G'MIC(1)

NAME
gmic - Perform image processing operations using the G'MIC framework.

HELP
  gmic: GREYC's Magic for Image Computing: Command-Line Interface
        Version 3.7.3
        (https://gmic.eu)
        Copyright (c) 2008-2026, David Tschumperlé / GREYC / CNRS.
        (https://www.greyc.fr)
  1. Usage
     -----
    gmic [command_1 [arg_1_1,arg_1_2,..]] .. [command_N [arg_N_1,arg_N_2,
   ..]]
  'gmic' is the open-source interpreter of the G'MIC language, a
   scripting programming language dedicated to the design of possibly complex
   image processing pipelines and operators.
  It can be used to convert, manipulate, filter and visualize image datasets made
   of one or several 1D/2D or 3D multi-spectral images.
  This reference documentation describes all the technical aspects of the
   G'MIC framework, in its current version 3.7.3.
  As a starting point, you may want to visit our detailed tutorial pages, at:
   https://gmic.eu/tutorial/
  2. Overall Context
     ---------------
   * At any time, G'MIC manages one list of numbered (and optionally named)
   pixel-based images, entirely stored in computer memory (uncompressed).
   * The first image of the list has index '0' and is denoted by
   '[0]'. The second image of the list is denoted by '[1]', the third
   by '[2]' and so on.
   * Negative indices are treated in a periodic way: '[-1]' refers to the
   last image of the list, '[-2]' to the penultimate one, etc. Thus, if the
   list has 4 images, '[1]' and '[-3]' both designate the second image
   of the list.
   * A named image may be also indicated by '[name]', if 'name' uses
   the character set '[a-zA-Z0-9_]' and does not start with a number. Image
   names can be set or reassigned at any moment during the processing pipeline
   (see command name for this purpose).
   * G'MIC defines a set of various commands and substitution mechanisms to
   allow the design of complex pipelines and operators managing this list of
   images, in a very flexible way: You can insert or remove images in the list,
   rearrange image order, process images (individually or grouped), merge image
   data together, display and output image files, etc.
   * Such a pipeline can define a new custom G'MIC command (stored in a user
   command file), and re-used afterwards as a regular command, in a larger
   pipeline if necessary.
  3. Image Definition and Terminology
     --------------------------------
   * In G'MIC, each image is modeled as a 1D, 2D, 3D or 4D array of scalar
   values, uniformly discretized on a rectangular/parallelepipedic domain.
   * The four dimensions of this array are respectively denoted by:
     - 'width', the number of image columns (size along the
   'x-axis').
     - 'height', the number of image rows (size along the 'y-axis').
     - 'depth', the number of image slices (size along the
   'z-axis'). The depth is equal to '1' for usual color or grayscale
   2D images.
     - 'spectrum', the number of image channels (size along the
   'c-axis'). The spectrum is respectively equal to '3' and '4'
   for usual 'RGB' and 'RGBA' color images.
   * There are no hard limitations on the size of the image along each dimension.
   For instance, the number of image slices or channels can be of arbitrary size
   within the limits of the available memory.
   * The 'width', 'height' and 'depth' of an image are
   considered as spatial dimensions, while the 'spectrum' has a
   multi-spectral meaning. Thus, a 4D image in G'MIC should be most often
   regarded as a 3D dataset of multi-spectral voxels. Most of the G'MIC
   commands will stick with this idea (e.g. by default, command blur blurs
   4D images only along the three spatial 'xyz'-axes).
   * G'MIC stores all the image data as buffers of 'float' values (32
   bits, value range '[-3.4E38,+3.4E38]'. It performs all its image
   processing operations with floating point numbers. Each image pixel takes then
   32bits/channel (except if double-precision buffers have been enabled during the
   compilation of the software, in which case 64bits/channel can be the default).
   * Considering 'float'-valued pixels ensure to keep numerical precision
   when executing image processing pipelines. For image input/output operations,
   you may want to prescribe the image datatype to be different than 'float'
   (like 'bool', 'char', 'int', etc.). This is possible by
   specifying it as a file option when using I/O commands (see section
   Input/Output Properties to learn more about file options).
  4. Items of a Processing Pipeline
     ------------------------------
   * In G'MIC, an image processing pipeline is described as a sequence of
   items separated by the space character. Such items are interpreted and
   executed from the left to the right. For instance, the expression:
    filename.jpg blur 3,0 sharpen 10 resize 200%,200% output file_out.jpg
  defines a valid pipeline composed of nine G'MIC items.
   * Each G'MIC item is either a command, a list of command
   arguments, a filename or a special input string.
   * Escape characters '' and double quotes '"' can be used to define items
   containing spaces or other special characters. For instance, the two strings
   'single item' and '"single item"' both define the same single item,
    with a space in it.
  5. Input Data
     ----------
   * If a specified G'MIC item appears to be an existing filename, the
   corresponding image data are loaded and inserted at the end of the image list
   (which is equivalent to the use of 'input filename').
   * Special filenames '-' and '-.ext' stand for the standard
   input/output streams, optionally forced to be in a specific 'ext' file
   format (e.g. '-.jpg' or '-.png').
   * The following special input strings may be used as G'MIC items to create
   and insert new images with prescribed values, at the end of the image list:
     - '[selection]' or '[selection]xN': Insert 1 or N copies of
   already existing images. 'selection' may represent one or several images
   (see section Command Items and Selections to learn more about selections).
     - 'width[%],_height[%],_depth[%],_spectrum[%],_values[xN]': Insert
   one or N images with specified size and values (adding '%' to a dimension
   means "percentage of the size along the same axis", taken from the last
   image '[-1]'). Any specified dimension can be also written as
   '[image]', and is then set to the size (along the same axis) of the
   existing specified image '[image]'. 'values' can be either a
   sequence of numbers separated by commas ',', or a mathematical expression,
    as e.g. in input item '256,256,1,3,[x,y,128]' which creates a
   '256x256' RGB color image with a spatial shading on the red and green
   channels. (see section Mathematical Expressions to learn more about
   mathematical expressions).
     - '(v1,v2,..[:delimiter | axis_order])[xN]': Insert one or 'N'
   new images from specified prescribed values. Value separator inside parentheses
   can be ',' (column separator), ';' (row separator), '/'
   (slice separator) or '^' (channel separator). For instance, expression
   '(1,2,3;4,5,6;7,8,9)' creates a 3x3 matrix (scalar image), with values
   running from 1 to 9.
     - '('string'[:delimiter])[xN]': Insert one or N new images from
   specified string, by filling the images with the character codes composing the
   string. When specified, 'delimiter' tells about the main orientation of
   the image. Delimiter can be 'x' (eq. to ',' which is the default),
   'y' (eq. to ';'), 'z' (eq. to '/') or 'c' (eq. to
   '^'). When specified delimiter is ',', ';', '/' or
   '^', the expression is actually equivalent to
   '({'string'[:delimiter]})[xN]' (see section Substitution Rules for
   more information on the syntax).
     - '0[xN]': Insert one or N new 'empty' images, containing no
   pixel data. Empty images are used only in rare occasions.
   * Input item 'name=value' declares a new variable 'name', or
   assign a new string value to an existing variable. Variable names must use the
   character set '[a-zA-Z0-9_]' and cannot start with a number.
   * A variable definition is always local to the current command except :
     - When it starts by the underscore character '_'. In that case, it
   becomes also accessible by any command invoked outside the current command
   scope (global variable).
     - When it is defined in a shared variable command, a variable becomes
   also accessible in the calling (parent) command. A shared variable command
   is a command whose name starts with '__' (e.g. '__foo').
   * If a variable name starts with two underscores '__', the global
   variable is also shared among different threads and can be read/set by commands
   running in parallel (see command parallel for this purpose). Otherwise,
   it remains local to the thread that defined it.
   * Numerical variables can be updated with the use of these special operators:
   '+=' (addition), '-=' (subtraction), '*=' (multiplication),
   '/=' (division), '%=' (modulo), '&=' (bitwise and),
   '|=' (bitwise or), '^=' (power), '<<=' and '>>'
   (bitwise left and right shifts). For instance, 'foo=1' 'foo+=3'.
   * Input item 'name.=string' appends specified 'string' at the end
   of variable 'name'.
   * Input item 'name..=string' prepends specified 'string' at the
   beginning of variable 'name'.
   * Multiple variable assignments and updates are allowed, with expressions:
   'name1,name2,...,nameN=value' or 'name1,name2,...,nameN=value1,value2,
   ...,valueN' where assignment operator '=' can be replaced by one of
   the allowed operators (e.g. '+=').
   * Variables usually store numbers or strings. Use command store to
   assign variables from image data (and syntax 'input $variable' to bring
   them back on the image list afterwards).
  6. Command Items and Selections
     ----------------------------
   * A G'MIC item that is not a filename nor a special input string designates
   a 'command' most of the time. Generally, commands perform image
   processing operations on one or several available images of the list.
   * Recurrent commands have two equivalent names ('regular' and
   'short'). For instance, command names 'resize' and 'r' refer
   to the same image resizing action.
   * A G'MIC command may have mandatory or optional arguments. Command
   arguments must be specified in the next item on the command line. Commas ',
   ' are used to separate multiple arguments of a single command, when required.
   * The execution of a G'MIC command may be restricted only to a subset
   of the image list, by appending '[selection]' to the command name.
   Examples of valid syntaxes for 'selection' are:
     - 'command[-2]': Apply command only on the penultimate image
   '[-2]' of the list.
     - 'command[0,1,3]': Apply command only on images '[0]',
   '[1]' and '[3]'.
     - 'command[3-6]': Apply command only on images '[3]' to
   '[6]' (i.e, '[3]', '[4]', '[5]' and '[6]').
     - 'command[50%-100%]': Apply command only on the second half of the
   image list.
     - 'command[0,-4--1]': Apply command only on the first image and the
   last four images.
     - 'command[0-9:3]': Apply command only on images '[0]' to
   '[9]', with a step of 3 (i.e. on images '[0]', '[3]',
   '[6]' and '[9]').
     - 'command[0--1:2]': Apply command only on images of the list with
   even indices.
     - 'command[0,2-4,50%--1]': Apply command on images '[0]',
   '[2]', '[3]', '[4]' and on the second half of the image list.
     - 'command[^0,1]': Apply command on all images except the first two.
     - 'command[name1,name2]': Apply command on named images 'name1'
   and 'name2'.
   * Indices in selections are always sorted in increasing order, and duplicate
   indices are discarded. For instance, selections '[3-1,1-3]' and '[1,1,
   1,3,2]' are both equivalent to '[1-3]'. If you want to repeat a single
   command multiple times on an image, use a 'repeat..done' loop instead.
   Inverting the order of images for a command is achieved by explicitly inverting
   the order of the images in the list, with command 'reverse[selection]'.
   * Command selections '[-1]', '[-2]' and '[-3]' are so often
   used they have their own shortcuts, respectively '.', '..' and
   '...'. For instance, command 'blur..' is equivalent to
   'blur[-2]'. These shortcuts work also when specifying command arguments.
   * G'MIC commands invoked without '[selection]' are applied on all
   images of the list, i.e. the default selection is '[0--1]' (except for
   command input whose default selection is '[-1]'').
   * Prepending a single hyphen '-' to a G'MIC command is allowed. This
   may be useful to recognize command items more easily in a one-liner pipeline
   (typically invoked from a shell).
   * A G'MIC command prepended with a plus sign '+' does not act
   in-place but inserts its result as one or several new images at the end
   of the image list.
   * There are two different types of commands that can be run by the G'MIC
   interpreter:
     - Built-in commands are the hard-coded functionalities in the
   interpreter core. They are thus compiled as binary code and run fast, most of
   the time. Omitting an argument when invoking a built-in command is not
   permitted, except if all following arguments are also omitted. For instance,
   invoking 'blur 1,,1' is invalid but 'blur 1' is correct.
     - Custom commands, are defined as G'MIC pipelines of built-in or
   other custom commands. They are parsed by the G'MIC interpreter, and thus
   run a bit slower than built-in commands. Omitting arguments when invoking a
   custom command is permitted. For instance, expressions 'flower ,,,100,,2'
   or 'flower ,' are correct.
   * Most of the existing commands in G'MIC are actually defined as custom
   commands.
   * A user can easily add its own custom commands to the G'MIC interpreter
   (see section Adding Custom Commands for more details). New built-in
   commands cannot be added (unless you modify the G'MIC interpreter source
   code and recompile it).
  7. Input/Output Properties
     -----------------------
   * G'MIC is able to read/write most of the classical image file formats,
   including:
     - 2D grayscale/color files: '.png', '.jpeg', '.gif',
   '.pnm', '.tif', '.bmp', ...
     - 3D volumetric files: '.dcm', '.hdr', '.nii',
   '.cube', '.pan', '.inr', '.pnk', ...
     - Video files: '.mpeg', '.avi', '.mp4', '.mov',
   '.ogg', '.flv', ...
     - Generic text or binary data files: '.gmz', '.cimg',
   '.cimgz', 'flo', 'ggr', 'gpl', '.dlm',
   '.asc', '.pfm', '.raw', '.txt', '.h'.
     - 3D mesh files: '.off', '.obj'.
   * When dealing with color images, G'MIC generally reads, writes and
   displays data using the usual sRGB color space.
   * When loading a '.png' and '.tiff' file, the bit-depth of the
   input image(s) is returned to the status.
   * G'MIC is able to manage 3D mesh objects that may be read from files
   or generated by G'MIC commands. A 3D object is stored as a one-column scalar
   image containing the object data, in the following order: { magic_number;
   sizes; vertices; primitives; colors; opacities }. These 3D representations
   can be then processed as regular images (see command split3d for
   accessing each of these 3D object data separately).
   * Be aware that usual file formats may be sometimes not adapted to store all
   the available image data, since G'MIC uses float-valued image buffers. For
   instance, saving an image that was initially loaded as a 16bits/channel image,
   as a '.jpg' file will result in a loss of information. Use the
   G'MIC-specific file extension '.gmz' to ensure that all data precision
   is preserved when saving images.
   * Sometimes, file options may/must be set for file formats:
     - Video files: Only sub-frames of an image sequence may be loaded,
   using the input expression 'filename.ext,[first_frame[,last_frame[,
   step]]]'. Set 'last_frame==-1' to tell it must be the last frame of
   the video. Set 'step' to '0' to force an opened video file to be
   opened/closed. Output framerate and codec can be also set by using the output
   expression 'filename.avi,_fps,_codec,_keep_open' where 'keep_open'
   can be { 0:No (default) | 1:Yes }. 'codec' is a 4-char string (see
   http://www.fourcc.org/codecs.php ) or '0' for the default codec.
   'keep_open' tells if the output video file must be kept open for
   appending new frames afterwards.
     - '.cimg[z]' files: Only crops and sub-images of .cimg files
   can be loaded, using the input expressions 'filename.cimg,N0,N1',
   'filename.cimg,N0,N1,x0,x1', 'filename.cimg,N0,N1,x0,y0,x1,y1',
   'filename.cimg,N0,N1,x0,y0,z0,x1,y1,z1' or 'filename.cimg,N0,N1,x0,y0,
   z0,c0,x1,y1,z1,c1'. Specifying '-1' for one coordinates stands for the
   maximum possible value. Output expression 'filename.cimg[z][,datatype]'
   can be used to force the output pixel type. 'datatype' can be { auto |
   bool | uint8 | int8 | uint16 | int16 | uint32 | int32 | uint64 | int64 |
   float32 | float64 }.
     - '.raw' binary files: Image dimensions and input pixel type
   may be specified when loading '.raw' files with input expression
   'filename.raw[,datatype][,width][,height[,depth[,dim[,offset]]]]]'. If no
   dimensions are specified, the resulting image is a one-column vector with
   maximum possible height. Pixel type can also be specified with the output
   expression 'filename.raw[,datatype]'. 'datatype' can be the same as
   for '.cimg[z]' files.
     - '.yuv' files: Image dimensions must be specified when loading,
    and only sub-frames of an image sequence may be loaded, using the input
   expression 'filename.yuv,width,height[,chroma_subsampling[,first_frame[,
   last_frame[,step]]]'. 'chroma_subsampling' can be { 420 | 422 | 444
   }. When saving, chroma subsampling mode can be specified with output
   expression 'filename.yuv[,chroma_subsampling]'.
     - '.tiff' files: Only sub-images of multi-pages tiff files can
   be loaded, using the input expression 'filename.tif,_first_frame,_last_frame,
   _step'. Output expression 'filename.tiff,_datatype,_compression,
   _force_multipage,_use_bigtiff' can be used to specify the output pixel type,
   as well as the compression method. 'datatype' can be the same as for
   '.cimg[z]' files. 'compression' can be { none | adobe_deflate |
   ccitt_t4 | ccitt_t6 | ccittfax3 | ccittfax4 | ccittrle | ccittrlew | dcs |
   deflate | it8bl | it8ctpad | it8lw | it8mp | jbig | jp2000 | jpeg | jpg | jxl |
   lerc | lzma | lzw | next | ojpeg | packbits | pixarfilm | pixarlog | sgilog |
   sgilog24 | t43 | t85 | thunderscan | webp | zip | zstd }.
   'force_multipage' can be { 0:No (default) | 1:Yes }.
   'use_bigtiff' can be { 0:No | 1:Yes (default) }.
     - '.pdf' files: When loading a file, the rendering resolution
   can be specified using the input expression 'filename.pdf,resolution',
   where 'resolution' is an unsigned integer value.
     - '.gif' files: Animated gif files can be saved, using the
   input expression 'filename.gif,fps>0,nb_loops'. Specify
   'nb_loops=0' to get an infinite number of animation loops (this is the
   default behavior).
     - '.jpeg' and '.webp' files: The output quality may be
   specified (in %), using the output expression 'filename.jpg,30' (here, to
   get a 30% quality output). '100' is the default.
     - '.png' files: The bit depth can be specified (8 or 16), using
   the output expression 'filename.png,16' (here, to get a 16 bit depth
   output file). By default, G'MIC guesses the best bit depth automatically.
     - '.mnc' files: The output header can set from another file,
   using the output expression 'filename.mnc,header_template.mnc'.
     - '.pan', '.cpp', '.hpp', '.c' and '.h'
   files: The output datatype can be selected with output expression
   'filename[,datatype]'. 'datatype' can be the same as for
   '.cimg[z]' files.
     - '.gmic' files: These filenames are assumed to be G'MIC
   custom commands files. Loading such a file will add the commands it defines to
   the interpreter. Debug information can be enabled/disabled by the input
   expression 'filename.gmic[,add_debug_info]' where 'debug_info' can
   be { 0:False | 1:True }.
     - Inserting 'ext:' on the beginning of a filename (e.g.
   'jpg:filename') forces G'MIC to read/write the file as it would have
   been done if it had the specified extension '.ext'.
   * Some input/output formats and options may not be supported, depending on the
   configuration flags that have been set during the build of the G'MIC
   software.
  8. Substitution Rules
     ------------------
   * G'MIC items containing '$' or '{}' are substituted before
   being interpreted. Use these substituting expressions to access various data
   from the interpreter environment.
   * '$name' and '${name}' are both substituted by the value of the
   specified named variable (set previously by the item 'name=value'). If
   this variable has not been already set, the expression is substituted by the
   highest positive index of the named image '[name]'. If no image has this
   name, the expression is substituted by the value of the OS environment variable
   with same name (it may be thus an empty string if it is not defined).
   * The following reserved variables are predefined by the G'MIC interpreter:
     - '$!': The current number of images in the list.
     - '$>' and '$<': The increasing/decreasing index of the latest
   (currently running) loop.'$>' goes from '0' (first loop iteration)
   to 'nb_iterations - 1' (last iteration). '$<' does the opposite
   (except for 'for' and 'while' loops, where '$<' is
   'nan' as the number of iterations is unknown).
     - '$%': A value that linearly goes from '0' (first iteration)
   to '1' (last iteration) in a running loop (equivalent to
   '$>/($>+$<)').
     - '$/': The current call stack. Stack items are separated by slashes
   '/'.
     - '$|': The current value (expressed in seconds) of a millisecond
   precision timer.
     - '$^': The current verbosity level.
     - '$_cpus': The number of computation cores available on your machine.
     - '$_flags': The list of enabled flags when G'MIC interpreter has
   been compiled.
     - '$_host': A string telling about the host running the G'MIC
   interpreter (e.g. 'cli' or 'gimp').
     - '$_os': A string describing the running operating system.
     - '$_path_rc': The path to the G'MIC folder used to store
   configuration files (its value is OS-dependent).
     - '$_path_user': The path to the G'MIC user file '.gmic' or
   'user.gmic' (its value is OS-dependent).
     - '$_path_commands': A list of all imported command files (stored as
   an image list).
     - '$_pid': The current process identifier, as an integer.
     - '$_pixeltype': The type of image pixels (default: 'float32').
     - '$_prerelease': For pre-releases, the date of the pre-release as
   'yymmdd'. For stable releases, this variable is set to '0'.
     - '$_version': A 3-digits number telling about the current version of
   the G'MIC interpreter (e.g. '373').
     - '$_vt100': Set to '1' if colored text output is allowed on
   the console. Otherwise, set to '0'.
   * '$$name' and '$${name}' are both substituted by the G'MIC
   script code of the specified named 'custom command', or by an empty
   string if no custom command with specified name exists.
   * '${"-pipeline"}' is substituted by the status value after the
   execution of the specified G'MIC pipeline (see command status).
   Expression '${}' thus stands for the current status value.
   * '{``string}' (starting with two backquotes) is substituted by a
   double-quoted version of the specified string.
   * '{/string}' is substituted by the escaped version of the specified
   string.
   * '{'string'[:delimiter]}' (between single quotes) is substituted by the
   sequence of character codes that composes the specified string, separated by
   specified delimiter. Possible delimiters are ',' (default), ';',
   '/', '^' or ' '. For instance, item '{'foo'}' is
   substituted by '102,111,111' and '{'foo':;}' by '102;111;111'.
   * '{image,feature[:delimiter]}' is substituted by a specific feature of
   the image '[image]'. 'image' can be either an image number or an
   image name. It can be also eluded, in which case, the last image '[-1]'
   of the list is considered for the requested feature. Specified 'feature'
   can be one of:
     - 'b': The image basename (i.e. filename without the folder path nor
   extension).
     - 'f': The image folder name.
     - 'n': The image name or filename (if the image has been read from a
   file).
     - 't': The text string from the image values regarded as character
   codes.
     - 'x': The image extension (i.e the characters after the last
   '.' in the image name).
     - '^': The sequence of all image values, separated by commas ',
   '.
     - '@subset': The sequence of image values corresponding to the
   specified subset, and separated by commas ','.
     - Any other 'feature' is considered as a mathematical
   expression associated to the image '[image]' and is substituted by the
   result of its evaluation (float value). For instance, expression '{0,
   w+h}' is substituted by the sum of the width and height of the first image
   (see section Mathematical Expressions for more details). If a
   mathematical expression starts with an underscore '_', the resulting
   value is truncated to a readable format. For instance, item '{_pi}' is
   substituted by '3.14159' (while '{pi}' is substituted by
   '3.141592653589793').
     - A 'feature' delimited by backquotes is replaced by a string whose
   character codes correspond to the list of values resulting from the evaluation
   of the specified mathematical expression. For instance, item '{`[102,111,
   111]`}' is substituted by 'foo' and item '{`vector8(65)`}' by
   'AAAAAAAA'.
   * '{*}' is substituted by the visibility state of the instant display
   window '#0' (can be { 0:Closed | 1:Visible }.
   * '{*[index],feature1,...,featureN[:delimiter]}' is substituted by a
   specific set of features of the instant display window '#0' (or
   '#index', if specified). Requested 'features' can be:
     - 'u': screen width (actually independent on the window size).
     - 'v': screen height (actually independent on the window size).
     - 'uv': screen width*screen height.
     - 'd': window width (i.e. width of the window widget).
     - 'e': window height (i.e. height of the window widget).
     - 'de': window width*window height.
     - 'w': display width (i.e. width of the display area managed by the
   window).
     - 'h': display height (i.e. height of the display area managed by the
   window).
     - 'wh': display width*display height.
     - 'i': X-coordinate of the display window.
     - 'j': Y-coordinate of the display window.
     - 'f': current fullscreen state of the instant display.
     - 'n': current normalization type of the instant display.
     - 't': window title of the instant display.
     - 'x': X-coordinate of the mouse position (or -1, if outside the
   display area).
     - 'y': Y-coordinate of the mouse position (or -1, if outside the
   display area).
     - 'b': state of the mouse buttons { 1:left-but. | 2:right-but. |
   4:middle-but. }.
     - 'o': state of the mouse wheel.
     - 'k': decimal code of the pressed key if any, 0 otherwise.
     - 'c': boolean (0 or 1) telling if the instant display has been
   closed recently.
     - 'r': boolean telling if the instant display has been resized
   recently.
     - 'm': boolean telling if the instant display has been moved recently.
     - Any other 'feature' stands for a keycode name (in capital letters),
   and is substituted by a boolean describing the current key state { 0:Pressed
   | 1:Released }.
     - You can also prepend a hyphen '-' to a 'feature' (that
   supports it) to flush the corresponding event immediately after reading its
   state (works for keys, mouse and window events).
   * Item substitution is never performed in items between double quotes.
   One must break the quotes to enable substitution if needed, as in '"3+8 kg =
   "{3+8}" kg"'. Using double quotes is then a convenient way to disable the
   substitutions mechanism in items, when necessary.
   * One can also disable the substitution mechanism on items outside double
   quotes, by escaping the '{', '}' or '$' characters, as in
   '3+4 doesn't evaluate'.
  9. Mathematical Expressions
     ------------------------
   * G'MIC has an embedded mathematical parser, used to evaluate
   (possibly complex) math expressions specified inside braces '{}', or
   formulas in commands that may take one as an argument (e.g. fill or
   eval).
   * When the context allows it, a formula is evaluated for each pixel of
   the selected images (e.g. fill or eval).
   * A math expression may return or take as an argument a scalar or a
   vector-valued result (with a fixed number of components).
  The mathematical parser understands the following set of functions, operators
   and variables:
  ## Usual math operators:
  '||' (logical or), '&&' (logical and), '|' (bitwise or),
   '&' (bitwise and), '!=', '==', '<=', '>=',
   '<', '>', '<<' (left bitwise shift), '>>' (right
   bitwise shift), '-', '+', '*', '/', '%' (modulo),
   '^' (power), '!' (logical not), '~' (bitwise not), '++',
    '--', '+=', '-=', '*=', '/=', '%=',
   '&=', '|=', '^=', '>>', '<<=' (in-place
   operators).
  ## Usual math functions:
  'abs()', 'acos()', 'acosh()', 'arg0()', 'arg1()',
   'argkth()', 'argmax()', 'argmaxabs()', 'argmin()',
   'argminabs()', 'asin()', 'asinh()', 'atan()',
   'atan2()', 'atanh()', 'avg()', 'bool()', 'cbrt()',
    'ceil()', 'cos()', 'cosh()', 'cut()',
   'deg2rad()', 'erf()', 'erfinv()', 'exp()',
   'fact()', 'fibo()', 'floor()', 'frac()',
   'gamma()', 'gauss()', 'gcd()', 'hypot()', 'int()',
    'isconst()', 'isfinite()', 'isnan()', 'isnum()',
   'isinf()', 'isint()', 'isbool()', 'isexpr()',
   'isfile()', 'isdir()', 'isin()', 'kth()', 'lcm()',
    'log()', 'log2()', 'log10()', 'logit()', 'max()',
    'maxabs()', 'med()', 'min()', 'minabs()',
   'narg()', 'prod()', 'rad2deg()', 'rol()' (left bit
   rotation), 'ror()' (right bit rotation), 'round()', 'sign()',
   'sigmoid()', 'sin()', 'sinc()', 'sinh()',
   'sqrt()', 'std()', 'srand(_seed)', 'sum()',
   'tan()', 'tanh()', 'var()', 'xor()'.
   * 'cov(A,B,_avgA,_avgB)' estimates the covariance between vectors
   'A' and 'B' (estimated averages of these vectors may be specified
   as arguments).
   * 'mse(A,B)' returns the mean-squared error between vectors 'A'
   and 'B'.
   * 'atan2(y,x)' is the version of 'atan()' with two arguments
   'y' and 'x' (as in C/C++).
   * 'perm(k,n,_with_order)' computes the number of permutations of
   'k' objects from a set of 'n' objects.
   * 'gauss(x,_sigma,_is_normalized)' returns
   'exp(-x^2/(2*s^2))/(is_normalized?sqrt(2*pi*sigma^2):1)'.
   * 'cut(x,min,_max)' returns 'x' if it is in range '[min,
   max]', or 'min' or 'max' otherwise.
   * 'abscut(x,min,_max,_offset)' returns 'cut(abs(x) + offset,min,
   max)*sign(x)'.
   * 'narg(a_1,...,a_N)' returns the number of specified arguments (here,
   'N').
   * 'arg0(i,a_0,..,a_N)' returns the 'i'-th argument 'a_i'
   (where index 'i' goes from '0' to 'N').
   * 'arg1(i,a_1,..,a_N)' does the same, but for index 'i' going from
   '1' to 'N'.
   * 'indexof(ref,a_0,..,a_N)' returns the index 'i' of the element
   'ref' in the list of elements 'a_k' (or '-1' if 'ref'
   is not present in the list). Index 'i' starts from '0'.
   * 'isnum()', 'isnan()', 'isinf()', 'isint()',
   'isbool()' test the type of the given number or expression, and return
   '0' (false) or '1' (true).
   * 'isfile('path')' (resp. 'isdir('path')') returns '0'
   (false) or '1' (true) whether its string argument is a path to an
   existing file (resp. to a directory) or not.
   * 'ispercentage(arg)' returns '1' (true) or '0' (false)
   whether 'arg' ends with a '%' or not.
   * 'isvarname('str')' returns '0' (false) or '1' (true)
   whether its string argument would be a valid to name a variable or not.
   * 'isvar(varname)' returns '0' (false) or '1' (true) whether
   'varname' is an already defined variable or not.
   * 'isin(v,a_1,...,a_n)' returns '0' (false) or '1' (true)
   whether the first argument 'v' appears in the set of other argument
   'a_i'.
   * 'isint(x,_xmin,_xmax)' returns '1' (true), if 'x' is an
   integer in range '[xmin,xmax]', otherwise '0' (false).
   * 'inrange(value,m,M,include_m,include_M)' returns '0' (false) or
   '1' (true) whether the specified value lies in range '[m,M]' or not
   ('include_m' and 'includeM' tells how boundaries 'm' and
   'M' are considered).
   * 'argkth()', 'argmin()', 'argmax()', 'argminabs()',
   'argmaxabs()'', 'avg()', 'kth()', 'min()',
   'max()', 'minabs()', 'maxabs()', 'med()',
   'prod()', 'std()', 'sum()' and 'var()' can be called
   with an arbitrary number of scalar/vector arguments.
   * 'vargkth()', 'vargmin()', 'vargmax()',
   'vargminabs()', 'vargmaxabs()', 'vavg()', 'vkth()',
   'vmin()', 'vmax()', 'vminabs()', 'vmaxabs()',
   'vmed()', 'vprod()', 'vstd()', 'vsum()' and
   'vvar()' are the versions of the previous function with vector-valued
   arguments.
   * 'wave(x,type)' defines a periodic function, period 1, with values in
   [-1,1]. 'type' sets the waveform. It can be { 0:Square | 1:Triangle |
   2:Ascending sawtooth | 3:Descending sawtooth | 4:Sine }.
   * 'round(value,rounding_value,direction)' returns a rounded value.
   'direction' can be { -1:To-lowest | 0:To-nearest | 1:To-highest }.
   * 'softmax(V,_temperature)' and 'softmin(V,_temperature)'
   respectively returns the softmax and softmin of specified vector 'V'.
   Default value for 'temperature' is 1.
   * 'softargmax(V,_temperature)' and 'softargmin(V,_temperature)'
   respectively returns the softargmax and softargmin of specified vector
   'V'. Default value for 'temperature' is 1.
   * 'lerp(a,b,t)' returns 'a*(1-t)+b*t'.
   * 'swap(a,b)' swaps the values of the given arguments. 'swap(#ind,
   offset0,offset1,_is_vector)' swaps pixels of image '[ind]', located at
   offsets 'offset0' and 'offset1'.
  ## Predefined variable names:
  Variable names below are pre-defined. They can be overridden though.
   * 'l': length of the associated list of images.
   * 'k': index of the associated image, in '[0,l-1]'.
   * 'w': width of the associated image, if any ('0' otherwise).
   * 'h': height of the associated image, if any ('0' otherwise).
   * 'd': depth of the associated image, if any ('0' otherwise).
   * 's': spectrum of the associated image, if any ('0' otherwise).
   * 'r': shared state of the associated image, if any ('0'
   otherwise).
   * 'wh': shortcut for 'width*height'.
   * 'whd': shortcut for 'width*height*depth'.
   * 'whds': shortcut for 'width*height*depth*spectrum' (i.e. number
   of image values).
   * 'im', 'iM', 'ia', 'iv', 'id', 'is',
   'ip', 'ic', 'in': Respectively the minimum, maximum, average,
   variance, standard deviation, sum, product, median value and L2-norm of the
   associated image, if any ('0' otherwise).
   * 'xm', 'ym', 'zm', 'cm': The pixel coordinates of the
   minimum value in the associated image, if any ('0' otherwise).
   * 'xM', 'yM', 'zM', 'cM': The pixel coordinates of the
   maximum value in the associated image, if any ('0' otherwise).
   * All these variables are considered as constant values by the math
   parser (for optimization purposes) which is indeed the case most of the time.
   Anyway, this might not be the case, if function 'resize(#ind,..)' is used
   in the math expression. If so, it is safer to invoke functions 'l()',
   'w(_#ind)', 'h(_#ind)', ... 's(_#ind)' and 'in(_#ind)'
   instead of the corresponding named variables.
   * 'i': current processed pixel value (i.e. value located at '(x,y,z,
   c)') in the associated image, if any ('0' otherwise).
   * 'iN': N-th channel value of current processed pixel (i.e. value
   located at '(x,y,z,N)' in the associated image, if any ('0'
   otherwise). 'N' must be an integer in range '[0,9]'.
   * 'R', 'G', 'B' and 'A' are equivalent to 'i0',
   'i1', 'i2' and 'i3' respectively.
   * 'I': current vector-valued processed pixel in the associated image, if
   any ('0' otherwise). The number of vector components is equal to the
   number of image channels (e.g. 'I' = '[ R,G,B ]' for a 'RGB'
   image).
   * '_0' and '_1': Vectors whose dimension is equal to the number of
   channels of the associated image, and whose values are all '0' or
   '1'.
   * You may add '#ind' to any of the variable name above to retrieve the
   information for any numbered image '[ind]' of the list (when this makes
   sense). For instance 'ia#0' denotes the average value of the first image
   of the list).
   * 'x': current processed column of the associated image, if any
   ('0' otherwise).
   * 'y': current processed row of the associated image, if any ('0'
   otherwise).
   * 'z': current processed slice of the associated image, if any
   ('0' otherwise).
   * 'c': current processed channel of the associated image, if any
   ('0' otherwise).
   * 't': thread id when an expression is evaluated with multiple threads
   ('0' means master thread).
   * 'n': maximum number of threads when expression is evaluated in
   parallel (so that 't' goes from '0' to 'n-1').
   * 'e': value of e, i.e. '2.71828...'.
   * 'pi': value of pi, i.e. '3.1415926...'.
   * 'eps': value of machine epsilon, that is the difference between 1.0
   and the next value representable by a double.
   * 'u': a random value between '[0,1]', following a uniform
   distribution.
   * 'v': a random integer that is either '0' or '1', following
   a uniform distribution.
   * 'g': a random value, following a gaussian distribution of variance 1
   (roughly in '[-6,6]').
   * 'interpolation': value of the default interpolation mode used when
   reading pixel values with the pixel access operators (i.e. when the
   interpolation argument is not explicitly specified, see below for more details
   on pixel access operators). Its initial default value is '0'.
   * 'boundary': value of the default boundary conditions used when reading
   pixel values with the pixel access operators (i.e. when the boundary condition
   argument is not explicitly specified, see below for more details on pixel
   access operators). Its initial default value is '0'.
   * The last image of the list is always associated to the evaluations of
   'expressions', e.g. G'MIC sequence
    256,128 fill {w}
   will create a 256x128 image filled with value 256.
  ## Vector-valued functions and operators:
  The math evaluator is able to work with vector-valued elements. A math function
   applied on a vector-valued argument usually returns a vector with same
   dimension, where each element of the input vector has been passed to the
   specified function (e.g. 'abs([-1,2,-3])' returns '[1,2,3]').
  There are specific functions and operators to define or compute vector-valued
   elements though :
   * '[a0,a1,...,aN-1]' defines a 'N'-dimensional vector with scalar
   coefficients 'ak'.
   * 'vectorN(a0,a1,,...,aN-1)' does the same, with the 'ak' being
   repeated periodically if only a few are specified.
   * 'vector(#N,a0,a1,,...,aN-1)' does the same, and can be used for any
   constant expression 'N'.
   * In previous expressions, the 'ak' can be vectors themselves, to be
   concatenated into a single vector.
   * The scalar element 'ak' of a vector 'X' is retrieved by
   'X[k]'.
   * The sub-vector '[X[p],X[p+s]...X[p+s*(q-1)]]' (of size 'q') of a
   vector 'X' is retrieved by 'X[p,q,s]'.
   * Equality/inequality comparisons between two vectors is done with operators
   '==' and '!='.
   * Some vector-specific functions can be used on vector values: 'cross(X,
   Y)' (cross product), 'dot(X,Y)' (dot product), 'size(X)' (vector
   dimension), 'sort(X,_is_increasing,_starting_index,_nb_elts,_size_elt,
   _sort_index)' (sorted values), 'reverse(A)' (reverse order of
   components), 'map(X,P,_nb_channelsX,_nb_channelsP,_boundary_conditions)',
   'shift(A,_length,_boundary_conditions)' and 'same(A,B,_nb_vals,
   _is_case_sensitive)' (vector equality test).
   * Function 'normP(u1,...,un)' computes the LP-norm of the specified
   vector ('P' being a constant or 'inf', as in e.g. 'norm1()').
   * Function 'normp(V,_p)' computes the Lp-norm of the specified vector
   'V'. Here, 'p' can be variable. Default value for 'p' is 2.
   * Function 'unitnorm(V,_p)' returns a normalized version
   'V/normp(V)' of specified vector 'V'. Default value for 'p'
   is 2.
   * Function 'resize(A,size,_interpolation,_boundary_conditions)' returns
   a resized version of a vector 'A' with specified interpolation mode.
   'interpolation' can be { -1:None (memory content) | 0:None | 1:Nearest
   | 2:Average | 3:Linear | 4:Grid | 5:Bicubic | 6:Lanczos }, and
   'boundary_conditions' can be { 0:Dirichlet | 1:Neumann | 2:Periodic |
   3:Mirror }.
   * Function 'find(A,B,_starting_index,_search_step)' returns the index
   where sub-vector 'B' appears in vector 'A', (or '-1' if
   'B' is not contained in 'A'). Argument 'A' can be also
   replaced by an image index '#ind'.
   * Specifying a vector-valued math expression as an argument of a command that
   operates on image values (e.g. 'fill') modifies the whole spectrum range
   of the processed image(s), for each spatial coordinates '(x,y,z)'. The
   command does not loop over the 'c'-axis in this case.
  ## Complex-valued functions:
  A '2'-dimensional vector may be seen as a complex number and used in
   those particular functions/operators: '**' (complex multiplication),
   '//' (complex division), '^^' (complex exponentiation), '**='
   (complex self-multiplication), '//=' (complex self-division), '^^='
   (complex self-exponentiation), 'cabs()' (complex modulus), 'carg()'
   (complex argument), 'cconj()' (complex conjugate), 'cexp()'
   (complex exponential), 'clog()' (complex logarithm), 'ccos()'
   (complex cosine), 'csin()' (complex sine), 'csqr()' (complex
   square), 'csqrt()' (complex square root), 'ctan()' (complex
   tangent), 'ccosh()' (complex hyperpolic cosine), 'csinh()' (complex
   hyperbolic sine) and 'ctanh()' (complex hyperbolic tangent).
  ## Matrix-valued functions:
  A 'MN'-dimensional vector may be seen as a 'M' x 'N' matrix
   and used in those particular functions/operators: '*' (matrix-vector
   multiplication), 'det(A)' (determinant), 'diag(V)' (diagonal matrix
   from a vector), 'eig(A)' (eigenvalues/eigenvectors), 'eye(n)' (n x
   n identity matrix), 'invert(A,_nb_colsA)' (matrix inverse), 'mul(A,B,
   _nb_colsB)' (matrix-matrix multiplication), 'qr(A,_nb_colsA,
   _is_reduced_form)', 'rot(u,v,w,angle)' (3D rotation matrix),
   'rot(angle)' (2D rotation matrix), 'solve(A,B,_nb_colsB)' (solver
   of linear system A.X = B), 'svd(A,_nb_colsA)' (singular value
   decomposition), 'trace(A)' (matrix trace) and 'transpose(A,
   nb_colsA)' (matrix transpose). Argument 'nb_colsB' may be omitted if
   it is equal to '1'.
  ## Image-valued functions:
  Some functions takes vector-valued arguments that represent image data :
   * Function 'expr(formula,_w,_h,_d,_s,_A)' outputs a vector of size
   'w*h*d*s' with values generated from the specified formula, as if one
   were filling an image with dimensions '(w,h,d,s)'. If 'A' is
   specified, it must be a vector of size 'w*h*d*N' ('N' can be
   different from 's'). It is then passed as a reference image (of size
   'w' x 'h' x 'd' x 'N') for the specified expression.
   * Function 'resize(A,wA,hA,dA,sA,nwA,_nhA,_ndA,_nsA,_interpolation,
   _boundary_conditions,_ax,_ay,_az,_ac)' is an extended version of the
   'resize()' function. It allows to resize the vector 'A', seen as an
   image of size '(ow,oh,od,os)' as a new image of size '(nw,nh,nd,
   ns)', with specified resizing options.
   * Function 'warp(A,wA,hA,dA,sA,B,wB,hB,dB,sB,_mode,_interpolation,
   _boundary_conditions)' returns the warped version of the image 'A' (of
   size '(wA,hA,dA,sA)', seen as a vector of size '(wA*hA*dA*sA)' by
   the warping field 'B' (of size '(wB,hB,dB,sB)'). The resulting
   image has size '(wB,hB,dB,sA)'. This is the math evaluator analog to
   command warp.
   * Function 'index(A,P,nb_channelsP,_dithering,_map_colors)' returns the
   indexed version of the image 'A' by the colormap 'P'. This is the
   math evaluator analog to command index.
   * Function 'permute(A,wA,hA,dA,sA,permutation)' returns a permuted
   version of the image 'A' (of size '(wA,hA,dA,sA)', seen as a vector
   of size 'wA*hA*dA*sA'). This is the math evaluator analog to command
   permute.
   * Function 'mirror(A,wA,hA,dA,sA,axes_string)' returns a mirrored
   version of the image 'A' (of size '(wA,hA,dA,sA)', seen as a vector
   of size 'wA*hA*dA*sA'). This is the math evaluator analog to command
   mirror.
   * Function 'cumulate(A,wA,hA,dA,sA,_axes_string)' returns a cumulated
   version of the image 'A' (of size '(wA,hA,dA,sA)', seen as a vector
   of size 'wA*hA*dA*sA'). This is the math evaluator analog to command
   cumulate.
   * Function 'histogram(A,nb_levels,_min_value,_max_value)' returns the
   histogram of the vector 'A'. This is the math evaluator analog to command
   histogram.
   * Function 'equalize(A,nb_levels,_min_value,_max_value)' returns the
   equalized version of the vector 'A'. This is the math evaluator analog to
   command equalize.
   * Function 'normalize(A,_min_value,_max_value)' returns the normalized
   version of the vector 'A'. This is the math evaluator analog to command
   normalize.
   * Function 'mproj(S,nb_colsS,D,nb_colsD,method,max_iter,max_residual)'
   projects a matrix 'S' onto a dictionary (matrix) 'D'. This is the
   math evaluator analog to command mproj.
   * Functions 'fft(R,I,w,h,d,s,_'axis')' computes the Fast Fourier
   Transform of the complex pair '[R,I]', seen as images of size '(w,h,d,
   s)'. Inverse fft is computed with similar function 'ifft()'. These
   functions replace the content of 'R' and 'I' in-place.
  This is the math evaluator analog to commands fft and ifft.
   * Function 'shift(A,wA,hA,dA,sA,_dx,_dy,_dz,_dc,_boundary_conditions)'
   return a shifted version of the vector 'A', seen as an image of size
   '(wA,hA,dA,sA)'. This is the math evaluator analog to command shift.
   * Function 'noise(A,amplitude,_noise_type)' returns the noisy version of
   the vector 'A'. This is the math evaluator analog to command noise.
   * Function 'rand(#size,_min_value,_max_value,_pdf,_precision)' returns
   the a vector of 'size' random values. This is the math evaluator analog
   to command rand.
  ## String manipulation:
  Character strings are defined as vectors objects and can be then managed as is.
   Dedicated functions and initializers to manage strings exist:
   * '['string']' and ''string'' define a vector whose values are the
   character codes of the specified 'character string' (e.g. ''foo''
   is equal to '[ 102,111,111 ]').
   * '_'character'' returns the (scalar) byte code of the specified
   character (e.g. '_'A'' is equal to '65').
   * A special case happens for empty strings: Values of both expressions
   '['']' and '''' are '0'.
   * Functions 'lowercase()' and 'uppercase()' return string with all
   string characters lowercased or uppercased.
   * Function 's2v(str,_starting_index,_is_strict)' parses specified string
   'str' and returns the value contained in it.
   * Function 'v2s(expr,_nb_digits,_siz)' returns a vector of size
   'siz' which contains the character representation of values described by
   expression 'expr'. 'nb_digits' can be { <-1:0-Padding of
   integers | -1:Auto-reduced | 0:All | >0:Max number of digits }.
   * Function 'echo(str1,str2,...,strN)' prints the concatenation of given
   string arguments on the console.
   * Function 'string(_#siz,str1,str2,...,strN)' generates a vector
   corresponding to the concatenation of given string/number arguments.
  ## Dynamic arrays:
  A dynamic array is defined as a one-column (or empty) image '[ind]' in
   the image list. It allows elements to be added or removed, each element having
   the same dimension (which is actually the number of channels of image
   '[ind]'). Dynamic arrays adapt their size to the number of elements they
   contain.
  A dynamic array can be manipulated in a math expression, with the following
   functions:
   * 'da_size(_#ind)': Return the number of elements in dynamic array
   '[ind]'.
   * 'da_back(_#ind)': Return the last element of the dynamic array
   '[ind]'.
   * 'da_insert(_#ind,pos,elt_1,_elt_2,...,_elt_N)': Insert 'N' new
   elements 'elt_k' starting from index 'pos' in dynamic array
   '[ind]'.
   * 'da_push(_#ind,elt1,_elt2,...,_eltN)': Insert 'N' new elements
   'elt_k' at the end of dynamic array '[ind]'.
   * 'da_pop(_#ind)': Same as 'da_back()' but also remove last
   element from the dynamic array '[ind]'.
   * 'da_push_heap(_#ind,elt1,_elt2,...,_eltN)' and
   'da_pop_heap(_#ind)' does the same but for a dynamic array seen as a
   min-heap structure.
   * 'da_insert_n(_#ind,n,pos,elt_1,_elt_2,...,_elt_N)',
   'da_push_n(_#ind,n,elt1,_elt2,...,_eltN)' and 'da_push_heap_n(_#ind,n,
   elt1,_elt2,...,_eltN)' are variants that insert the specified elements
   consecutively, 'n' times. If no elements are specified, they insert
   'n' empty elements.
   * 'da_remove(_#ind,_start,_end)': Remove elements located between
   indices 'start' and 'end' (included) in dynamic array '[ind]'.
   * 'da_freeze(_#ind)': Convert a dynamic array into a 1-column image with
   height 'da_size(#ind)'.
   * The value of the k-th element of dynamic array '[ind]' is retrieved
   with 'i[_#ind,k]' (if the element is a scalar value), or 'I[_#ind,
   k]' (if the element is a vector).
  In the functions above, argument '#ind' may be omitted in which case it
   is assumed to be '#-1'.
  ## Special operators:
   * ';': expression separator. The returned value is always the last
   encountered expression. For instance expression '1;2;pi' is evaluated as
   'pi'.
   * '=': variable assignment. Variables in mathematical parser can only
   refer to numerical values (vectors or scalars). Variable names are
   case-sensitive. Use this operator in conjunction with ';' to define more
   complex evaluable expressions, such as
    t = cos(x); 3*t^2 + 2*t + 1
  These variables remain local to the mathematical parser and cannot be
   accessed outside the evaluated expression.
   * Variables defined in math parser may have a constant property, by
   specifying keyword 'const' before the variable name (e.g. 'const foo =
   pi/4;'). The value set to such a variable must be indeed a constant
   scalar. Constant variables allows certain types of optimizations in the math
   JIT compiler.
  ## Specific functions:
   * 'addr(expr)': return the pointer address to the specified expression
   'expr'.
   * 'o2c(_#ind,offset,_boundary_conditions)' and 'c2o(_#ind,x,_y,_z,_c,
   _boundary_conditions)': Convert image offset to image coordinates and
   vice-versa. Argument 'boundary_conditions' can be { 0:None | 1:Return
   -1 if out-of-range }.
   * 'fill(target,expr)' or 'fill(target,index_name,expr)' fill the
   vector-valued variable 'target', using the specified expression, e.g.
   'V = vector16(); fill(V,k,k^2 + k + 1);'. This is basically equivalent
   to: 'for (index_name = 0, index_name<size(target), ++index_name,
   target[index_name] = expr);'.
   * 'fill(target,w,h,d,s,'expr')' fills the vector-valued variable
   'target', seen as an image of size '(w,h,d,s)', using the specified
   expression.
   * 'u(max)' or 'u(min,max,_include_min,_include_max)': return a
   random value in range '0...max' or 'min...max', following a uniform
   distribution. Each range extremum can be included (default) in the distribution
   or not.
   * 'v(max)' or 'v(min,max,_include_min,_include_max)' do the same
   but returns an integer in specified range.
   * 'f2ui(value)' and 'ui2f(value)': Convert a large unsigned
   integer as a negative floating point value (and vice-versa), so that 32bits
   floats can be used to store large integers while keeping a unitary precision.
   * 'i(_a,_b,_c,_d,_interpolation_type,_boundary_conditions)': return the
   value of the pixel located at position '(a,b,c,d)' in the associated
   image, if any ('0' otherwise). 'interpolation_type' can be {
   0:Nearest neighbor | 1:Linear | 2:Cubic }. 'boundary_conditions' can
   be { 0:Dirichlet | 1:Neumann | 2:Periodic | 3:Mirror }. Omitted
   coordinates are replaced by their default values which are respectively
   'x', 'y', 'z', 'c', 'interpolation' and
   'boundary'. For instance command
    fill 0.5*(i(x+1)-i(x-1))
   will estimate the X-derivative of an image with a classical finite difference
   scheme.
   * 'j(_dx,_dy,_dz,_dc,_interpolation_type,_boundary_conditions)' does the
   same for the pixel located at position '(x+dx,y+dy,z+dz,c+dc)' (pixel
   access relative to the current coordinates).
   * 'i[offset,_boundary_conditions]' returns the value of the pixel
   located at specified 'offset' in the associated image buffer (or
   '0' if offset is out-of-bounds).
   * 'j[offset,_boundary_conditions]' does the same for an offset relative
   to the current pixel coordinates '(x,y,z,c)'.
   * 'i(#ind,_x,_y,_z,_c,_interpolation,_boundary_conditions)', 'j(#ind,
   _dx,_dy,_dz,_dc,_interpolation,_boundary_conditions)', 'i[#ind,offset,
   _boundary_conditions]' and 'i[offset,_boundary_conditions]' are
   similar expressions used to access pixel values for any numbered image
   '[ind]' of the list.
   * 'I/J[_#ind,offset,_boundary_conditions]' and 'I/J(_#ind,_x,_y,_z,
   _interpolation,_boundary_conditions)' do the same as 'i/j[_#ind,offset,
   _boundary_conditions]' and 'i/j(_#ind,_x,_y,_z,_c,_interpolation,
   _boundary_conditions)' but return a vector instead of a scalar (e.g. a
   vector '[ R,G,B ]' for a pixel at '(a,b,c)' in a color image).
   * 'crop(_#ind,_x,_y,_z,_c,_dx,_dy,_dz,_dc,_boundary_conditions)' returns
   a vector whose values come from the cropped region of image '[ind]' (or
   from default image selected if 'ind' is not specified). Cropped region
   starts from point '(x,y,z,c)' and has a size of '(dx,dy,dz,dc)'.
   Arguments for coordinates and sizes can be omitted if they are not ambiguous
   (e.g. 'crop(#ind,x,y,dx,dy)' is a valid invocation of this function).
   * 'crop(S,w,h,d,s,_x,_y,_z,_c,_dx,_dy,_dz,_dc,_boundary_conditions)'
   does the same but extracts the cropped data from a vector 'S', seen as an
   image of size '(w,h,d,s)'.
   * 'draw(_#ind,S,_x,_y,_z,_c,_dx,_dy,_dz,_dc,_opacity,_opacity_mask,
   _max_opacity_mask)' draws a sprite 'S' in image '[ind]' (or in
   default image selected if 'ind' is not specified) at coordinates '(x,y,
   z,c)'.
   * 'draw(D,w,h,s,d,S,_x,_y,_z,_c,_dx,_dy,_dz,_dc,_opacity,_M,_max_M)'
   does the same but draw the sprite 'S' in the vector 'D', seen as an
   image of size '(w,h,d,s)'.
   * 'polygon(_#ind,nb_vertices,coords,_opacity,_color)' draws a filled
   polygon in image '[ind]' (or in default image selected if 'ind' is
   not specified) at specified coordinates. It draws a single line if
   'nb_vertices' is set to 2.
   * 'polygon(_#ind,-nb_vertices,coords,_opacity,_pattern,_color)' draws a
   outlined polygon in image '[ind]' (or in default image selected if
   'ind' is not specified) at specified coordinates and with specified line
   pattern. It draws a single line if 'nb_vertices' is set to 2.
   * 'ellipse(_#ind,xc,yc,radius1,_radius2,_angle,_opacity,_color)' draws a
   filled ellipse in image '[ind]' (or in default image selected if
   'ind' is not specified) with specified coordinates.
   * 'ellipse(_#ind,xc,yc,-radius1,-_radius2,_angle,_opacity,_pattern,
   _color)' draws an outlined ellipse in image '[ind]' (or in default
   image selected if 'ind' is not specified).
   * 'flood(_#ind,_x,_y,_z,_tolerance,_is_high_connectivity,_opacity,
   _color)' performs a flood fill in image '[ind]' (or in default image
   selected if 'ind' is not specified) with specified coordinates. This is
   the math evaluator analog to command flood.
   * 'resize(#ind,w,_h,_d,_s,_interp,_boundary_conditions,_cx,_cy,_cz,_cc)'
   resizes an image of the associated list with specified dimension and
   interpolation method. When using this function, you should consider retrieving
   the (non-constant) image dimensions using the dynamic functions
   'w(_#ind)', 'h(_#ind)', 'd(_#ind)', 's(_#ind)',
   'wh(_#ind)', 'whd(_#ind)' and 'whds(_#ind)' instead of the
   corresponding constant variables.
   * 'if(condition,expr_then,_expr_else)': return value of
   'expr_then' or 'expr_else', depending on the value of
   'condition' { 0:False | other:True }. 'expr_else' can be
   omitted in which case '0' is returned if the condition does not hold.
   Using the ternary operator 'condition?expr_then[:expr_else]' gives an
   equivalent expression. For instance, G'MIC commands
    fill if(!(x%10),255,i)
   and
    fill x%10?i:255
   both draw blank vertical lines on every 10th column of an image.
   * 'do(expression,_condition)' repeats the evaluation of
   'expression' until 'condition' vanishes (or until a 'break()'
   instruction is encountered if no 'condition' is specified). For instance,
   the expression:
    if(N<2,N,n=N-1;F0=0;F1=1;do(F2=F0+F1;F0=F1;F1=F2,n=n-1))
   returns the N-th value of the Fibonacci sequence, for 'N>=0' (e.g.,
   '46368' for 'N=24'). 'do(expression,condition)' always
   evaluates the specified expression at least once, then check for the loop
   condition. When done, it returns the last value of 'expression'.
   * 'for(init,condition,_procedure,body)' first evaluates the expression
   'init', then iteratively evaluates 'body' (followed by
   'procedure' if specified) while 'condition' holds (i.e. not zero).
   It may happen that no iterations are done, in which case the function returns
   'nan'. Otherwise, it returns the last value of 'body'. For instance,
    the expression:
    if(N<2,N,for(n=N;F0=0;F1=1,n=n-1,F2=F0+F1;F0=F1;F1=F2))
   returns the 'N'-th value of the Fibonacci sequence, for 'N>=0'
   (e.g., '46368' for 'N=24').
   * 'while(condition,expression)' is exactly the same as 'for(init,
   condition,expression)' without the specification of an initializing
   expression.
   * 'repeat(nb_iters,expr)' or 'fill(nb_iters,iter_name,expr)' run
   'nb_iters' iterations of the specified expression 'expr', e.g.
