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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/Histograms_Matching/EqualizeHist_Demo.cpp
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@@ -31,7 +31,7 @@ int main( int, char** argv )
}
/// Convert to grayscale
cvtColor( src, src, CV_BGR2GRAY );
cvtColor( src, src, COLOR_BGR2GRAY );
/// Apply Histogram Equalization
equalizeHist( src, dst );

2
samples/cpp/tutorial_code/Histograms_Matching/MatchTemplate_Demo.cpp
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@@ -74,7 +74,7 @@ void MatchingMethod( int, void* )
/// For SQDIFF and SQDIFF_NORMED, the best matches are lower values. For all the other methods, the higher the better
if( match_method == CV_TM_SQDIFF || match_method == CV_TM_SQDIFF_NORMED )
if( match_method == TM_SQDIFF || match_method == TM_SQDIFF_NORMED )
{ matchLoc = minLoc; }
else
{ matchLoc = maxLoc; }

2
samples/cpp/tutorial_code/Histograms_Matching/calcBackProject_Demo1.cpp
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@@ -28,7 +28,7 @@ int main( int, char** argv )
/// Read the image
src = imread( argv[1], 1 );
/// Transform it to HSV
cvtColor( src, hsv, CV_BGR2HSV );
cvtColor( src, hsv, COLOR_BGR2HSV );
/// Use only the Hue value
hue.create( hsv.size(), hsv.depth() );

2
samples/cpp/tutorial_code/Histograms_Matching/calcBackProject_Demo2.cpp
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@@ -31,7 +31,7 @@ int main( int, char** argv )
/// Read the image
src = imread( argv[1], 1 );
/// Transform it to HSV
cvtColor( src, hsv, CV_BGR2HSV );
cvtColor( src, hsv, COLOR_BGR2HSV );
/// Show the image
namedWindow( window_image, CV_WINDOW_AUTOSIZE );

6
samples/cpp/tutorial_code/Histograms_Matching/compareHist_Demo.cpp
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@@ -33,9 +33,9 @@ int main( int argc, char** argv )
src_test2 = imread( argv[3], 1 );
/// Convert to HSV
cvtColor( src_base, hsv_base, CV_BGR2HSV );
cvtColor( src_test1, hsv_test1, CV_BGR2HSV );
cvtColor( src_test2, hsv_test2, CV_BGR2HSV );
cvtColor( src_base, hsv_base, COLOR_BGR2HSV );
cvtColor( src_test1, hsv_test1, COLOR_BGR2HSV );
cvtColor( src_test2, hsv_test2, COLOR_BGR2HSV );
hsv_half_down = hsv_base( Range( hsv_base.rows/2, hsv_base.rows - 1 ), Range( 0, hsv_base.cols - 1 ) );

2
samples/cpp/tutorial_code/ImgProc/Threshold.cpp
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@@ -37,7 +37,7 @@ int main( int, char** argv )
src = imread( argv[1], 1 );
/// Convert the image to Gray
cvtColor( src, src_gray, CV_RGB2GRAY );
cvtColor( src, src_gray, COLOR_RGB2GRAY );
/// Create a window to display results
namedWindow( window_name, CV_WINDOW_AUTOSIZE );

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 );

4
samples/cpp/tutorial_code/ShapeDescriptors/findContours_demo.cpp
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@@ -30,7 +30,7 @@ int main( int, char** argv )
src = imread( argv[1], 1 );
/// Convert image to gray and blur it
cvtColor( src, src_gray, CV_BGR2GRAY );
cvtColor( src, src_gray, COLOR_BGR2GRAY );
blur( src_gray, src_gray, Size(3,3) );
/// Create Window
@@ -57,7 +57,7 @@ void thresh_callback(int, void* )
/// Detect edges using canny
Canny( src_gray, canny_output, thresh, thresh*2, 3 );
/// Find contours
findContours( canny_output, contours, hierarchy, CV_RETR_TREE, CV_CHAIN_APPROX_SIMPLE, Point(0, 0) );
findContours( canny_output, contours, hierarchy, RETR_TREE, CHAIN_APPROX_SIMPLE, Point(0, 0) );
/// Draw contours
Mat drawing = Mat::zeros( canny_output.size(), CV_8UC3 );

