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Make imgproc.hpp independent from C API

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This commit is contained in:
Andrey Kamaev
2013-04-06 18:16:51 +04:00
parent 7ac0d86992
commit 288a0634c2
95 changed files with 1400 additions and 1300 deletions
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2
samples/cpp/tutorial_code/ImgTrans/CannyDetector_Demo.cpp
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@@ -58,7 +58,7 @@ int main( int, char** argv )
dst.create( src.size(), src.type() );
/// Convert the image to grayscale
cvtColor( src, src_gray, CV_BGR2GRAY );
cvtColor( src, src_gray, COLOR_BGR2GRAY );
/// Create a window
namedWindow( window_name, CV_WINDOW_AUTOSIZE );

4
samples/cpp/tutorial_code/ImgTrans/HoughCircle_Demo.cpp
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@@ -26,7 +26,7 @@ int main(int, char** argv)
{ return -1; }
/// Convert it to gray
cvtColor( src, src_gray, CV_BGR2GRAY );
cvtColor( src, src_gray, COLOR_BGR2GRAY );
/// Reduce the noise so we avoid false circle detection
GaussianBlur( src_gray, src_gray, Size(9, 9), 2, 2 );
@@ -34,7 +34,7 @@ int main(int, char** argv)
vector<Vec3f> circles;
/// Apply the Hough Transform to find the circles
HoughCircles( src_gray, circles, CV_HOUGH_GRADIENT, 1, src_gray.rows/8, 200, 100, 0, 0 );
HoughCircles( src_gray, circles, HOUGH_GRADIENT, 1, src_gray.rows/8, 200, 100, 0, 0 );
/// Draw the circles detected
for( size_t i = 0; i < circles.size(); i++ )

6
samples/cpp/tutorial_code/ImgTrans/HoughLines_Demo.cpp
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@@ -46,7 +46,7 @@ int main( int, char** argv )
}
/// Pass the image to gray
cvtColor( src, src_gray, CV_RGB2GRAY );
cvtColor( src, src_gray, COLOR_RGB2GRAY );
/// Apply Canny edge detector
Canny( src_gray, edges, 50, 200, 3 );
@@ -85,7 +85,7 @@ void help()
void Standard_Hough( int, void* )
{
vector<Vec2f> s_lines;
cvtColor( edges, standard_hough, CV_GRAY2BGR );
cvtColor( edges, standard_hough, COLOR_GRAY2BGR );
/// 1. Use Standard Hough Transform
HoughLines( edges, s_lines, 1, CV_PI/180, min_threshold + s_trackbar, 0, 0 );
@@ -112,7 +112,7 @@ void Standard_Hough( int, void* )
void Probabilistic_Hough( int, void* )
{
vector<Vec4i> p_lines;
cvtColor( edges, probabilistic_hough, CV_GRAY2BGR );
cvtColor( edges, probabilistic_hough, COLOR_GRAY2BGR );
/// 2. Use Probabilistic Hough Transform
HoughLinesP( edges, p_lines, 1, CV_PI/180, min_threshold + p_trackbar, 30, 10 );

2
samples/cpp/tutorial_code/ImgTrans/Laplace_Demo.cpp
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@@ -34,7 +34,7 @@ int main( int, char** argv )
GaussianBlur( src, src, Size(3,3), 0, 0, BORDER_DEFAULT );
/// Convert the image to grayscale
cvtColor( src, src_gray, CV_RGB2GRAY );
cvtColor( src, src_gray, COLOR_RGB2GRAY );
/// Create window
namedWindow( window_name, CV_WINDOW_AUTOSIZE );

2
samples/cpp/tutorial_code/ImgTrans/Remap_Demo.cpp
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@@ -47,7 +47,7 @@ int main( int, char** argv )
/// Update map_x & map_y. Then apply remap
update_map();
remap( src, dst, map_x, map_y, CV_INTER_LINEAR, BORDER_CONSTANT, Scalar(0, 0, 0) );
remap( src, dst, map_x, map_y, INTER_LINEAR, BORDER_CONSTANT, Scalar(0, 0, 0) );
// Display results
imshow( remap_window, dst );

2
samples/cpp/tutorial_code/ImgTrans/Sobel_Demo.cpp
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@@ -33,7 +33,7 @@ int main( int, char** argv )
GaussianBlur( src, src, Size(3,3), 0, 0, BORDER_DEFAULT );
/// Convert it to gray
cvtColor( src, src_gray, CV_RGB2GRAY );
cvtColor( src, src_gray, COLOR_RGB2GRAY );
/// Create window
namedWindow( window_name, CV_WINDOW_AUTOSIZE );