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/** \file
* \brief Image Processing - Global Operations
*
* See Copyright Notice in im_lib.h
*/
#ifndef __IM_PROCESS_GLO_H
#define __IM_PROCESS_GLO_H
#include "im_image.h"
#if defined(__cplusplus)
extern "C" {
#endif
/** \defgroup transform Other Domain Transform Operations
* \par
* Hough, Distance.
*
* See \ref im_process_glo.h
* \ingroup process */
/** Hough Lines Transform. \n
* It will detect white lines in a black background. So the source image must be a IM_BINARY image
* with the white lines of interest enhanced. The better the threshold with the white lines the better
* the line detection. \n
* The destiny image must have IM_GRAY, IM_INT, hg_width=180, hg_height=2*rmax+1,
* where rmax is the image diagonal/2 (rmax = srqrt(width*width + height*height)). \n
* The hough transform defines "cos(theta) * X + sin(theta) * Y = rho" and the parameters are in the interval: \n
* theta = "0 .. 179", rho = "-hg_height/2 .. hg_height/2" .\n
* Where rho is the perpendicular distance from the center of the image and theta the angle with the normal.
* So do not confuse theta with the line angle, they are perpendicular. \n
* Returns zero if the counter aborted. \n
* Inspired from ideas in XITE, Copyright 1991, Blab, UiO \n
* http://www.ifi.uio.no/~blab/Software/Xite/
*
* \verbatim im.ProcessHoughLines(src_image: imImage, dst_image: imImage) -> counter: boolean [in Lua 5] \endverbatim
* \verbatim im.ProcessHoughLinesNew(image: imImage) -> counter: boolean, new_image: imImage [in Lua 5] \endverbatim
* \ingroup transform */
int imProcessHoughLines(const imImage* src_image, imImage* dst_image);
/** Draw detected hough lines. \n
* The source image must be IM_GRAY and IM_BYTE. The destiny image can be a clone of the source image or
* it can be the source image for in place processing. \n
* If the hough transform is not NULL, then the hough points are filtered to include only lines
* that are significally different from each other. \n
* The hough image is the hough transform image, but it is optional and can be NULL.
* If not NULL then it will be used to filter lines that are very similar. \n
* The hough points image is a hough transform image that was thresholded to a IM_BINARY image,
* usually using a Local Max threshold operation (see \ref imProcessLocalMaxThreshold). Again the better the threshold the better the results. \n
* The destiny image will be set to IM_MAP, and the detected lines will be drawn using a red color. \n
* Returns the number of detected lines.
*
* \verbatim im.ProcessHoughLinesDraw(src_image: imImage, hough: imImage, hough_points: imImage, dst_image: imImage) -> lines: number [in Lua 5] \endverbatim
* \verbatim im.ProcessHoughLinesDrawNew(image: imImage, hough: imImage, hough_points: imImage) -> lines: number, new_image: imImage [in Lua 5] \endverbatim
* \ingroup transform */
int imProcessHoughLinesDraw(const imImage* src_image, const imImage* hough, const imImage* hough_points, imImage* dst_image);
/** Calculates the Cross Correlation in the frequency domain. \n
* CrossCorr(a,b) = IFFT(Conj(FFT(a))*FFT(b)) \n
* Images must be of the same size and only destiny image must be of type complex.
*
* \verbatim im.ProcessCrossCorrelation(src_image1: imImage, src_image2: imImage, dst_image: imImage) [in Lua 5] \endverbatim
* \verbatim im.ProcessCrossCorrelationNew(image1: imImage, image2: imImage) -> new_image: imImage [in Lua 5] \endverbatim
* \ingroup transform */
void imProcessCrossCorrelation(const imImage* src_image1, const imImage* src_image2, imImage* dst_image);
/** Calculates the Auto Correlation in the frequency domain. \n
* Uses the cross correlation.
* Images must be of the same size and only destiny image must be of type complex.
*
* \verbatim im.ProcessAutoCorrelation(src_image: imImage, dst_image: imImage) [in Lua 5] \endverbatim
* \verbatim im.ProcessAutoCorrelationNew(image: imImage) -> new_image: imImage [in Lua 5] \endverbatim
* \ingroup transform */
void imProcessAutoCorrelation(const imImage* src_image, imImage* dst_image);
/** Calculates the Distance Transform of a binary image
* using an aproximation of the euclidian distance.\n
* Each white pixel in the binary image is
* assigned a value equal to its distance from the nearest
* black pixel. \n
* Uses a two-pass algorithm incrementally calculating the distance. \n
* Source image must be IM_BINARY, destiny must be IM_FLOAT.
