G'MIC(1) General Commands Manual 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 3.5.4
(https://gmic.eu)
Copyright (c) 2008-2025, David Tschumperle / GREYC / CNRS.
(https://www.greyc.fr)
1. Usage
-----
gmic [command1 [arg1_1,arg1_2,..]] .. [commandN [argN_1,argN_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 3.5.4.
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 33 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.
* Reccurent 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 (default) | lzw |
jpeg }. '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 '$<': The increasing/decreasing index of the latest
(currently running) 'repeat...done' loop. '$>' goes from '0'
(first loop iteration) to 'nb_iterations - 1' (last iteration).
'$<' does the opposite.
- '$/': 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'.
- '$_version': A 3-digits number telling about the current
version of
the G'MIC interpreter (e.g. '354').
- '$_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()', 'arg()', 'arg0()',
'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()', 'max()',
'maxabs()', 'med()', 'min()', 'minabs()',
'narg()', 'prod()', 'rad2deg()', 'rol()' (left bit
rotation), 'ror()' (right bit rotation), 'round()', 'sign()',
'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').
* 'arg(i,a_1,..,a_N)' returns the 'i'-th argument 'a_i'.
* '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 | 3: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).
* 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,_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,_use_LU,_lambda)' (matrix
inverse), 'mul(A,B,_nb_colsB)' (matrix-matrix multiplication),
'rot(u,
v,w,angle)' (3D rotation matrix), 'rot(angle)' (2D rotation matrix),
'solve(A,B,_nb_colsB,_use_LU)' (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)' 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)'.
* 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)', viewed 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_string)' returns a
permuted version of the image 'A' (of size '(wA,hA,dA,sA)', viewed
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)', viewed 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)', viewed 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.
* '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.
* 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 viewed as
a
min-heap structure.
* '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
content of the specified target (often vector-valued) using a given
expression,
e.g. 'V = vector16(); fill(V,k,k^2 + k + 1);'. For a vector-valued
target,
it is basically equivalent to: 'for (index_name = 0,
index_name<size(target), ++index_name, target[index_name] = expr);'.
* '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',
viewed 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', viewed 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
'expression' vanishes 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).
* '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 image data are large enough and you have several CPUs
available, it
is likely that the math expression passed to a 'fill', 'eval' or
'input' commands is evaluated in parallel, using multiple
computation
threads.
* Starting an expression with ':' or '*' forces the evaluations
required for an image to be run in parallel, even if the amount of
data to
process is small (beware, it may be slower to evaluate in this
case!). Specify
':' (rather than '*') to avoid possible image copy done before
evaluating the expression (this saves memory, but do this only if
you are sure
this step is not required!)
* Expression starting with '+' are evaluated in a single-threaded
way,
with possible image copy.
* If the specified expression starts with '>' or '<', the pixel
access operators 'i()', 'i[]', 'j()' and 'j[]' return
values of the image being currently modified, in forward ('>') or
backward ('<') order. The multi-threading evaluation of the
expression is
disabled in this case.
* Function 'critical(expr)' forces the execution of the given
expression
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.'
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 filenames 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.shtml
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 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=1'.
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_cube:
"filename"
Output selected CLUTs as a .cube file (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'.
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_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_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/tutorial/trigometric-and-inverse-trigo-
metric-commands.shtml
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/tutorial/trigometric-and-inverse-trigo-
metric-commands.shtml
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/tutorial/trigometric-and-inverse-trigo-
metric-commands.shtml
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/tutorial/trigometric-and-inverse-trigo-
metric-commands.shtml
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/tutorial/trigometric-and-inverse-trigo-
metric-commands.shtml
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
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)}
| (+):
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/tutorial/trigometric-and-inverse-trigo-
metric-commands.shtml
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/tutorial/trigometric-and-inverse-trigo-
metric-commands.shtml
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
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 33
tensor fields.
Tutorial: https://gmic.eu/tutorial/_eigen2tensor.shtml
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.shtml
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/_index.shtml
ir:
Shortcut for command 'inrange'.
