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~40 warnings under VS2008

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HAVE_CONFIG_H -> HAVE_CVCONFIG_H
This commit is contained in:
Anatoly Baksheev
2011-06-11 17:24:09 +00:00
parent dc8572dc7b
commit 8f4c7db3f6
33 changed files with 343 additions and 308 deletions
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443
modules/features2d/src/blobdetector.cpp
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@@ -1,44 +1,44 @@
/*M///////////////////////////////////////////////////////////////////////////////////////
//
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
// By downloading, copying, installing or using the software you agree to this license.
// If you do not agree to this license, do not download, install,
// copy or use the software.
//
//
// License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
// Third party copyrights are property of their respective owners.
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
//
// * Redistribution's in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
//
// * The name of the copyright holders may not be used to endorse or promote products
// derived from this software without specific prior written permission.
//
// This software is provided by the copyright holders and contributors "as is" and
// any express or implied warranties, including, but not limited to, the implied
// warranties of merchantability and fitness for a particular purpose are disclaimed.
// In no event shall the Intel Corporation or contributors be liable for any direct,
// indirect, incidental, special, exemplary, or consequential damages
// (including, but not limited to, procurement of substitute goods or services;
// loss of use, data, or profits; or business interruption) however caused
// and on any theory of liability, whether in contract, strict liability,
// or tort (including negligence or otherwise) arising in any way out of
// the use of this software, even if advised of the possibility of such damage.
//
//M*/
//
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
// By downloading, copying, installing or using the software you agree to this license.
// If you do not agree to this license, do not download, install,
// copy or use the software.
//
//
// License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
// Third party copyrights are property of their respective owners.
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
//
// * Redistribution's in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
//
// * The name of the copyright holders may not be used to endorse or promote products
// derived from this software without specific prior written permission.
//
// This software is provided by the copyright holders and contributors "as is" and
// any express or implied warranties, including, but not limited to, the implied
// warranties of merchantability and fitness for a particular purpose are disclaimed.
// In no event shall the Intel Corporation or contributors be liable for any direct,
// indirect, incidental, special, exemplary, or consequential damages
// (including, but not limited to, procurement of substitute goods or services;
// loss of use, data, or profits; or business interruption) however caused
// and on any theory of liability, whether in contract, strict liability,
// or tort (including negligence or otherwise) arising in any way out of
// the use of this software, even if advised of the possibility of such damage.
//
//M*/
#include "precomp.hpp"
#include <iterator>
@@ -52,241 +52,242 @@
using namespace cv;
/*
* SimpleBlobDetector
