Package 'pliman'

Description

• analyze_objects() provides tools for counting and extracting object features (e.g., area, perimeter, radius, pixel intensity) in an image. See more at the Details section.

• analyze_objects_iter() provides an iterative section to measure object features using an object with a known area.

• plot.anal_obj() produces a histogram for the R, G, and B values when argument object_index is used in the function analyze_objects().

Usage

analyze_objects(
  img,
  foreground = NULL,
  background = NULL,
  pick_palettes = FALSE,
  segment_objects = TRUE,
  viewer = get_pliman_viewer(),
  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,
  veins = FALSE,
  sigma_veins = 1,
  ab_angles = FALSE,
  ab_angles_percentiles = c(0.25, 0.75),
  width_at = FALSE,
  width_at_percentiles = c(0.05, 0.25, 0.5, 0.75, 0.95),
  haralick = FALSE,
  har_nbins = 32,
  har_scales = 1,
  har_band = 1,
  pcv = FALSE,
  pcv_niter = 100,
  resize = FALSE,
  trim = FALSE,
  fill_hull = FALSE,
  erode = FALSE,
  dilate = FALSE,
  opening = FALSE,
  closing = FALSE,
  filter = FALSE,
  invert = FALSE,
  object_size = "medium",
  index = "NB",
  r = 1,
  g = 2,
  b = 3,
  re = 4,
  nir = 5,
  object_index = NULL,
  pixel_level_index = FALSE,
  return_mask = FALSE,
  efourier = FALSE,
  nharm = 10,
  threshold = "Otsu",
  k = 0.1,
  windowsize = NULL,
  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,
  randomize = TRUE,
  nrows = 1000,
  plot = TRUE,
  show_original = TRUE,
  show_chull = FALSE,
  show_contour = TRUE,
  contour_col = "red",
  contour_size = 1,
  show_lw = FALSE,
  show_background = TRUE,
  show_segmentation = FALSE,
  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'
plot(
  x,
  which = "measure",
  measure = "area",
  type = c("density", "histogram"),
  ...
)

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

analyze_objects_iter(pattern, known_area, verbose = TRUE, ...)

Arguments

Details

A binary image is first generated to segment the foreground and background. The argument index is useful to choose a proper index to segment the image (see image_binary() for more details). It is also possible to provide color palettes for background and foreground (arguments background and foreground, respectively). When this is used, a general linear model (binomial family) fitted to the RGB values to segment fore- and background.

Then, the number of objects in the foreground is counted. By setting up arguments such as lower_size and upper_size, it is possible to set a threshold for lower and upper sizes of the objects, respectively. The argument object_size can be used to set up pre-defined values of tolerance and extension depending on the image resolution. This will influence the watershed-based object segmentation. Users can also tune up tolerance and extension explicitly for a better precision of watershed segmentation.

If watershed = FALSE is used, all pixels for each connected set of foreground pixels in img are set to a unique object. This is faster, especially for a large number of objects, but it is not able to segment touching objects.

There are some ways to correct the measures based on a reference object. If a reference object with a known area (reference_area) is used in the image and reference = TRUE is used, the measures of the objects will be corrected, considering the unit of measure informed in reference_area. There are two main ways to work with reference objects.

• The first, is to provide a reference object that has a contrasting color with both the background and object of interest. In this case, the arguments back_fore_index and fore_ref_index can be used to define an index to first segment the reference object and objects to be measured from the background, then the reference object from objects to be measured.

• The second one is to use a reference object that has a similar color to the objects to be measured, but has a contrasting size. For example, if we are counting small brown grains, we can use a brown reference template that has an area larger (says 3 times the area of the grains) and then uses reference_larger = TRUE. With this, the larger object in the image will be used as the reference object. This is particularly useful when images are captured with background light, such as the example 2. Some types: (i) It is suggested that the reference object is not too much larger than the objects of interest (mainly when the watershed = TRUE). In some cases, the reference object can be broken into several pieces due to the watershed algorithm. (ii) Since the reference object will increase the mean area of the object, the argument lower_noise can be increased. By default (lower_noise = 0.1) objects with lesser than 10% of the mean area of all objects are removed. Since the mean area will be increased, increasing lower_noise will remove dust and noises more reliably. The argument reference_smaller can be used in the same way

By using pattern, it is possible to process several images with common pattern names that are stored in the current working directory or in the subdirectory informed in dir_original. To speed up the computation time, one can set parallel = TRUE.

analyze_objects_iter() can be used to process several images using an object with a known area as a template. In this case, all the images in the current working directory that match the pattern will be processed. For each image, the function will compute the features for the objects and show the identification (id) of each object. The user only needs to inform which is the id of the known object. Then, given the known_area, all the measures will be adjusted. In the end, a data.frame with the adjusted measures will be returned. This is useful when the images are taken at different heights. In such cases, the image resolution cannot be conserved. Consequently, the measures cannot be adjusted using the argument dpi from get_measures(), since each image will have a different resolution. NOTE: This will only work in an interactive section.

• Additional measures: By default, some measures are not computed, mainly due to computational efficiency when the user only needs simple measures such as area, length, and width.

  • If haralick = TRUE, The function computes 13 Haralick texture features for each object based on a gray-level co-occurrence matrix (Haralick et al. 1979). Haralick features depend on the configuration of the parameters har_nbins and har_scales. har_nbins controls the number of bins used to compute the Haralick matrix. A smaller har_nbins can give more accurate estimates of the correlation because the number of events per bin is higher. While a higher value will give more sensitivity. har_scales controls the number of scales used to compute the Haralick features. Since Haralick features compute the correlation of intensities of neighboring pixels it is possible to identify textures with different scales, e.g., a texture that is repeated every two pixels or 10 pixels. By default, the Haralick features are computed with the R band. To chance this default, use the argument har_band. For example, har_band = 2 will compute the features with the green band. Additionaly, har_band = "GRAY" can be used. In this case, a grayscale (0.299 * R + 0.587 * G + 0.114 * B) is used.

  • If efourier = TRUE is used, an Elliptical Fourier Analysis (Kuhl and Giardina, 1982) is computed for each object contour using efourier().

