Reduce a color, color near-infrared, or grayscale images to a binary image using a given color channel (red, green blue) or even color indexes. The Otsu's thresholding method (Otsu, 1979) is used to automatically perform clustering-based image thresholding.
image_binary(
img,
index = "R",
r = 1,
g = 2,
b = 3,
re = 4,
nir = 5,
return_class = "ebimage",
threshold = c("Otsu", "adaptive"),
k = 0.1,
windowsize = NULL,
has_white_bg = FALSE,
resize = FALSE,
fill_hull = FALSE,
filter = FALSE,
invert = FALSE,
plot = TRUE,
nrow = NULL,
ncol = NULL,
parallel = FALSE,
workers = NULL,
verbose = TRUE
)A list containing binary images. The length will depend on the number of indexes used.
An image object.
A character value (or a vector of characters) specifying the
target mode for conversion to binary image. See the available indexes with
pliman_indexes() and image_index() for more details.
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.
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.
The theshold method to be used.
By default (threshold = "Otsu"), a threshold value based
on Otsu's method is used to reduce the grayscale image to a binary image. If
a numeric value is informed, this value will be used as a threshold.
If threshold = "adaptive", adaptive thresholding (Shafait et al. 2008)
is used, and will depend on the k and windowsize arguments.
If any non-numeric value different than "Otsu" and "adaptive" is used,
an iterative section will allow you to choose the threshold based on a
raster plot showing pixel intensity of the index.
a numeric in the range 0-1. when k is high, local threshold
values tend to be lower. when k is low, local threshold value tend to be
higher.
windowsize controls the number of local neighborhood in
adaptive thresholding. By default it is set to 1/3 * minxy, where
minxy is the minimum dimension of the image (in pixels).
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.
Resize the image before processing? Defaults to FALSE. Use a
numeric value as the percentage of desired resizing. For example, if
resize = 30, the resized image will have 30% of the size of original
image.
Fill holes in the objects? Defaults to FALSE.
Performs median filtering in the binary image? (Defaults to
FALSE). Provide a positive integer > 1 to indicate the size of the median
filtering. Higher values are more efficient to remove noise in the
background but can dramatically impact the perimeter of objects, mainly for
irregular perimeters such as leaves with serrated edges.
Inverts the binary image, if desired.
Show image after processing?
The number of rows or columns in the plot grid. Defaults to
NULL, i.e., a square grid is produced.
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.
A positive numeric scalar or a function specifying the maximum number of parallel processes that can be active at the same time.
If TRUE (default) a summary is shown in the console.
Tiago Olivoto tiagoolivoto@gmail.com
Otsu, N. 1979. Threshold selection method from gray-level histograms. IEEE Trans Syst Man Cybern SMC-9(1): 62–66. tools:::Rd_expr_doi("10.1109/tsmc.1979.4310076")
Shafait, F., D. Keysers, and T.M. Breuel. 2008. Efficient implementation of local adaptive thresholding techniques using integral images. Document Recognition and Retrieval XV. SPIE. p. 317–322 tools:::Rd_expr_doi("10.1117/12.767755")
library(pliman)
img <- image_pliman("soybean_touch.jpg")
image_binary(img, index = c("R, G"))
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