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fixed warnings in linemod on Windows

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pull/13383/head
Alexander Shishkov 13 years ago
parent e7e373307b
commit 0279ba953e
2 changed files with 37 additions and 37 deletions
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  1. 10
      modules/objdetect/include/opencv2/objdetect/objdetect.hpp
  2. 64
      modules/objdetect/src/linemod.cpp

10
modules/objdetect/include/opencv2/objdetect/objdetect.hpp
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@ -774,7 +774,7 @@ protected:
*
* \todo Max response, to allow optimization of summing (255/MAX) features as uint8
*/
class Modality
class CV_EXPORTS Modality
{
public:
// Virtual destructor
@ -821,7 +821,7 @@ protected:
/**
* \brief Modality that computes quantized gradient orientations from a color image.
*/
class ColorGradient : public Modality
class CV_EXPORTS ColorGradient : public Modality
{
public:
/**
@ -856,7 +856,7 @@ protected:
/**
* \brief Modality that computes quantized surface normals from a dense depth map.
*/
class DepthNormal : public Modality
class CV_EXPORTS DepthNormal : public Modality
{
public:
/**
@ -900,7 +900,7 @@ void colormap(const Mat& quantized, Mat& dst);
/**
* \brief Represents a successful template match.
*/
struct Match
struct CV_EXPORTS Match
{
Match()
{
@ -1020,7 +1020,7 @@ public:
int numTemplates() const;
int numTemplates(const std::string& class_id) const;
int numClasses() const { return class_templates.size(); }
int numClasses() const { return static_cast<int>(class_templates.size()); }
std::vector<std::string> classIds() const;

64
modules/objdetect/src/linemod.cpp
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@ -292,11 +292,11 @@ void quantizedOrientations(const Mat& src, Mat& magnitude,
float * ptr0y = (float *)sobel_dy.data;
float * ptrmg = (float *)magnitude.data;
const int length1 = sobel_3dx.step1();
const int length2 = sobel_3dy.step1();
const int length3 = sobel_dx.step1();
const int length4 = sobel_dy.step1();
const int length5 = magnitude.step1();
const int length1 = static_cast<const int>(sobel_3dx.step1());
const int length2 = static_cast<const int>(sobel_3dy.step1());
const int length3 = static_cast<const int>(sobel_dx.step1());
const int length4 = static_cast<const int>(sobel_dy.step1());
const int length5 = static_cast<const int>(magnitude.step1());
const int length0 = sobel_3dy.cols * 3;
for (int r = 0; r < sobel_3dy.rows; ++r)
@ -539,7 +539,7 @@ bool ColorGradientPyramid::extractTemplate(Template& templ) const
std::stable_sort(candidates.begin(), candidates.end());
// Use heuristic based on surplus of candidates in narrow outline for initial distance threshold
float distance = candidates.size() / num_features + 1;
float distance = static_cast<float>(candidates.size() / num_features + 1);
selectScatteredFeatures(candidates, templ.features, num_features, distance);
// Size determined externally, needs to match templates for other modalities
@ -690,9 +690,9 @@ void quantizedNormals(const Mat& src, Mat& dst, int distance_threshold,
/// @todo Magic number 1150 is focal length? This is something like
/// f in SXGA mode, but in VGA is more like 530.
