This function is used to automatically detect tree ring borders along the user-defined path.
ring_detect(ring.data, seg = 1, auto.path = TRUE, manual = FALSE,
method = "canny", incline = FALSE, sample.yr = NULL,
watershed.threshold = "auto", watershed.adjust = 0.8,
struc.ele1 = NULL, struc.ele2 = NULL, marker.correction = FALSE,
default.canny = TRUE, canny.t1, canny.t2, canny.smoothing = 2,
canny.adjust = 1.4, path.dis = 1, origin = 0,
border.color = "black", border.type = 16, label.color = "black",
label.cex = 1.2)A magick image object produced by ring_read.
An integer specifying the number of image segments.
A logical value. If TRUE, a path is automatically
created at the center of the image. If FALSE, the function allows
the user to create a sub-image and a path by interactive clickings.
See details below.
A logical value indicating whether to skip the automatic
detection. If TRUE, ring borders are visually identified after
creating the path. See ring_modify to learn how to mark
tree rings by clicking on the image.
A character string specifying how ring borders are detected.
It requires one of the following characters: "watershed",
"canny", or "lineardetect". See details below.
A logical value indicating whether to correct ring widths.
If TRUE, two horizontal paths are added to the image.
NULL or an integer giving the year of formation
of the left-most ring. If NULL, use the current year.
The threshold used for producing the marker image, either a numeric from 0 to 1, or the character "auto" (using the Otsu algorithm), or a character of the form "XX%" (e.g., "58%").
A numeric used to adjust the Otsu threshold.
The default is 1 which means that the threshold will not be adjusted.
The sizes of early-wood regions in the marker image will reduce along
with the decrease of watershed.adjust.
NULL or a vector of length two specifying the
width and height of the first structuring element. If NULL, the
size of the structuring element is determined by the argument dpi.
NULL or a vector of length two specifying the
width and height of the second structuring element. If NULL, the
size of the structuring element is determined by the argument dpi.
A logical value indicating whether to relabel early-wood regions by comparing the values of their left-side neighbours.
A logical value. If TRUE, upper and lower
Canny thresholds are determined automatically.
A numeric giving the threshold for weak edges.
A numeric giving the threshold for strong edges.
An integer specifying the degree of smoothing.
A numeric used as a sensitivity control factor for the Canny edge detector. The default is 1 which means that the sensitivity will not be adjusted. The number of detected borders will reduce along with the increase of this value.
A numeric specifying the perpendicular distance between
two paths when the argument incline = TRUE. The unit is in mm.
A numeric specifying the origin in smoothed gray to find
ring borders. See ringBorders from the package
measuRing.
Color for ring borders.
Symbol for ring borders. See pch in
points for possible values and their shapes.
Color for years and border numbers.
The magnification to be used for years and border numbers.
A matrix (grayscale image) or array (color image) representing the tree-ring image.
If auto.path = FALSE, the user can create a rectangular sub-image
and a horizontal path by interactively clicking on the tree ring image.
The automatic detection will be performed within this rectangular
sub-image.
To create a sub-image and a path, follow these steps.
Step 1. Select the left and right edges of the rectangle
The user can point the mouse at any desired locations and click the left mouse button to add each edge.
Step 2. Select the top and bottom edges of the rectangle
The user can point the mouse at any desired locations and click the left mouse button to add each edge. The width of the rectangle is defined as the distance between the top and bottom edges, and should not be unnecessarily large to reduce time consumption and memory usage. Creating a long and narrow rectangle if possible.
Step 3. Create a path
After creating the rectangular sub-image, the user can add a horizontal
path by left-clicking on the sub-image (generally at the center of the
sub-image, try to choose a clean defect-free area). Ring borders and
other markers are plotted along this path. If incline = TRUE,
two paths are added simultaneously.
After creating the sub-image and the path, this function will open several
graphics windows and plot detected ring borders on image segments. The
number of image segments is controlled by the argument seg.
Argument method determines how ring borders are identified.
If method = "watershed", this function uses the watershed algorithm
to obtain ring borders (Soille and Misson, 2001).
If method = "canny", this function uses the Canny algorithm
to detect borders.
If method = "lineardetect", a linear detection algorithm from the
package measuRing is used to identify ring borders (Lara
et al., 2015). Note that incline = TRUE is not supported in this
mode, and path will be automatically created at the center of the image.
If the argument method = "watershed" or "canny", the original
image is processed by morphological openings and closings using rectangular
structuring elements of increasing size before detecting borders. The first
small structuring element is used to remove smaller dark spots in early
wood regions, and the second large structuring element is used to remove
light strips in late wood regions. More details about morphological
processing can be found at Soille and Misson (2001).
Soille, P., Misson, L. (2001) Tree ring area measurements using morphological image analysis. Canadian Journal of Forest Research 31, 1074-1083. doi: 10.1139/cjfr-31-6-1074
Lara, W., Bravo, F., Sierra, C.A. (2015) measuRing: An R package to measure tree-ring widths from scanned images. Dendrochronologia 34, 43-50. doi: 10.1016/j.dendro.2015.04.002
# NOT RUN {
img.path <- system.file("001.png", package = "MtreeRing")
## Read a tree ring image:
t1 <- ring_read(img = img.path, dpi = 1200, plot = FALSE)
## Split a long core sample into 3 pieces to
## get better display performance and use the
## watershed algorithm to detect ring borders:
t2 <- ring_detect(t1, seg = 3, method = 'watershed', border.color = 'green')
# }
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