*/
* SimpleBlobDetector
*/
SimpleBlobDetector::Params::Params()
{
thresholdStep = 10;
minThreshold = 50;
maxThreshold = 220;
minRepeatability = 2;
minDistBetweenBlobs = 10;
thresholdStep = 10;
minThreshold = 50;
maxThreshold = 220;
minRepeatability = 2;
minDistBetweenBlobs = 10;
filterByColor = true;
blobColor = 0;
filterByColor = true;
blobColor = 0;
filterByArea = true;
minArea = 25;
maxArea = 5000;
filterByArea = true;
minArea = 25;
maxArea = 5000;
filterByCircularity = false;
minCircularity = 0.8f;
maxCircularity = std::numeric_limits<float>::max();
filterByCircularity = false;
minCircularity = 0.8f;
maxCircularity = std::numeric_limits<float>::max();
filterByInertia = true;
//minInertiaRatio = 0.6;
minInertiaRatio = 0.1f;
maxInertiaRatio = std::numeric_limits<float>::max();
filterByInertia = true;
//minInertiaRatio = 0.6;
minInertiaRatio = 0.1f;
maxInertiaRatio = std::numeric_limits<float>::max();
filterByConvexity = true;
//minConvexity = 0.8;
minConvexity = 0.95f;
maxConvexity = std::numeric_limits<float>::max();
filterByConvexity = true;
//minConvexity = 0.8;
minConvexity = 0.95f;
maxConvexity = std::numeric_limits<float>::max();
}
SimpleBlobDetector::SimpleBlobDetector(const SimpleBlobDetector::Params &parameters) :
params(parameters)
params(parameters)
{
}
void SimpleBlobDetector::findBlobs(const cv::Mat &image, const cv::Mat &binaryImage, vector<Center> &centers) const
{
centers.clear();
(void)image;
centers.clear();
vector < vector<Point> > contours;
Mat tmpBinaryImage = binaryImage.clone();
findContours(tmpBinaryImage, contours, CV_RETR_LIST, CV_CHAIN_APPROX_NONE);
vector < vector<Point> > contours;
Mat tmpBinaryImage = binaryImage.clone();
findContours(tmpBinaryImage, contours, CV_RETR_LIST, CV_CHAIN_APPROX_NONE);
#ifdef DEBUG_BLOB_DETECTOR
// Mat keypointsImage;
// cvtColor( binaryImage, keypointsImage, CV_GRAY2RGB );
//
// Mat contoursImage;
// cvtColor( binaryImage, contoursImage, CV_GRAY2RGB );
// drawContours( contoursImage, contours, -1, Scalar(0,255,0) );
// imshow("contours", contoursImage );
// Mat keypointsImage;
// cvtColor( binaryImage, keypointsImage, CV_GRAY2RGB );
//
// Mat contoursImage;
// cvtColor( binaryImage, contoursImage, CV_GRAY2RGB );
// drawContours( contoursImage, contours, -1, Scalar(0,255,0) );
// imshow("contours", contoursImage );
#endif
for (size_t contourIdx = 0; contourIdx < contours.size(); contourIdx++)
{
Center center;
center.confidence = 1;
Moments moms = moments(Mat(contours[contourIdx]));
if (params.filterByArea)
{
double area = moms.m00;
if (area < params.minArea || area >= params.maxArea)
continue;
}
for (size_t contourIdx = 0; contourIdx < contours.size(); contourIdx++)
{
Center center;
center.confidence = 1;
Moments moms = moments(Mat(contours[contourIdx]));
if (params.filterByArea)
{
double area = moms.m00;
if (area < params.minArea || area >= params.maxArea)
continue;
}
if (params.filterByCircularity)
{
double area = moms.m00;
double perimeter = arcLength(Mat(contours[contourIdx]), true);
double ratio = 4 * CV_PI * area / (perimeter * perimeter);
if (ratio < params.minCircularity || ratio >= params.maxCircularity)
continue;
}
if (params.filterByCircularity)
{
double area = moms.m00;
double perimeter = arcLength(Mat(contours[contourIdx]), true);
double ratio = 4 * CV_PI * area / (perimeter * perimeter);
if (ratio < params.minCircularity || ratio >= params.maxCircularity)
continue;
}
if (params.filterByInertia)
{
double denominator = sqrt(pow(2 * moms.mu11, 2) + pow(moms.mu20 - moms.mu02, 2));
const double eps = 1e-2;
double ratio;
if (denominator > eps)
{
double cosmin = (moms.mu20 - moms.mu02) / denominator;
