ImageMagick v6 Examples --
Color Modifications

ImageMagick Examples Preface and Index
Converting Color to Gray-Scale (Making grayscale images)
Image Level Adjustments (adjusting the colors in images)
Automatic Level Adjustments (automatic enhancement of images)
DIY Level Adjustments (general tinting operators)
Tinting Images (general tinting operators)
Global Color Modifiers
Replacing Colors in an Image (replacing individual colors)
Recoloring Images with Gradients
Miscellaneous Color Operators (special recoloring operators)
Color modification of images without changing the overall image itself is a very common requirement of ImageMagick. Whether it is to lighten or darken the image, or more drastic color modifications.

We will need a test image... Don't worry above how I actually generated this image, it is not important for the exercise. I did design it to contain a range of colors, transparencies and other features, specifically to give IM a good workout when used. [IM Output]

If you are really interested in the commands used to generate this image you can look at the special script, "generate_test", I use to create it.

Converting Color to Gray-Scale

Gray scale images can be very useful for many uses. Either as furthering the processing of the original image, or for use in background compositions. The best method of converting an image to gray-scale is to just ask IM to convert the image into a gray-scale Color Space representation for the image.

  convert  test.png  -colorspace Gray   gray_colorspace.png
[IM Output] ==> [IM Output]

Note how the blue is much darker than the red, due the weighting to match the intensity as they seem to appear to the human eye. That is 'red' is quite a bright color compared to 'blue' which looks darker.

However there a many other methods, and meanings of 'gray-scale'...

For example, you can drain all the color out of the the image by using "-modulate", to set all color saturation levels to zero.

  convert test.png  -modulate 100,0  gray_modulate.png
[IM Output]

Note how the IM 'green' color I used in my test image is not a pure green, but the half-bright green defined by the new SVG -- Scalable Vector Graphics standard. If you need a pure RGB green you can use the color 'lime' instead.

Another way is to use the FX DIY operator to average the three channels together to get a pure mathematical meaning of gray-scale.

  convert test.png -fx '(r+g+b)/3' gray_fx_average.png
[IM Output]

You can use the same technique to control the weighting of the individual color channels. For example this is the normal IM meaning of 'gray-scale' for an RGB image.

  convert test.png -fx '0.3*r+0.6*g+0.1*b' gray_diy.png
[IM Output]

You can also use 'intensity' if you want the same meaning within the "-fx" operator.

  convert  test.png  -fx intensity  gray_intensity.png
[IM Output]

Another technique is to simply add all three channels together and while the resulting image will not loose information due to 'quantum rounding' effects, you may loose information about the brightest colors.

  convert test.png -separate -background black \
          -compose plus -flatten   gray_added.png
[IM Output]

This grayscale image is particularly well suited for generating masks from Difference Images.

However as the FX DIY operator is interpreted, it can run very very slowly. For more complex operations you can use the simpler Evaluate Operator, "-evaluate".

For example here is a 2/5/3 ratio gray-scaled image, though I make no attempt to preserve the transparency channel of the original image.

  convert test.png -channel R -evaluate multiply .2 \
                   -channel G -evaluate multiply .5 \
                   -channel B -evaluate multiply .3 \
                   +channel -separate -compose add -flatten gray_253.png
[IM Output]

The above would suffer from 'quantization' effects for a ImageMagick compiled at a 'Q8' Quality Level. That is because the results of the "-evaluate" will be saved into a small 8 bit integer, used for image values. Only later are those values added together with the resulting loss of accuracy.

An ImageMagick compiled with 'Q16', or better still the HDRI, quality compile options will produce a much more exact result.

A similar technique can be used to generate a pure mathematical gray-scale, by directly averaging the three RGB channels equally.

  convert test.png -separate -average  gray_average.png
[IM Output]

However as you can see, I did not attempt to preserve the alpha channel of the resulting image.

Another fast alternative is to use the "-recolor" color matrix operator, which will let you specify the weighting of the three color channels.

  convert test.png -recolor '.2 .5 .3
                             .2 .5 .3
                             .2 .5 .3'   gray_recolor.png
[IM Output]

Basically the first tree numbers is the channel weighting for the resulting images red channel, next 3 for green, and the final three numbers for blue.

A much more interesting technique is to extract a variety of different meanings of brightness by extracting the appropriate Color Channel from various Color Space representations of the image. The first image is the normal recommended method.

  convert rose: -colorspace Gray                      channel_gray.gif
  convert rose: -colorspace CMYK -channel K -negate -separate channel_black.gif
  convert rose: -colorspace HSB  -channel B -separate channel_brilliance.gif
  convert rose: -colorspace HSL  -channel B -separate channel_lightness.gif
  convert rose: -colorspace YUV  -channel R -separate channel_luma.gif
[IM Output] ==> [IM Output]
[IM Output]
Neg Black
[IM Output]
[IM Output]
[IM Output]
Luma (Y)

Note that none of the gray-scale results are quite the same due to the different meanings of 'brightness' in the various colorspaces.

Alternatively you can use "-type" to tell IM to treat the image as gray-scale, when either reading or writing the image.

  convert  test.png  -type GrayScaleMatte  gray_type.png
[IM Output]

The "-type" setting is generally only used when an image is being read or written to a file. As such its action is delayed to the final write of the image. Its effect is also highly dependant on the capabilities of the image file format involved and is used to override ImageMagick's normal determination during that process. See the Type examples for more information.

Before IM v6.3.5-9 the above will have removed any transparency in the written image (equivalent of a "-type Grayscale") due to a bug. This was fixed as soon as I noted the problem and reported it. (There is a lesson here :-)

Image Level Adjustments

The most basic form of adjustment you can make to images are known as 'level' adjustments. This basically means taking the individual RGB color values (or even the matte/alpha channel values) and adjusting them so as to either stretch or compress those values.

As only channel values are being adjusted, they are best demonstrated on a gray-scale image, rather than a color image. However if you adjust all the color channels of an image by the same amount you can use them with color images, for the purposes of either enhancing, or adjusting the image. Do not confuse this with the more automatic form of level adjustments, which we will look at in the next major section of examples below, Auto-Level Adjustments. This function will do exactly the same operation regardless of the actual content of the image. It does not matter if the image is bright, or dark, or has a blue, or yellow tint. The operations are blind to the actual image content.

[IM Graph] In demonstrating these operations I will be using a modified "gnuplot" graph such as shown to the right, which I generate using a special script "im_graph_levels". The graph has a red line which maps the given original 'x' value (representing the gray-scale value of the top most gradient) to the 'y' value shown. The resulting color gradient is also shown underneath the input linear gradient.

The graph shown to right is of the IM "-noop" operator which actually does nothing to an image. As such each of the image's color values are just mapped to exactly the same value without change. The lower gradient is thus the same as the upper gradient.

Image Negation

The simplest and most basic global level adjustment you can make is to negate the image, using the "-negate" image operator.

Essentially this makes   white, black,   and   black, white,  , adjusting all the colors to match. That is it will make the color red, its complementary color of cyan,   and blue, yellow, etc.

You can see this with the mapping graph shown below, as I use the "-negate" operator on both the 'test' image and the standard IM 'rose' built-in image. Note how the lower gradient in the mapping graph image is now reversed, so that black and white are swapped, and the same reversal appearing in the negated 'test' image.

  convert  test.png  -negate  test_negate.png
  convert  rose:     -negate  rose_negate.gif
[IM Output]
[IM Output]
==> [IM Graph] ==> [IM Output]
[IM Output]

Internally negate is actually rather stupid. It handles the three color channels independently, and by default ignores the alpha or matte channel. If this was not the case, you would get a very silly result like this...

  convert  test.png -channel RGBA  -negate  negate_rgba.png
[IM Output] ==> [IM Output]

The image is negated, as you can see by the semi-transparent color gradient. But as the transparency channel has also been negated you loose all the opaque colors in the image. This is why the default setting for "-channel" is 'RGB'. See Color Channels for more information.

You can limit the negation to just one channel, say the green color channel. This may not seem very useful, but at times it is vitality important.

  convert  test.png -channel green  -negate  negate_green.png
[IM Output] ==> [IM Output]

The "-negate" operator is actually its own inverse. Doing two negations with the same "-channel" setting cancels each other out.

  convert  negate_green.png  -channel green  -negate  negate_restore.png
[IM Output] ==> [IM Output]

Negation is extremely common in image processing, particularly when dealing with gray-scale images as a step before or after other processing options. As such I recommend you play with it and keep it in mind whenever you are doing anything, as working with negated images can solve some otherwise difficult problems.

Direct Level Adjustments

The "-level" operator is the more general level adjustment operator. You basically give it two values a 'black_point' and a 'white_point', as well as an optional third value (gamma adjustment), which I will look at later.

What it does is map any color values in the image that is equal to or less than the 'black_point', and make them black (or a 0 value). Similarly, any color values which are equal to or brighter that the 'white_point' will make them white (or a Maximum value). The colors in between these two points are then 'stretched' linearly to fill the complete range of values.

The effect of this is to improve the contrast, enhancing the colors within an image. For example here is a 25% contrast enhancement of our test image, using the same values as shown by the graph.

As you commonly adjust both the black and white points by the same amount from the 0% and 100% amounts, you can just specify the 'black_point' only. The white point will be adjusted by the same amount inward.

  convert  test.png  -level 25%,75%  test_level.png
  convert  rose:     -level 25%      rose_level.gif
[IM Output]
[IM Output]
==> [IM Graph] ==> [IM Output]
[IM Output]

Note that 25% is a huge contrast enhancement for any image, but it clearly shows what it does.

You don't have to change both the 'black' and 'white' points. Instead it is quite permissible to just adjust only one end of the color range. For example we can make a very light, or a very dark rose image.

  convert  rose:  -level 0,75%     rose_level_light.gif
  convert  rose:  -level 25%,100%  rose_level_dark.gif
[IM Graph] ==> [IM Output]    [IM Graph] ==> [IM Output]

However I again warn you that the colors outside the given range are 'clipped' or 'burned', and as such will no longer be available for later image processing. This is the biggest problem with using a "-level" operator.

[IM Graph] By using a negative value you can do some rough de-contrasting of an image.

