Type:	Package
Title:	Advanced Graphics and Image-Processing in R
Version:	2.8.5
Description:	Bindings to 'ImageMagick': the most comprehensive open-source image processing library available. Supports many common formats (png, jpeg, tiff, pdf, etc) and manipulations (rotate, scale, crop, trim, flip, blur, etc). All operations are vectorized via the Magick++ STL meaning they operate either on a single frame or a series of frames for working with layers, collages, or animation. In RStudio images are automatically previewed when printed to the console, resulting in an interactive editing environment. The latest version of the package includes a native graphics device for creating in-memory graphics or drawing onto images using pixel coordinates.
License:	MIT + file LICENSE
URL:	https://docs.ropensci.org/magick/ https://ropensci.r-universe.dev/magick
BugReports:	https://github.com/ropensci/magick/issues
SystemRequirements:	ImageMagick++: ImageMagick-c++-devel (rpm) or libmagick++-dev (deb)
VignetteBuilder:	knitr
Imports:	Rcpp (≥ 0.12.12), magrittr, curl
LinkingTo:	Rcpp
Suggests:	av (≥ 0.3), spelling, jsonlite, methods, knitr, rmarkdown, rsvg, webp, pdftools, ggplot2, gapminder, IRdisplay, tesseract (≥ 2.0), gifski
Encoding:	UTF-8
RoxygenNote:	7.3.2
Language:	en-US
NeedsCompilation:	yes
Packaged:	2024-09-19 16:13:03 UTC; jeroen
Author:	Jeroen Ooms [aut, cre]
Maintainer:	Jeroen Ooms <jeroenooms@gmail.com>
Repository:	CRAN
Date/Publication:	2024-09-20 08:40:10 UTC

Magick Image Processing

Description

The magick package for graphics and image processing in R. Important resources:

• R introduction vignette: getting started

• Magick++ API and Magick++ STL detailed descriptions of methods and parameters

Details

Documentation is split into the following pages:

• analysis - metrics and calculations: compare, fft

• animation - manipulate or combine multiple frames: animate, morph, mosaic, montage, average, append, apply

• attributes - image properties: comment, info

• color - contrast, brightness, colors: modulate, quantize, map, transparent, background, colorize, contrast, normalize, enhance, equalize, median

• composite - advanced joining: composite, border, frame

• device - creating graphics and drawing on images

• editing - basic image IO: read, write, convert, join, display, brose

• effects - fun effects: despecle, reducenoise, noise, blur, charcoal, edge, oilpaint, emboss, implode, negate

• geometry - specify points, areas and sizes using geometry syntax

• ocr - extract text from image using tesseract package

• options - list option types and values supported in your version of ImageMagick

• painting - flood fill and annotating text

• transform - shape operations: trim, chop, rotate, resize, scale, sample crop, flip, flop, deskew, page

See Also

Other image: analysis, animation, attributes(), color, composite, defines, device, edges, editing, effects(), fx, geometry, morphology, ocr, options(), painting, segmentation, transform(), video

Image Analysis

Description

Functions for image calculations and analysis. This part of the package needs more work.

Usage

image_compare(image, reference_image, metric = "", fuzz = 0)

image_compare_dist(image, reference_image, metric = "", fuzz = 0)

image_fft(image)

Arguments

Details

For details see Image++ documentation. Short descriptions:

• image_compare calculates a metric by comparing image with a reference image.

• image_fft returns Discrete Fourier Transform (DFT) of the image as a magnitude / phase image pair. I wish I knew what this means.

Here image_compare() is vectorized over the first argument and returns the diff image with the calculated distortion value as an attribute.

See Also

Other image: _index_, animation, attributes(), color, composite, defines, device, edges, editing, effects(), fx, geometry, morphology, ocr, options(), painting, segmentation, transform(), video

Examples

out1 <- image_blur(logo, 3)
out2 <- image_oilpaint(logo, 3)
input <- c(logo, out1, out2, logo)
if(magick_config()$version >= "6.8.7"){
  diff_img <- image_compare(input, logo, metric = "AE")
  attributes(diff_img)
}

Image Frames and Animation

Description

Operations to manipulate or combine multiple frames of an image. Details below.

Usage

image_animate(
  image,
  fps = 10,
  delay = NULL,
  loop = 0,
  dispose = c("background", "previous", "none"),
  optimize = FALSE
)

image_coalesce(image)

image_morph(image, frames = 8)

image_mosaic(image, operator = NULL)

image_flatten(image, operator = NULL)

image_average(image)

image_append(image, stack = FALSE)

image_apply(image, FUN, ...)

image_montage(
  image,
  geometry = NULL,
  tile = NULL,
  gravity = "Center",
  bg = "white",
  shadow = FALSE
)

Arguments

Details

For details see Magick++ STL documentation. Short descriptions:

• image_animate coalesces frames by playing the sequence and converting to gif format.

• image_morph expands number of frames by interpolating intermediate frames to blend into each other when played as an animation.

• image_mosaic inlays images to form a single coherent picture.

• image_montage creates a composite image by combining frames.