  • If veins = TRUE (experimental), vein features are computed. This will call object_edge() and applies the Sobel-Feldman Operator to detect edges. The result is the proportion of edges in relation to the entire area of the object(s) in the image. Note that THIS WILL BE AN OPERATION ON AN IMAGE LEVEL, NOT an OBJECT LEVEL! So, If vein features need to be computed for leaves, it is strongly suggested to use one leaf per image.

  • If ab_angles = TRUE the apex and base angles of each object are computed with poly_apex_base_angle(). By default, the function computes the angle from the first pixel of the apex of the object to the two pixels that slice the object at the 25th percentile of the object height (apex angle). The base angle is computed in the same way but from the first base pixel.

  • If width_at = TRUE, the width at the 5th, 25th, 50th, 75th, and 95th percentiles of the object height are computed by default. These quantiles can be adjusted with the width_at_percentiles argument.

Value

analyze_objects() returns a list with the following objects:

• results A data frame with the following variables for each object in the image:

  • id: object identification.

  • x,y: x and y coordinates for the center of mass of the object.

  • area: area of the object (in pixels).

  • area_ch: the area of the convex hull around object (in pixels).

  • perimeter: perimeter (in pixels).

  • radius_min, radius_mean, and radius_max: The minimum, mean, and maximum radius (in pixels), respectively.

  • radius_sd: standard deviation of the mean radius (in pixels).

  • diam_min, diam_mean, and diam_max: The minimum, mean, and maximum diameter (in pixels), respectively.

  • major_axis, minor_axis: elliptical fit for major and minor axes (in pixels).

  • caliper: The longest distance between any two points on the margin of the object. See poly_caliper() for more details

  • length, width The length and width of objects (in pixels). These measures are obtained as the range of x and y coordinates after aligning each object with poly_align().

  • radius_ratio: radius ratio given by radius_max / radius_min.

  • theta: object angle (in radians).

  • eccentricity: elliptical eccentricity computed using the ratio of the eigen values (inertia axes of coordinates).

  • form_factor (Wu et al., 2007): the difference between a leaf and a circle. It is defined as 4*pi*A/P, where A is the area and P is the perimeter of the object.

  • narrow_factor (Wu et al., 2007): Narrow factor (caliper / length).

  • asp_ratio (Wu et al., 2007): Aspect ratio (length / width).

  • rectangularity (Wu et al., 2007): The similarity between a leaf and a rectangle (length * width/ area).

  • pd_ratio (Wu et al., 2007): Ratio of perimeter to diameter (perimeter / caliper)

  • plw_ratio (Wu et al., 2007): Perimeter ratio of length and width (perimeter / (length + width))

  • solidity: object solidity given by area / area_ch.

  • convexity: The convexity of the object computed using the ratio between the perimeter of the convex hull and the perimeter of the polygon.

  • elongation: The elongation of the object computed as 1 - width / length.

  • circularity: The object circularity given by perimeter ^ 2 / area.

  • circularity_haralick: The Haralick's circularity (CH), computed as CH = m/sd, where m and sd are the mean and standard deviations from each pixels of the perimeter to the centroid of the object.

  • circularity_norm: The normalized circularity (Cn), to be unity for a circle. This measure is computed as Cn = perimeter ^ 2 / 4*pi*area and is invariant under translation, rotation, scaling transformations, and dimensionless.

  • asm: The angular second-moment feature.

  • con: The contrast feature

  • cor: Correlation measures the linear dependency of gray levels of neighboring pixels.

  • var: The variance of gray levels pixels.

  • idm: The Inverse Difference Moment (IDM), i.e., the local homogeneity.

  • sav: The Sum Average.

  • sva: The Sum Variance.

  • sen: Sum Entropy.

  • dva: Difference Variance.

  • den: Difference Entropy

  • f12: Difference Variance.

  • f13: The angular second-moment feature.

• statistics: A data frame with the summary statistics for the area of the objects.

• count: If pattern is used, shows the number of objects in each image.

• obj_rgb: If object_index is used, returns the R, G, and B values for each pixel of each object.

• object_index: If object_index is used, returns the index computed for each object.

• Elliptical Fourier Analysis: If efourier = TRUE is used, the following objects are returned.

  • efourier: The Fourier coefficients. For more details see efourier().

  • efourier_norm: The normalized Fourier coefficients. For more details see efourier_norm().

  • efourier_error: The error between original data and reconstructed outline. For more details see efourier_error().

  • efourier_power: The spectrum of harmonic Fourier power. For more details see efourier_power().

• veins: If veins = TRUE is used, returns, for each image, the proportion of veins (in fact the object edges) related to the total object(s)' area.

• analyze_objects_iter() returns a data.frame containing the features described in the results object of analyze_objects().

• plot.anal_obj() returns a trellis object containing the distribution of the pixels, optionally for each object when facet = TRUE is used.

Author(s)

Tiago Olivoto [email protected]

References

Claude, J. (2008) Morphometrics with R, Use R! series, Springer 316 pp.

Gupta, S., Rosenthal, D. M., Stinchcombe, J. R., & Baucom, R. S. (2020). The remarkable morphological diversity of leaf shape in sweet potato (Ipomoea batatas): the influence of genetics, environment, and G×E. New Phytologist, 225(5), 2183–2195. doi:10.1111/NPH.16286

Haralick, R.M., K. Shanmugam, and I. Dinstein. 1973. Textural Features for Image Classification. IEEE Transactions on Systems, Man, and Cybernetics SMC-3(6): 610–621. doi:10.1109/TSMC.1973.4309314

Kuhl, F. P., and Giardina, C. R. (1982). Elliptic Fourier features of a closed contour. Computer Graphics and Image Processing 18, 236–258. doi: doi:10.1016/0146-664X(82)90034-X

Lee, Y., & Lim, W. (2017). Shoelace Formula: Connecting the Area of a Polygon and the Vector Cross Product. The Mathematics Teacher, 110(8), 631–636. doi:10.5951/mathteacher.110.8.0631

Montero, R. S., Bribiesca, E., Santiago, R., & Bribiesca, E. (2009). State of the Art of Compactness and Circularity Measures. International Mathematical Forum, 4(27), 1305–1335.

Chen, C.H., and P.S.P. Wang. 2005. Handbook of Pattern Recognition and Computer Vision. 3rd ed. World Scientific.

