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Merge pull request #17722 from pemmanuelviel:pev--replace-asserts

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* Clean: replace C style asserts by CV_Assert and CV_DbgAssert

* Try fixing warning on Windows compilation

* Another way trying to fix warnings on Win

* Fixing warnings with some compilers:
Some compilers warn on systematic exit preventing to execute the code that follows.
This is why assert(0) that exits only in debug was working, but not CV_Assert or CV_Error
that exit both in release and debug, even if with different behavior.
In addition, other compilers complain when return 0 is removed from getKey(),
even if before we have a statement leading to systematic exit.

* Disable "unreachable code" warnings for Win compilers so we can use proper CV_Error
This commit is contained in:
pemmanuelviel 2020-07-04 19:15:44 +02:00 committed by GitHub
parent 8f5b453a96
commit 65f87b114b
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GPG Key ID: 4AEE18F83AFDEB23
11 changed files with 48 additions and 38 deletions
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2
modules/flann/include/opencv2/flann/autotuned_index.h
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@ -497,7 +497,7 @@ private:
const int nn = 1;
const size_t SAMPLE_COUNT = 1000;
assert(bestIndex_ != NULL); // must have a valid index
CV_Assert(bestIndex_ != NULL && "Requires a valid index"); // must have a valid index
float speedup = 0;

1
modules/flann/include/opencv2/flann/flann_base.hpp
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@ -34,7 +34,6 @@
//! @cond IGNORED
#include <vector>
#include <cassert>
#include <cstdio>
#include "general.h"

9
modules/flann/include/opencv2/flann/hierarchical_clustering_index.h
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@ -35,7 +35,6 @@
#include <algorithm>
#include <map>
#include <cassert>
#include <limits>
#include <cmath>
@ -153,7 +152,7 @@ private:
int n = indices_length;
int rnd = rand_int(n);
assert(rnd >=0 && rnd < n);
CV_DbgAssert(rnd >=0 && rnd < n);
centers[0] = dsindices[rnd];
@ -208,7 +207,7 @@ private:
// Choose one random center and set the closestDistSq values
int index = rand_int(n);
assert(index >=0 && index < n);
CV_DbgAssert(index >=0 && index < n);
centers[0] = dsindices[index];
// Computing distance^2 will have the advantage of even higher probability further to pick new centers
@ -295,7 +294,7 @@ private:
// Choose one random center and set the closestDistSq values
int index = rand_int(n);
assert(index >=0 && index < n);
CV_DbgAssert(index >=0 && index < n);
centers[0] = dsindices[index];
for (int i = 0; i < n; i++) {
@ -564,10 +563,10 @@ public:
NodePtr node = branch.node;
findNN(node, result, vec, checks, maxChecks, heap, checked);
}
assert(result.full());
delete heap;
CV_Assert(result.full());
}
IndexParams getParameters() const CV_OVERRIDE

1
modules/flann/include/opencv2/flann/index_testing.h
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@ -34,7 +34,6 @@
//! @cond IGNORED
#include <cstring>
#include <cassert>
#include <cmath>
#include "matrix.h"

5
modules/flann/include/opencv2/flann/kdtree_index.h
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@ -35,7 +35,6 @@
#include <algorithm>
#include <map>
#include <cassert>
#include <cstring>
#include "general.h"
@ -433,7 +432,7 @@ private:
if (trees_>0) {
searchLevelExact(result, vec, tree_roots_[0], 0.0, epsError);
}
assert(result.full());
CV_Assert(result.full());
}
/**
@ -462,7 +461,7 @@ private:
delete heap;
assert(result.full());
CV_Assert(result.full());
}

11
modules/flann/include/opencv2/flann/kdtree_single_index.h
View File

@ -35,7 +35,6 @@
#include <algorithm>
#include <map>
#include <cassert>
#include <cstring>
#include "general.h"
@ -214,11 +213,11 @@ public:
*/
void knnSearch(const Matrix<ElementType>& queries, Matrix<int>& indices, Matrix<DistanceType>& dists, int knn, const SearchParams& params) CV_OVERRIDE
{
assert(queries.cols == veclen());
assert(indices.rows >= queries.rows);
assert(dists.rows >= queries.rows);
assert(int(indices.cols) >= knn);
assert(int(dists.cols) >= knn);
CV_Assert(queries.cols == veclen());
CV_Assert(indices.rows >= queries.rows);
CV_Assert(dists.rows >= queries.rows);
CV_Assert(int(indices.cols) >= knn);
CV_Assert(int(dists.cols) >= knn);
KNNSimpleResultSet<DistanceType> resultSet(knn);
for (size_t i = 0; i < queries.rows; i++) {