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%
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.shtml
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_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/_norm.shtml
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.shtml
oneminus:
For each selected image, compute one minus image.
Example:
[#1] image.jpg normalize 0,1 +oneminus
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
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
clut:
"clut_name",_resolution>0,_cut_and_round={ 0:No | 1:Yes }
Insert one of the 1149 pre-defined CLUTs at the end of the image
list.
'clut_name' can be { 12_years_a_slave | 1917 | 2-strip-process |
60s | 60s_faded | 60s_faded_alt | 7drk_21 | action_magenta_01 |
action_red_01 | ad_astra | adventure_1453 |
agfa_apx_100 | agfa_apx_25 | agfa_precisa_100 |
agfa_ultra_color_100 | agfa_vista_200 |
agressive_highligjtes_recovery_5 | 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 | 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_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_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 |
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_rec709_basic |
slog_to_rec709_contrasty | slog_to_rec709_crush_shadows |
slog_to_rec709_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 | tasdemirrr_1 | taiga | 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 | 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
hald2clut:
Convert selected 2D HaldCLUTs to 3D CLUTs.
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_colors' entries, to index
selected images.
Set 'nb_levels==0' to extract all existing colors of an image.
'sort_vectors' can be { 0:Unsorted | 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/tutorial/_colormap.shtml
compose_channels:
Compose all channels of each selected image, using specified
arithmetic operator (+,-,or,min,...).
Default value: '1=+'.
Example:
[#1] image.jpg +compose_channels and
Tutorial: https://gmic.eu/tutorial/compose_channels
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/tutorial/_fill_color.shtml
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]
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_icp:
[reference_image],_precision>0,_transformation_variable
Transform selected set of d-dimensional vectors to match specified
set of reference vectors, using ICP (Iterative Closest Point)
algorithm.
A description of ICP is available at
https://en.wikipedia.org/wiki/Iterative_closest_point.
Return the L2 alignment error.
Default values: 'precision=1e-2' and
'transformation_variable=(undefined)'.
sample lena,earth +match_icp[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:
[target],_gamma>=0,_regularization>=0,_luminosity_constraints>=0,_rgb_resolution>=0,_is_constraints={
0:No | 1:Yes }
Transfer colors from selected source images to selected reference
image (given as argument).
'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 33 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 333333 random 3D color LUT.
Example:
[#1] image.jpg random_clut +map_clut.. .
random_clut:
_seed = { >=0 | -1 }
Generate a 333333 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'.
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/tutorial/_select_color.shtml
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 Tschumperle.
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%
diagonal:
Transform selected vectors as diagonal matrices.
Example:
[#1] 1,10,1,1,'y' +diagonal
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'.
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_string
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'.
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 ,
scalex:
Triple XY-size of selected images, using the Scale3 algorithm.
Example:
[#1] image.jpg threshold 50% resize 33%,33% +scale3
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 +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 |
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 too).
(equivalent to shortcut command 's').
'split_mode' can be { 0:Split according to constant values |
>0:Split in N parts | <0:split in parts of size -N }.
Default value: 'split_mode=-1'.
Example:
[#1] image.jpg split c
[#2] image.jpg split y,3
[#3] image.jpg split x,-128
[#4] 1,20,1,1,"1,2,3,4" +split -,2,3 append[1--1] y
[#5] (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/tutorial/_bandpass.shtml
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/tutorial/_blur.shtml
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/tutorial/_blur_angular.shtml
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/tutorial/_blur_linear.shtml
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/tutorial/_blur_radial.shtml
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%,128,1,1 blur 5% 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
compose_freq:
Compose selected low and high frequency parts into new images.
Example:
[#1] image.jpg split_freq 2% mirror[-1] x compose_freq
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/tutorial/_convolve.shtml
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/tutorial/_dct-and-idct.shtml
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 +denoise 5,5,8
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/tutorial/_deriche.shtml
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/tutorial/_diffusiontensors.shtml
edges:
_threshold[%]>=0
Estimate contours of selected images.