4
samples/cpp/tutorial_code/ShapeDescriptors/generalContours_demo1.cpp
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@@ -30,7 +30,7 @@ int main( int, char** argv )
src = imread( argv[1], 1 );
/// Convert image to gray and blur it
cvtColor( src, src_gray, CV_BGR2GRAY );
cvtColor( src, src_gray, COLOR_BGR2GRAY );
blur( src_gray, src_gray, Size(3,3) );
/// Create Window
@@ -57,7 +57,7 @@ void thresh_callback(int, void* )
/// Detect edges using Threshold
threshold( src_gray, threshold_output, thresh, 255, THRESH_BINARY );
/// Find contours
findContours( threshold_output, contours, hierarchy, CV_RETR_TREE, CV_CHAIN_APPROX_SIMPLE, Point(0, 0) );
findContours( threshold_output, contours, hierarchy, RETR_TREE, CHAIN_APPROX_SIMPLE, Point(0, 0) );
/// Approximate contours to polygons + get bounding rects and circles
vector<vector<Point> > contours_poly( contours.size() );

4
samples/cpp/tutorial_code/ShapeDescriptors/generalContours_demo2.cpp
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@@ -30,7 +30,7 @@ int main( int, char** argv )
src = imread( argv[1], 1 );
/// Convert image to gray and blur it
cvtColor( src, src_gray, CV_BGR2GRAY );
cvtColor( src, src_gray, COLOR_BGR2GRAY );
blur( src_gray, src_gray, Size(3,3) );
/// Create Window
@@ -57,7 +57,7 @@ void thresh_callback(int, void* )
/// Detect edges using Threshold
threshold( src_gray, threshold_output, thresh, 255, THRESH_BINARY );
/// Find contours
findContours( threshold_output, contours, hierarchy, CV_RETR_TREE, CV_CHAIN_APPROX_SIMPLE, Point(0, 0) );
findContours( threshold_output, contours, hierarchy, RETR_TREE, CHAIN_APPROX_SIMPLE, Point(0, 0) );
/// Find the rotated rectangles and ellipses for each contour
vector<RotatedRect> minRect( contours.size() );

4
samples/cpp/tutorial_code/ShapeDescriptors/hull_demo.cpp
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@@ -30,7 +30,7 @@ int main( int, char** argv )
src = imread( argv[1], 1 );
/// Convert image to gray and blur it
cvtColor( src, src_gray, CV_BGR2GRAY );
cvtColor( src, src_gray, COLOR_BGR2GRAY );
blur( src_gray, src_gray, Size(3,3) );
/// Create Window
@@ -59,7 +59,7 @@ void thresh_callback(int, void* )
threshold( src_gray, threshold_output, thresh, 255, THRESH_BINARY );
/// Find contours
findContours( threshold_output, contours, hierarchy, CV_RETR_TREE, CV_CHAIN_APPROX_SIMPLE, Point(0, 0) );
findContours( threshold_output, contours, hierarchy, RETR_TREE, CHAIN_APPROX_SIMPLE, Point(0, 0) );
/// Find the convex hull object for each contour
vector<vector<Point> >hull( contours.size() );

4
samples/cpp/tutorial_code/ShapeDescriptors/moments_demo.cpp
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@@ -30,7 +30,7 @@ int main( int, char** argv )
src = imread( argv[1], 1 );
/// Convert image to gray and blur it
cvtColor( src, src_gray, CV_BGR2GRAY );
cvtColor( src, src_gray, COLOR_BGR2GRAY );
blur( src_gray, src_gray, Size(3,3) );
/// Create Window
@@ -57,7 +57,7 @@ void thresh_callback(int, void* )
/// Detect edges using canny
Canny( src_gray, canny_output, thresh, thresh*2, 3 );
/// Find contours
findContours( canny_output, contours, hierarchy, CV_RETR_TREE, CV_CHAIN_APPROX_SIMPLE, Point(0, 0) );
findContours( canny_output, contours, hierarchy, RETR_TREE, CHAIN_APPROX_SIMPLE, Point(0, 0) );
/// Get the moments
vector<Moments> mu(contours.size() );

2
samples/cpp/tutorial_code/TrackingMotion/cornerDetector_Demo.cpp
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@@ -40,7 +40,7 @@ int main( int, char** argv )
{
/// Load source image and convert it to gray
src = imread( argv[1], 1 );
cvtColor( src, src_gray, CV_BGR2GRAY );
cvtColor( src, src_gray, COLOR_BGR2GRAY );
/// Set some parameters
int blockSize = 3; int apertureSize = 3;