9
modules/flann/include/opencv2/flann/kmeans_index.h
View File

@ -35,7 +35,6 @@
#include <algorithm>
#include <map>
#include <cassert>
#include <limits>
#include <cmath>
@ -152,7 +151,7 @@ public:
int n = indices_length;
int rnd = rand_int(n);
assert(rnd >=0 && rnd < n);
CV_DbgAssert(rnd >=0 && rnd < n);
centers[0] = indices[rnd];
@ -207,7 +206,7 @@ public:
// Choose one random center and set the closestDistSq values
int index = rand_int(n);
assert(index >=0 && index < n);
CV_DbgAssert(index >=0 && index < n);
centers[0] = indices[index];
for (int i = 0; i < n; i++) {
@ -502,9 +501,9 @@ public:
KMeansNodePtr node = branch.node;
findNN(node, result, vec, checks, maxChecks, heap);
}
assert(result.full());
delete heap;
CV_Assert(result.full());
}
}

20
modules/flann/include/opencv2/flann/lsh_index.h
View File

@ -38,7 +38,6 @@
//! @cond IGNORED
#include <algorithm>
#include <cassert>
#include <cstring>
#include <map>
#include <vector>
@ -53,6 +52,11 @@
#include "random.h"
#include "saving.h"
#ifdef _MSC_VER
#pragma warning(push)
#pragma warning(disable: 4702) //disable unreachable code
#endif
namespace cvflann
{
@ -191,11 +195,11 @@ public:
*/
virtual void knnSearch(const Matrix<ElementType>& queries, Matrix<int>& indices, Matrix<DistanceType>& dists, int knn, const SearchParams& params) CV_OVERRIDE
{
assert(queries.cols == veclen());
assert(indices.rows >= queries.rows);
assert(dists.rows >= queries.rows);
assert(int(indices.cols) >= knn);
assert(int(dists.cols) >= knn);
CV_Assert(queries.cols == veclen());
CV_Assert(indices.rows >= queries.rows);
CV_Assert(dists.rows >= queries.rows);
CV_Assert(int(indices.cols) >= knn);
CV_Assert(int(dists.cols) >= knn);
KNNUniqueResultSet<DistanceType> resultSet(knn);
@ -391,6 +395,10 @@ private:
};
}
#ifdef _MSC_VER
#pragma warning(pop)
#endif
//! @endcond
#endif //OPENCV_FLANN_LSH_INDEX_H_

16
modules/flann/include/opencv2/flann/lsh_table.h
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@ -58,6 +58,12 @@
#include "dynamic_bitset.h"
#include "matrix.h"
#ifdef _MSC_VER
#pragma warning(push)
#pragma warning(disable: 4702) //disable unreachable code
#endif
namespace cvflann
{
@ -162,8 +168,7 @@ public:
{
feature_size_ = feature_size;
CV_UNUSED(key_size);
std::cerr << "LSH is not implemented for that type" << std::endl;
assert(0);
CV_Error(cv::Error::StsUnsupportedFormat, "LSH is not implemented for that type" );
}
/** Add a feature to the table
@ -243,8 +248,7 @@ public:
*/
size_t getKey(const ElementType* /*feature*/) const
{
std::cerr << "LSH is not implemented for that type" << std::endl;
assert(0);
CV_Error(cv::Error::StsUnsupportedFormat, "LSH is not implemented for that type" );
return 0;
}
@ -510,6 +514,10 @@ inline LshStats LshTable<unsigned char>::getStats() const
}
}
#ifdef _MSC_VER
#pragma warning(pop)
#endif
////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
//! @endcond

10
modules/flann/include/opencv2/flann/nn_index.h
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@ -69,11 +69,11 @@ public:
*/
virtual void knnSearch(const Matrix<ElementType>& queries, Matrix<int>& indices, Matrix<DistanceType>& dists, int knn, const SearchParams& params)
{
assert(queries.cols == veclen());
assert(indices.rows >= queries.rows);
assert(dists.rows >= queries.rows);
assert(int(indices.cols) >= knn);
assert(int(dists.cols) >= knn);
CV_Assert(queries.cols == veclen());
CV_Assert(indices.rows >= queries.rows);
CV_Assert(dists.rows >= queries.rows);
CV_Assert(int(indices.cols) >= knn);
CV_Assert(int(dists.cols) >= knn);
#if 0
KNNResultSet<DistanceType> resultSet(knn);

2
modules/flann/include/opencv2/flann/simplex_downhill.h
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@ -72,7 +72,7 @@ float optimizeSimplexDownhill(T* points, int n, F func, float* vals = NULL )
{
const int MAX_ITERATIONS = 10;
assert(n>0);
CV_DbgAssert(n>0);
T* p_o = new T[n];
T* p_r = new T[n];