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refactoring latentSVM

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This commit is contained in:
Evgeniy Kozinov
2011-07-12 18:43:41 +00:00
parent ea8e27961e
commit 07eed8c489
10 changed files with 354 additions and 409 deletions
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74
modules/objdetect/src/matching.cpp
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@@ -33,7 +33,7 @@ int convolution(const CvLSVMFilterObject *Fi, const CvLSVMFeatureMap *map, float
m1 = map->sizeX;
n2 = Fi->sizeY;
m2 = Fi->sizeX;
p = map->p;
p = map->numFeatures;
diff1 = n1 - n2 + 1;
diff2 = m1 - m2 + 1;
@@ -51,7 +51,7 @@ int convolution(const CvLSVMFilterObject *Fi, const CvLSVMFeatureMap *map, float
{
for (j2 = 0; j2 < m2; j2++)
{
pMap = map->Map + (i1 + i2) * m1 * p + (j1 + j2) * p;//sm2
pMap = map->map + (i1 + i2) * m1 * p + (j1 + j2) * p;//sm2
pH = Fi->H + (i2 * m2 + j2) * p;//sm2
for (k = 0; k < p/4; k++)
{
@@ -210,14 +210,14 @@ int getFFTImageFilterObject(const CvLSVMFilterObject *filter,
mapSize = mapDimX * mapDimY;
newFilter = (float *)malloc(sizeof(float) * (2 * mapSize));
rot2PIFilter = (float *)malloc(sizeof(float) * filterSize);
res = allocFFTImage(image, filter->p, mapDimX, mapDimY);
res = allocFFTImage(image, filter->numFeatures, mapDimX, mapDimY);
if (res != LATENT_SVM_OK)
{
return res;
}
for (i = 0; i < filter->p; i++)
for (i = 0; i < filter->numFeatures; i++)
{
rot2PI(filter->H, filter->sizeX, filter->sizeY, rot2PIFilter, filter->p, i);
rot2PI(filter->H, filter->sizeX, filter->sizeY, rot2PIFilter, filter->numFeatures, i);
addNullableBars(rot2PIFilter, filter->sizeX, filter->sizeY,
newFilter, mapDimX, mapDimY);
fft2d(newFilter, (*image)->channels[i], mapDimY, mapDimX);
@@ -241,14 +241,14 @@ int getFFTImageFeatureMap(const CvLSVMFeatureMap *map, CvLSVMFftImage **image)
{
int i, j, size;
float *buf;
allocFFTImage(image, map->p, map->sizeX, map->sizeY);
allocFFTImage(image, map->numFeatures, map->sizeX, map->sizeY);
size = map->sizeX * map->sizeY;
buf = (float *)malloc(sizeof(float) * (2 * size));
for (i = 0; i < map->p; i++)
for (i = 0; i < map->numFeatures; i++)
{
for (j = 0; j < size; j++)
{
buf[2 * j] = map->Map[j * map->p + i];
buf[2 * j] = map->map[j * map->numFeatures + i];
buf[2 * j + 1] = 0.0;
}
fft2d(buf, (*image)->channels[i], map->sizeY, map->sizeX);
@@ -282,7 +282,7 @@ int convFFTConv2d(const CvLSVMFftImage *featMapImage, const CvLSVMFftImage *filt
imagesMultRes = (float *)malloc(sizeof(float) * size);
fftImagesMulti(featMapImage->channels[0], filterImage->channels[0],
featMapImage->dimY, featMapImage->dimX, imagesMultRes);
for (i = 1; (i < (int)featMapImage->p) && (i < (int)filterImage->p); i++)
for (i = 1; (i < (int)featMapImage->numFeatures) && (i < (int)filterImage->numFeatures); i++)
{
fftImagesMulti(featMapImage->channels[i],filterImage->channels[i],
featMapImage->dimY, featMapImage->dimX, imagesMult);
@@ -343,7 +343,7 @@ int filterDispositionLevel(const CvLSVMFilterObject *Fi, const CvLSVMFeatureMap
m1 = pyramid->sizeX;