Wu, S. G., Bao, F. S., Xu, E. Y., Wang, Y.-X., Chang, Y.-F., and Xiang, Q.-L. (2007). A Leaf Recognition Algorithm for Plant Classification Using Probabilistic Neural Network. in 2007 IEEE International Symposium on Signal Processing and Information Technology, 11–16. doi:10.1109/ISSPIT.2007.4458016

Examples

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

########################### Example 1 #########################
# Enumerate the objects in the original image
# Return the top-5 grains with the largest area
top <-
 analyze_objects(img,
                 marker = "id",
                 topn_upper = 5)
top$results


#' ########################### Example 1 #########################
# Correct the measures based on the area of the largest ob
ject
# note that since the reference object

img <- image_pliman("flax_grains.jpg")
res <-
  analyze_objects(img,
                  index = "GRAY",
                  marker = "point",
                  show_contour = FALSE,
                  reference = TRUE,
                  reference_area = 6,
                  reference_larger = TRUE,
                  lower_noise = 0.3)
}

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(img,
                   marker = "id",
                   object_index = "B",
                   pixel_level_index = TRUE)
# density of area
plot(rgb)

# histogram of perimeter
plot(rgb, measure = "perimeter", type = "histogram") # or 'hist'

# density of the blue (B) index
plot(rgb, which = "index")
}

Description

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

Author(s)

Tiago Olivoto [email protected]

Examples

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'
}

Description

Analyzes objects using shapefiles

Usage

analyze_objects_shp(
  img,
  nrow = 1,
  ncol = 1,
  buffer_x = 0,
  buffer_y = 0,
  prepare = FALSE,
  segment_objects = TRUE,
  viewer = get_pliman_viewer(),
  index = "R",
  r = 1,
  g = 2,
  b = 3,
  re = 4,
  nir = 5,
  shapefile = NULL,
  interactive = FALSE,
  plot = FALSE,
  parallel = FALSE,
  workers = NULL,
  watershed = TRUE,
  opening = FALSE,
  closing = FALSE,
  filter = FALSE,
  erode = FALSE,
  dilate = FALSE,
  object_size = "medium",
  efourier = FALSE,
  object_index = NULL,
  veins = FALSE,
  width_at = FALSE,
  verbose = TRUE,
  invert = FALSE,
  ...
)

Arguments

Details

The analyze_objects_shp function performs object analysis on an image and generates shapefiles representing the analyzed objects. The function first prepares the image for analysis using the image_prepare() function if the prepare argument is set to TRUE. If a shapefile object is provided, the number of rows and columns for splitting the image is obtained from the shapefile. Otherwise, the image is split into multiple sub-images based on the specified number of rows and columns using the object_split_shp() function. The objects in each sub-image are analyzed using the analyze_objects() function, and the results are stored in a list. If parallel processing is enabled, the analysis is performed in parallel using multiple workers.

The output object provides access to various components of the analysis results, such as the analyzed object coordinates and properties. Additionally, the shapefiles representing the analyzed objects are included in the output object for further analysis or visualization.

Value

An object of class anal_obj. See more details in the Value section of analyze_objects().

Examples

if (interactive() && requireNamespace("EBImage")) {
library(pliman)

# Computes the DGCI index for each flax leaf
flax <- image_pliman("flax_leaves.jpg", plot =TRUE)
res <-
   analyze_objects_shp(flax,
                       nrow = 3,
                       ncol = 5,
                       plot = FALSE,
                       object_index = "DGCI")
plot(flax)
plot(res$shapefiles)
plot_measures(res, measure = "DGCI")
}

Description

Most of the functions in pliman can be applied to a list of images, but this can be not ideal to deal with lots of images, mainly if they have a high resolution. For curiosity, a 6000 x 4000 image use nearly 570 Megabytes of RAM. So, it would be impossible to deal with lots of images within R. apply_fun_to_img() applies a function to images stored in a given directory as follows:

• Create a vector of image names that contain a given pattern of name.

• Import each image of such a list.

• Apply a function to the imported image.

• Export the mutated image to the computer.

If parallel is set to FALSE (default), the images are processed sequentially, which means that one image needs to be imported, processed, and exported so that the other image can be processed. If parallel is set to TRUE, the images are processed asynchronously (in parallel) in separate R sessions (3) running in the background on the same machine. It may speed up the processing time when lots of images need to be processed.

Usage

apply_fun_to_imgs(
  pattern,
  fun,
  ...,
  dir_original = NULL,
  dir_processed = NULL,
  prefix = "",
  suffix = "",
  parallel = FALSE,
  workers = 3,
  verbose = TRUE
)

Arguments

Value

Nothing. The processed images are saved to the current working directory.

Examples

# apply_fun_to_imgs("pattern", image_resize, rel_size = 50)

Description

This function is a simple wrapper around EBImage::Image().

Usage

as_image(data, ...)

Arguments

Value

An Image object.

Examples

if (interactive() && requireNamespace("EBImage")) {
library(pliman)
img <-
as_image(rnorm(150 * 150 * 3),
         dim = c(150, 150, 3),
         colormode = 'Color')
plot(img)
}

Description

Calibrating the actual size is possible if any interlandmark distance on the image is known. calibrate() can be used to determine the size of a known distance (cm) on the graph. I invite users to photograph the object together with a scale (e.g., ruler, micrometer...).

Usage

calibrate(img, viewer = get_pliman_viewer())

Arguments

Value

A numeric (double) scalar value indicating the scale (in pixels per unit of known distance).

References

Claude, J. (2008) Morphometrics with R, Use R! series, Springer 316 pp.

Examples

if(isTRUE(interactive())){
library(pliman)
#### compute scale (dots per unit of known distance) ####
# only works in an interactive section
# objects_300dpi.jpg has a known resolution of 300 dpi
img <- image_pliman("objects_300dpi.jpg")
# Larger square: 10 x 10 cm
# 1) Run the function calibrate()
# 2) Use the left mouse button to create a line in the larger square
# 3) Declare a known distance (10 cm)
# 4) See the computed scale (pixels per cm)
calibrate(img)

# scale ~118
# 118 * 2.54 ~300 DPI
}

Description

A list of contour outlines from five leaves. It may be used as example in some functions such as efourier()

Format

A list with five objects

• leaf_1

• leaf_2

• leaf_3

• leaf_4

• leaf_5

Each object is a data.frame with the coordinates for the outline perimeter

Author(s)

Tiago Olivoto [email protected]

Source

Personal data. The images were obtained in the Flavia data set downlodable at https://flavia.sourceforge.net/

Description

This function generates a custom color palette using the specified colors and number of colors.