• image_flatten merges frames as layers into a single frame using a given operator.

• image_average averages frames into single frame.

• image_append stack images left-to-right (default) or top-to-bottom.

• image_apply applies a function to each frame

The image_apply function calls an image function to each frame and joins results back into a single image. Because most operations are already vectorized this is often not needed. Note that FUN() should return an image. To apply other kinds of functions to image frames simply use lapply, vapply, etc.

See Also

Other image: _index_, analysis, attributes(), color, composite, defines, device, edges, editing, effects(), fx, geometry, morphology, ocr, options(), painting, segmentation, transform(), video

Examples

# Combine images
logo <- image_read("https://jeroen.github.io/images/Rlogo.png")
oldlogo <- image_read("https://jeroen.github.io/images/Rlogo-old.png")

# Create morphing animation
both <- image_scale(c(oldlogo, logo), "400")
image_average(image_crop(both))
image_animate(image_morph(both, 10))

# Create thumbnails from GIF
banana <- image_read("https://jeroen.github.io/images/banana.gif")
length(banana)
image_average(banana)
image_flatten(banana)
image_append(banana)
image_append(banana, stack = TRUE)

# Append images together
wizard <- image_read("wizard:")
image_append(image_scale(c(image_append(banana[c(1,3)], stack = TRUE), wizard)))

image_composite(banana, image_scale(logo, "300"))

# Break down and combine frames
front <- image_scale(banana, "300")
background <- image_background(image_scale(logo, "400"), 'white')
frames <- image_apply(front, function(x){image_composite(background, x, offset = "+70+30")})
image_animate(frames, fps = 10)
# Simple 4x3 montage
input <- rep(logo, 12)
image_montage(input, geometry = 'x100+10+10', tile = '4x3', bg = 'pink', shadow = TRUE)

# With varying frame size
input <- c(wizard, wizard, logo, logo)
image_montage(input, tile = '2x2', bg = 'pink', gravity = 'southwest')

Convert to EBImage

Description

Convert a Magick image to EBImage class. Note that EBImage only supports multi-frame images in greyscale.

Usage

as_EBImage(image)

Arguments

Image Attributes

Description

Attributes are properties of the image that might be present on some images and might affect image manipulation methods.

Usage

image_comment(image, comment = NULL)

image_info(image)

Arguments

Details

Each attribute can be get and set with the same function. The image_info() function returns a data frame with some commonly used attributes.

See Also

Other image: _index_, analysis, animation, color, composite, defines, device, edges, editing, effects(), fx, geometry, morphology, ocr, options(), painting, segmentation, transform(), video

RStudio Graphics AutoViewer

Description

This enables a addTaskCallback that automatically updates the viewer after the state of a magick graphics device has changed. This is enabled by default in RStudio.

Usage

autoviewer_enable()

autoviewer_disable()

Examples

# Only has effect in RStudio (or other GUI with a viewer):
autoviewer_enable()

img <- magick::image_graph()
plot(1)
abline(0, 1, col = "blue", lwd = 2, lty = "solid")
abline(0.1, 1, col = "red", lwd = 3, lty = "dotted")

autoviewer_disable()
abline(0.2, 1, col = "green", lwd = 4, lty = "twodash")
abline(0.3, 1, col = "black", lwd = 5, lty = "dotdash")

autoviewer_enable()
abline(0.4, 1, col = "purple", lwd = 6, lty = "dashed")
abline(0.5, 1, col = "yellow", lwd = 7, lty = "longdash")

Magick Configuration

Description

ImageMagick can be configured to support various additional tool and formats via external libraries. These functions show which features ImageMagick supports on your system.

Usage

coder_info(format)

magick_config()

magick_set_seed(seed)

Arguments

Details

Note that coder_info raises an error for unsupported formats.

References

https://www.imagemagick.org/Magick++/CoderInfo.html

Examples

coder_info("png")
coder_info("jpg")
coder_info("pdf")
coder_info("tiff")
coder_info("gif")
# Reproduce random image
magick_set_seed(123)
image_blank(200,200, pseudo_image = "plasma:fractal")

Image Color

Description

Functions to adjust contrast, brightness, colors of the image. Details below.

Usage

image_modulate(image, brightness = 100, saturation = 100, hue = 100)

image_quantize(
  image,
  max = 256,
  colorspace = "rgb",
  dither = TRUE,
  treedepth = NULL
)

image_map(image, map, dither = FALSE)

image_ordered_dither(image, threshold_map)

image_channel(image, channel = "lightness")

image_separate(image, channel = "default")

image_combine(image, colorspace = "sRGB", channel = "default")

image_transparent(image, color, fuzz = 0)

image_background(image, color, flatten = TRUE)

image_colorize(image, opacity, color)

image_contrast(image, sharpen = 1)

image_normalize(image)

image_enhance(image)

image_equalize(image)

image_median(image, radius = 1)

Arguments

Details

For details see Magick++ STL documentation. Short descriptions:

• image_modulate adjusts brightness, saturation and hue of image relative to current.

• image_quantize reduces number of unique colors in the image.

• image_ordered_dither reduces number of unique colors using a dithering threshold map.