   'V = vector16(); repeat(16,k,V[k] = k^2 + k + 1);'. It is basically
   equivalent to: 'for (iter_name = 0, iter_name<nb_iters, ++iter_name,
   expr);'.
   * 'break()' and 'continue()' respectively breaks and continues the
   current running block.
   * 'fsize('filename')' returns the size of the specified 'filename'
   (or '-1' if file does not exist).
   * 'date(attr,'path')' returns the date attribute for the given
   'path' (file or directory), with 'attr' being { 0:Year | 1:Month
   | 2:Day | 3:Day of week | 4:Hour | 5:Minute | 6:Second }, or a vector of
   those values.
   * 'date(_attr)' returns the specified attribute for the current (locale)
   date (attributes being { 0...6:Same meaning as above | 7:Milliseconds }).
   * 'epoch(_year,_month,_day,_hour,_minute,_second)' converts the
   specified date into Epoch. If no arguments, are specified, current Epoch is
   returned.
   * 'print(expr1,expr2,...)' or 'print(#ind)' prints the value of
   the specified expressions (or image information) on the console, and returns
   the value of the last expression (or 'nan' in case of an image). Function
   'prints(expr)' also prints the string composed of the character codes
   defined by the vector-valued expression (e.g. 'prints('Hello')').
   * 'debug(expression)' prints detailed debug info about the sequence of
   operations done by the math parser to evaluate the expression (and returns its
   value).
   * 'display(_X,_w,_h,_d,_s)' or 'display(#ind)' display the
   contents of the vector 'X' (or specified image) and wait for user events.
   if no arguments are provided, a memory snapshot of the math parser environment
   is displayed instead.
   * 'begin(expression)' and 'end(expression)' evaluates the
   specified expressions only once, respectively at the beginning and end of the
   evaluation procedure, and this, even when multiple evaluations are required
   (e.g. in 'fill ">begin(foo = 0); ++foo"').
   * 'copy(dest,src,_nb_elts,_inc_d,_inc_s,_opacity)' copies an entire
   memory block of 'nb_elts' elements starting from a source value
   'src' to a specified destination 'dest', with increments defined by
   'inc_d' and 'inc_s' respectively for the destination and source
   pointers.
   * 'stats(_#ind)' returns the statistics vector of the running image
   '[ind]', i.e the vector '[ im,iM,ia,iv,xm,ym,zm,cm,xM,yM,zM,cM,is,ip
   ]' (14 values).
   * 'stats(X,w,h,d,s)' returns the statistics vector of the vector
   'X', seen as an image of size '(w,h,d,s)'.
   * 'ref(expr,a)' references specified expression 'expr' as variable
   name 'a'.
   * 'unref(a,b,...)' destroys references to the named variable given as
   arguments.
   * 'breakpoint()' inserts a possible computation breakpoint (useless with
   the cli interface).
   * '_(comment) expr' just returns expression 'expr' (useful for
   inserting inline comments in math expressions).
   * 'run('pipeline')' executes the specified G'MIC pipeline as if it
   was called outside the currently evaluated expression.
   * 'set('variable_name',A)' set the G'MIC variable
   '$variable_name' with the value of expression 'A'. If 'A' is
   a vector-valued variable, it is assumed to encode a string.
   * 'store('variable_name',A,_w,_h,_d,_s,_is_compressed)' transfers the
   data of vector 'A' as a '(w,h,d,s)' image to the G'MIC variable
   '$variable_name'. Thus, the data becomes available outside the math
   expression (that is equivalent to using the regular command store, but
   directly in the math expression).
   * 'get('variable_name',_size,_return_as_string)' returns the value of
   the specified variable, as a vector of 'size' values, or as a scalar (if
   'size' is zero or not specified).
   * 'name(_#ind,size)' returns a vector of size 'size', whose values
   are the characters codes of the name of image '[ind]' (or default image
   selected if 'ind' is not specified).
   * 'correlate(I,wI,hI,dI,sI,K,wK,hK,dK,sK,_boundary_conditions,
   _is_normalized,_channel_mode,_xcenter,_ycenter,_zcenter,_xstride,_ystride,
   _zstride,_xdilation,_ydilation,_zdilation,_xoffset,_yoffset,_zoffset,_xsize,
   _ysize,_zsize)' returns the correlation, unrolled as a vector, of the
   '(wI,hI,dI,sI)'-sized image 'I' with the '(wK,hK,dK,
   sK)'-sized kernel 'K' (the meaning of the other arguments are the same
   as in command 'correlate'). Similar function 'convolve(...)' is
   also defined for computing the convolution between 'I' and 'K'.
  ## User-defined macros:
   * Custom macro functions can be defined in a math expression, using the
   assignment operator '=', e.g.
    foo(x,y) = cos(x + y); result = foo(1,2) + foo(2,3)
   * Trying to override a built-in function (e.g. 'abs()') has no effect.
   * Overloading macros with different number of arguments is possible.
   Re-defining a previously defined macro with the same number of arguments
   discards its previous definition.
   * Macro functions are indeed processed as macros by the mathematical
   evaluator. You should avoid invoking them with arguments that are themselves
   results of assignments or self-operations. For instance,
    foo(x) = x + x; z = 0; foo(++z)
   returns '4' rather than expected value '2'.
   * When substituted, macro arguments are placed inside parentheses, except if a
   number sign '#' is located just before or after the argument name. For
   instance, expression
    foo(x,y) = x*y; foo(1+2,3)
   returns '9' (being substituted as '(1+2)*(3)'), while expression
    foo(x,y) = x#*y#; foo(1+2,3)
   returns '7' (being substituted as '1+2*3').
   * Number signs appearing between macro arguments function actually count for
   empty separators. They may be used to force the substitution of macro
   arguments in unusual places, e.g. as in
    str(N) = ['I like N#'];
   * Macros with variadic arguments can be defined, by specifying a single
   argument name followed by '...'. For instance,
    foo(args...) = sum([ args ]^2);
   defines a macro that returns the sum of its squared arguments, so 'foo(1,2,
   3)' returns '14' and 'foo(4,5)' returns '41'.
  ## Multi-threaded and in-place evaluation:
   * If your images are large enough and you have several CPU cores available, it
   is possible that the math expression passed to a 'fill', 'eval' or
   'input' commands can be evaluated in parallel, using multiple
   computation threads.
   * G'MIC tries to auto-detect if an expression can be parallelized, and
   if it can be evaluated in-place (otherwise, a temporary copy of the image is
   allocated). However, this auto-detection can be overridden, by starting the
   mathematical expression for the 'fill' or 'eval' commands, with a
   single character, that can be:
     - '*': Evaluate expression in parallel, eventually using a
   temporary image copy.
     - ':': Evaluate expression in parallel, but in-place.
     - '+': Evaluate expression in a single thread, eventually using a
   temporary image copy (in scanline order).
     - '>': Evaluate expression in a single thread, but in-place (in
   scanline order).
     - '<': Evaluate expression in a single thread, but in-place (in
   reverse scanline order).
   * Beware, depending on the amount of data to process, parallel evaluations may
   be slower than using single-thread evaluations (e.g. for small images).
   * For parallel evaluations, function 'critical(expr)' forces the
   execution of the given sub-expression 'expr' in a single thread at a time.
   * 'begin_t(expr)' and 'end_t(expr)' evaluates the specified
   expression once for each running thread (so possibly several times) at the
   beginning and the end of the evaluation procedure.
   * 'merge(variable,operator)' tells to merge the local variable value
   computed by threads, with the specified operator, when all threads have
   finished computing.
   * Expressions 'i(_#ind,x,_y,_z,_c)=value', 'j(_#ind,x,_y,_z,
   _c)=value', 'i[_#ind,offset]=value' and 'j[_#ind,offset]=value'
   set a pixel value at a different location than the running one in the image
   '[ind]' (or in the associated image if argument '#ind' is omitted),
   either with global coordinates/offsets (with 'i(...)' and 'i[...]'),
    or relatively to the current position '(x,y,z,c)' (with 'j(...)'
   and 'j[...]'). These expressions always return 'value'.
  10. Adding Custom Commands
      ----------------------
   * New custom commands can be added by the user, through the use of G'MIC
   custom commands files.
   * A command file is a simple text file, where each line starts either by
    command_name: command_definition
   or
    command_definition (continuation)
   * At startup, G'MIC automatically includes user's command file
   '$HOME/.gmic' (on Unix) or '%USERPROFILE%ser.gmic' (on
   Windows). The CLI tool 'gmic' automatically runs the command
   'cli_start' if defined.
   * Custom command names must use character set '[a-zA-Z0-9_]' and cannot
   start with a number.
   * Any '# comment' expression found in a custom commands file is
   discarded by the G'MIC parser, wherever it is located in a line.
   * In a custom command, the following '$-expressions' are recognized and
   substituted:
     - '$*' is substituted by a verbatim copy of the specified string of
   arguments (do not include arguments set to default values).
     - '$"*"' is substituted by the sequence of specified arguments,
   separated by commas ',', each being double-quoted (include arguments set
   to default values).
     - '$#' is substituted by the maximum index of known arguments (either
   specified by the user or set to a default value in the custom command).
     - '$[]' is substituted by the list of selected image indices that
   have been specified in the command invocation.
     - '$?' is substituted by a printable version of '$[]' to be
   used in command descriptions.
     - '$i' and '${i}' are both substituted by the 'i'-th
   specified argument. Negative indices such as '${-j}' are allowed and
   refer to the 'j'-th latest argument. '$0' is substituted by the
   custom command name.
     - '${i=default}' is substituted by the value of '$i' (if
   defined) or by its new value set to 'default' otherwise ('default'
   may be a '$-expression' as well).
     - '${subset}' is substituted by the argument values (separated by
   commas ',') of a specified argument subset. For instance expression
   '${2--2}' is substituted by all specified command arguments except the
   first and the last one. Expression '${^0}' is then substituted by all
   arguments of the invoked command (eq. to '$*' if all arguments have been
   indeed specified).
     - '$=var' is substituted by the set of instructions that will assign
   each argument '$i' to the named variable 'var$i' (for i in
   '[0...$#]'. This is particularly useful when a custom command want to
   manage variable numbers of arguments. Variables names must use character set
   '[a-zA-Z0-9_]' and cannot start with a number.
   * These particular '$-expressions' for custom commands are always
   substituted, even in double-quoted items or when the dollar sign '$'
   is escaped with a backslash '$'. To avoid substitution, place an empty
   double quoted string just after the '$' (as in '$""1').
   * Specifying arguments may be skipped when invoking a custom command, by
   replacing them by commas ',' as in expression
    flower ,,3
   Omitted arguments are set to their default values, which must be thus
   explicitly defined in the code of the corresponding custom command (using
   default argument expressions as '${1=default}').
   * If one numbered argument required by a custom command misses a value, an
   error is thrown by the G'MIC interpreter.
   * It is possible to specialize the invocation of a '+command' by
   defining it as
    +command_name: command_definition
   * A +-specialization takes priority over the regular command definition when
   the command is invoked with a prepended '+'.
   * When only a +-specialization of a command is defined, invoking
   'command' is actually equivalent to '+command'.
  11. List of Commands
      ----------------
  All available G'MIC commands are listed below, by categories. An argument
   specified between '[]' or starting by '_' is optional except when
   standing for an existing image '[image]', where 'image' can be
   either an index number or an image name. In this case, the '[]'
   characters are mandatory when writing the item. Note that all images that serve
   as illustrations in this reference documentation are normalized in range '[0,
   255]' before being displayed. You may need to do this explicitly (command
   'normalize 0,255') if you want to save and view images with the same
   aspect than those illustrated in the example codes.
  The examples accompanying this 'List of Commands' illustrate the use of
   the G'MIC language and are written as they would appear in a custom command.
   While some examples may work if entered directly at a shell prompt, there is no
   guarantee. No attempt has been made to escape special characters in these
   examples, which many shells reserve.
  11.1. Global Options
        --------------
  debug (+):
    Activate debug mode.
    When activated, the G'MIC interpreter becomes very verbose and outputs additional log
    messages about its internal state on the standard output (stdout).
    This option is useful for developers or to report possible bugs of the interpreter.
  h:
      Shortcut for command 'help'.
  help:
      command |
      (no arg)
    Display help (optionally for specified command only) and exit.
    (equivalent to shortcut command 'h').
  version:
    Display current version number on stdout.
  11.2. Input / Output
        --------------
  camera (+):
      _camera_index>=0,_nb_frames>0,_skip_frames>=0,_capture_width>=0,_capture_height>=0
    Insert one or several frames from specified camera.
    When 'nb_frames==0', the camera stream is released instead of capturing new images.
    This command requires features from the OpenCV library (not enabled in G'MIC by default).
    Default values: 'camera_index=0' (default camera), 'nb_frames=1', 'skip_frames=0' and 'capture_width=capture_height=0' (default size).
  m (+):
      Shortcut for command 'command'.
  command (+):
      _add_debug_info={ 0:No | 1:Yes },{ filename | http[s]://URL | "string" }
    Import G'MIC custom commands from specified file, URL or string.
    (equivalent to shortcut command 'm').
    Imported commands are available directly after the 'command' invocation.
    Specified filename is not allowed to contain colons ':'.
    Default value: 'add_debug_info=1' (except for a "string" argument, in which case 'add_debug_info=0').
    Example:
      [#1] image.jpg command "foo : mirror y deform $""1" +foo[0] 5 +foo[0] 15
  compress_to_keypoints:
      _method,_max_keypoints>=0,_err_avg[%]>=0,_err_max[%]>=0,_"err_command"
    Compress each of the selected images into a set of keypoints that can be further decompressed using command decompress_from_keypoints.
    Beware: This type of compression is effective only for images with very smooth content.
    'method' can be { 0:PDE | 1:RBF }. Add '2' to 'method' to skip the point removal step.
     * 'max_keypoints' is the maximal number of keypoints generated by the compression method. If 'max_keypoints<0', the removal step is not done when number of maximal
    keypoints has been reached. 'max_keypoints=0' means 'no limits'.
     * 'err_avg' is the desired average compression error.
     * 'err_max' is the desired pointwise max compression error.
     * 'err_command' is the code of a command that inputs the two images '[reference]' and '[compressed]' and compute a single error map as a last image.
    Default values: 'method=3', 'max_keypoints=0', 'err_avg=1%', 'err_max=5%' and 'err_command=-. [0] norm.'
  createdir (+):
      force_overwrite={ 0:No | 1:Yes },directory_path_1,_directory_path_2,...
    Create one or several new directories 'directory_path_k' on the filesystem.
    If a specified directory path contains itself several subdirectories, they are all created if necessary.
    Default value: 'force_overwrite=1'.
  cursor (+):
      _mode = { 0:hide | 1:show }
    Show or hide mouse cursor for selected instant display windows.
    Command selection (if any) stands for instant display window indices instead of image indices.
    Default value: 'mode=1'.
  delete (+):
      filename1[,filename2,...]
    Delete specified files or directories on disk. Multiple filenames must be separated by commas.
  d:
      Shortcut for command 'display'.
  display:
    Display selected images in an interactive window.
    (equivalent to shortcut command 'd').
    When invoked with a '+' prefix (i.e. '+display'), the command outputs its log messages on 'stdout' rather than on 'stderr'.
    Display window #0 is used as the default window for the display, if already opened.
    Available controls are shown below (where 'LMB' = Left mouse button, 'RMB' = Right mouse button, 'MMB' = Middle mouse button and 'MW' = Mouse wheel).
     * Thumbnail navigation bar:
    'TAB': Show/hide thumbnails - 'LMB': Select thumbnail or shift thumbnail bar - '0'-'9','ARROWS' (opt. '+SHIFT'),'B','BACKSPACE',
    'C','E','END','H','HOME','SPACE': Navigate and select thumbnails (add 'CTRL' if mouse pointer is outside thumbnail bar).
     * Image view:
    'LMB' or 'MMB': Image pan - 'RMB' or 'MW': Image zoom - 'ARROWS' (opt. '+SHIFT'),'HOME','END': Shift view - 'A': Switch
    alpha rendering - 'C': Center view - 'E': Go to lower-right corner - 'RETURN': Reset view - 'G': Toggle grid - 'H': Go to upper-left corner -
    'K': Switch background - 'M': Toggle 3D view - 'N': Switch normalization - 'P': Print info about current image pixel on 'stdout' - 'PAGEUP' or
    'PAGEDOWN': Raise/lower base channel - 'R': Rotate image - 'T': Plot as a 1D curve - 'V': Crop image - 'Z': Switch zoom factor -
    '0'-'9': Set zoom factor.
     * 3D mesh view:
    'LMB': Mesh rotation - 'CTRL+LMB' or 'MMB': Mesh pan - 'RMB': Mesh zoom - 'A': Toggle axes - 'D': Switch face side mode - 'F': Change
    focale - 'J': Start/stop animation - 'K': Switch background - 'O': Switch outline mode - 'P': Print 3D pose matrix on 'stdout' - 'R': Switch
    rendering mode - 'T': Switch motion rendering mode - 'X': Show/hide bounding-box - 'U': Switch animation mode - 'Z': Toggle z-buffer.
     * 2D images specific:
    'CTRL+LMB': Rectangular selection.
     * 3D volumetric images specific:
    'CTRL+MW': Pan along orthogonal axis - 'X': Reset area layout.
     * Window size, decoration and data I/O:
    'CTRL+C': Decrease window size - 'CTRL+D': Increase window size - 'CTRL+F': Toggle fullscreen - 'CTRL+I': Toggle info label - 'CTRL+O': Save copy
    of image as a '.gmz' file - 'CTRL+L': Save copy of image list as '.gmz' file - 'CTRL+S': Save screenshot as a '.png' file - 'CTRL+W': Start/stop window
    recording - 'CTRL+X': Toggle cursor.
     * Configuration variables:
    The viewer configuration can be tuned by assigning the following variables:
       - '_display_selected' is an integer or an image name that tells which image is selected by default.
       - '_display_alpha' can be { 0:Off | 1:On | 2:Over black | 3:Over gray | 4:Over white } (default value: '0').
       - '_display_background', an integer in range [ 0,9 ] (default value: '3').
       - '_display_cursor' can be { 0:Off | 1:On (2D only) | 2:On (+3D volumetric images) } (default value: '1').
       - '_display_is_grid' can be { 0:Off | 1:On } (default value: '1').
       - '_display_is_info' can be { 0:Off | 1:On } (default value: '1').
       - '_display_normalization' can be { -1:Auto | 0:Off | 1:Cut | 2:Stretch channelwise | 3:Stretch global | 4: stretch (global-once) } (default value: '-1').
       - '_display_print_images' can be { 0:Off | N>0 } (default value: '5'). It sets the max number 'N' of images whose information is initially printed on
    'stderr' or 'stdout'.
       - '_display_3d_is_rendered' can be { 0:Off | 1:On } (default value: '1').
       - '_display_3d_rendering_mode' can be { 0:Dots | 1:Wireframe | 2:Flat | 3:Flat-shaded | 4:Gouraud-shaded | 5=Phong-shaded } (default value: '4').
       - '_display_3d_outline_mode' can be { 0:No-outline | 1:Black-outline | 2:Gray-outline | 3:Red-outline | 4:Green-outline | 5:Blue-outline | 6:White-outline }
    (default value: '0').
       - '_display_3d_motion_rendering_mode' can be { -1:Bounding-box | 0:Dots | 1:Wireframe | 2:Flat | 3:Flat-shaded | 4:Gouraud-shaded | 5=Phong-shaded } (default
    value: '3').
       - '_display_3d_motion_time_limit' is specified in ms. Above this time, motion rendering toggle to 'bounding-box' mode (default value: '300').
       - '_display_3d_side_mode' can be { 0:Single-sided | 1:Double-sided | 2:Single-sided (flipped) } (default value: '0').
       - '_display_3d_is_zbuffer' can be { 0:Off | 1:On } (default value: '1').
       - '_display_3d_focale' can be { <0:Perspective projection w/o sprite zooming, 0:Parallel projection | >0:Perspective projection } (default value: 1.5).
       - '_display_3d_is_axes' can be { 0:Off | 1:On } (default value: '1').
       - '_display_3d_is_bounding_box' can be { 0:Off | 1:On } (default value: '0').
       - '_display_3d_background' is an unsigned integer in range [0,11] (default value: '11').
       - '_display_3d_pose' is a sequence of 12 values that defines the current 3D pose matrix (read/write).
       - '_display_3d_animation' can be { 0:Off | 1:Forward | 2:Backward } (default value: '0').
       - '_display_3d_animation_mode' can be { 0-3:X-axis | 4-7:Y-axis | 8-11:Z-axis | 12-15:XYZ-axes } (default value: '4').
  d0:
      Shortcut for command 'display0'.
  display0:
    Display selected images in an interactive window, without normalization and alpha mode activated.
  da:
      Shortcut for command 'display_array'.
  display_array:
      _width>0,_height>0
    Display images in interactive windows where pixel neighborhoods can be explored.
    Default values: 'width=13' and 'height=width'.
  dc:
      Shortcut for command 'display_camera'.
  display_camera:
    Open camera viewer.
    This command requires features from the OpenCV library (not enabled in G'MIC by default).
  dclut:
      Shortcut for command 'display_clut'.
  display_clut:
      _image_resolution>0,_clut_resolution>0
    Display selected 3D color LUTs.
    Default values: 'image_resolution=320' and 'clut_resolution=33'.
    Example:
      [#1] clut tealorange clut summer clut 60s display_clut 400
  dfft:
      Shortcut for command 'display_fft'.
  display_fft:
    Display fourier transform of selected images, with centered log-module and argument.
    (equivalent to shortcut command 'dfft').
    Example:
      [#1] image.jpg +display_fft
  dg:
      Shortcut for command 'display_graph'.
  display_graph:
      _width>=0,_height>=0,_plot_type,_vertex_type,_xmin,_xmax,_ymin,_ymax,_xlabel,_ylabel,_frame_size>=0
    Render graph plot from selected image data.
    'plot_type' can be { 0:None | 1:Lines | 2:Splines | 3:Bar }.
    'vertex_type' can be { 0:None | 1:Points | 2,3:Crosses | 4,5:Circles | 6,7:Squares }.
    'xmin','xmax','ymin','ymax' set the coordinates of the displayed xy-axes.
    if specified 'width' or 'height' is '0', then image size is set to half the screen size.
    Default values: 'width=0', 'height=0', 'plot_type=1', 'vertex_type=1', 'xmin=xmax=ymin=ymax=0 (auto)', 'xlabel="x-axis"',
     'ylabel="y-axis"' and 'frame_size=32'.
    Example:
      [#1] 128,1,1,1,'cos(x/10+u)' +display_graph 400,300,3
  dh:
      Shortcut for command 'display_histogram'.
  display_histogram:
      _width>=0,_height>=0,_clusters>0,_min_value[%],_max_value[%],_show_axes={ 0:No | 1:Yes },_expression.
    Render a channel-by-channel histogram.
    If selected images have several slices, the rendering is performed for all input slices.
    'expression' is a mathematical expression used to transform the histogram data for visualization purpose.
    (equivalent to shortcut command 'dh').
    if specified 'width' or 'height' is '0', then image size is set to half the screen size.
    Default values: 'width=0', 'height=0', 'clusters=256', 'min_value=0%', 'max_value=100%', 'show_axes=1' and 'expression=i'.
    Example:
      [#1] image.jpg +display_histogram 512,300
  display_parametric:
      _width>0,_height>0,_outline_opacity,_vertex_radius>=0,_is_antialiased={ 0:No | 1:Yes },_is_decorated={ 0:No | 1:Yes },_xlabel,_ylabel
    Render 2D or 3D parametric curve or point clouds from selected image data.
    Curve points are defined as pixels of a 2 or 3-channel image.
    If the point image contains more than 3 channels, additional channels define the (R,G,B) color for each vertex.
    If 'outline_opacity>1', the outline is colored according to the specified vertex colors and
    'outline_opacity-1' is used as the actual drawing opacity.
    Default values: 'width=512', 'height=width', 'outline_opacity=3', 'vertex_radius=0', 'is_antialiased=1','is_decorated=1',
     'xlabel="x-axis"' and 'ylabel="y-axis"'.
    Example:
      [#1] 1024,1,1,2,'t=x/40;(!c?sin(t):cos(t))*(exp(cos(t))-2*cos(4*t)-sin(t/12)^5)' display_parametric 512,512
      [#2] 1000,1,1,2,u(-100,100) quantize 4,1 noise 12 channels 0,2 +normalize 0,255 append c display_parametric 512,512,0.1,8
  display_polar:
      _width>32,_height>32,_outline_type,_fill_R,_fill_G,_fill_B,_theta_start,_theta_end,_xlabel,_ylabel
    Render polar curve from selected image data.
    'outline_type' can be { r<0:Dots with radius -r | 0:No outline | r>0:Lines+dots with radius r }.
    'fill_color' can be { -1:No fill | R,G,B:Fill with specified color }.
    Default values: 'width=500', 'height=width', 'outline_type=1', 'fill_R=fill_G=fill_B=200', 'theta_start=0', 'theta_end=360',
     'xlabel="x-axis"' and 'ylabel="y-axis"'.
    Example:
      [#1] 300,1,1,1,'0.3+abs(cos(10*pi*x/w))+u(0.4)' display_polar 512,512,4,200,255,200
      [#2] 3000,1,1,1,'x^3/1e10' display_polar 400,400,1,-1,,,0,{15*360}
  dq:
      Shortcut for command 'display_quiver'.
  display_quiver:
      _size_factor>0,_arrow_size>=0,_color_mode={ 0:Monochrome | 1:Grayscale | 2:Color }
    Render selected images of 2D vectors as a field of 2D arrows.
    (equivalent to shortcut command 'dq').
    Default values: 'size_factor=16', 'arrow_size=1.5' and 'color_mode=1'.
    Example:
      [#1] image.jpg +luminance gradient[-1] xy rv[-2,-1] *[-2] -1 a[-2,-1] c crop 60,10,90,30 +display_quiver[1] ,
  drgba:
      Shortcut for command 'display_rgba'.
  display_rgba:
      _background_RGB_color
    Render selected RGBA images over a checkerboard or colored background.
    (equivalent to shortcut command 'drgba').
    Default values: 'background_RGB_color=undefined' (checkerboard).
    Example:
      [#1] image.jpg +norm threshold[-1] 40% blur[-1] 3 normalize[-1] 0,255 append c display_rgba
  dt:
      Shortcut for command 'display_tensors'.
  display_tensors:
      _size_factor>0,_ellipse_size>=0,_color_mode={ 0:Monochrome | 1:Grayscale | 2:Color },_outline>=0
    Render selected images of tensors as a field of 2D ellipses.
    (equivalent to shortcut command 'dt').
    Default values: 'size_factor=16', 'ellipse_size=1.5', 'color_mode=2' and 'outline=2'.
    Example:
      [#1] image.jpg +diffusiontensors 0.1,0.9 rescale2d. 64 +display_tensors. 16,2
    Tutorial: https://gmic.eu/oldtutorial/_display_tensors
  dv3d:
      Shortcut for command 'display_voxels3d'.
  display_voxels3d:
    Display selected images as set of 3D voxels.
    (equivalent to shortcut command 'dv3d').
  dw:
      Shortcut for command 'display_warp'.
  display_warp:
      _cell_size>0
    Render selected 2D warping fields.
    (equivalent to shortcut command 'dw').
    Default value: 'cell_size=15'.
    Example:
      [#1] 400,400,1,2,'x=x-w/2;y=y-h/2;r=sqrt(x*x+y*y);a=atan2(y,x);5*sin(r/10)*[cos(a),sin(a)]' +display_warp 10
  e (+):
      Shortcut for command 'echo'.
  echo (+):
      message
    Output specified message on the error output.
    (equivalent to shortcut command 'e').
    Command selection (if any) stands for displayed call stack subset instead of image indices.
    When invoked with a '+' prefix (i.e. '+echo'), the command output its message on stdout rather than stderr.
  echo_file:
      filename,message
    Output specified message, appending it to specified output file.
    (similar to 'echo' for specified output file stream).
  font:
      { 'Font_name' | font_number | font.gmz },_font_height[%]>0,_is_bold={ 0:No | 1:Yes }
    Return font identifier (variable name) that can be further used in command 'text' as a custom font.
    'Font name' can be { Acme | Arial | ArialBlack | BlackOpsOne | BlackChancery | CabinSketch | Caprasimo | CarnevaleeFreakshow | CheeseBurger | Cheque | ChequeBlack |
    Chlorinar | ComicSansMS | CourierNew | Creepster | Georgia | Hidayatullah | Impact | Jaro | Lobster | LuckiestGuy | Macondo | MedievalSharp | OdinRounded | Oswald |
    PalatinoLinotype | PlayfairDisplay | Roboto | Satisfy | Sofia | SundayMilk | TexGyreAdventor | TimesNewRoman | TitanOne | Typewriter | Verdana }.
    If a filename 'font.gmz' is specified, it must be a file converted with command 'font2gmz'.
    Default values: 'font_height=64' and 'is_bold=0'.
    Example:
      [#1] 400,300,1,3 text "Hello World!",0.5~,0.5~,${"font
  font2gmz:
      _font_name,_font_size>0,_font_qualifier
    Convert specified font to G'MIC format, so that it can be used as a custom font for command 'text'.
    'font_name' can be either a filename as 'font.ttf', or a 'Google Font Name'.
    This command requires the command line tool 'cutycapt' to be installed on your system.
    Beware, 'font_size' is the size of font used for the rendering, it does not correspond to the font height.
    Default values: 'font_name=Sofia', 'font_size=24' and 'font_qualifier=""'.
  function1d:
      0<=smoothness<=1,x0>=0,y0,x1>=0,y1,...,xn>=0,yn
    Insert continuous 1D spline function from specified list of keypoints (xk,yk)
    in range [0,max(xk)] (xk are positive integers).
    Example:
      [#1] function1d 1,0,0,10,30,40,20,70,30,80,0 +display_graph 400,300
  identity:
      _width>=0,_height>=0,_depth>=0
    Insert an identity map of given size at the end of the image list.
    Default values: 'height=width' and 'depth=1'.
    Example:
      [#1] identity 5,1 identity 8,8
  i (+):
      Shortcut for command 'input'.
  input (+):
      [type:]filename |
      [type:]http://URL |
      [selection]x_nb_copies>0 |
      { width[%]>0 | [image_w] },{ _height[%]>0 | [image_h] },{ _depth[%]>0 | [image_d] },{ _spectrum[%]>0 | [image_s] },_{ value1,_value2,... | 'formula' } |
      (value1{,|;|/|^}value2{,|;|/|^}...[:{x|y|z|c|,|;|/|^}]) |
      0
    Insert a new image taken from a filename or from a copy of an existing image [index],
    or insert new image with specified dimensions and values. Single quotes may be omitted in
    'formula'. Specifying argument '0' inserts an 'empty' image.
    (equivalent to shortcut command 'i').
    Default values: 'nb_copies=1', 'height=depth=spectrum=1' and 'value1=0'.
    Example:
      [#1] input image.jpg
      [#2] input (1,2,3;4,5,6;7,8,9^9,8,7;6,5,4;3,2,1)
      [#3] image.jpg (1,2,3;4,5,6;7,8,9) (255^128^64) 400,400,1,3,'(x>w/2?x:y)*c'
    Tutorial: https://gmic.eu/tutorial/input
  input_565:
      filename,width>0,height>0,reverse_endianness={ 0:No | 1:Yes }
    Insert image data from a raw RGB-565 file, at the end of the list.
    Default value: 'reverse_endianness=0'.
  ib:
      Shortcut for command 'input_bytes'.
  input_bytes:
      filename
    Input specified filename as a 1D array of bytes.
    (equivalent to shortcut command 'ib').
  input_csv:
      "filename",_read_data_as={ 0:Numbers | 1:Strings | _Variable_name }
    Insert number of string array from specified .csv file.
    If 'variable_name' is provided, the string of each cell is stored in a numbered variable '_variable_name_x_y', where 'x' and 'y' are the indices of the
    cell column and row respectively (starting from '0').
    Otherwise, a 'WxH' image is inserted at the end of the list, with each vector-valued pixel 'I(x,y)' encoding the number or the string of each cell.
    This command returns the 'W,H' dimension of the read array, as the status.
    Default value: 'read_data_as=1'.
  input_cube:
      "filename",_convert_1d_cluts_to_3d={ 0:No | 1:Yes }.
    Insert CLUT data from a .cube filename (Adobe CLUT file format).
    Default value: 'convert_1d_cluts_to_3d=0'.
  input_flo:
      "filename"
    Insert optical flow data from a .flo filename (vision.middlebury.edu file format).
  ig:
      Shortcut for command 'input_glob'.
  input_glob:
      pattern
    Insert new images from several filenames that match the specified glob pattern.
    (equivalent to shortcut command 'ig').
  input_gpl:
      filename
    Input specified filename as a .gpl palette data file.
  input_cached:
      "basename.ext",_downloading_from_gmic_server
    Input specified filename, assumed to be stored in one of the G'MIC resource folder.
    If file not found and 'try_downloading=1', file is downloaded from the G'MIC server and stored
    in the '${-path_cache}' folder.
    This command returns the full path to the corresponding file.
    'downloading_from_gmic_server' can be { 0:No | 1:Yes, if file not found | 2:Yes, if file not in regular cache folder }.
    Default value: 'try_downloading_from_gmic_server=1'.
  in:
      Shortcut for command 'input_normalized'.
  input_normalized:
      filename
    Input specified filename and constrain its value range to be in [0,255].
    (equivalent to shortcut command 'in').
  input_obj:
      filename
    Input specified 3D mesh from a .obj Wavefront file.
  it:
      Shortcut for command 'input_text'.
  input_text:
      filename
    Input specified text-data filename as a new image.
    (equivalent to shortcut command 'it').
  lorem:
      _width>0,_height>0
    Input random image of specified size, retrieved from Internet.
    Default values: 'width=height=800'.
  network (+):
      mode={ -1=Disabled | 0:Enabled w/o timeout | >0:Enabled w/ specified timeout in seconds }
    Enable/disable load-from-network and set corresponding timeout.
    (Default mode is 'enabled w/o timeout').
  o (+):
      Shortcut for command 'output'.
  output (+):
      [type:]filename,_format_options
    Output selected images as one or several numbered file(s).
    (equivalent to shortcut command 'o').
    Default value: 'format_options'=(undefined).
  output_565:
      "filename",reverse_endianness={ 0:No | 1:Yes }
    Output selected images as raw RGB-565 files.
    Default value: 'reverse_endianness=0'.
  output_csv:
      "filename",_column_separator
    Output selected ascii-encoded arrays as .csv files.
    Default value: 'column_separator=,'.
  output_cube:
      "filename"
    Output selected CLUTs as .cube files (Adobe CLUT format).
  output_flo:
      "filename"
    Output selected optical flow as a .flo file (vision.middlebury.edu file format).
  output_ggr:
      filename,_gradient_name
    Output selected images as .ggr gradient files (GIMP).
    If no gradient name is specified, it is deduced from the filename.
  output_gmz:
      filename,_datatype
    Output selected images as .gmz files (G'MIC native file format).
    'datatype' can be { bool | uint8 | int8 | uint16 | int16 | uint32 | int32 | uint64 | int64 | float32 | float64 }.
  output_obj:
      filename,_save_materials={ 0:No | 1:Yes }
    Output selected 3D meshes as Wavefront 3D object files.
    Set 'save_materials' to '1' to produce a corresponding material file ('.mtl') and eventually texture files.
    Beware, the export to '.obj' files may be quite slow for large 3D objects.
    Default value: 'save_materials=1'.
  ot:
      Shortcut for command 'output_text'.
  output_text:
      filename
    Output selected images as text-data filenames.
    (equivalent to shortcut command 'ot').
  on:
      Shortcut for command 'outputn'.
  outputn:
      filename,_index
    Output selected images as automatically numbered filenames in repeat...done loops.
    (equivalent to shortcut command 'on').
  op:
      Shortcut for command 'outputp'.
  outputp:
      prefix
    Output selected images as prefixed versions of their original filenames.
    (equivalent to shortcut command 'op').
    Default value: 'prefix=_'.
  ow:
      Shortcut for command 'outputw'.
  outputw:
    Output selected images by overwriting their original location.
    (equivalent to shortcut command 'ow').
  ox:
      Shortcut for command 'outputx'.
  outputx:
      extension1,_extension2,_...,_extensionN,_output_at_same_location={ 0:No | 1:Yes }
    Output selected images with same base filenames but for N different extensions.
    (equivalent to shortcut command 'ox').
    Default value: 'output_at_same_location=0'.
  parse_cli:
      _output_mode,_{ * | command_name }
    Parse definition of '@cli'-documented commands and output info about them in specified output mode.
    'output_mode' can be { ascii | bashcompletion | html | images | print }.
    Default values: 'output_mode=print' and 'command_name=*'.
  parse_gmd:
    Parse and tokenize selected images, viewed as text strings formatted with the G'MIC markdown syntax.
  gmd2html:
      _include_default_header_footer={ 0:None | 1:Reference | 2:Tutorial | 3:News } |
      (no arg)
    Convert selected gmd-formatted text images to html format.
    Default values: 'include_default_header_footer=1'.
  gmd2ascii:
      _max_line_length>0,_indent_forced_newlines>=0 |
      (no arg)
    Convert selected gmd-formatted text images to ascii format.
    Default values: 'max_line_length=80' and 'indent_forced_newline=0'.
  parse_gui:
      _outputmode,_{ * | filter_name}
    Parse selected filter definitions and generate info about filters in selected output mode.
    'outputmode' can be { gmicol | images | json | list | print | strings | update | zart }.
    It is possible to define a custom output mode, by implementing the following commands
    ('outputmode' must be replaced by the name of the custom user output mode):
    . 'parse_gui_outputmode' : A command that outputs the parsing information with a custom format.
    . 'parse_gui_parseparams_outputmode' (optional): A simple command that returns 0 or 1. It tells the parser whether parameters of matching filter must be analyzed (slower)
    or not.
    . 'parse_gui_trigger_outputmode' (optional): A command that is called by the parser just before parsing the set of each matching filters.
    Here is the list of global variables set by the parser, accessible in command 'parse_gui_outputmode':
    '$_nb_filters': Number of matching filters.
    '$_nongui' (stored as an image): All merged lines in the file that do not correspond to '#@gui' lines.
    For each filter '
     * '$_fF_name' : Filter name.
     * '$_fF_path' : Full path.
     * '$_fF_locale' : Filter locale (empty, if not specified).
     * '$_fF_command' : Filter command.
     * '$_fF_command_preview' : Filter preview command (empty, if not specified).
     * '$_fF_zoom_factor' : Default zoom factor (empty, if not specified).
     * '$_fF_preview_accuracy' : Preview accuracy (can be { 0:Does not support zoom in/out | 1:Support zoom in/out | 2:Pixel-perfect }).
     * '$_fF_input_mode' : Default preferred input mode (empty, if not specified).
     * '$_fF_hide' : Path of filter hid by current filter (for localized filters, empty if not specified).
     * '$_fF_nb_params' : Number of parameters.
    For each parameter '
     * '$_fF_pP_name' : Parameter name.
     * '$_fF_pP_type' : Parameter type.
     * '$_fF_pP_responsivity' : Parameter responsivity (can be { 0:No | 1:Yes }).
     * '$_fF_pP_randomizable' : Randomizable property of the parameter (can be { 0:No | 1:Yes }).
     * '$_fF_pP_visibility' : Parameter visibility.
     * '$_fF_pP_propagation' : Propagation of the parameter visibility.
     * '$_fF_pP_nb_args' : Number of parameter arguments.
    For each argument '
     * '$_fF_pP_aA' : Argument value
    Default parameters: 'filter_name=*' and 'output_format=print'.
  pass (+):
      _shared_state={ -1:Status only | 0:Non-shared (copy) | 1:Shared | 2:Adaptive }
    Insert images from parent context of a custom command or a local environment.
    Command selection (if any) stands for a selection of images in the parent context.
    By default (adaptive shared state), selected images are inserted in a shared state if they do not belong
    to the context (selection) of the current custom command or local environment as well.
    Typical use of command 'pass' concerns the design of custom commands that take images as arguments.
    This commands return the list of corresponding indices in the status.
    Default value: 'shared_state=2'.
    Example:
      [#1] command "average : pass$""1 add[^-1] [-1] remove[-1] div 2" sample ? +mirror y +average[0] [1]
  plot:
      _plot_type,_vertex_type,_xmin,_xmax,_ymin,_ymax,_exit_on_anykey={ 0:No | 1:Yes }
    Display selected images or formula in an interactive viewer (use the instant display window [0] if opened).
    'plot_type' can be { 0:None | 1:Lines | 2:Splines | 3:Bar }.
    'vertex_type' can be { 0:None | 1:Points | 2,3:Crosses | 4,5:Circles | 6,7:Squares }.
    'xmin', 'xmax', 'ymin', 'ymax' set the coordinates of the displayed xy-axes.
    Default values: 'plot_type=1', 'vertex_type=1', 'xmin=xmax=ymin=ymax=0 (auto)' and 'exit_on_anykey=0'.
  poincare_disk:
      _size>=0,_p>2,_q>2,_angle,_tiling={ 0:Triangular | 1:Polygonal },_nb_max_iter>=0,_xmin,_ymin,_xmax,_ymax
    Return a 3-channels image of a poincare disk. Output channels are '[x,y,it]'.
    Default values: 'size=1024', 'p=5', 'q=3', 'angle=0', 'tiling=0', 'nb_max_iter=20', 'xmin=ymin=-1' and 'xmax=ymax=1'.
    Example:
      [#1] repeat 4 { poincare_disk 1024,{3+$>} channels[-1] 2 mod[-1] 3 neq[-1] 2 } rescale2d 50%
  portrait:
      _size>0
    Input random portrait image of specified size, retrieved from Internet.
    Default values: 'size=800'.
  p:
      Shortcut for command 'print'.
  print:
    Print information on selected images, on the standard error ('stderr').
    (equivalent to shortcut command 'p').
    When invoked with a '+' prefix (i.e. '+print'), the command outputs on 'stdout' rather than on 'stderr'.
  random_patches:
      _nb_patches>0,_patch_size>0
    Generate a set of square color patches containing random geometric shapes, returned as a volumetric image.
    Default values: 'nb_patches=16' and 'patch_size=64'.
    Example:
      [#1] random_patches 16,64 split z append_tiles ,
  random_pattern:
      _width>0,_height>0,_min_detail_level>=0
    Insert a new RGB image of specified size at the end of the image list, rendered with a random pattern.
    Default values: 'width=height=512' and 'min_detail_level=2'.
    Example:
      [#1] repeat 6 { random_pattern 256 }
  screen (+):
      _x0[%],_y0[%],_x1[%],_y1[%]
    Take screenshot, optionally grabbed with specified coordinates, and insert it
    at the end of the image list.
  select:
      feature_type,_X[%]>=0,_Y[%]>=0,_Z[%]>=0,_exit_on_anykey={ 0:No | 1:Yes },_is_multiaxes_selection={ 0:No | 1:Yes }
    Interactively select a feature from selected images (use the instant display window [0] if opened).
    'feature_type' can be { 0:Point | 1:Segment | 2:Rectangle | 3:Ellipse }.
    Arguments 'X','Y','Z' determine the initial selection view, for 3D volumetric images.
    The retrieved feature is returned as a 3D vector (if 'feature_type==0') or as a 6d vector
    (if 'feature_type!=0') containing the feature coordinates.
    Default values: 'feature_type=2', 'X=Y=Z=50%', 'exit_on_anykey=0' and 'is_multiaxes_selection=1'.
  serialize (+):
      _datatype,_is_compressed={ 0:No | 1:Yes },_store_names={ 0:No | 1:Yes }
    Serialize selected list of images into a single image, optionally in a compressed form.
    'datatype' can be { auto | uint8 | int8 | uint16 | int16 | uint32 | int32 | uint64 | int64 | float32 | float64 }.
    Specify 'datatype' if all selected images have a range of values constrained to a particular datatype,
    in order to minimize the memory footprint.
    The resulting image has only integers values in [0,255] and can then be saved as a raw image of
    unsigned chars (doing so will output a valid .cimg[z] or .gmz file).
    If 'store_names' is set to '1', serialization uses the .gmz format to store data in memory
    (otherwise the .cimg[z] format).
    Default values: 'datatype=auto', 'is_compressed=1' and 'store_names=1'.
    Example:
      [#1] image.jpg +serialize uint8 +unserialize[-1]
  shape_circle:
      _size>=0
    Input a 2D circle binary shape with specified size.
    Default value: 'size=512'.
    Example:
      [#1] shape_circle ,
  shape_cupid:
      _size>=0
    Input a 2D cupid binary shape with specified size.
    Default value: 'size=512'.
    Example:
      [#1] shape_cupid ,
  shape_diamond:
      _size>=0
    Input a 2D diamond binary shape with specified size.
    Default value: 'size=512'.
    Example:
      [#1] shape_diamond ,
  shape_dragon:
      _size>=0,_recursion_level>=0,_angle
    Input a 2D Dragon curve with specified size.
    Default value: 'size=512', 'recursion_level=18' and 'angle=0'.
    Example:
      [#1] shape_dragon ,
  shape_fern:
      _size>=0,_density[%]>=0,_angle,0<=_opacity<=1,_type={ 0:Asplenium adiantum-nigrum | 1:Thelypteridaceae }
    Input a 2D Barnsley fern with specified size.
    Default value: 'size=512', 'density=50%', 'angle=30', 'opacity=0.3' and 'type=0'.
    Example:
      [#1] shape_fern ,
  shape_gear:
      _size>=0,_nb_teeth>0,0<=_height_teeth<=100,0<=_offset_teeth<=100,0<=_inner_radius<=100
    Input a 2D gear binary shape with specified size.
    Default value: 'size=512', 'nb_teeth=12', 'height_teeth=20', 'offset_teeth=0' and 'inner_radius=40'.
    Example:
      [#1] shape_gear ,
  shape_heart:
      _size>=0
    Input a 2D heart binary shape with specified size.
    Default value: 'size=512'.
    Example:
      [#1] shape_heart ,
  shape_hilbert:
      _size>=0,_nb_recursions>=0
    Input a 2D hilbert curve with specified size.
    Default values: 'size=512' and 'nb_recursions=5'.
    Example:
      [#1] shape_hilbert ,
  shape_menger:
      _nb_iterations>=0
    Input a 3D voxelized representation of the Menger sponge.
    Default value: 'nb_iterations=3'.
    Example:
      [#1] shape_menger 4 surfels3d , color3d 200 m3d 3
  shape_mosely:
      _nb_iterations>=0
    Input a 3D voxelized representation of the Mosely snowflake.
    Default value: 'nb_iterations=3'.
    Example:
      [#1] shape_mosely 4 surfels3d , color3d 200 m3d 3
  shape_polygon:
      _size>=0,_nb_vertices>=3,_angle
    Input a 2D polygonal binary shape with specified geometry.
    Default value: 'size=512', 'nb_vertices=5' and 'angle=0'.
    Example:
      [#1] repeat 6 { shape_polygon 256,{3+$>} }
  shape_rays:
      _size>=0,_xcenter[%],_ycenter[%],_branches>0,_angle[%],_twist,0<=_perspective<=1,_is_antialias={ 0:No | 1:Yes }
    Input a 3D binary spiral with specified size and attributes.
    Default values: 'size=512', 'xcenter=50%', 'ycenter=50%', 'branches=7', 'angle=50%', 'twist=0', 'perspective=0.35' and
     'is_antialias=0'.
    Example:
      [#1] shape_rays 400,50%,50%,7 shape_rays 400,50%,50%,3,0,3
  shape_snowflake:
      size>=0,0<=_nb_recursions<=6
    Input a 2D snowflake binary shape with specified size.
    Default values: 'size=512' and 'nb_recursions=5'.
    Example:
      [#1] repeat 6 { shape_snowflake 256,$> }
  shape_star:
      _size>=0,_nb_branches>0,0<=_thickness<=1
    Input a 2D star binary shape with specified size.
    Default values: 'size=512', 'nb_branches=5' and 'thickness=0.38'.
    Example:
      [#1] repeat 9 { shape_star 256,{$>+2} }
  sh (+):
      Shortcut for command 'shared'.
  shared (+):
      x0[%],x1[%],y[%],z[%],c[%] |
      y0[%],y1[%],z[%],c[%] |
      z0[%],z1[%],c[%] |
      c0[%],c1[%] |
      c0[%] |
      (no arg)
    Insert shared buffers from (opt. points/rows/planes/channels of) selected images.
    Shared buffers cannot be returned by a command, nor a local environment.
    (equivalent to shortcut command 'sh').
    Example:
      [#1] image.jpg shared 1 blur[-1] 3 remove[-1]
      [#2] image.jpg repeat s { shared 25%,75%,0,$> mirror[-1] x remove[-1] }
    Tutorial: https://gmic.eu/oldtutorial/_shared
  sp:
      Shortcut for command 'sample'.
  sample:
      _name1={ ? | apples | balloons | barbara | boats | bottles | butterfly | cameraman | car | cat | cliff | chick | colorful | david | dog | duck | eagle | elephant | earth |
        flower | fruits | gmicky | gmicky_mahvin | gmicky_wilber | greece | gummy | house | inside | landscape | leaf | lena | leno | lion | mandrill | monalisa | monkey |
        parrots | pencils | peppers | portrait0 | portrait1 | portrait2 | portrait3 | portrait4 | portrait5 | portrait6 | portrait7 | portrait8 | portrait9 | roddy | rooster |
        rose | square | swan | teddy | tiger | tulips | wall | waterfall | zelda },_name2,...,_nameN,_width={ >=0 | 0 (auto) },_height = { >=0 | 0 (auto) } |
      (no arg)
    Input a new sample RGB image (opt. with specified size).
    (equivalent to shortcut command 'sp').
    Argument 'name' can be replaced by an integer which serves as a sample index.
    Example:
      [#1] repeat 6 { sample }
  srand (+):
      value |
      (no arg)
    Set random generator seed.
    If no argument is specified, a random value is used as the random generator seed.
  store (+):
      _is_compressed={ 0:No | 1:Yes },variable_name1,_variable_name2,...
    Store selected images into one or several named variables.
    Selected images are transferred to the variables, and are so removed from the image list.
    (except if the prepended variant of the command '+store[selection]' is used).
    If a single variable name is specified, all images of the selection are assigned
    to the named variable. Otherwise, there must be as many variable names as images
    in the selection, and each selected image is assigned to each specified named variable.
    Use command 'input $variable_name' to bring the stored images back in the list.
    Default value: 'is_compressed=0'.
    Example:
      [#1] sample eagle,earth store img1,img2 input $img2 $img1
    Tutorial: https://gmic.eu/tutorial/store
  testimage2d:
      _width>0,_height>0,_spectrum>0
    Input a 2D synthetic image.
    Default values: 'width=512', 'height=width' and 'spectrum=3'.
    Example:
      [#1] testimage2d 512
  um:
      Shortcut for command 'uncommand'.
  uncommand (+):
      command_name[,_command_name2,...] |
      *
    Discard definition of specified custom commands.
    Set argument to '*' for discarding all existing custom commands.
    (equivalent to shortcut command 'um').
  uniform_distribution:
      nb_levels>=1,spectrum>=1
    Input set of uniformly distributed spectrum-d points in [0,1]^spectrum.
    Example:
      [#1] uniform_distribution 64,3 * 255 +distribution3d circles3d[-1] 10
  unserialize (+):
    Recreate lists of images from serialized image buffers, obtained with command 'serialize'.
  up:
      Shortcut for command 'update'.
  update:
    Update commands from the latest definition file on the G'MIC server.
    (equivalent to shortcut command 'up').
  v (+):
      Shortcut for command 'verbose'.
  verbose (+):
      level |
      { + | - }
    Set or increment/decrement the verbosity level. Default level is 0.
    (equivalent to shortcut command 'v').
    When 'level>0', G'MIC log messages are displayed on the standard error (stderr).
    Default value: 'level=1'.
  wait (+):
      delay |
      (no arg)
    Wait for a given delay (in ms), optionally since the last call to 'wait'.
    or wait for a user event occurring on the selected instant display windows.
    'delay' can be { <0:Delay+flush events | 0:Event | >0:Delay }.
    Command selection (if any) stands for instant display window indices instead of image indices.
    If no window indices are specified and if 'delay' is positive, the command results
    in a 'hard' sleep during specified delay.
    Default value: 'delay=0'.
  warn (+):
      _force_visible={ 0:No | 1:Yes },_message
    Print specified warning message, on the standard error (stderr).
    Command selection (if any) stands for displayed call stack subset instead of image indices.
  w (+):
      Shortcut for command 'window'.
  window (+):
      _width[%]>=-1,_height[%]>=-1,_normalization,_fullscreen,_pos_x[%],_pos_y[%],_title
    Display selected images into an instant display window with specified size, normalization type,
    fullscreen mode and title.
    (equivalent to shortcut command 'w').
    If 'width' or 'height' is set to -1, the corresponding dimension is adjusted to the window
    or image size.
    Specify 'pos_x' and 'pos_y' arguments only if the window has to be moved to the specified
    coordinates. Otherwise, they can be avoided.
    'width'=0 or 'height'=0 closes the instant display window.
    'normalization' can be { -1:Keep same | 0:None | 1:Always | 2:1st-time | 3:Auto }.
    'fullscreen' can be { -1:Keep same | 0:No | 1:Yes }.
    You can manage up to 10 different instant display windows by using the numbered variants
    'w0' (default, eq. to 'w'),'w1',...,'w9' of the command 'w'.
    Invoke 'window' with no selection to make the window visible, if it has been closed by the user.
    Default values: 'width=height=normalization=fullscreen=-1' and 'title=(undefined)'.
  11.3. List Manipulation
        -----------------
  k (+):
      Shortcut for command 'keep'.
  keep (+):
    Keep only selected images.
    (equivalent to shortcut command 'k').
    Example:
      [#1] image.jpg split x keep[0-50%:2] append x
      [#2] image.jpg split x keep[^30%-70%] append x
  kn:
      Shortcut for command 'keep_named'.
  keep_named:
      "name1","name2",...
    Keep all images with specified names from the list of images.
    Remove all images if no images with those names exist.
    (equivalent to shortcut command 'kn').
  mv (+):
      Shortcut for command 'move'.
  move (+):
      position[%]
    Move selected images at specified position.
    Images are actually inserted between current positions 'position-1' and 'position'.
    (equivalent to shortcut command 'mv').
    Example:
      [#1] image.jpg split x,3 move[1] 0
      [#2] image.jpg split x move[50%--1:2] 0 append x
  nm (+):
      Shortcut for command 'name'.
  => (+):
      Shortcut for command 'name'.
  name (+):
      "name1","name2",...,"nameN"
    Set names of selected images.
     * If no explicit image selection is given, image selection is assumed to be '[-N--1]', where 'N' is the number of specified arguments.
     * If 'N' is higher than the number of images in selection, an error is thrown.
     * If 'N' is lower than the number of images in selection, image names are assigned in a periodic way, i.e. 'name(selection[k]) = arg[k%N]'.
    (equivalent to shortcut command '=>').
    Example:
      [#1] image.jpg name image blur[image] 2
    Tutorial: https://gmic.eu/tutorial/name
  rm (+):
      Shortcut for command 'remove'.
  remove (+):
    Remove selected images.
    (equivalent to shortcut command 'rm').
    Example:
      [#1] image.jpg split x remove[30%-70%] append x
      [#2] image.jpg split x remove[0-50%:2] append x
  remove_duplicates:
    Remove duplicates images in the selected images list.
    Example:
      [#1] (1,2,3,4,2,4,3,1,3,4,2,1) split x remove_duplicates append x
  remove_empty:
    Remove empty images in the selected image list.
  rmn:
      Shortcut for command 'remove_named'.
  remove_named:
      "name1","name2",...
    Remove all images with specified names from the list of images.
    Does nothing if no images with those names exist.
    (equivalent to shortcut command 'rmn').
  rv (+):
      Shortcut for command 'reverse'.
  reverse (+):
    Reverse positions of selected images.
    (equivalent to shortcut command 'rv').
    Example:
      [#1] image.jpg split x,3 reverse[-2,-1]
      [#2] image.jpg split x,-16 reverse[50%-100%] append x
  sort_list:
      _ordering={ +:Increasing | -:Decreasing },_criterion
    Sort list of selected images according to the specified image criterion.
    Default values: 'ordering=+', 'criterion=i'.
    Example:
      [#1] (1;4;7;3;9;2;4;7;6;3;9;1;0;3;3;2) split y sort_list +,i append y
  11.4. Mathematical Operators
        ----------------------
  abs (+):
    Compute the pointwise absolute values of selected images.
    Example:
      [#1] image.jpg +sub {ia} abs[-1]
      [#2] 300,1,1,1,'cos(20*x/w)' +abs display_graph 400,300
  abscut (+):
      min,_max,_offset
    Cut the absolute values of pixel values in selected images, with specified range.
    For each value 'i' of the selected images, compute 'cut(abs(i) + offset,min,max)*sign(i)'.
    Thus, it only clamp/shift the absolute value of each pixel value while keeping its sign unchanged.
    Default values: 'max=inf' and 'offset=0'.
    Example:
      [#1] 300,1,1,1,'cos(20*x/w)' +abscut 0,0.5 append c display_graph 400,300
  acos (+):
    Compute the pointwise arccosine of selected images.
    Example:
      [#1] image.jpg +normalize -1,1 acos[-1]
      [#2] 300,1,1,1,'cut(x/w+0.1*u,0,1)' +acos display_graph 400,300
    Tutorial: https://gmic.eu/oldtutorial/trigometric-and-inverse-trigometric-commands
  acosh (+):
    Compute the pointwise hyperbolic arccosine of selected images.