n2 = Fi->sizeY;
m2 = Fi->sizeX;
p = pyramid->p;
p = pyramid->numFeatures;
(*scoreFi) = NULL;
(*pointsX) = NULL;
(*pointsY) = NULL;
@@ -429,7 +429,7 @@ int filterDispositionLevelFFT(const CvLSVMFilterObject *Fi, const CvLSVMFftImage
m1 = featMapImage->dimX;
n2 = Fi->sizeY;
m2 = Fi->sizeX;
p = featMapImage->p;
p = featMapImage->numFeatures;
(*scoreFi) = NULL;
(*pointsX) = NULL;
(*pointsY) = NULL;
@@ -525,8 +525,8 @@ int addNullableBorder(CvLSVMFeatureMap *map, int bx, int by)
float *new_map;
sizeX = map->sizeX + 2 * bx;
sizeY = map->sizeY + 2 * by;
new_map = (float *)malloc(sizeof(float) * sizeX * sizeY * map->p);
for (i = 0; i < sizeX * sizeY * map->p; i++)
new_map = (float *)malloc(sizeof(float) * sizeX * sizeY * map->numFeatures);
for (i = 0; i < sizeX * sizeY * map->numFeatures; i++)
{
new_map[i] = 0.0;
}
@@ -534,17 +534,17 @@ int addNullableBorder(CvLSVMFeatureMap *map, int bx, int by)
{
for (j = bx; j < map->sizeX + bx; j++)
{
for (k = 0; k < map->p; k++)
for (k = 0; k < map->numFeatures; k++)
{
new_map[(i * sizeX + j) * map->p + k] =
map->Map[((i - by) * map->sizeX + j - bx) * map->p + k];
new_map[(i * sizeX + j) * map->numFeatures + k] =
map->map[((i - by) * map->sizeX + j - bx) * map->numFeatures + k];
}
}
}
map->sizeX = sizeX;
map->sizeY = sizeY;
free(map->Map);
map->Map = new_map;
free(map->map);
map->map = new_map;
return LATENT_SVM_OK;
}
@@ -558,19 +558,19 @@ CvLSVMFeatureMap* featureMapBorderPartFilter(CvLSVMFeatureMap *map,
computeBorderSize(maxXBorder, maxYBorder, &bx, &by);
sizeX = map->sizeX + 2 * bx;
sizeY = map->sizeY + 2 * by;
allocFeatureMapObject(&new_map, sizeX, sizeY, map->p, map->xp);
for (i = 0; i < sizeX * sizeY * map->p; i++)
allocFeatureMapObject(&new_map, sizeX, sizeY, map->numFeatures);
for (i = 0; i < sizeX * sizeY * map->numFeatures; i++)
{
new_map->Map[i] = 0.0;
new_map->map[i] = 0.0f;
}
for (i = by; i < map->sizeY + by; i++)
{
for (j = bx; j < map->sizeX + bx; j++)
{
for (k = 0; k < map->p; k++)
for (k = 0; k < map->numFeatures; k++)
{
new_map->Map[(i * sizeX + j) * map->p + k] =
map->Map[((i - by) * map->sizeX + j - bx) * map->p + k];
new_map->map[(i * sizeX + j) * map->numFeatures + k] =
map->map[((i - by) * map->sizeX + j - bx) * map->numFeatures + k];
}
}
}
@@ -640,7 +640,7 @@ int maxFunctionalScoreFixedLevel(const CvLSVMFilterObject **all_F, int n,
dimY = H->pyramid[level]->sizeY;
// Number of features
p = H->pyramid[level]->p;
p = H->pyramid[level]->numFeatures;
// Getting dimension of root filter
nF0 = all_F[0]->sizeY;
@@ -888,7 +888,7 @@ int thresholdFunctionalScoreFixedLevel(const CvLSVMFilterObject **all_F, int n,
dimY = H->pyramid[level]->sizeY;
// Number of features
p = H->pyramid[level]->p;
p = H->pyramid[level]->numFeatures;
// Getting dimension of root filter
nF0 = all_F[0]->sizeY;
@@ -1109,7 +1109,7 @@ int maxFunctionalScore(const CvLSVMFilterObject **all_F, int n,
// Computation the number of levels for seaching object,
// first lambda-levels are used for computation values
// of score function for each position of root filter