• image_map replaces colors of image with the closest color from a reference image.

• image_channel extracts a single channel from an image and returns as grayscale.

• image_transparent sets pixels approximately matching given color to transparent.

• image_background sets background color. When image is flattened, transparent pixels get background color.

• image_colorize overlays a solid color frame using specified opacity.

• image_contrast enhances intensity differences in image

• image_normalize increases contrast by normalizing the pixel values to span the full range of colors

• image_enhance tries to minimize noise

• image_equalize equalizes using histogram equalization

• image_median replaces each pixel with the median color in a circular neighborhood

Note that colors are also determined by image properties imagetype and colorspace which can be modified via image_convert().

See Also

Other image: _index_, analysis, animation, attributes(), composite, defines, device, edges, editing, effects(), fx, geometry, morphology, ocr, options(), painting, segmentation, transform(), video

Examples

# manually adjust colors
logo <- image_read("logo:")
image_modulate(logo, brightness = 200)
image_modulate(logo, saturation = 150)
image_modulate(logo, hue = 200)

# Reduce image to 10 different colors using various spaces
image_quantize(logo, max = 10, colorspace = 'gray')
image_quantize(logo, max = 10, colorspace = 'rgb')
image_quantize(logo, max = 10, colorspace = 'cmyk')

image_ordered_dither(logo, 'o8x8')
# Change background color
translogo <- image_transparent(logo, 'white')
image_background(translogo, "pink", flatten = TRUE)

# Compare to flood-fill method:
image_fill(logo, "pink", fuzz = 20)

# Other color tweaks
image_colorize(logo, 50, "red")
image_contrast(logo)
image_normalize(logo)
image_enhance(logo)
image_equalize(logo)
image_median(logo)

# Alternate way to convert into black-white
image_convert(logo, type = 'grayscale')

Image Composite

Description

Similar to the ImageMagick composite utility: compose an image on top of another one using a CompositeOperator.

Usage

image_composite(
  image,
  composite_image,
  operator = "atop",
  offset = "+0+0",
  gravity = "northwest",
  compose_args = ""
)

image_border(image, color = "lightgray", geometry = "10x10", operator = "copy")

image_frame(image, color = "lightgray", geometry = "25x25+6+6")

image_shadow_mask(image, geometry = "50x10+30+30")

image_shadow(
  image,
  color = "black",
  bg = "none",
  geometry = "50x10+30+30",
  operator = "copy",
  offset = "+20+20"
)

image_shade(image, azimuth = 30, elevation = 30, color = FALSE)

Arguments

Details

The image_composite function is vectorized over both image arguments: if the first image has n frames and the second m frames, the output image will contain n * m frames.

The image_border function creates a slightly larger solid color frame and then composes the original frame on top. The image_frame function is similar but has an additional feature to create a shadow effect on the border (which is really ugly).

See Also

Other image: _index_, analysis, animation, attributes(), color, defines, device, edges, editing, effects(), fx, geometry, morphology, ocr, options(), painting, segmentation, transform(), video

Examples

# Compose images using one of many operators
imlogo <- image_scale(image_read("logo:"), "x275")
rlogo <- image_read("https://jeroen.github.io/images/Rlogo-old.png")

# Standard is atop
image_composite(imlogo, rlogo)

# Same as 'blend 50' in the command line
image_composite(imlogo, rlogo, operator = "blend", compose_args="50")

# Offset can be geometry or gravity
image_composite(logo, rose, offset = "+100+100")
image_composite(logo, rose, gravity = "East")

# Add a border frame around the image
image_border(imlogo, "red", "10x10")
image_frame(imlogo)
image_shadow(imlogo)
image_shade(imlogo)

Set encoder defines

Description

So called 'defines' are properties that are passed along to external filters and libraries. Usually defines are used in image_read or image_write to control the image encoder/decoder, but you can also set these manually on the image object.

Usage

image_set_defines(image, defines)

Arguments

Details

The defines values must be a character string, where the names contain the defines keys. Each name must be of the format "enc:key" where the first part is the encoder or filter to which the key is passed. For example "png:...." defines can control the encoding and decoding of png images.

The image_set_defines function does not make a copy of the image, so the defined values remain in the image object until they are overwritten or unset.

See Also

Other image: _index_, analysis, animation, attributes(), color, composite, device, edges, editing, effects(), fx, geometry, morphology, ocr, options(), painting, segmentation, transform(), video

Examples

# Write an image
x <- image_read("https://jeroen.github.io/images/frink.png")
image_write(x, "frink.png")

# Pass some properties to PNG encoder
defines <- c("png:compression-filter" = "1", "png:compression-level" = "0")
image_set_defines(x, defines)
image_write(x, "frink-uncompressed.png")

# Unset properties
defines[1:2] = NA
image_set_defines(x, defines)
image_write(x, "frink-final.png")

# Compare size and cleanup
file.info(c("frink.png", "frink-uncompressed.png", "frink-final.png"))
unlink(c("frink.png", "frink-uncompressed.png", "frink-final.png"))

Magick Graphics Device

Description

Graphics device that produces a Magick image. Can either be used like a regular device for making plots, or alternatively via image_draw to open a device which draws onto an existing image using pixel coordinates. The latter is vectorized, i.e. drawing operations are applied to each frame in the image.

