procimg: Process images

In pavo: Perceptual Analysis, Visualization and Organization of Spectral Colour Data

Process images

Description

Specify scales, resize, and/or define focal objects within images.

Usage

procimg(
  image,
  resize = NULL,
  rotate = NULL,
  scaledist = NULL,
  outline = FALSE,
  reclass = NULL,
  smooth = FALSE,
  iterations = 1L,
  col = "red",
  obj_dist = NULL,
  obj_width = NULL,
  eye_res = NULL,
  plotnew = FALSE,
  ...
)

Arguments

image	(required) image data. Either a single image array, or a number of images stored in a list. Preferably the result of getimg().
resize	an integer specifying a percentage for resizing images, if so desired. E.g. 50 to half the size of an image, 200 to double it.
rotate	an integer specifying the angle of image rotation, in degrees. Images are rotated around the centre, and linearly interpolated.
scaledist	an integer, or numeric vector equal in length to the number of images, specifying the length of the scale in the image(s). Image(s) will then be presented, and the user asked to select either end of the scale corresponding to the input value.
outline	interactively specify the focal object in an image by clicking around its outline. The xy-coordinates of the resulting closed polygon are saved as an attribute, for use in generating a masking layer & separating animals/plants from backgrounds in further analyses. This is particularly useful when backgrounds are complex, such as in natural settings.
reclass	interactively specify an area on a colour-classified image that is to be reclassified as the numeric value provided. e.g. when reclass = 1, the user will be asked to select a polygon on the image, within which all colour-category values will be changes to 1.
smooth	should the polygon specified when outline = TRUE be smoothed using Chaikin's corner-cuting algorithm? Defaults to FALSE.
iterations	the number of smoothing iterations, when smooth = TRUE. Defaults to 1.
col	the color of the marker points and/or line, when using interactive options.
obj_dist, obj_width, eye_res	blur the image to model the visual acuity of non-human animals as per Caves & Johnsen (2018)'s AcuityView 2.0 algorithm. The procedure requires three arguments; obj_dist is the real-world distance between the viewer and the focal object in the image in the image, obj_width is the real-world width of the entire image; eye_res is the minimum resolvable angle of the viewer in degrees. All three arguments are numeric, and any units of measurement are suitable for obj_dist and obj_width, but they must match. Note that this is the more flexible v2.0 implementation meaning that any rectangular image is suitable; it need not be square with dimensions a power of 2. If using this capability, please cite Caves & Johnsen (2018), as per the included reference, and see note below.
plotnew	should plots be opened in a new window? Defaults to FALSE.
...	additional graphical parameters. Also see par().

Value

an image, or list of images, for use in further pavo functions.

Note

There are several caveats that should be considered when using the AcuityView algorithm. First and foremost, the converted image is not what the animal actually sees. For example, it does not account for edge enhancement and other processing by the retina and brain that may alter an image. It does, however, show what spatial information can be detected and then processed by the visual system. Second, the converted image is static, which does not allow one to assess how movement may reveal the presence of an otherwise indiscernible object. Third, AcuityView makes several assumptions about the Modulation Transfer Function (MTF), which describes how the optical system affects image contrast as a function of the level of detail. These assumptions include that the MTF is constant over the region of the retina that views the scene, is circularly symmetrical, and is wavelength independent. For a full discussion and details, please do read Caves & Johnsen (2018).

Author(s)

Thomas E. White [e-mail]

References

Caves, E. M., & Johnsen, S. (2018). AcuityView: An r package for portraying the effects of visual acuity on scenes observed by an animal. Methods in Ecology and Evolution, 9(3), 793-797 \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1111/2041-210X.12911")}.

Chaikin, G. 1974. An algorithm for high speed curve generation. Computer Graphics and Image Processing 3, 346-349.

Examples

if (interactive()) {
  # Interactively add a scale to a single image
  papilio <- getimg(system.file("testdata/images/butterflies/papilio.png", package = "pavo"))
  papilio <- procimg(papilio, scaledist = 10)

  # Interactively assign individual scales to each image,
  # after slightly reducing their size (to 90% of original).
  snakes <- getimg(system.file("testdata/images/snakes", package = "pavo"))
  snakes <- procimg(snakes, scaledist = c(10, 14), resize = 90)

  # Model the appearance of a butterfly given the reduced visual acuity of another
  # animal viewer as per the AcuityView algorithm. Here our butterfly is 60 cm away,
  # the image width is 10 cm, and the minimum resolvable angle of the viewer is 0.2-degrees.
  tiger <- getimg(system.file("testdata/images/tiger.png", package = "pavo"))
  tiger_acuity <- procimg(tiger, obj_dist = 60, obj_width = 10, eye_res = 0.2)
}

pavo documentation built on Sept. 24, 2023, 5:06 p.m.