Default value: 'edges=15%'
Example:
[#1] image.jpg +edges 15%
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/tutorial/_fft.shtml
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 }.
'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/tutorial/_gradient.shtml
gradient_norm:
Compute gradient norm of selected images.
Example:
[#1] image.jpg gradient_norm equalize
Tutorial: https://gmic.eu/tutorial/_gradient_norm.shtml
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/tutorial/_haar.shtml
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/tutorial/_dct-and-idct.shtml
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/tutorial/_fft.shtml
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,'!c?x-w/2:y-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^2*log(1e-5+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/tutorial/_smooth.shtml
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/tutorial/_structuretensors.shtml
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 noise 0.2,2 eq 1 +label_fg 0 voronoi[-1]
+gradient[-1] xy,1 append[-2,-1] c norm[-1] ==[-1] 0 map[-2] 2,2
mul[-2,-1] normalize[-2] 0,255 dilate_circ[-2] 4 reverse max
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/tutorial/_area.shtml
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 (+):
[source_image],_smoothness,_precision>=0,_nb_scales>=0,_iteration_max>=0,is_backward={
0:No | 1:Yes },_[guide]
Estimate displacement field between specified source 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=5', 'nb_scales=0',
'iteration_max=10000', 'is_backward=1' 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/tutorial/_distance.shtml
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'.
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/tutorial/_label.shtml
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'.
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
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/tutorial/_gaussian.shtml
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
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
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=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]
pack_sprites:
_nb_scales>=0,0<=_min_scale<=100,_allow_rotation={ 0:0 deg. |
1:180 deg. | 2:90 deg. | 3:any
},_spacing,_precision>=0,max_iterations>=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.
First selected image is the canvas that will be filled with the
sprites.
Its last channel must be a binary mask whose zero values represent
potential locations for drawing the sprites.
All other selected images represent the sprites considered for
packing.
Their last channel must be a binary mask that represents the sprite
shape (i.e. a 8-connected component).
The order of sprite packing follows the order of specified sprites
in the image list.
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.
'precision' tells about the desired number of failed trials before
ending the filling process.
Default values: 'nb_scales=5', 'min_scale=25', 'allow_rotation=3',
'spacing=1', 'precision=7' and 'max_iterations=256'.
Example:
[#1] 512,512,1,3,"min(255,y*c/2)" 100%,100% circle
50%,50%,100,1,255 append c image.jpg rescale2d[-1] ,24 to_rgba
pack_sprites 3,25
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/tutorial/_plasma.shtml
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/tutorial/_turbulence.shtml
yinyang:
Draw a yin-yang symbol on selected images.
Example:
[#1] 400,400 yinyang
11.11. Matrix Computation
------------------
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 33 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 (+):
_use_LU={ 0:SVD | 1:LU },_lambda>=0
Inverse selected matrices (or compute Moore-Penrose pseudoinverse
for non-square matrices).
SVD solver is slower but more precise than LU.
'lambda' is used only in the Moore-Penrose pseudoinverse, by
estimating A^t.(A^t.A + lambda.Id)^-1.
Default value: 'use_LU=0' and 'lambda=0'.
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'.
solve (+):
[image],_use_LU={ 0:SVD | 1:LU }
Solve linear system AX = B for selected B-matrices and specified A-
matrix.
If the system is under- or over-determined, the least squares
solution is returned.
Default value: 'use_LU=0'.
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 $>%2 rotate3d[-1] 0,1,0,90 fi add3d[-1] 70 add3d rotate3d[-1]
0,0,1,18 } double3d 0
animate3d:
nb_frames>0,_step_angle_x,_step_angle_y,_step_angle_z,_zoom_factor,0<=_fake_shadow_level<=100,_[background]
Generate 3D animation frames of rotating 3D objects.
Frames are stacked along the z-axis (volumetric image).
