Using threshold-based segmentation, objects are first isolated from background. Then, a new image is created for each single object. A list of images is returned.
object_split(
img,
index = "NB",
lower_size = NULL,
watershed = TRUE,
invert = FALSE,
fill_hull = FALSE,
filter = 2,
threshold = "Otsu",
extension = NULL,
tolerance = NULL,
object_size = "medium",
edge = 3,
remove_bg = FALSE,
plot = TRUE,
verbose = TRUE,
...
)A list of objects of class Image.
The image to be analyzed.
A character value specifying the target mode for conversion to
binary image when foreground and background are not declared. Defaults
to "NB" (normalized blue). See image_index() for more details. User can
also calculate your own index using the bands names, e.g. index = "R+B/G"
Plant images often contain dirt and dust. To prevent dust from
affecting the image analysis, objects with lesser than 10% of the mean of all objects
are removed. Set lower_limit = 0 to keep all the objects.
If TRUE (default) performs watershed-based object
detection. This will detect objects even when they are touching one other.
If FALSE, all pixels for each connected set of foreground pixels are set
to a unique object. This is faster but is not able to segment touching
objects.
Inverts the binary image if desired. This is useful to process
images with a black background. Defaults to FALSE. If reference = TRUE
is use, invert can be declared as a logical vector of length 2 (eg.,
invert = c(FALSE, TRUE). In this case, the segmentation of objects and
reference from the foreground using back_fore_index is performed using the
default (not inverted), and the segmentation of objects from the reference
is performed by inverting the selection (selecting pixels higher than the
threshold).
Fill holes in the binary image? Defaults to FALSE. This is
useful to fill holes in objects that have portions with a color similar to
the background. IMPORTANT: Objects touching each other can be combined into
one single object, which may underestimate the number of objects in an
image.
Performs median filtering in the binary image? See more at
image_filter(). Defaults to FALSE. Use a positive integer to define the
size of the median filtering. Larger values are effective at removing noise,
but adversely affect edges.
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.
Radius of the neighborhood in pixels for the detection of neighboring objects. Higher value smooths out small objects.
The minimum height of the object in the units of image intensity between its highest point (seed) and the point where it contacts another object (checked for every contact pixel). If the height is smaller than the tolerance, the object will be combined with one of its neighbors, which is the highest.
The size of the object. Used to automatically set up
tolerance and extension parameters. One of the following. "small"
(e.g, wheat grains), "medium" (e.g, soybean grains), "large"(e.g, peanut
grains), and "elarge" (e.g, soybean pods)`.
The number of pixels to be added in the edge of the segmented object. Defaults to 5.
If TRUE, the pixels that are not part of objects are
converted to white.
Show image after processing?
If TRUE (default) a summary is shown in the console.
Additional arguments passed on to image_combine()
analyze_objects(), image_binary()
library(pliman)
img <- image_pliman("la_leaves.jpg", plot = TRUE)
imgs <- object_split(img) # set to NULL to use 50% of the cores
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