cleanup moved console_app to tools/similarity
git-svn-id: http://find-object.googlecode.com/svn/trunk/find_object@363 620bd6b2-0a58-f614-fd9a-1bd335dccda9
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matlabbe
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SET(SRC_FILES
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main.cpp
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)
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SET(INCLUDE_DIRS
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${OpenCV_INCLUDE_DIRS}
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)
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SET(LIBRARIES
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${OpenCV_LIBS}
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)
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# Make sure the compiler can find include files from our library.
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INCLUDE_DIRECTORIES(${INCLUDE_DIRS})
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# Add binary called "console" that is built from the source file "main.cpp".
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# The extension is automatically found.
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ADD_EXECUTABLE(console ${SRC_FILES})
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TARGET_LINK_LIBRARIES(console ${LIBRARIES})
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SET_TARGET_PROPERTIES( console
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PROPERTIES OUTPUT_NAME ${PROJECT_PREFIX}-console)
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INSTALL(TARGETS console
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RUNTIME DESTINATION bin COMPONENT runtime
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BUNDLE DESTINATION "${CMAKE_BUNDLE_LOCATION}" COMPONENT runtime)
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@ -1,164 +0,0 @@
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#include <stdio.h>
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#include <stdlib.h>
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// OpenCV stuff
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#include <opencv2/core/core.hpp>
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#include <opencv2/highgui/highgui.hpp>
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#include <opencv2/features2d/features2d.hpp>
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#include <opencv2/nonfree/features2d.hpp>
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#include <opencv2/calib3d/calib3d.hpp> // for homography
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void showUsage()
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{
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printf(
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"\n"
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"Return similarity between two images (the number of similar features between the images).\n"
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"Usage :\n"
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" ./find_object-console [option] object.png scene.png\n"
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"Options: \n"
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" -inliers return inliers percentage : inliers / (inliers + outliers)\n"
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" -quiet don't show messages\n");
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exit(-1);
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}
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enum {mTotal, mInliers};
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int main(int argc, char * argv[])
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{
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bool quiet = false;
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int method = mTotal; //total matches
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if(argc<3)
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{
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printf("Two images required!\n");
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showUsage();
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}
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else if(argc>3)
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{
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for(int i=1; i<argc-2; ++i)
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{
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if(std::string(argv[i]).compare("-inliers") == 0)
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{
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method = mInliers;
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}
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else if(std::string(argv[i]).compare("-quiet") == 0)
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{
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quiet = true;
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}
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else
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{
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printf("Option %s not recognized!", argv[1]);
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showUsage();
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}
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}
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}
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//Load as grayscale
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cv::Mat objectImg = cv::imread(argv[argc-2], cv::IMREAD_GRAYSCALE);
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cv::Mat sceneImg = cv::imread(argv[argc-1], cv::IMREAD_GRAYSCALE);
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int value = 0;
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if(!objectImg.empty() && !sceneImg.empty())
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{
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std::vector<cv::KeyPoint> objectKeypoints;
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std::vector<cv::KeyPoint> sceneKeypoints;
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cv::Mat objectDescriptors;
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cv::Mat sceneDescriptors;
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////////////////////////////
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// EXTRACT KEYPOINTS
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////////////////////////////
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cv::SIFT sift;
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sift.detect(objectImg, objectKeypoints);
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sift.detect(sceneImg, sceneKeypoints);
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////////////////////////////
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// EXTRACT DESCRIPTORS
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////////////////////////////
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sift.compute(objectImg, objectKeypoints, objectDescriptors);
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sift.compute(sceneImg, sceneKeypoints, sceneDescriptors);
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////////////////////////////
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// NEAREST NEIGHBOR MATCHING USING FLANN LIBRARY (included in OpenCV)
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////////////////////////////
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cv::Mat results;
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cv::Mat dists;
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std::vector<std::vector<cv::DMatch> > matches;
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int k=2; // find the 2 nearest neighbors
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// Create Flann KDTree index
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cv::flann::Index flannIndex(sceneDescriptors, cv::flann::KDTreeIndexParams(), cvflann::FLANN_DIST_EUCLIDEAN);
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results = cv::Mat(objectDescriptors.rows, k, CV_32SC1); // Results index
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dists = cv::Mat(objectDescriptors.rows, k, CV_32FC1); // Distance results are CV_32FC1
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// search (nearest neighbor)
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flannIndex.knnSearch(objectDescriptors, results, dists, k, cv::flann::SearchParams() );
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////////////////////////////
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// PROCESS NEAREST NEIGHBOR RESULTS
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////////////////////////////
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// Find correspondences by NNDR (Nearest Neighbor Distance Ratio)
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float nndrRatio = 0.6f;
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std::vector<cv::Point2f> mpts_1, mpts_2; // Used for homography
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std::vector<int> indexes_1, indexes_2; // Used for homography
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std::vector<uchar> outlier_mask; // Used for homography
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// Check if this descriptor matches with those of the objects
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for(int i=0; i<objectDescriptors.rows; ++i)
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{
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// Apply NNDR
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if(dists.at<float>(i,0) <= nndrRatio * dists.at<float>(i,1))
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{
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mpts_1.push_back(objectKeypoints.at(i).pt);
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indexes_1.push_back(i);
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mpts_2.push_back(sceneKeypoints.at(results.at<int>(i,0)).pt);
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indexes_2.push_back(results.at<int>(i,0));
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}
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}
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if(method == mInliers)
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{
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// FIND HOMOGRAPHY
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unsigned int minInliers = 8;
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if(mpts_1.size() >= minInliers)
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{
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cv::Mat H = findHomography(mpts_1,
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mpts_2,
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cv::RANSAC,
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1.0,
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outlier_mask);
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int inliers=0, outliers=0;
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for(unsigned int k=0; k<mpts_1.size();++k)
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{
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if(outlier_mask.at(k))
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{
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++inliers;
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}
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else
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{
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++outliers;
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}
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}
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if(!quiet)
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printf("Total=%d Inliers=%d Outliers=%d\n", (int)mpts_1.size(), inliers, outliers);
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value = (inliers*100) / (inliers+outliers);
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}
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}
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else
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{
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value = mpts_1.size();
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}
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}
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else
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{
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printf("Images are not valid!\n");
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showUsage();
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}
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if(!quiet)
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printf("Similarity = %d\n", value);
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return value;
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}
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