  + (+):
      Shortcut for command 'add'.
  add (+):
      value[%] |
      [image] |
      'formula' |
      (no arg)
    Add specified value, image or mathematical expression to selected images, or compute the pointwise sum of selected images.
    (equivalent to shortcut command '+').
    Example:
      [#1] image.jpg +add 30% cut 0,255
      [#2] image.jpg +blur 5 normalize 0,255 add[1] [0]
      [#3] image.jpg add '80*cos(80*(x/w-0.5)*(y/w-0.5)+c)' cut 0,255
      [#4] image.jpg repeat 9 { +rotate[0] {$>*36},1,0,50%,50% } add div 10
  & (+):
      Shortcut for command 'and'.
  and (+):
      value[%] |
      [image] |
      'formula' |
      (no arg)
    Compute the bitwise AND of selected images with specified value, image or mathematical expression, or compute the pointwise sequential bitwise AND of selected images.
    (equivalent to shortcut command '&').
    Example:
      [#1] image.jpg and {128+64}
      [#2] image.jpg +mirror x and
  argmax:
    Compute the argmax of selected images. Returns a single image
    with each pixel value being the index of the input image with maximal value.
    Example:
      [#1] image.jpg sample lena,lion,square +argmax
  argmaxabs:
    Compute the argmaxabs of selected images. Returns a single image
    with each pixel value being the index of the input image with maxabs value.
  argmin:
    Compute the argmin of selected images. Returns a single image
    with each pixel value being the index of the input image with minimal value.
    Example:
      [#1] image.jpg sample lena,lion,square +argmin
  argminabs:
    Compute the argminabs of selected images. Returns a single image
    with each pixel value being the index of the input image with minabs value.
  asin (+):
    Compute the pointwise arcsine of selected images.
    Example:
      [#1] image.jpg +normalize -1,1 asin[-1]
      [#2] 300,1,1,1,'cut(x/w+0.1*u,0,1)' +asin display_graph 400,300
    Tutorial: https://gmic.eu/oldtutorial/trigometric-and-inverse-trigometric-commands
  asinh (+):
    Compute the pointwise hyperbolic arcsine of selected images.
  atan (+):
    Compute the pointwise arctangent of selected images.
    Example:
      [#1] image.jpg +normalize 0,8 atan[-1]
      [#2] 300,1,1,1,'4*x/w+u' +atan display_graph 400,300
    Tutorial: https://gmic.eu/oldtutorial/trigometric-and-inverse-trigometric-commands
  atan2 (+):
      [x_argument]
    Compute the pointwise oriented arctangent of selected images.
    Each selected image is regarded as the y-argument of the arctangent function, while the
    specified image gives the corresponding x-argument.
    Example:
      [#1] (-1,1) (-1;1) resize 400,400,1,1,3 atan2[1] [0] keep[1] mod {pi/8}
    Tutorial: https://gmic.eu/oldtutorial/trigometric-and-inverse-trigometric-commands
  atanh (+):
    Compute the pointwise hyperbolic arctangent of selected images.
  << (+):
      Shortcut for command 'bsl'.
  bsl (+):
      value[%] |
      [image] |
      'formula' |
      (no arg)
    Compute the bitwise left shift of selected images with specified value, image or mathematical expression, or compute the pointwise sequential bitwise left shift of selected
    images.
    (equivalent to shortcut command '<<').
    Example:
      [#1] image.jpg bsl 'round(3*x/w,0)' cut 0,255
  >> (+):
      Shortcut for command 'bsr'.
  bsr (+):
      value[%] |
      [image] |
      'formula' |
      (no arg)
    Compute the bitwise right shift of selected images with specified value, image or mathematical expression, or compute the pointwise sequential bitwise right shift of selected
    images.
    (equivalent to shortcut command '>>').
    Example:
      [#1] image.jpg bsr 'round(3*x/w,0)' cut 0,255
  cos (+):
    Compute the pointwise cosine of selected images.
    Example:
      [#1] image.jpg +normalize 0,{2*pi} cos[-1]
      [#2] 300,1,1,1,'20*x/w+u' +cos display_graph 400,300
    Tutorial: https://gmic.eu/oldtutorial/trigometric-and-inverse-trigometric-commands
  cosh (+):
    Compute the pointwise hyperbolic cosine of selected images.
    Example:
      [#1] image.jpg +normalize -3,3 cosh[-1]
      [#2] 300,1,1,1,'4*x/w+u' +cosh display_graph 400,300
  c (+):
      Shortcut for command 'cut'.
  cut (+):
      { value0[%] | [image0] },{ value1[%] | [image1] } |
      [image]
    Cut values of selected images in specified range.
    (equivalent to shortcut command 'c').
    Example:
      [#1] image.jpg +add 30% cut[-1] 0,255
      [#2] image.jpg +cut 25%,75%
  deg2rad:
    Convert pointwise angle values of selected images, from degrees to radians (apply 'i*pi/180').
  / (+):
      Shortcut for command 'div'.
  div (+):
      value[%] |
      [image] |
      'formula' |
      (no arg)
    Divide selected images by specified value, image or mathematical expression, or compute the pointwise quotient of selected images.
    (equivalent to shortcut command '/').
    Example:
      [#1] image.jpg div '1+abs(cos(x/10)*sin(y/10))'
      [#2] image.jpg +norm add[-1] 1 +div
  == (+):
      Shortcut for command 'eq'.
  eq (+):
      value[%] |
      [image] |
      'formula' |
      (no arg)
    Compute the boolean equality of selected images with specified value, image or mathematical expression, or compute the boolean equality of selected images.
    (equivalent to shortcut command '==').
    Example:
      [#1] image.jpg round 40 eq {round(ia,40)}
      [#2] image.jpg +mirror x eq
  erf (+):
    Compute the pointwise error function of selected images.
    Example:
      [#1] image.jpg +normalize 0,2 erf[-1]
      [#2] 300,1,1,1,'7*x/w-3.5+u' +erf display_graph 400,300
  exp (+):
    Compute the pointwise exponential of selected images.
    Example:
      [#1] image.jpg +normalize 0,2 exp[-1]
      [#2] 300,1,1,1,'7*x/w+u' +exp display_graph 400,300
  >= (+):
      Shortcut for command 'ge'.
  ge (+):
      value[%] |
      [image] |
      'formula' |
      (no arg)
    Compute the boolean 'greater or equal than' of selected images with specified value, image
    or mathematical expression, or compute the boolean 'greater or equal than' of selected images.
    (equivalent to shortcut command '>=').
    Example:
      [#1] image.jpg ge {ia}
      [#2] image.jpg +mirror x ge
  > (+):
      Shortcut for command 'gt'.
  gt (+):
      value[%] |
      [image] |
      'formula' |
      (no arg)
    Compute the boolean 'greater than' of selected images with specified value, image or mathematical expression, or compute the boolean 'greater than' of selected
    images.
    (equivalent to shortcut command '>').
    Example:
      [#1] image.jpg gt {ia}
      [#2] image.jpg +mirror x gt
  inrange:
      min[%],max[%],_include_min_boundary={ 0:No | 1:Yes },_include_max_boundary={ 0:No | 1:Yes }
    Detect pixels whose values are in specified range '[min,max]', in selected images.
    (equivalent to shortcut command 'ir').
    Default value: 'include_min_boundary=include_max_boundary=1'.
    Example:
      [#1] image.jpg +inrange 25%,75%
  isinf:
    Select 'inf' values in selected images.
  isnan:
    Select 'nan' values in selected images.
  <= (+):
      Shortcut for command 'le'.
  le (+):
      value[%] |
      [image] |
      'formula' |
      (no arg)
    Compute the boolean 'less or equal than' of selected images with specified value, image or mathematical expression, or compute the boolean 'less or equal than' of selected
    images.
    (equivalent to shortcut command '<=').
    Example:
      [#1] image.jpg le {ia}
      [#2] image.jpg +mirror x le
  < (+):
      Shortcut for command 'lt'.
  lt (+):
      value[%] |
      [image] |
      'formula' |
      (no arg)
    Compute the boolean 'less than' of selected images with specified value, image or mathematical expression, or compute the boolean 'less than' of selected images.
    (equivalent to shortcut command '<').
    Example:
      [#1] image.jpg lt {ia}
      [#2] image.jpg +mirror x lt
  log (+):
    Compute the pointwise base-e logarithm of selected images.
    Example:
      [#1] image.jpg +add 1 log[-1]
      [#2] 300,1,1,1,'7*x/w+u' +log display_graph 400,300
  log10 (+):
    Compute the pointwise base-10 logarithm of selected images.
    Example:
      [#1] image.jpg +add 1 log10[-1]
      [#2] 300,1,1,1,'7*x/w+u' +log10 display_graph 400,300
  log2 (+):
    Compute the pointwise base-2 logarithm of selected images
    Example:
      [#1] image.jpg +add 1 log2[-1]
      [#2] 300,1,1,1,'7*x/w+u' +log2 display_graph 400,300
  max (+):
      value[%] |
      [image] |
      'formula' |
      (no arg)
    Compute the maximum between selected images and specified value, image or mathematical expression, or compute the pointwise maxima between selected images.
    Example:
      [#1] image.jpg +mirror x max
      [#2] image.jpg max 'R=((x/w-0.5)^2+(y/h-0.5)^2)^0.5;255*R'
  maxabs (+):
      value[%] |
      [image] |
      'formula' |
      (no arg)
    Compute the maxabs between selected images and specified value, image or mathematical expression, or compute the pointwise maxabs between selected images.
  m/ (+):
      Shortcut for command 'mdiv'.
  mdiv (+):
      value[%] |
      [image] |
      'formula' |
      (no arg)
    Compute the matrix division of selected matrices/vectors by specified value, image or mathematical expression, or compute the matrix division of selected images.
    (equivalent to shortcut command 'm/').
  med:
    Compute the median of selected images.
    Example:
      [#1] image.jpg sample lena,lion,square +med
  min (+):
      value[%] |
      [image] |
      'formula' |
      (no arg)
    Compute the minimum between selected images and specified value, image or mathematical expression, or compute the pointwise minima between selected images.
    Example:
      [#1] image.jpg +mirror x min
      [#2] image.jpg min 'R=((x/w-0.5)^2+(y/h-0.5)^2)^0.5;255*R'
  minabs (+):
      value[%] |
      [image] |
      'formula' |
      (no arg)
    Compute the minabs between selected images and specified value, image or mathematical expression, or compute the pointwise minabs between selected images.
  % (+):
      Shortcut for command 'mod'.
  mod (+):
      value[%] |
      [image] |
      'formula' |
      (no arg)
    Compute the modulo of selected images with specified value, image or mathematical expression, or compute the pointwise sequential modulo of selected images.
    (equivalent to shortcut command '%').
    Example:
      [#1] image.jpg +mirror x n. 1,255 round. mod
      [#2] image.jpg mod 'R=((x/w-0.5)^2+(y/h-0.5)^2)^0.5;255*R'
  m* (+):
      Shortcut for command 'mmul'.
  mmul (+):
      value[%] |
      [image] |
      'formula' |
      (no arg)
    Compute the matrix right multiplication of selected matrices/vectors by specified value, image or mathematical expression, or compute the matrix right multiplication of selected
    images.
    If the right-hand side image is vector-valued, this command multiplies each vector-valued pixels by the specified left-hand matrix.
    (equivalent to shortcut command 'm*').
    Example:
      [#1] (0,1,0;0,0,1;1,0,0) (1;2;3) +mmul
      [#2] (0,1,0;0,0,1;1,0,0) image.jpg +mmul
  * (+):
      Shortcut for command 'mul'.
  mul (+):
      value[%] |
      [image] |
      'formula' |
      (no arg)
    Multiply selected images by specified value, image or mathematical expression, or compute the pointwise product of selected images.
    (equivalent to shortcut command '*').
    See also: add, sub, div.
    Example:
      [#1] image.jpg +mul 2 cut 0,255
      [#2] image.jpg (1,2,3,4,5,6,7,8) ri[-1] [0] mul[0] [-1]
      [#3] image.jpg mul '1-3*abs(x/w-0.5)' cut 0,255
      [#4] image.jpg +luminance negate[-1] +mul
  != (+):
      Shortcut for command 'neq'.
  neq (+):
      value[%] |
      [image] |
      'formula' |
      (no arg)
    Compute the boolean inequality of selected images with specified value, image or mathematical expression, or compute the boolean inequality of selected images.
    (equivalent to shortcut command '!=').
    Example:
      [#1] image.jpg round 40 neq {round(ia,40)}
  oneminus:
    For each selected image, compute one minus image.
    Example:
      [#1] image.jpg normalize 0,1 +oneminus
  | (+):
      Shortcut for command 'or'.
  or (+):
      value[%] |
      [image] |
      'formula' |
      (no arg)
    Compute the bitwise OR of selected images with specified value, image or mathematical expression, or compute the pointwise sequential bitwise OR of selected images.
    (equivalent to shortcut command '|').
    Example:
      [#1] image.jpg or 128
      [#2] image.jpg +mirror x or
  ^ (+):
      Shortcut for command 'pow'.
  pow (+):
      value[%] |
      [image] |
      'formula' |
      (no arg)
    Raise selected images to the power of specified value, image or mathematical expression, or compute the pointwise sequential powers of selected images.
    (equivalent to shortcut command '^').
    Example:
      [#1] image.jpg div 255 +pow 0.5 mul 255
      [#2] image.jpg gradient pow 2 add pow 0.2
  rad2deg:
    Convert pointwise angle values of selected images, from radians to degrees (apply 'i*180/pi').
  rol (+):
      value[%] |
      [image] |
      'formula' |
      (no arg)
    Compute the bitwise left rotation of selected images with specified value, image or mathematical expression, or compute the pointwise sequential bitwise left rotation of
    selected images.
    Example:
      [#1] image.jpg rol 'round(3*x/w,0)' cut 0,255
  ror (+):
      value[%] |
      [image] |
      'formula' |
      (no arg)
    Compute the bitwise right rotation of selected images with specified value, image or mathematical expression, or compute the pointwise sequential bitwise right rotation of
    selected images.
    Example:
      [#1] image.jpg ror 'round(3*x/w,0)' cut 0,255
  sign (+):
    Compute the pointwise sign of selected images.
    Example:
      [#1] image.jpg +sub {ia} sign[-1]
      [#2] 300,1,1,1,'cos(20*x/w+u)' +sign display_graph 400,300
  sin (+):
    Compute the pointwise sine of selected images.
    Example:
      [#1] image.jpg +normalize 0,{2*pi} sin[-1]
      [#2] 300,1,1,1,'20*x/w+u' +sin display_graph 400,300
    Tutorial: https://gmic.eu/oldtutorial/trigometric-and-inverse-trigometric-commands
  sinc (+):
    Compute the pointwise sinc function of selected images.
    Example:
      [#1] image.jpg +normalize {-2*pi},{2*pi} sinc[-1]
      [#2] 300,1,1,1,'20*x/w+u' +sinc display_graph 400,300
  sinh (+):
    Compute the pointwise hyperbolic sine of selected images.
    Example:
      [#1] image.jpg +normalize -3,3 sinh[-1]
      [#2] 300,1,1,1,'4*x/w+u' +sinh display_graph 400,300
  softmax:
      _temperature>0
    Compute the softmax of selected images.
    Default value: 'temperature=1'.
    Example:
      [#1] 100,1,1,1,"x<w/2?x:w-x" +softmax 10 display_graph ,
  softmin:
      _temperature>0
    Compute the softmin of selected images.
    Default value: 'temperature=1'.
    Example:
      [#1] 100,1,1,1,"x<w/2?x:w-x" +softmin 10 display_graph ,
  sqr (+):
    Compute the pointwise square function of selected images.
    Example:
      [#1] image.jpg +sqr
      [#2] 300,1,1,1,'40*x/w+u' +sqr display_graph 400,300
  sqrt (+):
    Compute the pointwise square root of selected images.
    Example:
      [#1] image.jpg +sqrt
      [#2] 300,1,1,1,'40*x/w+u' +sqrt display_graph 400,300
  - (+):
      Shortcut for command 'sub'.
  sub (+):
      value[%] |
      [image] |
      'formula' |
      (no arg)
    Subtract specified value, image or mathematical expression to selected images, or compute the pointwise difference of selected images.
    (equivalent to shortcut command '-').
    Example:
      [#1] image.jpg +sub 30% cut 0,255
      [#2] image.jpg +mirror x sub[-1] [0]
      [#3] image.jpg sub 'i(w/2+0.9*(x-w/2),y)'
      [#4] image.jpg +mirror x sub
  tan (+):
    Compute the pointwise tangent of selected images.
    Example:
      [#1] image.jpg +normalize {-0.47*pi},{0.47*pi} tan[-1]
      [#2] 300,1,1,1,'20*x/w+u' +tan display_graph 400,300
    Tutorial: https://gmic.eu/oldtutorial/trigometric-and-inverse-trigometric-commands
  tanh (+):
    Compute the pointwise hyperbolic tangent of selected images.
    Example:
      [#1] image.jpg +normalize -3,3 tanh[-1]
      [#2] 300,1,1,1,'4*x/w+u' +tanh display_graph 400,300
  xor (+):
      value[%] |
      [image] |
      'formula' |
      (no arg)
    Compute the bitwise XOR of selected images with specified value, image or mathematical expression, or compute the pointwise sequential bitwise XOR of selected images.
    Example:
      [#1] image.jpg xor 128
      [#2] image.jpg +mirror x xor
  11.5. Values Manipulation
        -------------------
  apply_curve:
      0<=smoothness<=1,x0,y0,x1,y1,x2,y2,...,xN,yN
    Apply curve transformation to image values.
    Default values: 'smoothness=1', 'x0=0', 'y0=100'.
    Example:
      [#1] image.jpg +apply_curve 1,0,0,128,255,255,0
  apply_gamma:
      gamma>=0
    Apply gamma correction to selected images.
    Example:
      [#1] image.jpg +apply_gamma 2
  apply_mask:
      "command",[opacity_mask],_max_opacity_mask
    Apply specified command on selected images but only inside the specified mask.
    Default value: 'max_opacity_mask=1'.
    Example:
      [#1] image.jpg 100%,100% noise. 10 blur. 2%,0 ge. 50% blur. 0.5% normalize. 0,1 +apply_mask.. "sepia whirls",[-1]
  balance_gamma:
      _ref_color1,...
    Compute gamma-corrected color balance of selected image, with respect to specified reference color.
    Default value: 'ref_color1=128'.
    Example:
      [#1] image.jpg +balance_gamma 128,64,64
  cast:
      datatype_source,datatype_target
    Cast datatype of image buffer from specified source type to specified target type.
    'datatype_source' and 'datatype_target' can be { uint8 | int8 | uint16 | int16 | uint32 | int32 | uint64 | int64 | float32 | float64 }.
  complex2polar:
    Compute complex to polar transforms of selected images.
    Example:
      [#1] image.jpg +fft complex2polar[-2,-1] log[-2] shift[-2] 50%,50%,0,0,2 remove[-1]
  compress_clut:
    Compress selected color LUTs as sequences of colored keypoints.
  compress_huffman:
      [huffman_tree],_max_leaf_value
    Compress selected images with Huffman coding.
    See also: decompress_huffman, huffman_tree.
  compress_rle:
      _is_binary_data={ 0:No | 1:Yes },_maximum_sequence_length>=0
    Compress selected images as 2xN data matrices, using RLE algorithm.
    Set 'maximum_sequence_length=0' to disable maximum length constraint.
    Default values: 'is_binary_data=0' and 'maximum_sequence_length=0'.
    Example:
      [#1] image.jpg rescale2d ,100 quantize 4 round +compress_rle , +decompress_rle[-1]
  cumulate (+):
      { x | y | z | c }...{ x | y | z | c } |
      (no arg)
    Compute the cumulative function of specified image data, optionally along the specified axes.
    Example:
      [#1] image.jpg +histogram 256 +cumulate[-1] display_graph[-2,-1] 400,300,3
  decompress_clut:
      _width>0,_height>0,_depth>0
    Decompress selected colored keypoints into 3D CLUTs, using a mixed RBF/PDE approach.
    Default values: 'width=height=depth=33' and 'reconstruction_colorspace=0'.
  decompress_from_keypoints:
      _width>0,_height>0,_depth>0 |
      (no arg)
    Decompress selected sets of keypoints as images (opt. of specified size).
    A set of keypoints is defined as a vector-valued image, such that:
     * The first pixel is a vector which encodes the '[ Width,Height,Depth ]' of the decompressed image.
     * The second pixel is a vector which encodes '[ Min,Max,Use_RBF ]', where 'Min' and 'Max' defines the value range of the decompressed image, and 'Use_RBF'
    tells is the decompression scheme must use RBFs ('Use_RBF=1') or Multiscale Diffusion PDE's ('Use_RBF=0').
     * The remaining pixels define the keypoint coordinates and values, as:
       - '[ x_k,y_k,z_k, v1_k,...,vN_k ]' for a 3D target image of N-valued vectors.
       - '[ x_k,y_k, v1_k,...,vN_k ]' for a 2D target image of N-valued vectors.
       - '[ x_k, v1_k,...,vN_k ]' for a 1D target image of N-valued vectors.
    where the coordinates 'x_k', 'y_k' and 'z_k' are defined respectively in ranges '[0,Width-1]', '[0,Height-1]' and '[0,Depth-1]'.
    If the 'width', 'height' and 'depth' arguments are provided, they define the size of the decompressed image, : overriding then the original image size '[ Width,
    Height,Depth ]' defined in the keypoints header.
  decompress_huffman:
      [huffman_tree]
    Decompress selected images with Huffman decoding.
    See also: compress_huffman, huffman_tree.
    Example:
      [#1] image.jpg huffman_tree compress_huffman.. . +decompress_huffman.. .
  decompress_rle:
    Decompress selected data vectors, using RLE algorithm.
  discard (+):
      _value1,_value2,... |
      { x | y | z | c}...{ x | y | z | c},_value1,_value2,... |
      (no arg)
    Discard specified values in selected images or discard neighboring duplicate values,
    optionally only for the values along the first of a specified axis.
    If no arguments are specified, neighboring duplicate values are discarded.
    If all pixels of a selected image are discarded, an empty image is returned.
    Example:
      [#1] (1;2;3;4;3;2;1) +discard 2
      [#2] (1,2,2,3,3,3,4,4,4,4) +discard x
  eigen2tensor:
    Recompose selected pairs of eigenvalues/eigenvectors as 2x2 or 3x3 tensor fields.
    Tutorial: https://gmic.eu/tutorial/eigen2tensor
  endian (+):
      _datatype
    Reverse data endianness of selected images, eventually considering the pixel being of the specified datatype.
    'datatype' can be { bool | uint8 | int8 | uint16 | int16 | uint32 | int32 | uint64 | int64 | float32 | float64 }.
    This command does nothing for 'bool', 'uint8' and 'int8' datatypes.
  equalize (+):
      _nb_levels[%]>0,_value_min[%],_value_max[%] |
      (no arg)
    Equalize histograms of selected images.
    If value range is specified, the equalization is done only for pixels in the specified
    value range.
    Default values: 'nb_levels=256', 'value_min=0%' and 'value_max=100%'.
    Example:
      [#1] image.jpg +equalize
      [#2] image.jpg +equalize 4,0,128
  f (+):
      Shortcut for command 'fill'.
  fill (+):
      value1,_value2,... |
      [image] |
      'formula'
    Fill selected images with values read from the specified value list, existing image
    or mathematical expression. Single quotes may be omitted in 'formula'.
    (equivalent to shortcut command 'f').
    Example:
      [#1] 4,4 fill 1,2,3,4,5,6,7
      [#2] 4,4 (1,2,3,4,5,6,7) fill[-2] [-1]
      [#3] 400,400,1,3 fill "X=x-w/2; Y=y-h/2; R=sqrt(X^2+Y^2); a=atan2(Y,X); R<=180?255*abs(cos(c+200*(x/w-0.5)*(y/h-0.5))):850*(a%(0.1*(c+1)))"
    Tutorial: https://gmic.eu/tutorial/fill
  icp:
      [reference],_affine_mode,_precision>0,_iter_max>=0
    Apply affine transformation on vector-valued points of selected images, to match specified set of reference vectors, using the ICP algorithm (Iterative Closest Point).
    A description of ICP is available at https://en.wikipedia.org/wiki/Iterative_closest_point.
    Argument 'affine_mode' tells about the type of affine transform applied. It can be: { 0:Free | 1:Rotation+Scaling | 2:Rotation-Only }.
    Default values: 'affine_mode=0', 'precision=1e-3' and 'iter_max=1000'.
  index (+):
      { [palette] | palette_name },0<=_dithering<=1,_map_colors={ 0:No | 1:Yes }
    Index selected vector-valued images by specified vector-valued palette.
    'palette_name' can be { default | hsv | lines | hot | cool | jet | flag | cube | rainbow | algae | amp |balance | curl | deep | delta | dense | diff | haline | ice |
    matter | oxy | phase | rain | solar | speed | tarn |tempo | thermal | topo | turbid | aurora | hocuspocus | srb2 | uzebox }
    Default values: 'dithering=0' and 'map_colors=0'.
    Example:
      [#1] image.jpg +index 1,1,1
      [#2] image.jpg (0;255;255^0;128;255^0;0;255) +index[-2] [-1],1,1
    Tutorial: https://gmic.eu/tutorial/gindex
  ir:
      Shortcut for command 'inrange'.
  map (+):
      [palette],_boundary_conditions |
      palette_name,_boundary_conditions
    Map specified vector-valued palette to selected indexed images.
    Each output image has 'M*N' channels, where 'M' and 'N' are the numbers of channels of, respectively, the corresponding input image and the 'palette'
    image.
    'palette_name' can be { default | hsv | lines | hot | cool | jet | flag | cube | rainbow | algae | amp | balance | curl | deep | delta | dense | diff | gray | haline |
    ice | matter | oxy | phase | rain | solar | speed | tarn | tempo | thermal | topo | turbid | aurora | hocuspocus | srb2 | uzebox }
    'boundary_conditions' can be { 0:Dirichlet | 1:Neumann | 2:Periodic | 3:Mirror }.
    Default value: 'boundary_conditions=0'.
    Example:
      [#1] image.jpg +luminance map[-1] 3
      [#2] image.jpg +rgb2ycbcr split[-1] c (0,255,0) resize[-1] 256,1,1,1,3 map[-4] [-1] remove[-1] append[-3--1] c ycbcr2rgb[-1]
    Tutorial: https://gmic.eu/tutorial/map
  mix_channels:
      (a00,...,aMN) |
      [matrix]
    Apply specified matrix to channels of selected images.
    Example:
      [#1] image.jpg +mix_channels (0,1,0;1,0,0;0,0,1)
  negate:
      base_value |
      (no arg)
    Negate image values.
    Default value: 'base_value=(undefined)'.
    Example:
      [#1] image.jpg +negate
  noise (+):
      amplitude[%]>=0,_noise_type
    Add random noise to selected images.
    'noise_type' can be { 0:Gaussian | 1:Uniform | 2:Salt&pepper | 3:Poisson | 4:Rice }.
    Default value: 'noise_type=0'.
    Example:
      [#1] image.jpg +noise[0] 50,0 +noise[0] 50,1 +noise[0] 10,2 cut 0,255
      [#2] 300,300,1,3 [0] noise[0] 20,0 noise[1] 20,1 +histogram 100 display_graph[-2,-1] 400,300,3
  noise_hurl:
      _amplitude>=0
    Add hurl noise to selected images.
    Default value: 'amplitude=10'.
    Example:
      [#1] image.jpg +noise_hurl ,
  noise_perlin:
      _scale_x[%]>0,_scale_y[%]>0,_scale_z[%]>0,_seed_x,_seed_y,_seed_z
    Render 2D or 3D Perlin noise on selected images, from specified coordinates.
    The Perlin noise is a specific type of smooth noise,
    described here : https://en.wikipedia.org/wiki/Perlin_noise.
    Default values: 'scale_x=scale_y=scale_z=16' and 'seed_x=seed_y=seed_z=0'.
    Example:
      [#1] 500,500,1,3 noise_perlin ,
  noise_poissondisk:
      _radius[%]>0,_max_sample_attempts>0,_p_norm>0
    Add poisson disk sampling noise to selected images.
    Implements the algorithm from the article "Fast Poisson Disk Sampling in Arbitrary Dimensions",
    by Robert Bridson (SIGGRAPH'2007).
    Default values: 'radius=8', 'max_sample_attempts=30' and 'p_norm=2'.
    Example:
      [#1] 300,300 noise_poissondisk 8
  normp:
      p>=0
    Compute the pointwise Lp-norm norm of vector-valued pixels in selected images.
    Default value: 'p=2'.
    Example:
      [#1] image.jpg +normp[0] 0 +normp[0] 1 +normp[0] 2 +normp[0] inf
  norm1:
    Compute the pointwise L1-norm of vector-valued pixels in selected images.
    Example:
      [#1] image.jpg +norm1
    Tutorial: https://gmic.eu/tutorial/norm1
  norm:
      Shortcut for command 'norm2'.
  norm2:
    Compute the pointwise L2-norm (euclidean norm) of vector-valued pixels in selected images.
    Example:
      [#1] image.jpg +norm
    Tutorial: https://gmic.eu/tutorial/norm2
  n (+):
      Shortcut for command 'normalize'.
  normalize (+):
      { value0[%] | [image0] },{ value1[%] | [image1] },_constant_case_ratio |
      [image]
    Linearly normalize values of selected images in specified range.
    (equivalent to shortcut command 'n').
    Example:
      [#1] image.jpg split x,2 normalize[-1] 64,196 append x
    Tutorial: https://gmic.eu/tutorial/normalize
  normalize_l2:
    Normalize selected images such that they have a unit L2 norm.
  normalize_sum:
    Normalize selected images such that they have a unit sum.
    Example:
      [#1] image.jpg +histogram 256 normalize_sum[-1] display_graph[-1] 400,300
  orientation:
    Compute the pointwise orientation of vector-valued pixels in selected images.
    Example:
      [#1] image.jpg +orientation +norm[-2] negate[-1] mul[-2] [-1] reverse[-2,-1]
    Tutorial: https://gmic.eu/tutorial/orientation
  otsu:
      _nb_levels>0
    Hard-threshold selected images using Otsu's method.
    The computed thresholds are returned as a list of values in the status.
    Default value: 'nb_levels=256'.
    Example:
      [#1] image.jpg luminance +otsu ,
  polar2complex:
    Compute polar to complex transforms of selected images.
  quantize:
      nb_levels>=1,_keep_values={ 0:No | 1:Yes },_quantization_type={ -1:Median-cut | 0:K-means | 1:Uniform }
    Quantize selected images.
    Default value: 'keep_values=1' and 'quantization_type=0'.
    Example:
      [#1] image.jpg luminance +quantize 3
      [#2] 200,200,1,1,'cos(x/10)*sin(y/10)' +quantize[0] 6 +quantize[0] 4 +quantize[0] 3 +quantize[0] 2
  quantize_area:
      _min_area>0
    Quantize selected images such that each flat region has an area greater or equal to 'min_area'.
    Default value: 'min_area=10'.
    Example:
      [#1] image.jpg quantize 3 +blur 1 round[-1] +quantize_area[-1] 2
  rand (+):
      { value0[%] | [image0] },_{ value1[%] | [image1] },_[pdf],_precision[%] |
      [image]
    Fill selected images with random values in the specified range.
    If no '[pdf]' (probability density function) is specified, random values follow a uniform distribution.
    Argument 'precision' tells about the number of distinct values that can be generated when a '[pdf]' is specified.
    Example:
      [#1] 400,400,1,3 rand -10,10 +blur 10 sign[-1]
      [#2] (8,2,1) 50,50 rand[-1] 0,255,[-2]
      [#3] 256 gaussian[-1] 30 line[-1] 47%,0,53%,0,1,0 500,500 rand[-1] 0,255,[-2] +histogram[-1] 256 display_graph[0,2] 640,480,3,0
  rand_sum:
      sum>0,_random_function
    Fill selected images with strictly positive, random, integer values, that sums to 'sum'.
    For each image, 'sum' must be greater or equal than 'width*height*depth*spectrum'.
    Default value: 'random_function=u'.
    Example:
      [#1] 100 rand_sum 1000
  replace:
      source,target
    Replace pixel values in selected images.
    Example:
      [#1] (1;2;3;4) +replace 2,3
  replace_inf:
      _expression
    Replace all infinite values in selected images by specified expression.
    Example:
      [#1] (0;1;2) log +replace_inf 2
  replace_infnan:
      _expression
    Replace all NaN and infinite values in selected images by specified expression.
  replace_nan:
      _expression
    Replace all NaN values in selected images by specified expression.
    Example:
      [#1] (-1;0;2) sqrt +replace_nan 2
  replace_seq:
      "search_seq","replace_seq"
    Search and replace a sequence of values in selected images.
    Example:
      [#1] (1;2;3;4;5) +replace_seq "2,3,4","7,8"
  replace_str:
      "search_str","replace_str"
    Search and replace a string in selected images (viewed as strings, i.e. sequences of character codes).
    Example:
      [#1] ('"Hello there, how are you ?"') +replace_str "Hello there","Hi David"
  round (+):
      rounding_value>=0,_rounding_type |
      (no arg)
    Round values of selected images.
    'rounding_type' can be { -1:Backward | 0:Nearest | 1:Forward }.
    Default value: 'rounding_type=0'.
    Example:
      [#1] image.jpg +round 100
      [#2] image.jpg mul {pi/180} sin +round
  roundify:
      gamma>=0
    Apply roundify transformation on float-valued data, with specified gamma.
    Default value: 'gamma=0'.
    Example:
      [#1] 1000 fill '4*x/w' repeat 5 { +roundify[0] {$>*0.2} } append c display_graph 400,300
  = (+):
      Shortcut for command 'set'.
  set (+):
      value,_x[%],_y[%],_z[%],_c[%]
    Set pixel value in selected images, at specified coordinates.
    (equivalent to shortcut command '=').
    If specified coordinates are outside the image bounds, no action is performed.
    Default values: 'x=y=z=c=0'.
    Example:
      [#1] 2,2 set 1,0,0 set 2,1,0 set 3,0,1 set 4,1,1
      [#2] image.jpg repeat 10000 { set 255,{u(100)}%,{u(100)}%,0,{u(100)}% }
  threshold:
      value[%],_is_soft_thresholding={ 0:No | 1:Yes }
    Threshold values of selected images.
    'soft' can be { 0:Hard-thresholding | 1:Soft-thresholding }.
    Default value: 'is_soft=0'.
    Example:
      [#1] image.jpg +threshold[0] 50% +threshold[0] 50%,1
    Tutorial: https://gmic.eu/tutorial/threshold
  unitnorm:
      _p>=0
    Compute the pointwise Lp-norm in selected images.
    Default value: 'p=2'.
    Example:
      [#1] image.jpg +unitnorm 2
  vector2tensor:
    Convert selected vector fields to corresponding tensor fields.
  11.6. Colors
        ------
  adjust_colors:
      -100<=_brightness<=100,-100<=_contrast<=100,-100<=_gamma<=100,-100<=_hue_shift<=100,-100<=_saturation<=100,_value_min,_value_max
    Perform a global adjustment of colors on selected images.
    Range of correct image values are considered to be in [value_min,value_max] (e.g. [0,255]).
    If 'value_min==value_max==0', value range is estimated from min/max values of selected images.
    Processed images have pixel values constrained in [value_min,value_max].
    Default values: 'brightness=0', 'contrast=0', 'gamma=0', 'hue_shift=0', 'saturation=0', 'value_min=value_max=0'.
    Example:
      [#1] image.jpg +adjust_colors 0,30,0,0,30
  ac:
      Shortcut for command 'apply_channels'.
  apply_channels:
      "command",color_channels,_value_action={ 0:None | 1:Cut | 2:Normalize }
    Apply specified command on the chosen color channel(s) of each selected images.
    (equivalent to shortcut command 'ac').
    Argument 'color_channels' refers to a colorspace, and can be basically one of
    { all | rgba | [s]rgb | ryb | lrgb | ycbcr | lab | lch | hsv | hsi | hsl | cmy | cmyk | yiq }.
    You can also make the processing focus on a few particular channels of this colorspace,
    by setting 'color_channels' as 'colorspace_channel' (e.g. 'hsv_h' for the hue).
    All channel values are considered to be provided in the [0,255] range.
    Default value: 'value_action=0'.
    Example:
      [#1] image.jpg +apply_channels "equalize blur 2",ycbcr_cbcr
  autoindex:
      nb_colors>0,0<=_dithering<=1,_method={ 0:Median-cut | 1:K-means }
    Index selected vector-valued images by adapted colormaps.
    Default values: 'dithering=0' and 'method=1'.
    Example:
      [#1] image.jpg +autoindex[0] 4 +autoindex[0] 8 +autoindex[0] 16
  bayer2rgb:
      _GM_smoothness,_RB_smoothness1,_RB_smoothness2
    Transform selected RGB-Bayer sampled images to color images.
    Default values: 'GM_smoothness=RB_smoothness=1' and 'RB_smoothness2=0.5'.
    Example:
      [#1] image.jpg rgb2bayer 0 +bayer2rgb 1,1,0.5
  brightness:
      strength
    Change contrast of selected images, with specified strength.
     * If 'strength' is positive, image brightness is amplified.
     * If 'strength' is negative, image brightness is reduced.
    A typical value range for parameter 'strength' is [-100,100].
    Example:
      [#1] image.jpg +brightness 20
  clut:
      "clut_name",_resolution>0,_cut_and_round={ 0:No | 1:Yes }
    Insert one of the 1189 pre-defined CLUTs at the end of the image list.
    'clut_name' can be { 12_years_a_slave | 1917 | 2-strip-process | 400_ft | 60s | 60s_faded | 60s_faded_alt | 7drk_21 | action_magenta_01 | action_red_01 | ad_astra |
    adventure_1453 | aerospace | agfa_apx_100 | agfa_apx_25 | agfa_precisa_100 | agfa_ultra_color_100 | agfa_vista_200 | agressive_highligjtes_recovery_5 | airdrop | aladdin |
    alberto_street | alien_green | ampio | amstragram | amstragram+ | analog_film_1 | analogfx_anno_1870_color | analogfx_old_style_i | analogfx_old_style_ii | analogfx_old_style_iii
    | analogfx_sepia_color | analogfx_soft_sepia_i | analogfx_soft_sepia_ii | anime | ant-man | apocalypse_this_very_moment | aqua | aqua_and_orange_dark | aquaman | arabica_12 |
    asistas | atomic_pink | atusa | autumn | autumn_leaves | ava_614 | avalanche | avengers_endgame | azrael_93 | baby_driver | bad_boys_for_life | basuco | bboyz_2 | bc_darkum |
    beach_aqua_orange | beach_faded_analog | beati | beauty_and_the_beast | berlin_sky | bisogno | black_and_white | black_panther | black_star | black_white_01 | black_white_02 |
    black_white_03 | black_white_04 | black_white_05 | black_white_06 | blade_runner | bleach_bypass | bleachbypass_1 | bleachbypass_2 | bleachbypass_3 | bleachbypass_4 |
    bleech_bypass_green | bleech_bypass_yellow_01 | blue_cold_fade | blue_dark | blue_house | blue_ice | blue_love_39 | blue_mono | blue_shadows_01 | bluearchitecture | bluehour |
    blues | bob_ford | bohemian_rhapsody | bombshell | bourbon_64 | boyado | bright_green_01 | bright_teal_orange | bright_warm | brightgreen | brown_mobster | brownbm | brownish |
    bw_1 | bw_10 | bw_2 | bw_3 | bw_4 | bw_5 | bw_6 | bw_7 | bw_8 | bw_9 | bw_but_yellow | byers_11 | calidum | candlelight | captain_marvel | caribe | chemical_168 | chrome_01 |
    cine-1 | cine-10 | cine-11 | cine-12 | cine-13 | cine-14 | cine-15 | cine-16 | cine-17 | cine-18 | cine-19 | cine-2 | cine-20 | cine-21 | cine-22 | cine-23 | cine-24 | cine-25 |
    cine-26 | cine-27 | cine-28 | cine-29 | cine-3 | cine-30 | cine-4 | cine-5 | cine-6 | cine-7 | cine-8 | cine-9 | cineblue | cinebm_4k | cinema | cinema_2 | cinema_3 | cinema_4 |
    cinema_5 | cinema_noir | cinematic-1 | cinematic-10 | cinematic-2 | cinematic-3 | cinematic-4 | cinematic-5 | cinematic-6 | cinematic-7 | cinematic-8 | cinematic-9 | cinematic_01
    | cinematic_02 | cinematic_03 | cinematic_04 | cinematic_05 | cinematic_06 | cinematic_07 | cinematic_for_flog | cinematic_forest | cinematic_lady_bird | cinematic_mexico | city
    | city_7 | city_dust | city_of_god | classic_films_01 | classic_films_02 | classic_films_03 | classic_films_04 | classic_films_05 | classic_teal_and_orange | clayton_33 | clear |
    clear_teal_fade | clouseau_54 | cobi_3 | coffee_44 | cold_clear_blue | cold_clear_blue_1 | cold_ice | cold_simplicity_2 | coldchrome | color_rich | colore | colorful_0209 |
    colornegative | conflict_01 | contrail_35 | contrast_with_highlights_protection | contrasty_afternoon | contrasty_green | convold | cosa | creed_2 | crispautumn | crispromance |
    crispwarm | crispwinter | cross_process_cp_130 | cross_process_cp_14 | cross_process_cp_15 | cross_process_cp_16 | cross_process_cp_18 | cross_process_cp_3 | cross_process_cp_4 |
    cross_process_cp_6 | crushin | cubicle_99 | culor | d_o_1 | dark_blues_in_sunlight | dark_green_02 | dark_green_1 | dark_man_x | dark_orange_teal | dark_place_01 | darkandsomber
    | darkness | date_39 | day_4nite | day_for_night | day_to_night_kings_blue | deep | deep_blue | deep_dark_warm | deep_high_contrast | deep_teal_fade | deep_warm_fade |
    deepskintones_2 | deepskintones_3 | delicatessen | denoiser_simple_40 | desert_gold_37 | dimension | dimmer | directions_23 | django_25 | doctor_strange | domingo_145 | dream_1 |
    dream_85 | drop_green_tint_14 | dropblues | dunkirk | duotone_blue_red | earth_tone_boost | eda_0_2 | edgyember | elegance_38 | enchanted | ensaya | eterna_for_flog | expired_69
    | expired_fade | expired_polaroid | extreme | fade | fade_to_green | faded | faded_47 | faded_alt | faded_analog | faded_extreme | faded_green | faded_pink-ish | faded_print |
    faded_retro_01 | faded_retro_02 | faded_vivid | fadedlook | fallcolors | falua | farkling | fatos | faux_infrared | faux_infrared_bw_1 | faux_infrared_color_p_2 |
    faux_infrared_color_p_3 | faux_infrared_color_r_0a | faux_infrared_color_r_0b | faux_infrared_color_yp_1 | fezzle | fg_cinebasic | fg_cinebright | fg_cinecold | fg_cinedrama |
    fg_cinetealorange_1 | fg_cinetealorange_2 | fg_cinevibrant | fg_cinewarm | fgcinebasic | fgcinebright | fgcinecold | fgcinedrama | fgcinetealorange_1 | fgcinetealorange_2 |
    fgcinevibrant | fgcinewarm | fight_club | film_0987 | film_9879 | film_gb-19 | film_high_contrast | film_print_01 | film_print_02 | filmic | filo | flat_30 | flat_blue_moon |
    flavin | flog_to_rec_709 | foggynight | folger_50 | ford_v_ferrari | foresta | formula_b | french_comedy | frosted | frostedbeachpicnic | fuji_160c | fuji_160c_+ | fuji_160c_++ |
    fuji_160c_- | fuji_3510_constlclip | fuji_3510_constlmap | fuji_3510_cuspclip | fuji_3513_constlclip | fuji_3513_constlmap | fuji_3513_cuspclip | fuji_400h | fuji_400h_+ |
    fuji_400h_++ | fuji_400h_- | fuji_800z | fuji_800z_+ | fuji_800z_++ | fuji_800z_- | fuji_astia_100_generic | fuji_astia_100f | fuji_fp-100c | fuji_fp-100c_+ | fuji_fp-100c_++ |
    fuji_fp-100c_+++ | fuji_fp-100c_++_alt | fuji_fp-100c_- | fuji_fp-100c_-- | fuji_fp-100c_alt | fuji_fp-100c_cool | fuji_fp-100c_cool_+ | fuji_fp-100c_cool_++ |
    fuji_fp-100c_cool_- | fuji_fp-100c_cool_-- | fuji_fp-100c_negative | fuji_fp-100c_negative_+ | fuji_fp-100c_negative_++ | fuji_fp-100c_negative_+++ | fuji_fp-100c_negative_++_alt
    | fuji_fp-100c_negative_- | fuji_fp-100c_negative_-- | fuji_fp-3000b | fuji_fp-3000b_+ | fuji_fp-3000b_++ | fuji_fp-3000b_+++ | fuji_fp-3000b_- | fuji_fp-3000b_-- |
    fuji_fp-3000b_hc | fuji_fp-3000b_negative | fuji_fp-3000b_negative_+ | fuji_fp-3000b_negative_++ | fuji_fp-3000b_negative_+++ | fuji_fp-3000b_negative_- |
    fuji_fp-3000b_negative_-- | fuji_fp-3000b_negative_early | fuji_fp_100c | fuji_hdr | fuji_neopan_1600 | fuji_neopan_1600_+ | fuji_neopan_1600_++ | fuji_neopan_1600_- |
    fuji_neopan_acros_100 | fuji_provia_100_generic | fuji_provia_100f | fuji_provia_400f | fuji_provia_400x | fuji_sensia_100 | fuji_superia_100 | fuji_superia_100_+ |
    fuji_superia_100_++ | fuji_superia_100_- | fuji_superia_1600 | fuji_superia_1600_+ | fuji_superia_1600_++ | fuji_superia_1600_- | fuji_superia_200 | fuji_superia_200_xpro |
    fuji_superia_400 | fuji_superia_400_+ | fuji_superia_400_++ | fuji_superia_400_- | fuji_superia_800 | fuji_superia_800_+ | fuji_superia_800_++ | fuji_superia_800_- |
    fuji_superia_hg_1600 | fuji_superia_reala_100 | fuji_superia_x-tra_800 | fuji_velvia_100_generic | fuji_velvia_50 | fuji_xtrans_iii_acros | fuji_xtrans_iii_acros+g |
    fuji_xtrans_iii_acros+r | fuji_xtrans_iii_acros+ye | fuji_xtrans_iii_astia | fuji_xtrans_iii_classic_chrome | fuji_xtrans_iii_mono | fuji_xtrans_iii_mono+g |
    fuji_xtrans_iii_mono+r | fuji_xtrans_iii_mono+ye | fuji_xtrans_iii_pro_neg_hi | fuji_xtrans_iii_pro_neg_std | fuji_xtrans_iii_provia | fuji_xtrans_iii_sepia |
    fuji_xtrans_iii_velvia | fusion_88 | futuristicbleak_1 | futuristicbleak_2 | futuristicbleak_3 | futuristicbleak_4 | going_for_a_walk | golden | golden_bright | golden_fade |
    golden_mono | golden_night_softner_43 | golden_sony_37 | golden_vibrant | goldengate | goldentime | goldfx_bright_spring_breeze | goldfx_bright_summer_heat |
    goldfx_hot_summer_heat | goldfx_perfect_sunset_01min | goldfx_perfect_sunset_05min | goldfx_perfect_sunset_10min | goldfx_spring_breeze | goldfx_summer_heat | good_morning |
    green_15 | green_2025 | green_action | green_afternoon | green_and_orange | green_blues | green_book | green_conflict | green_day_01 | green_day_02 | green_g_09 | green_indoor |
    green_light | green_mono | green_yellow | greenish_contrasty | greenish_fade | greenish_fade_1 | gremerta | greyhound | hackmanite | hallowen_dark | happyness_133 |
    hard_teal_orange | hardboost | harsh_day | harsh_sunset | helios | herderite | heulandite | hiddenite | highlights_protection | hilutite | hitman | hlg_1_1 | honey_light |
    hong_kong | horrorblue | howlite | huesio | husmes | huyan | hydracore | hyla_68 | hypersthene | hypnosis | hypressen | i_tonya | ideo | ilford_delta_100 | ilford_delta_3200 |
    ilford_delta_3200_+ | ilford_delta_3200_++ | ilford_delta_3200_- | ilford_delta_400 | ilford_fp_4_plus_125 | ilford_hp_5 | ilford_hp_5_+ | ilford_hp_5_++ | ilford_hp_5_- |
    ilford_hp_5_plus_400 | ilford_hps_800 | ilford_pan_f_plus_50 | ilford_xp_2 | inception | indoor_blue | industrial_33 | infrared_-_dust_pink | instantc | j | jarklin | jojo_rabbit
    | joker | jumanji_the_next_level | jurassic_world_fallen_kingdom | justice_league | justpeachy | jwick_21 | k_tone_vintage_kodachrome | kahve_3 | kh_1 | kh_10 | kh_2 | kh_3 |
    kh_4 | kh_5 | kh_6 | kh_7 | kh_8 | kh_9 | killstreak | kingsman_the_golden_circle | knives_out | kodak_2383_constlclip | kodak_2383_constlmap | kodak_2383_cuspclip |
    kodak_2393_constlclip | kodak_2393_constlmap | kodak_2393_cuspclip | kodak_bw_400_cn | kodak_e-100_gx_ektachrome_100 | kodak_ektachrome_100_vs | kodak_ektachrome_100_vs_generic |
    kodak_ektar_100 | kodak_elite_100_xpro | kodak_elite_chrome_200 | kodak_elite_chrome_400 | kodak_elite_color_200 | kodak_elite_color_400 | kodak_elite_extracolor_100 |
    kodak_hie_hs_infra | kodak_kodachrome_200 | kodak_kodachrome_25 | kodak_kodachrome_64 | kodak_kodachrome_64_generic | kodak_portra_160 | kodak_portra_160_+ | kodak_portra_160_++
    | kodak_portra_160_- | kodak_portra_160_nc | kodak_portra_160_nc_+ | kodak_portra_160_nc_++ | kodak_portra_160_nc_- | kodak_portra_160_vc | kodak_portra_160_vc_+ |
    kodak_portra_160_vc_++ | kodak_portra_160_vc_- | kodak_portra_400 | kodak_portra_400_+ | kodak_portra_400_++ | kodak_portra_400_- | kodak_portra_400_nc | kodak_portra_400_nc_+ |
    kodak_portra_400_nc_++ | kodak_portra_400_nc_- | kodak_portra_400_uc | kodak_portra_400_uc_+ | kodak_portra_400_uc_++ | kodak_portra_400_uc_- | kodak_portra_400_vc |
    kodak_portra_400_vc_+ | kodak_portra_400_vc_++ | kodak_portra_400_vc_- | kodak_portra_800 | kodak_portra_800_+ | kodak_portra_800_++ | kodak_portra_800_- | kodak_portra_800_hc |
    kodak_t-max_100 | kodak_t-max_3200 | kodak_t-max_400 | kodak_tmax_3200 | kodak_tmax_3200_+ | kodak_tmax_3200_++ | kodak_tmax_3200_- | kodak_tmax_3200_alt | kodak_tri-x_400 |
    kodak_tri-x_400_+ | kodak_tri-x_400_++ | kodak_tri-x_400_- | kodak_tri-x_400_alt | korben_214 | la_la_land | landscape | landscape_01 | landscape_02 | landscape_03 | landscape_04
    | landscape_05 | landscape_1 | landscape_10 | landscape_2 | landscape_3 | landscape_4 | landscape_5 | landscape_6 | landscape_7 | landscape_8 | landscape_9 |
    lateafternoonwanderlust | latesunset | lavark | lc_1 | lc_10 | lc_2 | lc_3 | lc_4 | lc_5 | lc_6 | lc_7 | lc_8 | lc_9 | lenox_340 | levex | life_giving_tree | light | light_blown
    | litore | little_women | logan | lomo | lomography_redscale_100 | lomography_x-pro_slide_200 | london_nights | longbeachmorning | loro | lotta | louetta | low_altitude |
    low_contrast_blue | low_key_01 | lucky_64 | lushgreen | lushgreensummer | mad_max_fury_road | maesky | magenta_day | magenta_day_01 | magenta_dream | magenta_yellow |
    magentacoffee | magichour | marriage_story | matrix | mckinnon_75 | memories | mercato | metropolis | milo_5 | minimalistcaffeination | modern_film | modern_films_01 |
    modern_films_02 | modern_films_03 | modern_films_04 | modern_films_05 | modern_films_06 | modern_films_07 | molti | mono_2 | mono_tinted | monochrome | monochrome_1 |
    monochrome_2 | moody_1 | moody_10 | moody_2 | moody_3 | moody_4 | moody_5 | moody_6 | moody_7 | moody_8 | moody_9 | moonlight | moonlight_01 | moonlight_2 | moonrise | morning_6
    | morroco_16 | mostly_blue | mother! | motus | moviz_1 | moviz_10 | moviz_11 | moviz_12 | moviz_13 | moviz_14 | moviz_15 | moviz_16 | moviz_17 | moviz_18 | moviz_19 | moviz_2 |
    moviz_20 | moviz_21 | moviz_22 | moviz_23 | moviz_24 | moviz_25 | moviz_26 | moviz_27 | moviz_28 | moviz_29 | moviz_3 | moviz_30 | moviz_31 | moviz_32 | moviz_33 | moviz_34 |
    moviz_35 | moviz_36 | moviz_37 | moviz_38 | moviz_39 | moviz_4 | moviz_40 | moviz_41 | moviz_42 | moviz_43 | moviz_44 | moviz_45 | moviz_46 | moviz_47 | moviz_48 | moviz_5 |
    moviz_6 | moviz_7 | moviz_8 | moviz_9 | mucca | mute_shift | muted_01 | muted_fade | mysticpurplesunset | nah | natural_01 | natural_02 | natural_03 | natural_vivid |
    naturalboost | negative | nemesis | neon_770 | neutral | neutral_pump | neutral_teal_orange | neutral_warm_fade | newspaper | night_01 | night_02 | night_03 | night_04 | night_05
    | night_blade_4 | night_king_141 | night_spy | night_view | nightfromday | nightlife | nigrum | no_time_to_die | nostalgiahoney | nostalgic | nw-1 | nw-10 | nw-2 | nw-3 | nw-4 |
    nw-5 | nw-6 | nw-7 | nw-8 | nw-9 | old_west | once_upon_a_time | once_upon_a_time_in_hollywood | onda | only_red | only_red_and_blue | operation_yellow | orange_dark_4 |
    orange_dark_7 | orange_dark_look | orange_tone | orange_underexposed | orangeandblue | oranges | padre | paladin | paladin_1875 | parasite | partia | pasadena_21 | passing_by |
    perso | picola | pink_fade | pirates_of_the_caribbean | pitaya_15 | pmcinematic_01 | pmcinematic_02 | pmcinematic_03 | pmcinematic_04 | pmcinematic_05 | pmcinematic_06 |
    pmcinematic_07 | pmnight_01 | pmnight_02 | pmnight_03 | pmnight_04 | pmnight_05 | polaroid_664 | polaroid_665 | polaroid_665_+ | polaroid_665_++ | polaroid_665_- |
    polaroid_665_-- | polaroid_665_negative | polaroid_665_negative_+ | polaroid_665_negative_- | polaroid_665_negative_hc | polaroid_667 | polaroid_669 | polaroid_669_+ |
    polaroid_669_++ | polaroid_669_+++ | polaroid_669_- | polaroid_669_-- | polaroid_669_cold | polaroid_669_cold_+ | polaroid_669_cold_- | polaroid_669_cold_-- | polaroid_672 |
    polaroid_690 | polaroid_690_+ | polaroid_690_++ | polaroid_690_- | polaroid_690_-- | polaroid_690_cold | polaroid_690_cold_+ | polaroid_690_cold_++ | polaroid_690_cold_- |
    polaroid_690_cold_-- | polaroid_690_warm | polaroid_690_warm_+ | polaroid_690_warm_++ | polaroid_690_warm_- | polaroid_690_warm_-- | polaroid_polachrome | polaroid_px-100uv+_cold
    | polaroid_px-100uv+_cold_+ | polaroid_px-100uv+_cold_++ | polaroid_px-100uv+_cold_+++ | polaroid_px-100uv+_cold_- | polaroid_px-100uv+_cold_-- | polaroid_px-100uv+_warm |
    polaroid_px-100uv+_warm_+ | polaroid_px-100uv+_warm_++ | polaroid_px-100uv+_warm_+++ | polaroid_px-100uv+_warm_- | polaroid_px-100uv+_warm_-- | polaroid_px-680 |
    polaroid_px-680_+ | polaroid_px-680_++ | polaroid_px-680_- | polaroid_px-680_-- | polaroid_px-680_cold | polaroid_px-680_cold_+ | polaroid_px-680_cold_++ |
    polaroid_px-680_cold_++_alt | polaroid_px-680_cold_- | polaroid_px-680_cold_-- | polaroid_px-680_warm | polaroid_px-680_warm_+ | polaroid_px-680_warm_++ | polaroid_px-680_warm_-
    | polaroid_px-680_warm_-- | polaroid_px-70 | polaroid_px-70_+ | polaroid_px-70_++ | polaroid_px-70_+++ | polaroid_px-70_- | polaroid_px-70_-- | polaroid_px-70_cold |
    polaroid_px-70_cold_+ | polaroid_px-70_cold_++ | polaroid_px-70_cold_- | polaroid_px-70_cold_-- | polaroid_px-70_warm | polaroid_px-70_warm_+ | polaroid_px-70_warm_++ |
    polaroid_px-70_warm_- | polaroid_px-70_warm_-- | polaroid_time_zero_expired | polaroid_time_zero_expired_+ | polaroid_time_zero_expired_++ | polaroid_time_zero_expired_- |
    polaroid_time_zero_expired_-- | polaroid_time_zero_expired_--- | polaroid_time_zero_expired_cold | polaroid_time_zero_expired_cold_- | polaroid_time_zero_expired_cold_-- |
    polaroid_time_zero_expired_cold_--- | portrait | portrait_1 | portrait_10 | portrait_2 | portrait_3 | portrait_4 | portrait_5 | portrait_6 | portrait_7 | portrait_8 | portrait_9
    | progressen | protect_highlights_01 | prussian_blue | pseudogrey | purple | purple_2 | quraqqq_12 | radar | randas | red_afternoon_01 | red_day_01 | red_dream_01 | redblueyellow
    | reds | reds_oranges_yellows | reeve_38 | remy_24 | rest_33 | retro | retro_brown_01 | retro_magenta_01 | retro_summer_3 | retro_yellow_01 | rocketman | rollei_ir_400 |
    rollei_ortho_25 | rollei_retro_100_tonal | rollei_retro_80s | rotate_muted | rotate_vibrant | rotated | rotated_crush | satid | saturated_blue | saving_private_damon | scala |
    science_fiction | scrittle | sea | seges | selor | sensum | separation | serenity | seringe_4 | serpent | seventies_magazine | sevsuz | shade_kings_ink | shadow_king_39 | shine |
    sicario | sino | skin_tones | slog_to_rec_709_basic | slog_to_rec_709_contrasty | slog_to_rec_709_crush_shadows | slog_to_rec_709_green_correction | smart_contrast | smokey |
    smooth_clear | smooth_cromeish | smooth_fade | smooth_green_orange | smooth_sailing | smooth_teal_orange | soft_fade | softblackandwhite | softwarming | solarized_color |
    solarized_color_2 | soldi | spider-man_far_from_home | spotlight | springmorning | sprocket_231 | spy_29 | standard | star_wars_the_rise_of_skywalker | strano | street | stringa
    | studio_skin_tone_shaper | subtle_blue | subtle_green | subtle_yellow | sully | summer | summer_alt | sunlight_love_11 | sunlightlove | sunny | sunny_alt | sunny_rich |
    sunny_warm | sunset | sunset_aqua_orange | sunset_intense_violet_blue | sunset_violet_mood | super_warm | super_warm_rich | sutro_fx | sweet_bubblegum | sweet_gelatto |
    taşdemirrr_1 | taiga | take_off | tarraco | teal-orange_for_flog | teal_fade | teal_moonlight | tealmagentagold | tealorange | tealorange_1 | tealorange_2 | tealorange_3 |
    technicalfx_backlight_filter | teigen_28 | tenet | tensiongreen_1 | tensiongreen_2 | tensiongreen_3 | tensiongreen_4 | terra_4 | the_dark_knight | the_darkest_hour |
    the_gentelmen | the_grand_budapest_hotel | the_hurt_locker | the_irishman | the_lighthouse | the_lobster | the_martian | the_matrices | the_revenant | the_shape_of_water |
    the_social_network | the_two_popes | the_way_back | thor_ragnarok | thriller_2 | tirare | toastedgarden | top_gun_maverick | trent_18 | true_colors_8 | turkiest_42 | tutto |
    tweed_71 | ultra_water | uncut_gems | undeniable | undeniable_2 | underwater | unknown | upglow | urban_01 | urban_02 | urban_03 | urban_04 | urban_05 | urban_cowboy |
    uzbek_bukhara | uzbek_marriage | uzbek_samarcande | valize | valsky | velvetia | venom | very_warm_greenish | vfb_21 | vibrant | vibrant_alien | vibrant_contrast |
    vibrant_cromeish | victory | vintage | vintage_01 | vintage_02 | vintage_03 | vintage_04 | vintage_05 | vintage_163 | vintage_alt | vintage_brighter | vintage_chrome |
    vintage_mob | vintage_warmth_1 | violet_taste | vireo_37 | vita | vivid | vubes | war_for_the_planet_of_the_apes | warm | warm_dark_contrasty | warm_fade | warm_fade_1 |
    warm_highlight | warm_neutral | warm_sunset_red | warm_teal | warm_vintage | warm_yellow | wavefire | waves | well_see | western | western_6 | westernlut_2 | westernlut_2_13 |
    whiter_whites | wingspan | winterlighthouse | wipe | wolf_of_wall_street | wonder_woman | wooden_gold_20 | x-men_dark_phoenix | yangabuz_8 | yellow_55b | yellow_film_01 |
    yellowstone | you_can_do_it | zed_32 | zeke_39 | zilverfx_bw_solarization | zilverfx_infrared | zilverfx_vintage_bw | zombieland_double_tap }
    Default values: 'resolution=33' and 'cut_and_round=1'.