2
samples/cpp/tutorial_code/TrackingMotion/cornerHarris_Demo.cpp
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@@ -31,7 +31,7 @@ int main( int, char** argv )
{
/// Load source image and convert it to gray
src = imread( argv[1], 1 );
cvtColor( src, src_gray, CV_BGR2GRAY );
cvtColor( src, src_gray, COLOR_BGR2GRAY );
/// Create a window and a trackbar
namedWindow( source_window, CV_WINDOW_AUTOSIZE );

2
samples/cpp/tutorial_code/TrackingMotion/cornerSubPix_Demo.cpp
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@@ -32,7 +32,7 @@ int main( int, char** argv )
{
/// Load source image and convert it to gray
src = imread( argv[1], 1 );
cvtColor( src, src_gray, CV_BGR2GRAY );
cvtColor( src, src_gray, COLOR_BGR2GRAY );
/// Create Window
namedWindow( source_window, CV_WINDOW_AUTOSIZE );

2
samples/cpp/tutorial_code/TrackingMotion/goodFeaturesToTrack_Demo.cpp
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@@ -32,7 +32,7 @@ int main( int, char** argv )
{
/// Load source image and convert it to gray
src = imread( argv[1], 1 );
cvtColor( src, src_gray, CV_BGR2GRAY );
cvtColor( src, src_gray, COLOR_BGR2GRAY );
/// Create Window
namedWindow( source_window, CV_WINDOW_AUTOSIZE );

2
samples/cpp/tutorial_code/calib3d/camera_calibration/camera_calibration.cpp
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@@ -291,7 +291,7 @@ int main(int argc, char* argv[])
if( s.calibrationPattern == Settings::CHESSBOARD)
{
Mat viewGray;
cvtColor(view, viewGray, CV_BGR2GRAY);
cvtColor(view, viewGray, COLOR_BGR2GRAY);
cornerSubPix( viewGray, pointBuf, Size(11,11),
Size(-1,-1), TermCriteria( CV_TERMCRIT_EPS+CV_TERMCRIT_ITER, 30, 0.1 ));
}

4
samples/cpp/tutorial_code/core/interoperability_with_OpenCV_1/interoperability_with_OpenCV_1.cpp
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@@ -46,7 +46,7 @@ int main( int argc, char** argv )
// convert image to YUV color space. The output image will be created automatically.
Mat I_YUV;
cvtColor(I, I_YUV, CV_BGR2YCrCb);
cvtColor(I, I_YUV, COLOR_BGR2YCrCb);
vector<Mat> planes; // Use the STL's vector structure to store multiple Mat objects
split(I_YUV, planes); // split the image into separate color planes (Y U V)
@@ -115,7 +115,7 @@ int main( int argc, char** argv )
merge(planes, I_YUV); // now merge the results back
cvtColor(I_YUV, I, CV_YCrCb2BGR); // and produce the output RGB image
cvtColor(I_YUV, I, COLOR_YCrCb2BGR); // and produce the output RGB image
namedWindow("image with grain", CV_WINDOW_AUTOSIZE); // use this to create images

2
samples/cpp/tutorial_code/objectDetection/objectDetection.cpp
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@@ -68,7 +68,7 @@ void detectAndDisplay( Mat frame )
std::vector<Rect> faces;
Mat frame_gray;
cvtColor( frame, frame_gray, CV_BGR2GRAY );
cvtColor( frame, frame_gray, COLOR_BGR2GRAY );
equalizeHist( frame_gray, frame_gray );
//-- Detect faces
face_cascade.detectMultiScale( frame_gray, faces, 1.1, 2, 0|CV_HAAR_SCALE_IMAGE, Size(30, 30) );

2
samples/cpp/tutorial_code/objectDetection/objectDetection2.cpp
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@@ -68,7 +68,7 @@ void detectAndDisplay( Mat frame )
std::vector<Rect> faces;
Mat frame_gray;
cvtColor( frame, frame_gray, CV_BGR2GRAY );
cvtColor( frame, frame_gray, COLOR_BGR2GRAY );
equalizeHist( frame_gray, frame_gray );
//-- Detect faces