Find white regions in binary image. \n * Result is IM_GRAY/IM_USHORT type. Regions can be 4 connected or 8 connected. \n * Returns the number of regions found. Background is marked as 0. \n * Regions touching the border are considered only if touch_border=1. * Not using OpenMP when enabled. * * \verbatim im.AnalyzeFindRegions(src_image: imImage, dst_image: imImage, connect: number, touch_border: boolean) -> count: number [in Lua 5] \endverbatim * \verbatim im.AnalyzeFindRegionsNew(image: imImage, connect: number, touch_border: boolean) -> count: number, new_image: imImage [in Lua 5] \endverbatim * \ingroup analyze
Measure the actual area of all regions. Holes are not included. \n * This is the number of pixels of each region. \n * Source image is IM_GRAY/IM_USHORT type (the result of \ref imAnalyzeFindRegions). \n * area has size the number of regions. * * \verbatim im.AnalyzeMeasureArea(image: imImage, [region_count: number]) -> area: table of numbers [in Lua 5] \endverbatim * The returned table is zero indexed. * \ingroup analyze
Calculate the centroid position of all regions. Holes are not included. \n * Source image is IM_GRAY/IM_USHORT type (the result of \ref imAnalyzeFindRegions). \n * area, cx and cy have size the number of regions. If area is NULL will be internally calculated. * * \verbatim im.AnalyzeMeasureCentroid(image: imImage, [area: table of numbers], [region_count: number]) -> cx: table of numbers, cy: table of numbers [in Lua 5] \endverbatim * The returned tables are zero indexed. * \ingroup analyze
Measure the number of holes of all regions. Optionally computes the holes area and holes perimeter of all regions. \n * Source image is IM_GRAY/IM_USHORT type (the result of \ref imAnalyzeFindRegions). \n * count, area and perim has size the number of regions, if some is NULL it will be not calculated. * Not using OpenMP when enabled. * * \verbatim im.AnalyzeMeasureHoles(image: imImage, connect: number, [region_count: number]) -> holes_count: number, holes_area: table of numbers, holes_perim: table of numbers [in Lua 5] \endverbatim * The returned tables are zero indexed. * \ingroup analyze
Measure the polygonal area limited by the perimeter line of all regions. Holes are not included. \n * Notice that some regions may have polygonal area zero. \n * Source image is IM_GRAY/IM_USHORT type (the result of \ref imAnalyzeFindRegions). \n * perimarea has size the number of regions. * * \verbatim im.AnalyzeMeasurePerimArea(image: imImage, [region_count: number]) -> perimarea: table of numbers [in Lua 5] \endverbatim * The returned table is zero indexed. * \ingroup analyze
Measure the total perimeter of all regions (external and internal). \n * Source image is IM_GRAY/IM_USHORT type (the result of imAnalyzeFindRegions). \n * It uses a half-pixel inter distance for 8 neighbors in a perimeter of a 4 connected region. \n * This function can also be used to measure line length. \n * perim has size the number of regions. * * \verbatim im.AnalyzeMeasurePerimeter(image: imImage) -> perim: table of numbers [in Lua 5] \endverbatim * \ingroup analyze
Calculate the principal major axis slope of all regions. \n * Source image is IM_GRAY/IM_USHORT type (the result of \ref imAnalyzeFindRegions). \n * data has size the number of regions. If area or centroid are NULL will be internally calculated. \n * Principal (major and minor) axes are defined to be those axes that pass through the * centroid, about which the moment of inertia of the region is, respectively maximal or minimal. * Partially using OpenMP when enabled. * * \verbatim im.AnalyzeMeasurePrincipalAxis(image: imImage, [area: table of numbers], [cx: table of numbers], [cy: table of numbers], [region_count: number]) -> major_slope: table of numbers, major_length: table of numbers, minor_slope: table of numbers, minor_length: table of numbers [in Lua 5] \endverbatim * The returned tables are zero indexed. * \ingroup analyze
Calculates the histogram of a IM_BYTE data. \n * Histogram is always 256 positions long. \n * When cumulative is different from zero it calculates the cumulative histogram. * Not available in Lua. * \ingroup stats
Count the number of different colors in an image. \n * Image must be IM_BYTE, but can has all color spaces except IM_CMYK. * Data type can be also IM_SHORT or IM_USHORT if color space is IM_GRAY, IM_BINARY or IM_MAP. * Not using OpenMP when enabled, when color space depth is greater than 1. * * \verbatim im.CalcCountColors(image: imImage) -> count: number [in Lua 5] \endverbatim * \ingroup stats
Calculates the gray histogram of an image. \n * Image must be (IM_BYTE, IM_SHORT or IM_USHORT)/(IM_RGB, IM_GRAY, IM_BINARY or IM_MAP). \n * If the image is IM_RGB then the histogram of the luma component is calculated. \n * Histogram is always 256 or 65536 positions long. \n * When cumulative is different from zero it calculates the cumulative histogram. * * \verbatim im.CalcGrayHistogram(image: imImage, cumulative: boolean) -> histo: table of numbers [in Lua 5] \endverbatim * \ingroup stats