Usage

image_graph(
  width = 800,
  height = 600,
  bg = "white",
  pointsize = 12,
  res = 72,
  clip = TRUE,
  antialias = TRUE
)

image_draw(image, pointsize = 12, res = 72, antialias = TRUE, ...)

image_capture()

Arguments

Details

The device is a relatively recent feature of the package. It should support all operations but there might still be small inaccuracies. Also it is a bit slower than some of the other devices, in particular for rendering text and clipping. Hopefully this can be optimized in the next version.

By default image_draw sets all margins to 0 and uses graphics coordinates to match image size in pixels (width x height) where (0,0) is the top left corner. Note that this means the y axis increases from top to bottom which is the opposite of typical graphics coordinates. You can override all this by passing custom xlim, ylim or mar values to image_draw.

The image_capture function returns the current device as an image. This only works if the current device is a magick device or supports dev.capture.

See Also

Other image: _index_, analysis, animation, attributes(), color, composite, defines, edges, editing, effects(), fx, geometry, morphology, ocr, options(), painting, segmentation, transform(), video

Examples

# Regular image
frink <- image_read("https://jeroen.github.io/images/frink.png")

# Produce image using graphics device
fig <- image_graph(res = 96)
ggplot2::qplot(mpg, wt, data = mtcars, colour = cyl)
dev.off()

# Combine
out <- image_composite(fig, frink, offset = "+70+30")
print(out)

# Or paint over an existing image
img <- image_draw(frink)
rect(20, 20, 200, 100, border = "red", lty = "dashed", lwd = 5)
abline(h = 300, col = 'blue', lwd = '10', lty = "dotted")
text(10, 250, "Hoiven-Glaven", family = "monospace", cex = 4, srt = 90)
palette(rainbow(11, end = 0.9))
symbols(rep(200, 11), seq(0, 400, 40), circles = runif(11, 5, 35),
  bg = 1:11, inches = FALSE, add = TRUE)
dev.off()
print(img)


# Vectorized example with custom coordinates
earth <- image_read("https://jeroen.github.io/images/earth.gif")
img <- image_draw(earth, xlim = c(0,1), ylim = c(0,1))
rect(.1, .1, .9, .9, border = "red", lty = "dashed", lwd = 5)
text(.5, .9, "Our planet", cex = 3, col = "white")
dev.off()
print(img)

Edge / Line Detection

Description

Best results are obtained by finding edges with image_canny() and then performing Hough-line detection on the edge image.

Usage

image_edge(image, radius = 1)

image_canny(image, geometry = "0x1+10%+30%")

image_hough_draw(
  image,
  geometry = NULL,
  color = "red",
  bg = "transparent",
  size = 3,
  overlay = FALSE
)

image_hough_txt(image, geometry = NULL, format = c("mvg", "svg"))

Arguments

Details

For Hough-line detection, the geometry format is ⁠{W}x{H}+{threshold}⁠ defining the size and threshold of the filter used to find 'peaks' in the intermediate search image. For canny edge detection the format is ⁠{radius}x{sigma}+{lower%}+{upper%}⁠. More details and examples are available at the imagemagick website.

See Also

Other image: _index_, analysis, animation, attributes(), color, composite, defines, device, editing, effects(), fx, geometry, morphology, ocr, options(), painting, segmentation, transform(), video

Examples

if(magick_config()$version > "6.8.9"){
shape <- demo_image("shape_rectangle.gif")
rectangle <- image_canny(shape)
rectangle |> image_hough_draw('5x5+20')
rectangle |> image_hough_txt(format = 'svg') |> cat()
}

Image Editing

Description

Read, write and join or combine images. All image functions are vectorized, meaning they operate either on a single frame or a series of frames (e.g. a collage, video, or animation). Besides paths and URLs, image_read() supports commonly used bitmap and raster object types.

Usage

image_read(
  path,
  density = NULL,
  depth = NULL,
  strip = FALSE,
  coalesce = TRUE,
  defines = NULL
)

image_read_svg(path, width = NULL, height = NULL)

image_read_pdf(path, pages = NULL, density = 300, password = "")

image_read_video(path, fps = 1, format = "png")

image_write(
  image,
  path = NULL,
  format = NULL,
  quality = NULL,
  depth = NULL,
  density = NULL,
  comment = NULL,
  flatten = FALSE,
  defines = NULL,
  compression = NULL
)

image_convert(
  image,
  format = NULL,
  type = NULL,
  colorspace = NULL,
  depth = NULL,
  antialias = NULL,
  matte = NULL,
  interlace = NULL,
  profile = NULL
)

image_data(image, channels = NULL, frame = 1)

image_raster(image, frame = 1, tidy = TRUE)

image_display(image, animate = TRUE)

image_browse(image, browser = getOption("browser"))

image_strip(image)

image_blank(width, height, color = "none", pseudo_image = "", defines = NULL)

image_destroy(image)

image_join(...)

image_attributes(image)

image_get_artifact(image, artifact = "")

demo_image(path)

Arguments

Details

All standard base vector methods such as [, [[, c(), as.list(), as.raster(), rev(), length(), and print() can be used to work with magick image objects. Use the standard img[i] syntax to extract a subset of the frames from an image. The img[[i]] method is an alias for image_data() which extracts a single frame as a raw bitmap matrix with pixel values.