double sinmin = 2 * moms.mu11 / denominator;
double cosmax = -cosmin;
double sinmax = -sinmin;
if (params.filterByInertia)
{
double denominator = sqrt(pow(2 * moms.mu11, 2) + pow(moms.mu20 - moms.mu02, 2));
const double eps = 1e-2;
double ratio;
if (denominator > eps)
{
double cosmin = (moms.mu20 - moms.mu02) / denominator;
double sinmin = 2 * moms.mu11 / denominator;
double cosmax = -cosmin;
double sinmax = -sinmin;
double imin = 0.5 * (moms.mu20 + moms.mu02) - 0.5 * (moms.mu20 - moms.mu02) * cosmin - moms.mu11 * sinmin;
double imax = 0.5 * (moms.mu20 + moms.mu02) - 0.5 * (moms.mu20 - moms.mu02) * cosmax - moms.mu11 * sinmax;
ratio = imin / imax;
}
else
{
ratio = 1;
}
double imin = 0.5 * (moms.mu20 + moms.mu02) - 0.5 * (moms.mu20 - moms.mu02) * cosmin - moms.mu11 * sinmin;
double imax = 0.5 * (moms.mu20 + moms.mu02) - 0.5 * (moms.mu20 - moms.mu02) * cosmax - moms.mu11 * sinmax;
ratio = imin / imax;
}
else
{
ratio = 1;
}
if (ratio < params.minInertiaRatio || ratio >= params.maxInertiaRatio)
continue;
if (ratio < params.minInertiaRatio || ratio >= params.maxInertiaRatio)
continue;
center.confidence = ratio * ratio;
}
center.confidence = ratio * ratio;
}
if (params.filterByConvexity)
{
vector < Point > hull;
convexHull(Mat(contours[contourIdx]), hull);
double area = contourArea(Mat(contours[contourIdx]));
double hullArea = contourArea(Mat(hull));
double ratio = area / hullArea;
if (ratio < params.minConvexity || ratio >= params.maxConvexity)
continue;
}
if (params.filterByConvexity)
{
vector < Point > hull;
convexHull(Mat(contours[contourIdx]), hull);
double area = contourArea(Mat(contours[contourIdx]));
double hullArea = contourArea(Mat(hull));
double ratio = area / hullArea;
if (ratio < params.minConvexity || ratio >= params.maxConvexity)
continue;
}
center.location = Point2d(moms.m10 / moms.m00, moms.m01 / moms.m00);
center.location = Point2d(moms.m10 / moms.m00, moms.m01 / moms.m00);
if (params.filterByColor)
{
if (binaryImage.at<uchar> (cvRound(center.location.y), cvRound(center.location.x)) != params.blobColor)
continue;
}
if (params.filterByColor)
{
if (binaryImage.at<uchar> (cvRound(center.location.y), cvRound(center.location.x)) != params.blobColor)
continue;
}
//compute blob radius
{
vector<double> dists;
for (size_t pointIdx = 0; pointIdx < contours[contourIdx].size(); pointIdx++)
{
Point2d pt = contours[contourIdx][pointIdx];
dists.push_back(norm(center.location - pt));
}
std::sort(dists.begin(), dists.end());
center.radius = (dists[(dists.size() - 1) / 2] + dists[dists.size() / 2]) / 2.;
}
//compute blob radius
{
vector<double> dists;
for (size_t pointIdx = 0; pointIdx < contours[contourIdx].size(); pointIdx++)
{
Point2d pt = contours[contourIdx][pointIdx];
dists.push_back(norm(center.location - pt));
}
std::sort(dists.begin(), dists.end());
center.radius = (dists[(dists.size() - 1) / 2] + dists[dists.size() / 2]) / 2.;
}
centers.push_back(center);
centers.push_back(center);
#ifdef DEBUG_BLOB_DETECTOR
// circle( keypointsImage, center.location, 1, Scalar(0,0,255), 1 );
// circle( keypointsImage, center.location, 1, Scalar(0,0,255), 1 );
#endif
}
}
#ifdef DEBUG_BLOB_DETECTOR
// imshow("bk", keypointsImage );
// waitKey();
// imshow("bk", keypointsImage );
// waitKey();
#endif
}
void SimpleBlobDetector::detectImpl(const cv::Mat& image, std::vector<cv::KeyPoint>& keypoints, const cv::Mat&) const
{
//TODO: support mask
keypoints.clear();
Mat grayscaleImage;
if (image.channels() == 3)
cvtColor(image, grayscaleImage, CV_BGR2GRAY);
else
grayscaleImage = image;
//TODO: support mask
keypoints.clear();
Mat grayscaleImage;
if (image.channels() == 3)