float l_nx = 1150 * l_ddx;
float l_ny = 1150 * l_ddy;
float l_nz = -l_det * l_d;
float l_nx = static_cast<float>(1150 * l_ddx);
float l_ny = static_cast<float>(1150 * l_ddy);
float l_nz = static_cast<float>(-l_det * l_d);
float l_sqrt = sqrtf(l_nx * l_nx + l_ny * l_ny + l_nz * l_nz);
@ -706,9 +706,9 @@ void quantizedNormals(const Mat& src, Mat& dst, int distance_threshold,
//*lp_norm = fabs(l_nz)*255;
int l_val1 = l_nx * l_offsetx + l_offsetx;
int l_val2 = l_ny * l_offsety + l_offsety;
int l_val3 = l_nz * GRANULARITY + GRANULARITY;
int l_val1 = static_cast<int>(l_nx * l_offsetx + l_offsetx);
int l_val2 = static_cast<int>(l_ny * l_offsety + l_offsety);
int l_val3 = static_cast<int>(l_nz * GRANULARITY + GRANULARITY);
*lp_norm = NORMAL_LUT[l_val3][l_val2][l_val1];
}
@ -856,8 +856,8 @@ bool DepthNormalPyramid::extractTemplate(Template& templ) const
std::stable_sort(candidates.begin(), candidates.end());
// Use heuristic based on object area for initial distance threshold
int area = no_mask ? normal.total() : countNonZero(local_mask);
float distance = sqrtf(area) / sqrtf(num_features) + 1.5f;
int area = static_cast<int>(no_mask ? normal.total() : countNonZero(local_mask));
float distance = sqrtf(static_cast<float>(area)) / sqrtf(static_cast<float>(num_features)) + 1.5f;
selectScatteredFeatures(candidates, templ.features, num_features, distance);
// Size determined externally, needs to match templates for other modalities
@ -1000,8 +1000,8 @@ void spread(const Mat& src, Mat& dst, int T)
int height = src.rows - r;
for (int c = 0; c < T; ++c)
{
orUnaligned8u(&src.at<unsigned char>(r, c), src.step1(), dst.ptr(),
dst.step1(), src.cols - c, height);
orUnaligned8u(&src.at<unsigned char>(r, c), static_cast<const int>(src.step1()), dst.ptr(),
static_cast<const int>(dst.step1()), src.cols - c, height);
}
}
}
@ -1366,7 +1366,7 @@ void addSimilarities(const std::vector<Mat>& similarities, Mat& dst)
{
// NOTE: add() seems to be rather slow in the 8U + 8U -> 16U case
dst.create(similarities[0].size(), CV_16U);
addUnaligned8u16u(similarities[0].ptr(), similarities[1].ptr(), dst.ptr<ushort>(), dst.total());
addUnaligned8u16u(similarities[0].ptr(), similarities[1].ptr(), dst.ptr<ushort>(), static_cast<int>(dst.total()));
/// @todo Optimize 16u + 8u -> 16u when more than 2 modalities
for (size_t i = 2; i < similarities.size(); ++i)
@ -1385,7 +1385,7 @@ Detector::Detector()
Detector::Detector(const std::vector< Ptr<Modality> >& modalities,
const std::vector<int>& T_pyramid)
: modalities(modalities),
pyramid_levels(T_pyramid.size()),
pyramid_levels(static_cast<int>(T_pyramid.size())),
T_at_level(T_pyramid)
{
}
@ -1396,7 +1396,7 @@ void Detector::match(const std::vector<Mat>& sources, float threshold, std::vect
{
matches.clear();
if (quantized_images.needed())
quantized_images.create(1, pyramid_levels * modalities.size(), CV_8U);
quantized_images.create(1, static_cast<int>(pyramid_levels * modalities.size()), CV_8U);
assert(sources.size() == modalities.size());
// Initialize each modality with our sources
@ -1441,7 +1441,7 @@ void Detector::match(const std::vector<Mat>& sources, float threshold, std::vect
linearize(response_maps[j], memories[j], T);
if (quantized_images.needed()) //use copyTo here to side step reference semantics.
quantized.copyTo(quantized_images.getMatRef(l*quantizers.size() + i));
quantized.copyTo(quantized_images.getMatRef(static_cast<int>(l*quantizers.size() + i)));
}
sizes.push_back(quantized.size());
@ -1496,13 +1496,13 @@ void Detector::matchClass(const LinearMemoryPyramid& lm_pyramid,
// Compute similarity maps for each modality at lowest pyramid level
std::vector<Mat> similarities(modalities.size());
int lowest_start = tp.size() - modalities.size();
int lowest_start = static_cast<int>(tp.size() - modalities.size());
int lowest_T = T_at_level.back();
int num_features = 0;
for (int i = 0; i < (int)modalities.size(); ++i)
{
const Template& templ = tp[lowest_start + i];
num_features += templ.features.size();
num_features += static_cast<int>(templ.features.size());
similarity(lowest_lm[i], templ, similarities[i], sizes.back(), lowest_T);
}
@ -1515,7 +1515,7 @@ void Detector::matchClass(const LinearMemoryPyramid& lm_pyramid,
// threshold scales from half the max response (what you would expect from applying
// the template to a completely random image) to the max response.
