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Extending template_matching tutorial with Java (#8043)

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* Extending template_matching tutorial with Java

* adding mask to java version of the tutorial

* adding the python toggle and code

* updating table of content

* adding py and java to table of content

* adding mask to python

* going back to markdown with duplicated text

* non duplicated text
This commit is contained in:
Cartucho
2017-05-11 22:42:04 +01:00
committed by Maksim Shabunin
parent 3b669149d2
commit 2055bcc807
6 changed files with 473 additions and 97 deletions
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samples/cpp/tutorial_code/Histograms_Matching/MatchTemplate_Demo.cpp
+24 -1
View File
@@ -12,6 +12,7 @@
using namespace std;
using namespace cv;
//! [declare]
/// Global Variables
bool use_mask;
Mat img; Mat templ; Mat mask; Mat result;
@@ -20,6 +21,7 @@ const char* result_window = "Result window";
int match_method;
int max_Trackbar = 5;
//! [declare]
/// Function Headers
void MatchingMethod( int, void* );
@@ -36,6 +38,7 @@ int main( int argc, char** argv )
return -1;
}
//! [load_image]
/// Load image and template
img = imread( argv[1], IMREAD_COLOR );
templ = imread( argv[2], IMREAD_COLOR );
@@ -50,19 +53,26 @@ int main( int argc, char** argv )
cout << "Can't read one of the images" << endl;
return -1;
}
//! [load_image]
//! [create_windows]
/// Create windows
namedWindow( image_window, WINDOW_AUTOSIZE );
namedWindow( result_window, WINDOW_AUTOSIZE );
//! [create_windows]
//! [create_trackbar]
/// Create Trackbar
const char* trackbar_label = "Method: \n 0: SQDIFF \n 1: SQDIFF NORMED \n 2: TM CCORR \n 3: TM CCORR NORMED \n 4: TM COEFF \n 5: TM COEFF NORMED";
createTrackbar( trackbar_label, image_window, &match_method, max_Trackbar, MatchingMethod );
//! [create_trackbar]
MatchingMethod( 0, 0 );
//! [wait_key]
waitKey(0);
return 0;
//! [wait_key]
}
/**
@@ -71,44 +81,57 @@ int main( int argc, char** argv )
*/
void MatchingMethod( int, void* )
{
//! [copy_source]
/// Source image to display
Mat img_display;
img.copyTo( img_display );
//! [copy_source]
//! [create_result_matrix]
/// Create the result matrix
int result_cols = img.cols - templ.cols + 1;
int result_rows = img.rows - templ.rows + 1;
result.create( result_rows, result_cols, CV_32FC1 );
//! [create_result_matrix]
//! [match_template]
/// Do the Matching and Normalize
bool method_accepts_mask = (CV_TM_SQDIFF == match_method || match_method == CV_TM_CCORR_NORMED);
if (use_mask && method_accepts_mask)
{ matchTemplate( img, templ, result, match_method, mask); }
else
{ matchTemplate( img, templ, result, match_method); }
//! [match_template]
//! [normalize]
normalize( result, result, 0, 1, NORM_MINMAX, -1, Mat() );
//! [normalize]
//! [best_match]
/// Localizing the best match with minMaxLoc
double minVal; double maxVal; Point minLoc; Point maxLoc;
Point matchLoc;
minMaxLoc( result, &minVal, &maxVal, &minLoc, &maxLoc, Mat() );
//! [best_match]
//! [match_loc]
/// For SQDIFF and SQDIFF_NORMED, the best matches are lower values. For all the other methods, the higher the better
if( match_method == TM_SQDIFF || match_method == TM_SQDIFF_NORMED )
{ matchLoc = minLoc; }
else
{ matchLoc = maxLoc; }
//! [match_loc]
//! [imshow]
/// Show me what you got
rectangle( img_display, matchLoc, Point( matchLoc.x + templ.cols , matchLoc.y + templ.rows ), Scalar::all(0), 2, 8, 0 );
rectangle( result, matchLoc, Point( matchLoc.x + templ.cols , matchLoc.y + templ.rows ), Scalar::all(0), 2, 8, 0 );
imshow( image_window, img_display );
imshow( result_window, result );
//! [imshow]
return;
}