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added histograms comparison into opencv_stitching

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This commit is contained in:
Alexey Spizhevoy
2011-05-20 07:32:25 +00:00
parent 63ac784ea0
commit 767a6e8e78
5 changed files with 65 additions and 15 deletions
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50
modules/stitching/matchers.cpp
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@@ -1,5 +1,6 @@
#include <algorithm>
#include <functional>
#include <opencv2/highgui/highgui.hpp>
#include <opencv2/calib3d/calib3d.hpp>
#include <opencv2/gpu/gpu.hpp>
#include "matchers.hpp"
@@ -9,6 +10,31 @@ using namespace std;
using namespace cv;
using namespace cv::gpu;
//////////////////////////////////////////////////////////////////////////////
void FeaturesFinder::operator ()(const vector<Mat> &images, vector<ImageFeatures> &features)
{
features.resize(images.size());
// Calculate histograms
for (size_t i = 0; i < images.size(); ++i)
{
Mat hsv;
cvtColor(images[i], hsv, CV_BGR2HSV);
int hbins = 30, sbins = 32, vbins = 30;
int hist_size[] = { hbins, sbins, vbins };
float hranges[] = { 0, 180 };
float sranges[] = { 0, 256 };
float vranges[] = { 0, 256 };
const float* ranges[] = { hranges, sranges, vranges };
int channels[] = { 0, 1, 2 };
calcHist(&hsv, 1, channels, Mat(), features[i].hist, 3, hist_size, ranges);
}
find(images, features);
}
//////////////////////////////////////////////////////////////////////////////
namespace
@@ -19,7 +45,7 @@ namespace
inline CpuSurfFeaturesFinder(double hess_thresh, int num_octaves, int num_layers,
int num_octaves_descr, int num_layers_descr)
{
detector_ = new /*FastFeatureDetector;*/SurfFeatureDetector(hess_thresh, num_octaves, num_layers);
detector_ = new SurfFeatureDetector(hess_thresh, num_octaves, num_layers);
extractor_ = new SurfDescriptorExtractor(num_octaves_descr, num_layers_descr);
}
@@ -149,14 +175,32 @@ void FeaturesMatcher::operator ()(const vector<Mat> &images, const vector<ImageF
for (size_t i = 0; i < images.size(); ++i)
{
LOGLN("Processing image " << i << "... ");
for (size_t j = 0; j < images.size(); ++j)
for (size_t j = i + 1; j < images.size(); ++j)
{
if (i == j)
// Save time by ignoring poor pairs
if (compareHist(features[i].hist, features[j].hist, CV_COMP_INTERSECT)
< min(images[i].size().area(), images[j].size().area()) * 0.4)
{
//LOGLN("Ignoring (" << i << ", " << j << ") pair...");
continue;
}
size_t pair_idx = i * images.size() + j;
(*this)(images[i], features[i], images[j], features[j], pairwise_matches[pair_idx]);
pairwise_matches[pair_idx].src_img_idx = i;
pairwise_matches[pair_idx].dst_img_idx = j;
// Set up dual pair matches info
size_t dual_pair_idx = j * images.size() + i;
pairwise_matches[dual_pair_idx] = pairwise_matches[pair_idx];
pairwise_matches[dual_pair_idx].src_img_idx = j;
pairwise_matches[dual_pair_idx].dst_img_idx = i;
if (!pairwise_matches[pair_idx].H.empty())
pairwise_matches[dual_pair_idx].H = pairwise_matches[pair_idx].H.inv();
for (size_t i = 0; i < pairwise_matches[dual_pair_idx].matches.size(); ++i)
swap(pairwise_matches[dual_pair_idx].matches[i].queryIdx,
pairwise_matches[dual_pair_idx].matches[i].trainIdx);
}
}
}