Calculates some extra statistics about the image histogram data.\n * There is one stats for each depth plane. \n * Only IM_BYTE, IM_SHORT and IM_USHORT images are supported. \n * mode will be -1 if more than one max is found. * * \verbatim im.CalcHistoImageStatistics(image: imImage) -> median: number, mode: number [in Lua 5] \endverbatim * \ingroup stats
Calculates the histogram of an image plane. \n * Image can be IM_BYTE, IM_SHORT or IM_USHORT. \n * Histogram is always 256 or 65536 positions long. \n * Where plane is the depth plane to calculate the histogram. \n * When cumulative is different from zero it calculates the cumulative histogram. * * \verbatim im.CalcHistogram(image: imImage, plane: number, cumulative: boolean) -> histo: table of numbers [in Lua 5] \endverbatim * The returned table is zero indexed. * \ingroup stats
Calculates the statistics about the image histogram data.\n * There is one stats for each depth plane. For ex: stats[0]=red stats, stats[0]=green stats, ... \n * Only IM_BYTE, IM_SHORT and IM_USHORT images are supported. * * \verbatim im.CalcHistogramStatistics(image: imImage) -> stats: table [in Lua 5] \endverbatim * \ingroup stats
Calculates the statistics about the image data. \n * There is one stats for each depth plane. For ex: stats[0]=red stats, stats[0]=green stats, ... \n * Supports all data types except complex. \n * * \verbatim im.CalcImageStatistics(image: imImage) -> stats: table [in Lua 5] \endverbatim * Table contains the following fields: max, min, positive, negative, zeros, mean, stddev. * If image depth > 1 then table contains several tables with the previous fields, one for each plane, * starting at 0. * The same as the \ref imStats structure. * \ingroup stats
Calculates the minimum and maximum levels * ignoring a given percentage of the histogram count.\n * Used by \ref imProcessExpandHistogram. \n * Only IM_BYTE, IM_SHORT and IM_USHORT images are supported. \n * * \verbatim im.CalcPercentMinMax(image: imImage, percent: number, ignore_zero: boolean) -> min, max: number [in Lua 5] \endverbatim * \ingroup stats
Calculates the RMS error between two images (Root Mean Square Error). * * \verbatim im.CalcRMSError(image1: imImage, image2: imImage) -> rms: number [in Lua 5] \endverbatim * \ingroup stats
Calculates the SNR of an image and its noise (Signal Noise Ratio). * * \verbatim im.CalcSNR(src_image: imImage, noise_image: imImage) -> snr: number [in Lua 5] \endverbatim * \ingroup stats
Calculates the histogram of a IM_SHORT data. \n * Histogram is always 65536 positions long. \n * Zero is located at 32768 index. \n * When cumulative is different from zero it calculates the cumulative histogram. \n * Not available in Lua. * \ingroup stats
Calculates the histogram of a IM_USHORT data. \n * Histogram is always 65536 positions long. \n * When cumulative is different from zero it calculates the cumulative histogram. \n * Not available in Lua. * \ingroup stats
Returns the histogram size based on the image data type. \n * Not available in Lua. * \ingroup stats
Alocates an histogram data based on the image data type. \n * Data type can be IM_BYTE, IM_SHORT or IM_USHORT. \n * Not available in Lua. * \ingroup stats
Releases the histogram data. \n * Not available in Lua. * \ingroup stats
Short data type stores the histogram values of negative indices starting at 0. * So the real level is obtained by shifting the zero based index. \n * Not available in Lua. * \ingroup stats
Fill holes inside white regions. \n * Source and target are a binary images. Regions can be 4 connected or 8 connected. \n * Can be done in-place. * * \verbatim im.ProcessFillHoles(src_image: imImage, dst_image: imImage, connect: number) [in Lua 5] \endverbatim * \verbatim im.ProcessFillHolesNew(image: imImage, connect: number) -> new_image: imImage [in Lua 5] \endverbatim * \ingroup analyze
Isolates the perimeter line of gray integer images. Background is defined as being black (0). \n * It just checks if at least one of the 4 connected neighbors is non zero. Image borders are extended with zeros. * * \verbatim im.ProcessPerimeterLine(src_image: imImage, dst_image: imImage) [in Lua 5] \endverbatim * \verbatim im.ProcessPerimeterLineNew(image: imImage) -> new_image: imImage [in Lua 5] \endverbatim * \ingroup analyze
Eliminates regions that have area size outside or inside the given interval. \n * Source and target are a binary images. Regions can be 4 connected or 8 connected. \n * Can be done in-place. end_size can be zero to indicate no upper limit or an area with width*height size. \n * When searching inside the region the limits are inclusive (<= size >=), when searching outside the limits are exclusive (> size <). * * \verbatim im.ProcessRemoveByArea(src_image: imImage, dst_image: imImage, connect: number, start_size: number, end_size: number, inside: boolean) [in Lua 5] \endverbatim * \verbatim im.ProcessRemoveByAreaNew(image: imImage, connect: number, start_size: number, end_size: number, inside: boolean) -> new_image: imImage [in Lua 5] \endverbatim * \ingroup analyze
\brief Numerical Statistics Structure * \ingroup stats
\file \brief Image Statistics and Analysis
See Copyright Notice in im_lib.d