x1*scratchWeight) + (unit.x1*unitWeight);
y = (scratch.y1*scratchWeight) + (unit.y1*unitWeight);
w = (scratch.w*scratchWeight) + (unit.w*unitWeight);
h = (scratch.h*scratchWeight) + (unit.h*unitWeight);
// area
area = (scratch.area*scratchWeight) + (unit.area*unitWeight);
// colour
l = (scratch.l*scratchWeight) + (unit.l*unitWeight);
u = (scratch.u*scratchWeight) + (unit.u*unitWeight);
v = (scratch.v*scratchWeight) + (unit.v*unitWeight);
// Number of merges
if (scratch.numMerges < 1 and unit.numMerges < 1) { // both units are patches
numMerges = 1;
} else if (scratch.numMerges < 1 and unit.numMerges >= 1) { // unit A is a patch, B a percept
numMerges = unit.numMerges + 1;
} else if (scratch.numMerges >= 1 and unit.numMerges < 1) { // unit A is a percept, B a patch.
numMerges = scratch.numMerges + 1;
cout << "merged scratch??" <<endl;
// TODO this may be an impossible case.
} else { // both units are percepts
numMerges = scratch.numMerges + unit.numMerges;
cout << "Merging two already merged Percepts" <<endl;
// TODO this may be an impossible case.
}
// Create unit.
mergedUnit = percepUnit(accumulation, accumulationMask, x, y, w, h, area); // time is the earliest value in times?
mergedUnit.l = l; // members not in the constrcutor.
mergedUnit.u = u;
mergedUnit.v = v;
mergedUnit.numMerges = numMerges;
mergedUnit.meanColour = meanColour;
mergedUnit.pActivated = unit.pActivated; // new clusters retain parent's history of activation.
mergedUnit.scratch = false;
mergedUnit.habituation = unit.habituation; // we inherent the habituation of the cluster we merged with.
}
EDIT2
Changing the copy and assignment operators had performance side-effects and did not seem to resolve the problem. So I've added a custom function to do the replacement, which just like the copy operator makes copies of each member and make's sure those copies are deep. The problem is that I still end up with a leak.
So I've changed this line: *(distIter->unit) = newUnit;
to this: (*(distIter->unit)).clone(newUnit)
Where the clone method is as follows:
// Deep Copy of members
void percepUnit::clone(const percepUnit &source) {
// Deep copy of Mats
this->image = source.image.clone();
this->mask = source.mask.clone();
this->alphaImage = source.alphaImage.clone();
this->meanColour = source.meanColour.clone();
// shallow copies of everything else
this->alpha = source.alpha;
this->fadingIn = source.fadingIn;
this->fadingHold = source.fadingHold;
this->fadingOut = source.fadingOut;
this->l = source.l;
this->u = source.u;
this->v = source.v;
this->x1 = source.x1;
this->y1 = source.y1;
this->w = source.w;
this->h = source.h;
this->x2 = source.x2;
this->y2 = source.y2;
this->cx = source.cx;
this->cy = source.cy;
this->numMerges = source.numMerges;
this->id = source.id;
this->area = source.area;
this->features = source.features;
this->featuresNorm = source.featuresNorm;
this->remove = source.remove;
this->fgKnockout = source.fgKnockout;
this->colourCalculated = source.colourCalculated;
this->normalized = source.normalized;
this->activation = source.activation;
this->activated = source.activated;
this->pActivated = source.pActivated;
this->habituation = source.habituation;
this->scratch = source.scratch;
this->FGBG = source.FGBG;
}
And yet, I still see a memory increase. The increase does not happen if I comment out that single replacement line. So I'm still stuck.
EDIT3
I can prevent memory from increasing if I disable the cv::Mat cloning code in the function above:
// Deep Copy of members
void percepUnit::clone(const percepUnit &source) {
/* try releasing Mats first?
// No effect on memory increase, but the refCount is decremented.
this->image.release();
this->mask.release();
this->alphaImage.release();
this->meanColour.release();*/
/* Deep copy of Mats
this->image = source.image.clone();
this->mask = source.mask.clone();
this->alphaImage = source.alphaImage.clone();
this->meanColour = source.meanColour.clone();*/
// shallow copies of everything else
this->alpha = source.alpha;
this->fadingIn = source.fadingIn;
this->fadingHold = source.fadingHold;
this->fadingOut = source.fadingOut;
this->l = source.l;
this->u = source.u;
this->v = source.v;
this->x1 = source.x1;
this->y1 = source.y1;
this->w = source.w;
this->h = source.h;
this->x2 = source.x2;
this->y2 = source.y2;
this->cx = source.cx;
this->cy = source.cy;
this->numMerges = source.numMerges;
this->id = source.id;
this->area = source.area;
this->features = source.features;
this->featuresNorm = source.featuresNorm;
this->remove = source.remove;
this->fgKnockout = source.fgKnockout;
this->colourCalculated = source.colourCalculated;
this->normalized = source.normalized;
this->activation = source.activation;
this->activated = source.activated;
this->pActivated = source.pActivated;
this->habituation = source.habituation;
this->scratch = source.scratch;
this->FGBG = source.FGBG;
}
EDIT4
While I still can't explain this issue, I did notice another hint. I realized that this leak can also be stopped if I don't normalize those features I use to cluster via featureDist() (but continue to clone cv::Mats). The really odd thing is that I rewrote that code entirely and still the problem persists.