// NOTE: This assumes max per-feature response is 4, so we scale between [2*nf, 4*nf].
int raw_threshold = 2*num_features + (threshold / 100.f) * (2*num_features) + 0.5f;
int raw_threshold = static_cast<int>(2*num_features + (threshold / 100.f) * (2*num_features) + 0.5f);
// Find initial matches
std::vector<Match> candidates;
@ -1530,8 +1530,8 @@ void Detector::matchClass(const LinearMemoryPyramid& lm_pyramid,
int offset = lowest_T / 2 + (lowest_T % 2 - 1);
int x = c * lowest_T + offset;
int y = r * lowest_T + offset;
int score = (raw_score * 100.f) / (4 * num_features) + 0.5f;
candidates.push_back(Match(x, y, score, class_id, template_id));
float score =(raw_score * 100.f) / (4 * num_features) + 0.5f;
candidates.push_back(Match(x, y, score, class_id, static_cast<int>(template_id)));
}
}
}
@ -1541,7 +1541,7 @@ void Detector::matchClass(const LinearMemoryPyramid& lm_pyramid,
{
const std::vector<LinearMemories>& lms = lm_pyramid[l];
int T = T_at_level[l];
int start = l * modalities.size();
int start = static_cast<int>(l * modalities.size());
Size size = sizes[l];
int border = 8 * T;
int offset = T / 2 + (T % 2 - 1);
@ -1569,7 +1569,7 @@ void Detector::matchClass(const LinearMemoryPyramid& lm_pyramid,
for (int i = 0; i < (int)modalities.size(); ++i)
{
const Template& templ = tp[start + i];
num_features += templ.features.size();
num_features += static_cast<int>(templ.features.size());
similarityLocal(lms[i], templ, similarities[i], size, T, Point(x, y));
}
addSimilarities(similarities, total_similarity);
@ -1610,9 +1610,9 @@ void Detector::matchClass(const LinearMemoryPyramid& lm_pyramid,
int Detector::addTemplate(const std::vector<Mat>& sources, const std::string& class_id,
const Mat& object_mask, Rect* bounding_box)
{
int num_modalities = modalities.size();
int num_modalities = static_cast<int>(modalities.size());
std::vector<TemplatePyramid>& template_pyramids = class_templates[class_id];
int template_id = template_pyramids.size();
int template_id = static_cast<int>(template_pyramids.size());
TemplatePyramid tp;
tp.resize(num_modalities * pyramid_levels);
@ -1646,7 +1646,7 @@ int Detector::addTemplate(const std::vector<Mat>& sources, const std::string& cl
int Detector::addSyntheticTemplate(const std::vector<Template>& templates, const std::string& class_id)
{
std::vector<TemplatePyramid>& template_pyramids = class_templates[class_id];
int template_id = template_pyramids.size();
int template_id = static_cast<int>(template_pyramids.size());
template_pyramids.push_back(templates);
return template_id;
}
@ -1664,7 +1664,7 @@ int Detector::numTemplates() const
int ret = 0;
TemplatesMap::const_iterator i = class_templates.begin(), iend = class_templates.end();
for ( ; i != iend; ++i)
ret += i->second.size();
ret += static_cast<int>(i->second.size());
return ret;
}
@ -1673,7 +1673,7 @@ int Detector::numTemplates(const std::string& class_id) const
TemplatesMap::const_iterator i = class_templates.find(class_id);
if (i == class_templates.end())
return 0;
return i->second.size();
return static_cast<int>(i->second.size());
}
std::vector<std::string> Detector::classIds() const

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