Added console app

git-svn-id: http://find-object.googlecode.com/svn/trunk/find_object@191 620bd6b2-0a58-f614-fd9a-1bd335dccda9
This commit is contained in:
matlabbe 2013-04-25 00:11:56 +00:00
parent 7921affc14
commit 2ca50933d2
3 changed files with 198 additions and 3 deletions

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@ -39,7 +39,7 @@ SET(CMAKE_ARCHIVE_OUTPUT_DIRECTORY ${PROJECT_SOURCE_DIR}/lib)
####### DEPENDENCIES #######
FIND_PACKAGE(OpenCV REQUIRED) # tested on 2.3.1
FIND_PACKAGE(Qt4 COMPONENTS QtCore QtGui) # tested on Qt4.7
#FIND_PACKAGE(Qt4 COMPONENTS QtCore QtGui) # tested on Qt4.7
####### OSX BUNDLE CMAKE_INSTALL_PREFIX #######
OPTION(BUILD_AS_BUNDLE "Set to ON to build as bundle (DragNDrop)" OFF)
@ -60,8 +60,9 @@ IF(APPLE AND BUILD_AS_BUNDLE)
ENDIF(APPLE AND BUILD_AS_BUNDLE)
####### SOURCES (Projects) #######
ADD_SUBDIRECTORY( app )
ADD_SUBDIRECTORY( example )
#ADD_SUBDIRECTORY( app )
#ADD_SUBDIRECTORY( example )
ADD_SUBDIRECTORY( console_app )

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@ -0,0 +1,25 @@
SET(SRC_FILES
main.cpp
)
SET(INCLUDE_DIRS
${OpenCV_INCLUDE_DIRS}
)
SET(LIBRARIES
${OpenCV_LIBS}
)
# Make sure the compiler can find include files from our library.
INCLUDE_DIRECTORIES(${INCLUDE_DIRS})
# Add binary called "console" that is built from the source file "main.cpp".
# The extension is automatically found.
ADD_EXECUTABLE(console ${SRC_FILES})
TARGET_LINK_LIBRARIES(console ${LIBRARIES})
SET_TARGET_PROPERTIES( console
PROPERTIES OUTPUT_NAME ${PROJECT_PREFIX}-console)

169
console_app/main.cpp Normal file
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@ -0,0 +1,169 @@
#include <stdio.h>
#include <stdlib.h>
// OpenCV stuff
#include <opencv2/core/core.hpp>
#include <opencv2/highgui/highgui.hpp>
#include <opencv2/features2d/features2d.hpp>
#include <opencv2/nonfree/features2d.hpp>
#include <opencv2/calib3d/calib3d.hpp> // for homography
void showUsage()
{
printf(
"\n"
"Return similarity between two images.\n"
"Usage :\n"
" ./find_object-console [option] object.png scene.png\n"
"Options: \n"
" -total return total matches (default total)\n"
" -inliers return inliers percentage : inliers / (inliers + outliers)\n"
" -quiet don't show messages\n");
exit(-1);
}
enum {mTotal, mInliers};
int main(int argc, char * argv[])
{
bool quiet = false;
int method = mTotal; //total matches
if(argc<3)
{
printf("Two images required!\n");
showUsage();
}
else if(argc>3)
{
for(int i=1; i<argc-2; ++i)
{
if(std::string(argv[1]).compare("-total") == 0)
{
method = mTotal;
}
else if(std::string(argv[1]).compare("-inliers") == 0)
{
method = mInliers;
}
else if(std::string(argv[1]).compare("-quiet") == 0)
{
quiet = true;
}
else
{
printf("Option %s not recognized!", argv[1]);
showUsage();
}
}
}
//Load as grayscale
cv::Mat objectImg = cv::imread(argv[argc-2], cv::IMREAD_GRAYSCALE);
cv::Mat sceneImg = cv::imread(argv[argc-1], cv::IMREAD_GRAYSCALE);
int value = 0;
if(!objectImg.empty() && !sceneImg.empty())
{
std::vector<cv::KeyPoint> objectKeypoints;
std::vector<cv::KeyPoint> sceneKeypoints;
cv::Mat objectDescriptors;
cv::Mat sceneDescriptors;
////////////////////////////
// EXTRACT KEYPOINTS
////////////////////////////
cv::SIFT sift;
sift.detect(objectImg, objectKeypoints);
sift.detect(sceneImg, sceneKeypoints);
////////////////////////////
// EXTRACT DESCRIPTORS
////////////////////////////
sift.compute(objectImg, objectKeypoints, objectDescriptors);
sift.compute(sceneImg, sceneKeypoints, sceneDescriptors);
////////////////////////////
// NEAREST NEIGHBOR MATCHING USING FLANN LIBRARY (included in OpenCV)
////////////////////////////
cv::Mat results;
cv::Mat dists;
std::vector<std::vector<cv::DMatch> > matches;
int k=2; // find the 2 nearest neighbors
// Create Flann KDTree index
cv::flann::Index flannIndex(sceneDescriptors, cv::flann::KDTreeIndexParams(), cvflann::FLANN_DIST_EUCLIDEAN);
results = cv::Mat(objectDescriptors.rows, k, CV_32SC1); // Results index
dists = cv::Mat(objectDescriptors.rows, k, CV_32FC1); // Distance results are CV_32FC1
// search (nearest neighbor)
flannIndex.knnSearch(objectDescriptors, results, dists, k, cv::flann::SearchParams() );
////////////////////////////
// PROCESS NEAREST NEIGHBOR RESULTS
////////////////////////////
// Find correspondences by NNDR (Nearest Neighbor Distance Ratio)
float nndrRatio = 0.6;
std::vector<cv::Point2f> mpts_1, mpts_2; // Used for homography
std::vector<int> indexes_1, indexes_2; // Used for homography
std::vector<uchar> outlier_mask; // Used for homography
// Check if this descriptor matches with those of the objects
for(int i=0; i<objectDescriptors.rows; ++i)
{
// Apply NNDR
if(dists.at<float>(i,0) <= nndrRatio * dists.at<float>(i,1))
{
mpts_1.push_back(objectKeypoints.at(i).pt);
indexes_1.push_back(i);
mpts_2.push_back(sceneKeypoints.at(results.at<int>(i,0)).pt);
indexes_2.push_back(results.at<int>(i,0));
}
}
if(method == mInliers)
{
// FIND HOMOGRAPHY
unsigned int minInliers = 8;
if(mpts_1.size() >= minInliers)
{
cv::Mat H = findHomography(mpts_1,
mpts_2,
cv::RANSAC,
1.0,
outlier_mask);
int inliers=0, outliers=0;
for(unsigned int k=0; k<mpts_1.size();++k)
{
if(outlier_mask.at(k))
{
++inliers;
}
else
{
++outliers;
}
}
if(!quiet)
printf("Total=%d Inliers=%d Outliers=%d\n", (int)mpts_1.size(), inliers, outliers);
value = (inliers*100) / (inliers+outliers);
}
}
else
{
value = mpts_1.size();
}
}
else
{
printf("Images are not valid!\n");
showUsage();
}
if(!quiet)
printf("Similarity = %d\n", value);
return value;
}