cvtColor(image, grayscaleImage, CV_BGR2GRAY);
else
grayscaleImage = image;
vector < vector<Center> > centers;
for (double thresh = params.minThreshold; thresh < params.maxThreshold; thresh += params.thresholdStep)
{
Mat binarizedImage;
threshold(grayscaleImage, binarizedImage, thresh, 255, THRESH_BINARY);
vector < vector<Center> > centers;
for (double thresh = params.minThreshold; thresh < params.maxThreshold; thresh += params.thresholdStep)
{
Mat binarizedImage;
threshold(grayscaleImage, binarizedImage, thresh, 255, THRESH_BINARY);
#ifdef DEBUG_BLOB_DETECTOR
// Mat keypointsImage;
// cvtColor( binarizedImage, keypointsImage, CV_GRAY2RGB );
// Mat keypointsImage;
// cvtColor( binarizedImage, keypointsImage, CV_GRAY2RGB );
#endif
vector < Center > curCenters;
findBlobs(grayscaleImage, binarizedImage, curCenters);
vector < vector<Center> > newCenters;
for (size_t i = 0; i < curCenters.size(); i++)
{
vector < Center > curCenters;
findBlobs(grayscaleImage, binarizedImage, curCenters);
vector < vector<Center> > newCenters;
for (size_t i = 0; i < curCenters.size(); i++)
{
#ifdef DEBUG_BLOB_DETECTOR
// circle(keypointsImage, curCenters[i].location, curCenters[i].radius, Scalar(0,0,255),-1);
// circle(keypointsImage, curCenters[i].location, curCenters[i].radius, Scalar(0,0,255),-1);
#endif
bool isNew = true;
for (size_t j = 0; j < centers.size(); j++)
{
double dist = norm(centers[j][ centers[j].size() / 2 ].location - curCenters[i].location);
isNew = dist >= params.minDistBetweenBlobs && dist >= centers[j][ centers[j].size() / 2 ].radius && dist >= curCenters[i].radius;
if (!isNew)
{
centers[j].push_back(curCenters[i]);
bool isNew = true;
for (size_t j = 0; j < centers.size(); j++)
{
double dist = norm(centers[j][ centers[j].size() / 2 ].location - curCenters[i].location);
isNew = dist >= params.minDistBetweenBlobs && dist >= centers[j][ centers[j].size() / 2 ].radius && dist >= curCenters[i].radius;
if (!isNew)
{
centers[j].push_back(curCenters[i]);
size_t k = centers[j].size() - 1;
while( k > 0 && centers[j][k].radius < centers[j][k-1].radius )
{
centers[j][k] = centers[j][k-1];
k--;
}
centers[j][k] = curCenters[i];
size_t k = centers[j].size() - 1;
while( k > 0 && centers[j][k].radius < centers[j][k-1].radius )
{
centers[j][k] = centers[j][k-1];
k--;
}
centers[j][k] = curCenters[i];
break;
}
}
if (isNew)
{
newCenters.push_back(vector<Center> (1, curCenters[i]));
//centers.push_back(vector<Center> (1, curCenters[i]));
}
}
std::copy(newCenters.begin(), newCenters.end(), std::back_inserter(centers));
break;
}
}
if (isNew)
{
newCenters.push_back(vector<Center> (1, curCenters[i]));
//centers.push_back(vector<Center> (1, curCenters[i]));
}
}
std::copy(newCenters.begin(), newCenters.end(), std::back_inserter(centers));
#ifdef DEBUG_BLOB_DETECTOR
// imshow("binarized", keypointsImage );
//waitKey();
// imshow("binarized", keypointsImage );
//waitKey();
#endif
}
}
for (size_t i = 0; i < centers.size(); i++)
{
if (centers[i].size() < params.minRepeatability)
continue;
Point2d sumPoint(0, 0);
double normalizer = 0;
for (size_t j = 0; j < centers[i].size(); j++)
{
sumPoint += centers[i][j].confidence * centers[i][j].location;
normalizer += centers[i][j].confidence;
}
sumPoint *= (1. / normalizer);
KeyPoint kpt(sumPoint, centers[i][centers[i].size() / 2].radius);
keypoints.push_back(kpt);
}
for (size_t i = 0; i < centers.size(); i++)
{
if (centers[i].size() < params.minRepeatability)
continue;
Point2d sumPoint(0, 0);
double normalizer = 0;
for (size_t j = 0; j < centers[i].size(); j++)
{
sumPoint += centers[i][j].confidence * centers[i][j].location;
normalizer += centers[i][j].confidence;
}
sumPoint *= (1. / normalizer);