Usage

custom_palette(
  colors = c("yellow", "#53CC67", "#009B95", "#00588B", "#4B0055"),
  n = 5
)

Arguments

Value

A vector of colors representing the custom color palette.

Examples

# Generate a custom color palette with default colors and 10 colors
custom_palette()

# Generate a custom color palette with specified colors and 20 colors
custom_palette(colors = c("blue", "red"), n = 20)

# example code
library(pliman)
custom_palette(n = 5)

Description

Computes the distance map transform of a binary image. The distance map is a matrix which contains for each pixel the distance to its nearest background pixel.

Usage

dist_transform(binary)

Arguments

Value

An Image object or an array, with pixels containing the distances to the nearest background points

Examples

if (interactive() && requireNamespace("EBImage")) {
library(pliman)
img <- image_pliman("soybean_touch.jpg")
binary <- image_binary(img, "B")[[1]]
wts <- dist_transform(binary)
range(wts)
}

Description

Computes Elliptical Fourier Analysis of closed outlines based on x and y-coordinates coordinates.

Usage

efourier(x, nharm = 10, align = FALSE, center = FALSE, smooth_iter = 0)

Arguments

Details

Adapted from Claude (2008). pp. 222-223.

Value

A list of class efourier with:

• the harmonic coefficients (an, bn, cn and dn)

• the estimates of the coordinates of the centroid of the configuration (a0 and c0).

• The number of rows (points) of the perimeter outline (nr).

• The number of harmonics used (nharm).

• The original coordinates (coords).

If x is a list of perimeter coordinates, a list of efourier objects will be returned as an object of class iefourier_lst.

References

Claude, J. (2008) Morphometrics with R, Use R! series, Springer 316 pp.

Kuhl, F. P., and Giardina, C. R. (1982). Elliptic Fourier features of a closed contour. Computer Graphics and Image Processing 18, 236–258. doi: doi:10.1016/0146-664X(82)90034-X

Examples

if (interactive() && requireNamespace("EBImage")) {
library(pliman)
leaf1 <- contours[[4]]
plot_polygon(leaf1)

#### default options
# 10 harmonics (default)
# without alignment

ef <- efourier(leaf1)
efourier_coefs(ef)

# object is aligned along the major caliper with `poly_align()`
# object is centered on the origin with `poly_center()`
# using a list of object coordinates
ef2 <- efourier(contours, align = TRUE, center = TRUE)
efourier_coefs(ef2)

# reconstruct the perimeter of the object
# Use only the first one for simplicity
plot_polygon(contours[[1]] |> poly_align() |> poly_center())
efourier_inv(ef2[[1]]) |> plot_contour(col = "red", lwd = 4)
}

Description

Extracts the Fourier coefficients from objects computed with efourier() and efourier_norm() returning a 'ready-to-analyze' data frame.

Usage

efourier_coefs(x)

Arguments

Value

A data.frame object

Examples

if (interactive() && requireNamespace("EBImage")) {
library(pliman)

# a list of objects
efourier(contours) |> efourier_coefs()

# one object, normalized coefficients
efourier(contours[[4]]) |>
  efourier_norm() |>
  efourier_coefs()
}

Description

Computes the sum of squared distances between the original data and reconstructed outline. It allows examining reconstructed outlines with the addition of successive contributing harmonics indicated in the argument nharm.

Usage

efourier_error(
  x,
  nharm = NULL,
  type = c("error", "outline", "deviations"),
  plot = TRUE,
  ncol = NULL,
  nrow = NULL
)

Arguments

Value

A list with the objects:

• dev_points A list with the deviations (distances) from original and predicted outline for each pixel of the outline.

• data.frame object with the minimum, maximum and average deviations (based on the outline points).

If x is an object of class efourier_lst, a list will be returned.

Examples

if (interactive() && requireNamespace("EBImage")) {
library(pliman)
ef <-
  contours[[1]] |>
  efourier(nharm = 30)

efourier_error(ef)

efourier_error(ef,
               nharm = 30,
               type = "outline")

efourier_error(ef,
               nharm = c(1, 4, 20),
               type = "deviations")
}

Description

Performs an inverse elliptical Fourier transformation to construct a shape, given a list with Fourier coefficients computed with efourier().

Usage

efourier_inv(x, nharm = NULL, a0 = NULL, c0 = NULL, npoints = 500)

Arguments

Details

Adapted from Claude (2008). pp. 223.

References

Claude, J. (2008) Morphometrics with R, Use R! series, Springer 316 pp.

Examples

if (interactive() && requireNamespace("EBImage")) {
library(pliman)
plot_polygon(contours, aspect_ratio = 1)
# without alignment
ef <- efourier(contours, nharm = 10, align = FALSE)
ief <- efourier_inv(ef)
plot_contour(ief, col = "red", lwd = 2)
}

Description

The first harmonic defines an ellipse that best fits the outlines. One can use the parameters of the first harmonic to “normalize” the data so that they can be invariant to size, rotation, and starting position of the outline trace. This approach is referred to in the literature as the normalized elliptic Fourier. efourier_norm() calculates a new set of Fourier coefficients An, Bn, Cn, Dn that one can use for further multivariate analyses (Claude, 2008).

Usage

efourier_norm(x, start = FALSE)

Arguments

Details

Adapted from Claude (2008). pp. 226.

Value

A list with the following components:

• A, B, C, D for harmonic coefficients.

• size the magnitude of the semi-major axis of the first fitting ellipse.

• theta angle, in radians, between the starting and the semi-major axis of the first fitting ellipse.

• psi orientation of the first fitting ellipse

• a0 and c0, harmonic coefficients.

• lnef the concatenation of coefficients.

• nharm the number of harmonics used.

References

Claude, J. (2008) Morphometrics with R, Use R! series, Springer 316 pp.

Examples

if (interactive() && requireNamespace("EBImage")) {
library(pliman)
leaf1 <- contours[[4]]
plot_polygon(leaf1)

# compute the Fourier coefficients
ef <- efourier(leaf1)
efourier_coefs(ef)

# Normalized Fourier coefficients

efn <- efourier_norm(ef)
efourier_coefs(efn)
}

Description

Computes an spectrum of harmonic Fourier power. The power is proportional to the harmonic amplitude and can be considered as a measure of shape information. As the rank of harmonic increases, the power decreases and adds less and less information. We can evaluate the number of harmonics that we must select, so their cumulative power gathers 99% of the total cumulative power (Claude, 2008).