What this means is that rather than providing a color value for the values to be mapped to 'black' and 'white' and thus stretching out the colors in between, you instead compress the color values so as to map the imaginary negative color to black or white. The result is a general graying of the image.

  convert  rose:  -level -25%  rose_decontrast.gif
[IM Output]

This method of decontrasting an image however is very inaccurate and not recommended, unless you have a IM older than version 6.4.2 where you don't have access to the new Reversed Level Operator.

[IM Graph] You can use the "-level" operator to negate an image, just by swapping the 'black' and 'white' point values given, using "-level 100%,0".
[IM Graph] Or by setting them to the same value, you can effectively call all the color values in the image to be thresholded. Using "-level" to threshold an image is exactly the same as if you used the "-threshold" operator with the value. The mapping graph shown right, shows the results of a "-level 50%,50%" operation, and its effect on a grayscale gradient.

And here is the result of applying this thresholding "-level" operation on the built-in rose image. Note that unlike "-threshold" the image is not converted to a grayscale image as part of the thresholding, when used with the default "-channel" setting (see Thresholding Images).

  convert  rose:  -level 50%,50%  rose_level_thres.gif
[IM Output]

The general nature of level to linearly modify an image, makes the "-level" operator a very good for general gray-scale image, and mask adjustments. Add the fact that you can modify individual channels (using the "-channel" setting) as opposed to the whole image, makes it one of the basic color modification operators available to IM users.

Be warned that the "-level" operator treats the transparency channel as 'matte' values. As such 100% is fully transparent and 0% is opaque. Please take this into account when using -level with a blurred shape image. This is most typically done after blurring an 'shape' image, to expand and stretch the results. For examples of this see Soft Edges, and Shadow Outlines.

Note you can also use the Evaluate and Function Operators for a more direct mathematical modification of the color values, to achieve the same results for -level both + and - forms).

Reversed Level Adjustments

As of IM version 6.4.2 the Level Operator was expanded to provide a 'reversed' form "+level" (note the 'plus'). Alternatively you can use the original "-level" form of the operator but add a '!' to the level argument given (for older API interfaces).

The arguments for this variant is exactly the same, but instead of stretching the values so as to map the 'black_point' and 'white_point' to 'black' and 'white', it maps 'black' and 'white' to the given points. In other words "+level" is the exact reverse of "-level".

For example here we map 'black' to a 25% gray, and white to 75% gray, effectively de-contrasting the image in a very exact way.

  convert  test.png  +level 25%   test_level_plus.png
  convert  rose:     +level 25%   rose_level_plus.gif
[IM Output]
[IM Output]
==> [IM Graph] ==> [IM Output]
[IM Output]

If you compare the above "+level 25%" operation with the use of a a negative de-contrasting, "-level -25%" operator we showed previously, you will see that are not the same. The 'plus' version produces a much stronger de-contrasted image (it is greyer), but does so by mapping to the exact values you give the operator, and not the 'imaginary' values the 'minus' form used. This exact value usage is important, and one of the reasons why the 'plus' form of the operator was added.

Of course a '25%' is again a very large value, and it is not recommended for use with typical image work.

Note that the "-level" and "+level", are in actual fact the exact reverse of each other when given the same argument. That is one maps values to the range extremes, while the other maps from the range extremes.

However while you can use one to 'undo' the other, the result may not be exactly the same, due to 'clipping' and 'quantum rounding' effects on the image values.

For example here we compress the colors of the test image using "+level", then decompress them again using "-level", so as to restore the image close to its original appearance.

  convert  test.png  +level 20%  -level 20%  test_level_undo.png
[IM Output] ==> [IM Graph] ==> [IM Output]

The two images appear to be very very similar, and as I am using a high quality 'Q16' version of IM, you will be hard pressed to notice any difference at all. However the values may not be exactly the same (especially with a Q8 version of IM), as you have effectively compressed, the un-compressed the image, which can produce some 'rounding' effects (unless you use a floating point HDRI version of ImageMagick).

Unfortunately, doing these two operations in the opposite order (stretch, then compress the color values) produces a very different result...

  convert  test.png  -level 20%  +level 20%  test_level_undo2.png
[IM Output] ==> [IM Graph] ==> [IM Output]

Notice how the center values of the color range are restored correctly, but the bright and dark ends have been clipped during the stretching by the "-level" operator. Then when the "+level" operator was applied the 'clipped' or 'burned' values was moved to the given grey levels, effectively clipping the brightest and darkest pixels to specific values. This can be a extremely useful technique.

One other useful aspect of the "+level" operator is that you can completely compress all the color values in an image to the same gray-scale level.

  convert  test.png  +level 30%,30%  test_level_const.png
[IM Output] ==> [IM Graph] ==> [IM Output]

By specifying levels according to the values of specific colors for each individual channel, you can effectively convert a greyscale gradient into a specific color gradient. However this is rather difficult to calculate and do. As such a "-level-colors" operator has also been provided that will let you specify the black and white points in terms of specific colors rather than 'level' values. See Level by Color below.

Level Gamma Adjustments

Both the above "-level" variants also allow you to use a third setting. The 'gamma' adjustment value. By default this is set to a value of 1.0', which does not do any sort of mid-tone adjustment of the resulting image, producing a pure linear mapping of the values from the old image to the new image.

However by making this value larger, you will curve the resulting line so as to brighten the image, while shrinking that value will darken the image.

For example here I use just the 'gamma' setting to brighten and darken just the mid-tones of the image.

  convert  rose:  -level 0%,100%,2.0   rose_level_gamma_light.gif
  convert  rose:  -level 0%,100%,0.5   rose_level_gamma_dark.gif
[IM Graph] ==> [IM Output]    [IM Graph] ==> [IM Output]

Values generally range from 10 for a blinding bright image, to .2 for very dark image. As mentioned a value of 1.0 will make no 'gamma' changes to the image. However the special value of '2.0' (see above) can be used to get the square root of the images normalized color.

Both versions of the "-level" operate handles 'gamma' in the same way. This means you can combine the level adjustment of the 'black' and 'white' ends with a non-linear 'gamma' adjustment. You can also only adjust a single channel of an image. For example, here we give an image a subtle tint at the black end of just the blue channel, while using gamma to preserve the mid-tone color levels of the image.

  convert  test.png  -channel B +level 25%,100%,.6 test_blue_tint.png
[IM Output] ==> [IM Graph] ==> [IM Output]

This specific example, could be used to tint a weather satellite photo, where only the sea is pure black, while land is more grey. Other alturnatives to this blue channel adjustment is given below in DIY Mathematical Non-linear Adjustments.

The "-gamma" operator is also provided, and has exactly the same effect as the 'gamma' setting in the "-level" operator. However it will let you adjust the 'gamma' adjustment level for each individual channel as well.

For example here we brighten the image differently for each individual RGB channel.

  convert  rose:  -gamma 0.8,1.3,1.0  gamma_channel.gif
[IM Output]

As you can see this can be used to do some subtle tinting and color adjustments to an image, or correct images with contain too much of a specific color.

One of the most important things when resizing, filtering or modifying images (even more important anything else) is to do it in linear space, so if your image is gamma corrected, you should transform it to linear space, scale and then transform back to gamma space.

One less obvious use of "-gamma" is to zero out specific image channels (see Zeroing Color Channels). Or color an image completely 'black', 'white' or some other primary color (see Primary Colored Canvases).

Level Adjustment by Color

The "-level-colors" operator was added to IM v6.2.4-1. Essentially, it is exactly the same as the Level Operator we discussed above, but with the value for each channel specified as a color value.

That is the "-level-colors" option will map the given colors to 'black' and 'white' and stretching all the other colors between then linearly. This effectively removes the range of colors given from the image.

And while this works, it is not particularly useful, as it is prone to fail for colors which have common values in some channel. For example the colors 'DodgerBlue' and 'White' have the same color values in the blue channel, as such "-level-colors DodgerBlue,White" will not convert those colors to black and white.

The better technique in that case is to extract a greyscale image of the channel with the highest differences (such as red) and level or normalize that channel.

WARNING: watch out for 'transparent' colors.

The plus form of the operator "+level-colors" on the other hand is extremely useful as it will map the 'black' and 'white' color to the given values compressing all the other colors linearly to fit the two colors you give.

For example lets map 'black' and 'white' to 'green', and 'gold'...

  convert  test.png  +level-colors green,gold   levelc_grn-gold.png
[IM Output] ==> [IM Output]

As you can see the grayscale gradient is remapped into the a gradient bound by the colors given, and although colors outside a gray-scale range are also modified, they will also follow the basic style of the color range specified. This makes the "+level-colors" operator an extremely useful one, especially when mapping grayscale images.

If you only supply one colorname but include a comma, the missing color will default either to 'black' or 'white' as appropriate.

  convert test.png  +level-colors ,DodgerBlue   levelc_dodger.png
  convert test.png  +level-colors ,Gold         levelc_gold.png
  convert test.png  +level-colors ,Lime         levelc_lime.png
  convert test.png  +level-colors ,Red          levelc_red.png

  convert test.png  +level-colors Navy,         levelc_navy.png
  convert test.png  +level-colors DarkGreen,    levelc_darkgreen.png
  convert test.png  +level-colors Firebrick,    levelc_firebrick.png
[IM Output] [IM Output] [IM Output] [IM Output]
[IM Output] [IM Output] [IM Output]

This makes it easy to convert grayscale images into a gradient for any color you like, without aliasing problems you may get using Direct Color Replacement.

  convert cow.gif   +level-colors red,   cow_red.gif
[IM Output] ==> [IM Output]

Of course if you want one of the colors to be made transparent instead you are better off using the -alpha Shape operator instead.

If you only specify a single color without any 'comma' separator, that color will be used for both black and white points. That means all the colors in the image will be reset to that one color. (according to the current "-channel" setting limitations).

  convert  test.png  +level-colors dodgerblue  levelc_blue.png
[IM Output]

If you want to set the images transparency setting as well you will need to set "-channel" to include the transparency channel, OR set the Alpha Channel to fully-opaque, using either "-alpha opaque" or "-alpha off.

  convert  test.png -channel ALL +level-colors dodgerblue levelc_blue2.png
[IM Output]

Also see Blanking Existing Images.

