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Merge pull request #55 from oreillymedia/xfeatures2d
Chap16 xfeatures2d
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‎CMakeLists.txt‎

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@@ -69,6 +69,7 @@ add_executable( example_15-04 example_15-04.cpp )
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add_executable( example_15-05 example_15-05.cpp )
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add_executable( example_15-BackgroundSubtractor example_15-BackgroundSubtractor.cpp )
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add_executable( example_16-01 example_16-01.cpp )
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add_executable( example_16-02 example_16-02.cpp )
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add_executable( example_17-01 example_17-01.cpp )
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add_executable( example_17-02 example_17-02.cpp )
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add_executable( example_18-01 example_18-01.cpp )
@@ -146,6 +147,7 @@ target_link_libraries( example_15-04 ${OpenCV_LIBS} )
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target_link_libraries( example_15-05 ${OpenCV_LIBS} )
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target_link_libraries( example_15-BackgroundSubtractor ${OpenCV_LIBS} )
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target_link_libraries( example_16-01 ${OpenCV_LIBS} )
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target_link_libraries( example_16-02 ${OpenCV_LIBS} )
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target_link_libraries( example_17-01 ${OpenCV_LIBS} )
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target_link_libraries( example_17-02 ${OpenCV_LIBS} )
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target_link_libraries( example_18-01 ${OpenCV_LIBS} )

