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Analyzes objects in an image

Source: R/analyze_objects_minimal.R
analyze_objects_minimal.Rd

A lighter option to analyze_objects()

Usage

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 class 'anal_obj_minimal'
plot(
  x,
  which = "measure",
  measure = "area",
  type = c("density", "histogram"),
  ...
)

# S3 method for class 'anal_obj_ls_minimal'
plot(
  x,
  which = "measure",
  measure = "area",
  type = c("density", "histogram"),
  ...
)

Arguments

img

The image to be analyzed.

segment_objects

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.

reference

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.

reference_area

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.

back_fore_index

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.

fore_ref_index

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.

reference_larger, reference_smaller

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.

pattern

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).

parallel

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).

workers

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.

watershed

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_hull

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.

opening, closing, filter, erode, dilate

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.

invert

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).

object_size

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)`.

index

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"

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.

threshold

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.

tolerance

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.

extension

Radius of the neighborhood in pixels for the detection of neighboring objects. Higher value smooths out small objects.

lower_noise

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_size, upper_size

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.

topn_lower, topn_upper

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_eccent, upper_eccent, lower_circ, upper_circ

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).

plot

Show image after processing?

show_original

Show the count objects in the original image?

show_contour

Show a contour line around the objects? Defaults to TRUE.

contour_col, contour_size

The color and size for the contour line around objects. Defaults to contour_col = "red" and contour_size = 1.

col_foreground, col_background

Foreground and background color after image processing. Defaults to NULL, in which "black", and "white" are used, respectively.

marker, marker_col, marker_size

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_image

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.

prefix

The prefix to be included in the processed images. Defaults to "proc_".

dir_original, dir_processed

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".

verbose

If TRUE (default) a summary is shown in the console.

x

An object of class anal_obj.

which

Which to plot. Either 'measure' (object measures) or 'index' (object index). Defaults to "measure".

measure

The measure to plot. Defaults to "area".

type

The type of plot. Either "hist" or "density". Partial matches are recognized.

...

Depends on the function:

Author

Tiago Olivoto tiagoolivoto@gmail.com

Examples

if (interactive() && requireNamespace("EBImage")) {
library(pliman)
img <- image_pliman("soybean_touch.jpg")
obj <- analyze_objects(img)
obj$statistics

}

#>        stat        value
#> 1         n 3.000000e+01
#> 2  min_area 1.366000e+03
#> 3 mean_area 2.051300e+03
#> 4  max_area 2.436000e+03
#> 5   sd_area 2.300703e+02
#> 6  sum_area 6.153900e+04
#> 7  coverage 1.151122e-01

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'
}