Here are a few more examples of using this to adjust or 'tint' a colorful image, rather than a gray-scale image.

  convert rose: +level-colors             navy,lemonchiffon  levelc_faded.gif
  convert rose: +level-colors        firebrick,gold          levelc_fire.gif
  convert rose: +level-colors 'rgb(102,75,25)',lemonchiffon  levelc_tan.gif
[IM Output] ==> [IM Output] [IM Output] [IM Output]

In summary the "+level-colors" is a gradient color replacement, a linear tinting operator, and can also completely reset colors.

Sigmoidal Non-linearity Contrast

From a PDF paper on 'Fundamentals of Image Processing' (page 44) they present an alternative from the linear contrast control with gamma correction known as 'sigmoidal non-linearity contrast control'.

The result is a non-linear, smooth contrast change (a 'Sigmoidal Function' in mathematical terms) over the whole color range, preserving the white and black colors, much better for photo color adjustments.

The exact formula from the paper is very complex, and even has a mistake, but essentially requires with two adjustment values. A threshold level for the the contrast function to center on (typically '50%'), and a contrast factor ('10 being very high, and '0.5' very low).

For those interested, the corrected formula for the 'sigmoidal non-linearity contrast control' is...
(1/(1+exp(β(α-u))) - 1/(1+exp(β))) / (1/(1+exp(β(α-u))/(1+exp(β))))
Where α is the threshold level, and β the contrast factor to be applied.

The formula is actually very simple exponential curve, with the bulk of the above formula is designed to ensure that 0 remains 0 and 1 remains one. That is the graph always goes though the points 0,0 and 1,1. And the highest gradient of change is at the given threshold.

Here for example is a "-fx" implementation of the above formula, resulting from a very high contrast value of '10' and a '50%' threshold value. These values have been rolled into the floating point constants, to speed up the function.

  convert test.png  -fx '(1/(1+exp(10*(.5-u)))-0.0066928509)*1.0092503' \
[IM Output] ==> [IM Graph] ==> [IM Output]

Lucky for us IM v6.2.1 had this complex function built in as a new operator "-sigmoidal-contrast", allowing a much simpler application.

    convert test.png  -sigmoidal-contrast 10,50% test_sigmoidal.png
[IM Output]

As a bonus IM also provides the inverse, a 'sigmoidal contrast reduction' function (as plus '+' form of the operator), which if applied with the same arguments restores our original image (almost exactly).

    convert test_sigmoidal.png +sigmoidal-contrast 10,50% \
[IM Output]

And here we apply it to the rose image...

    convert  rose:  -sigmoidal-contrast 10,50%  rose_sigmoidal.gif
[IM Output]

I did say '10' was a very heavy contrast factor. In fact anything higher than this value can be considered to me more like a fuzzy threshold operation, rather than a contrast enhancement.

For a practical example of using this operator see the advanced "Gel" Effects Example, where it is used to sharpen the bright area being added to a shaped area color.

Miscellaneous Contrast Operators

Under Construction

   -contrast  and   +contrast
         Rather useless minor contrast adjustment operator

   Threshold the image, any value less than or equal to the given value is
   set to 0 and anything greater is set to the maximum value.

   Note that like level, this is a channel operator, but if the default
   'channel setting' is used only the gray-scale intensity of the image is
   thresholded producing a black and white image.

   convert rose: -threshold 45%  x:

   You can force normal channel behaviour, where each channel is thresholded
   individually buy using "-channel All"

   convert rose: -channel All -threshold 45%  x:

   This is like -threshold except that only one side of the threshold value is
   actually modified.

   For example, here anything that is darker than 30% is set to black.

   convert rose: -black-threshold 30%  x:
   convert rose: -white-threshold 50%  x:

   These operators however do not seem to be channel effected, so may only be
   suitable for gray-scale images!

-colorize, and -tint
-opaque  -transparency floodfill -fuzz

Automatic Level Adjustments

Being able to adjust the color range of a gray scale image can be crucial to general image manipulation. This is generally known as Histogram Adjustment. The following are just some of the methods that can be used to do this.


     Try to stretch the image uniformly across all color channels so as to
     enhance the image.

     The channel operator can be used to 'disconnect' the uniformity across
     all channels, or alternatively you can separate and normalize each
     channel individually.  However when recombined you may find you get some
     color distortion due to each channel being stretched out unequally.

     Normalizing a intensity, or equivalent channel in some other colorspace
     can also be used to ensure correct normalization of the image without
     color distortions, though this is a much more trickier method.

     See also equalize, and (future) -equalize-gaussian, also look at Fred
     Weinhaus's scripts for various methods of image enhancements.

     In reality this operator is equivalent to  "-contrast-stretch 2%,99%"
     Which means at least 2% of the darkest color values will be set to zero
     (black) while the top 1% of the brightest pixels will be set to maximum
     (white) all the other pixels in the image are the stretched out to fill
     the whole range from black to white.

     If you do not which to 'burn' those top and bottom pixels, use
     "-contrast-stretch 0" to do a more 'logical normalize' of the image,
     without clipping or burning the top and bottom values.

     normalize the image so that the given percentage of pixels in a greyscale
     image are mapped to black and white respectively.

     That is -contrast-stretch 5% will make at least 5% of all pixels in a
     greyscale image black, and at least 5% of the pixels white.

     NOTE that -normalize is defined as   -contrast-stretch 2%,99%

     If you really want a 'true' normalize using the exact minimum and maximum
     values, use   -contrast-stretch 0

     ASIDE: This currently does the value remapping using 'binned' color
     values, as as a result produces a slightly distorted 'stretch'.
     We are endeavouring to have this fixed to stretch the color values using
     the internal -level operator.

     Example:  an image that does not span the histogram. You
     can show a histogram plot or list. Then do -contrast-stretch 0% to
     show that the min and max of the image are now at black and white,
     resp. I think this is a good example as it stretches the histogram
     just enough to span the range as opposed to normalize with clip or
     burns or whatever you want to call it by 2% on the low end and 1% on
     the high end.

     People need to know that they can stretch the image to just exactly span
     the histogram and do not have to use -normalize and lose data.

     Finds the 'level' points for stretching by counting the number of pixels
     from each end, until it exceeds the counts given.

-equalize         histogram equalization of the image
     When one wishes to compare two or more images on a specific basis, such
     as texture, it is common to first normalize their histograms to a
     "standard" histogram.  The most common histogram normalization technique
     is histogram equalization where one attempts to change the histogram so
     that all the histogram colors are spread out equally over all brightness
     values. This would correspond to a brightness distribution where all
     values are equally probable. Unfortunately, for an arbitrary image, one
     can only approximate this result.

     Is it a image comparison technique?

Expand or Normalize gray-scale

To expand the gray scale image so it occupies the full range of gray values (maximize contrast) is straight forward using the "-normalize" operator. That is, the lightest gray becomes white and darkest gray, black.

Here we create a gray-scale gradient, and expand it to the full black and white range.

  convert -size 150x100 gradient:gray70-gray30 gray_range.jpg
  convert gray_range.jpg  -normalize  normalize_gray.jpg
[IM Output] ==> [IM Output]

For practical reasons to do with JPEG color inaccuracies (see JPEG Color Distortion for more details) and scanned image noise, "-normalize" does not expand the very brightest and darkest colors, but a little beyond those values. That is it is equivalent to a "-contrast-stretch" with a value of '2%,99%' (see below).

This means if highest and lowest color values are very close together, "-normalize" will fail, an no action will be taken.

If you really want to expand the exact brightest and darkest color values to their extremes use "-contrast-stretch" with a value of '0' instead.

Up until IM version 6.2.5-5, "-normalize" worked purely as a grayscale operator. That is each of the red, green, blue, and alpha channels were expanded independently of each other according to the "-channel" setting. As of IM version 6.2.5-5, if only the default "+channel" setting 'RGB' is given, then "-normalize" will tie together all the color channels, and normalizes them all by the same amount. This ensures that any grayscale that is in the image, remains grayscale. However if non-grayscale colors are present, it may not expand the image to produce a pure white or black level.

For example here we added some extra colors (a blue to navy gradient) to our normalization test image.

  convert -size 100x100 gradient:gray70-gray30 \
          -size  50x100 gradient:blue-navy  +append  color_range.jpg
  convert color_range.jpg -normalize  normalize.jpg
[IM Output] ==> [IM Output]

As you can see from the last example, for color images "-normalize" maximized all the channels together so one channel has a zero value, and another channel has a maximum value. That is, no black pixels were generated, as all the added blue colors already contains 'zero' values in the 'red' and 'green' channels. As such the lower bounds of the image did not expand.

If you want the old "-normalize" behaviour, you will need to use a different "-channel" setting than the default 'RGB' setting. For images that contain no alpha (or matte) channel, you can just use the 'all' channel setting.

  convert color_range.jpg -channel all  -normalize   normalize_all.jpg
[IM Output]

Alternatively, you can normalize each channel as a separate image using the "-separate" operator (as of IM v6.2.9-2), then "-combine" them back into a single image again.

  convert color_range.jpg -separate -normalize -combine normalize_sep.jpg
[IM Output]

The results of the above turns the grayscale areas of the image yellow As the 'red' and 'green' channels lightened. The 'blue' channel however is only darkened slightly.

This brings use to an important point
Normalise is really a grayscale operator,
caution is needed when used with color images.

contrast-stretch -- controlled normalize

The "-contrast-stretch" (added IM v6.2.6), is a more controlled version of "-normalize". It first finds the maximum and minimum bounds in the image, as normal, but then shifts those bounds further inward by the given amount of color inward before selecting the colors that will be mapped to white and black.

In other words it is still a "-normalize" type of operator, but then ignores the most extreme colors by the amount given (generally as a percentage of gray scale).

For example, this will replace both the top and bottom 15% of colors with their extremes (white and black), stretching the rest of the color to improve the overall contrast.

  convert gray_range.jpg  -contrast-stretch 15%  stretch_gray.jpg
[IM Output] ==> [IM Output]

And here I just grab the brightest 5% of colors, stretching them linearly, and making all other colors black.

  convert gray_range.jpg  -contrast-stretch 95x100%  stretch_black.jpg
[IM Output]

This can be quite useful, to find bright points in images. It is a bit like a normalized version "-black-threshold" operator, but with the other colors stretched to fill the full color range, rather than just turning the thresholded color black.