For reading svg or pdf it is recommended to use image_read_svg() and image_read_pdf() if the rsvg and pdftools R packages are available. These functions provide more rendering options (including rendering of literal svg) and better quality than built-in svg/pdf rendering delegates from imagemagick itself.

X11 is required for image_display() which is only works on some platforms. A more portable method is image_browse() which opens the image in a browser. RStudio has an embedded viewer that does this automatically which is quite nice.

Image objects are automatically released by the garbage collector when they are no longer reachable. Because the GC only runs once in a while, you can also call image_destroy() explicitly to release the memory immediately. This is usually only needed if you create a lot of images in a short period of time, and you might run out of memory.

See Also

Other image: _index_, analysis, animation, attributes(), color, composite, defines, device, edges, effects(), fx, geometry, morphology, ocr, options(), painting, segmentation, transform(), video

Examples

# Download image from the web
frink <- image_read("https://jeroen.github.io/images/frink.png")
worldcup_frink <- image_fill(frink, "orange", "+100+200", 20)
image_write(worldcup_frink, "output.png")

# extract raw bitmap array
bitmap <- frink[[1]]

# replace pixels with #FF69B4 ('hot pink') and convert back to image
bitmap[,50:100, 50:100] <- as.raw(c(0xff, 0x69, 0xb4, 0xff))
image_read(bitmap)

# Plot to graphics device via legacy raster format
raster <- as.raster(frink)
par(ask=FALSE)
plot(raster)

# Read bitmap arrays from other image packages
download.file("https://jeroen.github.io/images/example.webp", "example.webp", mode = 'wb')
if(require(webp)) image_read(webp::read_webp("example.webp"))
unlink(c("example.webp", "output.png"))
if(require(rsvg)){
tiger <- image_read_svg("http://jeroen.github.io/images/tiger.svg")
svgtxt <- '<?xml version="1.0" encoding="UTF-8"?>
<svg width="400" height="400" viewBox="0 0 400 400" fill="none">
 <circle fill="steelblue" cx="200" cy="200" r="100" />
 <circle fill="yellow" cx="200" cy="200" r="90" />
</svg>'
circles <- image_read_svg(svgtxt)
}
if(require(pdftools))
image_read_pdf(file.path(R.home('doc'), 'NEWS.pdf'), pages = 1, density = 100)
# create a solid canvas
image_blank(600, 400, "green")
image_blank(600, 400, pseudo_image = "radial-gradient:purple-yellow")
image_blank(200, 200, pseudo_image = "gradient:#3498db-#db3a34",
  defines = c('gradient:direction' = 'east'))

Image Effects

Description

High level effects applied to an entire image. These are mostly just for fun.

Usage

image_despeckle(image, times = 1L)

image_reducenoise(image, radius = 1L)

image_noise(image, noisetype = "gaussian")

image_blur(image, radius = 1, sigma = 0.5)

image_motion_blur(image, radius = 1, sigma = 0.5, angle = 0)

image_charcoal(image, radius = 1, sigma = 0.5)

image_oilpaint(image, radius = 1)

image_emboss(image, radius = 1, sigma = 0.5)

image_implode(image, factor = 0.5)

image_negate(image)

Arguments

See Also

Other image: _index_, analysis, animation, attributes(), color, composite, defines, device, edges, editing, fx, geometry, morphology, ocr, options(), painting, segmentation, transform(), video

Examples

logo <- image_read("logo:")
image_despeckle(logo)
image_reducenoise(logo)
image_noise(logo)
image_blur(logo, 10, 10)
image_motion_blur(logo, 10, 10, 45)
image_charcoal(logo)
image_oilpaint(logo, radius = 3)
image_emboss(logo)
image_implode(logo)
image_negate(logo)

Image FX

Description

Apply a custom an fx expression to the image.

Usage

image_fx(image, expression = "p", channel = NULL)

image_fx_sequence(image, expression = "p")

Arguments

Details

There are two different interfaces. The image_fx function simply applies the same fx to each frame in the input image. The image_fx_sequence function on the other hand treats the entire input vector as a sequence, allowing you to apply an expression with multiple input images. See examples.