    Example:
      [#1] clut summer clut alien_green,17 clut orange_dark4,48
  clut2hald:
    Convert selected 3D CLUTs to 2D HaldCLUTs.
    Example:
      [#1] clut summer +clut2hald
  cmy2rgb:
    Convert color representation of selected images from CMY to RGB.
  cmyk2rgb:
    Convert color representation of selected images from CMYK to RGB.
  colorblind:
      type={ 0:Protanopia | 1:Protanomaly | 2:Deuteranopia | 3:Deuteranomaly | 4:Tritanopia | 5:Tritanomaly | 6:Achromatopsia | 7:Achromatomaly }
    Simulate color blindness vision.
    Simulation method of Vienot, Brettel & Mollon 1999, "Digital video colourmaps for checking the legibility of displays by dichromats".
    The dichromacy matrices of the paper were adapted to sRGB (RGB->XYZ).
    Anomalous trichromacy simulated via linear interpolation with the identity and a factor of 0.6.
    Example:
      [#1] image.jpg +colorblind 0
  colormap:
      nb_levels>=0,_method={ 0:Median-cut | 1:K-means },_sort_vectors
    Estimate best-fitting colormap with 'nb_levels' entries, to index selected images.
    Specify 'nb_levels==0' to extract all existing colors of an image.
    'sort_vectors' can be { 0:Do not sort | 1:By increasing norm | 2:By decreasing occurrence }.
    Default value: 'method=1' and 'sort_vectors=1'.
    Example:
      [#1] image.jpg +colormap[0] 4 +colormap[0] 8 +colormap[0] 16
    Tutorial: https://gmic.eu/oldtutorial/_colormap
  compose_channels:
      _operator
    Compose all channels of each selected image, using specified arithmetic operator (+,-,or,min,...).
    Default value: 'operator=+'.
    Example:
      [#1] image.jpg +compose_channels and
    Tutorial: https://gmic.eu/tutorial/compose_channels
  contrast:
      strength
    Change contrast of selected images, with specified strength.
     * If 'strength' is positive, image contrast is amplified.
     * If 'strength' is negative, image contrast is reduced.
    A typical value range for parameter 'strength' is [-100,100].
    Example:
      [#1] image.jpg +contrast 20
  count_colors:
      _count_until={ 0:None | >0:Max number of counted colors }
    Count number of distinct colors in selected images until it reaches the specified max number of counted colors.
    Set 'count_until' to '0' to disable limit on counted colors.
    This command returns the number of distinct colors for each image (separated by commas).
  deltaE:
      [image],_metric={ 0:DeltaE_1976 | 1:DeltaE_2000 },"_to_Lab_command"
    Compute the CIE DeltaE color difference between selected images and specified [image].
    Argument 'to_Lab_command' is a command able to convert colors of [image] into a Lab representation.
    Default values: 'metric=1' and 'to_Lab_command="srgb2lab"'.
    Example:
      [#1] image.jpg +blur 2 +deltaE[0] [1],1,srgb2lab
  direction2rgb:
    Compute RGB representation of selected 2D direction fields.
    Example:
      [#1] image.jpg luminance gradient append c blur 2 orientation +direction2rgb
  ditheredbw:
    Create dithered B&W version of selected images.
    Example:
      [#1] image.jpg +equalize ditheredbw[-1]
  fc:
      Shortcut for command 'fill_color'.
  fill_color:
      col1,...,colN
    Fill selected images with specified color.
    (equivalent to shortcut command 'fc').
    Example:
      [#1] image.jpg +fill_color 255,0,255
    Tutorial: https://gmic.eu/oldtutorial/_fill_color
  gradient2rgb:
      _is_orientation={ 0:No | 1:Yes }
    Compute RGB representation of 2D gradient of selected images.
    Default value: 'is_orientation=0'.
    Example:
      [#1] image.jpg +gradient2rgb 0 equalize[-1]
  hald2clut:
    Convert selected 2D HaldCLUTs to 3D CLUTs.
  hcy2rgb:
    Convert color representation of selected images from HCY to RGB.
  hsi2rgb:
    Convert color representation of selected images from HSI to RGB.
  hsi82rgb:
    Convert color representation of selected images from HSI8 to RGB.
  hsl2rgb:
    Convert color representation of selected images from HSL to RGB.
  hsl82rgb:
    Convert color representation of selected images from HSL8 to RGB.
  hsv2rgb:
    Convert color representation of selected images from HSV to RGB.
    Example:
      [#1] (0,360;0,360^0,0;1,1^1,1;1,1) resize 400,400,1,3,3 hsv2rgb
  hsv82rgb:
    Convert color representation of selected images from HSV8 to RGB.
  int2rgb:
    Convert color representation of selected images from INT24 to RGB.
  ipremula:
    Convert selected images with premultiplied alpha colors to normal colors.
    See also: premula.
  jzazbz2rgb:
      illuminant={ 0:D50 | 1:D65 | 2:E } |
      (no arg)
    Convert color representation of selected images from RGB to Jzazbz.
    Default value: 'illuminant=2'.
  jzazbz2xyz:
    Convert color representation of selected images from RGB to XYZ.
  lab2lch:
    Convert color representation of selected images from Lab to Lch.
  lab2rgb:
      illuminant={ 0:D50 | 1:D65 | 2:E } |
      (no arg)
    Convert color representation of selected images from Lab to RGB.
    Default value: 'illuminant=2'.
    Example:
      [#1] (50,50;50,50^-3,3;-3,3^-3,-3;3,3) resize 400,400,1,3,3 lab2rgb
  lab2srgb:
      illuminant={ 0:D50 | 1:D65 | 2:E } |
      (no arg)
    Convert color representation of selected images from Lab to sRGB.
    Default value: 'illuminant=2'.
    Example:
      [#1] (50,50;50,50^-3,3;-3,3^-3,-3;3,3) resize 400,400,1,3,3 lab2rgb
  lab82srgb:
      illuminant={ 0:D50 | 1:D65 | 2:E } |
      (no arg)
    Convert color representation of selected images from Lab8 to sRGB.
    Default value: 'illuminant=2'.
    Example:
      [#1] (50,50;50,50^-3,3;-3,3^-3,-3;3,3) resize 400,400,1,3,3 lab2rgb
  lab2xyz:
      illuminant={ 0:D50 | 1:D65 | 2:E } |
      (no arg)
    Convert color representation of selected images from Lab to XYZ.
    Default value: 'illuminant=2'.
  lab82rgb:
      illuminant={ 0:D50 | 1:D65 | 2:E } |
      (no arg)
    Convert color representation of selected images from Lab8 to RGB.
    Default value: 'illuminant=2'.
  lch2lab:
    Convert color representation of selected images from Lch to Lab.
  lch2rgb:
      illuminant={ 0:D50 | 1:D65 | 2:E } |
      (no arg)
    Convert color representation of selected images from Lch to RGB.
    Default value: 'illuminant=2'.
  lch82rgb:
      illuminant={ 0:D50 | 1:D65 | 2:E } |
      (no arg)
    Convert color representation of selected images from Lch8 to RGB.
    Default value: 'illuminant=2'.
  luminance:
    Compute luminance of selected sRGB images.
    Example:
      [#1] image.jpg +luminance
    Tutorial: https://gmic.eu/tutorial/luminance
  lightness:
    Compute lightness of selected sRGB images.
    Example:
      [#1] image.jpg +lightness
  lut_contrast:
      _nb_colors>1,_min_rgb_value
    Generate a RGB colormap where consecutive colors have high contrast.
    This function performs a specific score maximization to generate the result, so
    it may take some time when 'nb_colors' is high.
    Default values: 'nb_colors=256' and 'min_rgb_value=64'.
  map_clut:
      [clut] | "clut_name"
    Map specified RGB color LUT to selected images.
    Example:
      [#1] image.jpg uniform_distribution {2^6},3 mirror[-1] x +map_clut[0] [1]
  match_histogram:
      [reference_image],_nb_levels>0,_color_channels
    Transfer histogram of the specified reference image to selected images.
    Argument 'color channels' is the same as with command 'apply_channels'.
    Default value: 'nb_levels=256' and 'color_channels=all'.
    Example:
      [#1] image.jpg 100,100,1,3,"u([256,200,100])" +match_histogram[0] [1]
  match_pca:
      [reference_image],_color_channels
    Transfer mean and covariance matrix of specified vector-valued reference image to selected images.
    Argument 'color channels' is the same as with command 'apply_channels'.
    Default value: 'color_channels=all'.
    Example:
      [#1] sample lena,earth +match_pca[0] [1]
  match_rgb:
      [reference],_gamma>=0,_regularization>=0,_luminosity_constraints>=0,_rgb_resolution>=0,_is_constraints={ 0:No | 1:Yes }
    Transfer colors from specified reference image (given as argument) to selected image.
    'gamma' determines the importance of color occurrences in the matching process (0:None to 1:Huge).
    'regularization' determines the number of guided filter iterations to remove quantization effects.
    'luminosity_constraints' tells if luminosity constraints must be applied on non-confident matched colors.
    'is_constraints' tells if additional hard color constraints must be set (opens an interactive window).
    Default values: 'gamma=0.3','regularization=8', 'luminosity_constraints=0.1', 'rgb_resolution=64' and 'is_constraints=0'.
    Example:
      [#1] sample pencils,wall +match_rgb[0] [1],0,0.01
  mix_rgb:
      a11,a12,a13,a21,a22,a23,a31,a32,a33
    Apply 3x3 specified matrix to RGB colors of selected images.
    Default values: 'a11=1', 'a12=a13=a21=0', 'a22=1', 'a23=a31=a32=0' and 'a33=1'.
    Example:
      [#1] image.jpg +mix_rgb 0,1,0,1,0,0,0,0,1
    Tutorial: https://gmic.eu/tutorial/mix_rgb
  oklab2rgb:
    Convert color representation of selected images from OKlab to RGB.
    (see colorspace definition at: https://bottosson.github.io/posts/oklab/ ).
    See also: rgb2oklab.
  palette:
      palette_name | palette_number
    Input specified color palette at the end of the image list.
    'palette_name' can be { default | hsv | lines | hot | cool | jet | flag | cube | rainbow | parula | spring | summer | autumn | winter | bone | copper | pink | vga |
    algae | amp | balance | curl | deep | delta | dense | diff | gray | haline | ice | matter | oxy | phase | rain | solar | speed | tarn | tempo | thermal | topo | turbid | aurora |
    hocuspocus | srb2 | uzebox | amiga7800 | amiga7800mess | fornaxvoid1 }
    Example:
      [#1] palette hsv
  premula:
    Convert selected images with normal colors to premultiplied alpha colors.
    After conversion, alpha channel of resulting images has value in [0,1] range.
    See also: ipremula.
  pseudogray:
      _max_increment>=0,_JND_threshold>=0,_bits_depth>0
    Generate pseudogray colormap with specified increment and perceptual threshold.
    If 'JND_threshold' is 0, no perceptual constraints are applied.
    Default values: 'max_increment=5', 'JND_threshold=2.3' and 'bits_depth=8'.
    Example:
      [#1] pseudogray 5
  random_clut:
    Generate a 33x33x33 random 3D color LUT.
    Example:
      [#1] image.jpg random_clut +map_clut.. .
  random_clut:
      _seed = { >=0 | -1 }
    Generate a 33x33x33 random 3D color LUT.
    If specified 'seed' is positive, it is used as a seed for the random number generator @cli : (so that using the same seed will return the same CLUT).
    Example:
      [#1] image.jpg random_clut +map_clut.. .
  replace_color:
      tolerance[%]>=0,smoothness[%]>=0,src1,src2,...,dest1,dest2,...
    Replace pixels from/to specified colors in selected images.
    Example:
      [#1] image.jpg +replace_color 40,3,204,153,110,255,0,0
  retinex:
      _value_offset>0,_colorspace={ hsi | hsv | lab | lrgb | rgb | ycbcr },0<=_min_cut<=100,0<=_max_cut<=100,_sigma_low>0,_sigma_mid>0,_sigma_high>0
    Apply multi-scale retinex algorithm on selected images to improve color consistency.
    (as described in the page http://www.ipol.im/pub/art/2014/107/).
    Default values: 'offset=1', 'colorspace=hsv', 'min_cut=1', 'max_cut=1', 'sigma_low=15','sigma_mid=80' and 'sigma_high=250'.
  rgb:
      _min_RGB_value,_max_RGB_value |
      (no arg)
    Return a random int-valued RGB color.
  rgba:
      _min_RGB_value,_max_RGB_value,_min_A_value,_max_A_value |
      (no_arg)
    Return a random int-valued RGBA color.
  rgb2bayer:
      _start_pattern=0,_color_grid=0
    Transform selected color images to RGB-Bayer sampled images.
    Default values: 'start_pattern=0' and 'color_grid=0'.
    Example:
      [#1] image.jpg +rgb2bayer 0
  rgb2cmy:
    Convert color representation of selected images from RGB to CMY.
    Example:
      [#1] image.jpg rgb2cmy split c
  rgb2cmyk:
    Convert color representation of selected images from RGB to CMYK.
    Example:
      [#1] image.jpg rgb2cmyk split c
      [#2] image.jpg rgb2cmyk split c fill[3] 0 append c cmyk2rgb
  rgb2hcy:
    Convert color representation of selected images from RGB to HCY.
    Example:
      [#1] image.jpg rgb2hcy split c
  rgb2hsi:
    Convert color representation of selected images from RGB to HSI.
    Example:
      [#1] image.jpg rgb2hsi split c
  rgb2hsi8:
    Convert color representation of selected images from RGB to HSI8.
    Example:
      [#1] image.jpg rgb2hsi8 split c
  rgb2hsl:
    Convert color representation of selected images from RGB to HSL.
    Example:
      [#1] image.jpg rgb2hsl split c
      [#2] image.jpg rgb2hsl +split c add[-3] 100 mod[-3] 360 append[-3--1] c hsl2rgb
  rgb2hsl8:
    Convert color representation of selected images from RGB to HSL8.
    Example:
      [#1] image.jpg rgb2hsl8 split c
  rgb2hsv:
    Convert color representation of selected images from RGB to HSV.
    Example:
      [#1] image.jpg rgb2hsv split c
      [#2] image.jpg rgb2hsv +split c add[-2] 0.3 cut[-2] 0,1 append[-3--1] c hsv2rgb
  rgb2hsv8:
    Convert color representation of selected images from RGB to HSV8.
    Example:
      [#1] image.jpg rgb2hsv8 split c
  rgb2int:
    Convert color representation of selected images from RGB to INT24 scalars.
    Example:
      [#1] image.jpg rgb2int
  rgb2jzazbz:
      illuminant={ 0:D50 | 1:D65 | 2:E } |
      (no arg)
    Convert color representation of selected images from RGB to Jzazbz.
    Default value: 'illuminant=2'.
  rgb2lab:
      illuminant={ 0:D50 | 1:D65 | 2:E } |
      (no arg)
    Convert color representation of selected images from RGB to Lab.
    Default value: 'illuminant=2'.
  rgb2lab8:
      illuminant={ 0:D50 | 1:D65 | 2:E } |
      (no arg)
    Convert color representation of selected images from RGB to Lab8.
    Default value: 'illuminant=2'.
    Example:
      [#1] image.jpg rgb2lab8 split c
  rgb2lch:
      illuminant={ 0:D50 | 1:D65 | 2:E } |
      (no arg)
    Convert color representation of selected images from RGB to Lch.
    Default value: 'illuminant=2'.
    Example:
      [#1] image.jpg rgb2lch split c
  rgb2lch8:
      illuminant={ 0:D50 | 1:D65 | 2:E } |
      (no arg)
    Convert color representation of selected images from RGB to Lch8.
    Default value: 'illuminant=2'.
    Example:
      [#1] image.jpg rgb2lch8 split c
  rgb2luv:
    Convert color representation of selected images from RGB to LUV.
    Example:
      [#1] image.jpg rgb2luv split c
  rgb2oklab:
    Convert color representation of selected images from RGB to Oklab.
    (see colorspace definition at: https://bottosson.github.io/posts/oklab/ ).
    See also: oklab2rgb.
  rgb2ryb:
    Convert color representation of selected images from RGB to RYB.
    Example:
      [#1] image.jpg rgb2ryb split c
  rgb2srgb:
    Convert color representation of selected images from linear RGB to sRGB.
  rgb2xyz:
      illuminant={ 0:D50 | 1:D65 | 2:E } |
      (no arg)
    Convert color representation of selected images from RGB to XYZ.
    Default value: 'illuminant=2'.
    Example:
      [#1] image.jpg rgb2xyz split c
  rgb2xyz8:
      illuminant={ 0:D50 | 1:D65 | 2:E } |
      (no arg)
    Convert color representation of selected images from RGB to XYZ8.
    Default value: 'illuminant=2'.
    Example:
      [#1] image.jpg rgb2xyz8 split c
  rgb2yiq:
    Convert color representation of selected images from RGB to YIQ.
    Example:
      [#1] image.jpg rgb2yiq split c
  rgb2yiq8:
    Convert color representation of selected images from RGB to YIQ8.
    Example:
      [#1] image.jpg rgb2yiq8 split c
  rgb2ycbcr:
    Convert color representation of selected images from RGB to YCbCr.
    Example:
      [#1] image.jpg rgb2ycbcr split c
  rgb2yuv:
    Convert color representation of selected images from RGB to YUV.
    Example:
      [#1] image.jpg rgb2yuv split c
  rgb2yuv8:
    Convert color representation of selected images from RGB to YUV8.
    Example:
      [#1] image.jpg rgb2yuv8 split c
  remove_opacity:
    Remove opacity channel of selected images.
  ryb2rgb:
    Convert color representation of selected images from RYB to RGB.
  select_color:
      tolerance[%]>=0,col1,...,colN
    Select pixels with specified color in selected images.
    Example:
      [#1] image.jpg +select_color 40,204,153,110
    Tutorial: https://gmic.eu/oldtutorial/_select_color
  sepia:
    Apply sepia tones effect on selected images.
    Example:
      [#1] image.jpg sepia
  solarize:
    Solarize selected images.
    Example:
      [#1] image.jpg solarize
  split_colors:
      _tolerance>=0,_max_nb_outputs>0,_min_area>0
    Split selected images as several image containing a single color.
    One selected image can be split as at most 'max_nb_outputs' images.
    Output images are sorted by decreasing area of extracted color regions and have an additional alpha-channel.
    Default values: 'tolerance=0', 'max_nb_outputs=256' and 'min_area=8'.
    Example:
      [#1] image.jpg quantize 5 +split_colors , display_rgba
  split_opacity:
    Split color and opacity parts of selected images.
    This command returns 1 or 2 images for each selected image, whether it has an opacity channel or not.
  split_vector:
      keep_splitting_values={ +:Increasing | -:Decreasing },value1,_value2,...
    Split selected images into multiple parts, where specified vector '[value1,_value2,...]' is the separator.
  srgb2lab:
      illuminant={ 0:D50 | 1:D65 | 2:E } |
      (no arg)
    Convert color representation of selected images from sRGB to Lab.
    Default value: 'illuminant=2'.
    Example:
      [#1] image.jpg srgb2lab split c
      [#2] image.jpg srgb2lab +split c mul[-2,-1] 2.5 append[-3--1] c lab2srgb
  srgb2lab8:
      illuminant={ 0:D50 | 1:D65 | 2:E } |
      (no arg)
    Convert color representation of selected images from sRGB to Lab8.
    Default value: 'illuminant=2'.
  srgb2rgb:
    Convert color representation of selected images from sRGB to linear RGB.
  to_a:
    Force selected images to have an alpha channel.
  to_color:
    Force selected images to be in color mode (RGB or RGBA).
  to_colormode:
      mode={ 0:Adaptive | 1:G | 2:GA | 3:RGB | 4:RGBA }
    Force selected images to be in a given color mode.
    Default value: 'mode=0'.
  to_gray:
    Force selected images to be in GRAY mode.
    Example:
      [#1] image.jpg +to_gray
  to_graya:
    Force selected images to be in GRAYA mode.
  to_pseudogray:
      _max_step>=0,_is_perceptual_constraint={ 0:No | 1:Yes },_bits_depth>0
    Convert selected scalar images ([0-255]-valued) to pseudo-gray color images.
    Default values: 'max_step=5', 'is_perceptual_constraint=1' and 'bits_depth=8'.
    The original pseudo-gray technique has been introduced by Rich Franzen http://r0k.us/graphics/pseudoGrey.html.
    Extension of this technique to arbitrary increments for more tones, has been done by David Tschumperlé.
  to_rgb:
    Force selected images to be in RGB mode.
  to_rgba:
    Force selected images to be in RGBA mode.
  to_automode:
    Force selected images to be in the most significant color mode.
    This commands checks for useless alpha channel (all values equal to 255), as well as
    detects grayscale images encoded as color images.
  xyz2jzazbz:
    Convert color representation of selected images from XYZ to RGB.
  xyz2lab:
      illuminant={ 0:D50 | 1:D65 | 2:E } |
      (no arg)
    Convert color representation of selected images from XYZ to Lab.
    Default value: 'illuminant=2'.
  xyz2rgb:
      illuminant={ 0:D50 | 1:D65 | 2:E } |
      (no arg)
    Convert color representation of selected images from XYZ to RGB.
    Default value: 'illuminant=2'.
  xyz82rgb:
      illuminant={ 0:D50 | 1:D65 | 2:E } |
      (no arg)
    Convert color representation of selected images from XYZ8 to RGB.
    Default value: 'illuminant=2'.
  ycbcr2rgb:
    Convert color representation of selected images from YCbCr to RGB.
  yiq2rgb:
    Convert color representation of selected images from YIQ to RGB.
  yiq82rgb:
    Convert color representation of selected images from YIQ8 to RGB.
  yuv2rgb:
    Convert color representation of selected images from YUV to RGB.
  yuv82rgb:
    Convert selected images from YUV8 to RGB color bases.
  11.7. Geometry Manipulation
        ---------------------
  a (+):
      Shortcut for command 'append'.
  append (+):
      [image],axis,_centering |
      axis,_centering
    Append specified image to selected images, or all selected images together, along specified axis.
    (equivalent to shortcut command 'a').
    'axis' can be { x | y | z | c }.
    Usual 'centering' values are { 0:left-justified | 0.5:centered | 1:right-justified }.
    Default value: 'centering=0'.
    Example:
      [#1] image.jpg split y,10 reverse append y
      [#2] image.jpg repeat 5 { +rows[0] 0,{10+18*$>}% } remove[0] append x,0.5
      [#3] image.jpg append[0] [0],y
  append_tiles:
      _M>=0,_N>=0,0<=_centering_x<=1,0<=_centering_y<=1
    Append MxN selected tiles as new images.
    If 'N' is set to 0, number of rows is estimated automatically.
    If 'M' is set to 0, number of columns is estimated automatically.
    If 'M' and 'N' are both set to '0', auto-mode is used.
    If 'M' or 'N' is set to 0, only a single image is produced.
    'centering_x' and 'centering_y' tells about the centering of tiles when they have different sizes.
    Default values: 'M=0', 'N=0', 'centering_x=centering_y=0.5'.
    Example:
      [#1] image.jpg split xy,4 append_tiles ,
  apply_scales:
      "command",number_of_scales>0,_min_scale[%]>=0,_max_scale[%]>=0,_scale_gamma>0,_interpolation
    Apply specified command on different scales of selected images.
    'interpolation' can be { 0:None | 1:Nearest | 2:Average | 3:Linear | 4:Grid | 5:Bicubic | 6:Lanczos }.
    Default value: 'min_scale=25%', 'max_scale=100%' and 'interpolation=3'.
    Example:
      [#1] image.jpg apply_scales "blur 5 sharpen 1000",4
  autocrop:
      _axes,_value1,_value2,...
    Autocrop selected images according to specified axes and values.
    'axes' can be { x | y | z | c | xy | xz | xc | yz | yc | zc | xyz | xyc | xzc | yzc | xyzc }.
    If no 'axes' are provided, autocrop is assumed to be spatial only (e.g. 'axes=xyz').
    If no value arguments are provided, cropping value is automatically guessed.
    Default values: 'axes=xyz'.
    See also: autocrop_coords.
    Example:
      [#1] 400,400,1,3 fill_color 64,128,255 ellipse 50%,50%,120,120,0,1,255 +autocrop
  autocrop_components:
      _threshold[%],_min_area[%]>=0,_is_high_connectivity={ 0:No | 1:Yes },_output_type={ 0:Crop | 1:Segmentation | 2:Coordinates }
    Autocrop and extract connected components in selected images, according to a mask given as the last channel of
    each of the selected image (e.g. alpha-channel).
    Default values: 'threshold=0%', 'min_area=0.1%', 'is_high_connectivity=0' and 'output_type=1'.
    Example:
      [#1] 256,256 noise 0.1,2 eq 1 dilate_circ 20 label_fg 0,1 normalize 0,255 +neq 0 *[-1] 255 append c +autocrop_components ,
  autocrop_coords:
      _axes,_value1,_value2,...
    Return coordinates of the bounding box that would be used to autocrop selected images, according to specified axes and values.
    'axes' can be { x | y | z | c | xy | xz | xc | yz | yc | zc | xyz | xyc | xzc | yzc | xyzc }.
    If no 'axes' are provided, autocrop is assumed to be spatial only (e.g. 'axes=xyz').
    If no value arguments are provided, cropping value is automatically guessed.
    If input image is constant and equal to the crop value, -1 is returned for all output coordinates.
    Default values: 'axes=xyz'.
    See also: autocrop.
  autocrop_seq:
      value1,value2,... | auto
    Autocrop selected images using the crop geometry of the last one by specified vector-valued intensity,
    or by automatic guessing the cropping value.
    Default value: auto mode.
    Example:
      [#1] image.jpg +fill[-1] 0 ellipse[-1] 50%,50%,30%,20%,0,1,1 autocrop_seq 0
  channels:
      c0[%],_c1[%],_boundary_conditions
    Keep only specified channels of selected images.
    'boundary_conditions' can be { 0:Dirichlet | 1:Neumann | 2:Periodic | 3:Mirror }.
    Default values: 'c1=c0' and 'boundary_conditions=0'.
    Example:
      [#1] image.jpg channels 1
      [#2] image.jpg luminance channels 0,2
  columns:
      x0[%],_x1[%],_boundary_conditions
    Keep only specified columns of selected images.
    'boundary_conditions' can be { 0:Dirichlet | 1:Neumann | 2:Periodic | 3:Mirror }.
    Default values: 'x1=x0' and 'boundary_conditions=0'.
    Example:
      [#1] image.jpg columns -25%,50%
  z (+):
      Shortcut for command 'crop'.
  crop (+):
      x0[%],x1[%],_boundary_conditions |
      x0[%],y0[%],x1[%],y1[%],_boundary_conditions |
      x0[%],y0[%],z0[%],x1[%],y1[%],z1[%],_boundary_conditions |
      x0[%],y0[%],z0[%],c0[%],x1[%],y1[%],z1[%],c1[%],_boundary_conditions
    Crop selected images with specified region coordinates.
    (equivalent to shortcut command 'z').
    'boundary_conditions' can be { 0:Dirichlet | 1:Neumann | 2:Periodic | 3:Mirror }.
    Default value: 'boundary_conditions=0'.
    Example:
      [#1] image.jpg +crop -230,-230,280,280,1 crop[0] -230,-230,280,280,0
      [#2] image.jpg crop 25%,25%,75%,75%
  elevate:
      _depth,_is_plain={ 0:No | 1:Yes },_is_colored={ 0:No | 1:Yes }
    Elevate selected 2D images into 3D volumes.
    Default values: 'depth=64', 'is_plain=1' and 'is_colored=1'.
    Example:
      [#1] sample colorful,64 +elevate 32
  expand:
      axes,size[%],_boundary_conditions={ 0:Dirichlet | 1:Neumann | 2:Periodic | 3:Mirror }
    Expand selected images along the specified axes.
    'axes' can be { x | y | z | c | xy | xz | xc | yz | yc | zc | xyz | xyc | xzc | yzc | xyzc }.
    Default value: 'boundary_conditions=0'.
    Example:
      [#1] image.jpg expand xy,30
  extract:
      "condition",_output_type={ 0:XYZC-coords | 1:XYZ-coords | 2:Scalar-values | 3:Vector-values | 4:XYZC-coords + scalar value | 5:XYZ-coords + vector-values }
    Extract a list of coordinates or values from selected image, where
    specified mathematical condition holds.
    For N coordinates matching, result is a 1xNx1x4 image.
    Default values: 'output_type=0'.
    Example:
      [#1] sp lena +extract "norm(I)>128",3
  extract_region:
      [label_image],_extract_xyz_coordinates={ 0:No | 1:Yes },_label_1,...,_label_M
    Extract all pixels of selected images whose corresponding label in '[label_image]' is equal to 'label_m',
    and output them as M column images.
    Default value: 'extract_xyz_coordinates=0'.
    Example:
      [#1] image.jpg +blur 3 quantize. 4,0 +extract_region[0] [1],0,1,3
  montage:
      "_layout_code",_montage_mode={ 0<=centering<=1 | 2<=scale+2<=3 },_output_mode={ 0:Single layer | 1:Multiple layers },"_processing_command"
    Create a single image montage from selected images, according to specified layout code :
     * 'X' to assemble all images using an automatically estimated layout.
     * 'H' to assemble all images horizontally.
     * 'V' to assemble all images vertically.
     * 'A' to assemble all images as an horizontal array.
     * 'B' to assemble all images as a vertical array.
     * 'Ha:b' to assemble two blocks 'a' and 'b' horizontally.
     * 'Va:b' to assemble two blocks 'a' and 'b' vertically.
     * 'Ra' to rotate a block 'a' by 90 deg. ('RRa' for 180 deg. and 'RRRa' for 270 deg.).
     * 'Ma' to mirror a block 'a' along the X-axis ('MRRa' for the Y-axis).
    A block 'a' can be an image index (treated periodically) or a nested layout expression 'Hb:c','Vb:c','Rb' or
    'Mb' itself.
    For example, layout code 'H0:V1:2' creates an image where image [0] is on the left, and images [1] and [2]
    vertically packed on the right.
    Default values: 'layout_code=X', 'montage_mode=2', output_mode='0' and 'processing_command=""'.
    Example:
      [#1] image.jpg sample ? +plasma[0] 1 shape_cupid 256 normalize 0,255 frame xy,3,0 frame xy,10,255 to_rgb +montage A +montage[^-1] H1:V0:VH2:1H0:3
  mirror (+):
      { x | y | z }...{ x | y | z }
    Mirror selected images along specified axes.
    Example:
      [#1] image.jpg +mirror y +mirror[0] c
      [#2] image.jpg +mirror x +mirror y append_tiles 2,2
  permute (+):
      permutation
    Permute selected image axes by specified permutation.
    'permutation' is a combination of the character set {x|y|z|c},
    e.g. 'xycz', 'cxyz', ...
    Example:
      [#1] image.jpg permute yxzc
  rs:
      Shortcut for command 'rescale2d'.
  rescale2d:
      _width[%]={ 0:Any | >0 },_height[%]={ 0:Any | >0 },-1=<_interpolation<=6,_mode={ 0:Inside | 1:Padded-inside | 2:Outside | 3:Cropped-outside }
    Resize selected 2D images while preserving aspect ratio.
    'interpolation' can be { -1:Status only | 0:None | 1:Nearest | 2:Average | 3:Linear | 4=Grid | 5=Bicubic | 6=Lanczos }.
    When 'interpolation==-1', image size is actually not modified, but the size that would have been used for the last selected image is returned in the status value.
    Each resized image size is computed according to the specified 'mode':
     * If 'mode==0', image size is at most '(width,height)'.
     * If 'mode==1' or 'mode==3', image size is exactly '(width,height)'.
     * If 'mode==2', image size is at least '(width,height)'.
    (equivalent to shortcut command 'rs').
    Default values: 'width=height=0', 'interpolation=2' and 'mode=0'.
  rs3d:
      Shortcut for command 'rescale3d'.
  rescale3d:
      _width[%]={ 0:Any | >0 },_height[%]={ 0:Any | >0 },_depth[%]={ 0:Any | >0 },-1=<_interpolation<=6,_mode={ 0:Inside | 1:Padded-inside | 2:Outside | 3 |
      Cropped-outside }
    Resize selected 3D images while preserving aspect ratio.
    'interpolation' can be { -1:Status only | 0:None | 1:Nearest | 2:Average | 3:Linear | 4=Grid | 5=Bicubic | 6=Lanczos }.
    When 'interpolation==-1', image size is actually not modified, but the size that would have been used for the last selected image is returned in the status value.
    Each resized image size is computed according to the specified 'mode':
     * If 'mode==0', image size is at most '(width,height)'.
     * If 'mode==1' or 'mode==3', image size is exactly '(width,height)'.
     * If 'mode==2', image size is at least '(width,height)'.
    (equivalent to shortcut command 'rs3d').
    Default values: 'width=height=depth=0', 'interpolation=2' and 'mode=0'.
  r (+):
      Shortcut for command 'resize'.
  resize (+):
      {[image_w] | width[%]>0},_{[image_h] | height[%]>0},_{[image_d] | depth[%]>0},_{[image_s] | spectrum[%]>0},_interpolation,_boundary_conditions,_ax,_ay,_az,_ac
    Resize selected images with specified geometry.
    (equivalent to shortcut command 'r').
    'interpolation' can be { -1:None (memory content) | 0:None | 1:Nearest | 2:Average | 3:Linear | 4=Grid | 5=Bicubic | 6=Lanczos }.
    'boundary_conditions' has different meanings, according to the chosen 'interpolation' mode :
    . When 'interpolation=={ -1 | 1 | 2 | 4 }', 'boundary_conditions' is meaningless.
    . When 'interpolation==0', 'boundary_conditions' can be { 0:Dirichlet | 1:Neumann | 2:Periodic | 3:Mirror }.
    . When 'interpolation=={ 3 | 5 | 6 }', 'boundary_conditions' can be { 0:None | 1:Neumann }.
    'ax,ay,az,ac' set the centering along each axis when 'interpolation=0 or 4'
    (set to '0' by default, must be defined in range [0,1]).
    Default values: 'interpolation=1', 'boundary_conditions=0' and 'ax=ay=az=ac=0'.
    Example:
      [#1] image.jpg +resize[-1] 256,128,1,3,2 +resize[-1] 120%,120%,1,3,0,1,0.5,0.5 +resize[-1] 120%,120%,1,3,0,0,0.2,0.2 +resize[-1] [0],[0],1,3,4
  ri:
      Shortcut for command 'resize_as_image'.
  resize_as_image:
      [reference],_interpolation,_boundary_conditions,_ax,_ay,_az,_ac
    Resize selected images to the geometry of specified [reference] image.
    (equivalent to shortcut command 'ri').
    Default values: 'interpolation=1', 'boundary_conditions=0' and 'ax=ay=az=ac=0'.
    Example:
      [#1] image.jpg sample duck +resize_as_image[-1] [-2]
  resize_displacement:
      width[%]>0,_height[%]>0,_depth[%]>0
    Resize selected displacement fields with specified geometry.
    During the process, the displacement vectors are also scaled by the corresponding ratios along each axis.
    Default values: 'height=100%' and 'depth=100%'.
  resize_mn:
      width[%]>=0,_height[%]>=0,_depth[%]>=0,_B_value,_C_value
    Resize selected images with Mitchell-Netravali filter (cubic).
    For details about the method, see: https://de.wikipedia.org/wiki/Mitchell-Netravali-Filter.
    Default values: 'height=100%', 'depth=100%', 'B=0.3333' and 'C=0.3333'.
    Example:
      [#1] image.jpg rescale2d 32 resize_mn 800%,800%
  resize_pow2:
      _interpolation,_boundary_conditions,_ax,_ay,_az,_ac
    Resize selected images so that each dimension is a power of 2.
    'interpolation' can be { -1:None (memory content) | 0:None | 1:Nearest | 2:Average | 3:Linear | 4:Grid | 5:Bicubic | 6:Lanczos }.
    'boundary_conditions' has different meanings, according to the chosen 'interpolation' mode :
    . When 'interpolation=={ -1 | 1 | 2 | 4 }', 'boundary_conditions' is meaningless.
    . When 'interpolation==0', 'boundary_conditions' can be { 0:Dirichlet | 1:Neumann | 2:Periodic | 3:Mirror }.
    . When 'interpolation=={ 3 | 5 | 6 }', 'boundary_conditions' can be { 0:None | 1:Neumann }.
    'ax,ay,az,ac' set the centering along each axis when 'interpolation=0'
    (set to '0' by default, must be defined in range [0,1]).
    Default values: 'interpolation=0', 'boundary_conditions=0' and 'ax=ay=az=ac=0'.
    Example:
      [#1] image.jpg +resize_pow2[-1] 0
  rotate (+):
      angle,_interpolation,_boundary_conditions,_center_x[%],_center_y[%] |
      u,v,w,angle,interpolation,boundary_conditions,_center_x[%],_center_y[%],_center_z[%]
    Rotate selected images with specified angle (in deg.), and optionally 3D axis (u,v,w).
    'interpolation' can be { 0:None | 1:Linear | 2:Bicubic }.
    'boundary_conditions' can be { 0:Dirichlet | 1:Neumann | 2:Periodic | 3:Mirror }.
    When a rotation center (cx,cy,_cz) is specified, the size of the image is preserved.
    Default values: 'interpolation=1', 'boundary_conditions=0' and 'center_x=center_y=(undefined)'.
    Example:
      [#1] image.jpg +rotate -25,1,2,50%,50% rotate[0] 25
  rotate_tileable:
      angle,_max_size_factor>=0
    Rotate selected images by specified angle and make them tileable.
    If resulting size of an image is too big, the image is replaced by a 1x1 image.
    Default values: 'max_size_factor=8'.
    Example:
      [#1] sample colorful,128 +rotate_tileable 16
  rows:
      y0[%],_y1[%],_boundary_conditions
    Keep only specified rows of selected images.
    'boundary_conditions' can be { 0:Dirichlet | 1:Neumann | 2:Periodic | 3:Mirror }.
    Default values: 'y1=y0' and 'boundary_conditions=0'.
    Example:
      [#1] image.jpg rows -25%,50%
  scale2x:
    Double XY-size of selected images, using the Scale2x algorithm.
    Example:
      [#1] image.jpg threshold 50% resize 50%,50% +scale2x
  scale2x_cnn:
      _sharpness>=0
    Double XY-size of selected images, using a convolutional neural network.
    Default value: 'sharpness=1.25'.
    Example:
      [#1] image.jpg rescale2d 128 +scale2x_cnn ,
  scale3x:
    Triple XY-size of selected images, using the Scale3x algorithm.
    Example:
      [#1] image.jpg threshold 50% resize 33%,33% +scale3x
  scale_dcci2x:
      _edge_threshold>=0,_exponent>0,_extend_1px={ 0:No | 1:Yes }
    Double XY-size of selected images, using a directional cubic convolution interpolation,
    as described in https://en.wikipedia.org/wiki/Directional_Cubic_Convolution_Interpolation.
    Default values: 'edge_threshold=1.15', 'exponent=5' and 'extend_1px=0'.
    Example:
      [#1] image.jpg rescale2d 128 +scale_dcci2x ,
  seamcarve:
      _width[%]>=0,_height[%]>=0,_is_priority_channel={ 0:No | 1:Yes },_is_antialiasing={ 0:No | 1:Yes },_maximum_seams[%]>=0
    Resize selected images with specified 2D geometry, using the seam-carving algorithm.
    Default values: 'height=100%', 'is_priority_channel=0', 'is_antialiasing=1' and 'maximum_seams=25%'.
    Example:
      [#1] image.jpg seamcarve 60%
  shift (+):
      vx[%],_vy[%],_vz[%],_vc[%],_boundary_conditions,_interpolation={ 0:Nearest_neighbor | 1:Linear }
    Shift selected images by specified displacement vector.
    Displacement vector can be non-integer in which case linear interpolation should be chosen.
    'boundary_conditions' can be { 0:Dirichlet | 1:Neumann | 2:Periodic | 3:Mirror }.
    Default value: 'boundary_conditions=0' and 'interpolation=0'.
    Example:
      [#1] image.jpg +shift[0] 50%,50%,0,0,0 +shift[0] 50%,50%,0,0,1 +shift[0] 50%,50%,0,0,2
  shrink:
      axes,size[%],_boundary_conditions={ 0:Dirichlet | 1:Neumann | 2:Periodic | 3:Mirror }
    Shrink selected images along the specified axes.
    'axes' can be { x | y | z | c | xy | xz | xc | yz | yc | zc | xyz | xyc | xzc | yzc | xyzc }.
    Default value: 'boundary_conditions=0'.
    Example:
      [#1] image.jpg shrink xy,100
  slices:
      z0[%],_z1[%],_boundary_conditions
    Keep only specified slices of selected images.
    'boundary_conditions' can be { 0:Dirichlet | 1:Neumann | 2:Periodic | 3:Mirror }.
    Default values: 'z1=z0' and 'boundary_conditions=0'.
  sort (+):
      _ordering={ +:Increasing | -:Decreasing },_axis={ x | y | z | c }
    Sort pixel values of selected images.
    If 'axis' is specified, the sorting is done according to the data of the first column/row/slice/channel
    of selected images.
    Default values: 'ordering=+' and 'axis=(undefined)'.
    Example:
      [#1] 64 rand 0,100 +sort display_graph 400,300,3
  s (+):
      Shortcut for command 'split'.
  split (+):
      { x | y | z | c }...{ x | y | z | c },_split_mode[%],_max_parts>0 |
      keep_splitting_values={ +:Increasing | -:Decreasing },_{ x | y | z | c }...{ x | y | z | c },value1,_value2,... |
      (no arg)
    Split selected images along specified axes, or regarding to a sequence of scalar values
    (optionally along specified axes).
    (equivalent to shortcut command 's').
    Argument 'split_mode' determines how the split is done. It can be :
     * '0': Split image according to consecutive constant values;
     * 'N' (where 'N' is a positive integer): Split image into 'N' homogeneous parts;
     * '-N[%]' (where 'N' is a positive integer): Split image as blocks of size 'N'(opt. specified as a percentage of the image dimension).
    When specified, 'max_parts' defines a limit in the number of resulting images.
    Default value: 'N=-1'.
    Example:
      [#1] image.jpg split c
      [#2] image.jpg split y,3
      [#3] image.jpg split x,-128
      [#4] image.jpg split x,-30%,2
      [#5] 1,20,1,1,"1,2,3,4" +split -,2,3 append[1--1] y
      [#6] (1,2,2,3,3,3,4,4,4,4) +split x,0 append[1--1] y
  split_tiles:
      M!=0,_N!=0,_is_homogeneous={ 0:No | 1:Yes }
    Split selected images as a MxN array of tiles.
    If M or N is negative, it stands for the tile size instead.
    Default values: 'N=M' and 'is_homogeneous=0'.
    Example:
      [#1] image.jpg +local split_tiles 5,4 blur 3,0 sharpen 700 append_tiles 4,5 done
  undistort:
      -1<=_amplitude<=1,_aspect_ratio,_zoom,_center_x[%],_center_y[%],_boundary_conditions
    Correct barrel/pincushion distortions occurring with wide-angle lens.
    References:
    [1] Zhang Z. (1999). Flexible camera calibration by viewing a plane from unknown orientation.
    [2] Andrew W. Fitzgibbon (2001). Simultaneous linear estimation of multiple view geometry and lens distortion.
    'boundary_conditions' can be { 0:Dirichlet | 1:Neumann | 2:Periodic | 3:Mirror }.
    Default values: 'amplitude=0.25', 'aspect_ratio=0', 'zoom=0', 'center_x=center_y=50%' and 'boundary_conditions=0'.
  y (+):
      Shortcut for command 'unroll'.
  unroll (+):
      _axis={ x | y | z | c }
    Unroll selected images along specified axis.
    (equivalent to shortcut command 'y').
    Default value: 'axis=y'.
    Example:
      [#1] (1,2,3;4,5,6;7,8,9) +unroll y
  upscale_smart:
      width[%],_height[%],_depth,_smoothness>=0,_anisotropy=[0,1],sharpening>=0
    Upscale selected images with an edge-preserving algorithm.
    Default values: 'height=100%', 'depth=100%', 'smoothness=2', 'anisotropy=0.4' and 'sharpening=10'.
    Example:
      [#1] image.jpg rescale2d ,100 +upscale_smart 500%,500% append x
  volumetric2d:
      _x[%],_y[%],_z[%],_separator_size>=0
    Convert selected 3D volumetric images into a 2D representation.
    Default values: 'x=y=z=50%' and 'separator_size=0'.
    Example:
      [#1] image.jpg rescale2d 64 animate noise,0,100,50 cut 0,255 append z volumetric2d 50%,50%,50%,1
  11.8. Filtering
        ---------
  bandpass:
      _min_freq[%],_max_freq[%]
    Apply bandpass filter to selected images.
    Default values: 'min_freq=0' and 'max_freq=20%'.
    Example:
      [#1] image.jpg bandpass 1%,3%
    Tutorial: https://gmic.eu/oldtutorial/_bandpass
  bilateral (+):
      [guide],std_deviation_s[%]>=0,std_deviation_r[%]>=0,_sampling_s>=0,_sampling_r>=0 |
      std_deviation_s[%]>=0,std_deviation_r[%]>=0,_sampling_s>=0,_sampling_r>=0
    Blur selected images by anisotropic (eventually joint/cross) bilateral filtering.
    If a guide image is provided, it is used for drive the smoothing filter.
    A guide image must be of the same xyz-size as the selected images.
    Set 'sampling' arguments to '0' for automatic adjustment.
    Example:
      [#1] image.jpg repeat 5 { bilateral 10,10 }
  b (+):
      Shortcut for command 'blur'.
  blur (+):
      std_deviation[%]>=0,_boundary_conditions,_kernel |
      axes,std_deviation[%]>=0,_boundary_conditions,_kernel
    Blur selected images by a Deriche or gaussian filter (recursive implementation).
    (equivalent to shortcut command 'b').
    'boundary_conditions' can be { 0:Dirichlet | 1:Neumann | 2:Periodic | 3:Mirror }.
    'kernel' can be { 0:Deriche | 1:Gaussian }.
    When specified, argument 'axes' is a sequence of { x | y | z | c }.
    Specifying one axis multiple times apply also the blur multiple times.
    Default values: 'boundary_conditions=1' and 'kernel=1'.
    Example:
      [#1] image.jpg +blur 5,0 +blur[0] 5,1
      [#2] image.jpg +blur y,10%
    Tutorial: https://gmic.eu/oldtutorial/_blur
  blur_angular:
      amplitude[%],_center_x[%],_center_y[%]
    Apply angular blur on selected images.
    Default values: 'center_x=center_y=50%'.
    Example:
      [#1] image.jpg blur_angular 2%
    Tutorial: https://gmic.eu/oldtutorial/_blur_angular
  blur_bloom:
      _amplitude>=0,_ratio>=0,_nb_iter>=0,_blend_operator={ + | max | min },_kernel={ 0:Deriche | 1:Gaussian | 2:Box | 3:Triangle | 4:Quadratic },_normalize_scales={ 0:No | 1:Yes },
        _axes
    Apply a bloom filter that blend multiple blur filters of different radii,
    resulting in a larger but sharper glare than a simple blur.
    When specified, argument 'axes' is a sequence of { x | y | z | c }.
    Specifying one axis multiple times apply also the blur multiple times.
    Reference: Masaki Kawase, "Practical Implementation of High Dynamic Range Rendering", GDC 2004.
    Default values: 'amplitude=1', 'ratio=2', 'nb_iter=5', 'blend_operator=+', 'kernel=1', 'normalize_scales=0' and 'axes=(all)'
    Example:
      [#1] image.jpg blur_bloom ,
  blur_linear:
      amplitude1[%],_amplitude2[%],_angle,_boundary_conditions={ 0:Dirichlet | 1:Neumann }
    Apply linear blur on selected images, with specified angle and amplitudes.
    Default values: 'amplitude2=0', 'angle=0' and 'boundary_conditions=1'.
    Example:
      [#1] image.jpg blur_linear 10,0,45
    Tutorial: https://gmic.eu/oldtutorial/_blur_linear
  blur_radial:
      amplitude[%],_center_x[%],_center_y[%]
    Apply radial blur on selected images.
    Default values: 'center_x=center_y=50%'.
    Example:
      [#1] image.jpg blur_radial 2%
    Tutorial: https://gmic.eu/oldtutorial/_blur_radial
  blur_selective:
      sigma>=0,_edges>0,_nb_scales>0
    Blur selected images using selective gaussian scales.
    Default values: 'sigma=5', 'edges=0.5' and 'nb_scales=5'.
    Example:
      [#1] image.jpg noise 20 cut 0,255 +local[-1] repeat 4 { blur_selective , } done
    Tutorial: https://gmic.eu/oldtutorial/_blur_selective
  boxfilter (+):
      size[%]>=0,_order,_boundary_conditions,_nb_iter>=0 |
      axes,size[%]>=0,_order,_boundary_conditions,_nb_iter>=0
    Blur selected images by a box filter of specified size (fast recursive implementation).
    'order' can be { 0:Smooth | 1:1st-derivative | 2:2nd-derivative }.
    'boundary_conditions' can be { 0:Dirichlet | 1:Neumann | 2:Periodic | 3:Mirror }.
    When specified, argument 'axes' is a sequence of { x | y | z | c }.
    Specifying one axis multiple times apply also the blur multiple times.
    Default values: 'order=0', 'boundary_conditions=1' and 'nb_iter=1'.
    Example:
      [#1] image.jpg +boxfilter 5%
      [#2] image.jpg +boxfilter y,3,1
  bump2normal:
    Convert selected bumpmaps to normalmaps.
    Example:
      [#1] 300,300 circle 50%,50%,128,1,255 blur 2% +bump2normal
  closing:
      size>=0 |
      size_x>=0,size_y>=0,_size_z>=0 |
      [kernel],_boundary_conditions,_is_real={ 0:Binary-mode | 1:Real-mode }
    Apply morphological closing to selected images.
    'boundary_conditions' can be { 0:Dirichlet | 1:Neumann | 2:Periodic | 3:Mirror }.
    Default values: 'size_z=1', 'boundary_conditions=1' and 'is_real=0'.
    Example:
      [#1] image.jpg +closing 10
  closing_circ:
      _size>=0,_is_real={ 0:No | 1:Yes }
    Apply circular dilation of selected images by specified size.
    Default values: 'boundary_conditions=1' and 'is_real=0'.
    Example:
      [#1] image.jpg +closing_circ 7
  convolve (+):
      [mask],_boundary_conditions,_is_normalized={ 0:No | 1:Yes },_channel_mode,_xcenter,_ycenter,_zcenter,_xstride>0,_ystride>0,_zstride>0,_xdilation,_ydilation,_zdilation,_xoffset,
        _yoffset,_zoffset,_xsize>=0,_ysize>=0,_zsize>=0
    Convolve selected images by specified mask.
    'boundary_conditions' can be { 0:Dirichlet | 1:Neumann | 2:Periodic | 3:Mirror }.
    'channel_mode' can be { 0:All | 1:One-for-one | 2:Partial sum | 3:Full sum }.
    Default values: 'boundary_conditions=1', 'is_normalized=0', 'channel_mode=1', 'xcenter=ycenter=zcenter=(undefined)',
     'xstride=ystride=zstride=1', 'xdilation=ydilation=zdilation=1','xoffset=yoffset=zoffset=0' and 'xsize=ysize=zsize=(input_size/stride)'.
    Example:
      [#1] image.jpg (0,1,0;1,-4,1;0,1,0) convolve[-2] [-1] keep[-2]
      [#2] image.jpg (0,1,0) resize[-1] 130,1,1,1,3 +convolve[0] [1]
    Tutorial: https://gmic.eu/oldtutorial/_convolve
  convolve_fft:
      [mask],_boundary_conditions
    Convolve selected images with specified mask, in the fourier domain.
    'boundary_conditions' can be { 0:Dirichlet | 1:Neumann | 2:Periodic | 3:Mirror }.
    Example:
      [#1] image.jpg 100%,100% gaussian[-1] 20,1,45 +convolve_fft[0] [1]
  correlate (+):
      [mask],_boundary_conditions,_is_normalized={ 0:No | 1:Yes },_channel_mode,_xcenter,_ycenter,_zcenter,_xstride>0,_ystride>0,_zstride>0,_xdilation,_ydilation,_zdilation,_xoffset,
        _yoffset,_zoffset,_xsize>=0,_ysize>=0,_zsize>=0
    Correlate selected images by specified mask.