Note that "-contrast-stretch" is not a true contrast operator, as it normalizes the image first. If you want to improve the contrast of an image by a fixed amount that is independent of the actual images current content, then you should use "-level" instead.

DIY Level Adjustments

Mathematical Linear Histogram Adjustments

The various basic forms of "Level Adjustments shown above linearly adjust the colors of the image.

These changes can be applied mathematically as well. For example by multiplying the image with a specific color, we set all pure white areas to that color. So lets just read in our image, create an image containing the color we want, then multiply the original image with this color using the IM free-form "-fx" or DIY Operator.

  convert test.png  -size 1x1 xc:Yellow \
          -fx 'u*v.p{0,0}'    fx_linear_white.png
[IM Output] ==> [IM Output]

By getting "-fx" to read the color from a second 'v' image makes it easy to change the color, without needing to convert colors to RGB values for use in the mathematics.

If you were using a fancy graphical image processing package like "Gimp" and "Photoshop" the above operation would have been applied to an image by adjusting the images color histogram graph 'curve'.

[IM Output] For example to the right is a "gnuplot" generated graph (See the script "im_histogram") of the mathematical formula showing what happens to just one of the three RGB channels. The original color (green line) is remapped to a darker color (red line) linearly.

Linearly tinting the black colors is also quite simple. For example to linear map 'black' to a gold like color 'rgb(204,153,51)', (while leaving 'white' as 'white'), would require a mathematical formula such as...
          result = 1-(1-color)*(1-intensity)
This formula negates the colors, multiples the image with the negated color wanted, and negates the image back again. The result is tinting of the black side of the gray scale, leaving white unchanged.

  convert test.png  -size 1x1 xc:'rgb(204,153,51)'  \
          -fx '1-(1-v.p{0,0})*(1-u)'   fx_linear_black.png
[IM Output] ==> [IM Output] ==> [IM Output]

A "gnuplot" histogram graph of the remapping formula is also displayed in the above for your reference.

With a slightly more complicated formula you can linearly replace both the 'black' and 'white' end of the grayscale with specific colors.

  convert test.png  -size 1x2  gradient:gold-firebrick \
          -fx 'v.p{0,0}*u+v.p{0,1}*(1-u)'   fx_linear_color.png
[IM Output] ==> [IM Output] ==> [IM Output]

The "-size 1x2 gradient:color1-color2" in the above is only used to generate a two color pixel image for the "-fx" formula to reference. The first color replaces white, while the second replaces black, while all others are interpolated between white and black. As is typical of a gray-scale operator, each RGB channel is treated as a separate gray scale channel, though the linear interpolation is different for each channel.

This by the way is exactly equivalent to the Level Adjustments by Color operator "+level-colors"

However unlike "+level-colors", the colors to use can of course come from any image source, and not just the color names provided as an argument. However even direct use of color names is posible.

  convert test.png   -fx "yellow*u+green*(1-u)"  fx_linear.png
[IM Output]

Mathematical Non-linear Histogram Adjustments

While linear color adjustments are important, and faster methods ar available, there are many situations where a linear 'level' adjustment, is not what is wanted, and this is where the "-fx" DIY Operator, becomes more useful.

Well an alternative formula for linear adjustment is "-fx 'v.p{0,1}+(v.p{0,0}-v.p{0,1})*u'", which has the advantage that the 'u' can be replaced by a single random function 'f(u)' to produce non-linear color change.

This lets you do more interesting things. For example what if in the last example you wanted to push all the colors toward the 'black' side, resulting in the image being a more 'firebrick' color.

  convert test.png -size 1x2  gradient:gold-firebrick \
          -fx 'v.p{0,1}+(v.p{0,0}-v.p{0,1})*u^4'  fx_non-linear.png
[IM Output] ==> [IM Output] ==> [IM Output]

In a more practical example, Adelmo Gomes needed a color adjustment for a automated Weather Map Recoloring script he was developing.

In this case he wanted to tint pure black parts of the image to a .25 blue, but leave the rest of the gray-scale alone, especially the white and mid-tone grays of the image. Only the blue color needed such adjustment, which he currently was doing by hand in an image editor.

For example you could use a quadratic formula like 'u^2' to tint the black end of the histogram to a '.25' blue color. Only the blue channel needs to be modified, so the value was inserted directly into the formula.

  convert test.png  -channel B  -fx '.25+(1-.25)*u^2'  fx_quadratic.png
[IM Output] ==> [IM Output] ==> [IM Output]

However while this produced a reasonable result it does darken the mid-tone grays slightly, producing a sickly off-yellow color.

To avoid this a 'exponential' function can be used instead, to give better control of the tinting process.

  convert test.png  -channel B  -fx '.3*exp(-u*4.9)+u'  fx_expotential.png
[IM Output] ==> [IM Output] ==> [IM Output]

Again the graph show how blue channel was modified to give black a distinctive dark blue tint.

The second value ('4.9') is the falloff back to a linear '+u' graph. The smaller this value is the slower the fall off, and the more linear the adjustment becomes. The larger the value, the more dramatic the 'fall-off'. The value may need to be adjusted for different color values, so this is not a good general formula for general black color tinting, but perfect for tinting weather maps.

Generally if you can express the color adjustment you want mathematically, you can then use "-fx" operator to achieve the results you want.

'Curves' Adjustments

[popup] Normally in a graphical photo editor you would be presented with a histogram 'curves' chart such as I have shown to the left. The user can then edit the 'curve' by moving four (or more) control points, and the histogram adjustment function will follow those points.

The control points generally specify that the first grayscale level is after adjustment to become the second grayscale level. So a point like 0.0,0.2 basically means that a 0% gray (black) should after adjustment be a 20% gray level.

Now IM does not allow you to directly specify 'control points' to generate a 'curve' adjustment, what it wants is the mathematical formula of that 'curve' generated. Lucky for us there are programs that can generate that curve formula from the control points, including "gnuplot", "fudgit", "mathematica", and "matlab", as well as many more mathematical software packages.

The following is one method you can use to generate the formula from four control points using "gnuplot" which is a standard extra package you can install on most linux distributions (and is available for Windows too)...

  ( echo "0.0 0.2";  echo "1.0 0.9"; \
    echo "0.2 0.8";  echo "0.7 0.5"; )   > fx_control.txt

  ( echo 'f(x) = a*x**3 + b*x**2 + c*x + d'; \
    echo 'fit f(x) "fx_control.txt" via a, b, c, d'; \
    echo 'print a,"*u^3 + ",b,"*u^2 + ",c,"*u + ",d'; \
  ) | gnuplot 2>&1 | tail -1             > fx_funct.txt
Control Points
==> [Gnuplot]
Gnuplot Fitted FX Function

Note that the number of parameters ('a' to 'd' in above) needed for curve fitting, must equal the number of control points you provide. As such if you want five control points you need to include another 'e' term to the function.

If your histogram curve goes though the fixed control points 0,0 and 1,1, you really only need two parameters as 'd' will be equal to '0', and 'c' will be equal to '1-a-b'.

As you can see from the extra "gnuplot" generated image above, the function generated fits the control points perfectly. Also as it generated a "-fx" style formula it can be used as is as an IM argument.

For example...

  convert test.png    -fx "`cat fx_funct.txt`"     fx_funct_curve.png
[IM Output]

To make it easier for users to convert control points into a histogram adjustment function, I have created a shell script called "im_fx_curves" to call "gnuplot", and output a nicer looking polynomial equation of the given the control points. Gabe Schaffer, also provided a perl version (using a downloaded "Math::Polynomial" library module) called "" to do the same thing. Either script can be used.

For example here is a different curve with 5 control points...

    im_fx_curves  0,0.2  0.3,0.7  0.6,0.5  0.8,0.8  1,0.6  > fx_curve.txt
[Gnuplot] ==>

However the FX function is very slow. But as of IM 6.4.8-9 you can now directly pass the discovered coefficents of the fitted polynomial expression directly into a Polynomial Function Method.

You can generate the comma seperated list of coefficients using "" with a special '-c' option...

    im_fx_curves -c  0,0.2  0.3,0.7  0.6,0.5  0.8,0.8  1,0.6  > coefficients.txt
[Gnuplot] ==>

For example lets apply those curves to our test image...

  convert test.png  -function Polynomial `cat coefficients.txt`  test_curves.png
[IM Output]

A more practical example of this method is detailed in the advanced "Aqua" Effects example.

Tinting Images

Uniformly Color Tinting Images

Typically tinting an image is achieved by blending the image with a color by a certain amount. This can be done using an Evaluate Operator or Blend Images techniques, but these are not simple to use.

Lucky for us a simpler method of bleading a uniform color into an image is available by using the "-colorize" image operator. This operator bleands the current "-fill" color, into all the images in the current image sequence. The alpha channel of the original image is preserved, with only the color channels being modified.

For example lighten an image (gray scale or otherwise) we use "-colorize" to blend some amount of white into the image, making it brighter without saturating the image completely.

  convert test.png  -fill white -colorize 50%  colorize_lighten.png
[IM Output] ==> [IM Output]

Similarly we can use a 'black' fill color to darken an image.

  convert test.png  -fill black -colorize 50%  colorize_darken.png
[IM Output] ==> [IM Output]

To gray both ends of the image toward the mid-tones, you would use a specific gray fill color. The color 'gray50' is the exact middle color of the RGB color spectrum.

  convert test.png  -fill gray50 -colorize 40%  colorize_grayer.png
[IM Output] ==> [IM Output]

The "-colorize" operator also allows you to specify dissolve percentages for each of the three color channels separately. This is useful for linearly darkening (or lightening) an image in a special way.

One common use of the "-colorize" operator is to simply replace all the colors in an existing image (tinting '100%') to set the color of a shape mask.

  convert test.png -fill blue -colorize 100%   colorize_shape.png
[IM Output]

Or by reseting the alpha channel, make a completely blank canvas..

  convert test.png -fill blue -colorize 100% -alpha opaque colorize_blank.png
[IM Output]

However these functions can be handles faster by using Level Adjustments by Color operator, with a single color. See also Blank Canvases.