Frame size is the same as the size of the '[background]' image (or
800x800 if no background specified).
Default values: 'step_angle_x=0', 'step_angle_y=5',
'step_angle_z=0', 'zoom_factor=1', 'fake_shadow_level=50' and
'background=(undefined)'.
apply_camera3d:
pos_x,pos_y,pos_z,target_x,target_y,target_z,up_x,up_y,up_z
Apply 3D camera matrix to selected 3D objects.
Default values: 'target_x=0', 'target_y=0', 'target_z=0', 'up_x=0',
'up_y=-1' and 'up_z=0'.
apply_matrix3d:
a11,a12,a13,...,a31,a32,a33
Apply specified 3D rotation matrix to selected 3D objects.
Example:
[#1] torus3d 10,1 +apply_matrix3d
{mul(rot(1,0,1,-15<degree>),[1,0,0,0,2,0,0,0,8],3)} double3d 0
array3d:
size_x>=1,_size_y>=1,_size_z>=1,_offset_x[%],_offset_y[%],_offset_y[%]
Duplicate a 3D object along the X,Y and Z axes.
Default values: 'size_y=1', 'size_z=1' and
'offset_x=offset_y=offset_z=100%'.
Example:
[#1] torus3d 10,1 +array3d 5,5,5,110%,110%,300%
arrow3d:
x0,y0,z0,x1,y1,z1,_radius[%]>=0,_head_length[%]>=0,_head_radius[%]>=0
Input 3D arrow with specified starting and ending 3D points.
Default values: 'radius=5%', 'head_length=25%' and
'head_radius=15%'.
Example:
[#1] repeat 10 { a:=$>*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 double3d 0 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,_is_closed_curve={
0:No | 1:Yes }
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 'is_closed_curve=0'.
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]
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,_opacity
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 double3d 0 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. ${-rgb} } add3d
lines3d:
_length>=0
Convert specified 3D objects to sets of 3D horizontal segments with
specified length.
Default values: 'length=1'.
Example:
[#1] torus3d 100,40 +lines3d 20
lissajous3d:
resolution>1,a,A,b,B,c,C
Input 3D lissajous curves 'x(t)=sin(a*t+A*2*pi)',
'y(t)=sin(b*t+B*2*pi)', 'z(t)=sin(c*t+C*2*pi)'.
Default values: 'resolution=1024', 'a=2', 'A=0', 'b=1', 'B=0',
'c=0' and 'C=0'.
Example:
[#1] lissajous3d ,
m3d:
Shortcut for command 'mode3d'.
mode3d:
_mode
Set static 3D rendering mode.
(equivalent to shortcut command 'm3d').
'mode' can be { -1:Bounding-box | 0:Dots | 1:Wireframe | 2:Flat |
3:Flat-shaded | 4:Gouraud-shaded | 5:Phong-shaded }.
Bounding-box mode ('mode==-1') is active only for the interactive
3D viewer.
Default value: 'mode=4'.
Example:
[#1] (0,1,2,3,4,5) double3d 0 repeat w { torus3d 100,30
rotate3d[-1] 1,1,0,60 mode3d {0,@$>} 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
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
o3d:
Shortcut for command 'opacity3d'.
opacity3d:
opacity
Set opacity of selected 3D objects.
(equivalent to shortcut command 'o3d').
Example:
[#1] torus3d 100,10 double3d 0 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 double3d 0 +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 double3d 0 repeat 7 { +rotate3d[-1] 1,0,0,20
} add3d
rotation3d:
u,v,w,angle
Input 33 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] ${-rgb}
size3d:
Return bounding box size of the last selected 3D object.
skeleton3d:
_metric,_frame_type={ 0:Squares | 1:Diamonds | 2:Circles | 3:Auto
},_skeleton_opacity,_frame_opacity,_is_frame_wireframe={ 0:No | 1:Yes }
Build 3D skeletal structure object from 2d binary shapes located in
selected images.