    'boundary_conditions' can be { 0:Dirichlet | 1:Neumann | 2:Periodic | 3:Mirror }.
    'channel_mode' can be { 0:All | 1:One-for-one | 2:Partial sum | 3:Full sum }.
    Default values: 'boundary_conditions=1', 'is_normalized=0', 'channel_mode=1', 'xcenter=ycenter=zcenter=-1', 'xstride=ystride=zstride=1',
     'xdilation=ydilation=zdilation=1','xoffset=yoffset=zoffset=0' and 'xsize=ysize=zsize=(input_size/stride)'.
    Example:
      [#1] image.jpg (0,1,0;1,-4,1;0,1,0) correlate[-2] [-1] keep[-2]
      [#2] image.jpg +crop 40%,40%,60%,60% +correlate[0] [-1],0,1
  cross_correlation:
      [mask]
    Compute cross-correlation of selected images with specified mask.
    Example:
      [#1] image.jpg +shift -30,-20 +cross_correlation[0] [1]
  curvature:
    Compute isophote curvatures on selected images.
    Example:
      [#1] image.jpg blur 10 curvature
  dct:
      _{ x | y | z }...{ x | y | z } |
      (no arg)
    Compute the discrete cosine transform of selected images, optionally along the specified axes only.
    Output images are always evenly sized, so this command may change the size of the selected images.
    Default values: (no arg)
    See also: idct.
    Example:
      [#1] image.jpg +dct +idct[-1] abs[-2] +[-2] 1 log[-2]
    Tutorial: https://gmic.eu/oldtutorial/_dct-and-idct
  deblur:
      amplitude[%]>=0,_nb_iter>=0,_dt>=0,_regul>=0,_regul_type={ 0:Tikhonov | 1:Meancurv. | 2:TV }
    Deblur image using a regularized Jansson-Van Cittert algorithm.
    Default values: 'nb_iter=10', 'dt=20', 'regul=0.7' and 'regul_type=1'.
    Example:
      [#1] image.jpg blur 3 +deblur 3,40,20,0.01
  deblur_goldmeinel:
      sigma>=0,_nb_iter>=0,_acceleration>=0,_kernel_type={ 0:Deriche | 1:Gaussian }.
    Deblur selected images using Gold-Meinel algorithm
    Default values: 'nb_iter=8', 'acceleration=1' and 'kernel_type=1'.
    Example:
      [#1] image.jpg +blur 1 +deblur_goldmeinel[-1] 1
  deblur_richardsonlucy:
      sigma>=0, nb_iter>=0, _kernel_type={ 0:Deriche | 1:Gaussian }.
    Deblur selected images using Richardson-Lucy algorithm.
    Default values: 'nb_iter=50' and 'kernel_type=1'.
    Example:
      [#1] image.jpg +blur 1 +deblur_richardsonlucy[-1] 1
  deconvolve_fft:
      [kernel],_regularization>=0
    Deconvolve selected images by specified mask in the fourier space.
    Default value: 'regularization>=0'.
    Example:
      [#1] image.jpg +gaussian 5 +convolve_fft[0] [1] +deconvolve_fft[-1] [1]
  deinterlace:
      _method
    Deinterlace selected images ('method' can be { 0:Standard | 1:Motion-compensated }).
    Default value: 'method=0'.
    Example:
      [#1] image.jpg +rotate 3,1,1,50%,50% resize 100%,50% resize 100%,200%,1,3,4 shift[-1] 0,1 add +deinterlace 1
  denoise (+):
      [guide],std_deviation_s[%]>=0,std_deviation_r[%]>=0,_patch_size>0,_lookup_size>0,_smoothness,_fast_approx={ 0:No | 1:Yes } |
      std_deviation_s[%]>=0,std_deviation_r[%]>=0,_patch_size>0,_lookup_size>0,_smoothness,_fast_approx={ 0:No | 1:Yes }
    Denoise selected images by non-local patch averaging.
    Default values: 'patch_size=5', 'lookup_size=6' and 'smoothness=1'.
    Example:
      [#1] image.jpg noise 20 cut 0,255 +denoise 8,8,7
  denoise_haar:
      _threshold>=0,_nb_scales>=0,_cycle_spinning>0
    Denoise selected images using haar-wavelet thresholding with cycle spinning.
    Set 'nb_scales==0' to automatically determine the optimal number of scales.
    Default values: 'threshold=1.4', 'nb_scale=0' and 'cycle_spinning=10'.
    Example:
      [#1] image.jpg noise 20 cut 0,255 +denoise_haar[-1] 0.8
  denoise_cnn:
      _noise_level>=0,_patch_size>0
    Denoise selected images using a convolutional neural network (CNN).
    Input value range should be [0,255]. Output value range is [0,255].
    If 'std_noise==0', the noise level is automatically estimated for each selected image.
    Default value: 'noise_level=0 (auto)' and 'patch_size=64'.
    Example:
      [#1] image.jpg noise 20 cut 0,255 +denoise_cnn 0
  denoise_patchpca:
      _strength>=0,_patch_size>0,_lookup_size>0,_spatial_sampling>0
    Denoise selected images using the patch-pca algorithm.
    Default values: 'patch_size=7', 'lookup_size=11', 'details=1.8' and 'spatial_sampling=5'.
    Example:
      [#1] image.jpg +noise 20 cut[-1] 0,255 +denoise_patchpca[-1] ,
  deriche (+):
      std_deviation[%]>=0,order={ 0 | 1 | 2 },axis={ x | y | z | c },_boundary_conditions
    Apply Deriche recursive filter on selected images, along specified axis and with
    specified standard deviation, order and boundary conditions.
    'boundary_conditions' can be { 0:Dirichlet | 1:Neumann | 2:Periodic | 3:Mirror }.
    Default value: 'boundary_conditions=1'.
    Example:
      [#1] image.jpg deriche 3,1,x
      [#2] image.jpg +deriche 30,0,x deriche[-2] 30,0,y add
    Tutorial: https://gmic.eu/oldtutorial/_deriche
  dilate (+):
      size[%]>=0 |
      size_x[%]>=0,size_y[%]>=0,size_z[%]>=0 |
      [kernel],_boundary_conditions,_is_real={ 0:Binary-mode | 1:Real-mode }
    Dilate selected images by a rectangular or the specified structuring element.
    'boundary_conditions' can be { 0:Dirichlet | 1:Neumann | 2:Periodic | 3:Mirror }.
    Default values: 'size_z=1', 'boundary_conditions=1' and 'is_real=0'.
    Example:
      [#1] image.jpg +dilate 10
  dilate_circ:
      _size[%]>=0,_boundary_conditions,_is_real={ 0:No | 1:Yes }
    Apply circular dilation of selected images by specified size.
    'boundary_conditions' can be { 0:Dirichlet | 1:Neumann | 2:Periodic | 3:Mirror }.
    Default values: 'boundary_conditions=1' and 'is_real=0'.
    Example:
      [#1] image.jpg +dilate_circ 7
  dilate_oct:
      _size[%]>=0,_boundary_conditions,_is_real={ 0:No | 1:Yes }
    Apply octagonal dilation of selected images by specified size.
    Default values: 'boundary_conditions=1' and 'is_real=0'.
    Example:
      [#1] image.jpg +dilate_oct 7
  dilate_threshold:
      size_x>=1,size_y>=1,size_z>=1,_threshold>=0,_boundary_conditions
    Dilate selected images in the (X,Y,Z,I) space.
    'boundary_conditions' can be { 0:Dirichlet | 1:Neumann | 2:Periodic | 3:Mirror }.
    Default values: 'size_y=size_x', 'size_z=1', 'threshold=255' and 'boundary_conditions=1'.
  divergence:
    Compute divergence of selected vector fields.
    Example:
      [#1] image.jpg luminance +gradient append[-2,-1] c divergence[-1]
  dog:
      _sigma1[%]>=0,_sigma2[%]>=0
    Compute difference of gaussian on selected images.
    Default values: 'sigma1=2%' and 'sigma2=3%'.
    Example:
      [#1] image.jpg dog 2,3
  diffusiontensors:
      _sharpness>=0,0<=_anisotropy<=1,_alpha[%],_sigma[%],is_sqrt={ 0:No | 1:Yes }
    Compute the diffusion tensors of selected images for edge-preserving smoothing algorithms.
    Default values: 'sharpness=0.7', 'anisotropy=0.3', 'alpha=0.6', 'sigma=1.1' and 'is_sqrt=0'.
    Example:
      [#1] image.jpg diffusiontensors 0.8 abs pow 0.2
    Tutorial: https://gmic.eu/oldtutorial/_diffusiontensors
  erode (+):
      size[%]>=0 |
      size_x[%]>=0,size_y[%]>=0,_size_z[%]>=0 |
      [kernel],_boundary_conditions,_is_real={ 0:Binary-mode | 1:Real-mode }
    Erode selected images by a rectangular or the specified structuring element.
    'boundary_conditions' can be { 0:Dirichlet | 1:Neumann | 2:Periodic | 3:Mirror }.
    Default values: 'size_z=1', 'boundary_conditions=1' and 'is_real=0'.
    Example:
      [#1] image.jpg +erode 10
  erode_circ:
      _size[%]>=0,_boundary_conditions,_is_real={ 0:No | 1:Yes }
    Apply circular erosion of selected images by specified size.
    'boundary_conditions' can be { 0:Dirichlet | 1:Neumann | 2:Periodic | 3:Mirror }.
    Default values: 'boundary_conditions=1' and 'is_real=0'.
    Example:
      [#1] image.jpg +erode_circ 7
  erode_oct:
      _size[%]>=0,_boundary_conditions,_is_real={ 0:No | 1:Yes }
    Apply octagonal erosion of selected images by specified size.
    Default values: 'boundary_conditions=1' and 'is_real=0'.
    Example:
      [#1] image.jpg +erode_oct 7
  erode_threshold:
      size_x>=1,size_y>=1,size_z>=1,_threshold>=0,_boundary_conditions
    Erode selected images in the (X,Y,Z,I) space.
    'boundary_conditions' can be { 0:Dirichlet | 1:Neumann | 2:Periodic | 3:Mirror }.
    Default values: 'size_y=size_x', 'size_z=1', 'threshold=255' and 'boundary_conditions=1'.
  fft (+):
      _{ x | y | z }...{ x | y | z }
    Compute the direct fourier transform (real and imaginary parts) of selected images,
    optionally along the specified axes only.
    See also: ifft.
    Example:
      [#1] image.jpg luminance +fft append[-2,-1] c norm[-1] log[-1] shift[-1] 50%,50%,0,0,2
      [#2] image.jpg w2:=int(w/2) h2:=int(h/2) fft shift $w2,$h2,0,0,2 ellipse $w2,$h2,30,30,0,1,0 shift -$w2,-$h2,0,0,2 ifft remove[-1]
    Tutorial: https://gmic.eu/oldtutorial/_fft
  g (+):
      Shortcut for command 'gradient'.
  gradient:
      { x | y | z | c }...{ x | y | z | c },_scheme,_boundary_conditions |
      (no arg)
    Compute the gradient components (first derivatives) of selected images, along specified axes.
    (equivalent to shortcut command 'g').
    'scheme' can be { -1:Backward | 0:Centered | 1:Forward | 2:Sobel | 3:Rotation-invariant (default) | 4:Deriche | 5:Vanvliet | 6:FB-Maxabs }.
    'boundary_conditions' can be { 0:Dirichlet | 1:Neumann | 2:Periodic | 3:Mirror }.
    (no arg) compute all significant components.
    Default values: 'scheme=0' and 'boundary_conditions=1'.
    Example:
      [#1] image.jpg gradient
    Tutorial: https://gmic.eu/oldtutorial/_gradient
  gradient_norm:
      _scheme,_boundary_conditions |
      (no arg)
    Compute the gradient norm of selected images.
    'scheme' can be { -1:Backward | 0:Centered | 1:Forward | 2:Sobel | 3:Rotation-invariant (default) | 4:Deriche | 5:Vanvliet | 6:FB-Maxabs }.
    'boundary_conditions' can be { 0:Dirichlet | 1:Neumann | 2:Periodic | 3:Mirror }.
    Default values: 'scheme=0' and 'boundary_conditions=1'.
    Example:
      [#1] image.jpg gradient_norm equalize
    Tutorial: https://gmic.eu/oldtutorial/_gradient_norm
  gradient_orientation:
      _dimension={ 1 | 2 | 3 }
    Compute N-d gradient orientation of selected images.
    Default value: 'dimension=3'.
    Example:
      [#1] image.jpg +gradient_orientation 2
  guided (+):
      [guide],radius[%]>=0,regularization[%]>=0 |
      radius[%]>=0,regularization[%]>=0
    Blur selected images by guided image filtering.
    If a guide image is provided, it is used to drive the smoothing process.
    A guide image must be of the same xyz-size as the selected images.
    This command implements the filtering algorithm described in:
    He, Kaiming; Sun, Jian; Tang, Xiaoou, "Guided Image Filtering",
    IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.35, no.6, pp.1397,1409, June 2013
    Example:
      [#1] image.jpg +guided 5,400
  haar:
      scale>0
    Compute the direct haar multiscale wavelet transform of selected images.
    See also: ihaar.
    Tutorial: https://gmic.eu/oldtutorial/_haar
  heat_flow:
      _nb_iter>=0,_dt,_keep_sequence={ 0:No | 1:Yes }
    Apply iterations of the heat flow on selected images.
    Default values: 'nb_iter=10', 'dt=30' and 'keep_sequence=0'.
    Example:
      [#1] image.jpg +heat_flow 20
  hessian:
      { xx | xy | xz | yy | yz | zz }...{ xx | xy | xz | yy | yz | zz },_boundary_conditions |
      (no arg) :
    Compute the hessian components (second derivatives) of selected images along specified axes.
    'boundary_conditions' can be { 0:Dirichlet | 1:Neumann | 2:Periodic | 3:Mirror }.
    (no arg) compute all significant components.
    Default value: 'boundary_conditions=1'.
    Example:
      [#1] image.jpg hessian
  idct:
      _{ x | y | z }...{ x | y | z } |
      (no arg)
    Compute the inverse discrete cosine transform of selected images, optionally along the specified axes only.
    Output images are always evenly sized, so this command may change the size of the selected images.
    (dct images obtained with the 'dct' command are evenly sized anyway).
    Default values: (no arg)
    See also: dct.
    Tutorial: https://gmic.eu/oldtutorial/_dct-and-idct
  iee:
    Compute gradient-orthogonal-directed 2nd derivative of image(s).
    Example:
      [#1] image.jpg iee
  ifft (+):
      _{ x | y | z }...{ x | y | z }
    Compute the inverse fourier transform (real and imaginary parts) of selected images.
    optionally along the specified axes only.
    See also: fft.
    Tutorial: https://gmic.eu/oldtutorial/_fft
  ihaar:
      scale>0
    Compute the inverse haar multiscale wavelet transform of selected images.
    See also: haar.
    Tutorial: https://gmic.eu/oldtutorial/_haar
  ilaplacian:
      { nb_iterations>0 | 0 },_[initial_estimate]
    Invert selected Laplacian images.
    If given 'nb_iterations' is '0', inversion is done in Fourier space (single iteration),
    otherwise, by applying 'nb_iterations' of a Laplacian-inversion PDE flow.
    Note that the resulting inversions are just estimation of possible/approximated solutions.
    Default values: 'nb_iterations=0', 'axes=(undefined)' and '[initial_estimated]=(undefined)'.
    Example:
      [#1] image.jpg +laplacian +ilaplacian[-1] 0
  inn:
    Compute gradient-directed 2nd derivative of image(s).
    Example:
      [#1] image.jpg inn
  inpaint (+):
      [mask] |
      [mask],0,_fast_method |
      [mask],_patch_size>=1,_lookup_size>=1,_lookup_factor>=0,_lookup_increment!=0,_blend_size>=0,0<=_blend_threshold<=1,_blend_decay>=0,_blend_scales>=1,_is_blend_outer={ 0:No |
        1:Yes }
    Inpaint selected images by specified mask.
    If no patch size (or 0) is specified, inpainting is done using a fast average or median algorithm.
    Otherwise, it used a patch-based reconstruction method, that can be very time consuming.
    'fast_method' can be { 0:Low-connectivity average | 1:High-connectivity average | 2:Low-connectivity median | 3:High-connectivity median }.
    Default values: 'patch_size=0', 'fast_method=1', 'lookup_size=22', 'lookup_factor=0.5', 'lookup_increment=1', 'blend_size=0',
     'blend_threshold=0', 'blend_decay=0.05', 'blend_scales=10' and 'is_blend_outer=1'.
    Example:
      [#1] image.jpg 100%,100% ellipse 50%,50%,30,30,0,1,255 ellipse 20%,20%,30,10,0,1,255 +inpaint[-2] [-1] remove[-2]
      [#2] image.jpg 100%,100% circle 30%,30%,30,1,255,0,255 circle 70%,70%,50,1,255,0,255 +inpaint[0] [1],5,15,0.5,1,9,0 remove[1]
  inpaint_pde:
      [mask],_nb_scales[%],_diffusion_type={ 0:Isotropic | 1:Delaunay-guided | 2:Edge-guided | 3:Mask-guided },_diffusion_iter>=0
    Inpaint selected images by specified mask using a multiscale transport-diffusion algorithm.
    Argument 'nb_scales' sets the number of scales used in the multi-scale resolution scheme.
     * When the '%' qualifier is used for 'nb_scales', the number of used scales is relative to 'nb_scales_max = ceil(log2(max(w,h,d)))'.
     * When 'nb_scales<0', it determines the minimum image size encountered at the lowest scale.
    If 'diffusion_type==3', non-zero values of the mask (e.g. a distance function) are used
    to guide the diffusion process.
    Default values: 'nb_scales=-9', 'diffusion_type=1' and 'diffusion_iter=20'.
    Example:
      [#1] image.jpg 100%,100% ellipse[-1] 30%,30%,40,30,0,1,255 +inpaint_pde[0] [1]
  inpaint_flow:
      [mask],_nb_global_iter>=0,_nb_local_iter>=0,_dt>0,_alpha>=0,_sigma>=0
    Apply iteration of the inpainting flow on selected images.
    Default values: 'nb_global_iter=10', 'nb_local_iter=100', 'dt=5', 'alpha=1' and 'sigma=3'.
    Example:
      [#1] image.jpg 100%,100% ellipse[-1] 30%,30%,40,30,0,1,255 inpaint_flow[0] [1]
  inpaint_holes:
      maximal_area[%]>=0,_tolerance>=0,_is_high_connectivity={ 0:No | 1:Yes }
    Inpaint all connected regions having an area less than specified value.
    Default values: 'maximal_area=4', 'tolerance=0' and 'is_high_connectivity=0'.
    Example:
      [#1] image.jpg noise 5%,2 +inpaint_holes 8,40
  inpaint_morpho:
      [mask]
    Inpaint selected images by specified mask using morphological operators.
    Example:
      [#1] image.jpg 100%,100% ellipse[-1] 30%,30%,40,30,0,1,255 +inpaint_morpho[0] [1]
  inpaint_matchpatch:
      [mask],_nb_scales={ 0:Auto | >0 },_patch_size>0,_nb_iterations_per_scale>0,_blend_size>=0,_allow_outer_blending={ 0:No | 1:Yes },_is_already_initialized={ 0:No | 1:Yes }
    Inpaint selected images by specified binary mask, using a multi-scale matchpatch algorithm.
    Default values: 'nb_scales=0', 'patch_size=9', 'nb_iterations_per_scale=10', 'blend_size=5','allow_outer_blending=1' and
     'is_already_initialized=0'.
    Example:
      [#1] image.jpg 100%,100% ellipse[-1] 30%,30%,40,30,0,1,255 +inpaint_matchpatch[0] [1]
  kuwahara:
      size>0
    Apply Kuwahara filter of specified size on selected images.
    Example:
      [#1] image.jpg kuwahara 9
  laplacian:
    Compute Laplacian of selected images.
    Example:
      [#1] image.jpg laplacian
  lic:
      _amplitude>0,_channels>0
    Render LIC representation of selected vector fields.
    Default values: 'amplitude=30' and 'channels=1'.
    Example:
      [#1] 400,400,1,2,'[x,y]-[w,h]/2' +lic 200,3 quiver[-2] [-2],10,1,1,1,255
  map_tones:
      _threshold>=0,_gamma>=0,_smoothness>=0,nb_iter>=0
    Apply tone mapping operator on selected images, based on Poisson equation.
    Default values: 'threshold=0.1', 'gamma=0.8', 'smoothness=0.5' and 'nb_iter=30'.
    Example:
      [#1] image.jpg +map_tones ,
  map_tones_fast:
      _radius[%]>=0,_power>=0
    Apply fast tone mapping operator on selected images.
    Default values: 'radius=3%' and 'power=0.3'.
    Example:
      [#1] image.jpg +map_tones_fast ,
  meancurvature_flow:
      _nb_iter>=0,_dt,_keep_sequence={ 0:No | 1:Yes }
    Apply iterations of the mean curvature flow on selected images.
    Default values: 'nb_iter=10', 'dt=30' and 'keep_sequence=0'.
    Example:
      [#1] image.jpg +meancurvature_flow 20
  median (+):
      size>=0,_threshold>0
    Apply (opt. thresholded) median filter on selected images with structuring element size x size.
    Example:
      [#1] image.jpg +median 5
  merge_alpha:
    Merge selected alpha detail scales into a single image.
    Alpha detail scales have been obtained with command split_alpha.
  nlmeans:
      [guide],_patch_radius>0,_spatial_bandwidth>0,_tonal_bandwidth>0,_patch_measure_command |
      _patch_radius>0,_spatial_bandwidth>0,_tonal_bandwidth>0,_patch_measure_command
    Apply non local means denoising of Buades et al, 2005. on selected images.
    The patch is a gaussian function of 'std_patch_radius'.
    The spatial kernel is a rectangle of radius 'spatial_bandwidth'.
    The tonal kernel is exponential ('exp(-d^2/_tonal_bandwidth^2)')
    with 'd' the euclidean distance between image patches.
    Default values: 'patch_radius=4', 'spatial_bandwidth=4', 'tonal_bandwidth=10' and 'patch_measure_command=-norm'.
    Example:
      [#1] image.jpg +noise 10 nlmeans[-1] 4,4,{0.6*${-std_noise}}
  nlmeans_core:
      _reference_image,_scaling_map,_patch_radius>0,_spatial_bandwidth>0
    Apply non local means denoising using a image for weight and a map for scaling
  normalize_local:
      _amplitude>=0,_radius>0,_n_smooth[%]>=0,_a_smooth[%]>=0,_is_cut={ 0:No | 1:Yes },_min=0,_max=255
    Normalize selected images locally.
    Default values: 'amplitude=3', 'radius=16', 'n_smooth=4%', 'a_smooth=2%', 'is_cut=1', 'min=0' and 'max=255'.
    Example:
      [#1] image.jpg normalize_local 8,10
  normalized_cross_correlation:
      [mask]
    Compute normalized cross-correlation of selected images with specified mask.
    Example:
      [#1] image.jpg +shift -30,-20 +normalized_cross_correlation[0] [1]
  opening:
      size>=0 |
      size_x>=0,size_y>=0,_size_z>=0 |
      [kernel],_boundary_conditions,_is_real={ 0:Binary-mode | 1:Real-mode }
    Apply morphological opening to selected images.
    'boundary_conditions' can be { 0:Dirichlet | 1:Neumann | 2:Periodic | 3:Mirror }.
    Default values: 'size_z=1', 'boundary_conditions=1' and 'is_real=0'.
    Example:
      [#1] image.jpg +opening 10
  opening_circ:
      _size>=0,_is_real={ 0:No | 1:Yes }
    Apply circular opening of selected images by specified size.
    Default values: 'boundary_conditions=1' and 'is_real=0'.
    Example:
      [#1] image.jpg +opening_circ 7
  percentile:
      [mask],0<=_min_percentile[%]<=100,0<=_max_percentile[%]<=100.
    Apply percentile averaging filter to selected images.
    Default values: 'min_percentile=0' and 'max_percentile=100'.
    Example:
      [#1] image.jpg shape_circle 11,11 +percentile[0] [1],25,75
  peronamalik_flow:
      K_factor>0,_nb_iter>=0,_dt,_keep_sequence={ 0:No | 1:Yes }
    Apply iterations of the Perona-Malik flow on selected images.
    Default values: 'K_factor=20', 'nb_iter=5', 'dt=5' and 'keep_sequence=0'.
    Example:
      [#1] image.jpg +heat_flow 20
  phase_correlation:
      [destination]
    Estimate translation vector between selected source images and specified destination.
    Example:
      [#1] image.jpg +shift -30,-20 +phase_correlation[0] [1] unroll[-1] y
  pde_flow:
      _nb_iter>=0,_dt,_velocity_command,_keep_sequence={ 0:No | 1:Yes }
    Apply iterations of a generic PDE flow on selected images.
    Default values: 'nb_iter=10', 'dt=30', 'velocity_command=laplacian' and 'keep_sequence=0'.
    Example:
      [#1] image.jpg +pde_flow 20
  periodize_poisson:
    Periodize selected images using a Poisson solver in Fourier space.
    Example:
      [#1] image.jpg +periodize_poisson array 2,2,2
  rbf:
      dx,_x0,_x1,_phi(r) |
      dx,dy,_x0,_y0,_x1,_y1,_phi(r) |
      dx,dy,dz,x0,y0,z0,x1,y1,z1,phi(r)
    Reconstruct 1D/2D or 3D image from selected sets of keypoints, by RBF-interpolation.
    A set of keypoints is represented by a vector-valued image, where each pixel represents a single keypoint.
    Vector components of a keypoint have the following meaning:
       - For 1D reconstruction: [ x_k, f1(x_k),...fN(x_k) ].
       - For 2D reconstruction: [ x_k,y_k, f1(x_k,y_k),...,fN(x_k,y_k) ].
       - For 3D reconstruction: [ x_k,y_k,z_k, f1(x_k,y_k,z_k),...,fN(x_k,y_k,z_k) ].
    Values 'x_k','y_k' and 'z_k' are the spatial coordinates of keypoint 'k'.
    Values 'f1(),..,fN()' are the 'N' components of the vector value of keypoint 'k'.
    The command reconstructs an image with specified size 'dx'x'dy'x'dz', with 'N' channels.
    Default values: 'x0=y0=z0=0', 'x1=dx-1', 'y1=dy-1', 'z1=dz-1', 'phi(r)=r*log(1+r)'.
    Example:
      [#1] sample colorful,400 100%,100% noise_poissondisk. 10 1,{is},1,5 eval[-2] "begin(p=0);i?(I[#-1,p++]=[x,y,I(#0)])" to_rgb[1] mul[0,1] dilate_circ[0] 5 +rbf[-1] {0,[w,h]}
       c[-1] 0,255
      [#2] 32,1,1,5,u([400,400,255,255,255]) rbf 400,400 c 0,255
  red_eye:
      0<=_threshold<=100,_smoothness>=0,0<=attenuation<=1
    Attenuate red-eye effect in selected images.
    Default values: 'threshold=75', 'smoothness=3.5' and 'attenuation=0.1'.
    Example:
      [#1] image.jpg +red_eye ,
  remove_hotpixels:
      _mask_size>0, _threshold[%]>0
    Remove hot pixels in selected images.
    Default values: 'mask_size=3' and 'threshold=10%'.
    Example:
      [#1] image.jpg noise 10,2 +remove_hotpixels ,
  remove_pixels:
      number_of_pixels[%]>=0
    Remove specified number of pixels (i.e. set them to 0) from the set of non-zero pixels in selected images.
    Example:
      [#1] image.jpg +remove_pixels 50%
  rolling_guidance:
      std_deviation_s[%]>=0,std_deviation_r[%]>=0,_precision>=0
    Apply the rolling guidance filter on selected image.
    Rolling guidance filter is a fast image abstraction filter, described in:
    "Rolling Guidance Filter", Qi Zhang Xiaoyong, Shen Li, Xu Jiaya Jia, ECCV'2014.
    Default values: 'std_deviation_s=4', 'std_deviation_r=10' and 'precision=0.5'.
    Example:
      [#1] image.jpg +rolling_guidance , +-
  sharpen:
      amplitude>=0 |
      amplitude>=0,edge>=0,_alpha[%],_sigma[%]
    Sharpen selected images by inverse diffusion or shock filters methods.
    'edge' must be specified to enable shock-filter method.
    Default values: 'edge=0', 'alpha=0' and 'sigma=0'.
    Example:
      [#1] image.jpg sharpen 300
      [#2] image.jpg blur 5 sharpen 300,1
  sharpen_alpha:
      _amplitude[%]>=0,_nb_scales>0,0<=_anisotropy<=1,0<=_minimize_alpha<=1
    Sharpen selected images using a multi-scale and alpha boosting algorithm.
    Default values: 'amplitude=1', 'nb_scales=5', 'anisotropy=0' and 'minimize_alpha=1'.
  smooth (+):
      amplitude[%]>=0,_sharpness>=0,0<=_anisotropy<=1,_alpha[%],_sigma[%],_dl>0,_da>0,_precision>0,_interpolation,_fast_approx={ 0:No | 1:Yes } |
      nb_iterations>=0,_sharpness>=0,_anisotropy,_alpha,_sigma,_dt>0,0 |
      [tensor_field],_amplitude>=0,_dl>0,_da>0,_precision>0,_interpolation,_fast_approx={ 0:No | 1:Yes } |
      [tensor_field],_nb_iters>=0,_dt>0,0
    Smooth selected images anisotropically using diffusion PDE's, with specified field of
    diffusion tensors.
    'interpolation' can be { 0:Nearest | 1:Linear | 2:Runge-kutta }.
    Default values: 'sharpness=0.7', 'anisotropy=0.3', 'alpha=0.6', 'sigma=1.1', 'dl=0.8', 'da=30', 'precision=2',
     'interpolation=0' and 'fast_approx=1'.
    Example:
      [#1] image.jpg repeat 3 smooth 40,0,1,1,2 done
      [#2] image.jpg 100%,100%,1,2 rand[-1] -100,100 repeat 2 smooth[-1] 100,0.2,1,4,4 done warp[0] [-1],1,1,1
    Tutorial: https://gmic.eu/oldtutorial/_smooth
  split_freq:
      smoothness[%]>0
    Split selected images into low and high frequency parts.
    Example:
      [#1] image.jpg split_freq 2%
  solve_poisson:
      "laplacian_command",_nb_iterations>=0,_time_step>0,_nb_scales>=0
    Solve Poisson equation so that applying 'laplacian[n]' is close to the result of 'laplacian_command[n]'.
    Solving is performed using a multi-scale gradient descent algorithm.
    If 'nb_scales=0', the number of scales is automatically determined.
    Default values: 'nb_iterations=60', 'dt=5' and 'nb_scales=0'.
    Example:
      [#1] image.jpg command "foo : gradient x" +solve_poisson foo +foo[0] +laplacian[1]
  split_alpha:
      _nb_scales[%]={ 0:Auto | -S<0 | N>0 },_subsample={ 0:No | 1:Yes },0<=_anisotropy<=1,0<=_minimize_alpha<=1
    Split selected images into alpha detail scales.
    If 'nb_scales==-S', the lowest scale has a size of at least 'SxS'.
    Parameter 'anisotropy' is only considered when 'subsample=0'.
    Image reconstruction is done with command merge_alpha.
    Default values: 'nb_scales=0', 'subsample=0', 'anisotropy=0' and 'minimize_alpha=1'.
  split_details:
      _nb_scales[%]={ 0:Auto | -S<0 | N>0 },_base_scale[%]>=0,_detail_scale[%]>=0
    Split selected images into 'nb_scales' detail scales.
    If 'base_scale' = 'detail_scale' = 0, the image decomposition is done with 'a trous' wavelets.
    Otherwise, it uses laplacian pyramids with linear standard deviations.
    Default values: 'nb_scales=0', 'base_scale=0' and 'detail_scale=0'.
    Example:
      [#1] image.jpg split_details ,
  structuretensors:
      _scheme={ 0:Centered | 1:Forward/backward }
    Compute the structure tensor field of selected images.
    Default value: 'scheme=0'.
    Example:
      [#1] image.jpg structuretensors abs pow 0.2
    Tutorial: https://gmic.eu/oldtutorial/_structuretensors
  solidify:
      _smoothness[%]>=0,_diffusion_type={ 0:Isotropic | 1:Delaunay-guided | 2:Edge-oriented },_diffusion_iter>=0
    Solidify selected transparent images.
    Default values: 'smoothness=75%', 'diffusion_type=1' and 'diffusion_iter=20'.
    Example:
      [#1] image.jpg 100%,100% circle[-1] 50%,50%,25%,1,255 append c +solidify , display_rgba
  syntexturize:
      _width[%]>0,_height[%]>0
    Resynthetize 'width'x'height' versions of selected micro-textures by phase randomization.
    The texture synthesis algorithm is a straightforward implementation of the method described in :
    http://www.ipol.im/pub/art/2011/ggm_rpn/.
    Default values: 'width=height=100%'.
    Example:
      [#1] image.jpg crop 2,282,50,328 +syntexturize 320,320
  syntexturize_matchpatch:
      _width[%]>0,_height[%]>0,_nb_scales>=0,_patch_size>0,_blending_size>=0,_precision>=0
    Resynthetize 'width'x'height' versions of selected micro-textures using a patch-matching algorithm.
    If 'nbscales==0', the number of scales used is estimated from the image size.
    Default values: 'width=height=100%', 'nb_scales=0', 'patch_size=7', 'blending_size=5' and 'precision=1'.
    Example:
      [#1] image.jpg crop 25%,25%,75%,75% syntexturize_matchpatch 512,512
  tv_flow:
      _nb_iter>=0,_dt,_keep_sequence={ 0:No | 1:Yes }
    Apply iterations of the total variation flow on selected images.
    Default values: 'nb_iter=10', 'dt=30' and 'keep_sequence=0'.
    Example:
      [#1] image.jpg +tv_flow 40
  unsharp:
      radius[%]>=0,_amount>=0,_threshold[%]>=0
    Apply unsharp mask on selected images.
    Default values: 'amount=2' and 'threshold=0'.
    Example:
      [#1] image.jpg blur 3 +unsharp 1.5,15 cut 0,255
  unsharp_octave:
      _nb_scales>0,_radius[%]>=0,_amount>=0,threshold[%]>=0
    Apply octave sharpening on selected images.
    Default values: 'nb_scales=4', 'radius=1', 'amount=2' and 'threshold=0'.
    Example:
      [#1] image.jpg blur 3 +unsharp_octave 4,5,15 cut 0,255
  vanvliet (+):
      std_deviation[%]>=0,order={ 0 | 1 | 2 | 3 },axis={ x | y | z | c },_boundary_conditions
    Apply Vanvliet recursive filter on selected images, along specified axis and with
    specified standard deviation, order and boundary conditions.
    'boundary_conditions' can be { 0:Dirichlet | 1:Neumann | 2:Periodic | 3:Mirror }.
    Default value: 'boundary_conditions=1'.
    Example:
      [#1] image.jpg +vanvliet 3,1,x
      [#2] image.jpg +vanvliet 30,0,x vanvliet[-2] 30,0,y add
  voronoi:
    Compute the discrete Voronoi diagram of non-zero pixels in selected images.
    Example:
      [#1] 400,400,1,3 eval "repeat(50,I(v([w,w]-1)) = v([255,255,255]))" +voronoi +gradient_norm. eq. 0 *[-2,-1]
  watermark_fourier:
      text,_size>0
    Add a textual watermark in the frequency domain of selected images.
    Default value: 'size=33'.
    Example:
      [#1] image.jpg +watermark_fourier "Watermarked!" +display_fft remove[-3,-1] normalize 0,255 append[-4,-2] y append[-2,-1] y
  watershed (+):
      [priority_image],_is_high_connectivity={ 0:No | 1:Yes }
    Compute the watershed transform of selected images.
    Default value: 'is_high_connectivity=1'.
    Example:
      [#1] 400,400 noise 0.2,2 eq 1 +distance 1 mul[-1] -1 label[-2] watershed[-2] [-1] mod[-2] 256 map[-2] 0 reverse
  11.9. Features Extraction
        -------------------
  area:
      tolerance>=0,is_high_connectivity={ 0:No | 1:Yes }
    Compute area of connected components in selected images.
    Default values: 'is_high_connectivity=0'.
    Example:
      [#1] image.jpg luminance stencil[-1] 1 +area 0
    Tutorial: https://gmic.eu/oldtutorial/_area
  area_fg:
      tolerance>=0,is_high_connectivity={ 0:No | 1:Yes }
    Compute area of connected components for non-zero values in selected images.
    Similar to 'area' except that 0-valued pixels are not considered.
    Default values: 'is_high_connectivity=0'.
    Example:
      [#1] image.jpg luminance stencil[-1] 1 +area_fg 0
  at_curve:
      x0[%],y0[%],z0[%],...,xN[%],yn[%],zn[%]
    Retrieve pixels of the selected images belonging to the specified cubic spline curve that passes across the specified points.
    Example:
      [#1] image.jpg +at_curve 0,0,0,80%,50%,0,100%,100%,0
  at_quadrangle:
      x0[%],y0[%],x1[%],y1[%],x2[%],y2[%],x3[%],y3[%],_interpolation,_boundary_conditions |
      x0[%],y0[%],z0[%],x1[%],y1[%],z1[%],x2[%],y2[%],z2[%],x3[%],y3[%],z3[%],_interpolation,_boundary_conditions
    Retrieve pixels of the selected images belonging to the specified 2D or 3D quadrangle.
    'interpolation' can be { 0:Nearest-neighbor | 1:Linear | 2:Cubic }.
    'boundary_conditions' can be { 0:Dirichlet | 1:Neumann | 2:Periodic | 3:Mirror }.
    Example:
      [#1] image.jpg params=5%,5%,95%,5%,60%,95%,40%,95% +at_quadrangle $params polygon.. 4,$params,0.5,255
  barycenter:
    Compute the barycenter vector of pixel values.
    Example:
      [#1] 256,256 ellipse 50%,50%,20%,20%,0,1,1 deform 20 +barycenter +ellipse[-2] {@0,1},5,5,0,10
  betti:
    Compute Betti numbers B0,B1 and B2 from selected 3D binary shapes.
    Values B0,B1 and B2 are returned in the status. When multiple images are selected, the B0,B1,B2 of each image are concatenated in the status.
    (see 'https://en.wikipedia.org/wiki/Betti_number' for details about Betti numbers).
  canny:
      _sigma[%]>=0,_low_threshold>=0,_high_threshold>=0
    Locate image edges using Canny edge detector.
    Default values: 'sigma=1', 'low_threshold=0.05', 'high_threshold=0.15'.
    Example:
      [#1] image.jpg canny 1
  delaunay:
      _output_type={ 0:Image | 1:Coordinates/triangles }
    Generate discrete 2D Delaunay triangulation of non-zero pixels in selected images.
    Input images must be scalar.
    Each pixel of the output image is a triplet (a,b,c) meaning the pixel belongs to
    the Delaunay triangle 'ABC' where 'a','b','c' are the labels of the pixels 'A','B','C'.
    Example:
      [#1] 400,400 rand 32,255 100%,100% noise. 0.4,2 eq. 1 mul +delaunay
      [#2] image.jpg 100%,100% noise. 2,2 eq. 1 delaunay. +blend shapeaverage0
  detect_skin:
      0<=tolerance<=1,_skin_x,_skin_y,_skin_radius>=0
    Detect skin in selected color images and output an appartenance probability map.
    Detection is performed using CbCr chromaticity data of skin pixels.
    If arguments 'skin_x', 'skin_y' and 'skin_radius' are provided, skin pixels are learnt
    from the sample pixels inside the circle located at ('skin_x','skin_y') with radius 'skin_radius'.
    Default value: 'tolerance=0.5' and 'skin_x=skiny=radius=-1'.
  displacement (+):
      [reference],_smoothness>=0,_precision>=0,_nb_scales>=0,_iteration_max>=0,mode={ 0:Backward | 1:Forward },_[guide]
    Estimate displacement field between specified reference image and selected (target) images.
    If 'smoothness>=0', regularization type is set to isotropic, else to anisotropic.
    If 'nbscales==0', the number of scales used is estimated from the image size.
    Default values: 'smoothness=0.1', 'precision=7', 'nb_scales=0', 'iteration_max=1000', 'mode=0' and '[guide]=(unused)'.
    Example:
      [#1] image.jpg +rotate 3,1,0,50%,50% +displacement[-1] [-2] quiver[-1] [-1],15,1,1,1,{1.5*iM}
  distance (+):
      isovalue[%],_metric |
      isovalue[%],[metric],_method
    Compute the unsigned distance function to specified isovalue, opt. according to a custom metric.
    'metric' can be { 0:Chebyshev | 1:Manhattan | 2:Euclidean | 3:Squared-euclidean }.
    'method' can be { 0:Fast-marching | 1:Low-connectivity dijkstra | 2:High-connectivity dijkstra | 3:1+Return path | 4:2+Return path }.
    Default value: 'metric=2' and 'method=0'.
    Example:
      [#1] image.jpg threshold 20% distance 0 pow 0.3
      [#2] 400,400 set 1,50%,50% +distance[0] 1,2 +distance[0] 1,1 distance[0] 1,0 mod 32 threshold 16 append c
    Tutorial: https://gmic.eu/oldtutorial/_distance
  edgels:
      x0,y0,_n0,_is_high_connectivity={ 0:No | 1:Yes }
    Extract one or several lists of edgels (and their normals) that defines a 2D binary silhouette.
    When specified (i.e. '!=-1'), arguments 'x0,y0,n0' are the coordinates of the starting edgel, which must be located on an edge of the binary silhouette.
     * If 'x0,y0' and 'n0' are specified, only a single list of edgels is returned.
     * If only 'x0,y0' are specified (meaning 'n0=-1'), up to 4 lists of edgels can be returned, all starting from the same point (x0,y0).
     * If no arguments are specified (meaning 'x0=y0=n0=-1'), all possible lists of edgels are returned.
    A list of edgels is returned as an image with 3 channels '[x,y,n]' where 'x' and 'y' are the 2D coordinates of the edgel pixel, and 'n' is the
    orientation of its associated canonical normal (which can be { 0:[1,0] | 1:[0,1] | 2:[-1,0] | 3:[0,-1] }.
    Default values: 'x0=y0=n0=-1' and 'is_high_connectivity=1'.
  edges:
      _threshold[%]>=0
    Estimate contours of selected images.
    Default value: 'edges=15%'
    Example:
      [#1] image.jpg +edges 15%
  fftpolar:
    Compute fourier transform of selected images, as centered magnitude/phase images.
    Example:
      [#1] image.jpg fftpolar ellipse 50%,50%,10,10,0,1,0 ifftpolar
  histogram (+):
      nb_levels[%]>0,_min_value[%],_max_value[%]
    Compute the histogram of selected images.
    If value range is set, the histogram is estimated only for pixels in the specified
    value range. Argument 'max_value' must be specified if 'min_value' is set.
    Default values: 'min_value=0%' and 'max_value=100%'.
    Example:
      [#1] image.jpg +histogram 64 display_graph[-1] 400,300,3
  histogram_masked:
      [mask],nb_levels[%]>0,_min_value[%],_max_value[%]
    Compute the masked histogram of selected images.
    Default values: 'min_value=0%' and 'max_value=100%'.
  histogram_nd:
      nb_levels[%]>0,_value0[%],_value1[%]
    Compute the 1D,2D or 3D histogram of selected multi-channels images (having 1,2 or 3 channels).
    If value range is set, the histogram is estimated only for pixels in the specified
    value range.
    Default values: 'value0=0%' and 'value1=100%'.
    Example:
      [#1] image.jpg channels 0,1 +histogram_nd 256
  histogram_cumul:
      _nb_levels>0,_is_normalized={ 0:No | 1:Yes },_val0[%],_val1[%]
    Compute cumulative histogram of selected images.
    Default values: 'nb_levels=256', 'is_normalized=0', 'val0=0%' and 'val1=100%'.
    Example:
      [#1] image.jpg +histogram_cumul 256 histogram[0] 256 display_graph 400,300,3
  histogram_pointwise:
      nb_levels[%]>0,_value0[%],_value1[%]
    Compute the histogram of each vector-valued point of selected images.
    If value range is set, the histogram is estimated only for values in the specified
    value range.
    Default values: 'value0=0%' and 'value1=100%'.
  hough:
      _width>0,_height>0,gradient_norm_voting={ 0:No | 1:Yes }
    Compute hough transform (theta,rho) of selected images.
    Default values: 'width=512', 'height=width' and 'gradient_norm_voting=1'.
    Example:
      [#1] image.jpg +blur 1.5 hough[-1] 400,400 blur[-1] 0.5 add[-1] 1 log[-1]
  huffman_tree:
    Generate Huffman coding tree from the statistics of all selected images.
    Huffman tree is returned as a 1xN image inserted at the end of the image list, representing the 'N' vector-valued leafs/nodes of the tree, encoded as '[ value,parent,
    child0,child1 ]'.
    Last row of the returned image corresponds to the tree root.
    Selected images must contain only positive integer values.
    Return maximal value of the input data in the status.
    See also: compress_huffman, decompress_huffman.
  ifftpolar:
    Compute inverse fourier transform of selected images, from centered magnitude/phase images.
  img2patches:
      patch_size>0,_overlap[%]>0,_boundary_conditions
    Decompose selected 2D images into (possibly overlapping) patches and stack them along the z-axis.
    'overlap' must be in range '[0,patch_size-1]'.
    'boundary_conditions' can be { 0:Dirichlet | 1:Neumann | 2:Periodic | 3:Mirror }.
    Default values: 'overlap=0' and 'boundary_conditions=0'.
    See also: patches2img.
    Example:
      [#1] image.jpg img2patches 64
  isophotes:
      _nb_levels>0
    Render isophotes of selected images on a transparent background.
    Default value: 'nb_levels=64'
    Example:
      [#1] image.jpg blur 2 isophotes 6 dilate_circ 5 display_rgba
  label (+):
      _tolerance>=0,is_high_connectivity={ 0:No | 1:Yes },_is_L2_norm={ 0:No | 1:Yes }
    Label connected components in selected images.
    If 'is_L2_norm=1', tolerances are compared against L2-norm, otherwise L1-norm is used.
    Default values: 'tolerance=0', 'is_high_connectivity=0' and 'is_L2_norm=1'.
    Example:
      [#1] image.jpg luminance threshold 60% label normalize 0,255 map 0
      [#2] 400,400 set 1,50%,50% distance 1 mod 16 threshold 8 label mod 255 map 2
    Tutorial: https://gmic.eu/oldtutorial/_label
  label_fg:
      tolerance>=0,is_high_connectivity={ 0:No | 1:Yes },_is_L2_norm={ 0:No | 1:Yes }
    Label connected components for non-zero values (foreground) in selected images.
    Similar to 'label' except that 0-valued pixels are not labeled.
    If 'is_L2_norm=1', tolerances are compared against L2-norm, otherwise L1-norm is used.
    Default value: 'is_high_connectivity=0' and 'is_L2_norm=1'.
  laar:
    Extract the largest axis-aligned rectangle in non-zero areas of selected images.
    Rectangle coordinates are returned in status, as a sequence of numbers x0,y0,x1,y1.
    Example:
      [#1] shape_cupid 256 coords=${-laar} normalize 0,255 to_rgb rectangle $coords,0.5,0,128,0
  max_patch:
      _patch_size>=1
    Return locations of maximal values in local patch-based neighborhood of given size for selected images.
    Default value: 'patch_size=16'.
    Example:
      [#1] image.jpg norm +max_patch 16
  min_patch:
      _patch_size>=1
    Return locations of minimal values in local patch-based neighborhood of given size for selected images.
    Default value: 'patch_size=16'.
    Example:
      [#1] image.jpg norm +min_patch 16
  minimal_path:
      x0[%]>=0,y0[%]>=0,z0[%]>=0,x1[%]>=0,y1[%]>=0,z1[%]>=0,_is_high_connectivity={ 0:No | 1:Yes }
    Compute minimal path between two points on selected potential maps.
    Default value: 'is_high_connectivity=0'.
    Example:
      [#1] image.jpg +gradient_norm fill[-1] 1/(1+i) minimal_path[-1] 0,0,0,100%,100%,0 pointcloud[-1] 0 *[-1] 280 to_rgb[-1] ri[-1] [-2],0 or
  mse:
      [reference]
    Return the MSE (Mean-Squared Error) between selected images and specified reference image.
    This command does not modify the images. It returns a value or a list of values in the status.
  mse_matrix:
    Compute MSE (Mean-Squared Error) matrix between selected images.
    Example:
      [#1] image.jpg +noise 30 +noise[0] 35 +noise[0] 38 cut. 0,255 +mse_matrix
  patches2img:
      width>0,height>0,_overlap[%]>0,_overlap_std[%]
    Recompose 2D images from their selected patch representations.
    'overlap' must be in range '[0,patch_size-1]' where 'patch_size' is the width/height of the selected image.
    'overlap_std' is the standard deviation of the gaussian weights used for reconstructing overlapping patches.
    If 'overlap_std' is set to '-1', uniform weights are used rather than gaussian.
    Default value: 'overlap=0' and 'overlap_std=-1'.
    See also: img2patches.
    Example:
      [#1] image.jpg +img2patches 32,0,3 mirror[-1] xy patches2img[-1] {0,[w,h]}
  patches:
      patch_width>0,patch_height>0,patch_depth>0,x0,y0,z0,_x1,_y1,_z1,...,_xN,_yN,_zN
    Extract N+1 patches from selected images, centered at specified locations.
    Example:
      [#1] image.jpg +patches 64,64,1,153,124,0,184,240,0,217,126,0,275,38,0
  pca:
      _output_mode,_target_dim[%]>0,_normalization_mode={ 0:None | 1:Center | 2:Center+scale }
    Perform PCA (Principal Component Analysis) on selected images, viewed as sets of vector-valued samples 'Xk'.
    Bits of 'output_mode' selects what output data are returned on the image stack:
     * '1': Output image of vector-valued samples 'Yk = Mt*(Xk - avg)/std', that are the projections of the 'Xk' in a vector sub-space of (lower) dimension
    'target_dim'. 'avg' is the average vector of all 'Xk' (or zero vector if 'normalization_mode==0'). 'std' is the standard deviation vector of all
    'Xk' (or all-ones vector if 'normalization_mode<2'). 'Mt' is the transpose of 'M', the matrix of the 'target_dim' first eigenvectors (arranged by
    columns) of the covariance matrix of the 'Xk'.
     * '2': Output vector 'avg': average of the 'Xk'.
     * '4': Output vector 'std': standard deviations of the 'Xk'.
     * '8': Output matrix 'M': projection matrix (orthonormal).
    Knowing 'avg', 'std' and 'M' makes it possible to (approximately) retro-project the 'Yk' in the initial vector space, by computing 'Xk = avg +
    std*M*Yk' for each image pixel.
    Default values: 'output_mode=15', 'target_dim=100%' and 'normalization_mode=1'.
    Example:
      [#1] shape_dragonfly 400 mul 128 0 eval.. "i?da_push([x,y])" da_freeze. pca. 11,2 eval "C = I[#-2,0]; M = transpose(crop(#-1),2); repeat(2,k,polygon(#0,-2,C-1000*M[2*k,2],
       C+1000*M[2*k,2],1,0xF0F0F0F0,180)); ellipse(#0,C,5,5,0,1,255)" k[0]
  matchpatch (+):
      [patch_image],patch_width>=1,_patch_height>=1,_patch_depth>=1,_nb_iterations>=0,_nb_randoms>=0,_occurence_penalization,_output_score={ 0:No | 1:Yes },_[guide]
    Estimate correspondence map between selected images and specified patch image, using
    a patch-matching algorithm.
    Each pixel of the returned correspondence map gives the location (p,q) of the closest patch in
    the specified patch image. If 'output_score=1', the third channel also gives the corresponding
    matching score for each patch as well.
    If 'patch_penalization' is >=0, SSD is penalized with patch occurrences.
    If 'patch_penalization' is <0, SSD is inf-penalized when distance between patches are less than '-patch_penalization'.
    Default values: 'patch_height=patch_width', 'patch_depth=1', 'nb_iterations=5', 'nb_randoms=5', 'occurence_penalization=0',
     'output_score=0' and 'guide=(undefined)'.
    Example:
      [#1] image.jpg sample colorful +matchpatch[0] [1],3 +warp[-2] [-1],0
  matchpatch_alt:
      [patch_image],_patch_width>=1,_patch_height>=1,_patch_depth>=1,_nb_iterations>=0,_nb_randoms>=0,_occurrence_penalization>=0,_output_score={ 0:No | 1:Yes },_[guide]
    Implementation of the matchpatch command as an alternative custom command (slower).
    Default values: 'patch_height=patch_width', 'patch_depth=1', 'nb_iterations=5', 'nb_randoms=5', 'occurrence_penalization=0',
     'output_score=0' and 'guide=(undefined)'.
    Example:
      [#1] image.jpg sample colorful +matchpatch_alt[0] [1],3 +warp[-2] [-1],0
  plot2value:
    Retrieve values from selected 2D graph plots.
    Example:
      [#1] 400,300,1,1,'y>300*abs(cos(x/10+2*u))' +plot2value +display_graph[-1] 400,300
  pointcloud:
      _type = { -X:-X-opacity | 0:Binary | 1:Cumulative | 2:Label | 3:Retrieve coordinates },_width,_height>0,_depth>0
    Render a set of point coordinates, as a point cloud in a 1D/2D or 3D binary image
    (or do the reverse, i.e. retrieve coordinates of non-zero points from a rendered point cloud).
    Input point coordinates can be a NxMx1x1, Nx1x1xM or 1xNx1xM image, where 'N' is the number of points,
    and M the point coordinates.
    If 'M'>3, the 3-to-M components sets the (M-3)-dimensional color at each point.
    Parameters 'width','height' and 'depth' are related to the size of the final image :
       - If set to 0, the size is automatically set along the specified axis.
       - If set to N>0, the size along the specified axis is N.
       - If set to N<0, the size along the specified axis is at most N.
    Points with coordinates that are negative or higher than specified ('width','height','depth')
    are not plotted.
    Default values: 'type=0' and 'max_width=max_height=max_depth=0'.
    Example:
      [#1] 3000,2 rand 0,400 +pointcloud 0 dilate[-1] 3
      [#2] 3000,2 rand 0,400 {w} {w},3 rand[-1] 0,255 append y +pointcloud 0 dilate[-1] 3
  psnr:
      [reference],_max_value>0
    Return PSNR (Peak Signal-to-Noise Ratio) between selected images and specified reference image.
    This command does not modify the images. It returns a value or a list of values in the status.
    Default value: 'max_value=255'.
  psnr_matrix:
      _max_value>0
    Compute PSNR (Peak Signal-to-Noise Ratio) matrix between selected images.
    Default value: 'max_value=255'.
    Example:
      [#1] image.jpg +noise 30 +noise[0] 35 +noise[0] 38 cut. 0,255 +psnr_matrix
  segment_watershed:
      _threshold>=0
    Apply watershed segmentation on selected images.
    Default values: 'threshold=2'.
    Example:
      [#1] image.jpg segment_watershed 2
  shape2bump:
      _resolution>=0,0<=_weight_std_max_avg<=1,_dilation,_smoothness>=0
    Estimate bumpmap from binary shape in selected images.
    Default value: 'resolution=256', 'weight_std_max=0.75', 'dilation=0' and 'smoothness=100'.
  skeleton:
      _boundary_conditions={ 0:Dirichlet | 1:Neumann }
    Compute skeleton of binary shapes using distance transform and constrained thinning.
    Default value: 'boundary_conditions=1'.
    Example:
      [#1] shape_cupid 320 +skeleton 0
  slic:
      size>0,_regularity>=0,_nb_iterations>0
    Segment selected 2D images with superpixels, using the SLIC algorithm (Simple Linear Iterative Clustering).
    Scalar images of increasingly labeled pixels are returned.
    Reference paper: Achanta, R., Shaji, A., Smith, K., Lucchi, A., Fua, P., & Susstrunk, S. (2010). SLIC Superpixels (No. EPFL-REPORT-149300).
    Default values: 'size=16', 'regularity=10' and 'nb_iterations=10'.
    Example:
      [#1] image.jpg +srgb2lab slic[-1] 16 +blend shapeaverage f[-2] "j(1,0)==i && j(0,1)==i" *[-1] [-2]
  ssd_patch:
      [patch],_use_fourier={ 0:No | 1:Yes },_boundary_conditions
    Compute fields of SSD between selected images and specified patch.
    Argument 'boundary_conditions' is valid only when 'use_fourier=0'.
    'boundary_conditions' can be { 0:Dirichlet | 1:Neumann | 2:Periodic | 3:Mirror }.
    Default value: 'use_fourier=0' and 'boundary_conditions=0'.
    Example:
      [#1] image.jpg +crop 20%,20%,35%,35% +ssd_patch[0] [1],0,0
  ssim:
      [reference],_patch_size>0,_max_value>0
    Compute the Structural Similarity Index Measure (SSIM) between selected images and specified reference image.
    This command does not modify the images, it just returns a value or a list of values in the status.
    When 'downsampling_factor' is specified with a ending '%', its value is equal to '1+(patch_size-1)*spatial_factor%'.
    SSIM is a measure introduced int the following paper:
    Wang, Zhou, et al., "Image quality assessment: from error visibility to structural similarity.",
    in IEEE transactions on image processing 13.4 (2004): 600-612.