*
* \verbatim im.ProcessDistanceTransform(src_image: imImage, dst_image: imImage) [in Lua 5] \endverbatim
* \verbatim im.ProcessDistanceTransformNew(image: imImage) -> new_image: imImage [in Lua 5] \endverbatim
* \ingroup transform */
void imProcessDistanceTransform(const imImage* src_image, imImage* dst_image);
/** Marks all the regional maximum of the distance transform. \n
* source is IMGRAY/IM_FLOAT destiny in IM_BINARY. \n
* We consider maximum all connected pixel values that have smaller pixel values around it.
*
* \verbatim im.ProcessRegionalMaximum(src_image: imImage, dst_image: imImage) [in Lua 5] \endverbatim
* \verbatim im.ProcessRegionalMaximumNew(image: imImage) -> new_image: imImage [in Lua 5] \endverbatim
* \ingroup transform */
void imProcessRegionalMaximum(const imImage* src_image, imImage* dst_image);
/** \defgroup fourier Fourier Transform Operations
* \par
* All Fourier transforms use FFTW library version 2.1.5. \n
* Although there are newer versions, we build binaries only to version 2
* because it is small and as fast as newer versions.
* Source code to use FFTW version 3 is available.
* \par
* FFTW Copyright Matteo Frigo, Steven G. Johnson and the MIT. \n
* http://www.fftw.org \n
* See "fftw.h"
* \par
* Must link with "im_fftw" library. \n
* \par
* The FFTW lib has a GPL license. The license of the "im_fftw" library is automatically the GPL.
* So you cannot use it for commercial applications without contacting the authors.
* \par
* See \ref im_process_glo.h
* \ingroup process */
/** Forward FFT. \n
* The result has its lowest frequency at the center of the image. \n
* This is an unnormalized fft. \n
* Images must be of the same size. Destiny image must be of type complex.
*
* \verbatim im.ProcessFFT(src_image: imImage, dst_image: imImage) [in Lua 5] \endverbatim
* \verbatim im.ProcessFFTNew(image: imImage) -> new_image: imImage [in Lua 5] \endverbatim
* \ingroup fourier */
void imProcessFFT(const imImage* src_image, imImage* dst_image);
/** Inverse FFT. \n
* The image has its lowest frequency restored to the origin before the transform. \n
* The result is normalized by (width*height). \n
* Images must be of the same size and both must be of type complex.
*
* \verbatim im.ProcessIFFT(src_image: imImage, dst_image: imImage) [in Lua 5] \endverbatim
* \verbatim im.ProcessIFFTNew(image: imImage) -> new_image: imImage [in Lua 5] \endverbatim
* \ingroup fourier */
void imProcessIFFT(const imImage* src_image, imImage* dst_image);
/** Raw in-place FFT (forward or inverse). \n
* The lowest frequency can be centered after forward, or
* can be restored to the origin before inverse. \n
* The result can be normalized after the transform by sqrt(w*h) [1] or by (w*h) [2],
* or left unnormalized [0]. \n
* Images must be of the same size and both must be of type complex.
*
* \verbatim im.ProcessFFTraw(image: imImage, inverse: number, center: number, normalize: number) [in Lua 5] \endverbatim
* \ingroup fourier */
void imProcessFFTraw(imImage* image, int inverse, int center, int normalize);
/** Auxiliary function for the raw FFT. \n
* This is the function used internally to change the lowest frequency position in the image. \n
* If the image size has even dimensions the flag "center2origin" is useless. But if it is odd,
* you must specify if its from center to origin (usually used before inverse) or
* from origin to center (usually used after forward). \n
* Notice that this function is used for images in the the frequency domain. \n
* Image type must be complex.
*
* \verbatim im.ProcessSwapQuadrants(image: imImage, center2origin: number) [in Lua 5] \endverbatim
* \ingroup fourier */
void imProcessSwapQuadrants(imImage* image, int center2origin);
#if defined(__cplusplus)
}
#endif
#endif
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