'metric' can be { 0:Chebyshev | 1:Manhattan | 2:Euclidean }.
Default values: 'metric=2', 'bones_type=3', 'skeleton_opacity=1'
and 'frame_opacity=0.1'.
Example:
[#1] shape_cupid 480 +skeleton3d ,
snapshot3d:
_size>0,_zoom>=0,_backgroundR,_backgroundG,_backgroundB,_backgroundA,_fov_angle>=0
|
[background_image],zoom>=0,_fov_angle>=0
Take 2D snapshots of selected 3D objects.
Set 'zoom' to 0 to disable object auto-scaling.
Default values: 'size=1024', 'zoom=1',
'[background_image]=(default)' and 'fov_angle=45'.
Example:
[#1] torus3d 100,20 rotate3d 1,1,0,60 snapshot3d
400,1.2,128,64,32
[#2] torus3d 100,20 rotate3d 1,1,0,60 sample ? +snapshot3d[0]
[1],1.2
sl3d:
Shortcut for command 'specl3d'.
specl3d:
value>=0
Set lightness of 3D specular light.
(equivalent to shortcut command 'sl3d').
Default value: 'value=0.15'.
Example:
[#1] (0,0.3,0.6,0.9,1.2) repeat w { torus3d 100,30 rotate3d[-1]
1,1,0,60 color3d[-1] 255,0,0 specl3d {0,@$>} 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=0.8'.
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] - [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 a0D 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,"command1","command2",...
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/tutorial/_repeat.shtml
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 ::
Return the list of library functions that has to be included in a
math expression,in order to use the neural network library.
nn_add:
out,in0,_in1
Add an 'add' layer to the current network.
'in0' or 'in1' can be layer names or constant values (both cannot
be constant values though).
Default value: 'in1=. (previous layer)'.
nn_append:
out,in0,in1,axis={ x | y | z | c }
Add an 'append' layer to the current network.
nn_avgpool2d:
out,_in,_patch_size>1
Add a 'avgpool2d' layer (2D average pooling) to the current
network.
Default value: 'in=. (previous layer)'.
nn_avgpool3d:
out,_in,_patch_size>1
Add a 'avgpool3d' layer (3D average pooling) to the current
network.
Default value: 'in=. (previous layer)'.
nn_check_layer:
layer_name
Check that layer with specified name exists in the current network.
nn_clone:
name0,name1,_in
Add a 'clone' layer to the current network.
Default value: 'in=. (previous layer)'.
nn_conv2d:
out,in,nb_channels>0,_kernel_size>0,_stride>0,_dilation,_shrink>=0,_boundary_conditions,_learning_mode,_regularization>=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 }.
Default values: 'kernel_size=3', 'stride=1', 'dilation=1',
'shrink=0', 'boundary_conditions=1', 'learning_mode=3',
'regularization=0'
and 'initialization=2'.
nn_conv2dnl:
out,in,nb_channels>0,_kernel_size>0,_stride>0,_dilation,_shrink>=0,_boundary_conditions,_activation,_learning_mode,regularization>=0,_initialization
Add a 'conv2dnl' (2D convolutional layer followed by a non-
linearity) 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 }.
Default values: 'kernel_size=3', 'stride=1', 'dilation=1',
'shrink=0', 'boundary_conditions=1', 'activation=leakyrelu',
'learning_mode=3', 'regularization=0' and 'initialization=2'.
nn_conv2dnnl:
out,in,nb_channels>0,_kernel_size>0,_stride>0,_dilation,_shrink>=0,_boundary_conditions,_activation,_learning_mode,_regularization>=0,_initialization
Add a 'conv2dnnl' (2D convolutional layer followed by a
normalization layer, then a non-linearity) 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 }.
Default values: 'kernel_size=3', 'stride=1', 'dilation=1',
'shrink=0', 'boundary_conditions=1', 'activation=leakyrelu',
'learning_mode=3', 'regularization=0' and 'initialization=2'.
nn_conv3d:
out,in,nb_channels>0,_kernel_size>0,_stride>0,_dilation,_shrink>=0,_boundary_conditions,_learning_mode,_regularization>=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 }.