Here is the featureDist function:
float clustering::featureDist(percepUnit unitA, percepUnit unitB, const string FGBG) {
float distance=0;
if (FGBG == "BG") {
for (unsigned int i=0; i<unitA.featuresNorm.rows; i++) {
distance += pow(abs(unitA.featuresNorm.at<float>(i) - unitB.featuresNorm.at<float>(i)),0.5);
//cout << "unitA.featuresNorm[" << i << "]: " << unitA.featuresNorm[i] << endl;
//cout << "unitB.featuresNorm[" << i << "]: " << unitB.featuresNorm[i] << endl;
}
// for FG, don't use normalized colour features.
// TODO To include the area use i=4
} else if (FGBG == "FG") {
for (unsigned int i=4; i<unitA.features.rows; i++) {
distance += pow(abs(unitA.features.at<float>(i) - unitB.features.at<float>(i)),0.5);
}
} else {
cout << "FGBG argument was not FG or BG, returning 0." <<endl;
return 0;
}
return pow(distance,2);
}
Features used to be a vector of floats, and thus the normalization code was as follows:
void clustering::normalize(list<percepUnit> &scratch, list<percepUnit> &units) {
list<percepUnit>::iterator unit;
list<percepUnit*>::iterator unitPtr;
vector<float> min,max;
list<percepUnit*> masterList; // list of pointers.
// generate pointers
for (unit = scratch.begin(); unit != scratch.end(); unit++)
masterList.push_back(&(*unit)); // add pointer to where unit points to.
for (unit = units.begin(); unit != units.end(); unit++)
masterList.push_back(&(*unit)); // add pointer to where unit points to.
int numFeatures = masterList.front()->features.size(); // all percepts have the same number of features.
min.resize(numFeatures); // allocate for the number of features we have.
max.resize(numFeatures);
// Loop through all units to get feature values
for (int i=0; i<numFeatures; i++) {
min[i] = masterList.front()->features[i]; // starting point.
max[i] = min[i];
// calculate min and max for each feature.
for (unitPtr = masterList.begin(); unitPtr != masterList.end(); unitPtr++) {
if ((*unitPtr)->features[i] < min[i])
min[i] = (*unitPtr)->features[i];
if ((*unitPtr)->features[i] > max[i])
max[i] = (*unitPtr)->features[i];
}
}
// Normalize features according to min/max.
for (int i=0; i<numFeatures; i++) {
for (unitPtr = masterList.begin(); unitPtr != masterList.end(); unitPtr++) {
(*unitPtr)->featuresNorm[i] = ((*unitPtr)->features[i]-min[i]) / (max[i]-min[i]);
(*unitPtr)->normalized = true;
}
}
}
I changed the features type to a cv::Mat so I could use the opencv normalization function, so I rewrote the normalization function as follows:
void clustering::normalize(list<percepUnit> &scratch, list<percepUnit> &units) {
Mat featureMat = Mat(1,units.size()+scratch.size(), CV_32FC1, Scalar(0));
list<percepUnit>::iterator unit;
// For each feature
for (int i=0; i< units.begin()->features.rows; i++) {
// for each unit in units
int j=0;
float value;
for (unit = units.begin(); unit != units.end(); unit++) {
// Populate featureMat j is the unit index, i is the feature index.
value = unit->features.at<float>(i);
featureMat.at<float>(j) = value;
j++;
}
// for each unit in scratch
for (unit = scratch.begin(); unit != scratch.end(); unit++) {
// Populate featureMat j is the unit index, i is the feature index.
value = unit->features.at<float>(i);
featureMat.at<float>(j) = value;
j++;
}
// Normalize this featureMat in place
cv::normalize(featureMat, featureMat, 0, 1, NORM_MINMAX);
// set normalized values in percepUnits from featureMat
// for each unit in units
j=0;
for (unit = units.begin(); unit != units.end(); unit++) {
// Populate percepUnit featuresNorm, j is the unit index, i is the feature index.
value = featureMat.at<float>(j);
unit->featuresNorm.at<float>(i) = value;
j++;
}
// for each unit in scratch
for (unit = scratch.begin(); unit != scratch.end(); unit++) {
// Populate percepUnit featuresNorm, j is the unit index, i is the feature index.
value = featureMat.at<float>(j);
unit->featuresNorm.at<float>(i) = value;
j++;
}
}
}
I can't understand what the interaction between mergePercepts and normalization, especially since normalization is an entirely rewritten function.
Update
Massif and my /proc memory reporting don't agree. Massif says there is no effect of normalization on memory usage, only commenting out the percepUnit::clone() operation bypasses the leak.
Here is all the code, in case the interaction is somewhere else I am missing.
Here is another version of the same code with the dependence on OpenCV GPU removed, to facilitate testing...

Answer 1

It was recommended by Nghia (on the opencv forum) that I try and make the percepts a constant size. Sure enough, if I fix the dimensions and type of the cv::Mat members of percepUnit, then the leak disappears.
So it seems to me this is a bug in OpenCV that effects calling clone() and copyTo() on Mats of different sizes that are class members. So far unable to reproduce in a simple program. The leak does seem small enough that it may be the headers leaking, rather than the underlying image data.