Usage

efourier_power(
  x,
  first = TRUE,
  thresh = c(0.8, 0.85, 0.9, 0.95, 0.99, 0.999),
  plot = TRUE,
  ncol = NULL,
  nrow = NULL
)

Arguments

Details

Most of the shape "information" is contained in the first harmonic. This is not surprising because this is the harmonic that best fits the outline, and the size of ellipses decreases as for explaining successive residual variation. However, one may think that the first ellipse does not contain relevant shape information, especially when differences one wants to investigate concern complex outlines. By using first = FALSE it is possible to remove the first harmonic for this computation. When working on a set of outlines, high-rank-harmonics can contain information that may allow groups to be distinguished (Claude, 2008).

Adapted from Claude (2008). pp. 229.

Value

A list with the objects:

• cum_power, a data.frame object with the accumulated power depending on the number of harmonics

• min_harm The minimum number of harmonics to achieve a given power.

References

Claude, J. (2008) Morphometrics with R, Use R! series, Springer 316 pp.

Examples

if (interactive() && requireNamespace("EBImage")) {
library(pliman)
pw <- efourier(contours) |> efourier_power()
}

Description

Calculates a 'Fourier elliptical shape' given Fourier coefficients

Usage

efourier_shape(
  an = NULL,
  bn = NULL,
  cn = NULL,
  dn = NULL,
  n = 1,
  nharm = NULL,
  npoints = 150,
  alpha = 4,
  plot = TRUE
)

Arguments

Details

efourier_shape can be used by specifying nharm and alpha. The coefficients are then sampled in an uniform distribution $(-\pi ; \pi)$ and this amplitude is then divided by $harmonicrank ^ alpha$. If alpha is lower than 1, consecutive coefficients will thus increase. See Claude (2008) pp.223 for the maths behind inverse ellipitical Fourier

Adapted from Claude (2008). pp. 223.

Value

A list with components:

• x vector of x-coordrdinates

• y vector of y-coordrdinates.

References

Claude, J. (2008) Morphometrics with R, Use R! series, Springer 316 pp.

Examples

if (interactive() && requireNamespace("EBImage")) {
library(pliman)
# approximation of the third leaf's perimeter
# 4 harmonics
image_pliman("potato_leaves.jpg", plot = TRUE)

efourier_shape(an = c(-7.34,  1.81,  -1.32, 0.50),
               bn = c(-113.88, 21.90, -0.31, -6.14),
               cn = c(-147.51, -20.89, 0.66, -14.06),
               dn = c(-0.48, 2.36, -4.36, 3.03))
}

Description

Produces a confidence ellipse that is an iso-contour of the Gaussian distribution, allowing to visualize a 2D confidence interval.

Usage

ellipse(
  x,
  conf = 0.95,
  np = 100,
  plot = TRUE,
  fill = "green",
  alpha = 0.3,
  random_fill = TRUE
)

Arguments

Value

A matrix with coordinates of points sampled on the ellipse.

Note

Borrowed from Claude (2008), pp. 85

References

Claude, J. (2008) Morphometrics with R, Use R! series, Springer 316 pp.

Examples

if (interactive() && requireNamespace("EBImage")) {
library(pliman)
ellipse(contours)
}

Description

Retrieves the current value of the pliman_viewer option used in the package.

Usage

get_pliman_viewer()

Value

The current value of the pliman_viewer option.

Description

Generate ggplot2

Usage

ggplot_color(n = 1)

Arguments

Examples

library(pliman)
ggplot_color(n = 3)

Description

image_align() rotate an image given a line of desired aligment along the y axis that corresponds to the alignment of the objects (e.g., field plots). By default, the aligment will be to the vertical, which means that if the drawed line have an angle < 90º parallel to the x axis, the rotation angle wil be negative (anticlocwise rotation).

Usage

image_align(
  img,
  align = c("vertical", "horizontal"),
  viewer = get_pliman_viewer(),
  plot = TRUE
)

Arguments

Details

The image_align function aligns an image along the vertical or horizontal axis based on user-selected points. The alignment can be performed in either the base plotting system or using the mapview package for interactive visualization. If the viewer option is set to "base", the function prompts the user to select two points on the image to define the alignment line. If the viewer option is set to "mapview", the function opens an interactive map where the user can draw a polyline to define the alignment line. The alignment angle is calculated based on the selected points, and the image is rotated accordingly using the image_rotate function. The function returns the aligned image object.

Value

The img aligned

Examples

if (interactive() && requireNamespace("EBImage")) {
library(pliman)
flax <- image_pliman("flax_leaves.jpg", plot = TRUE)
aligned <- image_align(flax)
}

Description

This function adds an alpha (transparency) layer to an RGB image using the EBImage package. The alpha layer can be specified as a single numeric value for uniform transparency or as a matrix/array matching the dimensions of the image for varying transparency.

Usage

image_alpha(img, mask)

Arguments

Value

An Image object with an added alpha layer, maintaining the RGBA format.

Examples

if (interactive() && requireNamespace("EBImage")) {
# Load the EBImage package
library(pliman)

# Load a sample RGB image
img <- image_pliman("soybean_touch.jpg")

# 50% transparency
image_alpha(img, 0.5) |> plot()

# transparent background
mask <- image_binary(img, "NB")[[1]]
img_tb <- image_alpha(img, mask)
plot(img_tb)

}

Description

This function takes an image and augments it by rotating it multiple times.

Usage

image_augment(
  img,
  pattern = NULL,
  times = 12,
  type = "export",
  dir_original = NULL,
  dir_processed = NULL,
  parallel = FALSE,
  verbose = TRUE
)

Arguments

Value

If type is "export," augmented images are saved. If type is "return," a list of augmented images is returned.

Examples

if (interactive() && requireNamespace("EBImage")) {
library(pliman)
img <- image_pliman("sev_leaf.jpg")
imgs <- image_augment(img, type = "return", times = 4)
image_combine(imgs)
}

Description

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.

