convert - convert an image or sequence of images
 

Synopsis

Description

Examples

Options

Image Segmentation

Environment

Authors

Copyright

convert [ options ... ] input_file output_file
 

Convert converts an input file using one image format to an output file with a differing image format. In addition, various types of image processing can be performed on the converted image during the conversion process. Convert recognizes the image formats listed in ImageMagick(1).
 

To make a thumbnail of a JPEG image, use:

    convert -size 120x120 cockatoo.jpg -resize 120x120 +profile '*' thumbnail.jpg

To convert a MIFF image of a cockatoo to a SUN raster image, use:

    convert cockatoo.miff sun:cockatoo.ras

To convert a multi-page PostScript document to individual FAX pages, use:

    convert -monochrome document.ps fax:page

To convert a TIFF image to a PostScript A4 page with the image in the lower left-hand corner, use:

    convert -page A4+0+0 image.tiff document.ps

To convert a raw Gray image with a 128 byte header to a portable graymap, use:

    convert -depth 8 -size 768x512+128 gray:raw image.pgm

To convert a Photo CD image to a TIFF image, use:

    convert -size 1536x1024 img0009.pcd image.tiff
    convert img0009.pcd[4] image.tiff

To create a visual image directory of all your JPEG images, use:

    convert 'vid:*.jpg' directory.miff

To annotate an image with blue text using font 12x24 at position (100,100), use:

    convert -font helvetica -fill blue -draw "text 100,100 Cockatoo"
            bird.jpg bird.miff

To tile a 640x480 image with a JPEG texture with bumps use:

    convert -size 640x480 tile:bumps.jpg tiled.png

To surround an icon with an ornamental border to use with Mosaic(1), use:

    convert -mattecolor "#697B8F" -frame 6x6 bird.jpg icon.png

To create a MNG animation from a DNA molecule sequence, use:

    convert -delay 20 dna.* dna.mng

Options are processed in command line order. Any option you specify on the command line remains in effect for the set of images that follows, until the set is terminated by the appearance of any option or -noop. Some options only affect the decoding of images and others only the encoding. The latter can appear after the final group of input images.

For a more detailed description of each option, see ImageMagick(1).
 

-adjoin

-affine <matrix>

-antialias

-append

-average

-background <color>

-blur <radius>x<sigma>

-border <width>x<height>

-bordercolor <color>

-box <color>

-cache <threshold>

-channel <type>

-charcoal <factor>

-chop <width>x<height>{+-}<x>{+-}<y>{%}

-clip

-coalesce

-colorize <value>

-colors <value>

-colorspace <value>

-comment <string>

-compose <operator>

-compress <type>

-contrast

-crop <width>x<height>{+-}<x>{+-}<y>{%}

-cycle <amount>

-debug

-deconstruct

-delay <1/100ths of a second>

-density <width>x<height>

-depth <value>

-despeckle

-display <host:display[.screen]>

-dispose <method>

-dither

-draw <string>

-edge <radius>

-emboss

-encoding <type>

-endian <type>

-enhance

-equalize

-fill <color>

-filter <type>

-flatten

-flip

-flop

-font <name>

-frame <width>x<height>+<outer bevel width>+<inner bevel width>

-fuzz <distance>{%}

-gamma <value>

-gaussian <radius>x<sigma>

-geometry <width>x<height>{+-}<x>{+-}<y>{%}{@} {!}{<}{>}

-gravity <type>

-help

-implode <factor>

-intent <type>

-interlace <type>

-label <name>

-level <value>

-list <type>

-loop <iterations>

-map <filename>

-mask <filename>

-matte

-median <radius>

-modulate <value>

-monochrome

-morph <frames>

-mosaic

-negate

-noise <radius|type>

-noop

-normalize

-opaque <color>

-page <width>x<height>{+-}<x>{+-}<y>{%}{!}{<}{>}

-paint <radius>

-pen <color>

-ping

-pointsize <value>

-preview <type>

-process <command>

-profile <filename>

-quality <value>

-raise <width>x<height>

-region <width>x<height>{+-}<x>{+-}<y>

-resize <width>x<height>{%}{@}{!}{<}{>}

-roll {+-}<x>{+-}<y>

-rotate <degrees>{<}{>}

-sample <geometry>

-sampling_factor <horizontal_factor>x<vertical_factor>

-scale <geometry>

-scene <value>

-seed <value>

-segment <cluster threshold>x<smoothing threshold>

-shade <azimuth>x<elevation>

-sharpen <radius>x<sigma>

-shave <width>x<height>

-shear <x degrees>x<y degrees>

-size <width>x<height>{+offset}

-solarize <factor>

-spread <amount>

-stroke <color>

-strokewidth <value>

-swirl <degrees>

-texture <filename>

-threshold <value>

-tile <filename>

-transform

-transparent <color>

-treedepth <value>

-trim

-type <type>

-units <type>

-unsharp <radius>x<sigma>

-use_pixmap

-verbose

-view <string>

-wave <amplitude>x<wavelength>

-write <filename>

For a more detailed description of each option, see ImageMagick(1).
 

Use -segment to segment an image by analyzing the histograms of the color components and identifying units that are homogeneous with the fuzzy c-means technique. The scale-space filter analyzes the histograms of the three color components of the image and identifies a set of classes. The extents of each class is used to coarsely segment the image with thresholding. The color associated with each class is determined by the mean color of all pixels within the extents of a particular class. Finally, any unclassified pixels are assigned to the closest class with the fuzzy c-means technique.

The fuzzy c-Means algorithm can be summarized as follows:

• Build a histogram, one for each color component of the image.
• For each histogram, successively apply the scale-space filter and build an interval tree of zero crossings in the second derivative at each scale. Analyze this scale-space "fingerprint" to determine which peaks or valleys in the histogram are most predominant.
• The fingerprint defines intervals on the axis of the histogram. Each interval contains either a minima or a maxima in the original signal. If each color component lies within the maxima interval, that pixel is considered "classified" and is assigned an unique class number.
• Any pixel that fails to be classified in the above thresholding pass is classified using the fuzzy c-Means technique. It is assigned to one of the classes discovered in the histogram analysis phase.

The fuzzy c-Means technique attempts to cluster a pixel by finding the local minima of the generalized within group sum of squared error objective function. A pixel is assigned to the closest class of which the fuzzy membership has a maximum value.

For additional information see:

Young Won Lim, Sang Uk Lee, ``On The Color Image Segmentation Algorithm Based on the Thresholding and the Fuzzy c-Means Techniques'', Pattern Recognition, Volume 23, Number 9, pages 935-952, 1990.

John Cristy, [email protected], ImageMagick Studio LLC,
Glenn Randers-Pehrson, [email protected], ImageMagick Studio LLC.
 

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