Default values: 'kernel_size=3', 'stride=1', 'dilation=1',
'shrink=0', 'boundary_conditions=1', 'learning_mode=3',
'regularization=0'
and 'initialization=2'.
nn_conv3dnl:
out,in,nb_channels>0,_kernel_size>0,_stride>0,_dilation>0,_shrink>=0,_boundary_conditions,_activation,_learning_mode,_regularization>=0,_initialization
Add a 'conv3dnl' (3D convolutional layer followed by a non-
linearity) 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 }.
Default values: 'kernel_size=3', 'stride=1', 'dilation=1',
'shrink=0', 'boundary_conditions=1', 'activation=leakyrelu',
'learning_mode=3', 'regularization=0' and 'initialization=2'.
nn_conv3dnnl:
out,in,nb_channels>0,_kernel_size>0,_stride>0,_dilation>0,_shrink>=0,_boundary_conditions,_activation,_learning_mode,_regularization>=0,_initialization
Add a 'conv3dnnl' (3D convolutional layer followed by a
normalization layer, then a non-linearity) 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 }.
Default values: 'kernel_size=3', 'stride=1', 'dilation=1',
'shrink=0', 'boundary_conditions=1', 'activation=leakyrelu',
'learning_mode=3', 'regularization=0' and 'initialization=2'.
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_distance:
out,in0,_in1,_metric={ 0:Squared-L2 | p>0:Lp-norm }
Add a 'distance' layer to the current network (distance between two
inputs, with specified metric).
Default value: 'in=. (previous layer)',
nn_div:
out,in0,_in1
Add a 'div' layer to the current network.
'in0' or 'in1' can be layer names or constant values (both cannot
be constant values though).
Default value: 'in1=. (previous layer)'.
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,_regularization>=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 }.
Default value: 'learning_mode=3', 'regularization=0' and
'initialization=2'.
nn_fcnl:
out,in,nb_channels>0,_activation,_learning_mode,_regularization>=0,_initialization
Add a 'fcnl' layer (fully connected layer followed by a non-
linearity) 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 }.
Default values: 'activation=leakyrelu', 'learning_mode=3',
'regularization=0' and 'initialization=2'.
nn_fcnnl:
out,in,nb_channels>0,_activation,_learning_mode,_regularization>=0,_initialization
Add a 'fcnnl' layer (fully connected layer followed by a
normalization layer, then a non-linearity) 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 }.
Default values: 'activation=leakyrelu', 'learning_mode=3',
'regularization=0' and 'initialization=2'.
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_load:
'filename.gmz',_include_trainer_data={ 0:No | 1:Yes }
Load and initialize network saved as a .gmz file.
Neural 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).
Default value: 'in1=. (previous loss)'.
nn_loss_binary_crossentropy:
out,in,ground_truth,_weight
Add a 'binary_crossentropy' loss to the current network (binary
cross entropy).
Default value: 'weight=1'.
nn_loss_crossentropy:
out,in,ground_truth,_weight
Add a 'crossentropy' loss to the current network (cross entropy).
Default value: 'weight=1'.
nn_loss_mse:
out,in,ground_truth,_weight
Add a 'mse' loss to the current network (mean-squared error).
Default value: 'weight=1'.
nn_loss_normp:
out,in,ground_truth,_metric>0,_weight
Add a 'normp' loss to the current network (||out -
ground_truth||_metric).
Default value: 'metric=1' and 'weight=1'.
nn_loss_softmax_crossentropy:
out,in,ground_truth,_weight
Add a 'softmax_crossentropy' loss to the current network (softmax
followed by cross entropy).
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: 'in=. (previous layer)', '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: 'in=. (previous layer)', 'patch_size=2' and
'is_maxabs=0'.
nn_mul:
out,in0,_in1
Add a 'mul' layer to the current network.