See Also

Other image: _index_, analysis, animation, attributes(), color, composite, defines, device, edges, editing, effects(), geometry, morphology, ocr, options(), painting, segmentation, transform(), video

Examples

# Show image_fx() expression
img <- image_convert(logo, colorspace = "Gray")
gradient_x <- image_convolve(img, kernel = "Prewitt")
gradient_y <- image_convolve(img, kernel = "Prewitt:90")
gradient <- c(image_fx(gradient_x, expression = "p^2"),
                image_fx(gradient_y, expression = "p^2"))
gradient <- image_flatten(gradient, operator = "Plus")
#gradient <- image_fx(gradient, expression = "sqrt(p)")
gradient


image_fx(img, expression = "pow(p, 0.5)")
image_fx(img, expression = "rand()")

# Use multiple source images

input <- c(logo, image_flop(logo))
image_fx_sequence(input, "(u+v)/2")

Geometry Helpers

Description

ImageMagick uses a handy geometry syntax to specify coordinates and shapes for use in image transformations. You can either specify these manually as strings or use the helper functions below.

Usage

geometry_point(x, y)

geometry_area(width = NULL, height = NULL, x_off = 0, y_off = 0)

geometry_size_pixels(width = NULL, height = NULL, preserve_aspect = TRUE)

geometry_size_percent(width = 100, height = NULL)

Arguments

Details

See ImageMagick Manual for details about the syntax specification. Examples of geometry strings:

• "500x300" – Resize image keeping aspect ratio, such that width does not exceed 500 and the height does not exceed 300.

• "500x300!" – Resize image to 500 by 300, ignoring aspect ratio

• "500x" – Resize width to 500 keep aspect ratio

• "x300" – Resize height to 300 keep aspect ratio

• "50%x20%" – Resize width to 50 percent and height to 20 percent of original

• "500x300+10+20" – Crop image to 500 by 300 at position 10,20

See Also

Other image: _index_, analysis, animation, attributes(), color, composite, defines, device, edges, editing, effects(), fx, morphology, ocr, options(), painting, segmentation, transform(), video

Examples

# Specify a point
logo <- image_read("logo:")
image_annotate(logo, "Some text", location = geometry_point(100, 200), size = 24)

# Specify image area
image_crop(logo, geometry_area(300, 300), repage = FALSE)
image_crop(logo, geometry_area(300, 300, 100, 100), repage = FALSE)

# Specify image size
image_resize(logo, geometry_size_pixels(300))
image_resize(logo, geometry_size_pixels(height = 300))
image_resize(logo, geometry_size_pixels(300, 300, preserve_aspect = FALSE))

# resize relative to current size
image_resize(logo, geometry_size_percent(50))
image_resize(logo, geometry_size_percent(50, 20))

Image to ggplot

Description

Create a ggplot with axes set to pixel coordinates and plot the raster image on it using ggplot2::annotation_raster. See examples for how to plot an image onto an existing ggplot.

Usage

image_ggplot(image, interpolate = FALSE)

Arguments

Examples

# Plot with base R
plot(logo)

# Plot image with ggplot2
library(ggplot2)
myplot <- image_ggplot(logo)
myplot + ggtitle("Test plot")

# Show that coordinates are reversed:
myplot + theme_classic()

# Or add to plot as annotation
image <- image_fill(logo, 'none')
raster <- as.raster(image)
myplot <- qplot(mpg, wt, data = mtcars)
myplot + annotation_raster(raster, 25, 35, 3, 5)

# Or overplot image using grid
library(grid)
qplot(speed, dist, data = cars, geom = c("point", "smooth"))
grid.raster(image)

Morphology

Description

Apply a morphology method. This is a very flexible function which can be used to apply any morphology method with custom parameters. See imagemagick website for examples.

Usage

image_morphology(
  image,
  method = "convolve",
  kernel = "Gaussian",
  iterations = 1,
  opts = list()
)

image_convolve(
  image,
  kernel = "Gaussian",
  iterations = 1,
  scaling = NULL,
  bias = NULL
)

Arguments

See Also

Other image: _index_, analysis, animation, attributes(), color, composite, defines, device, edges, editing, effects(), fx, geometry, ocr, options(), painting, segmentation, transform(), video

Examples

#example from IM website:
if(magick_config()$version > "6.8.8"){
pixel <- image_blank(1, 1, 'white') |> image_border('black', '5x5')

# See the effect of Dilate method
pixel |> image_scale('800%')
pixel |> image_morphology('Dilate', "Diamond") |> image_scale('800%')

# These produce the same output:
pixel |> image_morphology('Dilate', "Diamond", iter = 3) |> image_scale('800%')
pixel |> image_morphology('Dilate', "Diamond:3") |> image_scale('800%')

# Plus example
pixel |> image_morphology('Dilate', "Plus", iterations = 2) |> image_scale('800%')

# Rose examples
rose |> image_morphology('ErodeI', 'Octagon', iter = 3)
rose |> image_morphology('DilateI', 'Octagon', iter = 3)
rose |> image_morphology('OpenI', 'Octagon', iter = 3)
rose |> image_morphology('CloseI', 'Octagon', iter = 3)

# Edge detection
man <- demo_image('man.gif')
man |> image_morphology('EdgeIn', 'Octagon')
man |> image_morphology('EdgeOut', 'Octagon')
man |> image_morphology('Edge', 'Octagon')

# Octagonal Convex Hull
 man |>
   image_morphology('Close', 'Diamond') |>
   image_morphology('Thicken', 'ConvexHull', iterations = 1)

# Thinning down to a Skeleton
man |> image_morphology('Thinning', 'Skeleton', iterations = 1)

# Specify custom kernel matrix usingn a string:
img <- demo_image("test_mag.gif")
i <- image_convolve(img, kernel = '4x5:
       0 -1  0  0
      -1 +1 -1  0
      -1 +1 -1  0
      -1 +1 +1 -1
       0 -1 -1  0 ', bias = "50%")
}

Image Text OCR

Description

Extract text from an image using the tesseract package.

