image_index: Image indexes

In TiagoOlivoto/pliman: Tools for Plant Image Analysis

Image indexes

Description

image_index() Builds image indexes using Red, Green, Blue, Red-Edge, and NIR bands. See this page for a detailed list of available indexes.

The S3 method plot() can be used to generate a raster or density plot of the index values computed with image_index()

Usage

image_index(
  img,
  index = NULL,
  r = 1,
  g = 2,
  b = 3,
  re = 4,
  nir = 5,
  return_class = c("ebimage", "terra"),
  resize = FALSE,
  has_white_bg = FALSE,
  plot = TRUE,
  nrow = NULL,
  ncol = NULL,
  max_pixels = 1e+05,
  parallel = FALSE,
  workers = NULL,
  verbose = TRUE,
  ...
)

## S3 method for class 'image_index'
plot(x, type = c("raster", "density"), nrow = NULL, ncol = NULL, ...)

Arguments

img	An Image object. Multispectral mosaics can be converted to an Image object using mosaic_as_ebimage().
index	A character value (or a vector of characters) specifying the target mode for conversion to a binary image. Use pliman_indexes() or the details section to see the available indexes. Defaults to NULL (normalized Red, Green, and Blue). You can also use "RGB" for RGB only, "NRGB" for normalized RGB, "MULTISPECTRAL" for multispectral indices (provided NIR and RE bands are available) or "all" for all indexes. Users can also calculate their own index using the band names, e.g., index = "R+B/G".
r, g, b, re, nir	The red, green, blue, red-edge, and near-infrared bands of the image, respectively. Defaults to 1, 2, 3, 4, and 5, respectively. If a multispectral image is provided (5 bands), check the order of bands, which are frequently presented in the 'BGR' format.
return_class	The class of object to be returned. If ⁠"terra⁠ returns a SpatRaster object with the number of layers equal to the number of indexes computed. If "ebimage" (default) returns a list of Image objects, where each element is one index computed.
resize	Resize the image before processing? Defaults to resize = FALSE. Use resize = 50, which resizes the image to 50% of the original size to speed up image processing.
has_white_bg	Logical indicating whether a white background is present. If TRUE, pixels that have R, G, and B values equals to 1 will be considered as NA. This may be useful to compute an image index for objects that have, for example, a white background. In such cases, the background will not be considered for the threshold computation.
plot	Show image after processing?
nrow, ncol	The number of rows or columns in the plot grid. Defaults to NULL, i.e., a square grid is produced.
max_pixels	integer > 0. Maximum number of cells to plot the index. If max_pixels < npixels(img), downsampling is performed before plotting the index. Using a large number of pixels may slow down the plotting time.
parallel	Processes the images asynchronously (in parallel) in separate R sessions running in the background on the same machine. It may speed up the processing time when image is a list. The number of sections is set up to 70% of available cores.
workers	A positive numeric scalar or a function specifying the maximum number of parallel processes that can be active at the same time.
verbose	If TRUE (default) a summary is shown in the console.
...	Additional arguments passed to plot_index() for customization.
x	An object of class image_index.
type	The type of plot. Use type = "raster" (default) to produce a raster plot showing the intensity of the pixels for each image index or type = "density" to produce a density plot with the pixels' intensity.

Details

When type = "raster" (default), the function calls plot_index() to create a raster plot for each index present in x. If type = "density", a for loop is used to create a density plot for each index. Both types of plots can be arranged in a grid controlled by the ncol and nrow arguments.

Value

A list containing Grayscale images. The length will depend on the number of indexes used.

A NULL object

Author(s)

Tiago Olivoto [e-mail]

References

Nobuyuki Otsu, "A threshold selection method from gray-level histograms". IEEE Trans. Sys., Man., Cyber. 9 (1): 62-66. 1979. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1109/TSMC.1979.4310076")}

Karcher, D.E., and M.D. Richardson. 2003. Quantifying Turfgrass Color Using Digital Image Analysis. Crop Science 43(3): 943–951. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.2135/cropsci2003.9430")}

Bannari, A., D. Morin, F. Bonn, and A.R. Huete. 1995. A review of vegetation indices. Remote Sensing Reviews 13(1–2): 95–120. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1080/02757259509532298")}

Examples

library(pliman)
img <- image_pliman("soybean_touch.jpg")
image_index(img, index = c("R, NR"))

# Example for S3 method plot()
library(pliman)
img <- image_pliman("sev_leaf.jpg")
# compute the index
ind <- image_index(img, index = c("R, G, B, NGRDI"), plot = FALSE)
plot(ind)

# density plot
plot(ind, type = "density")

TiagoOlivoto/pliman documentation built on Sept. 14, 2024, 2:24 a.m.