'in0' or 'in1' can be layer names or constant values (both cannot
be constant values though).
Default value: 'in1=. (previous layer)'.
nn_nl:
out,_in,_activation
Add a 'nl' (nonlinearity) layer to the current network.
'activation' can be { elu | gelu | leakyrelu | linear | relu |
sigmoid | sin | sinc | softmax | sqr | sqrt | swish | tanh }.
Default values: 'in=. (previous layer)' and 'activation=leakyrelu'.
nn_normalize:
out,_in,_normalization_mode,_learning_mode
Add a 'normalize' layer to the current network.
'normalization_mode' can be { 0:Global parameters | 1:Channel-by-
channel parameters }
'learning_mode' can be { 0:No learning | 1:Alpha only | 2:Beta only
| 3:Alpha+beta }
Default values: 'in=. (previous layer)','normalization_mode=0' and
'learning_mode=3'.
nn_patchdown2d:
out,_in,_patch_size>1
Add a 'patchdown2d' (2D downscale by patch) layer to the current
network.
Default values: 'in=. (previous layer)' and 'patch_size=2'.
nn_patchdown3d:
out,_in,_patch_size>1
Add a 'patchdown3d' (3D downscale by patch) layer to the current
network.
Default values: 'in=. (previous layer)' and 'patch_size=2'.
nn_patchup2d:
out,_in,_patch_size>1
Add a 'patchup2d' (2D upscale by patch) layer to the current
network.
Default values: 'in=. (previous layer)' and 'patch_size=2'.
nn_patchup3d:
out,_in,_patch_size>1
Add a 'patchup3d' (3D upscale by patch) layer to the current
network.
Default values: 'in=. (previous layer)' and 'patch_size=2'.
nn_print:
Print info on current neural network.
nn_rename:
out,_in
Add a 'rename' layer to the current network.
Default value: 'in=. (previous layer)'.
nn_resconv2dnl:
out,_in,_kernel_size>0,_dilation>0,_boundary_conditions,_activation,_learning_mode,_regularization>=0
Add a 'resconv2dnl' (residual 2D convolutional layer followed by a
non-linearity) 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 }.
Default values: 'in=. (previous layer)', 'kernel_size=3',
'dilation=1', 'boundary_conditions=1', 'activation=leakyrelu',
'learning_mode=3'
and 'regularization=0'.
nn_resconv3dnl:
out,_in,_kernel_size>0,_dilation>0,_boundary_conditions,_activation,_learning_mode,_regularization>=0
Add a 'resconv3dnl' (residual 3D convolutional layer followed by a
non-linearity) 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 }.
Default values: 'in=. (previous layer)', 'kernel_size=3',
'dilation=1', 'boundary_conditions=1', activation='leakyrelu',
'learning_mode=3' and
'regularization=0'.
nn_resfcnl:
out,_in,_activation,_learning_mode,_regularization>=0
Add a 'resfcnl' (residual fully connecter layer followed by a non-
linearity) to the current network.
'learning_mode' can be { 0:No learning | 1:Weights only | 2:Biases
only | 3:Weights+biases }.
Default values: 'in=. (previous layer)', 'activation=leakyrelu',
'learning_mode=3' and 'regularization=0'.
nn_reshape:
out,in,width>0,height>0,depth>0,spectrum>0
Add a 'reshape' layer to the current network.
nn_resize:
out,in,width[%]>0,_height[%]>0,_depth[%]>0,_spectrum[%]>0,_interpolation
Add a 'resize' layer to the current network.
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_select:
_network_name
Select network with specified name as the current one.
nn_size:
Return size of the current network (i.e. number of stored
parameters).
nn_split:
out0,out1,in,axis={ x | y | z | c },size0>0
Add a 'split' layer to the current network.
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 to the current network.
'in0' or 'in1' can be layer names or constant values (both cannot
be constant values though).