Usage

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,
  erode = FALSE,
  dilate = FALSE,
  opening = FALSE,
  closing = FALSE,
  filter = FALSE,
  invert = FALSE,
  plot = TRUE,
  nrow = NULL,
  ncol = NULL,
  parallel = FALSE,
  workers = NULL,
  verbose = TRUE
)

Arguments

Value

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

Author(s)

Tiago Olivoto [email protected]

References

Otsu, N. 1979. Threshold selection method from gray-level histograms. IEEE Trans Syst Man Cybern SMC-9(1): 62–66. 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 doi:10.1117/12.767755

Examples

if (interactive() && requireNamespace("EBImage")) {
library(pliman)
img <- image_pliman("soybean_touch.jpg")
image_binary(img, index = c("R, G"))
}

Description

Combines several images to a grid

Usage

image_combine(
  ...,
  labels = NULL,
  nrow = NULL,
  ncol = NULL,
  col = "black",
  verbose = TRUE
)

Arguments

Value

A grid with the images in ...

Author(s)

Tiago Olivoto [email protected]

Examples

if (interactive() && requireNamespace("EBImage")) {
library(pliman)
img1 <- image_pliman("sev_leaf.jpg")
img2 <- image_pliman("sev_leaf_nb.jpg")
image_combine(img1, img2)
}

Description

image_create() can be used to create an Image object with a desired color and size.

Usage

image_create(color, width = 200, heigth = 200, plot = FALSE)

Arguments

Value

An object of class Image.

Examples

if (interactive() && requireNamespace("EBImage")) {
image_create("red")
image_create("#009E73", width = 300, heigth = 100)
}

Description

Expands an image towards the left, top, right, or bottom by sampling pixels from the image edge. Users can choose how many pixels (rows or columns) are sampled and how many pixels the expansion will have.

Usage

image_expand(
  img,
  left = NULL,
  top = NULL,
  right = NULL,
  bottom = NULL,
  edge = NULL,
  sample_left = 10,
  sample_top = 10,
  sample_right = 10,
  sample_bottom = 10,
  random = FALSE,
  filter = NULL,
  plot = TRUE
)

Arguments

Value

An Image object

Examples

if (interactive() && requireNamespace("EBImage")) {
library(pliman)
img <- image_pliman("soybean_touch.jpg")
image_expand(img, left = 200)
image_expand(img, right = 150, bottom = 250, filter = 5)
}

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

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 [email protected]

References

Nobuyuki Otsu, "A threshold selection method from gray-level histograms". IEEE Trans. Sys., Man., Cyber. 9 (1): 62-66. 1979. 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. 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. doi:10.1080/02757259509532298

Examples

if (interactive() && requireNamespace("EBImage")) {
library(pliman)
img <- image_pliman("soybean_touch.jpg")
image_index(img, index = c("R, NR"))
}
if (interactive() && requireNamespace("EBImage")) {
# 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")
}

Description

This function aligns and crops the image using either base or mapview visualization. This is useful to prepare the images to be analyzed with analyze_objects_shp()

Usage

image_prepare(
  img,
  viewer = get_pliman_viewer(),
  downsample = NULL,
  max_pixels = 1e+06
)

Arguments

Value

The alighed/cropped image for further visualization or analysis.

Examples

# Example usage:
if (interactive() && requireNamespace("EBImage")) {
img <- image_pliman("mult_leaves.jpg")
image_prepare(img, viewer = "mapview")
}

Description

• image_segment() reduces a color, color near-infrared, or grayscale images to a segmented image using a given color channel (red, green blue) or even color indexes (See image_index() for more details). The Otsu's thresholding method (Otsu, 1979) is used to automatically perform clustering-based image thresholding.

• image_segment_iter() Provides an iterative image segmentation, returning the proportions of segmented pixels.

Usage

image_segment(
  img,
  index = NULL,
  r = 1,
  g = 2,
  b = 3,
  re = 4,
  nir = 5,
  threshold = c("Otsu", "adaptive"),
  k = 0.1,
  windowsize = NULL,
  col_background = NULL,
  na_background = FALSE,
  has_white_bg = FALSE,
  fill_hull = FALSE,
  erode = FALSE,
  dilate = FALSE,
  opening = FALSE,
  closing = FALSE,
  filter = FALSE,
  invert = FALSE,
  plot = TRUE,
  nrow = NULL,
  ncol = NULL,
  parallel = FALSE,
  workers = NULL,
  verbose = TRUE
)

image_segment_iter(
  img,
  nseg = 2,
  index = NULL,
  invert = NULL,
  threshold = NULL,
  k = 0.1,
  windowsize = NULL,
  has_white_bg = FALSE,
  plot = TRUE,
  verbose = TRUE,
  nrow = NULL,
  ncol = NULL,
  parallel = FALSE,
  workers = NULL,
  ...
)

Arguments

Value

• image_segment() returns list containing n objects where n is the number of indexes used. Each objects contains:

  • image an image with the RGB bands (layers) for the segmented object.

  • mask A mask with logical values of 0 and 1 for the segmented image.

• image_segment_iter() returns a list with (1) a data frame with the proportion of pixels in the segmented images and (2) the segmented images.

Author(s)

Tiago Olivoto [email protected]

References

Nobuyuki Otsu, "A threshold selection method from gray-level histograms". IEEE Trans. Sys., Man., Cyber. 9 (1): 62-66. 1979. doi:10.1109/TSMC.1979.4310076

Examples

if (interactive() && requireNamespace("EBImage")) {
library(pliman)
img <- image_pliman("soybean_touch.jpg", plot = TRUE)
image_segment(img, index = c("R, G, B"))
}

Description

Segments image objects using clustering by the k-means clustering algorithm

Usage

image_segment_kmeans(
  img,
  bands = 1:3,
  nclasses = 2,
  invert = FALSE,
  opening = FALSE,
  closing = FALSE,
  filter = FALSE,
  erode = FALSE,
  dilate = FALSE,
  fill_hull = FALSE,
  plot = TRUE
)

Arguments

Value

A list with the following values:

• image The segmented image considering only two classes (foreground and background)

• clusters The class of each pixel. For example, if ncluster = 3, clusters will be a two-way matrix with values ranging from 1 to 3. masks A list with the binary matrices showing the segmentation.