numLevels = H->countLevel - H->lambda;
numLevels = H->numLevels - LAMBDA;
// Allocation memory for maximum value of score function for each level
tmpScore = (float *)malloc(sizeof(float) * numLevels);
@@ -1135,7 +1135,7 @@ int maxFunctionalScore(const CvLSVMFilterObject **all_F, int n,
}
// Set current value of the maximum of score function
res = maxFunctionalScoreFixedLevel(all_F, n, H, H->lambda, b,
res = maxFunctionalScoreFixedLevel(all_F, n, H, LAMBDA, b,
maxXBorder, maxYBorder,
&(tmpScore[0]),
tmpPoints[0],
@@ -1150,9 +1150,9 @@ int maxFunctionalScore(const CvLSVMFilterObject **all_F, int n,
file = fopen("maxScore.csv", "w+");
fprintf(file, "%i;%lf;\n", H->lambda, tmpScore[0]);
//*/
for (l = H->lambda + 1; l < H->countLevel; l++)
for (l = LAMBDA + 1; l < H->numLevels; l++)
{
k = l - H->lambda;
k = l - LAMBDA;
res = maxFunctionalScoreFixedLevel(all_F, n, H, l, b,
maxXBorder, maxYBorder,
&(tmpScore[k]),
@@ -1191,7 +1191,7 @@ int maxFunctionalScore(const CvLSVMFilterObject **all_F, int n,
if ((tmpScore[i] - maxScore) * (tmpScore[i] - maxScore) <= EPS)
{
// Computation the number of level
level = i + H->lambda;
level = i + LAMBDA;
// Addition a set of points
f += tmpKPoints[i];
@@ -1272,7 +1272,7 @@ int thresholdFunctionalScore(const CvLSVMFilterObject **all_F, int n,
// Computation the number of levels for seaching object,
// first lambda-levels are used for computation values
// of score function for each position of root filter
numLevels = H->countLevel - H->lambda;
numLevels = H->numLevels - LAMBDA;
// Allocation memory for values of score function for each level
// that exceed threshold
@@ -1305,9 +1305,9 @@ int thresholdFunctionalScore(const CvLSVMFilterObject **all_F, int n,
fprintf(file, "%i;%lf;\n", H->lambda, tmpScore[0]);
//*/
(*kPoints) = 0;
for (l = H->lambda; l < H->countLevel; l++)
for (l = LAMBDA; l < H->numLevels; l++)
{
k = l - H->lambda;
k = l - LAMBDA;
//printf("Score at the level %i\n", l);
res = thresholdFunctionalScoreFixedLevel(all_F, n, H, l, b,
maxXBorder, maxYBorder, scoreThreshold,
@@ -1339,7 +1339,7 @@ int thresholdFunctionalScore(const CvLSVMFilterObject **all_F, int n,
for (i = 0; i < numLevels; i++)
{
// Computation the number of level
level = i + H->lambda;
level = i + LAMBDA;
// Addition a set of points
f += tmpKPoints[i];
@@ -1411,7 +1411,7 @@ int createSchedule(const CvLSVMFeaturePyramid *H, const CvLSVMFilterObject **all
sumPartFiltersDim += all_F[i]->sizeX * all_F[i]->sizeY;
}
// Number of levels which are used for computation of score function
numLevels = H->countLevel - H->lambda;
numLevels = H->numLevels - LAMBDA;
// Allocation memory for saving number of dot products that will be
// computed for each level of feature pyramid
dotProd = (int *)malloc(sizeof(int) * numLevels);
@@ -1421,7 +1421,7 @@ int createSchedule(const CvLSVMFeaturePyramid *H, const CvLSVMFilterObject **all
dby = 2 * by;
// Total number of dot products for all levels
numDotProducts = 0;
lambda = H->lambda;
lambda = LAMBDA;
for (i = 0; i < numLevels; i++)
{
dotProd[i] = H->pyramid[i + lambda]->sizeX *