Default value: 'in1=. (previous layer)'.
nn_tconv2d:
out,in,nb_channels>0,_kernel_size>0,_stride>0,_dilation,_shrink>=0,_boundary_conditions,_learning_mode,_regularization>=0,_initialization
Add a 'tconv2d' layer (2D transposed 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 }.
Default values: 'kernel_size=3', 'stride=2', 'dilation=1',
'shrink=0', 'boundary_conditions=1', 'learning_mode=3',
'regularization=0'
and 'initialization=2'.
nn_tconv2dnl:
out,in,nb_channels>0,_kernel_size>0,_stride>0,_dilation,_shrink>=0,_boundary_conditions,_activation,_learning_mode,_regularization>=0,_initialization
Add a 'tconv2dnl' layer (2D transposed convolutional
layer+nonlinearity) 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 }.
Default values: 'kernel_size=3', 'stride=2', 'dilation=1',
'shrink=0', 'boundary_conditions=1', 'activation=leakyrelu'
'learning_mode=3', 'regularization=0' and 'initialization=2'.
nn_trainer:
out,_in,_learning_rate>0,_optimizer,_scheduler
Add a network trainer to the current network.
'optimizer' can be { sgd | rmsprop | adam | adamax }.
'scheduler' can be { constant | linear | exponential | adaptive }.
Default values: 'in=. (previous layer)', 'learning_rate=2.5e-4',
'optimizer=rmsprop' and 'scheduler=constant'.
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
Apply random smooth deformation on selected images.
'interpolation' can be { 0:None | 1:Linear | 2:Bicubic }.
Default value: 'amplitude=10'.
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
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 ,
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:
blue,burn,darken
[#4] command "ex : $""=arg repeat $""# +blend[0,1] ${arg{$>+1}}
text_outline[-1] Mode:
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:
luminance,multiply,negation,or,overlay
[#7] command "ex : $""=arg repeat $""# +blend[0,1] ${arg{$>+1}}
text_outline[-1] Mode:
saturation,screen,shapeaverage,softburn
[#8] command "ex : $""=arg repeat $""# +blend[0,1] ${arg{$>+1}}
text_outline[-1] Mode:
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=4'.
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=4', '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=4', '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=6' 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.'.
arg:
n>=1,_arg1,...,_argN
Return the n-th argument of the specified argument list.
arg0:
n>=0,_arg0,...,_argN
Return the n-th argument of the specified argument list (where 'n'
starts from '0').
arg2img:
argument_1,...,argument_N
Split specified list of arguments and return each as a new image
(as a null-terminated string).
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.
fibonacci:
N>=0
Return the Nth number of the Fibonacci sequence.
Example:
[#1] echo ${"fibonacci 10"}
[gmic]-0./ Start G'MIC interpreter.
[gmic]-0./ 55
[gmic]-0./ End G'MIC interpreter.
file_mv:
filename_src,filename_dest
Rename or move a file from a location $1 to another location $2.
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 subfolders from specified path.
'path' can be eventually a matching pattern.
'mode' can be { 0:Files only | 1:Folders only | 2:Files + folders
}.
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,_depth,_minimal_size[%],_maximal_size[%] |
[image],_minimal_size[%],_maximal_size[%]
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.
img2text:
_line_separator
Return text contained in a multi-line image.
Default value: 'line_separator= '.
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:
arg1,arg2,...,argN
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.
padint:
number,_size>0
Return a integer with 'size' digits (eventually left-padded with
'0').
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.
rgb:
Return a random int-valued RGB color.
rgba:
Return a random int-valued RGBA color.
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.
strreplace:
string,search,replace
Search and replace substrings in an input string.
strlowercase:
string
Return a lower-case version of the specified 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))'"
../img/example_elevation3d.png
[image: '3D Elevation']
### Plot the isosurface of a 3D volume:
$ gmic mode3d 5 moded3d 5 double3d 0 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
1../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 double3d 0
../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 **
G'MIC(1)
gmic 3.5.4 - Generated Sat May 10 08:07:17 CDT 2025