Calculates the Auto Correlation in the frequency domain. \n * Uses the cross correlation. * Images must be of the same size and only target 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
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 target 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
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, target 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
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. Target image must be of type float 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
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 float complex. * * \verbatim im.ProcessFFTraw(image: imImage, inverse: number, center: number, normalize: number) [in Lua 5] \endverbatim * \ingroup fourier
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 target 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/ \n * Not using OpenMP when enabled. * * \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
Draw detected hough lines. \n * The source and target images can be IM_MAP, IM_GRAY or IM_RGB, with data type IM_BYTE. \n * Can be done in-place. \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 detected lines will be drawn using a red color. * If the target image is IM_GRAY, it will be changed to IM_MAP. \n * If the target image is IM_RGB, then only the red plane will be changed. * Returns the number of detected lines. \n * Not using OpenMP when enabled. * * \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
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 float 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
Sets the minimum number of iterations to split into threads. \n * Default value is 250000, or an image with 500x500. \n * Returns the previous value. * * \verbatim im.ProcessOpenMPSetMinCount(min_count: number) -> old_min_count: number [in Lua 5] \endverbatim * \ingroup openmp
Sets the number of threads. \n * Does nothing if OpenMP is not enabled. \n * Returns the previous value. * * \verbatim im.ProcessOpenMPSetNumThreads(min_count: number) -> old_min_count: number [in Lua 5] \endverbatim * \ingroup openmp
Marks all the regional maximum of the distance transform. \n * source is IMGRAY/IM_FLOAT target 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
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 float complex. * * \verbatim im.ProcessSwapQuadrants(image: imImage, center2origin: number) [in Lua 5] \endverbatim * \ingroup fourier
\file \brief Image Processing - Global Operations
See Copyright Notice in im_lib.d