Midtone Color Tinting

While a "-colorize" operator applies the "-fill" color to tint all the colors in an image linearly, the "-tint" operator applies the "-fill" color in such a way as to only tint the mid-tone colors of an image.

The operator is a grayscale operator, and the color is moderated or enhanced by the percentage given (0 to 200). To limit its effects it is also adjusted using a a mathematical formula so that it will not effect black and white. but have the greatest effect on mid-tone colors of each color channel.

A "-tint 100" essentially will tint a perfect gray color so that it becomes the current fill color. A lower value will tint it to a darker color while a higher value will tint to a lighter shade of that color.

  convert  test.png  -fill red  -tint 40 tint_red.png
[IM Output] ==> [IM Output]

The green color in the test image is not a true RGB green, but a Scaled Vector Graphics 'green', which is only half as bright as a true green color. As such it is also a mid-tone color, and thus is effected by the "-tint" operator, becoming darker, unlike red and blue color spots of the test image.

Also you can tint the individual color components, by using a comma separated list of percentages. For example "-tint 30,40,20,10". This however can be tricky to use and may need some experimentation to get right.

The tinting operator is perfect to adjust the results of the output of "-shade", (See Shade Overlay Highlight Images), such as the examples in 3d Bullet Images.

[IM Output] The "-tint" operator works by taking the color and percentages given then then adjusting the individual colors in the image according to the "-fill" colors intensity, as per the following formula. (see graph right)
A quadratic function, the result of which is used as vector for the existing color in the image. As you can see gives a complete replacement of the color for a pure mid-gray, with no adjustment for either white or black.

You can also use "-tint" to brighten or darken the mid-tone colors of an image. This is sort of like a 'gamma adjustment' for images, though not exactly.

For example using a tint value greater than 100 with a 'white' color will brighten the mid-tones.

  convert  test.png  -fill white  -tint 130 tint_lighter.png
[IM Output] ==> [IM Output]

While a value less than 100 will darken colors.

  convert  test.png  -fill white  -tint 70 tint_darker.png
[IM Output] ==> [IM Output]

As "-tint" uses the color as a 'vector' in color space, a "-fill" color of 'black' will have no effect on the result, as it produces a zero color vector.

On the same note tinting with 100% 'white' generates no change in the mid-tone colors of the image. Other colors darkens the midtones so that perfect grays will become the color specified.

Of course their are other ways of color tinting images...

DIY Color Tinting

One of the biggest problems with "-tint" is that it is a grayscale (or vector) operator. That is it handles each of the red,green,blue channels completely separately to each other. That in turn means that a primary and secondary color like 'blue' or 'yellow' are not effected by "-tint", even though all the gray levels are.

However thanks to the "-fx" you can create your own tinting method, by using it to create a color overlay so that it works in a similar way to the "-colorize" operator. (see Uniformly Color Tinting Images).

For example, here I convert an image's 'intensity' or grayscale brightness level into a 'mid-tone tinting overlay' image to tint grayscale midtone 'gold'.

  convert test.png \( +clone -matte  -channel A \
          -fx 'tint=intensity-.5; (1-4*tint*tint)*a* 1.0' +channel \
          -fill gold -colorize 100% \) -composite  tint_diy.png
[IM Output]

The final '1.0' is equivalent to a 100% level of tinting, so you can reduce that figure to moderate the amount of tinting.

Note that while similar to "-tint" the above uses the "-colorize" overlay method of modifing the image colors color vector or color channel approach, so primary colors are also tinted 'gold' leaving only the pure 'white' and 'black' colors unchanged.

You can also change the 'intensity' to other things like 'lightness', for other tinting methods.

Overlay Color Tinting

The special Alpha Composition methods 'Overlay' and 'Hardlight' were actually designed with color (and pattern) tinting in mind. These compose methods also will replace mid-tone grays leaving black and white highlights in the image alone.

For example here I quickly generate a colored overlay image, and compose it to tint the original image.

  convert test.png \( +clone +matte -fill gold -colorize 100% \) \
          -compose overlay -composite  tint_overlay.png
[IM Output]

As you can see the alpha composition does not preserve any transparency of the original image, requiring the use of a second alpha composition operation to fix this problem.

  convert test.png \
          \( +clone +matte -fill gold -colorize 100% \
             +clone +swap -compose overlay -composite \) \
          -compose SrcIn -composite  tint_overlay_fixed.png
[IM Output]

Using 'Overlay' is much more linear form of tinting than the quadratic function used above, and like "-tint" is applied to each channel of the image separately such that primary and secondary colors are also left unchanged. The 'SVG green' color is of course effected as it is a half bright green.

Also no adjustment control is provided by this alpha composition method, so if you want to control the level of tinting, you will need to adjust the overlay image transparency before applying the tint.

Of course unlike the other tinting methods I have shown so far, you are not limited to tinting a simple color, but can apply a tint using an image, or tile pattern.

  convert test.png \
          \( -size 150x100 tile:tile_disks.jpg \
             +clone +swap -compose overlay -composite \) \
          -compose SrcIn -composite  tint_overlay_pattern.png
[IM Output]

This however is getting outside the scope of basic color handling so I'll leave image tinting at that.

The alpha composition method 'HardLight' will produce the same results as 'Overlay' but with the source and destination images swapped.

This could have been used instead of the "+swap" in the last few examples.

Global Color Modifiers

Modulate Brightness, Satuartion, and Hue

The "-modulate" operator is special in that it modifies an image in the special HSB (hue-saturation-brightness) colorspace. It converts each color pixel in into this color space and modifys it and converts it back to its original color space.

It takes three values (though later values are optional) as a percentage such that 100 will make no change to an image. For example..

  convert  rose:  -modulate 100,100,100  mod_noop.gif
[IM Output]

The first value, brightness is a multiplier of the images overall brightness.

  convert rose:   -modulate 0     mod_bright_0.gif
  convert rose:   -modulate 50    mod_bright_50.gif
  convert rose:   -modulate 80    mod_bright_80.gif
  convert rose:   -modulate 100   mod_bright_100.gif
  convert rose:   -modulate 150   mod_bright_150.gif
  convert rose:   -modulate 200   mod_bright_200.gif
[IM Output] [IM Output] [IM Output] [IM Output] [IM Output] [IM Output]

Note that while a brightness argument of '0' will produce a pure black image, you can not produce a pure white image using this operator on its own.

The second value saturation is also a multiplier adjusting the overall amount of color that is present in the image.

  convert rose:   -modulate 100,0     mod_sat_0.gif
  convert rose:   -modulate 100,20    mod_sat_20.gif
  convert rose:   -modulate 100,70    mod_sat_70.gif
  convert rose:   -modulate 100,100   mod_sat_100.gif
  convert rose:   -modulate 100,150   mod_sat_150.gif
  convert rose:   -modulate 100,200   mod_sat_200.gif
[IM Output] [IM Output] [IM Output] [IM Output] [IM Output] [IM Output]

A saturation of '0' will produce a grayscale image, as was exampled in Converting Color to Gray-Scale above. The gray however mixes all three color channels equally, as defined by the HSB colorspace. Small values produce more 'pastel' colors, while values larger than '100' will produce more cartoon-like colorful images.

The final value, Hue, rotates the colors of the image, in a cyclic manner.

  convert rose:   -modulate 100,100,0      mod_hue_0.gif
  convert rose:   -modulate 100,100,33.3   mod_hue_33.gif
  convert rose:   -modulate 100,100,66.6   mod_hue_66.gif
  convert rose:   -modulate 100,100,100    mod_hue_100.gif
  convert rose:   -modulate 100,100,133.3  mod_hue_133.gif
  convert rose:   -modulate 100,100,166.6  mod_hue_166.gif
  convert rose:   -modulate 100,100,200    mod_hue_200.gif
[IM Output] [IM Output] [IM Output] [IM Output] [IM Output] [IM Output] [IM Output]

As you can see a value of '33' produces a negative, or counter-clockwise rotation of all the colors by 60 degrees, effectivally mapping the red to blue, blue to green, and green to red. Using values of '0' or '200' produces a compete 180 degree negation of the colors, without negating the brightness of the image. A value of '300' will produce a 360 degree in color rotation resulting in no change to the image.

These type of operations and more can be applied using advanced Color Space techniques, such as demonstrated in Combining non-RGB Channel Images, but for basic 'modulation' of an image the above simplifies things.

Modulate Problems and Alternatives

The biggest problem with "-modulate" when handing images containing a lot of 'near white' colors. As it does its work in HSL colorscape, colors which are off white get exaggerated. You can see this in the white leaf of the rose image above, which shows lots of color artifacts.

This is especially a problem when dealing with JPEG image formats, as it tends to generate off-white colors as part of its compression algorithm. For example...

  convert wedding_party_sm.jpg  -modulate 85  modulate_off-white.png
[IM Output] ==> [IM Output]

The problem is that in HSL all the off white colors were packed into a small 'white point' of the color cones used. When brightness is then reduced the area off-white color get expanded as the cone color space expands, causing the off white color to generate a more colorful set of off-white colors. That is and small variations in color are exaggerated.

The solution to this is to do the equivalent of "-modulate" but in the HSB colorspace instead of HSL colorspace. In this colorspace 'white' is not a single point, but a large 'disk', and as such off-whites, are not 'close' to each other. As such when you reduce the brightness, the off-whites contract equallyx, reducing the variations rather than expanding them. As such the whites just become grayer, and not more colorful.

  convert wedding_party_sm.jpg -colorspace HSB \
          -channel B -evaluate multiply 0.85 +channel \
          -colorspace RGB   modulate_channel.png
[IM Output]

Another soltuion is to do the modulation of the image immediatally after resizing, before saving it to JPEG or some other image file format. By not saving the image to a intermedite file the modulation is performed on a higher quality image that does not contain these small bit-depth errors (in a Q16 version of IM that is), and will thus produce the right result.

For a larger discussion of this problem see the IM Forum thread Brightness Problem.