Usage

image_ocr(image, language = "eng", HOCR = FALSE, ...)

image_ocr_data(image, language = "eng", ...)

Arguments

Details

To use this function you need to tesseract first:

  install.packages("tesseract")

Best results are obtained if you set the correct language in tesseract. To install additional languages see instructions in tesseract_download().

See Also

Other image: _index_, analysis, animation, attributes(), color, composite, defines, device, edges, editing, effects(), fx, geometry, morphology, options(), painting, segmentation, transform(), video

Examples

if(require("tesseract")){
img <- image_read("http://jeroen.github.io/images/testocr.png")
image_ocr(img)
image_ocr_data(img)
}

Magick Options

Description

List option types and values supported in your version of ImageMagick. For descriptions see ImageMagick Enumerations.

Usage

magick_options()

magick_fonts()

option_types()

filter_types()

metric_types()

dispose_types()

compose_types()

colorspace_types()

channel_types()

image_types()

kernel_types()

noise_types()

gravity_types()

orientation_types()

morphology_types()

style_types()

decoration_types()

compress_types()

distort_types()

dump_option_info(option = "font")

Arguments

Details

The dump_option_info function is equivalent to calling convert -list [option] on the command line. It does not return anything, it only makes ImageMagick print stuff to the console, use only for debugging.

References

ImageMagick Manual: Enumerations

See Also

Other image: _index_, analysis, animation, attributes(), color, composite, defines, device, edges, editing, effects(), fx, geometry, morphology, ocr, painting, segmentation, transform(), video

Image Painting

Description

The image_fill() function performs flood-fill by painting starting point and all neighboring pixels of approximately the same color. Annotate prints some text on the image.

Usage

image_fill(image, color, point = "+1+1", fuzz = 0, refcolor = NULL)

image_annotate(
  image,
  text,
  gravity = "northwest",
  location = "+0+0",
  degrees = 0,
  size = 10,
  font = "",
  style = "normal",
  weight = 400,
  kerning = 0,
  decoration = NULL,
  color = NULL,
  strokecolor = NULL,
  strokewidth = NULL,
  boxcolor = NULL
)

Arguments

Details

Note that more sophisticated drawing mechanisms are available via the graphics device using image_draw.

Setting a font, weight, style only works if your imagemagick is compiled with fontconfig support.

See Also

Other image: _index_, analysis, animation, attributes(), color, composite, defines, device, edges, editing, effects(), fx, geometry, morphology, ocr, options(), segmentation, transform(), video

Examples

logo <- image_read("logo:")
logo <- image_background(logo, 'white')
image_fill(logo, "pink", point = "+450+400")
image_fill(logo, "pink", point = "+450+400", fuzz = 25)
# Add some text to an image
image_annotate(logo, "This is a test")
image_annotate(logo, "CONFIDENTIAL", size = 50, color = "red", boxcolor = "pink",
 degrees = 30, location = "+100+100")

# Setting fonts requires fontconfig support (and that you have the font)
image_annotate(logo, "The quick brown fox", font = "monospace", size = 50)

Objects exported from other packages

Description

These objects are imported from other packages. Follow the links below to see their documentation.

magrittr

%>%

Image Segmentation

Description

Basic image segmentation like connected components labelling, blob extraction and fuzzy c-means

Usage

image_connect(image, connectivity = 4)

image_split(image, keep_color = TRUE)

image_fuzzycmeans(image, min_pixels = 1, smoothing = 1.5)

Arguments

Details

• image_connect Connect adjacent pixels with the same pixel intensities to do blob extraction

• image_split Splits the image according to pixel intensities

• image_fuzzycmeans Fuzzy c-means segmentation of the histogram of color components

image_connect performs blob extraction by scanning the image, pixel-by-pixel from top-left to bottom-right where regions of adjacent pixels which share the same set of intensity values get combined.

See Also

Other image: _index_, analysis, animation, attributes(), color, composite, defines, device, edges, editing, effects(), fx, geometry, morphology, ocr, options(), painting, transform(), video

Examples

# Split an image by color
img <- image_quantize(logo, 4)
layers <- image_split(img)
layers

# This returns the original image
image_flatten(layers)

# From the IM website
objects <- image_convert(demo_image("objects.gif"), colorspace = "Gray")
objects


# Split image in blobs of connected pixel levels
if(magick_config()$version > "6.9.0"){
objects |>
  image_connect(connectivity = 4) |>
  image_split()

# Fuzzy c-means
image_fuzzycmeans(logo)

logo |>
  image_convert(colorspace = "HCL") |>
  image_fuzzycmeans(smoothing = 5)
}

Image thresholding

Description

Thresholding an image can be used for simple and straightforward image segmentation. The function image_threshold() allows to do black and white thresholding whereas image_lat() performs local adaptive thresholding.