    The implementation of this command is a direct translation of the reference code (in Matlab), found at :
    https://ece.uwaterloo.ca/~z70wang/research/ssim/
    Default values: 'patch_size=11', and 'max_value=255'.
  ssim_matrix:
      _patch_size>0,_max_value>0
    Compute SSIM (Structural Similarity Index Measure) matrix between selected images.
    Default values: 'patch_size=11', and 'max_value=255'.
    Example:
      [#1] image.jpg +noise 30 +noise[0] 35 +noise[0] 38 cut. 0,255 +ssim_matrix
  thinning:
      _boundary_conditions={ 0:Dirichlet | 1:Neumann }
    Compute skeleton of binary shapes using morphological thinning
    (beware, this is a quite slow iterative process)
    Default value: 'boundary_conditions=1'.
    Example:
      [#1] shape_cupid 320 +thinning
  tones:
      N>0
    Get N tones masks from selected images.
    Example:
      [#1] image.jpg +tones 3
  topographic_map:
      _nb_levels>0,_smoothness
    Render selected images as topographic maps.
    Default values: 'nb_levels=16' and 'smoothness=2'.
    Example:
      [#1] image.jpg topographic_map 10
  tsp:
      _precision>=0
    Try to solve the 'travelling salesman' problem, using a combination of greedy search and 2-opt algorithms.
    Selected images must have dimensions Nx1x1xC to represent N cities each with C-dimensional coordinates.
    This command re-order the selected data along the x-axis so that the point sequence becomes a shortest path.
    Default values: 'precision=256'.
    Example:
      [#1] 256,1,1,2 rand 0,512 tsp , 512,512,1,3 repeat w#0 circle[-1] {0,I[$>]},2,1,255,255,255 line[-1] {0,boundary=2;[I[$>],I[$>+1]]},1,255,128,0 done keep[-1]
  variance_patch:
      _patch_size>=1
    Compute variance of each images patch centered at (x,y), in selected images.
    Default value: 'patch_size=16'
    Example:
      [#1] image.jpg +variance_patch
  11.10. Image Drawing
         -------------
  arrow:
      x0[%],y0[%],x1[%],y1[%],_thickness[%]>=0,_head_length[%]>=0,_head_thickness[%]>=0,_opacity,_pattern,_color1,...
    Draw specified arrow on selected images.
    'pattern' is an hexadecimal number starting with '0x' which can be omitted
    even if a color is specified. If a pattern is specified, the arrow is
    drawn outlined instead of filled.
    Default values: 'thickness=1%', 'head_length=10%', 'head_thickness=3%', 'opacity=1', 'pattern=(undefined)' and 'color1=0'.
    Example:
      [#1] 400,400,1,3 repeat 100 arrow 50%,50%,{u(100)}%,{u(100)}%,3,20,10,0.3,${-rgb} done
  axes:
      x0,x1,y0,y1,_font_height>=0,_opacity,_pattern,_color1,...
    Draw xy-axes on selected images.
    'pattern' is an hexadecimal number starting with '0x' which can be omitted
    even if a color is specified.
    To draw only one x-axis at row Y, set both 'y0' and 'y1' to Y.
    To draw only one y-axis at column X, set both 'x0' and 'x1' to X.
    Default values: 'font_height=14', 'opacity=1', 'pattern=(undefined)' and 'color1=0'.
    Example:
      [#1] 400,400,1,3,255 axes -1,1,1,-1
  ball:
      _size>0,_R,_G,_B,_ambient>=0,_diffuse>=0,_specular>=0,_shininess>=0,_light_x,_light_y,_light_z
    Input a 2D RGBA colored ball sprite, rendered using the Phong illumination model.
    Default values: 'size=64', 'R=200', 'G=R', 'B=R', 'ambient=0.25', 'diffuse=1', 'specular=1', 'shininess=20',
     'light_x=1.5', 'light_y=-1.5' and 'light_z=1'.
    Example:
      [#1] repeat 9 { ball {int(1.5^($>+4))},${-rgb} } append_tiles 3,3
  chessboard:
      size1>0,_size2>0,_offset1,_offset2,_angle,_opacity,_color1,...,_color2,...
    Draw chessboard on selected images.
    Default values: 'size2=size1', 'offset1=offset2=0', 'angle=0', 'opacity=1', 'color1=0' and 'color2=255'.
    Example:
      [#1] image.jpg chessboard 32,32,0,0,25,0.3,255,128,0,0,128,255
  cie1931:
    Draw CIE-1931 chromaticity diagram on selected images.
    Example:
      [#1] 500,400,1,3 cie1931
  circle:
      x[%],y[%],R[%],_opacity,_pattern,_color1,...
    Draw specified colored circle on selected images.
    A radius of '100%' stands for 'sqrt(width^2+height^2)'.
    'pattern' is an hexadecimal number starting with '0x' which can be omitted
    even if a color is specified. If a pattern is specified, the circle is
    drawn outlined instead of filled.
    Default values: 'opacity=1', 'pattern=(undefined)' and 'color1=0'.
    Example:
      [#1] image.jpg repeat 300 circle {u(100)}%,{u(100)}%,{u(30)},0.3,${-rgb} done circle 50%,50%,100,0.7,255
  close_binary:
      0<=_endpoint_rate<=100,_endpoint_connectivity>=0,_spline_distmax>=0,_segment_distmax>=0,0<=_spline_anglemax<=180,_spline_roundness>=0,_area_min>=0,_allow_self_intersection={
        0:No | 1:Yes }
    Automatically close open shapes in binary images (defining white strokes on black background).
    Default values: 'endpoint_rate=75', 'endpoint_connectivity=2', 'spline_distmax=80', 'segment_distmax=20', 'spline_anglemax=90',
     'spline_roundness=1','area_min=100', 'allow_self_intersection=1'.
  curve:
      [xy_coordinates],_thickness>0,_tilt,_tilt_strength[%],_is_closed={ 0:No | 1:Yes },_opacity,_color1,...
    Draw specified parameterized curve on selected images.
    Arguments are:
     * '[xy_coordinates]' is the set of XY-coordinates of the curve, specified as a 2-channels image.
     * 'thickness' is the thickness of the drawing, specified in pixels.
     * 'tilt' is an angle, specified in degrees.
     * 'tilt_strength' must be a float value in [0,1] (or in [0,100] if specified as a percentage).
     * 'is_closed' is a boolean which tells if the curve is closed or not.
    Default values: 'thickness=0', 'tilt=45'
    Example:
      [#1] image.jpg srand 3 16,1,1,4,u s. c,2 rbf[-2,-1] 1000,0,1 n[-2] 10,{w#0-10} n[-1] 10,{h#0-10} a[-2,-1] c curve[-2] [-1],6,0,0,0,1,0,128,0
  ellipse (+):
      x[%],y[%],R[%],r[%],_angle,_opacity,_pattern,_color1,...
    Draw specified colored ellipse on selected images.
    A radius of '100%' stands for 'sqrt(width^2+height^2)'.
    'pattern' is an hexadecimal number starting with '0x' which can be omitted
    even if a color is specified. If a pattern is specified, the ellipse is
    drawn outlined instead of filled.
    Default values: 'opacity=1', 'pattern=(undefined)' and 'color1=0'.
    Example:
      [#1] image.jpg repeat 300 ellipse {u(100)}%,{u(100)}%,{u(30)},{u(30)},{u(180)},0.3,${-rgb} done ellipse 50%,50%,100,100,0,0.7,255
  flood (+):
      x[%],_y[%],_z[%],_tolerance>=0,_is_high_connectivity={ 0:No | 1:Yes },_opacity,_color1,...
    Flood-fill selected images using specified value and tolerance.
    Default values: 'y=z=0', 'tolerance=0', 'is_high_connectivity=0', 'opacity=1' and 'color1=0'.
    Example:
      [#1] image.jpg repeat 1000 flood {u(100)}%,{u(100)}%,0,20,0,1,${-rgb} done
  gaussian:
      _sigma1[%],_sigma2[%],_angle
    Draw a centered gaussian on selected images, with specified standard deviations and orientation.
    Default values: 'sigma1=3', 'sigma2=sigma1' and 'angle=0'.
    Example:
      [#1] 400,400 gaussian 100,30,45
    Tutorial: https://gmic.eu/oldtutorial/_gaussian
  graph:
      [function_image],_plot_type,_vertex_type,_ytop,_ybottom,_opacity,_pattern,_color1,...
    Draw specified function graph on selected images.
    'plot_type' can be { 0:None | 1:Lines | 2:Splines | 3:Bar }.
    'vertex_type' can be { 0:None | 1:Points | 2,3:Crosses | 4,5:Circles | 6,7:Squares }.
    'pattern' is an hexadecimal number starting with '0x' which can be omitted
    even if a color is specified.
    Default values: 'plot_type=1', 'vertex_type=1', 'ytop=ybottom=0 (auto)', 'opacity=1', 'pattern=(undefined)'
    and 'color1=0'.
    Example:
      [#1] image.jpg +rows 50% blur[-1] 3 split[-1] c div[0] 1.5 graph[0] [1],2,0,0,0,1,255,0,0 graph[0] [2],2,0,0,0,1,0,255,0 graph[0] [3],2,0,0,0,1,0,0,255 keep[0]
  grid:
      size_x[%]>=0,size_y[%]>=0,_offset_x[%],_offset_y[%],_opacity,_pattern,_color1,...
    Draw xy-grid on selected images.
    'pattern' is an hexadecimal number starting with '0x' which can be omitted
    even if a color is specified.
    Default values: 'offset_x=offset_y=0', 'opacity=1', 'pattern=(undefined)' and 'color1=0'.
    Example:
      [#1] image.jpg grid 10%,10%,0,0,0.5,255
      [#2] 400,400,1,3,255 grid 10%,10%,0,0,0.3,0xCCCCCCCC,128,32,16
  j (+):
      Shortcut for command 'image'.
  image (+):
      [sprite],_x[%|~],_y[%|~],_z[%|~],_c[%|~],_opacity,_[opacity_mask],_max_opacity_mask
    Draw specified sprite on selected images.
    (equivalent to shortcut command 'j').
    If one of the x,y,z or c argument ends with a '~', its value is expected to be
    a centering ratio (in [0,1]) rather than a position.
    Usual centering ratio are { 0:left-justified | 0.5:centered | 1:right-justified }.
    Default values: 'x=y=z=c=0', 'opacity=1', 'opacity_mask=(undefined)' and 'max_opacity_mask=1'.
    Example:
      [#1] image.jpg +crop 40%,40%,60%,60% resize[-1] 200%,200%,1,3,5 frame[-1] xy,2,0 image[0] [-1],30%,30% keep[0]
  ja:
      Shortcut for command 'imagealpha'.
  imagealpha:
      [sprite],_x[%|~],_y[%|~],_z[%|~],_c[%|~],_opacity
    Draw specified sprite on selected images, considering that the sprite's last channel is the drawing's alpha.
    (equivalent to shortcut command 'ja').
    If one of the x,y,z or c argument ends with a '~', its value is expected to be
    a centering ratio (in [0,1]) rather than a position.
    Usual centering ratio are { 0:left-justified | 0.5:centered | 1:right-justified }.
    Default values: 'x=y=z=c=0' and 'opacity=1'.
  line (+):
      x0[%],y0[%],x1[%],y1[%],_opacity,_pattern,_color1,...
    Draw specified colored line on selected images.
    'pattern' is an hexadecimal number starting with '0x' which can be omitted
    even if a color is specified.
    Default values: 'opacity=1', 'pattern=(undefined)' and 'color1=0'.
    Example:
      [#1] image.jpg repeat 500 line 50%,50%,{u(w)},{u(h)},0.5,${-rgb} done line 0,0,100%,100%,1,0xCCCCCCCC,255 line 100%,0,0,100%,1,0xCCCCCCCC,255
  line_aa:
      x0[%],y0[%],x1[%],y1[%],_opacity,_color1,...
    Draw specified antialiased colored line on selected images.
    Default values: 'opacity=1' and 'color1=0'.
    Example:
      [#1] 512,512,1,3 repeat 100 line_aa {v([w,h,w,h])-1},1,${-rgb} done
  spline:
      x0[%],y0[%],u0[%],v0[%],x1[%],y1[%],u1[%],v1[%],_opacity,_color1,...
    Draw specified colored spline curve on selected images (cubic hermite spline).
    Default values: 'opacity=1' and 'color1=0'.
    Example:
      [#1] image.jpg repeat 30 { spline {u(100)}%,{u(100)}%,{u(-600,600)},{u(-600,600)},{u(100)}%,{u(100)}%,{u(-600,600)},{u(-600,600)},1,${-rgb} }
  thickcircle:
      x[%],y[%],R[%],_thickness>=0,_opacity,_color1,...
    Draw specified colored thick outlined circle on selected images.
    Default values: 'thickness=3', 'opacity=1' and 'color1=0'.
    Example:
      [#1] 400,400 repeat 15 { R:=lerp(10,190,$%) thickcircle 200,200,$R,2,1,$R } n 0,255 map 7
  thickellipse:
      x[%],y[%],R[%],r[%],_angle,_thickness>=0,_opacity,_color1,...
    Draw specified colored thick outlined ellipse on selected images.
    Default values: 'thickness=3', 'opacity=1' and 'color1=0'.
    Example:
      [#1] image.jpg repeat 300 thickellipse {u(100)}%,{u(100)}%,{u(50)},{u(50)},{u(180)},3,0.6,${-rgb} done thickellipse 50%,50%,200,100,0,5,0.7,255
  thickline:
      x0[%],y0[%],x1[%],y1[%],_thickness,_opacity,_color1
    Draw specified colored thick line on selected images.
    Default values: 'thickness=2', 'opacity=1' and 'color1=0'.
    Example:
      [#1] 400,400,1,3 repeat 100 thickline {u([w,h,w,h,5])},0.5,${-rgb} done
  thickpolygon:
      N>=1,x1[%],y1[%],...,xN[%],yN[%],_thickness>=0,_opacity,_color1,... |
      [coords],_thickness>=0,_opacity,_color1,...
    Draw specified colored thick outlined N-vertices polygon on selected images.
    If 'thickness<0', the command draws an open polygon rather than a closed polygon.
    Default values: 'thickness=3', 'opacity=1', and 'color1=0'.
    Example:
      [#1] image.jpg thickpolygon 4,20%,20%,80%,30%,80%,70%,20%,80%,5,1,0,255,0
      [#2] image.jpg 2,16,1,1,'u(x?h#0:w#0)' thickpolygon[-2] [-1],5,1,255,0,255 remove[-1]
  thickspline:
      x0[%],y0[%],u0[%],v0[%],x1[%],y1[%],u1[%],v1[%],_thickness,_opacity,_color1,...
    Draw specified colored thick spline curve on selected images (cubic hermite spline).
    Default values: 'thickness=3', 'opacity=1' and 'color1=0'.
    Example:
      [#1] image.jpg repeat 30 { thickspline {u(100)}%,{u(100)}%,{u(-600,600)},{u(-600,600)},{u(100)}%,{u(100)}%,{u(-600,600)},{u(-600,600)},3,1,${-rgb} }
  mandelbrot:
      z0r,z0i,z1r,z1i,_iteration_max>=0,_is_julia={ 0:No | 1:Yes },_c0r,_c0i,_opacity
    Draw mandelbrot/julia fractal on selected images.
    Default values: 'iteration_max=100', 'is_julia=0', 'c0r=c0i=0' and 'opacity=1'.
    Example:
      [#1] 400,400 mandelbrot -2.5,-2,2,2,1024 map 0 +blur 2 elevation3d[-1] -0.2
  marble:
      _image_weight,_pattern_weight,_angle,_amplitude,_sharpness>=0,_anisotropy>=0,_alpha,_sigma,_cut_low>=0,_cut_high>=0
    Render marble like pattern on selected images.
    Default values: 'image_weight=0.2', 'pattern_weight=0.1', 'angle=45', 'amplitude=0', 'sharpness=0.4' and 'anisotropy=0.8',
    'alpha=0.6', 'sigma=1.1' and 'cut_low=cut_high=0'.
    Example:
      [#1] image.jpg +marble ,
  maze:
      _width>0,_height>0,_cell_size>0
    Input maze with specified size.
    Example:
      [#1] maze 30,20 negate normalize 0,255
  maze_mask:
      _cellsize>0
    Input maze according to size and shape of selected mask images.
    Mask may contain disconnected shapes.
    Example:
      [#1] 0 text "G'MIC",0,0,53,1,1 dilate 3 autocrop 0 frame xy,1,0 maze_mask 8 dilate 3 negate mul 255
  newton_fractal:
      z0r,z0i,z1r,z1i,_angle,0<=_descent_method<=2,_iteration_max>=0,_convergence_precision>0,_expr_p(z),_expr_dp(z),_expr_d2p(z)
    Draw newton fractal on selected images, for complex numbers in range (z0r,z0i) - (z1r,z1i).
    Resulting images have 3 channels whose meaning is [ last_zr, last_zi, nb_iter_used_for_convergence ].
    'descent_method' can be { 0:Secant | 1:Newton | 2:Householder }.
    Default values: 'angle=0', 'descent_method=1', 'iteration_max=200', 'convergence_precision=0.01', 'expr_p(z)=z^^3-1',
     'expr_dp(z)=3*z^^2' and 'expr_d2z(z)=6*z'.
    Example:
      [#1] 400,400 newton_fractal -1.5,-1.5,1.5,1.5,0,2,200,0.01,"z^^6 + z^^3 - 1","6*z^^5 + 3*z^^2","30*z^^4 + 6*z" f "[ atan2(i1,i0)*90+20,1,cut(i2/30,0.2,0.7) ]" hsl2rgb
  pack_sprites:
      [sprite_set],_nb_scales>=1,0<_min_scale<=100,_allow_rotation={ 0:None | 1:180 deg. | 2:90 deg. | 3:Any },_spacing,_max_attempts>0
    Try to randomly pack as many sprites as possible onto the 'empty' areas of an image.
    Sprites can be eventually rotated and scaled during the packing process.
    Selected image is the canvas that will be filled with the sprites.
    The zero values of its alpha channel defines the potential locations for drawing the sprites.
    '[sprite_set]' defines the set of sprites considered for filling the canvas.
    The zero values of their alpha channels defines the sprite shape.
    Sprite packing is done on random locations and iteratively with decreasing scales.
    'nb_scales' sets the number of decreasing scales considered for all specified sprites to be packed.
    'min_scale' (in %) sets the minimal size considered for packing (specified as a percentage of the
    original sprite size).
    'spacing' can be positive or negative.
    'max_attempts' defines the maximum number of attempts to pack a sprite for each scale.
    Default values: 'nb_scales=1', 'min_scale=25', 'allow_rotation=0', 'spacing=1' and 'max_attempts=512'.
    Example:
      [#1] shape_heart 512 negate normalize 0,255 channels -3,0 repeat 2 { ball 48,${-rgb} } +pack_sprites[0] [^0],5,15
  piechart:
      label_height>=0,label_R,label_G,label_B,"label1",value1,R1,G1,B1,...,"labelN",valueN,RN,GN,BN
    Draw pie chart on selected (RGB) images.
    Example:
      [#1] image.jpg piechart 25,0,0,0,"Red",55,255,0,0,"Green",40,0,255,0,"Blue",30,128,128,255,"Other",5,128,128,128
  plasma:
      _alpha,_beta,_scale>=0
    Draw a random colored plasma fractal on selected images.
    This command implements the so-called 'Diamond-Square' algorithm.
    Default values: 'alpha=1', 'beta=1' and 'scale=8'.
    Example:
      [#1] 400,400,1,3 plasma 1
    Tutorial: https://gmic.eu/oldtutorial/_plasma
  point (+):
      x[%],_y[%],_z[%],_opacity,_color1,...
    Set specified colored pixel on selected images.
    Default values: 'z=0', 'opacity=1' and 'color1=0'.
    Example:
      [#1] image.jpg repeat 10000 point {u(100)}%,{u(100)}%,0,1,${-rgb} done
  polka_dots:
      diameter>=0,_density,_offset1,_offset2,_angle,_aliasing,_shading,_opacity,_color,...
    Draw dots pattern on selected images.
    Default values: 'density=20', 'offset1=offset2=50', 'angle=0', 'aliasing=10', 'shading=1', 'opacity=1' and 'color=255'.
    Example:
      [#1] image.jpg polka_dots 10,15,0,0,20,10,1,0.5,0,128,255
  polygon (+):
      N>=1,x1[%],y1[%],...,xN[%],yN[%],_opacity,_pattern,_color1,... |
      [coords],_opacity,_pattern,_color1,...
    Draw specified colored N-vertices polygon on selected images.
    'pattern' is an hexadecimal number starting with '0x' which can be omitted
    even if a color is specified. If a pattern is specified, the polygon is
    drawn outlined instead of filled.
    Adding a '-' sign before 'pattern' makes the command draw an open polyline rather than a closed polygon.
    Default values: 'opacity=1', 'pattern=(undefined)' and 'color1=0'.
    Example:
      [#1] image.jpg polygon 4,20%,20%,80%,30%,80%,70%,20%,80%,0.3,0,255,0 polygon 4,20%,20%,80%,30%,80%,70%,20%,80%,1,0xCCCCCCCC,255
      [#2] image.jpg 2,16,1,1,'u(x?h#0:w#0)' polygon[-2] [-1],0.6,255,0,255 remove[-1]
  quiver:
      [function_image],_sampling[%]>0,_factor>=0,_is_arrow={ 0:No | 1:Yes },_opacity,_color1,...
    Draw specified 2D vector/orientation field on selected images.
    Default values: 'sampling=5%', 'factor=1', 'is_arrow=1', 'opacity=1', 'pattern=(undefined)'
    and 'color1=0'.
    Example:
      [#1] 100,100,1,2,'!c?x-w/2:y-h/2' 500,500,1,3,255 quiver[-1] [-2],10
      [#2] image.jpg +rescale2d ,600 luminance[0] gradient[0] mul[1] -1 reverse[0,1] append[0,1] c blur[0] 8 orientation[0] quiver[1] [0],20,1,1,0.8,255
  rectangle:
      x0[%],y0[%],x1[%],y1[%],_opacity,_pattern,_color1,...
    Draw specified colored rectangle on selected images.
    'pattern' is an hexadecimal number starting with '0x' which can be omitted
    even if a color is specified. If a pattern is specified, the rectangle is
    drawn outlined instead of filled.
    Default values: 'opacity=1', 'pattern=(undefined)' and 'color1=0'.
    Example:
      [#1] image.jpg repeat 30 { rectangle {u(100)}%,{u(100)}%,{u(100)}%,{u(100)}%,0.3,${-rgb} }
  rorschach:
      'smoothness[%]>=0','mirroring={ 0:None | 1:X | 2:Y | 3:XY }
    Render rorschach-like inkblots on selected images.
    Default values: 'smoothness=5%' and 'mirroring=1'.
    Example:
      [#1] 400,400 rorschach 3%
  sierpinski:
      recursion_level>=0
    Draw Sierpinski triangle on selected images.
    Default value: 'recursion_level=7'.
    Example:
      [#1] image.jpg sierpinski 7
  spiralbw:
      width>0,_height>0,_is_2dcoords={ 0:No | 1:Yes }
    Input a 2D rectangular spiral image with specified size.
    Default values: 'height=width' and 'is_2dcoords=0'.
    Example:
      [#1] spiralbw 16
      [#2] image.jpg spiralbw {[w,h]},1 +warp[0] [1],0,1,1 +warp[2] [1],2,1,1
  tetraedron_shade:
      x0,y0,z0,x1,y1,z1,x2,y2,z2,x3,y3,z3,R0,G0,B0,...,R1,G1,B1,...,R2,G2,B2,...,R3,G3,B3,...
    Draw tetraedron with interpolated colors on selected (volumetric) images.
  t (+):
      Shortcut for command 'text'.
  text (+):
      text,_x[%|~],_y[%|~],_{ font_height[%]>=0 | custom_font },_opacity,_color1,...
    Draw specified colored text string on selected images.
    (equivalent to shortcut command 't').
    If one of the x or y argument ends with a '~', its value is expected to be a centering ratio (in [0,1]) rather than a position.
    Usual centering ratio are { 0:left-justified | 0.5:centered | 1:right-justified }.
    Sizes '13' and '128' are special and correspond to binary fonts (no-antialiasing). Any other font size is rendered with anti-aliasing.
    Specifying an empty target image resizes it to new dimensions such that the image contains the entire text string.
    A custom font can be specified as a variable name that stores an image list of 256 or 512 items (512 for 256 character sprites + 256 associated opacities), or as an image
    selection that is a serialized version of such an image list.
    Default values: 'x=y=0.01~', 'font_height=16', 'opacity=1' and 'color1=0'.
    Example:
      [#1] image.jpg rescale2d ,600 div 2 y=0 repeat 30 { text {2*$>}" : This is a nice text!",10,$y,{2*$>},0.9,255 y+={2*$>} }
      [#2] 0 text "G'MIC",0,0,23,1,255
  to:
      Shortcut for command 'text_outline'.
  text_outline:
      text,_x[%|~],_y[%|~],{ _font_height[%]>0 | custom_font },_outline>=0,_opacity,_color1,...
    Draw specified colored and outlined text string on selected images.
    If one of the x or y argument ends with a '~', its value is expected to be
    a centering ratio (in [0,1]) rather than a position.
    Usual centering ratio are { 0:left-justified | 0.5:centered | 1:right-justified }.
    Default values: 'x=y=0.01~', 'font_height=7.5%', 'outline=2', 'opacity=1', 'color1=color2=color3=255' and 'color4=255'.
    Example:
      [#1] image.jpg text_outline "Hi there!",10,10,63,3
  triangle_shade:
      x0,y0,x1,y1,x2,y2,R0,G0,B0,...,R1,G1,B1,...,R2,G2,B2,...
    Draw triangle with interpolated colors on selected images.
    Example:
      [#1] image.jpg triangle_shade 20,20,400,100,120,200,255,0,0,0,255,0,0,0,255
  truchet:
      _scale>0,_radius>=0,_pattern_type={ 0:Straight | 1:Curved }
    Fill selected images with random truchet patterns.
    Default values: 'scale=32', 'radius=5' and 'pattern_type=1'.
    Example:
      [#1] 400,300 truchet ,
  turbulence:
      _radius>0,_octaves={ 1,2,3...,12 },_alpha>0,_difference={ -10,10 },_mode={ 0,1,2,3 }
    Render fractal noise or turbulence on selected images.
    Default values: 'radius=32', 'octaves=6', 'alpha=3', 'difference=0' and 'mode=0'.
    Example:
      [#1] 400,400,1,3 turbulence 16
    Tutorial: https://gmic.eu/oldtutorial/_turbulence
  yinyang:
    Draw a yin-yang symbol on selected images.
    Example:
      [#1] 400,400 yinyang
  11.11. Matrix Computation
         ------------------
  diagonal:
    Transform selected vectors as diagonal matrices.
    Example:
      [#1] 1,10,1,1,'y' +diagonal
  dijkstra:
      starting_vertex>=0,_ending_vertex={ -1:None | >=0 }
    Compute minimal distances/paths in selected graphs, from specified 'starting_vertex' to all other vertices (opt. only until 'ending_vertex' has been reached).
    A graph of 'N' vertices is specified as a 'NxN' adjacency matrix giving the weights of all edges connecting vertices (set to 'inf' when two vertices are not
    connected).
    This command return a '1xNx1x2' image containing the '[distance,parent]' information :
       - 'distance' is the minimal distance from vertex '#y' to the 'starting_vertex' (i.e. the sum of edge weights composing the minimal path between these two
    vertices).
       - 'parent' is the index of the next vertex that must be followed to reaches the 'starting_vertex' through the minimal path.
    Default value: 'ending_vertex=-1'
  eigen (+):
    Compute the eigenvalues and eigenvectors of selected symmetric matrices or matrix fields.
    If one selected image has 3 or 6 channels, it is regarded as a field of 2x2 or 3x3 symmetric matrices,
    whose eigen elements are computed at each point of the field.
    Example:
      [#1] (1,0,0;0,2,0;0,0,3) +eigen
      [#2] image.jpg structuretensors blur 2 eigen split[0] c
    Tutorial: https://gmic.eu/oldtutorial/_eigen
  eye:
      _size>0
    Insert an identity matrix of given size at the end of the image list.
    Example:
      [#1] eye 3 eye 7 eye 10
  fitsamples:
      nb_samples>0,_relevant_dimension[%]>0,_average_vector_varname,_dilation_vector_varname,_orientation_matrix_varname
    Generate 'nb_samples' vectors having the same multivariate gaussian distribution as the vectors of the selected images.
    Each input represents a set of 'M' vectors of dimension 'N' (with M>1) (specified as an image with size 'MxNx1x1', 'Mx1xNx1', 'Mx1x1xN',
    '1xMxNx1', '1xMx1xN' or '1x1xMxN').
    The command returns a new set of random vectors with similar geometry.
    Default values: 'relevant_dimension=100%', and 'average_vector_varname=orientation_matrix_varname=dilation_matrix_varname=(undefined)'.
  invert:
    Inverse selected matrices (or compute Moore-Penrose pseudoinverse for non-square matrices).
    Example:
      [#1] (0,1,0;0,0,1;1,0,0) +invert
  meigen:
      m>=1
    Compute an approximation of the 'm' largest eigenvalues and eigenvectors of selected symmetric matrices,
    using the Arnoldi iteration method (https://en.wikipedia.org/wiki/Arnoldi_iteration).
    A larger 'm' goes with better numerical precision.
    Example:
      [#1] (1,0,0;0,2,0;0,0,3) +meigen 3
  mproj (+):
      [dictionary],_method,_max_iter={ 0:Auto | >0 },_max_residual>=0
    Find best matching projection of selected matrices onto the span of an over-complete
    dictionary D, using the orthogonal projection or Matching Pursuit algorithm.
    Selected images are 2D-matrices in which each column represent a signal to project.
    '[dictionary]' is a matrix in which each column is an element of the dictionary D.
    'method' tells what projection algorithm must be applied. It can be:
       - 0 = orthogonal projection (least-squares solution using LU-based solver).
       - 1 = matching pursuit.
       - 2 = matching pursuit, with a single orthogonal projection step at the end.
       - >=3 = orthogonal matching pursuit where an orthogonal projection step is performed
               every 'method-2' iterations.
    'max_iter' sets the max number of iterations processed for each signal.
    If set to '0' (default), 'max_iter' is equal to the number of columns in D.
    (only meaningful for matching pursuit and its variants).
    'max_residual' gives a stopping criterion on signal reconstruction accuracy.
    (only meaningful for matching pursuit and its variants).
    For each selected image, the result is returned as a matrix W
    whose columns correspond to the weights associated to each column of D,
    such that the matrix product D*W is an approximation of the input matrix.
    Default values: 'method=0', 'max_iter=0' and 'max_residual=1e-6'.
  orthogonalize:
      _mode = { 0:orthogonalize | 1:orthonormalize }
    Orthogonalize or orthonormalize selected matrices, using Modified Gram-Schmidt process.
    Default value: 'mode=0'.
  poweriteration:
      _nb_eigenvectors>0,_epsilon>0,_max_iter>0
    Compute the 'nb_eigenvectors' largest eigenvectors of the selected symmetric matrices,
    using the power iteration algorithm.
    Default values: 'nb_eigenvectors=1', 'epsilon=1e-5' and 'max_iter=100'.
  qr (+):
      _is_reduced_form={ 0:No | 1:Yes }
    Compute QR decomposition of selected matrices.
    Assuming a selected image A is a matrix of size m×n (m rows, n columns), this command returns the matrices Q and R, so that A = Q*R.
     * Q is an orthogonal matrix, of size 'm×m' if 'is_reduced_form==0', or 'm×min(m,n)' otherwise.
     * R is an upper-trianguler matrix of size 'm×n' if 'is_reduced_form==0' or 'min(m,n)×n' otherwise.
     * Q^T*Q = Id.
     * If n>m, only the first m×m part of R is upper triangular.
    Default value: 'is_reduced_form=1'.
    Example:
      [#1] 10,10,1,1,'v(100)' +qr +mmul[-2,-1]
  solve (+):
      [image]
    Solve linear system AX = B for selected B-matrices and specified A-matrix.
    If the system is under- or over-determined, the least squares or minimal norm solution is returned.
    Example:
      [#1] (0,1,0;1,0,0;0,0,1) (1;2;3) +solve[-1] [-2]
  svd (+):
    Compute SVD decomposition of selected matrices.
    Example:
      [#1] 10,10,1,1,'x==y?x+u(-0.2,0.2):0' +svd
  transpose:
    Transpose selected matrices.
    Example:
      [#1] image.jpg +transpose
  trisolve:
      [image]
    Solve tridiagonal system AX = B for selected B-vectors and specified tridiagonal A-matrix.
    Tridiagonal matrix must be stored as a 3 column vector, where 2nd column contains the
    diagonal coefficients, while 1st and 3rd columns contain the left and right coefficients.
    Example:
      [#1] (0,0,1;1,0,0;0,1,0) (1;2;3) +trisolve[-1] [-2]
  11.12. 3D Meshes
         ---------
  +3d (+):
      Shortcut for command 'add3d'.
  add3d (+):
      tx,_ty,_tz |
      [object3d] |
      (no arg)
    Shift selected 3D objects with specified displacement vector, or merge them with specified
    3D object, or merge all selected 3D objects together.
    (equivalent to shortcut command '+3d').
    Default values: 'ty=tz=0'.
    Example:
      [#1] sphere3d 10 repeat 5 { +add3d[-1] 10,{u(-10,10)},0 color3d[-1] ${-rgb} } add3d
      [#2] repeat 20 { torus3d 15,2 color3d[-1] ${-rgb} mul3d[-1] 0.5,1 if $>%2 rotate3d[-1] 0,1,0,90 fi add3d[-1] 70 add3d rotate3d[-1] 0,0,1,18 }
  animate3d:
      nb_frames>0,_step_angle_x,_step_angle_y,_step_angle_z,_zoom_factor,0<=_fake_shadow_level<=100,_[background]
    Generate 3D animation frames of rotating 3D objects.
    Frames are stacked along the z-axis (volumetric image).
    Frame size is the same as the size of the '[background]' image (or 800x800 if no background specified).
    Default values: 'step_angle_x=0', 'step_angle_y=5', 'step_angle_z=0', 'zoom_factor=1', 'fake_shadow_level=50' and
     'background=(undefined)'.
  apply_camera3d:
      pos_x,pos_y,pos_z,target_x,target_y,target_z,up_x,up_y,up_z
    Apply 3D camera matrix to selected 3D objects.
    Default values: 'target_x=0', 'target_y=0', 'target_z=0', 'up_x=0', 'up_y=-1' and 'up_z=0'.
  apply_matrix3d:
      a11,a12,a13,...,a31,a32,a33
    Apply specified 3D rotation matrix to selected 3D objects.
    Example:
      [#1] torus3d 10,1 +apply_matrix3d {mul(rot(1,0,1,-15°),[1,0,0,0,2,0,0,0,8],3)}
  array3d:
      size_x>=1,_size_y>=1,_size_z>=1,_offset_x[%],_offset_y[%],_offset_y[%]
    Duplicate a 3D object along the X,Y and Z axes.
    Default values: 'size_y=1', 'size_z=1' and 'offset_x=offset_y=offset_z=100%'.
    Example:
      [#1] torus3d 10,1 +array3d 5,5,5,110%,110%,300%
  arrow3d:
      x0,y0,z0,x1,y1,z1,_radius[%]>=0,_head_length[%]>=0,_head_radius[%]>=0
    Input 3D arrow with specified starting and ending 3D points.
    Default values: 'radius=5%', 'head_length=25%' and 'head_radius=15%'.
    Example:
      [#1] repeat 10 { a:=$>*2*pi/10 arrow3d 0,0,0,{cos($a)},{sin($a)},-0.5 } +3d
  axes3d:
      _size_x,_size_y,_size_z,_font_size>0,_label_x,_label_y,_label_z,_is_origin={ 0:No | 1:Yes }
    Input 3D axes with specified sizes along the x,y and z orientations.
    Default values: 'size_x=size_y=size_z=1', 'font_size=23', 'label_x=X', 'label_y=Y', 'label_z=Z' and 'is_origin=1'
    Example:
      [#1] axes3d ,
  boundingbox3d:
    Replace selected 3D objects by their 3D bounding boxes.
    Example:
      [#1] torus3d 100,30 +boundingbox3d +3d[-1] [-2]
  box3d:
      _size_x,_size_y,_size_z
    Input 3D box at (0,0,0), with specified geometry.
    Default values: 'size_x=1' and 'size_z=size_y=size_x'.
    Example:
      [#1] box3d 100,40,30 +primitives3d 1 color3d[-2] ${-rgb}
  c3d:
      Shortcut for command 'center3d'.
  center3d:
    Center selected 3D objects at (0,0,0).
    (equivalent to shortcut command 'c3d').
    Example:
      [#1] repeat 100 { circle3d {u(100)},{u(100)},{u(100)},2 } add3d color3d[-1] 255,0,0 +center3d color3d[-1] 0,255,0 add3d
  chainring3d:
      _nb_links>=3,_x_scale>0,_y_scale>0,_z_scale>0
    Input 3D chain ring with specified geometry.
    'nb_links' should be preferably even.
    Default values: 'nb_links=16', 'x_scale=0.5', 'y_scale=1' and 'z_scale=1'.
    Example:
      [#1] chainring3d
  circle3d:
      _x0,_y0,_z0,_radius>=0
    Input 3D circle at specified coordinates.
    Default values: 'x0=y0=z0=0' and 'radius=1'.
    Example:
      [#1] repeat 500 { a:=$>*pi/250 circle3d {cos(3*$a)},{sin(2*$a)},0,{$a/50} color3d[-1] ${-rgb},0.4 } add3d
  circles3d:
      _radius>=0,_is_outlined={ 0:No | 1:Yes }
    Convert specified 3D objects to sets of 3D circles with specified radius.
    Default values: 'radius=1' and 'is_outlined=1'.
    Example:
      [#1] image.jpg luminance rescale2d ,40 threshold 50% * 255 pointcloud3d color3d[-1] 255,255,255 circles3d 0.7
  col3d:
      Shortcut for command 'color3d'.
  color3d:
      R,_G,_B,_opacity
    Set color (and optionally opacity) of selected 3D objects.
    (equivalent to shortcut command 'col3d').
    Default value: 'B=G=R' and 'opacity=(undefined)'.
    Example:
      [#1] torus3d 100,10 repeat 7 { +rotate3d[-1] 1,0,0,20 color3d[-1] ${-rgb} } add3d
  colorcube3d:
      _is_wireframe={ 0:No | 1:Yes }
    Input 3D color cube.
    Default value: 'is_wireframe=0'.
    Example:
      [#1] colorcube3d mode3d 2 +primitives3d 1
  colorize3d:
      _color_function,_passed_images_for_color_function
    Colorize primitives of selected 3D objects, according to a specified function.
     * 'color_function' returns a G,GA,RGB or RGBA vector that can depend on variables 'x','y' and 'z', which are defined as the barycenter coordinates for
    each primitive.
     * 'passed_images_for_color_function' can be specified as a selection (e.g. '[0,2]') of images that will be inserted at the end of the image list while modifying 3D
    objects, so that the 'color_function' can have access to their content.
    Default values: 'color_function=[x,y,z]' and 'passed_images_for_color_function='.
    Example:
      [#1] torus3d 100,40,640,100 c3d n3d mul3d 256 +3d 128,128,128 sample colorful,257 colorize3d[0] "I(#-1,x,y,0)",[1]
  cone3d:
      _radius,_height,_nb_subdivisions>0
    Input 3D cone at (0,0,0), with specified geometry.
    Default value: 'radius=1','height=1' and 'nb_subdivisions=24'.
    Example:
      [#1] cone3d 10,40 +primitives3d 1 color3d[-2] ${-rgb}
  cubes3d:
      _size>=0
    Convert specified 3D objects to sets of 3D cubes with specified size.
    Default value: 'size=1'.
    Example:
      [#1] image.jpg luminance rescale2d ,40 threshold 50% * 255 pointcloud3d color3d[-1] 255,255,255 cubes3d 1
  cup3d:
      _resolution>0
    Input 3D cup object.
    Default value: 'resolution=128'.
    Example:
      [#1] cup3d ,
  curve3d:
      _"x(t)",_"y(t)",_"z(t)",_"r(t)",_resolution>1,_tmin,_tmax,_nb_sides>=0,_cap_style={ 0:Open | 1 |
      Capped | 2 |
      Closed }
    Input 3D curve with specified parameterization.
    If 'r(t)==0' or 'nb_sides<3', the generated 3D object is composed of segments only.
    Default values: 'x(t)=cos(2*pi*t)', 'y(t)=sin(2*pi*t)', 'z(t)=t', 'r(t)=0.025', 'resolution=128', 'tmin=0', 'tmax=1',
     'nb_sides=16' and 'cap_style=1'.
    Example:
      [#1] curve3d ,
  cylinder3d:
      _radius,_height,_nb_subdivisions>0
    Input 3D cylinder at (0,0,0), with specified geometry.
    Default value: 'radius=1','height=1' and 'nb_subdivisions=24'.
    Example:
      [#1] cylinder3d 10,40 +primitives3d 1 color3d[-2] ${-rgb}
  delaunay3d:
    Generate 3D Delaunay triangulations from selected images.
    One assumes that the selected input images are binary images containing the set of points to mesh.
    The output 3D object is a mesh composed of non-oriented triangles.
    Example:
      [#1] 500,500 noise 0.05,2 eq 1 * 255 +delaunay3d color3d[1] 255,128,0 dilate_circ[0] 5 to_rgb[0] +object3d[0] [1],0,0,0,1,1 max[-1] [0]
  depthmap3d:
      [object3d],_x[%],_y[%],_z_,is_double_sided={ 0:No | 1:Yes },_is_zbuffer={ 0:No | 1:Yes },_focale
    Draw depth map of specified 3D object on selected images.
    Default values: 'x=y=50%', 'z=0' and 'is_zbuffer=1'. All other arguments take their default values from the 3D environment variables.
    Example:
      [#1] gmic3d center3d normalize3d rotate3d 0,1,0,45 mul3d 360 400,400 depthmap3d[-1] [-2] snapshot3d[-2] 400
  distribution3d:
    Get 3D color distribution of selected images.
    Example:
      [#1] image.jpg distribution3d colorcube3d primitives3d[-1] 1 add3d
  /3d (+):
      Shortcut for command 'div3d'.
  div3d (+):
      factor |
      factor_x,factor_y,_factor_z
    Scale selected 3D objects isotropically or anisotropically, with the inverse of specified
    factors.
    (equivalent to shortcut command '/3d').
    Default value: 'factor_z=1'.
    Example:
      [#1] torus3d 5,2 repeat 5 { +add3d[-1] 12,0,0 div3d[-1] 1.2 color3d[-1] ${-rgb} } add3d
  db3d:
      Shortcut for command 'double3d'.
  double3d:
      _is_double_sided={ 0:No | 1:Yes }
    Enable/disable double-sided mode for 3D rendering.
    (equivalent to shortcut command 'db3d').
    Default value: 'is_double_sided=1'.
    Example:
      [#1] mode3d 1 repeat 2 { torus3d 100,30 rotate3d[-1] 1,1,0,60 double3d $> snapshot3d[-1] 400 }
  elevation3d:
      { z-factor | [elevation_map] | 'formula' },base_height={ -1 | >=0 } |
      (no arg)
    Generate 3D elevation of selected images, opt. with a specified elevation map.
    When invoked with (no arg) or 'z-factor', the elevation map is computed as the pointwise L2 norm of the
    pixel values. Otherwise, the elevation map is taken from the specified image or formula.
    Example:
      [#1] image.jpg +blur 5 elevation3d. 0.75
      [#2] 128,128,1,3,u(255) plasma 10,3 blur 4 sharpen 10000 n 0,255 elevation3d[-1] 'X=(x-64)/6;Y=(y-64)/6;-100*exp(-(X^2+Y^2)/30)*abs(cos(X)*sin(Y))'
  empty3d:
    Input empty 3D object.
    Example:
      [#1] empty3d
  extract_textures3d:
    Extract texture data from selected 3D objects.
    Example:
      [#1] image.jpg imagesphere3d 10,10 +extract_textures3d
  extrude3d:
      _depth>0,_resolution>0,_smoothness[%]>=0
    Generate extruded 3D object from selected binary XY-profiles.
    Default values: 'depth=16', 'resolution=1024' and 'smoothness=0.5%'.
    Example:
      [#1] image.jpg threshold 50% extrude3d 16
  f3d:
      Shortcut for command 'focale3d'.
  focale3d:
      focale
    Set 3D focale.
    (equivalent to shortcut command 'f3d').
    Set 'focale' to 0 to enable parallel projection (instead of perspective).
    Set negative 'focale' will disable 3D sprite zooming.
    Default value: 'focale=700'.
    Example:
      [#1] repeat 5 { torus3d 100,30 rotate3d[-1] 1,1,0,60 focale3d {$<*90} snapshot3d[-1] 400 } remove[0]
  fov3d:
      fov_angle>=0,_image_resolution>0
    Set 3D focale to match specified field of vision angle (in degree) for rendering a 3D object in an image with specified resolution.
    Return corresponding value of the focale in status.
    Default value: 'fov_angle=45' and 'image_size=max(w,h)' (max size of the latest image).
  gaussians3d:
      _size>0
    Convert selected 3D objects into set of 3D gaussian-shaped sprites.
    Example:
      [#1] image.jpg rescale2d ,32 distribution3d gaussians3d 20 colorcube3d primitives3d[-1] 1 +3d
  gmic3d:
    Input a 3D G'MIC logo.
    Example:
      [#1] gmic3d +primitives3d 1
  gyroid3d:
      _resolution>0,_zoom
    Input 3D gyroid at (0,0,0), with specified resolution.
    Default values: 'resolution=32' and 'zoom=5'.
    Example:
      [#1] gyroid3d 48 +primitives3d 1
  histogram3d:
    Get 3D color histogram of selected images.
    Example:
      [#1] image.jpg rescale2d 64 histogram3d circles3d 3 opacity3d. 0.75 colorcube3d primitives3d[-1] 1 add3d
  image6cube3d:
    Generate 3D mapped cubes from 6-sets of selected images.
    Example:
      [#1] image.jpg animate flower,"30,0","30,5",6 image6cube3d
  imageblocks3d:
      _maximum_elevation,_smoothness[%]>=0
    Generate 3D blocks from selected images.
    Transparency of selected images is taken into account.
    Default values: 'maximum_elevation=10' and 'smoothness=0'.
    Example:
      [#1] image.jpg rescale2d ,32 imageblocks3d -20 mode3d 3
  imagecube3d:
    Generate 3D mapped cubes from selected images.
    Example:
      [#1] image.jpg imagecube3d
  imageplane3d:
    Generate 3D mapped planes from selected images.
    Example:
      [#1] image.jpg imageplane3d
  imagepyramid3d:
    Generate 3D mapped pyramids from selected images.
    Example:
      [#1] image.jpg imagepyramid3d
  imagerubik3d:
      _xy_tiles>=1,0<=xy_shift<=100,0<=z_shift<=100
    Generate 3D mapped rubik's cubes from selected images.
    Default values: 'xy_tiles=3', 'xy_shift=5' and 'z_shift=5'.
    Example:
      [#1] image.jpg imagerubik3d ,
  imagesphere3d:
      _resolution1>=3,_resolution2>=3
    Generate 3D mapped sphere from selected images.
    Default values: 'resolution1=32' and 'resolutions2=16'.
    Example:
      [#1] image.jpg imagesphere3d 32,16
  isoline3d (+):
      isovalue[%] |
      'formula',value,_x0,_y0,_x1,_y1,_size_x[%]>0,_size_y[%]>0
    Extract 3D isolines with specified value from selected images or from specified formula.
    Default values: 'x0=y0=-3', 'x1=y1=3' and 'size_x=size_y=256'.
    Example:
      [#1] image.jpg blur 1 isoline3d 50%
      [#2] isoline3d 'X=x-w/2;Y=y-h/2;(X^2+Y^2)%20',10,-10,-10,10,10
  isosurface3d (+):
      isovalue[%] |
      'formula',value,_x0,_y0,_z0,_x1,_y1,_z1,_size_x[%]>0,_size_y[%]>0,_size_z[%]>0
    Extract 3D isosurfaces with specified value from selected images or from specified formula.
    Default values: 'x0=y0=z0=-3', 'x1=y1=z1=3' and 'size_x=size_y=size_z=32'.
    Example:
      [#1] image.jpg rescale2d ,128 luminance threshold 50% expand z,2 blur 1 isosurface3d 50% mul3d 1,1,30
      [#2] isosurface3d 'x^2+y^2+abs(z)^abs(4*cos(x*y*z*3))',3
  label3d:
      "text",font_height>=0,_opacity,_color1,...
    Generate 3D text label.
    Default values: 'font_height=13', 'opacity=1' and 'color=255,255,255'.
  label_points3d:
      _label_size>0,_opacity
    Add a numbered label to all vertices of selected 3D objects.
    Default values: 'label_size=13' and 'opacity=0.8'.
    Example:
      [#1] torus3d 100,40,6,6 label_points3d 23,1 mode3d 1
  lathe3d:
      _resolution>0,_smoothness[%]>=0,_max_angle>=0
    Generate 3D object from selected binary XY-profiles.
    Default values: 'resolution=128', 'smoothness=0.5%' and 'max_angle=361'.
    Example:
      [#1] 300,300 rand -1,1 blur 40 sign normalize 0,255 lathe3d ,
  l3d (+):
      Shortcut for command 'light3d'.
  light3d (+):
      position_x,position_y,position_z |
      [texture] |
      (no arg)
    Set the light coordinates or the light texture for 3D rendering.
    (equivalent to shortcut command 'l3d').
    (no arg) resets the 3D light to default.
    Example:
      [#1] torus3d 100,30 specs3d 1.2 repeat 5 { light3d {$>*100},0,-300 +snapshot3d[0] 400 } remove[0]
  line3d:
      x0,y0,z0,x1,y1,z1
    Input 3D line at specified coordinates.
    Example:
      [#1] repeat 100 { a:=$>*pi/50 line3d 0,0,0,{cos(3*$a)},{sin(2*$a)},0 color3d. ${-rgb} } add3d
  lines3d:
      _length>=0
    Convert specified 3D objects to sets of 3D horizontal segments with specified length.
    Default values: 'length=1'.
    Example:
      [#1] torus3d 100,40 +lines3d 20
  lissajous3d:
      resolution>1,a,A,b,B,c,C
    Input 3D lissajous curves 'x(t)=sin(a*t+A*2*pi)', 'y(t)=sin(b*t+B*2*pi)', 'z(t)=sin(c*t+C*2*pi)'.
    Default values: 'resolution=1024', 'a=2', 'A=0', 'b=1', 'B=0', 'c=0' and 'C=0'.
    Example:
      [#1] lissajous3d ,
  m3d:
      Shortcut for command 'mode3d'.
  mode3d:
      _mode
    Set static 3D rendering mode.
    (equivalent to shortcut command 'm3d').
    'mode' can be { -1:Bounding-box | 0:Dots | 1:Wireframe | 2:Flat | 3:Flat-shaded | 4:Gouraud-shaded | 5:Phong-shaded }.
    Bounding-box mode ('mode==-1') is active only for the interactive 3D viewer.
    Default value: 'mode=4'.
    Example:
      [#1] (0,1,2,3,4,5) repeat w { torus3d 100,30 rotate3d[-1] 1,1,0,60 mode3d {0,@$>} snapshot3d[-1] 300 } remove[0]
  md3d:
      Shortcut for command 'moded3d'.
  moded3d:
      _mode
    Set dynamic 3D rendering mode for interactive 3D viewer.
    (equivalent to shortcut command 'md3d').
    'mode' can be { -1:Bounding-box | 0:Dots | 1:Wireframe | 2:Flat | 3:Flat-shaded | 4:Gouraud-shaded | 5:Phong-shaded }.
    Default value: 'mode=-1'.
  *3d (+):
      Shortcut for command 'mul3d'.
  mul3d (+):
      factor |
      factor_x,factor_y,_factor_z
    Scale selected 3D objects isotropically or anisotropically, with specified factors.
    (equivalent to shortcut command '*3d').
    Default value: 'factor_z=1'.
    Example:
      [#1] torus3d 5,2 repeat 5 { +add3d[-1] 10,0,0 mul3d[-1] 1.2 color3d[-1] ${-rgb} } add3d
  mt3d:
      Shortcut for command 'multithreaded3d'.
  multithreaded3d:
      _is_multi_threaded_rendering={ 0:No | 1:Yes }
    Enable/disable multi-threaded 3D rendering.
    (equivalent to shortcut command 'mt3d').
    Default value: 'is_multithreaded_rendering=0'.
  n3d:
      Shortcut for command 'normalize3d'.
  normalize3d:
    Normalize selected 3D objects to unit size.
    (equivalent to shortcut command 'n3d').
    Example:
      [#1] repeat 100 { circle3d {u(3)},{u(3)},{u(3)},0.1 } add3d color3d[-1] 255,0,0 +normalize3d[-1] color3d[-1] 0,255,0 add3d
  normalmap3d:
      [object3d],_x[%],_y[%],_z_,is_double_sided={ 0:No | 1:Yes },_is_zbuffer={ 0:No | 1:Yes },_focale
    Draw normal map of specified 3D object on selected images.
    Default values: 'x=y=50%', 'z=0' and 'is_zbuffer=1'. All other arguments take their default values from the 3D environment variables.
    Example:
      [#1] gmic3d center3d normalize3d rotate3d 0,1,0,45 mul3d 360 400,400,1,3 normalmap3d[-1] [-2] snapshot3d[-2] 400
  normals3d:
      output_mode={ 0:vertices | 1:primitives | 2:vertices+primitives }.
    Estimate normals of selected 3D meshes, for vertices, primitives, or both.
    This command returns one of two images, having 6 channels : [ x,y,z,u,v,w ], where (x,y,z) is the origin of the normal vector, and (u,v,z) is the unit normal vector.
    Default value: 'mode=2'.
  o3d:
      Shortcut for command 'opacity3d'.
  j3d (+):
      Shortcut for command 'object3d'.
  object3d (+):
      [object3d],_x[%],_y[%],_z,_opacity,_rendering_mode,_is_double_sided={ 0:No | 1:Yes },_is_zbuffer={ 0:No | 1:Yes },_focale,_light_x,_light_y,_light_z,_specular_lightness,
        _specular_shininess,_is_multithreaded_rendering={ 0:No | 1:Yes }
    Draw specified 3D object on selected images.
    (equivalent to shortcut command 'j3d').
    'rendering_mode' can be { 0:Dots | 1:Wireframe | 2:Flat | 3:Flat-shaded | 4:Gouraud-shaded | 5:Phong-shaded }.
    Default values: 'x=y=50%', 'z=0', 'opacity=1' and 'is_zbuffer=1'. All other arguments take their default values
    from the 3D environment variables.
    Example:
      [#1] image.jpg torus3d 100,10 cone3d 30,-120 add3d[-2,-1] rotate3d. 1,1,0,60 object3d[0] [-1],50%,50% keep[0]
  opacity3d:
      opacity
    Set opacity of selected 3D objects.
    (equivalent to shortcut command 'o3d').
    Example:
      [#1] torus3d 100,10 repeat 7 { +rotate3d[-1] 1,0,0,20 opacity3d[-1] {u} } add3d
  parametric3d:
      _x(a,b),_y(a,b),_z(a,b),_amin,_amax,_bmin,_bmax,_res_a>0,_res_b>0,_res_x>0,_res_y>0,_res_z>0,_smoothness>=0,_isovalue>=0
    Input 3D object from specified parametric surface '(a,b) ⟶ (x(a,b),y(a,b),z(a,b))'.
    Default values: 'x=(2+cos(b))*sin(a)', 'y=(2+cos(b))*cos(a)', 'c=sin(b)', 'amin=-pi', 'amax=pi', 'bmin=-pi', 'bmax=pi',
     'res_a=512', 'res_b=res_a', 'res_x=64', 'res_y=res_x', 'res_z=res_y', 'smoothness=2%' and 'isovalue=10%'.
    Example:
      [#1] parametric3d ,
  pca_patch3d:
      _patch_size>0,_M>0,_N>0,_normalize_input={ 0:No | 1:Yes },_normalize_output={ 0:No | 1:Yes },_lambda_xy
    Get 3D patch-pca representation of selected images.
    The 3D patch-pca is estimated from M patches on the input image, and displayed as a cloud of N 3D points.
    Default values: 'patch_size=7', 'M=1000', 'N=3000', 'normalize_input=1', 'normalize_output=0', and 'lambda_xy=0'.
    Example:
      [#1] image.jpg pca_patch3d 7
  plane3d:
      _size_x,_size_y,_nb_subdivisions_x>0,_nb_subdisivions_y>0
    Input 3D plane at (0,0,0), with specified geometry.
    Default values: 'size_x=1', 'size_y=size_x' and 'nb_subdivisions_x=nb_subdivisions_y=24'.
    Example:
      [#1] plane3d 50,30 +primitives3d 1 color3d[-2] ${-rgb}
  point3d:
      x0,y0,z0
    Input 3D point at specified coordinates.
    Example:
      [#1] repeat 1000 { a:=$>*pi/500 point3d {cos(3*$a)},{sin(2*$a)},0 color3d[-1] ${-rgb} } add3d
  pointcloud3d:
    Convert selected planar or volumetric images to 3D point clouds.