Recolor Matrix Operator

-recolor   translate, scale, shear, or rotate image colors
           (good for hue changes)

  Each of the rows of the matrix represents the value assignment
  for each channel in the image.
  As such the first three numbers is the color formula for the
  'red' channel. the next for 'green' and so on

  As such the following will swap the red and blue channel color,
  but leave the green channel as is.

        convert rose: -recolor ' 0 0 1
                                 0 1 0
                                 1 0 0 '  rose_blue.png

  And this example makes a grayscale image using a 2/5/3 ratio
  by using the same formula for all color channels.

        convert rose: -recolor ' .2 .5 .3
                                 .2 .5 .3
                                 .2 .5 .3 '  rose_gray_253.png

  The matrix is applied such that each row represents the formula
  for each channel in turn.  As such the first row translates to

       new_red =  .2*red + .5*green + .3*blue

  And so on, with all the colors being calculated before finally being
  assigned back into the original image.

  If a 4x4 matrix is used the transparency channel is also included
  While a 5x5 matrix will also include the 'blacK' channel for CMYKA images.

  Vivid colors, in a technique called  Digital Velvia
  For example this will produce a 20% vivid color image operation...
        convert rose: -recolor ' 1.2 -0.1 -0.1
                                -0.1  1.2 -0.1
                                -0.1 -0.1  1.2 '  rose_vivid.png

  These matrices brighten that color channel while subtracting
  the colors from the other channels, making colors more vivid
  in the RGB image.  Each row should add to a value of 1.0 to preserve
  the images overall brightness.

  Note that this is not the same as using -modulate to increase an images
  color saturation by 20%.

Replacing Colors in Images

ImageMagick naturally provides a number of options to replacing a specific and near match colors with another color. This is great when dealing with icons and 'bitmap' type images that contain very few colors, but tends to fail when dealing with images containing shades of colors or anti-aliasing edge pixels.

Basically you need to remember that colors are replaced by a single shade. So if you replace a set or neighbourhood of colors all those colors replaced by one color and not a range of colors, that matches the original. That is not to say it is imposible to do a shaded color replacement, just not simple to do at this time.

Even so as the GIF image does not allow semi-transparency, replacing colors in this way is a good method for controlling GIF background transparency (See GIFs on a background pattern for examples)

The other aspect is that while you can map all 'close colors' to a given colormap, using Pre-Defined Color Maps, you can not currently do a global remapping of one set of colors to another completely different set. A short comming that may change in a future version of IM.

With that caveat, lets look at the ways IM does provide to replace specific colors with another color, one color at a time.

Replace a Specific Color

The "-opaque" and "-transparent" operators are designed for replacing one color in an image with another.

For example to replace a 'blue' color with say 'white' you would use a command like this...

  convert balloon.gif  -fill white -opaque blue   balloon_white.gif
[IM Output] ==> [IM Output]

Basically any color that was 'blue' has been replaced with the current "-fill" color.

However as of IM v6.2.7, this operator limited by the -channel setting. As such to convert a color (say blue) to transparency you will need to specify a "-channel" to include the alpha channel in the output changes, You will also need to ensure the image has a 'matte' or alpha channel enabled.

  convert balloon.gif   -alpha set  -channel RGBA \
                        -fill none -opaque blue   balloon_none.gif
[IM Output] ==> [IM Output]

Because replacing a color with transparency is such a common operation the above has its own special replace with transparency operator "-transparent".

  convert balloon.gif  -alpha set  -transparent blue   balloon_trans.gif
[IM Output] ==> [IM Output]

As of IM version 6.3.7-10, the 'plus' versions of these operators invert the color selection. That is the colors that do NOT match the given color will be replaced. For example here I replace any color that is NOT black, with white, leaving just the pure black borders of the image.

  convert balloon.gif  -fill white +opaque black   balloon_borders.gif
[IM Output] ==> [IM Output]

This may not seem like much, but when you combine it with a Fuzz Factor see below, this becomes a very powerful tool.

Before IM v6.3.7-10, the inverse operation required the use of some trickiness using image masks. Basically you replace the color you want to preserve with transparency, then "-colorize" all the other colors to the desired color to create an overlay mask. This is then overlaid on the original image to 'mask out' the colors that did not match!

  convert balloon.gif \
          \( +clone -matte -transparent black \
             -fill white  -colorize 100% \) \
          -composite    balloon_mask_non-black.gif

[IM Output] ==> [IM Output]

As you can see the 'plus' form of the operator simplified the 'not this color' replacement operation enormously.

For more advanced replacement techniques, I suggest you look at Transparency Masking and Background Removal.

Be warned that as all matching colors (especially 'fuzzy matched colors', see below) are replaces with a single uniform color, you will not get any anti-aliasing of the edges of the colored areas. And you will lose any an all shaodw or other shading effects that may be present. This can have a seriously detrimental effect to the look off any non-cartoon like images.

This type of color replacement is not designed with practical real world images in mind, but more for image masking effects. Caution is advised.

The "-opaque" color replacement can not replace a color with a tiled pattern. It will only replace colors with another single specific color. However both the "-draw" and "-floodfill" color replacement methods can (see below).

Replace using a Image Color

You can also use Draw Color Replacement to recolor images based on colors present in the image itself, rather than a specific color.

  convert present.gif -fill red -draw 'color 0,0 replace' present_blue.gif
[IM Output] ==> [IM Output]

Note that I never specified the color to be replaced, only the location of the color to be replaced. It is the color at that location that is used for 'matching' what areas is to be filled, regardless of what that color is.

You can see in the above example the problem with color replacement, the specific color may appear in other places that you intend, giving us a line of red pixels within the 'present' image above.

Transparency also presents no problem, though some internal parts of the image was also made transparent just as they became red in the above...

  convert present.gif -matte -fill none \
                      -draw 'color 0,0 replace' present_none.gif
[IM Output] ==> [IM Output]

Note however that unlike "-opaque" and "-transparent" the Draw Color Replacement, does not let you invert the 'matching colors' to be replaced.

Draw also has a special Matte Replacement, where only the transparency of the fill color is replaced. That is you can make all matching colors transparent, or semi-transparent, without actually changing the color of the pixel itself. With the appropriate file format of course.

  convert present.gif -matte -fill '#00000080' \
            -draw 'matte 0,0 replace' present_semi.png
[IM Output] ==> [IM Output]

This becomes much more useful when a Fuzz Factor is also specified.

The biggest advantage of using "-draw" is that you can also replace the color with a tile pattern. For example..

  convert present.gif -tile pattern:right30 \
                -draw 'color 0,0 replace' present_tile.gif
[IM Output] ==> [IM Output]

For more advanced replacement techniques, I suggest you look at Transparency Masking and Background Removal.

Floodfill Draw

The Draw Color methods also provide you with a simple method of replacing a color by 'floodfilling'. That is, rather than replacing ALL the matching colors within the image, you can select just the colors which are 'connected to' or 'attached' to the specified point in the image.

Again the specified point given sets the starting (or center) of the colors which are to be replaced.

  convert present.gif -fill red -draw 'color 0,0 floodfill' present_fill.gif
[IM Output] ==> [IM Output]

Note that the red areas which was not 'attached' to the 0,0 pixel was note replaced.

For background replacing that can be a problem, but the solution is just as easy. Expand the image slightly so the floodfill can 'leak' into the image from all directions, then remove that extra space when finished.

  convert present.gif -bordercolor white -border 1x1 \
          -fill red     -draw 'color 0,0 floodfill' \
          -shave 1x1               present_bgnd.gif
[IM Output] ==> [IM Output]

Of course you can adjust what colors are 'matched' using the Fuzz Factor control setting below, which is especially important for JPEG images.

Floodfill Operator

The "-floodfill" operator was added to make floodfilling slightly easier, especially when you what to exactly specify the 'center' color for the Fuzz Factor color matching.

That is you not only specify the start or seed point for the flood fill, but also the center color, of the colors you are replacing.

  convert present.gif -bordercolor white -border 1x1 \
          -fill red    -floodfill +0+0 white \
          -shave 1x1               present_floodfill.gif
[IM Output] ==> [IM Output]

This will replace any color that is 'white' to 'red' that is directly part of the area surrounding the seed pixel starting at +0+0.

Note that the 'seeding pixel' must itself be match the 'white' color otherwise no action will be taken. This 'do nothing if no match' is particularly useful to ensure that the color of the area specified is the expected color, such as when doing a scripted color replacement.

Again if you are attempting to replace a background color, adding a border for the "-floodfill" to 'leak from is a good idea, such as we did above. That border should naturally be the same color that floodfill is replacing.

You can also floodfill with a tile pattern.

  convert present.gif -bordercolor white -border 1x1 \
                -tile pattern:left30   -floodfill +0+0 white \
                -shave 1x1           present_pattern.gif
[IM Output] ==> [IM Output]

Of course if the colors are not exact, as it is in the above I recommend that you also specify a "-fuzz" factor, so that nearby colors also 'match' the given color. See next set of examples.

For more advanced replacement techniques, I suggest you look at Transparency Masking and Background Removal.

Fuzz Factor - Matching Similar Colors

The overall results of just selecting a single color to replace, as shown in the previous examples is usually not very nice. The edges or areas of solid colors generally have a mix of colors at the edge, due to anti-aliasing (See Anti-Aliasing for more information. As such you should avoid direct color replace if possible.

EXAMPLE: floodfill 
You can improve the selection of the area being recolored, by setting a "-fuzz" factor setting. Here for example we tell IM that other colors 'close' to the one selected is also OK to be replaced.
EXAMPLE: fuzzy floodfill 
As you can see we now replaced even the pixels closer to the edge of the image. This isn't perfect and replacing the backgrounds of images like this is a difficult task. For more on this specific problem see Re-adding Transparency to an Image.

The fuzz factor, technically represents a 'similarity' match in multi-dimensional spherical distance between colors, using whatever color space the image is using.

Well okay lets try that in plain English. You have a specific color. Another color will be treated as being same as the first color if the difference between these two colors is less than the currently fuzz factor setting.

A "-fuzz" setting of '200' represents a distance of 200 units in the current color depth of the IM being used, for a IM Q16 (16 bit quality for color store) this is quite small, for a IM Q8 this is VERY large, and will cause a lot of colors to match each other.