Usage

image_threshold(
  image,
  type = c("black", "white"),
  threshold = "50%",
  channel = NULL
)

image_level(
  image,
  black_point = 0,
  white_point = 100,
  mid_point = 1,
  channel = NULL
)

image_lat(image, geometry = "10x10+5%")

Arguments

Details

• image_threshold(type = "black"): Forces all pixels below the threshold into black while leaving all pixels at or above the threshold unchanged

• image_threshold(type = "white"): Forces all pixels above the threshold into white while leaving all pixels at or below the threshold unchanged

• image_lat(): Local Adaptive Thresholding. Looks in a box (width x height) around the pixel neighborhood if the pixel value is bigger than the average minus an offset.

Examples

test <- image_convert(logo, colorspace = "Gray")
image_threshold(test, type = "black", threshold = "50%")
image_threshold(test, type = "white", threshold = "50%")

# Turn image into BW
test |>
  image_threshold(type = "white", threshold = "50%") |>
  image_threshold(type = "black", threshold = "50%")

# adaptive thresholding
image_lat(test, geometry = '10x10+5%')

Image Transform

Description

Basic transformations like rotate, resize, crop and flip. The geometry syntax is used to specify sizes and areas.

Usage

image_trim(image, fuzz = 0)

image_chop(image, geometry)

image_rotate(image, degrees)

image_resize(image, geometry = NULL, filter = NULL)

image_scale(image, geometry = NULL)

image_sample(image, geometry = NULL)

image_crop(image, geometry = NULL, gravity = NULL, repage = TRUE)

image_extent(image, geometry, gravity = "center", color = "none")

image_flip(image)

image_flop(image)

image_deskew(image, threshold = 40)

image_deskew_angle(image, threshold = 40)

image_page(image, pagesize = NULL, density = NULL)

image_repage(image)

image_orient(image, orientation = NULL)

image_shear(image, geometry = "10x10", color = "none")

image_distort(image, distortion = "perspective", coordinates, bestfit = FALSE)

Arguments

Details

For details see Magick++ STL documentation. Short descriptions:

• image_trim removes edges that are the background color from the image.

• image_chop removes vertical or horizontal subregion of image.

• image_crop cuts out a subregion of original image

• image_rotate rotates and increases size of canvas to fit rotated image.

• image_deskew auto rotate to correct skewed images

• image_resize resizes using custom filterType

• image_scale and image_sample resize using simple ratio and pixel sampling algorithm.

• image_flip and image_flop invert image vertically and horizontally

The most powerful resize function is image_resize which allows for setting a custom resize filter. Output of image_scale is similar to image_resize(img, filter = "point").

For resize operations it holds that if no geometry is specified, all frames are rescaled to match the top frame.

See Also

Other image: _index_, analysis, animation, attributes(), color, composite, defines, device, edges, editing, effects(), fx, geometry, morphology, ocr, options(), painting, segmentation, video

Examples

logo <- image_read("logo:")
logo <- image_scale(logo, "400")
image_trim(logo)
image_chop(logo, "100x20")
image_rotate(logo, 45)
# Small image
rose <- image_convert(image_read("rose:"), "png")

# Resize to 400 width or height:
image_resize(rose, "400x")
image_resize(rose, "x400")

# Resize keeping ratio
image_resize(rose, "400x400")

# Resize, force size losing ratio
image_resize(rose, "400x400!")

# Different filters
image_resize(rose, "400x", filter = "Triangle")
image_resize(rose, "400x", filter = "Point")
# simple pixel resize
image_scale(rose, "400x")
image_sample(rose, "400x")
image_crop(logo, "400x400+200+200")
image_extent(rose, '200x200', color = 'pink')
image_flip(logo)
image_flop(logo)
skewed <- image_rotate(logo, 5)
deskewed <- image_deskew(skewed)
attr(deskewed, 'angle')
if(magick_config()$version > "6.8.6")
  image_orient(logo)
image_shear(logo, "10x10")
building <- demo_image('building.jpg')
image_distort(building, 'perspective', c(7,40,4,30,4,124,4,123,85,122,100,123,85,2,100,30))

Write Video

Description

High quality video / gif exporter based on external packages gifski and av.

Usage

image_write_video(image, path = NULL, framerate = 10, ...)

image_write_gif(image, path = NULL, delay = 1/10, ...)

Arguments

Details

This requires an image with multiple frames. The GIF exporter accomplishes the same thing as image_animate but much faster and with better quality.

See Also

Other image: _index_, analysis, animation, attributes(), color, composite, defines, device, edges, editing, effects(), fx, geometry, morphology, ocr, options(), painting, segmentation, transform()

Example Images

Description

Example images included with ImageMagick:

Usage

logo

Format

An object of class magick-image of length 1.

Details

• logo: ImageMagick Logo, 640x480

• wizard: ImageMagick Wizard, 480x640

• rose : Picture of a rose, 70x46

• granite : Granite texture pattern, 128x128