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Warning fixes continued

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This commit is contained in:
Andrey Kamaev
2012-06-09 15:00:04 +00:00
parent f6b451c607
commit f2d3b9b4a1
127 changed files with 6298 additions and 6277 deletions
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modules/flann/include/opencv2/flann/allocator.h
+6 -6
View File
@@ -92,9 +92,9 @@ public:
/**
Default constructor. Initializes a new pool.
*/
PooledAllocator(int blocksize = BLOCKSIZE)
PooledAllocator(int blockSize = BLOCKSIZE)
{
this->blocksize = blocksize;
blocksize = blockSize;
remaining = 0;
base = NULL;
@@ -122,7 +122,7 @@ public:
*/
void* allocateMemory(int size)
{
int blocksize;
int blockSize;
/* Round size up to a multiple of wordsize. The following expression
only works for WORDSIZE that is a power of 2, by masking last bits of
@@ -138,11 +138,11 @@ public:
wastedMemory += remaining;
/* Allocate new storage. */
blocksize = (size + sizeof(void*) + (WORDSIZE-1) > BLOCKSIZE) ?
blockSize = (size + sizeof(void*) + (WORDSIZE-1) > BLOCKSIZE) ?
size + sizeof(void*) + (WORDSIZE-1) : BLOCKSIZE;
// use the standard C malloc to allocate memory
void* m = ::malloc(blocksize);
void* m = ::malloc(blockSize);
if (!m) {
fprintf(stderr,"Failed to allocate memory.\n");
return NULL;
@@ -155,7 +155,7 @@ public:
int shift = 0;
//int shift = (WORDSIZE - ( (((size_t)m) + sizeof(void*)) & (WORDSIZE-1))) & (WORDSIZE-1);
remaining = blocksize - sizeof(void*) - shift;
remaining = blockSize - sizeof(void*) - shift;
loc = ((char*)m + sizeof(void*) + shift);
}
void* rloc = loc;
modules/flann/include/opencv2/flann/dynamic_bitset.h
+5 -5
View File
@@ -66,9 +66,9 @@ public:
/** @param only constructor we use in our code
* @param the size of the bitset (in bits)
*/
DynamicBitset(size_t size)
DynamicBitset(size_t sz)
{
resize(size);
resize(sz);
reset();
}
@@ -116,10 +116,10 @@ public:
/** @param resize the bitset so that it contains at least size bits
* @param size
*/
void resize(size_t size)
void resize(size_t sz)
{
size_ = size;
bitset_.resize(size / cell_bit_size_ + 1);
size_ = sz;
bitset_.resize(sz / cell_bit_size_ + 1);
}
/** @param set a bit to true
modules/flann/include/opencv2/flann/heap.h
+3 -3
View File
@@ -67,12 +67,12 @@ public:
* Constructor.
*
* Params:
* size = heap size
* sz = heap size
*/
Heap(int size)
Heap(int sz)
{
length = size;
length = sz;
heap.reserve(length);
count = 0;
}
modules/flann/include/opencv2/flann/hierarchical_clustering_index.h
+22 -22
View File
@@ -106,7 +106,7 @@ private:
* indices_length = length of indices vector
*
*/
void chooseCentersRandom(int k, int* indices, int indices_length, int* centers, int& centers_length)
void chooseCentersRandom(int k, int* dsindices, int indices_length, int* centers, int& centers_length)
{
UniqueRandom r(indices_length);
@@ -122,7 +122,7 @@ private:
return;
}
centers[index] = indices[rnd];
centers[index] = dsindices[rnd];
for (int j=0; j<index; ++j) {
DistanceType sq = distance(dataset[centers[index]], dataset[centers[j]], dataset.cols);
@@ -147,14 +147,14 @@ private:
* indices = indices in the dataset
* Returns:
*/
void chooseCentersGonzales(int k, int* indices, int indices_length, int* centers, int& centers_length)
void chooseCentersGonzales(int k, int* dsindices, int indices_length, int* centers, int& centers_length)
{
int n = indices_length;
int rnd = rand_int(n);
assert(rnd >=0 && rnd < n);
centers[0] = indices[rnd];
centers[0] = dsindices[rnd];
int index;
for (index=1; index<k; ++index) {
@@ -162,9 +162,9 @@ private:
int best_index = -1;
DistanceType best_val = 0;
for (int j=0; j<n; ++j) {
DistanceType dist = distance(dataset[centers[0]],dataset[indices[j]],dataset.cols);
DistanceType dist = distance(dataset[centers[0]],dataset[dsindices[j]],dataset.cols);
for (int i=1; i<index; ++i) {
DistanceType tmp_dist = distance(dataset[centers[i]],dataset[indices[j]],dataset.cols);
DistanceType tmp_dist = distance(dataset[centers[i]],dataset[dsindices[j]],dataset.cols);
if (tmp_dist<dist) {
dist = tmp_dist;
}
@@ -175,7 +175,7 @@ private:
}
}
if (best_index!=-1) {
centers[index] = indices[best_index];