    Example:
      [#1] image.jpg luminance rescale2d ,100 threshold 50% mul 255 pointcloud3d color3d[-1] 255,255,255
  pose3d:
      p1,...,p12
    Apply 3D pose matrix to selected 3D objects.
    Example:
      [#1] torus3d 100,20 pose3d 0.152437,1.20666,-0.546366,0,-0.535962,0.559129,1.08531,0,1.21132,0.0955431,0.548966,0,0,0,-206,1 snapshot3d 400
  p3d:
      Shortcut for command 'primitives3d'.
  primitives3d:
      mode
    Convert primitives of selected 3D objects.
    (equivalent to shortcut command 'p3d').
    'mode' can be { 0:Points | 1:Outlines | 2:Non-textured }.
    Example:
      [#1] sphere3d 30 primitives3d 1 torus3d 50,10 color3d[-1] ${-rgb} add3d
  projections3d:
      _x[%],_y[%],_z[%],_is_bounding_box={ 0:No | 1:Yes },nb_subdivisions>0
    Generate 3D xy,xz,yz projection planes from specified volumetric images.
    Default values: 'x=y=z=50%', 'is_bounding_box=1' and 'nb_subdividions=5'
  pyramid3d:
      width,height
    Input 3D pyramid at (0,0,0), with specified geometry.
    Example:
      [#1] pyramid3d 100,-100 +primitives3d 1 color3d[-2] ${-rgb}
  quadrangle3d:
      x0,y0,z0,x1,y1,z1,x2,y2,z2,x3,y3,z3
    Input 3D quadrangle at specified coordinates.
    Example:
      [#1] quadrangle3d -10,-10,10,10,-10,10,10,10,10,-10,10,10 repeat 10 { +rotate3d[-1] 0,1,0,30 color3d[-1] ${-rgb},0.6 } add3d mode3d 2
  random3d:
      nb_points>=0
    Input random 3D point cloud in [0,1]^3.
    Example:
      [#1] random3d 100 circles3d 0.1 opacity3d 0.5
  rv3d:
      Shortcut for command 'reverse3d'.
  reverse3d:
    Reverse primitive orientations of selected 3D objects.
    (equivalent to shortcut command 'rv3d').
    Example:
      [#1] torus3d 100,40 +reverse3d
  r3d (+):
      Shortcut for command 'rotate3d'.
  rotate3d (+):
      u,v,w,angle
    Rotate selected 3D objects around specified axis with specified angle (in deg.).
    (equivalent to shortcut command 'r3d').
    Example:
      [#1] torus3d 100,10 repeat 7 { +rotate3d[-1] 1,0,0,20 } add3d
  rotation3d:
      u,v,w,angle
    Input 3x3 rotation matrix with specified axis and angle (in deg).
    Example:
      [#1] rotation3d 1,0,0,0 rotation3d 1,0,0,90 rotation3d 1,0,0,180
  sierpinski3d:
      _recursion_level>=0,_width,_height
    Input 3D Sierpinski pyramid.
    Example:
      [#1] sierpinski3d 3,100,-100 +primitives3d 1 color3d[-2] ${-rgb}
  size3d:
    Return bounding box size of the last selected 3D object.
  skeleton3d:
      _metric,_frame_type={ 0:Squares | 1:Diamonds | 2:Circles | 3:Auto },_skeleton_opacity,_frame_opacity,_is_frame_wireframe={ 0:No | 1:Yes }
    Build 3D skeletal structure object from 2d binary shapes located in selected images.
    'metric' can be { 0:Chebyshev | 1:Manhattan | 2:Euclidean }.
    Default values: 'metric=2', 'bones_type=3', 'skeleton_opacity=1' and 'frame_opacity=0.1'.
    Example:
      [#1] shape_cupid 480 +skeleton3d ,
  snapshot3d:
      _size>0,_zoom>=0,_backgroundR,_backgroundG,_backgroundB,_backgroundA,_fov_angle>=0 |
      [background_image],zoom>=0,_fov_angle>=0
    Take 2D snapshots of selected 3D objects.
    Set 'zoom' to 0 to disable object auto-scaling.
    Default values: 'size=1024', 'zoom=1', '[background_image]=(default)' and 'fov_angle=45'.
    Example:
      [#1] torus3d 100,20 rotate3d 1,1,0,60 snapshot3d 400,1.2,128,64,32
      [#2] torus3d 100,20 rotate3d 1,1,0,60 sample ? +snapshot3d[0] [1],1.2
  sl3d:
      Shortcut for command 'specl3d'.
  specl3d:
      value>=0
    Set lightness of 3D specular light.
    (equivalent to shortcut command 'sl3d').
    Default value: 'value=0.25'.
    Example:
      [#1] (0,0.3,0.6,0.9,1.2) repeat w { torus3d 100,30 rotate3d[-1] 1,1,0,60 color3d[-1] 255,0,0 specl3d {0,@$>} snapshot3d[-1] 400 } remove[0]
  ss3d:
      Shortcut for command 'specs3d'.
  specs3d:
      value>=0
    Set shininess of 3D specular light.
    (equivalent to shortcut command 'ss3d').
    Default value: 'value=0.1'.
    Example:
      [#1] (0,0.3,0.6,0.9,1.2) repeat w { torus3d 100,30 rotate3d[-1] 1,1,0,60 color3d[-1] 255,0,0 specs3d {0,@$>} snapshot3d[-1] 400 } remove[0]
  sphere3d:
      radius,_nb_recursions!=0 |
      radius,_nb_phi>=3,_nb_theta>=3
    Input 3D sphere at (0,0,0), with specified geometry.
     * If 2 arguments are specified:
       - If 'nb_recursions>0', the sphere is generated using recursive subdivisions of an icosahedron.
       - If 'nb_recursions<0', the sphere is generated using recursive subdividions of a cube.
     * If 3 arguments are specified, the sphere is generated using spherical coordinates discretization.
    Default value: 'nb_recursions=3'.
    Example:
      [#1] sphere3d 100 +primitives3d 1 color3d[-2] ${-rgb}
  spherical3d:
      "radius_function(phi,theta)",_nb_recursions!=0 |
      "radius_function(phi,theta)",_nb_phi>=3,_nb_theta>=3
    Input 3D spherical object at (0,0,0), with specified geometry.
    Second and third arguments are the same as in command sphere3d.
    Default values: 'nb_recursions=5'.
    Example:
      [#1] spherical3d "abs(1+0.5*cos(3*phi)*sin(4*theta))" +primitives3d 1
  spline3d:
      x0[%],y0[%],z0[%],u0[%],v0[%],w0[%],x1[%],y1[%],z1[%],u1[%],v1[%],w1[%],_nb_vertices>=2
    Input 3D spline with specified geometry.
    Default values: 'nb_vertices=128'.
    Example:
      [#1] repeat 100 { spline3d {u},{u},{u},{u},{u},{u},{u},{u},{u},{u},{u},{u},128 color3d[-1] ${-rgb} } box3d 1 primitives3d[-1] 1 add3d
  s3d:
      Shortcut for command 'split3d'.
  split3d:
    Split selected 3D objects into feature vectors :
    { header, sizes, vertices, primitives, colors, opacities }.
    (equivalent to shortcut command 's3d').
    To recreate the 3D object, append all produced images along the y-axis (with command 'append y').
    Example:
      [#1] box3d 100 +split3d
  sprite3d:
    Convert selected images as 3D sprites.
    Selected images with alpha channels are managed.
    Example:
      [#1] image.jpg sprite3d
  sprites3d:
      [sprite],_sprite_has_alpha_channel={ 0:No | 1:Yes }
    Convert selected 3D objects as a sprite cloud.
    Set 'sprite_has_alpha_channel' to 1 to make the last channel of the selected sprite be a transparency mask.
    Default value: 'mask_has_alpha_channel=0'.
    Example:
      [#1] torus3d 100,20 image.jpg rescale2d[-1] ,64 100%,100% gaussian[-1] 30%,30% *[-1] 255 append[-2,-1] c +sprites3d[0] [1],1 display_rgba[-2]
  star3d:
      _nb_branches>0,0<=_thickness<=1
    Input 3D star at position '(0,0,0)', with specified geometry.
    Default values: 'nb_branches=5' and 'thickness=0.38'.
    Example:
      [#1] star3d , +primitives3d 1 color3d[-2] ${-rgb}
  streamline3d (+):
      x[%],y[%],z[%],_L>=0,_dl>0,_interpolation,_is_backward={ 0:No | 1:Yes },_is_oriented={ 0:No | 1:Yes } |
      'formula',x,y,z,_L>=0,_dl>0,_interpolation,_is_backward={ 0:No | 1:Yes },_is_oriented={ 0:No | 1:Yes }
    Extract 3D streamlines from selected vector fields or from specified formula.
    'interpolation' can be { 0:Nearest integer | 1:1st-order | 2:2nd-order | 3:4th-order }.
    Default values: 'dl=0.1', 'interpolation=2', 'is_backward=0' and 'is_oriented=0'.
    Example:
      [#1] 100,100,100,3 rand -10,10 blur 3 repeat 300 { +streamline3d[0] {u(100)},{u(100)},{u(100)},1000,1,1 color3d[-1] ${-rgb} } remove[0] box3d 100 primitives3d[-1] 1 add3d
  -3d (+):
      Shortcut for command 'sub3d'.
  sub3d (+):
      tx,_ty,_tz
    Shift selected 3D objects with the opposite of specified displacement vector.
    (equivalent to shortcut command '3d').
    Default values: 'ty=tz=0'.
    Example:
      [#1] sphere3d 10 repeat 5 { +sub3d[-1] 10,{u(-10,10)},0 color3d[-1] ${-rgb} } add3d
  subdivide3d:
    Subdivide primitives of selected 3D objects.
  superformula3d:
      resolution>1,m>=1,n1,n2,n3
    Input 2D superformula curve as a 3D object.
    Default values: 'resolution=1024', 'm=8', 'n1=1', 'n2=5' and 'n3=8'.
    Example:
      [#1] superformula3d ,
  surfels3d:
      0<=_left_right_attenuation<=1,0<=_top_bottom_attenuation<=1,0<=_closer_further_attenuation<=1
    Convert selected images to 3D objects composed of 3D surfels (or 2D edgels for 2D images).
    The binary shape is composed of all non-zero voxels.
    The resulting 3D object is colored according to the color of non zero voxels.
    Default values: 'left_right_attenuation=1', 'top_bottom_attenuation=1' and 'closer_further_attenuation=1'.
    Example:
      [#1] 100,100,100 = 1,40%,40%,40% = 1,60%,60%,60% distance 1 lt 30% blur 3 gt 50% surfels3d 0.5,0.75,1
  tensors3d:
      _radius_factor>=0,_shape={ 0:Box | >=N:Ellipsoid },_radius_min>=0
    Generate 3D tensor fields from selected images.
    when 'shape'>0, it gives the ellipsoid shape precision.
    Default values: 'radius_factor=1', 'shape=2' and 'radius_min=0.05'.
    Example:
      [#1] 7,7,7,9,"U = unitnorm([x,y,z] - int([w,h,d]/2)); mul(U,U,3) + 0.3*eye(3)" tensors3d 0.8
  text_pointcloud3d:
      _"text1",_"text2",_smoothness
    Input 3D text pointcloud from the two specified strings.
    Default values: 'text1="text1"', 'text2="text2"' and 'smoothness=1'.
    Example:
      [#1] text_pointcloud3d "G'MIC","Rocks!"
  text3d:
      text,_{ font_height>=0 | custom_font },_depth>0,_smoothness
    Input a 3D text object from specified text.
    Default values: 'font_height=53', 'depth=10' and 'smoothness=1.5'.
    Example:
      [#1] text3d "G'MIC as aD logo!"
  t3d:
      Shortcut for command 'texturize3d'.
  texturize3d:
      [ind_texture],_[ind_coords]
    Texturize selected 3D objects with specified texture and coordinates.
    (equivalent to shortcut command 't3d').
    When '[ind_coords]' is omitted, default XY texture projection is performed.
    Default value: 'ind_coords=(undefined)'.
    Example:
      [#1] image.jpg torus3d 100,30 texturize3d[-1] [-2] keep[-1]
  torus3d:
      _radius1,_radius2,_nb_subdivisions1>2,_nb_subdivisions2>2
    Input 3D torus at (0,0,0), with specified geometry.
    Default values: 'radius1=1', 'radius2=0.3', 'nb_subdivisions1=24' and 'nb_subdivisions2=12'.
    Example:
      [#1] torus3d 10,3 +primitives3d 1 color3d[-2] ${-rgb}
  triangle3d:
      x0,y0,z0,x1,y1,z1,x2,y2,z2
    Input 3D triangle at specified coordinates.
    Example:
      [#1] repeat 100 { a:=$>*pi/50 triangle3d 0,0,0,0,0,3,{cos(3*$a)},{sin(2*$a)},0 color3d[-1] ${-rgb} } add3d
  volume3d:
    Transform selected 3D volumetric images as 3D parallelepipedic objects.
    Example:
      [#1] image.jpg animate blur,0,5,30 append z volume3d
  voxelize3d:
      _max_resolution>0,_fill_interior={ 0:No | 1:Yes },_preserve_colors={ 0:No | 1:Yes }
    Convert selected 3D objects as 3D volumetric images of binary voxels, using 3D mesh rasterization.
    Default values: 'max_resolution=128', 'fill_interior=1' and 'preserve_colors=0'.
  weird3d:
      _resolution>0
    Input 3D weird object at (0,0,0), with specified resolution.
    Default value: 'resolution=32'.
    Example:
      [#1] weird3d 48 +primitives3d 1 color3d[-2] ${-rgb}
  11.13. Flow Control
         ------------
  ap:
      Shortcut for command 'apply_parallel'.
  apply_parallel:
      "command"
    Apply specified command on each of the selected images, by parallelizing it for all image of the list.
    (equivalent to shortcut command 'ap').
    Example:
      [#1] image.jpg +mirror x +mirror y apply_parallel "blur 3"
  apc:
      Shortcut for command 'apply_parallel_channels'.
  apply_parallel_channels:
      "command"
    Apply specified command on each of the selected images, by parallelizing it for all channel
    of the images independently.
    (equivalent to shortcut command 'apc').
    Example:
      [#1] image.jpg apply_parallel_channels "blur 3"
  apo:
      Shortcut for command 'apply_parallel_overlap'.
  apply_parallel_overlap:
      "command",overlap[%],nb_threads={ 0:Auto | 1 | 2 | 4 | 8 | 16 }
    Apply specified command on each of the selected images, by parallelizing it on 'nb_threads'
    overlapped sub-images.
    (equivalent to shortcut command 'apo').
    'nb_threads' must be a power of 2.
    Default values: 'overlap=0','nb_threads=0'.
    Example:
      [#1] image.jpg +apply_parallel_overlap "smooth 500,0,1",1
  at:
      Shortcut for command 'apply_tiles'.
  apply_tiles:
      "command",_tile_width[%]>0,_tile_height[%]>0,_tile_depth[%]>0,_overlap_width[%]>=0,_overlap_height[%]>=0,_overlap_depth[%]>=0,_boundary_conditions={ 0:Dirichlet | 1:Neumann |
        2:Periodic | 3:Mirror }
    Apply specified command on each tile (neighborhood) of the selected images, eventually with overlapping tiles.
    (equivalent to shortcut command 'at').
    Default values: 'tile_width=tile_height=tile_depth=10%','overlap_width=overlap_height=overlap_depth=0' and 'boundary_conditions=1'.
    Example:
      [#1] image.jpg +equalize[0] 256 +apply_tiles[0] "equalize 256",16,16,1,50%,50%
  apply_timeout:
      "command",_timeout={ 0:No timeout | >0:With specified timeout (in seconds) }
    Apply a command with a timeout.
    Set variable '$_is_timeout' to '1' if timeout occurred, '0' otherwise.
    Default value: 'timeout=20'.
  check (+):
      condition
    Evaluate specified condition and display an error message if evaluated to false.
  check3d (+):
      _is_full_check={ 0:No | 1:Yes }
    Check validity of selected 3D vector objects, and display an error message
    if one of the selected images is not a valid 3D vector object.
    Full 3D object check is slower but more precise.
    Default value: 'is_full_check=1'.
  continue (+):
    Go to end of current 'do...while', 'for...done', 'foreach...done', 'local...done' or 'repeat...done' block.
    Example:
      [#1] image.jpg repeat 10 blur 1 if 1==1 continue fi deform 10 done
  break (+):
    Break current 'do...while', 'for...done', 'foreach...done', 'local...done' or 'repeat...done' block.
    Example:
      [#1] image.jpg repeat 10 blur 1 if 1==1 break fi deform 10 done
  do (+):
    Start a 'do...while' block.
    Example:
      [#1] image.jpg luminance i:=ia+2 do set 255,{u(100)}%,{u(100)}% while ia<$i
  done (+):
    End a 'for/foreach/local/repeat...done' block, and go to associated 'for/foreach/repeat' if iterations remain.
    (equivalent to shortcut command '}').
  elif (+):
      condition
    Start a 'elif...[else]...fi' block if previous 'if' was not verified
    and test if specified condition holds
    'condition' is a mathematical expression, whose evaluation is interpreted as { 0:False | other:True }..
    Tutorial: https://gmic.eu/tutorial/iffi
  else (+):
    Execute following commands if previous 'if' or 'elif' conditions failed.
    Tutorial: https://gmic.eu/tutorial/iffi
  fi (+):
    End a 'if...[elif]...[else]...fi' block.
    (equivalent to shortcut command 'fi').
    Tutorial: https://gmic.eu/tutorial/iffi
  error (+):
      message
    Print specified error message on the standard error (stderr) and exit interpreter, except
    if error is caught by a 'onfail' command.
    Command selection (if any) stands for displayed call stack subset instead of image indices.
  eval (+):
      expression
    Evaluate specified math expression.
     * If no image selection is specified, the expression is evaluated only once and its result is set to status.
     * If image selection is specified, the expression is evaluated for all pixel values of the selected images. Status is unchanged. In this setting, 'eval' is similar to
    fill without assigning the image values.
  x (+):
      Shortcut for command 'exec'.
  exec (+):
      _is_verbose={ 0:No | 1:Yes },"command"
    Execute external command using a system call.
    The status value is then set to the error code returned by the system call.
    If 'is_verbose=1', the executed command is allowed to output on stdout/stderr.
    (equivalent to shortcut command 'x').
    Default value: 'is_verbose=1'.
  xo:
      Shortcut for command 'exec_out'.
  exec_out:
      _mode,"command"
    Execute external command using a system call, and return resulting 'stdout' and/or 'stderr'.
    'mode' can be { 0:Stdout | 1:Stderr | 2:Stdout+stderr }.
  for (+):
      condition
    Start a 'for...done' block.
    Example:
      [#1] image.jpg rescale2d ,32 400,400,1,3 x=0 for $x<400 image[1] [0],$x,$x x+=40 done
  foreach (+):
    Start a 'foreach...done' block, that iterates over all images in the selection, with a separate local environment for each one.
    Example:
      [#1] sample colorful,earth,duck,dog foreach[^2] +blur 10 sub normalize 0,255 done
  if (+):
      condition
    Start a 'if...[elif]...[else]...fi' block and test if specified condition holds.
    'condition' is a mathematical expression, whose evaluation is interpreted as { 0:False | other:True }.
    Example:
      [#1] image.jpg if ia<64 add 50% elif ia<128 add 25% elif ia<192 sub 25% else sub 50% fi cut 0,255
    Tutorial: https://gmic.eu/tutorial/iffi
  l (+):
      Shortcut for command 'local'.
  local (+):
    Start a 'local...[onfail]...done' block, with selected images.
    (equivalent to shortcut command 'l').
    Example:
      [#1] image.jpg local[] 300,300,1,3 rand[0] 0,255 blur 4 sharpen 1000 done
      [#2] image.jpg +local repeat 3 { deform 20 } done
    Tutorial: https://gmic.eu/oldtutorial/_local
  noarg (+):
    Used in a custom command, 'noarg' tells the command that its argument list have not been used
    finally, and so they must be evaluated next in the G'MIC pipeline, just as if the custom
    command takes no arguments at all.
    Use this command to write a custom command which can decide if it takes arguments or not.
  onfail (+):
    Execute following commands when an error is encountered in the body of the 'local...done' block.
    The status value is set with the corresponding error message.
    Example:
      [#1] image.jpg +local blur -3 onfail mirror x done
  parallel (+):
      _wait_threads,"command_1","command_2",...
    Execute specified commands in parallel, each in a different thread.
    Parallel threads share the list of images.
    'wait_threads' can be { 0:When current environment ends | 1:Immediately }.
    Default value: 'wait_threads=1'.
    Example:
      [#1] image.jpg [0] parallel "blur[0] 3","mirror[1] c"
  progress (+):
      0<=value<=100 |
      -1
    Set the progress index of the current processing pipeline.
    This command is useful only when G'MIC is used by an embedding application.
  q (+):
      Shortcut for command 'quit'.
  quit (+):
    Quit G'MIC interpreter.
    (equivalent to shortcut command 'q').
  repeat (+):
      nb_iterations
    Start 'nb_iterations' iterations of a 'repeat...done' block.
    'nb_iterations' is a mathematical expression that will be evaluated.
    Example:
      [#1] image.jpg split y repeat $! n=$> shift[$n] $<,0,0,0,2 done append y
      [#2] image.jpg mode3d 2 repeat 4 imagecube3d rotate3d 1,1,0,40 snapshot3d 400,1.4 done
    Tutorial: https://gmic.eu/oldtutorial/_repeat
  return (+):
    Return from current custom command.
  rprogress:
      0<=value<=100 | -1 | "command",0<=value_min<=100,0<=value_max<=100
    Set the progress index of the current processing pipeline (relatively to
    previously defined progress bounds), or call the specified command with
    specified progress bounds.
  run:
      "G'MIC pipeline"
    Run specified G'MIC pipeline.
  skip (+):
      item
    Do nothing but skip specified item.
  u (+):
      Shortcut for command 'status'.
  status (+):
      status_string
    Set the current status. Used to define a returning value from a function.
    (equivalent to shortcut command 'u').
    Example:
      [#1] image.jpg command "foo : u0=Dark u1=Bright status ${u{ia>=128}}" text_outline ${-foo},2,2,23,2,1,255
  while (+):
      condition
    End a 'do...while' block and go back to associated 'do' if specified condition holds.
    'condition' is a mathematical expression, whose evaluation is interpreted as { 0:False | other:True }.
  11.14. Neural Networks
         ---------------
  nn_lib ::
    Define the math functions that has to be included in a math expression, when using 'nn_lib', the G'MIC neural network library. This command sets string variable
    '$_nn_lib' and returns its content.
  nn_add:
      out,in0,in1,_in2,_in3
    Add an 'add' layer (element-wise addition) to the current network.
    the 'in_k' arguments can be layer names or constant values (all cannot be constant values though).
  nn_append:
      out,in0,in1,_in2,_in3,axis={ x | y | z | c }
    Add an 'append' layer to the current network.
    All input layers 'in_k' must have same dimensions (except along specified 'axis').
  nn_check_layer:
      layer_name
    Check that layer with specified name exists in the current network.
  nn_clone:
      out0,out1,in
    Add a 'clone' layer to the current network.
  nn_columnpatch2d:
      out,in,out_width>0,out_height>0,patch_size>0,_overlap>0
    Add a 'columnpatch2d' layer (rearrangement of matrix columns as image patches) to the current network.
    Default values: 'overlap=0'.
  nn_conv:
      out,in,nb_channels>0,_kernel_width>0,_kernel_height>0,_kernel_depth>0,_stride_x>0,_stride_y>0,_stride_z>0,_dilation_x,_dilation_y,_dilation_z,_shrink_x>=0,_shrink_y>=0_,
        _shrink_z>=0,boundary_conditions,_learning_mode,_weight_decay>=0,_initialization
    Add a 'conv' layer (generic 1D/2D/3D convolutional layer) to the current network.
    'boundary_conditions' can be { 0:Dirichlet | 1:Neumann | 2:Periodic | 3:Mirror }.
    'learning_mode' can be { 0:No learning | 1:Weights only | 2:Biases only | 3:Weights+biases }.
    'initialization' can be { 0:Zero | 1:identity | 2:Lecun-initialization | 3:He-initialization | 4:Residual He-initialization }.
    Default values: 'stride_x=stride_y=stride_z=1', 'dilation_x=dilation_y=dilation_z=1', 'shrink_x=shrink_y=shrink_z=0', 'boundary_conditions=1',
     'learning_mode=3', 'weight_decay=0' and 'initialization=3'.
  nn_conv2d:
      out,in,nb_channels>0,_kernel_size>0,_stride>0,_dilation,_shrink>=0,_boundary_conditions,_learning_mode,_weight_decay>=0,_initialization
    Add a 'conv2d' layer (2D convolutional layer) to the current network.
    'boundary_conditions' can be { 0:Dirichlet | 1:Neumann | 2:Periodic | 3:Mirror }.
    'learning_mode' can be { 0:No learning | 1:Weights only | 2:Biases only | 3:Weights+biases }.
    'initialization' can be { 0:Zero | 1:identity | 2:Lecun-initialization | 3:He-initialization | 4:Residual He-initialization }.
    Default values: 'kernel_size=3', 'stride=1', 'dilation=1', 'shrink=0', 'boundary_conditions=1', 'learning_mode=3',
     'weight_decay=0' and 'initialization=3'.
  nn_conv3d:
      out,in,nb_channels>0,_kernel_size>0,_stride>0,_dilation,_shrink>=0,_boundary_conditions,_learning_mode,_weight_decay>=0,_initialization
    Add a 'conv3d' layer (3D convolutional layer) to the current network.
    'boundary_conditions' can be { 0:Dirichlet | 1:Neumann | 2:Periodic | 3:Mirror }.
    'learning_mode' can be { 0:No learning | 1:Weights only | 2:Biases only | 3:Weights+biases }.
    'initialization' can be { 0:Zero | 1:Identity | 2:Lecun-initialization | 3:He-initialization | 4:Residual He-initialization }.
    Default values: 'kernel_size=3', 'stride=1', 'dilation=1', 'shrink=0', 'boundary_conditions=1', 'learning_mode=3',
     'weight_decay=0' and 'initialization=3'.
  nn_crop:
      out,in,x0,y0,z0,c0,x1,y1,z1,c1,_boundary_conditions
    Add a 'crop' layer to the current network.
    'boundary_conditions' can be { 0:Dirichlet | 1:Neumann | 2:Periodic | 3:Mirror }.
    Default value: 'boundary_conditions=0'.
  nn_div:
      out,in0,in1
    Add a 'div' layer (element-wise division) to the current network.
    'in0' or 'in1' can be layer names or constant values (both cannot be constant values though).
  nn_dropout:
      out,in,0<=dropout_rate<1
    Add a 'dropout' layer to the current network.
  nn_fc:
      out,in,nb_channels>0,_learning_mode,_weight_decay>=0,_initialization
    Add a 'fc' layer (fully connected layer) to the current network.
    'learning_mode' can be { 0:No learning | 1:Weights only | 2:Biases only | 3:Weights+biases }.
    'initialization' can be { 0:Zero | 1:Identity | 2:Lecun-initialization | 3:He-initialization 4:Residual He-initialization }.
    Default value: 'learning_mode=3', 'weight_decay=0' and 'initialization=3'.
  nn_fft:
      out,in,io_mode,_is_inverse={ 0:No | 1:Yes },_axes.
    Add a 'fft' layer (Fourier Transform) to the current network.
    Argument 'io_mode' tells about the meaning of input/output data. It can be { 0:real → real | 1:(real,imag) → real | 2:real → (real,imag) | 3:(real,imag) → (real,
    imag) }
    When dealing with '(real,imag)' data, the real and imaginary parts of the data (N channels each) are concatenated into a 2N-channels image.
    Argument 'axes' can be { 0 (all) | x | y | z | xy | xz | yz | xyz }.
    Default value: 'is_inverse=0', 'axes=0 (all)'.
  nn_init:
      _network_name
    Initialize new network with specified name, and select it as the current one.
    Default value: 'network_name=nn'.
  nn_input:
      out,width,_height,_depth,_spectrum
    Add a new 'input' to the current network.
    Default values: 'height=1', 'depth=1' and 'spectrum=1'.
  nn_linear:
      out,in,nb_channels>0,_axis={ x | y | z | c},_learning_mode,_weight_decay>=0,_initialization
    Add a 'linear' layer to the current network.
    'learning_mode' can be { 0:No learning | 1:Weights only | 2:Biases only | 3:Weights+biases }.
    'initialization' can be { 0:Zero | 1:Identity | 2:Lecun-initialization | 3:He-initialization 4:Residual He-initialization }.
    Default value: 'learning_mode=3', 'weight_decay=0' and 'initialization=3'.
  nn_load:
      'filename.gmz',_include_trainer_data={ 0:No | 1:Yes }
    Load and initialize network (saved as a .gmz file).
    Network files can be only loaded in '.gmz' format.
    Default value: 'include_trainer_data=1'.
  nn_loss_add:
      out,in0,in1,...
    Add a 'add' loss to the current network (sum of losses).
  nn_loss_bce:
      out,in,ground_truth,_weight
    Add a 'bce' loss to the current network (binary cross entropy).
    'ground_truth' can be a layer output name or a scalar value (default: 0).
    Default value: 'weight=1'.
  nn_loss_ce:
      out,in,ground_truth,_weight
    Add a 'ce' loss to the current network (cross entropy).
    'ground_truth' can be a layer output name or a scalar value (default: 0).
    Default value: 'weight=1'.
  nn_loss_l1:
      out,in,_ground_truth,_weight
    Add a 'l1' loss to the current network (L1-norm).
    'ground_truth' can be a layer output name or a scalar value (default: 0).
    Default value: 'weight=1'.
  nn_loss_l2:
      out,in,_ground_truth,_weight
    Add a 'l2' loss to the current network (L2-norm).
    'ground_truth' can be a layer output name or a scalar value (default: 0).
    Default value: 'weight=1'.
  nn_loss_lp:
      out,in,_ground_truth,_p>0,_weight
    Add a 'lp' loss to the current network (Lp-norm).
    'ground_truth' can be a layer output name or a scalar value (default: 0).
    Default value: 'p=1.5' and 'weight=1'.
  nn_loss_mse:
      out,in,_ground_truth,_weight
    Add a 'mse' loss to the current network (mean-squared error).
    'ground_truth' can be a layer output name or a scalar value (default: 0).
    Default value: 'weight=1'.
  nn_loss_sigmoid_bce:
      out,in,ground_truth,_weight
    Add a 'sigmoid_bce' loss to the current network (sigmoid followed by binary cross entropy).
    'ground_truth' can be a layer output name or a scalar value (default: 0). Its value correspond to the desired probability (so, after the sigmoid).
    Default value: 'weight=1'.
  nn_loss_smoothl1:
      out,in,_ground_truth,_weight
    Add a 'smoothl1' loss to the current network (smooth L1-norm).
    'ground_truth' can be a layer output name or a scalar value (default: 0).
    Default value: 'weight=1'.
  nn_loss_softmax_ce:
      out,in,ground_truth,_weight
    Add a 'softmax_ce' loss to the current network (softmax followed by cross entropy).
    'ground_truth' can be a layer output name or a scalar value (default: 0).
    Default value: 'weight=1'.
  nn_maxpool2d:
      out,in,_patch_size>1,_is_maxabs={ 0:No | 1:Yes }
    Add a 'maxpool2d' layer (2D max pooling) to the current network.
    Default values: 'patch_size=2' and 'is_maxabs=0'.
  nn_maxpool3d:
      out,in,_patch_size>1,_is_maxabs={ 0:No | 1:Yes }
    Add a 'maxpool3d' layer (3d max pooling) to the current network.
    Default values: 'patch_size=2' and 'is_maxabs=0'.
  nn_mmul:
      out,in0,in1
    Add a 'mmul' layer (matrix multiplication 'in0*in1') to the current network.
    'in0' or 'in1' must be of compatible size.
  nn_mmult:
      out,in0,in1
    Add a 'mmult' layer (matrix multiplication 'in0*in1^t') to the current network.
    'in0' or 'in1' must be of compatible size.
  nn_mul:
      out,in0,in1,_in2,_in3
    Add a 'mul' layer (element-wise multiplication) to the current network.
    the 'in_k' arguments can be layer names or constant values (all cannot be constant values though).
  nn_network:
      _network_name
    Get/set current network name (and initialize network if it does not already exist).
    This command returns the name of the current selected network.
  nn_nl:
      out,in,_activation
    Add a 'nl' layer (nonlinearity) to the current network.
    'activation' can be { elu | gelu | leakyrelu | linear | log | normalize_l1 | normalize_l1_x | normalize_l1_y | normalize_l1_z | normalize_l1_c | normalize_l2 |
    normalize_l2_x | normalize_l2_y | normalize_l2_z | normalize_l2_c | relu | sigmoid | sin | sinc | softmax | softmax_x | softmax_y | softmax_z | softmax_c | msqr | sqrt | swish |
    tanh | z2 | z3 }.
    Default value: 'activation=leakyrelu'.
  nn_noise:
      out,in,_amplitude,_noise_type,_training_only={ 0:No | 1:Yes }
    Add a 'noise' layer to the current network.
    'noise_type' can be { 0:Gaussian | 1:Uniform | 2:Salt&pepper | 3:Poisson | 4:Rice }.
    If 'training_only' is set, the noise is applied on the input only during the network training phase.
    Default value: 'amplitude=0.1', 'noise_type=0' and 'training_only=1'.
  nn_norm:
      out,in,p>=0,_axis={ 0 | x | y | z | c }
    Add a 'norm' layer (Lp norm computation) to the current network.
    'p' can be { 0:Squared norm | >0:Lp-norm }.
    Default values: 'p=2' and 'axis=0'.
  nn_normalize:
      out,in,axes,_normalization_type={ 0:Avg_std | 1:RMS },_group_x>=0,_group_y>=0,_group_z>=0,_group_c>=0,_learning_mode
    Add a 'normalize' layer to the current network.
    This normalization layer is generic and can simulate the behavior of a Layernorm, an InstanceNorm, a GroupNorm, or a RMSNorm.
    'axes' defines the shape used to compute the mean/std of the normalization.
    'axes' can be { x | y | z | c | xy | xz | xc | yz | yc | xyz | xyc | xzc | yzc | xyzc }.
    For instance :
     * 'axes=c' means the vector-valued pixels of the input image areis normalized independently, according to the channel values.
     * 'axes=xyz' means each input channel of the input image is normalized independently, according to all (x,y,z) pixel values.
    When different from '0', a 'group_*' argument forces the corresponding complementary axis to be split into 'N' groups.
    'learning_mode' can be { 0:No learning | 1:Gamma only | 2:Beta only | 3:Gamma+beta }
    Default values: 'normalization_type=0', 'group_x=group_y=group_z=group_c=0' and 'learning_mode=3'.
  nn_parameters:
      out,width>0,_height>0,_depth>0,_spectrum>0,_learning_mode,_weight_decay>=0,_initialization
    Add a 'parameters' layer (4D grid of learnable parameters) to the current network.
    'learning_mode' can be { 0:No learning | 1:Weights }.
    'initialization' can be { 0:Zero | 1:Identity | 2:Lecun-initialization | 3:He-initialization 4:Residual He-initialization }.
    Default value: 'height=depth=spectrum=1', 'learning_mode=1', 'weight_decay=0' and 'initialization=3'.
  nn_patchcolumn2d:
      out,in,patch_size>0,_overlap>=0
    Add a 'patchcolumn2d' layer (rearrangement of image patches as matrix columns) to the current network.
    Default value: 'overlap=0'.
  nn_patchdown2d:
      out,in,_patch_size>1
    Add a 'patchdown2d' layer (2D downscale by patch) to the current network.
    Default value: 'patch_size=2'.
  nn_patchdown3d:
      out,in,_patch_size>1
    Add a 'patchdown3d' layer (3D downscale by patch) to the current network.
    Default value: 'patch_size=2'.
  nn_patchup2d:
      out,in,_patch_size>1
    Add a 'patchup2d' layer (2D upscale by patch) to the current network.
    Default value: 'patch_size=2'.
  nn_patchup3d:
      out,in,_patch_size>1
    Add a 'patchup3d' layer (3D upscale by patch) to the current network.
    Default value: 'patch_size=2'.
  nn_permute:
      out,in,permutation
    Add a 'permute' layer (axes permutation) to the current network.
  nn_print:
    Print info on current network.
  nn_rename:
      out,in
    Add a 'rename' layer to the current network.
  nn_reshape:
      out,in,width>0,_height>0,_depth>0,_spectrum>0
    Add a 'reshape' layer to the current network.
    Default values: 'height=depth=spectrum=1'.
  nn_resize:
      out,in,width[%]>0,_height[%]>0,_depth[%]>0,_spectrum[%]>0,_interpolation
    Add a 'resize' layer to the current network.
    'interpolation' can be { -1:None (memory content) | 0:None | 1:Nearest | 2:Average | 3:Linear | 4=Grid | 5=Bicubic | 6=Lanczos }.
    Default values: 'height=depth=spectrum=100%' and 'interpolation=3'.
  nn_run:
      out,in,"command",_width[%]>0,_height[%]>0,_depth[%]>0,_spectrum[%]>0
    Add a 'run' layer to the current network.
    Default values: 'width=height=depth=spectrum=100%'.
  nn_save:
      'filename.gmz',_include_trainer_data={ 0:No | 1:Yes }
    Save current network as a .gmz file.
    '.gmz' is mandatory extension, specifying another file extension will throw an error.
    Default value: 'include_trainer_data=1'.
  nn_size:
    Return size of the current network (i.e. number of stored parameters).
  nn_split:
      out0,out1,_out2,_out3,in,axis={ x | y | z | c },_size_out0>0,_size_out1>0,_size_out2>0,_size_out3>0
    Add a 'split' layer to the current network.
    Default values: 'size2=size1=size0'.
  nn_store:
      'variable_name',_include_trainer_data={ 0:No | 1:Yes }
    Store current network into a variable.
    Default value: 'include_trainer_data=1'.
  nn_sub:
      out,in0,in1
    Add a 'sub' layer (element-wise subtraction) to the current network.
    'in0' or 'in1' can be layer names or constant values (both cannot be constant values though).
  nn_sum:
      out,in,_is_average={ 0:No | 1:Yes },_axis={ 0 | x | y | z | c }
    Add a 'sum' layer (sum or average computation) to the current network.
    Default values: 'is_average=0' and 'axis=0'.
  nn_tmmul:
      out,in0,in1
    Add a 'tmmul' layer (matrix multiplication 'in0^t.in1') to the current network.
    'in0' or 'in1' must be of compatible size.
  nn_tmmult:
      out,in0,in1
    Add a 'tmmult' layer (matrix multiplication 'in0^t.in1^t') to the current network.
    'in0' or 'in1' must be of compatible size.
  nn_transpose:
      out,in
    Add a 'transpose' layer (matrix transpose) to the current network.
  nn_trainer:
      out,loss,_optimizer,_"optimizer_params",_scheduler,_"scheduler_params"
    Add a network trainer to the current network, with specified optimizer and scheduler.
    'optimizer' can be { adam | adamax | dtschump | sgd | rmsprop }.
    'scheduler' can be { constant | linear | exponential | adaptive }.
     * Value of 'optimizer_params' depends on the chosen optimizer:
    'adam', 'adamax': (learning_rate,beta1,beta2,weight_decay,clip_gradient_min,clip_gradient_max).
    'dtschump': ( learning_rate,momentum,weight_decay,clip_gradient_min,clip_gradient_max ).
    'rmsprop': ( learning_rate,momentum,beta,weight_decay,clip_gradient_min,clip_gradient_max ).
    'sgd': ( learning_rate,momentum,weight_decay,clip_gradient_min,clip_gradient_max ).
     * Value of 'scheduler_params' depends on the chosen scheduler:
    'constant' : (none).
    'adaptive' : ( lr_min,lr_max,nb_warmup_iterations,nb_cooldown_iterations,patience_down,factor_down,patience_up,factor_up ).
    Default values: 'optimizer=adam', 'optimizer_params=(default)', 'scheduler=constant' and 'scheduler_params=(default)'.
  nn_varname:
      layer_name
    Return the variable name used in the math evaluator to store the output of the specified network layer.
  11.15. Arrays, Tiles and Frames
         ------------------------
  array:
      M>0,_N>0,_expand_type={ 0:Min | 1:Max | 2:All }
    Create MxN array from selected images.
    Default values: 'N=M' and 'expand_type=0'.
    Example:
      [#1] image.jpg array 3,2,2
  array_fade:
      M>0,_N>0,0<=_fade_start<=100,0<=_fade_end<=100,_expand_type={0:Min | 1:Max | 2:All}
    Create MxN array from selected images.
    Default values: 'N=M', 'fade_start=60', 'fade_end=90' and 'expand_type=1'.
    Example:
      [#1] image.jpg array_fade 3,2
  array_mirror:
      N>=0,_dir={ 0:X | 1:Y | 2:XY | 3:Tri-XY },_expand_type={ 0:No | 1:Yes }
    Create 2^Nx2^N array from selected images.
    Default values: 'dir=2' and 'expand_type=0'.
    Example:
      [#1] image.jpg array_mirror 2
  array_random:
      Ms>0,_Ns>0,_Md>0,_Nd>0
    Create MdxNd array of tiles from selected MsxNs source arrays.
    Default values: 'Ns=Ms', 'Md=Ms' and 'Nd=Ns'.
    Example:
      [#1] image.jpg +array_random 8,8,15,10
  frame:
      axes,size[%]>=0,_col1,...,_colN
    Insert outer frame in selected images, along the specified axes.
    'axes' can be { x | y | z | xy | xz | yz | xyz }.
    Default value: 'axes=xy', 'size=10%', 'col1=col2=col3=col4=255'.
    Example:
      [#1] image.jpg frame xy,10%,255,128,0
  frame_blur:
      _sharpness>0,_size>=0,_smoothness,_shading,_blur
    Draw RGBA-colored round frame in selected images.
    Default values: 'sharpness=10', 'size=30', 'smoothness=0', 'shading=1' and 'blur=3%'.
    Example:
      [#1] image.jpg frame_blur 3,30,8,10%
  frame_cube:
      _depth>=0,_centering_x,_centering_y,_left_side={ 0:Normal | 1:Mirror-X | 2:Mirror-Y | 3:Mirror-XY },_right_side,_lower_side,_upper_side
    Insert 3D frames in selected images.
    Default values: 'depth=1', 'centering_x=centering_y=0' and 'left_side=right_side,lower_side=upper_side=0'.
    Example:
      [#1] image.jpg frame_cube ,
  frame_fuzzy:
      size_x[%]>=0,_size_y[%]>=0,_fuzzyness>=0,_smoothness[%]>=0,_R,_G,_B,_A
    Draw RGBA-colored fuzzy frame in selected images.
    Default values: 'size_y=size_x', 'fuzzyness=5', 'smoothness=1' and 'R=G=B=A=255'.
    Example:
      [#1] image.jpg frame_fuzzy 20
  frame_painting:
      _size[%]>=0,0<=_contrast<=1,_profile_smoothness[%]>=0,_R,_G,_B,_vignette_size[%]>=0,_vignette_contrast>=0,_defects_contrast>=0,0<=_defects_density<=100,_defects_size>=0,
        _defects_smoothness[%]>=0,_serial_number
    Add a painting frame to selected images.
    Default values: 'size=10%', 'contrast=0.4', 'profile_smoothness=6%', 'R=225', 'G=200', 'B=120', 'vignette_size=2%',
     'vignette_contrast=400', 'defects_contrast=50', 'defects_density=10', 'defects_size=1', 'defects_smoothness=0.5%' and
     'serial_number=123456789'.
    Example:
      [#1] image.jpg frame_painting ,
  frame_pattern:
      M>=3,_constrain_size={ 0:No | 1:Yes } |
      M>=3,_[frame_image],_constrain_size={ 0:No | 1:Yes }
    Insert selected pattern frame in selected images.
    Default values: 'pattern=0' and 'constrain_size=0'.
    Example:
      [#1] image.jpg frame_pattern 8
  frame_round:
      size_x[%]>=0,size_y[%]>=0,radius[%]>=0,_smoothness[%]>=0,_col1,...,_colN
    Insert an inner round frame in selected images.
    Default values: 'size_x=size_y=5%, 'radius=30%', 'smoothness=0' and 'col=0,0,0,255'.
  frame_seamless:
      frame_size>=0,_patch_size>0,_blend_size>=0,_frame_direction={ 0:Inner (preserve image size) | 1:Outer }
    Insert frame in selected images, so that tiling the resulting image makes less visible seams.
    Default values: 'patch_size=7', 'blend_size=5' and 'frame_direction=1'.
    Example:
      [#1] image.jpg +frame_seamless 30 array 2,2
  img2ascii:
      _charset,_analysis_scale>0,_analysis_smoothness[%]>=0,_synthesis_scale>0,_output_ascii_filename
    Render selected images as binary ascii art.
    This command returns the corresponding the list of widths and heights (expressed as a number of characters)
    for each selected image.
    Default values: 'charset=[ascii charset]', 'analysis_scale=16', 'analysis_smoothness=20%', 'synthesis_scale=16' and
     '_output_ascii_filename=[undefined]'.
    Example:
      [#1] image.jpg img2ascii ,
  imagegrid:
      M>0,_N>0
    Create MxN image grid from selected images.
    Default value: 'N=M'.
    Example:
      [#1] image.jpg imagegrid 16
  imagegrid_hexagonal:
      _resolution>0,0<=_outline<=1
    Create hexagonal grids from selected images.
    Default values: 'resolution=32', 'outline=0.1' and 'is_antialiased=1'.
    Example:
      [#1] image.jpg imagegrid_hexagonal 24
  imagegrid_triangular:
      pattern_width>=1,_pattern_height>=1,_pattern_type,0<=_outline_opacity<=1,_outline_color1,...
    Create triangular grids from selected images.
    'pattern type' can be { 0:Horizontal | 1:Vertical | 2:Crossed | 3:Cube | 4:Decreasing | 5:Increasing }.
    Default values: 'pattern_width=24', 'pattern_height=pattern_width', 'pattern_type=0', 'outline_opacity=0.1' and 'outline_color1=0'.
    Example:
      [#1] image.jpg imagegrid_triangular 6,10,3,0.5
  map_sprites:
      _nb_sprites>=1,_allow_rotation={ 0:None | 1:90 deg. | 2:180 deg. }
    Map set of sprites (defined as the 'nb_sprites' latest images of the selection) to other selected images,
    according to the luminosity of their pixel values.
    Example:
      [#1] image.jpg rescale2d ,48 repeat 16 ball {8+2*$>},${-rgb} mul[-1] {(1+$>)/16} done map_sprites 16
  pack:
      is_ratio_constraint={ 0:No | 1:Yes },_sort_criterion
    Pack selected images into a single image.
    The returned status contains the list of new (x,y) offsets for each input image.
    Parameter 'is_ratio_constraint' tells if the resulting image must tend to a square image.
    Default values: 'is_ratio_constraint=0' and 'sort_criterion=max(w,h)'.
    Example:
      [#1] image.jpg repeat 10 +rescale2d[-1] 75% balance_gamma[-1] ${-rgb} done pack 0
  puzzle:
      _width>0,_height>0,_M>=1,_N>=1,_curvature,_centering,_connectors_variability,_resolution>=1
    Input puzzle binary mask with specified size and geometry.
    Default values: 'width=height=512', 'M=N=5', 'curvature=0.5', 'centering=0.5', 'connectors_variability=0.5' and 'resolution=64'.
    Example:
      [#1] puzzle ,
  rotate_tiles:
      angle,_M>0,N>0
    Apply MxN tiled-rotation effect on selected images.
    Default values: 'M=8' and 'N=M'.
    Example:
      [#1] image.jpg to_rgba rotate_tiles 10,8 drop_shadow 10,10 display_rgba
  shift_tiles:
      M>0,_N>0,_amplitude
    Apply MxN tiled-shift effect on selected images.
    Default values: 'N=M' and 'amplitude=20'.
    Example:
      [#1] image.jpg +shift_tiles 8,8,10
  taquin:
      M>0,_N>0,_remove_tile={ 0:None | 1:First | 2:Last | 3:Random },_relief,_border_thickness[%],_border_outline[%],_outline_color
    Create MxN taquin puzzle from selected images.
    Default value: 'N=M', 'relief=50', 'border_thickness=5', 'border_outline=0' and 'remove_tile=0'.
    Example:
      [#1] image.jpg +taquin 8
  tunnel:
      _level>=0,_factor>0,_centering_x,_centering_y,_opacity,_angle
    Apply tunnel effect on selected images.
    Default values: 'level=9', 'factor=80%', 'centering_x=centering_y=0.5', 'opacity=1' and 'angle=0'
    Example:
      [#1] image.jpg tunnel 20
  11.16. Artistic
         --------
  boxfitting:
      _min_box_size>=1,_max_box_size>=0,_initial_density>=0,_min_spacing>0
    Apply box fitting effect on selected images, as displayed the web page:
    http://www.complexification.net/gallery/machines/boxFittingImg/.
    Default values: 'min_box_size=1', 'max_box_size=0', 'initial_density=0.25' and 'min_spacing=1'.
    Example:
      [#1] image.jpg boxfitting ,
  brushify:
      [brush],_brush_nb_sizes>=1,0<=_brush_min_size_factor<=1,_brush_nb_orientations>=1,_brush_light_type,0<=_brush_light_strength<=1,_brush_opacity,_painting_density[%]>=0,
        0<=_painting_contours_coherence<=1,0<=_painting_orientation_coherence<=1,_painting_coherence_alpha[%]>=0,_painting_coherence_sigma[%]>=0,_painting_primary_angle,
        0<=_painting_angle_dispersion<=1
    Apply specified brush to create painterly versions of specified images.
    'brush_light_type' can be { 0:None | 1:Flat | 2:Darken | 3:Lighten | 4:Full }.
    Default values: 'brush_nb_sizes=3', 'brush_min_size_factor=0.66', 'brush_nb_orientations=12', 'brush_light_type=0',
     'brush_light_strength=0.25', 'brush_opacity=0.8', 'painting_density=20%', 'painting_contours_coherence=0.9',
     'painting_orientation_coherence=0.9', 'painting_coherence_alpha=1', 'painting_coherence_sigma=1', 'painting_primary_angle=0',
     'painting_angle_dispersion=0.2'
    Example:
      [#1] image.jpg 40,40 gaussian[-1] 10,4 spread[-1] 10,0 brushify[0] [1],1
  cartoon:
      _smoothness,_sharpening,_threshold>=0,_thickness>=0,_color>=0,quantization>0
    Apply cartoon effect on selected images.
    Default values: 'smoothness=3', 'sharpening=150', 'threshold=20', 'thickness=0.25', 'color=1.5' and 'quantization=8'.
    Example:
      [#1] image.jpg cartoon 3,50,10,0.25,3,16
  color_ellipses:
      _count>0,_radius>=0,_opacity>=0
    Add random color ellipses to selected images.
    Default values: 'count=400', 'radius=5' and 'opacity=0.1'.
    Example:
      [#1] image.jpg +color_ellipses ,,0.15
  cubism:
      _density>=0,0<=_thickness<=50,_max_angle,_opacity,_smoothness>=0
    Apply cubism effect on selected images.
    Default values: 'density=50', 'thickness=10', 'max_angle=75', 'opacity=0.7' and 'smoothness=0'.
    Example:
      [#1] image.jpg cubism ,
  draw_whirl:
      _amplitude>=0
    Apply whirl drawing effect on selected images.
    Default value: 'amplitude=100'.
    Example:
      [#1] image.jpg draw_whirl ,
  drop_shadow:
      _offset_x[%],_offset_y[%],_smoothness[%]>=0,_curvature_x,_curvature_y,_expand_size={ 0:No | 1:Yes }
    Drop shadow behind selected images.
    Default values: 'offset_x=20', 'offset_y=offset_x', 'smoothness=5', 'curvature=0' and 'expand_size=1'.
    Example:
      [#1] image.jpg drop_shadow 10,20,5,0.5 expand. xy,20 display_rgba
  drop_shadow:
      _offset_x[%],_offset_y[%],_smoothness[%]>=0,curvature_x>=0,curvature_y>=0,_expand_size={ 0:No | 1:Yes },_output_separate_layers={ 0:No | 1:Yes }
    Drop shadow behind selected images.
    Default values: 'offset_x=20', 'offset_y=offset_x', 'smoothness=5', 'curvature_x=curvature_y=0', 'expand_size=1' and
     'output_separate_layers=0'.
    Example:
      [#1] image.jpg drop_shadow 10,20,5,0.5 display_rgba
  ellipsionism:
      _R[%]>0,_r[%]>0,_smoothness[%]>=0,_opacity,_outline>0,_density>0
    Apply ellipsionism filter to selected images.
    Default values: 'R=10', 'r=3', 'smoothness=1%', 'opacity=0.7', 'outline=8' and 'density=0.6'.
    Example:
      [#1] image.jpg ellipsionism ,
  fire_edges:
      _edges>=0,0<=_attenuation<=1,_smoothness>=0,_threshold>=0,_nb_frames>0,_starting_frame>=0,frame_skip>=0
    Generate fire effect from edges of selected images.
    Default values: 'edges=0.7', 'attenuation=0.25', 'smoothness=0.5', 'threshold=25', 'nb_frames=1', 'starting_frame=20' and
     'frame_skip=0'.
    Example:
      [#1] image.jpg fire_edges ,
  fractalize:
      0<=detail_level<=1
    Randomly fractalize selected images.
    Default value: 'detail_level=0.8'
    Example:
      [#1] image.jpg fractalize ,
  glow:
      _amplitude>=0
    Add soft glow on selected images.
    Default value: 'amplitude=1%'.
    Example:
      [#1] image.jpg glow ,
  halftone:
      nb_levels>=2,_size_dark>=2,_size_bright>=2,_shape={ 0:Square | 1:Diamond | 2:Circle | 3:inv-square | 4:inv-diamond | 5:inv-circle },_smoothness[%]>=0
    Apply halftone dithering to selected images.
    Default values: 'nb_levels=5', 'size_dark=8', 'size_bright=8', 'shape=5' and 'smoothnesss=0'.
    Example:
      [#1] image.jpg halftone ,
  hardsketchbw:
      _amplitude>=0,_density>=0,_opacity,0<=_edge_threshold<=100,_is_fast={ 0:No | 1:Yes }
    Apply hard B&W sketch effect on selected images.
    Default values: 'amplitude=1000', 'sampling=3', 'opacity=0.1', 'edge_threshold=20' and 'is_fast=0'.
    Example:
      [#1] image.jpg +hardsketchbw 200,70,0.1,10 median[-1] 2 +local reverse blur[-1] 3 blend[-2,-1] overlay done
  hearts:
      _density>=0
    Apply heart effect on selected images.
    Default value: 'density=10'.
    Example:
      [#1] image.jpg hearts ,
  houghsketchbw:
      _density>=0,_radius>0,0<=_threshold<=100,0<=_opacity<=1,_votesize[%]>0
    Apply hough B&W sketch effect on selected images.
    Default values: 'density=100', 'radius=3', 'threshold=100', 'opacity=0.1' and 'votesize=100%'.
    Example:
      [#1] image.jpg +houghsketchbw ,
  lightrays:
      100<=_density<=0,_center_x[%],_center_y[%],_ray_length>=0,_ray_attenuation>=0
    Generate ray lights from the edges of selected images.
    Default values: 'density=50%', 'center_x=50%', 'center_y=50%', 'ray_length=0.9' and 'ray_attenuation=0.5'.
    Example:
      [#1] image.jpg +lightrays , + cut 0,255
  light_relief:
      _ambient_light,_specular_lightness,_specular_size,_darkness,_light_smoothness,_xl,_yl,_zl,_zscale,_opacity_is_heightmap={ 0:No | 1:Yes }
    Apply relief light to selected images.
    Default values(s) : 'ambient_light=0.3', 'specular_lightness=0.5', 'specular_size=0.2', 'darkness=0', 'xl=0.2', 'yl=zl=0.5',
    'zscale=1', 'opacity=1' and 'opacity_is_heightmap=0'.
    Example:
      [#1] image.jpg blur 2 light_relief 0.3,4,0.1,0
  linify:
      0<=_density<=100,_spreading>=0,_resolution[%]>0,_line_opacity>=0,_line_precision>0,_mode={ 0:Subtractive | 1:Additive }
    Apply linify effect on selected images.
    The algorithm is inspired from the one described on the webpage http://linify.me/about.
    Default values: 'density=50', 'spreading=2', 'resolution=40%', 'line_opacity=10', 'line_precision=24' and 'mode=0'.
    Example:
      [#1] image.jpg linify 60
  mosaic:
      0<=_density<=100
    Create random mosaic from selected images.
    Default values: 'density=30'.
    Example:
      [#1] image.jpg mosaic , +fill "I!=J(1) || I!=J(0,1)?[0,0,0]:I"
  old_photo:
    Apply old photo effect on selected images.
    Example:
      [#1] image.jpg old_photo
  pencilbw:
      _size>=0,_amplitude>=0
    Apply B&W pencil effect on selected images.
    Default values: 'size=0.3' and 'amplitude=60'.
    Example:
      [#1] image.jpg pencilbw ,
  pixelsort:
      _ordering={ +:Increasing | -:Decreasing },_axis={ x | y | z | xy | yx },_[sorting_criterion],_[mask]
    Apply a 'pixel sorting' algorithm on selected images, as described in the page :
    http://satyarth.me/articles/pixel-sorting/.
    Default values: 'ordering=+', 'axis=x' and 'sorting_criterion=mask=(undefined)'.