References

Hartigan, J. A. and Wong, M. A. (1979). Algorithm AS 136: A K-means clustering algorithm. Applied Statistics, 28, 100–108. doi:10.2307/2346830

Examples

if (interactive() && requireNamespace("EBImage")) {
img <- image_pliman("la_leaves.jpg", plot = TRUE)
seg <- image_segment_kmeans(img)
seg <- image_segment_kmeans(img, fill_hull = TRUE, invert = TRUE, filter = 10)
}

Description

This R code is a function that allows the user to manually segment an image based on the parameters provided. This only works in an interactive section.

Usage

image_segment_manual(
  img,
  shape = c("free", "circle", "rectangle"),
  type = c("select", "remove"),
  viewer = get_pliman_viewer(),
  resize = TRUE,
  edge = 5,
  plot = TRUE
)

Arguments

Details

If the shape is "free", it allows the user to draw a perimeter to select/remove objects. If the shape is "circle", it allows the user to click on the center and edge of the circle to define the desired area. If the shape is "rectangle", it allows the user to select two points to define the area.

Value

A list with the segmented image and the mask used for segmentation.

Examples

if (interactive()) {
img <- image_pliman("la_leaves.jpg")
seg <- image_segment_manual(img)
plot(seg$mask)

}

Description

It combines make_mask() and make_brush() to segment an Image object using a brush of desired size, shape, and position.

Usage

image_segment_mask(
  img,
  size,
  shape = "disc",
  rel_pos_x = 0.5,
  rel_pos_y = 0.5,
  type = c("binary", "shadow"),
  col_background = "white",
  plot = TRUE,
  ...
)

Arguments

Value

A color Image object

Examples

if (interactive() && requireNamespace("EBImage")) {
img <- image_pliman("soybean_touch.jpg")
plot(img)
image_segment_mask(img, size = 601)
image_segment_mask(img,
                   size = 401,
                   shape = "diamond",
                   rel_pos_x = 0,
                   rel_pos_y = 0,
                   type = "shadow")
}

Description

Creates a list of object coordinates given the desired number of nrow and columns. It starts by selecting 4 points at the corners of objects of interest in the plot space. Then, given nrow and ncol, a grid is drawn and the objects' coordinates are returned.

Usage

image_shp(
  img,
  nrow = 1,
  ncol = 1,
  buffer_x = 0,
  buffer_y = 0,
  interactive = FALSE,
  viewer = get_pliman_viewer(),
  col_line = "red",
  size_line = 2,
  col_text = "red",
  size_text = 1,
  plot = TRUE
)

Arguments

Value

A list with row * col objects containing the plot coordinates.

Examples

if (interactive() && requireNamespace("EBImage")) {
library(pliman)
flax <- image_pliman("flax_leaves.jpg")
shape <- image_shp(flax, nrow = 3, ncol = 5)
}

Description

Converts a rectangular image into a square image by expanding the rows/columns using image_expand().

Usage

image_square(img, plot = TRUE, ...)

Arguments

Value

The modified Image object.

Examples

if (interactive() && requireNamespace("EBImage")) {
library(pliman)
img <- image_pliman("soybean_touch.jpg")
dim(img)
square <- image_square(img)
dim(square)
}

Description

This function performs the Guo-Hall thinning algorithm (Guo and Hall, 1989) on a binary image or a list of binary images.

Usage

image_thinning_guo_hall(
  img,
  parallel = FALSE,
  workers = NULL,
  verbose = TRUE,
  plot = FALSE,
  ...
)

Arguments

Value

If img is a single binary image, the function returns the thinned binary image. If img is a list of binary images, the function returns a list containing the thinned binary images.

References

Guo, Z., and R.W. Hall. 1989. Parallel thinning with two-subiteration algorithms. Commun. ACM 32(3): 359–373. doi:10.1145/62065.62074

Examples

if (interactive() && requireNamespace("EBImage")) {
library(pliman)
img <- image_pliman("potato_leaves.jpg", plot = TRUE)
image_thinning_guo_hall(img, index = "R", plot = TRUE)
}

Description

Given an object image, converts it into a data frame where each row corresponds to the intensity values of each pixel in the image.

Usage

image_to_mat(img, parallel = FALSE, workers = NULL, verbose = TRUE)

Arguments

Value

A list containing three matrices (R, G, and B), and a data frame containing four columns: the name of the image in image and the R, G, B values.

Author(s)

Tiago Olivoto [email protected]

Examples

if (interactive() && requireNamespace("EBImage")) {
library(pliman)
img <- image_pliman("sev_leaf.jpg")
dim(img)
mat <- image_to_mat(img)
dim(mat[[1]])
}

Description

This function allows users to interactively edit and analyze an image using mapview and mapedit packages.

Usage

image_view(
  img,
  object = NULL,
  r = 1,
  g = 2,
  b = 3,
  edit = FALSE,
  alpha = 0.7,
  attribute = "area",
  title = "Edit the image",
  show = c("rgb", "index"),
  index = "B",
  max_pixels = 1e+06,
  downsample = NULL,
  color_regions = custom_palette(),
  quantiles = c(0, 1),
  ...
)

Arguments

Value

An sf object, the same object returned by mapedit::editMap().

Examples

if (interactive() && requireNamespace("EBImage")) {
# Example usage:
img <- image_pliman("sev_leaf.jpg")
image_view(img)
}

Description

An interactive section where the user will be able to click on the image to select landmarks manually is open. With each mouse click, a point is drawn and an upward counter is shown in the console. After n counts or after the user press Esc, the interactive process is interrupted and a data.frame with the x and y coordinates for the landmarks is returned.

Usage

landmarks(
  img,
  n = Inf,
  viewer = get_pliman_viewer(),
  scale = NULL,
  calibrate = FALSE
)

Arguments

Value

A data.frame with the x and y-coordinates from the landmarks.

References

Claude, J. (2008) Morphometrics with R, Use R! series, Springer 316 pp.

Examples

if(isTRUE(interactive())){
library(pliman)
img <- image_pliman("potato_leaves.jpg")
x <- landmarks(img)
}

Description

Interpolates supplementary landmarks that correspond to the mean coordinates of two adjacent landmarks.

Usage

landmarks_add(x, n = 3, smooth_iter = 0, plot = TRUE, nrow = NULL, ncol = NULL)

Arguments

Value

A Matrix of interpolated coordinates.