centers[index] = dsindices[best_index];
}
else {
break;
@@ -198,7 +198,7 @@ private:
* indices = indices in the dataset
* Returns:
*/
void chooseCentersKMeanspp(int k, int* indices, int indices_length, int* centers, int& centers_length)
void chooseCentersKMeanspp(int k, int* dsindices, int indices_length, int* centers, int& centers_length)
{
int n = indices_length;
@@ -208,10 +208,10 @@ private:
// Choose one random center and set the closestDistSq values
int index = rand_int(n);
assert(index >=0 && index < n);
centers[0] = indices[index];
centers[0] = dsindices[index];
for (int i = 0; i < n; i++) {
closestDistSq[i] = distance(dataset[indices[i]], dataset[indices[index]], dataset.cols);
closestDistSq[i] = distance(dataset[dsindices[i]], dataset[dsindices[index]], dataset.cols);
currentPot += closestDistSq[i];
}
@@ -237,7 +237,7 @@ private:
// Compute the new potential
double newPot = 0;
for (int i = 0; i < n; i++) newPot += std::min( distance(dataset[indices[i]], dataset[indices[index]], dataset.cols), closestDistSq[i] );
for (int i = 0; i < n; i++) newPot += std::min( distance(dataset[dsindices[i]], dataset[dsindices[index]], dataset.cols), closestDistSq[i] );
// Store the best result
if ((bestNewPot < 0)||(newPot < bestNewPot)) {
@@ -247,9 +247,9 @@ private:
}
// Add the appropriate center
centers[centerCount] = indices[bestNewIndex];
centers[centerCount] = dsindices[bestNewIndex];
currentPot = bestNewPot;
for (int i = 0; i < n; i++) closestDistSq[i] = std::min( distance(dataset[indices[i]], dataset[indices[bestNewIndex]], dataset.cols), closestDistSq[i] );
for (int i = 0; i < n; i++) closestDistSq[i] = std::min( distance(dataset[dsindices[i]], dataset[dsindices[bestNewIndex]], dataset.cols), closestDistSq[i] );
}
centers_length = centerCount;
@@ -518,11 +518,11 @@ private:
void computeLabels(int* indices, int indices_length, int* centers, int centers_length, int* labels, DistanceType& cost)
void computeLabels(int* dsindices, int indices_length, int* centers, int centers_length, int* labels, DistanceType& cost)
{
cost = 0;
for (int i=0; i<indices_length; ++i) {
ElementType* point = dataset[indices[i]];
ElementType* point = dataset[dsindices[i]];
DistanceType dist = distance(point, dataset[centers[0]], veclen_);
labels[i] = 0;
for (int j=1; j<centers_length; ++j) {
@@ -547,13 +547,13 @@ private:
*
* TODO: for 1-sized clusters don't store a cluster center (it's the same as the single cluster point)
*/
void computeClustering(NodePtr node, int* indices, int indices_length, int branching, int level)
void computeClustering(NodePtr node, int* dsindices, int indices_length, int branching, int level)
{
node->size = indices_length;
node->level = level;
if (indices_length < leaf_size_) { // leaf node
node->indices = indices;
node->indices = dsindices;
std::sort(node->indices,node->indices+indices_length);
node->childs = NULL;
return;
@@ -563,10 +563,10 @@ private:
std::vector<int> labels(indices_length);
int centers_length;
(this->*chooseCenters)(branching, indices, indices_length, &centers[0], centers_length);
(this->*chooseCenters)(branching, dsindices, indices_length, &centers[0], centers_length);
if (centers_length<branching) {
node->indices = indices;
node->indices = dsindices;
std::sort(node->indices,node->indices+indices_length);
node->childs = NULL;
return;
@@ -575,7 +575,7 @@ private:
// assign points to clusters
DistanceType cost;
computeLabels(indices, indices_length, &centers[0], centers_length, &labels[0], cost);
computeLabels(dsindices, indices_length, &centers[0], centers_length, &labels[0], cost);
node->childs = pool.allocate<NodePtr>(branching);
int start = 0;
@@ -583,7 +583,7 @@ private:
for (int i=0; i<branching; ++i) {
for (int j=0; j<indices_length; ++j) {
if (labels[j]==i) {
std::swap(indices[j],indices[end]);
std::swap(dsindices[j],dsindices[end]);
std::swap(labels[j],labels[end]);
end++;
}
@@ -592,7 +592,7 @@ private:
node->childs[i] = pool.allocate<Node>();
node->childs[i]->pivot = centers[i];
node->childs[i]->indices = NULL;
computeClustering(node->childs[i],indices+start, end-start, branching, level+1);
computeClustering(node->childs[i],dsindices+start, end-start, branching, level+1);
start=end;
}
}