Here for example I change all the colors that are within 3000 color units of 'blue' to white. With my Q16 ImageMagick programs, that represents about the distance from 'blue to 'navy blue' (about 25% as a percentage, see below).

  convert colorwheel.png -fuzz 30000 -fill white -opaque blue opaque_blue.jpg
[IM Output]

To make this easier to understand here I invert the matched colors turning the unmatched colors to white.

  convert colorwheel.png \
          -fuzz 30000 -fill white +opaque blue \
[IM Output]

If your IM is older than version 6.3.7-10 when the 'plus' form of the "-opaque" operator was added, you can use this masking method to invert the result of the color match...

  convert colorwheel.png \
          \( +clone  -fuzz 30000 -transparent blue \
             -fill white  -colorize 100% \) \
          -composite   opaque_blue_inv.png
[IM Output]

As a matter of interest, in a IM with a Q8 compilation setting, a "-fuzz" factor of 256 (28) will make the colors 'black' and 'blue' the same. For a IM with a Q16 setting this number is 65536 (216).

To make 'blue' and 'red' colors match this number must be multiplied by the square root of 2, or 362 for IM Q8, and with 92682 for IM Q16.

Finally to make all colors match (eg colors 'black' and 'white') you will need to multiply by the square root of 3. In other words, a fuzz factor setting of 444 for IM Q8 and 113512 for IM Q16.

As you can see from the above formulas, direct color distances is definitely not a nice way of setting the fuzz factor to use, as it is also dependant on exactly what compile time Quality Setting is used.

Setting the "-fuzz" factor as a percentage, makes its use a lot simpler. In this case '100%' represents a large enough fuzz factor to cover all colors. That is it represents the color distance from 'black' to 'white', across the 3 dimensional diagonal of the RGB color cube.

To demonstrate lets change 90% of all the colors closest to 'white', white. This should result in only the last 10% colors near 'black' on the image, as black is on the opposite side of the RGB color cube.

  convert colorwheel.png -fuzz 90% -fill white -opaque white  opaque_w90.jpg
[IM Output]

Note that as the 90% represents a sphere of colors around 'white' in RGB color space, this is not the same as replacing the colors which are not within a 10% sphere of black.

  convert colorwheel.png -fuzz 10% -fill white +opaque black  opaque_k10.jpg
[IM Output]

As you can see 10% sphere of colors near the black is much more uniform, than selecting a 90% sphere of the colors around white.

A "-fuzz" factor of 100%, equates to the RGB color cube distance from 'black' to 'white'. From this we can calculate that a percentage of about 57.7% is the distance between 'black' and 'blue', and 81.6% is the distance from 'blue' to 'red' or from either of those colors to 'white'.

In summary, anything larger than about 25%, (just short of the RGB distance from 'blue' to 'navy blue' represents a very large color change.

To demonstrate the color distances more, lets use a progressively larger fuzz factor percentage around the blue colors...

  convert colorwheel.png -fuzz 10% -fill white -opaque blue opaque_b10.jpg
  convert colorwheel.png -fuzz 25% -fill white -opaque blue opaque_b25.jpg
  convert colorwheel.png -fuzz 57% -fill white -opaque blue opaque_b57.jpg
  convert colorwheel.png -fuzz 81% -fill white -opaque blue opaque_b81.jpg
  convert colorwheel.png -fuzz 95% -fill white -opaque blue opaque_b95.jpg
[IM Output] [IM Output] [IM Output] [IM Output] [IM Output]

From this you can clearly see that it isn't 'black', or 'white' that is the most distant color from 'blue', but that it is actually 'yellow' that is most distant within RGB color space.

Also not that a 81% color difference will juts miss matching a pure 'red' color, however while pure red does not match almost all of the other reds do. That is again due the to 'sphereical' nature of the color matching. The moral is that you are probably better of either using multiple small "-fuzz" factored matches or a smaller 'inverted match', that a large value.

Color matching is actually much more 'natural if the image was stored using some other color scheme than RGB, such as YIQ. The formula is still the same, just using a different colorspace. Similarly for other color spaces.

How to use YIQ or even CMYK with opaque tests ????
Colors do not seem to map correctly in mathcing to images in a different
Using a "-fuzz" factor becomes more complicated when matching involves transparent and semi-transparent colors, and recent work (for IM version 6.2.6-2) has adjusted the comparison algorithm so that fully-transparent colors will always match as being the same, no matter what other color components are present.

Comparing semi-transparent colors will results in the distance between the RGB color components being divided by amount of transparency involved, as such semi-transparent colors are thought of by IM as being closer than their fully-opaque equivalents.

This improves comparisons between image with transparencies, and also color reduction for images with some semi-transparency, with less semi-transparent colors being generated in color reductions.

The "-fuzz" operator effects just about any operator which compares specific colors within an image. This includes: "-opaque", "-transparent", "-floodfill", "-trim", "-deconstruct", "-layer", "-draw 'color'", "-draw 'matte'", and probably others.

Full Color Map Replacement

FUTURE: Replace all the colors in one color map to another.

Suggestions as to how is welcome, perhaps using the ideas presented in
Dithering with Symbols.

Recoloring Images with Gradients

While you can recolor images using the various histogram color adjustments shown above, there is another technique for recoloring images based on color lookups of pre-prepared color gradients.

Color Lookup Tables

A common requirement of a image processing tool is the ability to replace the whole range of colors, from a pre-prepared table of colors. This allows you to convert images of one set of colors (generally gray-scale) into completely different set of colors, just by looking up its replacement color from a special image known as a Color Lookup Table (or color LUT).

Of course you do need a 'Look Up Table' image from which to read the replacement colors. For these first few examples, I choose to use a vertical gradient of colors for the LUT so that the IM "gradient:" generator can be used to simplify the generation of the 'color lookup table'.

Well so much for the theory. Let try it out by recoloring a simple gray-Scale Plasma image, replacing the grayscale with a dark-blue to off-white gradient of colors.

  convert -size 100x100 plasma:fractal -virtual-pixel edge -blur 0x5 \
          -shade 140x45  -normalize \
          -size 1x100 xc:black -size 9x100 gradient: \
          +append  gray_image.jpg
  convert -size 10x100  gradient:navy-snow       gradient_ice-sea.png
  convert gray_image.jpg  gradient_ice-sea.png -clut  gray_recolored.jpg
[IM Output] ==> [IM Output] ==> [IM Output]

The "-clut" operator takes two images. The first is the image to replace color values in, the second is a gradient image that is either a single row, or a single column.

The "-clut" operator was added to IM v6.3.5-8.

If your IM is too old to understand the the "-clut" operator or you want to do something out of the ordinary, such as a 2 dimensional color lookup table, then you can roll your own using the General DIY Operator, FX. For example here is a slow, but equivalent command to the above.

  convert gray_image.jpg  gradient_ice-sea.png \
          -fx 'v.p{0,u*v.h}'  gray_recolored_fx.jpg
[IM Output]

The problem is that even for a simple process such as the above the "-fx" operator is very slow, and has to be designed specifically for either a row or column LUT. But it does work.

The LUT does not have to be very large. For example here we use a very small LUT, with a very limited number of colors.

  convert -size 1x6 gradient:navy-snow  gradient_levels.png
  convert gray_image.jpg  gradient_levels.png  -clut  gray_levels.jpg
[IM Output] ==> [IM Output] ==> [IM Output]

I enlarged the gradient image for the web page display above, otherwise it would be too small to see properly. The LUT is in actual fact only 6 pixels in size. However if you look at the result you will see that the Color Lookup Operator smoothes out those 6 colors into a smooth gradient.

What is happening is that IM is doing an Interpolated Lookup of the LUT image. That is, instead of just picking the color found, it does a weighted average of all the nearby colors to better represent the LUT. In this particular case, it used the default 'Bilinear' setting that just links each colored pixel together with linear line segments.

Different "-interpolate" settings generate different levels of smoothing of the colors when using a very small color LUT. Here for example I show a various type of interpolated smoothing of the LUT colors.

  convert gray_image.jpg  gradient_levels.png \
          -interpolate Integer         -clut  gray_levels_integer.jpg
  convert gray_image.jpg  gradient_levels.png \
          -interpolate NearestNeighbor -clut  gray_levels_nearest.jpg
  convert gray_image.jpg  gradient_levels.png \
          -interpolate Average         -clut  gray_levels_average.jpg
  convert gray_image.jpg  gradient_levels.png \
          -interpolate BiLinear       -clut  gray_levels_bilinear.jpg
  convert gray_image.jpg  gradient_levels.png \
          -interpolate BiCubic         -clut gray_levels_bicubic.jpg
  convert gray_image.jpg  gradient_levels.png \
          -interpolate Spline          -clut  gray_levels_spline.jpg
[IM Output]
[IM Output]
Nearest Neighbor
[IM Output]
[IM Output]
[IM Output]
[IM Output]

The 'Integer' and 'NearestNeighbor' settings are special in that they do no smoothing colors at all. That is no new 'mixed colors' will be added, only the exact color values present will be used used to color a grayscale image. However note how the lookup of the colors are differ between the two. It is a subtle difference but important.

The 'Average' setting on the other hand also generated bands of color but only using a mix of the colors, resulting in one less color than the size of the color lookup table image.

This type of color 'banding' (or Blocking Artifacts) is actually rather common for geographic maps, and temperature graphs, as it gives a better representation of the exact shape of the map. The sharp boundary edges being known as iso-lines. Adding a slight one pixel Blur to the final image can improve the look of those edges, making it look a little smoother, without destroying the color banding.

The 'BiLinear' setting will also generate banding but only in the form of sharp gradient changes, as will 'BiCubic' to a lesser extent. This is easily seen in the above.

To avoid this problem you would normally use much longer LUT to produce a larger range of intermediate colors. Ideally this should cover the full range of possible intensity values. For ImageMagick Q16 (compiled with 16 bit quality) that requires a LUT to have a height of 65536 pixels. However thanks to Pixel Interpolation, a LUT gradient image of 500 pixels or more is usually good enough for re-coloring most images quite well.

Note that the vertical gradient LUT used in the above examples appears upside-down to our eyes, as the black or '0' index is at the top of the image. Normally we humans prefer to see gradients with the black level at the bottom (thanks to our evolutionary past).