    Example:
      [#1] image.jpg +norm +ge[-1] 30% +pixelsort[0] +,y,[1],[2]
  polaroid:
      _size1>=0,_size2>=0
    Create polaroid effect in selected images.
    Default values: 'size1=10' and 'size2=20'.
    Example:
      [#1] image.jpg to_rgba polaroid 5,30 rotate 20 drop_shadow , drgba
  polygonize:
      _warp_amplitude>=0,_smoothness[%]>=0,_min_area[%]>=0,_resolution_x[%]>0,_resolution_y[%]>0
    Apply polygon effect on selected images.
    Default values: 'warp_amplitude=300', 'smoothness=2%', 'min_area=0.1%', 'resolution_x=resolution_y=10%'.
    Example:
      [#1] image.jpg +polygonize 100,10 fill[-1] "I!=J(1) || I!=J(0,1)?[0,0,0]:I"
  poster_edges:
      0<=_edge_threshold<=100,0<=_edge_shade<=100,_edge_thickness>=0,_edge_antialiasing>=0,0<=_posterization_level<=15,_posterization_antialiasing>=0
    Apply poster edges effect on selected images.
    Default values: 'edge_threshold=40', 'edge_shade=5', 'edge_thickness=0.5', 'edge_antialiasing=10', 'posterization_level=12' and
     'posterization_antialiasing=0'.
    Example:
      [#1] image.jpg poster_edges ,
  poster_hope:
      _smoothness>=0
    Apply Hope stencil poster effect on selected images.
    Default value: 'smoothness=3'.
    Example:
      [#1] image.jpg poster_hope ,
  rodilius:
      0<=_amplitude<=100,_0<=thickness<=100,_sharpness>=0,_nb_orientations>0,_offset,_color_mode={ 0:Darker | 1:Brighter }
    Apply rodilius (fractalius-like) filter on selected images.
    Default values: 'amplitude=10', 'thickness=10', 'sharpness=400', 'nb_orientations=7', 'offset=0' and 'color_mode=1'.
    Example:
      [#1] image.jpg rodilius 12,10,300,10 normalize_local 10,6
      [#2] image.jpg normalize_local 10,16 rodilius 10,4,400,16 smooth 60,0,1,1,4 normalize_local 10,16
  sketchbw:
      _nb_angles>0,_start_angle,_angle_range>=0,_length>=0,_threshold>=0,_opacity,_bgfactor>=0,_density>0,_sharpness>=0,_anisotropy>=0,_smoothness>=0,_coherence>=0,_is_boost={ 0:No |
        1:Yes },_is_curved={ 0:No | 1:Yes }
    Apply sketch effect to selected images.
    Default values: 'nb_angles=2', 'start_angle=45', 'angle_range=180', 'length=30', 'threshold=3', 'opacity=0.03', 'bgfactor=0',
     'density=0.6', 'sharpness=0.1', 'anisotropy=0.6', 'smoothness=0.25', 'coherence=1', 'is_boost=0' and 'is_curved=1'.
    Example:
      [#1] image.jpg +sketchbw 1 reverse blur[-1] 3 blend[-2,-1] overlay
  sponge:
      _size>0
    Apply sponge effect on selected images.
    Default value: 'size=13'.
    Example:
      [#1] image.jpg sponge ,
  stained_glass:
      _edges[%]>=0, shading>=0, is_thin_separators={ 0:No | 1:Yes }
    Generate stained glass from selected images.
    Default values: 'edges=40%', 'shading=0.2' and 'is_precise=0'.
    Example:
      [#1] image.jpg stained_glass 20%,1 cut 0,20
  stars:
      _density[%]>=0,_depth>=0,_size>0,_nb_branches>=1,0<=_thickness<=1,_smoothness[%]>=0,_R,_G,_B,_opacity
    Add random stars to selected images.
    Default values: 'density=10%', 'depth=1', 'size=32', 'nb_branches=5', 'thickness=0.38', 'smoothness=0.5', 'R=G=B=200' and
     'opacity=1'.
    Example:
      [#1] image.jpg stars ,
  stencil:
      _radius[%]>=0,_smoothness>=0,_iterations>=0
    Apply stencil filter on selected images.
    Default values: 'radius=3', 'smoothness=1' and 'iterations=8'.
    Example:
      [#1] image.jpg +norm stencil. 2,1,4 +mul rm[0]
  stencilbw:
      _edges>=0,_smoothness>=0
    Apply B&W stencil effect on selected images.
    Default values: 'edges=15' and 'smoothness=10'.
    Example:
      [#1] image.jpg +stencilbw 40,4
  stylize:
      [style_image],_fidelity_finest,_fidelity_coarsest,_fidelity_smoothness_finest>=0,_fidelity_smoothnes_coarsest>=0,0<=_fidelity_chroma<=1,_init_type,_init_resolution>=0,
        init_max_gradient>=0,_patch_size_analysis>0,_patch_size_synthesis>0,_patch_size_synthesis_final>0,_nb_matches_finest>=0,_nb_matches_coarsest>=0,_penalize_repetitions>=0,
        _matching_precision>=0,_scale_factor>1,_skip_finest_scales>=0,_"image_matching_command"
    Transfer colors and textures from specified style image to selected images, using a multi-scale patch-mathing algorithm.
    If instant display window[0] is opened, the steps of the image synthesis are displayed on it.
    'init_type' can be { 0:Best-match | 1:Identity | 2:Randomized }.
    Default values: 'fidelity_finest=0.5', 'fidelity_coarsest=2', 'fidelity_smoothness_finest=3', 'fidelity_smoothness_coarsest=0.5',
     'fidelity_chroma=0.1', 'init_type=0', 'init_resolution=16', 'init_max_gradient=0', 'patch_size_analysis=5', 'patch_size_synthesis=5',
     'patch_size_synthesis_final=5', 'nb_matches_finest=2', 'nb_matchesc_coarsest=30', 'penalize_repetitions=2', 'matching_precision=2',
     'scale_factor=1.85', 'skip_finest_scales=0' and 'image_matching_command'="s c,-3 match_pca[0] [2] b[0,2] xy,0.7 n[0,2] 0,255 n[1,2] 0,200 a[0,1] c a[1,2] c"'.
  tetris:
      _scale>0
    Apply tetris effect on selected images.
    Default value: 'scale=10'.
    Example:
      [#1] image.jpg +tetris 10
  warhol:
      _M>0,_N>0,_smoothness>=0,_color>=0
    Create MxN Andy Warhol-like artwork from selected images.
    Default values: 'M=3', 'N=M', 'smoothness=2' and 'color=20'.
    Example:
      [#1] image.jpg warhol 3,3,3,40
  weave:
      _density>=0,0<=_thickness<=100,0<=_shadow<=100,_shading>=0,_fibers_amplitude>=0,_fibers_smoothness>=0,_angle,-1<=_x_curvature<=1,-1<=_y_curvature<=1
    Apply weave effect to the selected images.
    'angle' can be { 0:0 deg. | 1:22.5 deg. | 2:45 deg. | 3:67.5 deg. }.
    Default values: 'density=6', 'thickness=65', 'shadow=40', 'shading=0.5', 'fibers_amplitude=0', _'fibers_smoothness=0', 'angle=0' and
     'curvature_x=curvature_y=0'
    Example:
      [#1] image.jpg weave ,
  whirls:
      _texture>=0,_smoothness>=0,_darkness>=0,_lightness>=0
    Add random whirl texture to selected images.
    Default values: 'texture=3', 'smoothness=6', 'darkness=0.5' and 'lightness=1.8'.
    Example:
      [#1] image.jpg whirls ,
  11.17. Warpings
         --------
  deform:
      _amplitude[%]>=0,_interpolation,_boundary_conditions
    Apply random smooth deformation on selected images.
    'interpolation' can be { 0:None | 1:Linear | 2:Bicubic }.
    'boundary_conditions' can be { 0:Dirichlet | 1:Neumann | 2:Periodic | 3:Mirror }.
    Default values: 'amplitude=10', 'interpolation=1' and 'boundary_conditions=3'.
    Example:
      [#1] image.jpg +deform[0] 10 +deform[0] 20
  euclidean2polar:
      _center_x[%],_center_y[%],_stretch_factor>0,_boundary_conditions={ 0:Dirichlet | 1:Neumann | 2:Periodic | 3:Mirror }
    Apply euclidean to polar transform on selected images.
    Default values: 'center_x=center_y=50%', 'stretch_factor=1' and 'boundary_conditions=3'.
    Example:
      [#1] image.jpg +euclidean2polar ,
  equirectangular2nadirzenith:
    Transform selected equirectangular images to nadir/zenith rectilinear projections.
  fisheye:
      _center_x,_center_y,0<=_radius<=100,_amplitude>=0
    Apply fish-eye deformation on selected images.
    Default values: 'x=y=50', 'radius=50' and 'amplitude=1.2'.
    Example:
      [#1] image.jpg +fisheye ,
  flower:
      _amplitude,_frequency,_offset_r[%],_angle,_center_x[%],_center_y[%],_boundary_conditions={ 0:Dirichlet | 1:Neumann | 2:Periodic | 3:Mirror }
    Apply flower deformation on selected images.
    Default values: 'amplitude=30', 'frequency=6', 'offset_r=0', 'angle=0', 'center_x=center_y=50%' and 'boundary_conditions=3'.
    Example:
      [#1] image.jpg +flower ,
  kaleidoscope:
      _center_x[%],_center_y[%],_radius,_angle,_boundary_conditions={ 0:Dirichlet | 1:Neumann | 2:Periodic | 3:Mirror }
    Create kaleidoscope effect from selected images.
    Default values: 'center_x=center_y=50%', 'radius=100', 'angle=30' and 'boundary_conditions=3'.
    Example:
      [#1] image.jpg kaleidoscope ,
  map_sphere:
      _width>0,_height>0,_radius,_dilation>0,_fading>=0,_fading_power>=0
    Map selected images on a sphere.
    Default values: 'width=height=512', 'radius=100', 'dilation=0.5', 'fading=0' and 'fading_power=0.5'.
    Example:
      [#1] image.jpg map_sphere ,
  nadirzenith2equirectangular:
    Transform selected nadir/zenith rectilinear projections to equirectangular images.
  polar2euclidean:
      _center_x[%],_center_y[%],_stretch_factor>0,_boundary_conditions={ 0:Dirichlet | 1:Neumann | 2:Periodic | 3:Mirror }
    Apply euclidean to polar transform on selected images.
    Default values: 'center_x=center_y=50%', 'stretch_factor=1' and 'boundary_conditions=3'.
    Example:
      [#1] image.jpg +euclidean2polar ,
  raindrops:
      _amplitude,_density>=0,_wavelength>=0,_merging_steps>=0
    Apply raindrops deformation on selected images.
    Default values: 'amplitude=80','density=0.1', 'wavelength=1' and 'merging_steps=0'.
    Example:
      [#1] image.jpg +raindrops ,
  ripple:
      _amplitude,_bandwidth,_shape={ 0:Block | 1:Triangle | 2:Sine | 3:Sine+ | 4:Random },_angle,_offset
    Apply ripple deformation on selected images.
    Default values: 'amplitude=10', 'bandwidth=10', 'shape=2', 'angle=0' and 'offset=0'.
    Example:
      [#1] image.jpg +ripple ,
  rotoidoscope:
      _center_x[%],_center_y[%],_tiles>0,_smoothness[%]>=0,_boundary_conditions={ 0:Dirichlet | 1:Neumann | 2:Periodic | 3:Mirror }
    Create rotational kaleidoscope effect from selected images.
    Default values: 'center_x=center_y=50%', 'tiles=10', 'smoothness=1' and 'boundary_conditions=3'.
    Example:
      [#1] image.jpg +rotoidoscope ,
  spherize:
      _radius[%]>=0,_strength,_smoothness[%]>=0,_center_x[%],_center_y[%],_ratio_x/y>0,_angle,_interpolation
    Apply spherize effect on selected images.
    Default values: 'radius=50%', 'strength=1', 'smoothness=0', 'center_x=center_y=50%', 'ratio_x/y=1', 'angle=0' and
     'interpolation=1'.
    Example:
      [#1] image.jpg grid 5%,5%,0,0,0.6,255 spherize ,
  symmetrize:
      _x[%],_y[%],_angle,_boundary_conditions={ 0:Dirichlet | 1:Neumann | 2:Periodic | 3:Mirror },_is_antisymmetry={ 0:No | 1:Yes },_swap_sides={ 0:No | 1:Yes }
    Symmetrize selected images regarding specified axis.
    Default values: 'x=y=50%', 'angle=90', 'boundary_conditions=3', 'is_antisymmetry=0' and 'swap_sides=0'.
    Example:
      [#1] image.jpg +symmetrize 50%,50%,45 +symmetrize[-1] 50%,50%,-45
  transform_polar:
      "expr_radius",_"expr_angle",_center_x[%],_center_y[%],_boundary_conditions={ 0:Dirichlet | 1:Neumann | 2:Periodic | 3:Mirror }
    Apply user-defined transform on polar representation of selected images.
    Default values: 'expr_radius=R-r', 'expr_rangle=a', 'center_x=center_y=50%' and 'boundary_conditions=3'.
    Example:
      [#1] image.jpg +transform_polar[0] R*(r/R)^2,a +transform_polar[0] r,2*a
  twirl:
      _amplitude,_center_x[%],_center_y[%],_boundary_conditions={ 0:Dirichlet | 1:Neumann | 2:Periodic | 3:Mirror }
    Apply twirl deformation on selected images.
    Default values: 'amplitude=1', 'center_x=center_y=50%' and 'boundary_conditions=3'.
    Example:
      [#1] image.jpg twirl 0.6
  warp (+):
      [warping_field],_mode,_interpolation,_boundary_conditions,_nb_frames>0
    Warp selected images with specified displacement field.
    'mode' can be { 0:Backward-absolute | 1:Backward-relative | 2:Forward-absolute | 3:Forward-relative }.
    'interpolation' can be { 0:Nearest-neighbor | 1:Linear | 2:Cubic }.
    'boundary_conditions' can be { 0:Dirichlet | 1:Neumann | 2:Periodic | 3:Mirror }.
    Default values: 'mode=0', 'interpolation=1', 'boundary_conditions=0' and 'nb_frames=1'.
    Example:
      [#1] image.jpg 100%,100%,1,2,'X=x/w-0.5;Y=y/h-0.5;R=(X*X+Y*Y)^0.5;A=atan2(Y,X);130*R*(!c?cos(4*A):sin(8*A))' warp[-2] [-1],1,1,0 quiver[-1] [-1],10,1,1,1,100
    Tutorial: https://gmic.eu/oldtutorial/_warp
  warp_patch:
      [displacement_map],patch_width>=1,_patch_height>=1,_patch_depth>=1,_std_factor>0,_boundary_conditions,_fast_approximation={ 0:No | 1:Yes }
    Patch-warp selected images, with specified 2D or 3D displacement map (in backward-absolute mode).
    Argument 'std_factor' sets the std of the gaussian weights for the patch overlap,
    equal to 'std = std_factor*patch_size'.
    'boundary_conditions' can be { 0:Dirichlet | 1:Neumann | 2:Periodic | 3:Mirror }.
    Default values: 'std_factor=0.3', 'boundary_conditions=3' and 'fast_approximation=0'.
  warp_perspective:
      _x-angle,_y-angle,_zoom>0,_x-center,_y-center,_boundary_conditions={ 0:Dirichlet | 1:Neumann | 2:Periodic | 3:Mirror }
    Warp selected images with perspective deformation.
    Default values: 'x-angle=1.5', 'y-angle=0', 'zoom=1', 'x-center=y-center=50' and 'boundary_conditions=2'.
    Example:
      [#1] image.jpg warp_perspective ,
  warp_rbf:
      xs0[%],ys0[%],xt0[%],yt0[%],...,xsN[%],ysN[%],xtN[%],ytN[%]
    Warp selected images using RBF-based interpolation.
    Each argument (xsk,ysk)-(xtk,ytk) corresponds to the coordinates of a keypoint
    respectively on the source and target images. The set of all keypoints define the overall image deformation.
    Example:
      [#1] image.jpg +warp_rbf 0,0,0,0,100%,0,100%,0,100%,100%,100%,100%,0,100%,0,100%,50%,50%,70%,50%,25%,25%,25%,75%
  warp_seamless:
      [displacement_map],_sigma[%]>0,_blend_dimension={ 0:Auto | 1:1D | 2:2D | 3:3D }
    Warp selected 2D or 3D images by specified displacement field, using seamless blending.
    Default values: 'sigma=5%' and 'blend_dimension=0'.
    Example:
      [#1] sp colorful,512 100%,100%,1,2,[x,y] l. { s xy,8 sort_list +,u append_tiles , } +warp[0] [1] +warp_seamless[0] [1]
  water:
      _amplitude,_smoothness>=0,_angle
    Apply water deformation on selected images.
    Default values: 'amplitude=30', 'smoothness=1.5' and 'angle=45'.
    Example:
      [#1] image.jpg water ,
  wave:
      _amplitude>=0,_frequency>=0,_center_x,_center_y
    Apply wave deformation on selected images.
    Default values: 'amplitude=4', 'frequency=0.4' and 'center_x=center_y=50'.
    Example:
      [#1] image.jpg wave ,
  wind:
      _amplitude>=0,_angle,0<=_attenuation<=1,_threshold
    Apply wind effect on selected images.
    Default values: 'amplitude=20', 'angle=0', 'attenuation=0.7' and 'threshold=20'.
    Example:
      [#1] image.jpg +wind ,
  zoom:
      _factor,_cx,_cy,_cz,_boundary_conditions={ 0:Dirichlet | 1:Neumann | 2:Periodic | 3:Mirror }
    Apply zoom factor to selected images.
    Default values: 'factor=1', 'cx=cy=cz=0.5' and 'boundary_conditions=0'.
    Example:
      [#1] image.jpg +zoom[0] 0.6 +zoom[0] 1.5
  11.18. Degradations
         ------------
  cracks:
      0<=_density<=100,_is_relief={ 0:No | 1:Yes },_opacity,_color1,...
    Draw random cracks on selected images with specified color.
    Default values: 'density=25', 'is_relief=0', 'opacity=1' and 'color1=0'.
    Example:
      [#1] image.jpg +cracks ,
  light_patch:
      _density>0,_darkness>=0,_lightness>=0
    Add light patches to selected images.
    Default values: 'density=10', 'darkness=0.9' and 'lightness=1.7'.
    Example:
      [#1] image.jpg +light_patch 20,0.9,4
  pixelize:
      _scale_x>0,_scale_y>0,_scale_z>0
    Pixelize selected images with specified scales.
    Default values: 'scale_x=20' and 'scale_y=scale_z=scale_x'.
    Example:
      [#1] image.jpg +pixelize ,
  scanlines:
      _amplitude,_bandwidth,_shape={ 0:Block | 1:Triangle | 2:Sine | 3:Sine+ | 4:Random },_angle,_offset
    Apply ripple deformation on selected images.
    Default values: 'amplitude=60', 'bandwidth=2', 'shape=0', 'angle=0' and 'offset=0'.
    Example:
      [#1] image.jpg +scanlines ,
  shade_stripes:
      _frequency>=0,_direction={ 0:Horizontal | 1:Vertical },_darkness>=0,_lightness>=0
    Add shade stripes to selected images.
    Default values: 'frequency=5', 'direction=1', 'darkness=0.8' and 'lightness=2'.
    Example:
      [#1] image.jpg +shade_stripes 30
  shadow_patch:
      _opacity>=0
    Add shadow patches to selected images.
    Default value: 'opacity=0.7'.
    Example:
      [#1] image.jpg +shadow_patch 0.4
  shuffle:
    Shuffle vectors of selected images with Fisher-Yates algorithm, as described in https://en.wikipedia.org/wiki/Fisher%E2%80%93Yates_shuffle.
    Example:
      [#1] uniform_distribution 8,3 shuffle
  spread:
      _dx[%]>=0,_dy[%]>=0,_dz[%]>=0
    Spread pixel values of selected images randomly along x,y and z.
    Default values: 'dx=3', 'dy=dx' and 'dz=0'.
    Example:
      [#1] image.jpg +spread 3
  stripes_y:
      _frequency>=0
    Add vertical stripes to selected images.
    Default value: 'frequency=10'.
    Example:
      [#1] image.jpg +stripes_y ,
  texturize_canvas:
      _amplitude>=0,_fibrousness>=0,_emboss_level>=0
    Add paint canvas texture to selected images.
    Default values: 'amplitude=20', 'fibrousness=3' and 'emboss_level=0.6'.
    Example:
      [#1] image.jpg +texturize_canvas ,
  texturize_paper:
    Add paper texture to selected images.
    Example:
      [#1] image.jpg +texturize_paper
  vignette:
      _strength>=0,0<=_radius_min<=100,0<=_radius_max<=100
    Add vignette effect to selected images.
    Default values: 'strength=100', 'radius_min=70' and 'radius_max=90'.
    Example:
      [#1] image.jpg vignette ,
  watermark_visible:
      _text,0<_opacity<1,_{ size>0 | font },_angle,_mode={ 0:Remove | 1:Add },_smoothness>=0
    Add or remove a visible watermark on selected images (value range must be [0,255]).
    Default values: 'text=(c) G'MIC', 'opacity=0.3', 'size=53', 'angle=25', 'mode=1' and 'smoothness=0'.
    Example:
      [#1] image.jpg watermark_visible ,0.7
  11.19. Blending and Fading
         -------------------
  blend:
      [layer],blending_mode,_opacity[%],_selection_is={ 0:Base-layers | 1:Top-layers } |
      blending_mode,_opacity[%]
    Blend selected G,GA,RGB or RGBA images by specified layer or blend all selected images together,
    using specified blending mode.
    'blending_mode' can be { add | alpha | and | average | blue | burn | darken | difference |
    divide | dodge | edges | exclusion | freeze | grainextract | grainmerge | green | hardlight |
    hardmix | hue | interpolation | lchlightness | lighten | lightness | linearburn | linearlight | luminance |
    multiply | negation | or | overlay | pinlight | red | reflect | saturation |
    screen | seamless | seamless_mixed | shapeareamax | shapeareamax0 | shapeareamin | shapeareamin0 |
    shapeaverage | shapeaverage0 | shapemedian | shapemedian0 | shapemin | shapemin0 | shapemax | shapemax0 |
    shapeprevalent | softburn | softdodge | softlight | stamp | subtract | value | vividlight | xor }.
    'opacity' must be in range '[0,1]' (or '[0%,100%]').
    Default values: 'blending_mode=alpha', 'opacity=1' and 'selection_is=0'.
    Example:
      [#1] image.jpg +drop_shadow , rescale2d[-1] ,200 rotate[-1] 20 +blend alpha display_rgba[-2]
      [#2] image.jpg testimage2d {w},{h} blend overlay
      [#3] command "ex : $""=arg repeat $""# +blend[0,1] ${arg{$>+1}} text_outline[-1] Mode:
       average,blue,burn,darken
      [#4] command "ex : $""=arg repeat $""# +blend[0,1] ${arg{$>+1}} text_outline[-1] Mode:
       divide,dodge,exclusion,freeze,grainextract,grainmerge
      [#5] command "ex : $""=arg repeat $""# +blend[0,1] ${arg{$>+1}} text_outline[-1] Mode:
       hardmix,hue,interpolation,lighten,lightness
      [#6] command "ex : $""=arg repeat $""# +blend[0,1] ${arg{$>+1}} text_outline[-1] Mode:
       linearlight,luminance,multiply,negation,or,overlay
      [#7] command "ex : $""=arg repeat $""# +blend[0,1] ${arg{$>+1}} text_outline[-1] Mode:
       reflect,saturation,screen,shapeaverage,softburn
      [#8] command "ex : $""=arg repeat $""# +blend[0,1] ${arg{$>+1}} text_outline[-1] Mode:
       softlight,stamp,subtract,value,vividlight,xor
  nblend:
      [layer],blending_mode,_opacity[%],_selection_is={ 0:Base-layers | 1:Top-layers } |
      blending_mode,_opacity[%]
  blend_edges:
      smoothness[%]>=0
    Blend selected images togethers using 'edges' mode.
    Example:
      [#1] image.jpg testimage2d {w},{h} +blend_edges 0.8
  blend_fade:
      [fading_shape]
    Blend selected images together using specified fading shape.
    Example:
      [#1] image.jpg testimage2d {w},{h} 100%,100%,1,1,'cos(y/10)' normalize[-1] 0,1 +blend_fade[0,1] [2]
  blend_median:
    Blend selected images together using 'median' mode.
    Example:
      [#1] image.jpg testimage2d {w},{h} +mirror[0] y +blend_median
  blend_seamless:
      _is_mixed_mode={ 0:No | 1:Yes },_inner_fading[%]>=0,_outer_fading[%]>=0
    Blend selected images using a seamless blending mode (Poisson-based).
    Default values: 'is_mixed=0', 'inner_fading=0' and 'outer_fading=100%'.
  fade_diamond:
      0<=_start<=100,0<=_end<=100
    Create diamond fading from selected images.
    Default values: 'start=80' and 'end=90'.
    Example:
      [#1] image.jpg testimage2d {w},{h} +fade_diamond 80,85
  fade_linear:
      _angle,0<=_start<=100,0<=_end<=100
    Create linear fading from selected images.
    Default values: 'angle=45', 'start=30' and 'end=70'.
    Example:
      [#1] image.jpg testimage2d {w},{h} +fade_linear 45,48,52
  fade_radial:
      0<=_start<=100,0<=_end<=100
    Create radial fading from selected images.
    Default values: 'start=30' and 'end=70'.
    Example:
      [#1] image.jpg testimage2d {w},{h} +fade_radial 30,70
  fade_x:
      0<=_start<=100,0<=_end<=100
    Create horizontal fading from selected images.
    Default values: 'start=30' and 'end=70'.
    Example:
      [#1] image.jpg testimage2d {w},{h} +fade_x 30,70
  fade_y:
      0<=_start<=100,0<=_end<=100
    Create vertical fading from selected images.
    Default values: 'start=30' and 'end=70'.
    Example:
      [#1] image.jpg testimage2d {w},{h} +fade_y 30,70
  fade_z:
      0<=_start<=100,0<=_end<=100
    Create transversal fading from selected images.
    Default values: 'start=30' and 'end=70'.
  sub_alpha:
      [base_image],0<=_minimize_alpha<=1
    Compute the alpha-channel difference (opposite of alpha blending) between the selected images
    and the specified base image.
    The alpha difference A-B is defined as the image having 'minimal' opacity, such that alpha_blend(B,A-B) = A.
    The 'min_alpha' argument is used to relax the alpha minimality constraint. When set to '1', alpha is constrained to be minimal. When set to '0', alpha is
    maximal (i.e. '255').
    Default value: 'minimize_alpha=1'.
    Example:
      [#1] image.jpg testimage2d {w},{h} +sub_alpha[0] [1] display_rgba
  11.20. Image Sequences and Videos
         --------------------------
  animate:
      filter_name,"param1_start,...,paramN_start","param1_end,...,paramN_end",nb_frames>=0,_output_frames={ 0:No | 1:Yes },_output_filename |
      delay>0,_back and forth={ 0:No | 1:Yes }
    Animate filter from starting parameters to ending parameters or animate selected images
    in a display window.
    Default value: 'delay=30'.
    Example:
      [#1] image.jpg animate flower,"0,3","20,8",9
  apply_camera:
      _"command",_camera_index>=0,_skip_frames>=0,_output_filename
    Apply specified command on live camera stream, and display it on display window [0].
    This command requires features from the OpenCV library (not enabled in G'MIC by default).
    Default values: 'command=""', 'camera_index=0' (default camera), 'skip_frames=0' and 'output_filename=""'.
  apply_files:
      "filename_pattern",_"command",_first_frame>=0,_last_frame={ >=0 | -1:Last },_frame_step>=1,_output_filename
    Apply a G'MIC command on specified input image files, in a streamed way.
    If a display window is opened, rendered frames are displayed in it during processing.
    The output filename may have extension '.avi' or '.mp4' (saved as a video), or any other usual image file
    extension (saved as a sequence of images).
    Default values: 'command=(undefined)', 'first_frame=0', 'last_frame=-1', 'frame_step=1' and 'output_filename=(undefined)'.
  apply_video:
      video_filename,_"command",_first_frame>=0,_last_frame={ >=0 | -1:Last },_frame_step>=1,_output_filename
    Apply a G'MIC command on all frames of the specified input video file, in a streamed way.
    If a display window is opened, rendered frames are displayed in it during processing.
    The output filename may have extension '.avi' or '.mp4' (saved as a video), or any other usual image
    file extension (saved as a sequence of images).
    This command requires features from the OpenCV library (not enabled in G'MIC by default).
    Default values: 'first_frame=0', 'last_frame=-1', 'frame_step=1' and 'output_filename=(undefined)'.
  average_files:
      "filename_pattern",_first_frame>=0,_last_frame={ >=0 | -1:Last },_frame_step>=1,_output_filename
    Average specified input image files, in a streamed way.
    If a display window is opened, rendered frames are displayed in it during processing.
    The output filename may have extension '.avi' or '.mp4' (saved as a video), or any other usual image
    file extension (saved as a sequence of images).
    Default values: 'first_frame=0', 'last_frame=-1', 'frame_step=1' and 'output_filename=(undefined)'.
  average_video:
      video_filename,_first_frame>=0,_last_frame={ >=0 | -1:Last },_frame_step>=1,_output_filename
    Average frames of specified input video file, in a streamed way.
    If a display window is opened, rendered frames are displayed in it during processing.
    The output filename may have extension '.avi' or '.mp4' (saved as a video), or any other usual image
    file extension (saved as a sequence of images).
    This command requires features from the OpenCV library (not enabled in G'MIC by default).
    Default values: 'first_frame=0', 'last_frame=-1', 'frame_step=1' and 'output_filename=(undefined)'.
  fade_files:
      "filename_pattern",_nb_inner_frames>0,_first_frame>=0,_last_frame={ >=0 | -1:Last },_frame_step>=1,_output_filename
    Generate a temporal fading from specified input image files, in a streamed way.
    If a display window is opened, rendered frames are displayed in it during processing.
    The output filename may have extension 'avi' or 'mp4' (saved as a video), or any other usual image
    file extension (saved as a sequence of images).
    Default values: 'nb_inner_frames=10', 'first_frame=0', 'last_frame=-1', 'frame_step=1' and 'output_filename=(undefined)'.
  fade_video:
      video_filename,_nb_inner_frames>0,_first_frame>=0,_last_frame={ >=0 | -1:Last },_frame_step>=1,_output_filename
    Create a temporal fading sequence from specified input video file, in a streamed way.
    If a display window is opened, rendered frames are displayed in it during processing.
    This command requires features from the OpenCV library (not enabled in G'MIC by default).
    Default values: 'nb_inner_frames=10', 'first_frame=0', 'last_frame=-1', 'frame_step=1' and 'output_filename=(undefined)'.
  files2video:
      "filename_pattern",_output_filename,_fps>0,_codec
    Convert several files into a single video file.
    Default values: 'output_filename=output.mp4', 'fps=25' and 'codec=mp4v'.
  median_files:
      "filename_pattern",_first_frame>=0,_last_frame={ >=0 | -1:Last },_frame_step>=1,_frame_rows[%]>=1,_is_fast_approximation={ 0:No | 1:Yes }
    Compute the median frame of specified input image files, in a streamed way.
    If a display window is opened, rendered frame is displayed in it during processing.
    Default values: 'first_frame=0', 'last_frame=-1', 'frame_step=1', 'frame_rows=20%' and 'is_fast_approximation=0'.
  median_video:
      video_filename,_first_frame>=0,_last_frame={ >=0 | -1:Last },_frame_step>=1,_frame_rows[%]>=1,_is_fast_approximation={ 0:No | 1:Yes }
    Compute the median of all frames of an input video file, in a streamed way.
    If a display window is opened, rendered frame is displayed in it during processing.
    This command requires features from the OpenCV library (not enabled in G'MIC by default).
    Default values: 'first_frame=0', 'last_frame=-1', 'frame_step=1', 'frame_rows=100%' and 'is_fast_approximation=1'.
  morph:
      nb_inner_frames>=1,_smoothness>=0,_precision>=0
    Create morphing sequence between selected images.
    Default values: 'smoothness=0.1' and 'precision=7'.
    Example:
      [#1] image.jpg +rotate 20,1,1,50%,50% morph 9
  morph_files:
      "filename_pattern",_nb_inner_frames>0,_smoothness>=0,_precision>=0,_first_frame>=0,_last_frame={ >=0 | -1:Last },_frame_step>=1,_output_filename
    Generate a temporal morphing from specified input image files, in a streamed way.
    If a display window is opened, rendered frames are displayed in it during processing.
    The output filename may have extension '.avi' or '.mp4' (saved as a video), or any other usual image
    file extension (saved as a sequence of images).
    Default values: 'nb_inner_frames=10', 'smoothness=0.1', 'precision=7', 'first_frame=0', 'last_frame=-1', 'frame_step=1' and
     'output_filename=(undefined)'.
  morph_rbf:
      nb_inner_frames>=1,xs0[%],ys0[%],xt0[%],yt0[%],...,xsN[%],ysN[%],xtN[%],ytN[%]
    Create morphing sequence between selected images, using RBF-based interpolation.
    Each argument (xsk,ysk)-(xtk,ytk) corresponds to the coordinates of a keypoint
    respectively on the source and target images. The set of all keypoints define the overall image deformation.
  morph_video:
      video_filename,_nb_inner_frames>0,_smoothness>=0,_precision>=0,_first_frame>=0,_last_frame={ >=0 | -1:Last },_frame_step>=1,_output_filename
    Generate a temporal morphing from specified input video file, in a streamed way.
    If a display window is opened, rendered frames are displayed in it during processing.
    The output filename may have extension '.avi' or '.mp4' (saved as a video), or any other usual image
    file extension (saved as a sequence of images).
    This command requires features from the OpenCV library (not enabled in G'MIC by default).
    Default values: 'nb_inner_frames=10', 'smoothness=0.1', 'precision=7', 'first_frame=0', 'last_frame=-1', 'frame_step=1' and
     'output_filename=(undefined)'.
  register_nonrigid:
      [destination],_smoothness>=0,_precision>0,_nb_scale>=0
    Register selected source images with specified destination image, using non-rigid warp.
    Default values: 'smoothness=0.2', 'precision=7' and 'nb_scale=0(auto)'.
    Example:
      [#1] image.jpg +rotate 20,1,1,50%,50% +register_nonrigid[0] [1]
  register_rigid:
      [destination],_smoothness>=0,_boundary_conditions={ 0:Dirichlet | 1:Neumann | 2:Periodic | 3:Mirror }
    Register selected source images with specified destination image, using rigid warp (shift).
    Default values: 'smoothness=0.1%' and 'boundary_conditions=0'.
    Example:
      [#1] image.jpg +shift 30,20 +register_rigid[0] [1]
  transition:
      [transition_shape],nb_added_frames>=0,100>=shading>=0,_single_frame_only={ -1:Disabled | >=0 }
    Generate a transition sequence between selected images.
    Default values: 'shading=0' and 'single_frame_only=-1'.
    Example:
      [#1] image.jpg +mirror c 100%,100% plasma[-1] 1,1,6 transition[0,1] [2],5
  transition3d:
      _nb_frames>=2,_nb_xtiles>0,_nb_ytiles>0,_axis_x,_axis_y,_axis_z,_is_antialias={ 0:No | 1:Yes }
    Create 3D transition sequence between selected consecutive images.
    'axis_x', 'axis_y' and 'axis_z' can be set as mathematical expressions, depending on 'x' and 'y'.
    Default values: 'nb_frames=10', 'nb_xtiles=nb_ytiles=3', 'axis_x=1', 'axis_y=1', 'axis_z=0' and 'is_antialias=1'.
    Example:
      [#1] image.jpg +blur 5 transition3d 9 display_rgba
  video2files:
      input_filename,_output_filename,_first_frame>=0,_last_frame={ >=0 | -1:Last },_frame_step>=1
    Split specified input video file into image files, one for each frame.
    First and last frames as well as step between frames can be specified.
    Default values: 'output_filename=frame.png', 'first_frame=0', 'last_frame=-1' and 'frame_step=1'.
  11.21. Convenience Functions
         ---------------------
  add_copymark:
    Add copymark suffix in names of selected images.
  alert:
      _title,_message,_label_button1,_label_button2,...
    Display an alert box and wait for user's choice.
    If a single image is in the selection, it is used as an icon for the alert box.
    Default values: 'title=[G'MIC Alert]' and 'message=This is an alert box.'.
  arg0:
      n>=0,_arg_0,...,_arg_N
    Return the n-th argument of the specified argument list (where 'n' goes from '0' to 'N').
  arg1:
      n>=1,_arg_1,...,_arg_N
    Return the n-th argument of the specified argument list (where 'n' goes from '1' to 'N').
  arg2var:
      variable_name,argument_1,...,argument_N
    For each i in [1...N], set 'variable_name$i=argument_i'.
    The variable name should be global to make this command useful (i.e. starts by an underscore).
  average_vectors:
    Return the vector-valued average of the latest of the selected images.
  base642img:
      "base64_string"
    Decode given base64-encoded string as a newly inserted image at the end of the list.
    The argument string must have been generated using command 'img2base64'.
  base642uint8:
      "base64_string"
    Decode given base64-encoded string as a newly inserted 1-column image at the end of the list.
    The argument string must have been generated using command 'uint82base64'.
  basename:
      file_path,_variable_name_for_folder
    Return the basename of a file path, and opt. its folder location.
    When specified 'variable_name_for_folder' must starts by an underscore
    (global variable accessible from calling function).
  bin:
      binary_int1,...
    Print specified binary integers into their octal, decimal, hexadecimal and string representations.
  bin2dec:
      binary_int1,...
    Convert specified binary integers into their decimal representations.
  cat:
      filename,_display_line_numbers={ 0:No | 1:Yes },_line_selection,
    Print specified line selection of given filename on stdout.
    Default values: 'display_line_numbers=1' and 'line_selection=^'.
  color2name:
      R,G,B
    Return the name (as a string, in English) that most matches the specified color.
  covariance_vectors:
      _avg_outvarname
    Return the covariance matrix of the vector-valued colors in the latest of the selected images
    (for arbitrary number of channels).
    Parameter 'avg_outvarname' is used as a variable name that takes the value of the average vector-value.
  da_freeze:
    Convert each of the selected dynamic arrays into a 1-column image whose height is the number of array elements.
  date:
    Return current date as a string 'YYYY/MM/DD'.
  dec:
      decimal_int1,...
    Print specified decimal integers into their binary, octal, hexadecimal and string representations.
  dec2str:
      decimal_int1,...
    Convert specifial decimal integers into its string representation.
  dec2bin:
      decimal_int1,...
    Convert specified decimal integers into their binary representations.
  dec2hex:
      decimal_int1,...
    Convert specified decimal integers into their hexadecimal representations.
  dec2oct:
      decimal_int1,...
    Convert specified decimal integers into their octal representations.
  filename:
      filename,_number1,_number2,...,_numberN
    Return a filename numbered with specified indices.
  filename_rand:
    Return a random filename for storing temporary data.
  filename_dated:
      filename
    Convert specified filename to one stamped with the current date ('filename_YYYYMMDD_HHMMSS.ext').
  files (+):
      _mode,path
    Return the list of files and/or subdirectories from specified path.
    'path' can be eventually a matching pattern.
    'mode' can be { 0:Files only | 1:Directories only | 2:Files + Directories }.
    Add '3' to 'mode' to return full paths instead of filenames only.
    Default value: 'mode=5'.
  files2img:
      _mode,path
    Insert a new image where each vector-valued pixel is a string encoding the filenames returned by command files.
    Useful to manage list of filenames containing characters that have a special meaning in the G'MIC language,such as spaces or commas.
  fitratio_wh:
      min_width,min_height,ratio_wh
    Return a 2D size 'width,height' which is bigger than 'min_width,min_height' and has the specified w/h ratio.
  fitscreen:
      width,height,_minimal_size[%]>0,_maximal_size[%]>0 |
      [image],_minimal_size[%]>0,_maximal_size[%]>0
    Return the 'ideal' size WxH for a window intended to display an image of specified size on screen.
    Default values: 'depth=1', 'minimal_size=128' and 'maximal_size=85%'.
  fontchart:
      display_mode.
    Insert G'MIC font chart at the end of the image list.
    'display_mode' can be { 0: List of characters | N: List of fonts with height 'N'}.
    Default value: 'display_mode=0'.
    Example:
      [#1] fontchart 0 fontchart 64
  fps:
    Return the number of time this function is called per second, or -1 if this info is not yet available.
    Useful to display the framerate when displaying animations.
  hex:
      hexadecimal_int1,...
    Print specified hexadecimal integers into their binary, octal, decimal and string representations.
  hex2dec:
      hexadecimal_int1,...
    Convert specified hexadecimal integers into their decimal representations.
  hex2img:
      "hexadecimal_string"
    Insert new image 1xN at the end of the list with values specified by the given hexadecimal-encoded string.
  hex2str:
      hexadecimal_string
    Convert specified hexadecimal string into a string.
    See also: str2hex.
  img2base64:
      _encoding={ 0:Base64 | 1:Base64url },_store_names={ 0:No | 1:Yes }
    Encode selected images as a base64-encoded string.
    The images can be then decoded using command 'base642img'.
    Default values: 'encoding=0' and 'store_names=1'.
  img2hex:
    Return representation of last image as an hexadecimal-encoded string.
    Input image must have values that are integers in [0,255].
  img2str:
    Return the content of the selected images, as special G'MIC input strings.
  indexof:
      string,_arg_0,...,_arg_N
    Return the lowest index 'i' such that 'arg_i==string'.
    If no 'arg_i' matches, '-1' is returned.
  is_mesh3d:
    Return 1 if all of the selected images are 3D meshes, 0 otherwise.
  is_change:
      _value={ 0:False | 1:True }
    Set or unset the 'is_change' flag associated to the image list.
    This flag tells the interpreter whether or not the image list should be displayed when the pipeline ends.
    Default value: 'value=1'.
  is_half:
    Return 1 if the type of image pixels is limited to half-float.
  is_ext:
      filename,_extension
    Return 1 if specified filename has a given extension.
  is_image_arg:
      string
    Return 1 if specified string is a valid single image argument like '[ind]'.
  is_pattern:
      string
    Return 1 if specified string looks like a drawing pattern '0x......'.
  is_videofilename:
      filename
    Return 1 if extension of specified filename is typical from video files.
  is_macos:
    Return 1 if current computer OS is Darwin (MacOS), 0 otherwise.
  is_windows:
    Return 1 if current computer OS is Windows, 0 otherwise.
  lof:
      feature
    Return the list of specified features (separated by commas) for each selected images.
  math_lib:
    Return string that defines a set of several useful macros for the embedded math evaluator.
  mad:
    Return the MAD (Maximum Absolute Deviation) of the last selected image.
    The MAD is defined as MAD = med_i|x_i-med_j(x_j)|
  max_w:
    Return the maximal width between selected images.
  max_h:
    Return the maximal height between selected images.
  max_d:
    Return the maximal depth between selected images.
  max_s:
    Return the maximal spectrum between selected images.
  max_wh:
    Return the maximal wxh size of selected images.
  max_whd:
    Return the maximal wxhxd size of selected images.
  max_whds:
    Return the maximal wxhxdxs size of selected images.
  median_vectors:
    Return the median vector value of the last selected image (median computed channel by channel)
  min_w:
    Return the minimal width between selected images.
  min_h:
    Return the minimal height between selected images.
  min_d:
    Return the minimal depth between selected images.
  min_s:
    Return the minimal s size of selected images.
  min_wh:
    Return the minimal wxh size of selected images.
  min_whd:
    Return the minimal wxhxd size of selected images.
  min_whds:
    Return the minimal wxhxdxs size of selected images.
  name2color:
      name
    Return the R,G,B color that matches the specified color name.
  nmd (+):
      Shortcut for command 'named'.
  named (+):
      _mode,"name1","name2",...
    Return the set of indices corresponding to images of the selection with specified names.
    After this command returns, the status contains a list of indices (unsigned integers),
    separated by commas (or an empty string if no images with those names have been found).
    (equivalent to shortcut command 'nmd').
    'mode' can be { 0:All indices (default) | 1:Lowest index | 2:Highest index | 3:All indices (case insensitive) | 4:Lowest index (case insensitive) | 5:Highest index
    (case insensitive)}
  narg:
      arg_1,arg_2,...,arg_N
    Return number of specified arguments.
  normalize_filename:
      filename
    Return a "normalized" version of the specified filename, without spaces and capital letters.
  oct:
      octal_int1,...
    Print specified octal integers into their binary, decimal, hexadecimal and string representations.
  oct2dec:
      octal_int1,...
    Convert specified octal integers into their decimal representations.
  path_cache:
    Return a path to store G'MIC data files for one user (whose value is OS-dependent).
  path_cached_file:
      "filename"
    Return path of the specified cached file, or "" if file has not been yet cached.
  path_current:
    Return current folder from where G'MIC has been run.
  path_gimp:
    Return a path to store GIMP configuration files for one user (whose value is OS-dependent).
  path_tmp:
    Return a path to store temporary files (whose value is OS-dependent).
  remove_copymark:
    Remove copymark suffix in names of selected images.
  reset:
    Reset global parameters of the interpreter environment.
  shell_cols:
    Return the estimated number of columns of the current shell.
  size_value:
    Return the size (in bytes) of image values.
  std_noise:
    Return the estimated noise standard deviation of the last selected image.
  str:
      string
    Print specified string into its binary, octal, decimal and hexadecimal representations.
  strbuffer:
      buffer_size
    Return a string describing a size for the specified buffer size.
  str2hex:
      "string"
    Convert specified string argument into a sequence of hexadecimal values (returned as a string).
    See also: hex2str.
    Example:
      [#1] hex=${"str2hex
      [gmic]-0./ Start G'MIC interpreter.
      [gmic]-0./ 48656c6c6f206d7920667269656e6473
      [gmic]-0./ End G'MIC interpreter.
  strcapitalize:
      string
    Capitalize specified string.
  strcontains:
      string1,string2
    Return 1 if the first string contains the second one.
  strclut:
      "string"
    Return simplified version of the specified string that can be used as a CLUT name.
  strlen:
      string1
    Return the length of specified string argument.
  strlowercase:
      string
    Return a lower-case version of the specified string.
  strreplace:
      string,search,replace
    Search and replace substrings in an input string.
  struppercase:
      string
    Return an upper-case version of the specified string.
  strvar:
      "string"
    Return a simplified version of the specified string, that can be used as a variable name.
    (version that creates a lowercase result, no longer than 128 chars).
  strcasevar:
      "string"
    Return a simplified version of the specified string, that can be used as a variable name.
    (version that keeps original case of specified string, no longer than 128 chars).
  strver:
      _version,_prerelease
    Return the specified version number of the G'MIC interpreter, as a string.
    Default value: 'version=$_version' and 'prerelease='.
  tic:
    Initialize tic-toc timer.
    Use it in conjunction with 'toc'.
  time:
    Return current time as a string 'hh:mm:ss'.
  toc:
    Display elapsed time of the tic-toc timer since the last call to 'tic'.
    This command returns the elapsed time in the status value.
    Use it in conjunction with 'tic'.
  uint82base64:
      _encoding={ 0:Base64 | 1:Base64url }
    Encode the values of the latest of the selected images as a base64-encoded string.
    The string can be decoded using command 'base642uint8'.
    Selected images must have values that are integers in [0,255].
    Default values: 'encoding=0'.
  11.22. Other Interactive Commands
         --------------------------
  demos:
      _run_in_parallel={ 0:No | 1:Yes | 2:Auto }
    Show a menu to select and view all G'MIC interactive demos.
  x_2048:
    Launch the 2048 game.
  x_blobs:
    Launch the blobs editor.
    ../img/x_blobs.jpg [image: 'x_blobs']
  x_bouncing:
    Launch the bouncing balls demo.
  x_color_curves:
      _colorspace={ rgb | cmy | cmyk | hsi | hsl | hsv | lab | lch | ycbcr | last }
    Apply color curves on selected RGB[A] images, using an interactive window.
    Set 'colorspace' to 'last' to apply last defined color curves without opening interactive windows.
    Default value: 'colorspace=rgb'.
  x_colorize:
      _is_lineart={ 0:No | 1:Yes },_max_resolution={ 0 | >=128 },_multichannels_output={ 0:No | 1:Yes },_[palette1],_[palette2],_[grabber1]
    Colorized selected B&W images, using an interactive window.
    When >0, argument 'max_resolution' defines the maximal image resolution used in the interactive window.
    Default values: 'is_lineart=1', 'max_resolution=1024' and 'multichannels_output=0'.
  x_connect4:
    Launch the Connect Four game.
  xz:
      Shortcut for command 'x_crop'.
  x_crop:
    Crop selected images interactively.
    If multiple input images are selected, the same crop is applied to all images.
    (equivalent to shortcut command 'xz').
  x_cut:
    Cut selected images interactively.
  x_fire:
    Launch the fire effect demo.
  x_fireworks:
    Launch the fireworks demo.
  x_fisheye:
    Launch the fish-eye effect demo.
  x_fourier:
    Launch the fourier filtering demo.
  x_grab_color:
      _variable_name
    Open a color grabber widget from the first selected image.
    Argument 'variable_name' specifies the variable that contains the selected color values at any time.
    Assigning '-1' to it forces the interactive window to close.
    Default values: 'variable_name=xgc_variable'.
  x_hanoi:
    Launch the Tower of Hanoi game.
  x_histogram:
    Launch the histogram demo.
  x_hough:
    Launch the hough transform demo.
  x_jawbreaker:
      0<_width<20,0<_height<20,0<_balls<=8
    Launch the Jawbreaker game.
  x_landscape:
    Launch the virtual landscape demo.
  x_life:
    Launch the game of life.
  x_light:
    Launch the light effect demo.
  x_mandelbrot:
      _is_julia={ 0:No | 1:Yes },_c0r,_c0i
    Launch Mandelbrot/Julia explorer.
  x_mask_color:
      _colorspace={ all | rgb | lrgb | ycbcr | lab | lch | hsv | hsi | hsl | cmy | cmyk | yiq },_spatial_tolerance>=0,_color_tolerance>=0
    Interactively select a color, and add an alpha channel containing the corresponding color mask.
    Argument 'colorspace' refers to the color metric used to compute color similarities, and can be basically
    one of { rgb | lrgb | ycbcr | lab | lch | hsv | hsi | hsl | cmy | cmyk | yiq }.
    You can also select one one particular channel of this colorspace, by setting 'colorspace' as
    'colorspace_channel' (e.g. 'hsv_h' for the hue).
    Default values: 'colorspace=all', 'spatial_tolerance=5' and 'color_tolerance=5'.
  x_metaballs3d:
    Launch the 3D metaballs demo.
  x_minesweeper:
      8<=_width=<20,8<=_height<=20
    Launch the Minesweeper game.
  x_minimal_path:
    Launch the minimal path demo.
  x_morph:
      _nb_frames>=2,_preview_fidelity={ 0:Coarsest | 1:Coarse | 2:Normal | 3:Fine | 4:Finest }
    Launch the interactive image morpher.
    Default values: 'nb_frames=16' and 'preview_fidelity=3'.
  x_pacman:
    Launch pacman game.
  x_paint:
    Launch the interactive painter.
  x_plasma:
    Launch the plasma effect demo.
  x_quantize_rgb:
      _nbcolors>=2
    Launch the RGB color quantization demo.
  x_reflection3d:
    Launch the 3D reflection demo.
  x_rubber3d:
    Launch the 3D rubber object demo.
  x_segment:
      _max_resolution={ 0:Auto | >=128 }
    Segment foreground from background in selected opaque RGB images, interactively.
    Return RGBA images with binary alpha-channels.
    Default value: 'max_resolution=1024'.
  x_select_color:
      _variable_name
    Display a RGB or RGBA color selector.
    Argument 'variable_name' specifies the variable that contains the selected color values (as R,G,B,[A])
    at any time.
    Its value specifies the initial selected color. Assigning '-1' to it forces the interactive window to close.
    Default value: 'variable_name=xsc_variable'.
  x_select_function1d:
      _variable_name,_background_curve_R,_background_curve_G,_background_curve_B
    Open an interactive window, where the user can defined its own 1D function.
    If an image is selected, it is used to display additional information :
       - The first row defines the values of a background curve displayed on the window (e.g. an histogram).
       - The 2nd, 3rd and 4th rows define the R,G,B color components displayed beside the X and Y axes.
    Argument 'variable_name' specifies the variable that contains the selected function keypoints at any time.
    Assigning '-1' to it forces the interactive window to close.
    Default values: 'variable_name=xsf_variable', 'background_curve_R=220', 'background_curve_G=background_curve_B=background_curve_T'.
  x_select_palette:
      _variable_name,_number_of_columns={ 0:Auto | >0 }
    Open a RGB or RGBA color selector widget from a palette.
    The palette is given as a selected image.
    Argument 'variable_name' specifies the variable that contains the selected color values (as R,G,B,[A])
    at any time.
    Assigning '-1' to it forces the interactive window to close.
    Default values: 'variable_name=xsp_variable' and 'number_of_columns=2'.
  x_shadebobs:
    Launch the shade bobs demo.
  x_spline:
    Launch spline curve editor.
  x_starfield3d:
    Launch the 3D starfield demo.
  x_tetris:
    Launch tetris game.
  x_threshold:
    Threshold selected images interactively.
  x_tictactoe:
    Launch tic-tac-toe game.
  x_tixy:
      "expression"
    Animate specified mathematical expression with a 16x16 grid of circles, using the rules described at https://tixy.land.
  x_warp:
      _nb_keypoints_xgrid>=2,_nb_keypoints_ygrid>=2,_nb_keypoints_contours>=0,_preview_fidelity={ 0:Coarsest | 1:Coarse | 2:Normal | 3:Fine | 4:Finest },_[background_image],
        0<=_background_opacity<=1
    Launch the interactive image warper.
    Default values: 'nb_keypoints_xgrid=nb_keypoints_ygrid=2', 'nb_keypoints_contours=0' and 'preview_fidelity=1'.
  x_waves:
    Launch the image waves demo.
  x_whirl:
      _opacity>=0
    Launch the fractal whirls demo.
    Default values: 'opacity=0.2'.
  12. Examples of Use
      ---------------
  'gmic' is a generic image processing tool which can be used in a wide variety of situations. The few examples below illustrate possible uses of this tool:
  ### View a list of images:
    $ gmic file1.bmp file2.jpeg
  ### Convert an image file:
    $ gmic input.bmp output output.jpg
  ### Create a volumetric image from a movie sequence:
    $ gmic input.mpg append z output output.hdr
  ### Compute image gradient norm:
    $ gmic input.bmp gradient_norm
  ### Denoise a color image:
    $ gmic image.jpg denoise 30,10 output denoised.jpg
  ### Compose two images using overlay layer blending:
    $ gmic image1.jpg image2.jpg blend overlay output blended.jpg
  ### Evaluate a mathematical expression:
    $ gmic echo "cos(pi/4)^2+sin(pi/4)^2={cos(pi/4)^2+sin(pi/4)^2}"
  ### Plot a 2D function:
    $ gmic 1000,1,1,2 fill "X=3*(x-500)/500;X^2*sin(3*X^2)+(!c?u(0,-1):cos(X*10))" plot
                        ../img/example_plot.png [image: '2D Plot']
  ### Plot a 3D elevated function in random colors:
    $ gmic 128,128,1,3,"u(0,255)" plasma 10,3 blur 4 sharpen 10000 n 0,255 elevation3d[-1] "'X=(x-64)/6;Y=(y-64)/6;100*exp(-(X^2+Y^2)/30)*abs(cos(X)*sin(Y))'"
                        0../img/example_elevation3d.png [image: '3D Elevation']
  ### Plot the isosurface of a 3D volume:
    $ gmic mode3d 5 moded3d 5 isosurface3d "'x^2+y^2+abs(z)^abs(4*cos(x*y*z*3))'",3
                        "../img/example_isosurface3d.png [image: '3D Isosurface']
  ### Render a G'MIC 3D logo:
    $ gmic 0 text G'MIC,0,0,53,1,1,1,1 expand xy,10 blur 1 normalize 0,100 +plasma 0.4 add blur 1 elevation3d -0.1 moded3d 4
                        ,../img/example_logo.png [image: '3D G'MIC Logo']
  ### Generate a 3D ring of torii:
    $ gmic repeat 20 torus3d 15,2 color3d[-1] "{u(60,255)},{u(60,255)},{u(60,255)}" *3d[-1] 0.5,1 if "{$>%2}" rotate3d[-1] 0,1,0,90 fi add3d[-1] 70 add3d rotate3d 0,0,1,18 done
   moded3d 3 mode3d 5
                        ../img/example_torii.png [image: '3D Ring']
  ### Create a vase from a 3D isosurface:
    $ gmic moded3d 4 isosurface3d "'x^2+2*abs(y/2)*sin(2*y)^2+z^2-3',0" sphere3d 1.5 sub3d[-1] 0,5 plane3d 15,15 rotate3d[-1] 1,0,0,90 center3d[-1] add3d[-1] 0,3.2 color3d[-1] 180,
   150,255 color3d[-2] 128,255,0 color3d[-3] 255,128,0 add3d
                        ../img/example_vase.png [image: '3D Vase']
  ### Launch a set of interactive demos:
    $ gmic demos
  ** G'MIC comes with ABSOLUTELY NO WARRANTY; for details visit: https://gmic.eu **