Struct opencv::types::PtrOfFilterEngine
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pub struct PtrOfFilterEngine {
pub ptr: *mut c_void,
}The Main Class for Image Filtering.
The class can be used to apply an arbitrary filtering operation to an image. It contains all the necessary intermediate buffers, it computes extrapolated values of the "virtual" pixels outside of the image etc. Pointers to the initialized cv::FilterEngine instances are returned by various OpenCV functions, such as cv::createSeparableLinearFilter(), cv::createLinearFilter(), cv::createGaussianFilter(), cv::createDerivFilter(), cv::createBoxFilter() and cv::createMorphologyFilter().
Using the class you can process large images by parts and build complex pipelines that include filtering as some of the stages. If all you need is to apply some pre-defined filtering operation, you may use cv::filter2D(), cv::erode(), cv::dilate() etc. functions that create FilterEngine internally.
Here is the example on how to use the class to implement Laplacian operator, which is the sum of second-order derivatives. More complex variant for different types is implemented in cv::Laplacian().
\code void laplace_f(const Mat& src, Mat& dst) { CV_Assert( src.type() == CV_32F ); // make sure the destination array has the proper size and type dst.create(src.size(), src.type());
// get the derivative and smooth kernels for d2I/dx2. // for d2I/dy2 we could use the same kernels, just swapped Mat kd, ks; getSobelKernels( kd, ks, 2, 0, ksize, false, ktype );
// let's process 10 source rows at once
int DELTA = std::min(10, src.rows);
Ptr
int y = Fxx->start(src), dsty = 0, dy = 0; Fyy->start(src); const uchar* sptr = src.data + y*src.step;
// allocate the buffers for the spatial image derivatives; // the buffers need to have more than DELTA rows, because at the // last iteration the output may take max(kd.rows-1,ks.rows-1) // rows more than the input. Mat Ixx( DELTA + kd.rows - 1, src.cols, dst.type() ); Mat Iyy( DELTA + kd.rows - 1, src.cols, dst.type() );
// inside the loop we always pass DELTA rows to the filter // (note that the "proceed" method takes care of possibe overflow, since // it was given the actual image height in the "start" method) // on output we can get: // * < DELTA rows (the initial buffer accumulation stage) // * = DELTA rows (settled state in the middle) // * > DELTA rows (then the input image is over, but we generate // "virtual" rows using the border mode and filter them) // this variable number of output rows is dy. // dsty is the current output row. // sptr is the pointer to the first input row in the portion to process for( ; dsty < dst.rows; sptr += DELTA*src.step, dsty += dy ) { Fxx->proceed( sptr, (int)src.step, DELTA, Ixx.data, (int)Ixx.step ); dy = Fyy->proceed( sptr, (int)src.step, DELTA, d2y.data, (int)Iyy.step ); if( dy > 0 ) { Mat dstripe = dst.rowRange(dsty, dsty + dy); add(Ixx.rowRange(0, dy), Iyy.rowRange(0, dy), dstripe); } } } \endcode
Fields
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