If you rather save the gradient image the 'right way up' you can "-flip" the image as you reading it in. For example lets try a more complex LUT, flipping the vertical gradient before using it on the image.

  convert -size 1x33 gradient:wheat-brown gradient:brown-lawngreen \
          gradient:dodgerblue-navy   -append  gradient_planet.png
  convert gray_image.jpg \
          \( gradient_planet.png -flip \) -clut   gray_planet.jpg
[IM Output] ==> [IM Output] ==> [IM Output]

As you can see for a vertical gradient, flipping it before using makes a lot of sense.

For more examples of generating gradients see Gradients of Color.

You may also be interested in a way of tiling greyscale images using a image for each grey level, which can produce even better 'map' like images. See Dithering with Patterns.

Function to LUT Conversion

These pre-prepared "Lookup Table Images" (or LUTs) can also be used to greatly increase the speed of very complex and thus slow "-fx" operations, so instead of IM interpreting the functional string 3 or 4 times per pixel, it can do a much faster lookup of the replacement color.

The procedure for doing this is quite straight forward, either apply the function to a unmodified linear gradient, or replace the 'u' in the function with the value '(i/w)' for a row lut image, or '(j/h)' for a column lut image.

For example, in the advanced 'Aqua' Effects example, I used a complex "-fx" function to adjust the gray-scale output of "-shade". Also as this gray-scale adjustment is also overlaid onto a 'DodgerBlue' shape, there is no reason why the results of both of these operators could not be combined into a single gradient lookup table.

That is we generate a LUT from the "-fx" formula and the color overlay. Also for these examples I decided to generate a single row of pixels rather than a column as I did previously.

  convert -size 1x512 gradient: -rotate 90 +matte \
          -fx '3.5u^3 - 5.05u^2 + 2.05u + 0.3' \
          -size 512x1 xc:DodgerBlue -compose Overlay -composite \
[IM Output]

The polynomial "-fx" in the above can now be generated faster using a Polynomial Function Method. For example
"-function Polynomial 3.5,-5.05,2.05,0.3"

This pre-generated LUT can now be applied to the shaded shape much quicker at the minimal cost for the storage of a small image.

  convert -font Candice -pointsize 72  label:A -trim +repage -negate \
          \( +clone -blur 0x8 -shade 110x45 -normalize \
             aqua_gradient.png -clut \) \
          +matte +swap -compose CopyOpacity -composite \
WARNING: the above is incomplete (edges have not been darkened)
[IM Output]

As you can see, the result is very effective, and once appropraite LUT gradient has been generated, you can use the same gradient over and over.

Color Replacement with Transparency

The "-clut" operator is controlled by the "-channel" setting, and in reality it only replaces the individual channel values within the image.

That means that normally each individual channel of the source image is used to 'lookup' the replacement value for just that channel from the color lookup table. That includes the alpha/matte channel which is usually very inconvenient, and difficult to apply.

Typically the "-clut" operator is used to either colorize a gray-scale source image, (see previous examples), OR it is used to do a histogram adjustment of a color image using a gray-scale CLUT (Color Lookup Table).

As of IM v6.4.9-8, if a "-channel" setting specifies that you are wanting to replace/adjust the alpha channel of an image (an 'A' is present), and the source or CLUT image has no alpha/matte channel defined, then IM will assume that you are doing a color replacement of a gray-scale, and will act accordingally.

For example, here I generate a simple blurred triangle, which I can then color using a Color Lookup Table that includes transparency. I did not flip the CLUT image so the black replacement will be at the top and white replacement at the bottom.

  convert -size 100x100 xc:  -draw 'polygon 50,10 10,80 90,80' \
          -blur 0x10  blurred_shape.jpg
  convert -size 1x5 xc:none \
          -draw 'fill red    point 0,2' \
          -draw 'fill yellow rectangle 0,0 0,1'   gradient_border.png
  convert blurred_shape.jpg -alpha off    gradient_border.png \
          -channel RGBA  -interpolate integer clut_shape.png
[IM Output] ==> [IM Output] ==> [IM Output]

Remember the above will only work as expected if the gray-scale image has no alpha channel (turned off using either "-alpha off" or "+matte"), and you specify that you also want to lookup alpha channel values (using "-channel RGBA").

The other special case is if you are modifying the alpha channel of some image, but this time the you only give a gray scale lookup table (no alpha channel) but you again specified alpha channel replacement. IM will assume you want to use the gray-scale as a histogram adjustment for the alpha channel. For example here I generated triangle from colored tile, But I want to roughly feather its outline.

  convert -size 100x100 xc:none -draw 'polygon 50,10 10,80 90,80' \
          tile_disks.jpg -compose In -composite shape_triangle.gif
  convert shape_triangle.gif -channel A -blur 0x10 +channel shape_blurred.png
  convert -size 1x50 gradient: xc:black -append -flip \
          -sigmoidal-contrast 6x0%  feather_histogram.jpg
  convert shape_blurred.png \( feather_histogram.jpg -alpha off \) \
          -channel A    -clut    shape_feathered.png
[IM Output] ==> [IM Output] ==> [IM Output] ==> [IM Output]

Note that I ensure that the CLUT image does not have a alpha channel by using a "-alpha off" operator after reading just that image in.

The 'black' halo in the intermedite image is caused by the "-blur" operation making the fully-transparent areas surrounding the triangle visible. As fully-transparent has an undefined color, IM defaults to black. The CLUT image itself was designed to ensure that any pixel which was less than 50% transparent will be turned fully-transparent, effectively making the previously fully transparent parts of the image, transparent again.

For demonstration purposes I overdo the initial 'blur', then over-correct the alpha channel adjustment. The result is a sever rounding of the points of the triangle. You would typically use much smaller values for both the "-blur" and the "-sigmoidal-contrast" alpha adjustment.

Also the above blurred-feathering method, will add undefined semi-transparent pixels on concave internal facing corners of a mask image. This problem however could be solved by masking the image with a thresholded mask of the original outline to ensure no pixel that was fully-transparent in the original image is added as a semi-transparent pixel in the final feathered result.

Fred Weinhaus, has implemented a blurred feathering technique in his "feather" script, to make it easier to use.

Miscellaneous Color Operators

Sepia Tone Coloring

A special photographic recoloring technique, "-sepia-tone" is basically consists to converting the image into a grayscale, and coloring all the mid-tones to a special brown color.

  convert rose:   -sepia-tone 65%     sepia-tone.jpg
[IM Output]

The argument in the above is the 'mid-point' that is to become the closest to the sepia-tone brown color.

The most common use of this simple color operator is probably generating 'old looking' photos.

The actual formula used is not a simple one, and one I have not been able to figure out myself.

Solarize Coloring

To "-solarize" an image is to basically 'burn' the brightest colors black. The brighter the color, the darker the solarized color is. This happens in photography when chemical film is over exposed.

  convert rose:   -solarize 90%     solarize.jpg
[IM Output]

Basically anything above the grayscale level given is negated. So if you give a argument of '0%' you basically have a poorman's "-negate" operator.

For example here is a faked "-solarize" using a "-fx" mathematical formula.

  convert rose:   -fx  '.9>u ? u : 1-u'     solarize_fx.jpg
[IM Output]

This operator is particularly well suited to extracting the midtone gray colors from images.

For example here I use very strong Sigmoidal Contrast operation to produce a sort of 'fuzzy' threshold at 70% gray. I then Solarize that to generate a 'fuzzy' spike at that gray-scale level. Then using a level adjustment the spike is enlarged to generate a 'filament' effect.

  convert -size 10x300 gradient: -rotate 90 \
                         -sigmoidal-contrast 30x70%   fuzzy_thres.png
  convert fuzzy_thres.png  -solarize 50%   fuzzy_spike.png
  convert fuzzy_spike.png  -level 0,50%    filament.png
[IM Output] ==> [IM Output] ==> [IM Output]

ASIDE: The above images showing 'profile' graphs of the gradient, was generated using the "im_profile" in the IM Examples, Scripts directory.

Note how anything that is white becomes black, while the midtone gray around the central spike are preserved. The fuzziness and placement of the spike is determined by "-sigmoidal-contrast" operator.

I call it a 'filament' is in grayscale gradient images the result looks remarkably like glowing electrical fillaments, or lightning discharges. See Random Flux for an example of this effect.

This extraction of midtone grays is also put to good use in techniques for generating Edge Outlines from Bitmap Shapes.

Another novel use of this operation is in determining if an image is basically a pure black and white sketch or drawing (such as from a book), rather than a shaded grayscale or color images, See Determining if an image is: Pure Black and White, or Grayscale

Under Construction

More color options yet to be looked at in detail...

    -cycle     shift colormap (for animations of fractals???)


   -threshold     Looked at in quantization as a 'simplistic dither'
                  Now look at it as a color operator
      Threshold operations which only threshold half the image.

   -lat low,high     local adaptive thresholding???

Color maths (get the average of two or more colors)....

  Example Averaging two colors... Say '#000000'  and  '#DDDDDD'

  Generally the colors are added to images, and the result output as a
  single pixel 'txt:-' image, which which the color can be extracted.

  * use -resize to merge the colors

      convert -size 2x1 xc:'#000000' -fill '#DDDDDD' \
              -draw 'point 0,0'  -resize 1x1  txt:-

  * Use -average on them!

      convert -size 1x1 xc:'#000000' xc:'#DDDDDD' \
              -average  txt:-

    Or for a lot of colors you can use the 'Box' resize filter
      convert rose: -filter Box -resize 1x1\! txt:
      # ImageMagick pixel enumeration: 1,1,255,RGB
      0,0: (145, 89, 80) #915950

  * Use -fx to apply whatever formula you want

      convert -size 1x1 xc:'#000000' xc:'#DDDDDD' \
              -fx '(u+v)/2'  txt:-

  With a ImageMagick API the results can be more directly retrieved from the

Chromaticity Color Points???
   –white-point x,y
   –red-primary x,y
   –green-primary x,y
   –blue-primary x,y

Created: 19 December 2003
Updated: 20 October 2008
Author: Anthony Thyssen, <>
Examples Generated with: [version image]