Examples

library(pliman)

# equally spaced landmarks
plot_polygon(contours[[4]])
ldm <- landmarks_regradi(contours[[4]], plot = FALSE)
points(ldm$coords, pch = 16)
segments(mean(ldm$coords[,1]),
         mean(ldm$coords[,2]),
         ldm$coords[,1],
         ldm$coords[,2])

ldm_add <- landmarks_add(ldm, plot = FALSE)
points(ldm_add, col = "red")
points(ldm$coords, pch = 16)

# smoothed version
ldm_add_smo <- landmarks_add(ldm, plot = FALSE, smooth_iter = 10)
lines(ldm_add_smo, col = "blue", lwd = 3)

Description

Computes the angle from two interlandmark vectors using the difference of their arguments using complex vectors (Claude, 2008).

Usage

landmarks_angle(x, unit = c("rad", "deg"))

Arguments

Value

A matrix with the angles for each landmark combination.

Note

Borrowed from Claude (2008), pp. 50

References

Claude, J. (2008) Morphometrics with R, Use R! series, Springer 316 pp.

Examples

if(isTRUE(interactive())){
library(pliman)
img <- image_pliman("potato_leaves.jpg")
x <- landmarks(img)
landmarks_angle(x)
}

Description

Computes the distance between two landmarks as the square root of the sum of the squared differences between each coordinate (Claude, 2008).

Usage

landmarks_dist(x)

Arguments

Value

A matrix with the distances for each landmark combination.

Note

Borrowed from Claude (2008), pp. 49

References

Claude, J. (2008) Morphometrics with R, Use R! series, Springer 316 pp.

Examples

if(isTRUE(interactive())){
library(pliman)
img <- image_pliman("potato_leaves.jpg")
x <- landmarks(img)
landmarks_dist(x)
}

Description

Select n landmarks that are spaced with a regular sequence of angles taken between the outline coordinates and the centroid.

Usage

landmarks_regradi(
  x,
  n = 50,
  close = TRUE,
  plot = TRUE,
  ncol = NULL,
  nrow = NULL
)

Arguments

Value

A list with the following objects:

• pixindices: Vector of radius indices.

• radii: Vector of sampled radii lengths.

• Xc: The centroid coordinate of x axis.

• Yc: The centroid coordinate of y axis.

• coords: Coordinates of sampled points arranged in a two-column matrix.

If x is a list, a list of objects described above is returned.

Note

Borrowed from Claude (2008), pp. 53

References

Claude, J. (2008) Morphometrics with R, Use R! series, Springer 316 pp.

Examples

library(pliman)
plot_polygon(contours[[1]])
ldm <- landmarks_regradi(contours)

Description

Add n leading zeros to a numeric sequence. This is useful to create a character vector to rename files in a folder.

Usage

leading_zeros(x, n = 3)

Arguments

Value

A character vector or a list of character vectors.

Examples

library(pliman)
leading_zeros(1:5)
leading_zeros(list(a = 1:3,
                   b = 1:5),
              n = 2)

Description

Generates brushes of various sizes and shapes that can be used as structuring elements. See EBImage::makeBrush().

Usage

make_brush(size, shape = "disc", ...)

Arguments

Value

A 2D matrix of 0s and 1s containing the desired brush.

Examples

if (interactive() && requireNamespace("EBImage")) {
make_brush(size = 51) |> image()
make_brush(size = 51, shape = "diamond") |> image()
}

Description

Make a mask using an Image object and a brush.

Usage

make_mask(img, brush, rel_pos_x = 0.5, rel_pos_y = 0.5, plot = TRUE)

Arguments

Details

It applies a brush to an Image, selecting the Image pixels that match the brush values equal to 1. The position of the brush in the original image is controlled by the relative positions x (rel_pos_x) and y (rel_pos_y) arguments. The size of the brush must be smaller or equal to the smaller dimension of image.

Value

A binary image with 0s and 1s.

Examples

if (interactive() && requireNamespace("EBImage")) {
img <- image_pliman("soybean_touch.jpg")
make_mask(img, brush = make_brush(size = 201))
make_mask(img,
          brush = make_brush(size = 401, shape = "diamond"),
          rel_pos_x = 0.1,
          rel_pos_y = 0.8)
}

Description

• measure_disease() computes the percentage of symptomatic leaf area and (optionally) counts and compute shapes (area, perimeter, radius, etc.) of lesions in a sample or entire leaf using color palettes. See more at Details.

• measure_disease_iter() provides an iterative section for measure_disease(), where the user picks up samples in the image to create the needed color palettes.

Usage

measure_disease(
  img,
  img_healthy = NULL,
  img_symptoms = NULL,
  img_background = NULL,
  pattern = NULL,
  opening = c(10, 0),
  closing = c(0, 0),
  filter = c(0, 0),
  erode = c(0, 0),
  dilate = c(0, 0),
  parallel = FALSE,
  workers = NULL,
  resize = FALSE,
  fill_hull = TRUE,
  index_lb = NULL,
  index_dh = "GLI",
  has_white_bg = FALSE,
  threshold = NULL,
  invert = FALSE,
  lower_noise = 0.1,
  lower_size = NULL,
  upper_size = NULL,
  topn_lower = NULL,
  topn_upper = NULL,
  randomize = TRUE,
  nsample = 3000,
  watershed = FALSE,
  lesion_size = "medium",
  tolerance = NULL,
  extension = NULL,
  show_features = FALSE,
  show_segmentation = FALSE,
  plot = TRUE,
  show_original = TRUE,
  show_background = TRUE,
  show_contour = TRUE,
  contour_col = "white",
  contour_size = 1,
  col_leaf = NULL,
  col_lesions = NULL,
  col_background = NULL,
  marker = FALSE,
  marker_col = NULL,
  marker_size = NULL,
  save_image = FALSE,
  prefix = "proc_",
  name = NULL,
  dir_original = NULL,
  dir_processed = NULL,
  verbose = TRUE
)

measure_disease_iter(
  img,
  has_background = TRUE,
  r = 2,
  viewer = get_pliman_viewer(),
  opening = c(10, 0),
  closing = c(0, 0),
  filter = c(0, 0),
  erode = c(0, 0),
  dilate = c(0, 0),
  show = "rgb",
  index = "NGRDI",
  ...
)

Arguments