A lighter option to analyze_objects()
analyze_objects_minimal(
img,
segment_objects = TRUE,
reference = FALSE,
reference_area = NULL,
back_fore_index = "R/(G/B)",
fore_ref_index = "B-R",
reference_larger = FALSE,
reference_smaller = FALSE,
pattern = NULL,
parallel = FALSE,
workers = NULL,
watershed = TRUE,
fill_hull = FALSE,
opening = FALSE,
closing = FALSE,
filter = FALSE,
erode = FALSE,
dilate = FALSE,
invert = FALSE,
object_size = "medium",
index = "NB",
r = 1,
g = 2,
b = 3,
re = 4,
nir = 5,
threshold = "Otsu",
tolerance = NULL,
extension = NULL,
lower_noise = 0.1,
lower_size = NULL,
upper_size = NULL,
topn_lower = NULL,
topn_upper = NULL,
lower_eccent = NULL,
upper_eccent = NULL,
lower_circ = NULL,
upper_circ = NULL,
plot = TRUE,
show_original = TRUE,
show_contour = TRUE,
contour_col = "red",
contour_size = 1,
col_foreground = NULL,
col_background = NULL,
marker = FALSE,
marker_col = NULL,
marker_size = NULL,
save_image = FALSE,
prefix = "proc_",
dir_original = NULL,
dir_processed = NULL,
verbose = TRUE
)# S3 method for anal_obj_minimal
plot(
x,
which = "measure",
measure = "area",
type = c("density", "histogram"),
...
)
# S3 method for anal_obj_ls_minimal
plot(
x,
which = "measure",
measure = "area",
type = c("density", "histogram"),
...
)
The image to be analyzed.
Segment objects in the image? Defaults to TRUE. In
this case, objects are segmented using the index defined in the index
argument, and each object is analyzed individually. If segment_objects = FALSE is used, the objects are not segmented and the entire image is
analyzed. This is useful, for example, when analyzing an image without
background, where an object_index could be computed for the entire image,
like the index of a crop canopy.
Logical to indicate if a reference object is present in the image. This is useful to adjust measures when images are not obtained with standard resolution (e.g., field images). See more in the details section.
The known area of the reference objects. The measures of all the objects in the image will be corrected using the same unit of the area informed here.
A character value to indicate the index to segment the
foreground (objects and reference) from the background. Defaults to
"R/(G/B)". This index is optimized to segment white backgrounds from green
leaves and a blue reference object.
A character value to indicate the index to segment
objects and the reference object. It can be either an available index in
pliman (see pliman_indexes() or an own index computed with the R, G, and
B bands. Defaults to "B-R". This index is optimized to segment green
leaves from a blue reference object after a white background has been
removed.
Logical argument indicating when the
larger/smaller object in the image must be used as the reference object.
This only is valid when reference is set to TRUE and reference_area
indicates the area of the reference object. IMPORTANT. When
reference_smaller is used, objects with an area smaller than 1% of the
mean of all the objects are ignored. This is used to remove possible noise
in the image such as dust. So, be sure the reference object has an area that
will be not removed by that cutpoint.
A pattern of file name used to identify images to be imported.
For example, if pattern = "im" all images in the current working directory
that the name matches the pattern (e.g., img1.-, image1.-, im2.-) will be
imported as a list. Providing any number as pattern (e.g., pattern = "1")
will select images that are named as 1.-, 2.-, and so on. An error will be
returned if the pattern matches any file that is not supported (e.g.,
img1.pdf).
If TRUE 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, especially when pattern is used is informed.
When object_index is informed, multiple sections will be used to extract
the RGB values for each object in the image. This may significantly speed up
processing time when an image has lots of objects (say >1000).
A positive numeric scalar or a function specifying the number of parallel processes that can be active at the same time. By default, the number of sections is set up to 30% of available cores.
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.
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.
Morphological operations (brush size)
dilate puts the mask over every background pixel, and sets it to
foreground if any of the pixels covered by the mask is from the foreground.
erode puts the mask over every foreground pixel, and sets it to
background if any of the pixels covered by the mask is from the background.
opening performs an erosion followed by a dilation. This helps to
remove small objects while preserving the shape and size of larger objects.
closing performs a dilatation followed by an erosion. This helps to
fill small holes while preserving the shape and size of larger objects.
filter performs median filtering in the binary image. 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. 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).
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)`.
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"
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 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.
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.
Radius of the neighborhood in pixels for the detection of neighboring objects. Higher value smooths out small objects.
To prevent noise from affecting the image analysis, objects
with lesser than 10% of the mean area of all objects are removed
(lower_noise = 0.1). Increasing this value will remove larger noises (such
as dust points), but can remove desired objects too. To define an explicit
lower or upper size, use the lower_size and upper_size arguments.
Lower and upper limits for size for the image
analysis. Plant images often contain dirt and dust. Upper limit is set to
NULL, i.e., no upper limit used. One can set a known area or use
lower_size = 0 to select all objects (not advised). Objects that matches
the size of a given range of sizes can be selected by setting up the two
arguments. For example, if lower_size = 120 and upper_size = 140,
objects with size greater than or equal 120 and less than or equal 140 will
be considered.
Select the top n objects based on its area.
topn_lower selects the n elements with the smallest area whereas
topn_upper selects the n objects with the largest area.
Lower and upper limit
for object eccentricity/circularity for the image analysis. Users may use
these arguments to remove objects such as square papers for scale (low
eccentricity) or cut petioles (high eccentricity) from the images. Defaults
to NULL (i.e., no lower and upper limits).
Show image after processing?
Show the count objects in the original image?
Show a contour line around the objects? Defaults to
TRUE.
The color and size for the contour line around
objects. Defaults to contour_col = "red" and contour_size = 1.
Foreground and background color after
image processing. Defaults to NULL, in which "black", and "white" are
used, respectively.
The type, color and size of the object
marker. Defaults to NULL, which plots the object id. Use marker = "point" to show a point in each object or marker = FALSE to omit object
marker.
Save the image after processing? The image is saved in the
current working directory named as proc_* where * is the image name
given in img.
The prefix to be included in the processed images. Defaults to
"proc_".
The directory containing the original and
processed images. Defaults to NULL. In this case, the function will search
for the image img in the current working directory. After processing, when
save_image = TRUE, the processed image will be also saved in such a
directory. It can be either a full path, e.g., "C:/Desktop/imgs", or a
subfolder within the current working directory, e.g., "/imgs".
If TRUE (default) a summary is shown in the console.
An object of class anal_obj.
Which to plot. Either 'measure' (object measures) or 'index'
(object index). Defaults to "measure".
The measure to plot. Defaults to "area".
The type of plot. Either "hist" or "density". Partial matches
are recognized.
Depends on the function:
For analyze_objects_iter(), further arguments passed on to
analyze_objects().
Tiago Olivoto tiagoolivoto@gmail.com
if (interactive() && requireNamespace("EBImage")) {
library(pliman)
img <- image_pliman("soybean_touch.jpg")
obj <- analyze_objects(img)
obj$statistics
}
if (interactive() && requireNamespace("EBImage")) {
library(pliman)
img <- image_pliman("soy_green.jpg")
# Segment the foreground (grains) using the normalized blue index (NB, default)
# Shows the average value of the blue index in each object
rgb <- analyze_objects_minimal(img)
# density of area
plot(rgb)
# histogram of area
plot(rgb, type = "histogram") # or 'hist'
}
Run the code above in your browser using DataLab