‎example_16-01.cpp‎

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@@ -1,11 +1,14 @@
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// Pyramid L-K optical flow example
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// Example 16-1. Pyramid L-K optical flow
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//
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#include <opencv2/opencv.hpp>
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#include <iostream>
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using namespace std;
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#include <vector>
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#include <opencv2/opencv.hpp>
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static const int MAX_CORNERS = 1000;
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using std::cout;
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using std::endl;
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using std::vector;
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void help( char** argv ) {
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cout << "\nExample 16-1: Pyramid L-K optical flow example.\n" << endl;
@@ -15,90 +18,90 @@ void help( char** argv ) {
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}
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int main(int argc, char** argv) {
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if (argc != 3) {
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help(argv);
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exit(-1);
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}
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// Initialize, load two images from the file system, and
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// allocate the images and other structures we will need for
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// results.
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//
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cv::Mat imgA = cv::imread(argv[1], CV_LOAD_IMAGE_GRAYSCALE);
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cv::Mat imgB = cv::imread(argv[2], CV_LOAD_IMAGE_GRAYSCALE);
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cv::Size img_sz = imgA.size();
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int win_size = 10;
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cv::Mat imgC = cv::imread(argv[2], CV_LOAD_IMAGE_UNCHANGED);
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// The first thing we need to do is get the features
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// we want to track.
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//
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vector< cv::Point2f > cornersA, cornersB;
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const int MAX_CORNERS = 500;
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cv::goodFeaturesToTrack(
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imgA, // Image to track
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cornersA, // Vector of detected corners (output)
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MAX_CORNERS, // Keep up to this many corners
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0.01, // Quality level (percent of maximum)
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5, // Min distance between corners
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cv::noArray(), // Mask
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3, // Block size
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false, // true: Harris, false: Shi-Tomasi
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0.04 // method specific parameter
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);
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19-
if( argc != 3 ) { help( argv ); exit( -1 ); }
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// Initialize, load two images from the file system, and
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// allocate the images and other structures we will need for
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// results.
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//
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cv::Mat imgA = cv::imread( argv[1], CV_LOAD_IMAGE_GRAYSCALE );
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cv::Mat imgB = cv::imread( argv[2], CV_LOAD_IMAGE_GRAYSCALE );
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cv::Size img_sz = imgA.size();
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int win_size = 10;
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cv::Mat imgC = cv::imread( argv[2], CV_LOAD_IMAGE_UNCHANGED );
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// The first thing we need to do is get the features
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// we want to track.
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//
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vector< cv::Point2f > cornersA, cornersB;
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const int MAX_CORNERS = 500;
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cv::goodFeaturesToTrack(
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imgA, // Image to track
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cornersA, // Vector of detected corners (output)
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MAX_CORNERS, // Keep up to this many corners
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0.01, // Quality level (percent of maximum)
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5, // Min distance between corners
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cv::noArray(), // Mask
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3, // Block size
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false, // true: Harris, false: Shi-Tomasi
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0.04 // method specific parameter
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);
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cv::cornerSubPix(
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imgA, // Input image
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cornersA, // Vector of corners (input and output)
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cv::Size(win_size, win_size), // Half side length of search window
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cv::Size(-1,-1), // Half side length of dead zone (-1=none)
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cv::TermCriteria(
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cv::TermCriteria::MAX_ITER | cv::TermCriteria::EPS,
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20, // Maximum number of iterations
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0.03 // Minimum change per iteration
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)
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);
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// Call the Lucas Kanade algorithm
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//
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vector<uchar> features_found;
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cv::calcOpticalFlowPyrLK(
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imgA, // Previous image
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imgB, // Next image
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cornersA, // Previous set of corners (from imgA)
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cornersB, // Next set of corners (from imgB)
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features_found, // Output vector, each is 1 for tracked
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cv::noArray(), // Output vector, lists errors (optional)
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cv::Size( win_size*2+1, win_size*2+1 ), // Search window size
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5, // Maximum pyramid level to construct
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cv::TermCriteria(
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cv::TermCriteria::MAX_ITER | cv::TermCriteria::EPS,
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20, // Maximum number of iterations
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0.3 // Minimum change per iteration
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)
78-
);
51+
cv::cornerSubPix(
52+
imgA, // Input image
53+
cornersA, // Vector of corners (input and output)
54+
cv::Size(win_size, win_size), // Half side length of search window
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cv::Size(-1, -1), // Half side length of dead zone (-1=none)
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cv::TermCriteria(
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cv::TermCriteria::MAX_ITER | cv::TermCriteria::EPS,
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20, // Maximum number of iterations
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0.03 // Minimum change per iteration
60+
)
61+
);
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// Now make some image of what we are looking at:
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// Note that if you want to track cornersB further, i.e.
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// pass them as input to the next calcOpticalFlowPyrLK,
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// you would need to "compress" the vector, i.e., exclude points for which
84-
// features_found[i] == false.
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for( int i = 0; i < (int)cornersA.size(); i++ ) {
86-
if( !features_found[i] )
87-
continue;
88-
line(
89-
imgC, // Draw onto this image
90-
cornersA[i], // Starting here
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cornersB[i], // Ending here
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cv::Scalar(0,255,0), // This color
93-
2, // This many pixels wide
94-
cv::LINE_AA // Draw line in this style
63+
// Call the Lucas Kanade algorithm
64+
//
65+
vector<uchar> features_found;
66+
cv::calcOpticalFlowPyrLK(
67+
imgA, // Previous image
68+
imgB, // Next image
69+
cornersA, // Previous set of corners (from imgA)
70+
cornersB, // Next set of corners (from imgB)
71+
features_found, // Output vector, each is 1 for tracked
72+
cv::noArray(), // Output vector, lists errors (optional)
73+
cv::Size(win_size * 2 + 1, win_size * 2 + 1), // Search window size
74+
5, // Maximum pyramid level to construct
75+
cv::TermCriteria(
76+
cv::TermCriteria::MAX_ITER | cv::TermCriteria::EPS,
77+
20, // Maximum number of iterations
78+
0.3 // Minimum change per iteration
79+
)
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);
96-
}
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98-
cv::imshow( "ImageA", imgA );
99-
cv::imshow( "ImageB", imgB );
100-
cv::imshow( "LK Optical Flow Example", imgC );
101-
cv::waitKey(0);
82+
// Now make some image of what we are looking at:
83+
// Note that if you want to track cornersB further, i.e.
84+
// pass them as input to the next calcOpticalFlowPyrLK,
85+
// you would need to "compress" the vector, i.e., exclude points for which
86+
// features_found[i] == false.
87+
for (int i = 0; i < static_cast<int>(cornersA.size()); ++i) {
88+
if (!features_found[i]) {
89+
continue;
90+
}
91+
line(
92+
imgC, // Draw onto this image
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cornersA[i], // Starting here
94+
cornersB[i], // Ending here
95+
cv::Scalar(0, 255, 0), // This color
96+
1, // This many pixels wide
97+
cv::LINE_AA // Draw line in this style
98+
);
99+
}
100+
101+
cv::imshow("ImageA", imgA);
102+
cv::imshow("ImageB", imgB);
103+
cv::imshow("LK Optical Flow Example", imgC);
104+
cv::waitKey(0);
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103-
return 0;
106+
return 0;
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}

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