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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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88
modules/objdetect/src/linemod.cpp
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@@ -451,15 +451,15 @@ protected:
float strong_threshold;
};
ColorGradientPyramid::ColorGradientPyramid(const Mat& src, const Mat& mask,
float weak_threshold, size_t num_features,
float strong_threshold)
: src(src),
mask(mask),
ColorGradientPyramid::ColorGradientPyramid(const Mat& _src, const Mat& _mask,
float _weak_threshold, size_t _num_features,
float _strong_threshold)
: src(_src),
mask(_mask),
pyramid_level(0),
weak_threshold(weak_threshold),
num_features(num_features),
strong_threshold(strong_threshold)
weak_threshold(_weak_threshold),
num_features(_num_features),
strong_threshold(_strong_threshold)
{
update();
}
@@ -557,10 +557,10 @@ ColorGradient::ColorGradient()
{
}
ColorGradient::ColorGradient(float weak_threshold, size_t num_features, float strong_threshold)
: weak_threshold(weak_threshold),
num_features(num_features),
strong_threshold(strong_threshold)
ColorGradient::ColorGradient(float _weak_threshold, size_t _num_features, float _strong_threshold)
: weak_threshold(_weak_threshold),
num_features(_num_features),
strong_threshold(_strong_threshold)
{
}
@@ -751,13 +751,13 @@ protected:
int extract_threshold;
};
DepthNormalPyramid::DepthNormalPyramid(const Mat& src, const Mat& mask,
int distance_threshold, int difference_threshold, size_t num_features,
int extract_threshold)
: mask(mask),
DepthNormalPyramid::DepthNormalPyramid(const Mat& src, const Mat& _mask,
int distance_threshold, int difference_threshold, size_t _num_features,
int _extract_threshold)
: mask(_mask),
pyramid_level(0),
num_features(num_features),
extract_threshold(extract_threshold)
num_features(_num_features),
extract_threshold(_extract_threshold)
{
quantizedNormals(src, normal, distance_threshold, difference_threshold);
}
@@ -876,12 +876,12 @@ DepthNormal::DepthNormal()
{
}
DepthNormal::DepthNormal(int distance_threshold, int difference_threshold, size_t num_features,
int extract_threshold)
: distance_threshold(distance_threshold),
difference_threshold(difference_threshold),
num_features(num_features),
extract_threshold(extract_threshold)
DepthNormal::DepthNormal(int _distance_threshold, int _difference_threshold, size_t _num_features,
int _extract_threshold)
: distance_threshold(_distance_threshold),
difference_threshold(_difference_threshold),
num_features(_num_features),
extract_threshold(_extract_threshold)
{
}
@@ -1388,9 +1388,9 @@ Detector::Detector()
{
}
Detector::Detector(const std::vector< Ptr<Modality> >& modalities,
Detector::Detector(const std::vector< Ptr<Modality> >& _modalities,
const std::vector<int>& T_pyramid)
: modalities(modalities),
: modalities(_modalities),
pyramid_levels(static_cast<int>(T_pyramid.size())),
T_at_level(T_pyramid)
{
@@ -1480,7 +1480,7 @@ void Detector::match(const std::vector<Mat>& sources, float threshold, std::vect
// Used to filter out weak matches
struct MatchPredicate
{
MatchPredicate(float threshold) : threshold(threshold) {}
MatchPredicate(float _threshold) : threshold(_threshold) {}
bool operator() (const Match& m) { return m.similarity < threshold; }
float threshold;
};
@@ -1554,13 +1554,13 @@ void Detector::matchClass(const LinearMemoryPyramid& lm_pyramid,
int max_x = size.width - tp[start].width - border;
int max_y = size.height - tp[start].height - border;
std::vector<Mat> similarities(modalities.size());
Mat total_similarity;
std::vector<Mat> similarities2(modalities.size());
Mat total_similarity2;
for (int m = 0; m < (int)candidates.size(); ++m)
{
Match& match = candidates[m];
int x = match.x * 2 + 1; /// @todo Support other pyramid distance
int y = match.y * 2 + 1;
Match& match2 = candidates[m];
int x = match2.x * 2 + 1; /// @todo Support other pyramid distance
int y = match2.y * 2 + 1;
// Require 8 (reduced) row/cols to the up/left
x = std::max(x, border);
@@ -1571,22 +1571,22 @@ void Detector::matchClass(const LinearMemoryPyramid& lm_pyramid,
y = std::min(y, max_y);
// Compute local similarity maps for each modality
int num_features = 0;
int numFeatures = 0;
for (int i = 0; i < (int)modalities.size(); ++i)
{
const Template& templ = tp[start + i];
num_features += static_cast<int>(templ.features.size());
similarityLocal(lms[i], templ, similarities[i], size, T, Point(x, y));
numFeatures += static_cast<int>(templ.features.size());
similarityLocal(lms[i], templ, similarities2[i], size, T, Point(x, y));
}
addSimilarities(similarities, total_similarity);
addSimilarities(similarities2, total_similarity2);
// Find best local adjustment
int best_score = 0;
int best_r = -1, best_c = -1;
for (int r = 0; r < total_similarity.rows; ++r)
for (int r = 0; r < total_similarity2.rows; ++r)
{
ushort* row = total_similarity.ptr<ushort>(r);
for (int c = 0; c < total_similarity.cols; ++c)
ushort* row = total_similarity2.ptr<ushort>(r);
for (int c = 0; c < total_similarity2.cols; ++c)
{
int score = row[c];
if (score > best_score)
@@ -1598,9 +1598,9 @@ void Detector::matchClass(const LinearMemoryPyramid& lm_pyramid,
}
}
// Update current match
match.x = (x / T - 8 + best_c) * T + offset;
match.y = (y / T - 8 + best_r) * T + offset;
match.similarity = (best_score * 100.f) / (4 * num_features);
match2.x = (x / T - 8 + best_c) * T + offset;
match2.y = (y / T - 8 + best_r) * T + offset;
match2.similarity = (best_score * 100.f) / (4 * numFeatures);
}
// Filter out any matches that drop below the similarity threshold
@@ -1763,10 +1763,10 @@ void Detector::write(FileStorage& fs) const
tps[template_id].resize(templates_fn.size());
FileNodeIterator templ_it = templates_fn.begin(), templ_it_end = templates_fn.end();
int i = 0;
int idx = 0;
for ( ; templ_it != templ_it_end; ++templ_it)
{
tps[template_id][i++].read(*templ_it);
tps[template_id][idx++].read(*templ_it);
}
}