KeyPoint kpt(sumPoint, (float)(centers[i][centers[i].size() / 2].radius));
keypoints.push_back(kpt);
}
#ifdef DEBUG_BLOB_DETECTOR
namedWindow("keypoints", CV_WINDOW_NORMAL);
Mat outImg = image.clone();
for(size_t i=0; i<keypoints.size(); i++)
{
circle(outImg, keypoints[i].pt, keypoints[i].size, Scalar(255, 0, 255), -1);
}
//drawKeypoints(image, keypoints, outImg);
imshow("keypoints", outImg);
waitKey();
namedWindow("keypoints", CV_WINDOW_NORMAL);
Mat outImg = image.clone();
for(size_t i=0; i<keypoints.size(); i++)
{
circle(outImg, keypoints[i].pt, keypoints[i].size, Scalar(255, 0, 255), -1);
}
//drawKeypoints(image, keypoints, outImg);
imshow("keypoints", outImg);
waitKey();
#endif
}

2
modules/features2d/src/precomp.hpp
View File

@@ -47,7 +47,7 @@
#pragma warning( disable: 4251 4512 4710 4711 4514 4996 )
#endif
#ifdef HAVE_CONFIG_H
#ifdef HAVE_CVCONFIG_H
#include "cvconfig.h"
#endif

16
modules/features2d/src/sift.cpp
View File

@@ -1111,7 +1111,7 @@ static double*** descr_hist( IplImage* img, int r, int c, double ori,
bins_per_rad = n / PI2;
exp_denom = d * d * 0.5;
hist_width = SIFT_DESCR_SCL_FCTR * scl;
radius = hist_width * sqrt(2.0) * ( d + 1.0 ) * 0.5 + 0.5;
radius = (int)(hist_width * sqrt(2.0) * ( d + 1.0 ) * 0.5 + 0.5);
for( i = -radius; i <= radius; i++ )
for( j = -radius; j <= radius; j++ )
{
@@ -1191,7 +1191,7 @@ static void hist_to_descr( double*** hist, int d, int n, struct feature* feat )
/* convert floating-point descriptor to integer valued descriptor */
for( i = 0; i < k; i++ )
{
int_val = SIFT_INT_DESCR_FCTR * feat->descr[i];
int_val = (int)(SIFT_INT_DESCR_FCTR * feat->descr[i]);
feat->descr[i] = MIN( 255, int_val );
}
}
@@ -1207,7 +1207,7 @@ static void hist_to_descr( double*** hist, int d, int n, struct feature* feat )
@return Returns 1 if feat1's scale is greater than feat2's, -1 if vice versa,
and 0 if their scales are equal
*/
static int feature_cmp( void* feat1, void* feat2, void* param )
static int feature_cmp( void* feat1, void* feat2, void* /*param*/ )
{
struct feature* f1 = (struct feature*) feat1;
struct feature* f2 = (struct feature*) feat2;
@@ -1478,9 +1478,9 @@ struct SiftParams
inline KeyPoint featureToKeyPoint( const feature& feat )
{
float size = feat.scl * SIFT::DescriptorParams::GET_DEFAULT_MAGNIFICATION() * 4; // 4==NBP
float angle = feat.ori * a_180divPI;
return KeyPoint( feat.x, feat.y, size, angle, 0, feat.feature_data->octv, 0 );
float size = (float)(feat.scl * SIFT::DescriptorParams::GET_DEFAULT_MAGNIFICATION() * 4); // 4==NBP
float angle = (float)(feat.ori * a_180divPI);
return KeyPoint( (float)feat.x, (float)feat.y, size, angle, 0, feat.feature_data->octv, 0 );
}
static void fillFeatureData( feature& feat, const SiftParams& params )
@@ -1551,7 +1551,7 @@ static void fillFeatureData( feature& feat, const SiftParams& params )
float s, phi;
phi = static_cast<float>(log( sigma / params.sigma0 ) / log(2.0));
o = std::floor( phi - (float(params.smin)+.5)/params.S );
o = (int)std::floor( phi - (float(params.smin)+.5)/params.S );
o = std::min(o, params.omin+params.O-1);
o = std::max(o, params.omin);
s = params.S * (phi - o);
@@ -1640,7 +1640,7 @@ void SIFT::operator()(const Mat& image, const Mat& mask,
ImagePyrData pyrImages( &img, commParams.nOctaves, commParams.nOctaveLayers, SIFT_SIGMA, SIFT_IMG_DBL );
int feature_count = 0;
compute_features( &pyrImages, &features, feature_count, detectorParams.threshold, detectorParams.edgeThreshold );
compute_features( &pyrImages, &features, feature_count, detectorParams.threshold, (int)detectorParams.edgeThreshold );
// convert to KeyPoint structure
keypoints.resize( feature_count );