This function can remove existing ring borders or add new borders.
ring_modify(ring.data, del = NULL, del.u = NULL, del.l = NULL, add = FALSE)
A matrix or array produced by
A numeric vector giving the border numbers to be removed.
A numeric vector giving the border numbers to be removed on the upper path.
A numeric vector giving the border numbers to be removed on the lower path.
A logical value indicating whether to add new ring borders.
A matrix (grayscale image) or array (color image) representing the tree ring image.
This function is used to remove existing ring borders, or to add new borders by interactively clicking on the image segments.
If the user creates one path (
incline = FALSE), the argument
del is used to remove ring borders. If the user creates two paths
incline = TRUE), arguments
del.l are used
to remove ring borders.
add = TRUE, graphics windows opened by
will be activated sequentially. When a graphics window is activated,
the user can add new borders by left-clicking the mouse along the path.
Every click draws a point representing the ring border.
vignette('detection-MtreeRing') to see
an example of adding ring borders.
The identification process does not automatically stop by itself.
On the Windows system, the identification process can be terminated by pressing the right mouse button and selecting Stop from the menu.
On the MacOS system, for a X11 device the identification process is terminated by pressing any mouse button other than the first, and for a quartz device this process is terminated by pressing the ESC key.
Once the user terminates the identification process, the current
graphics window will be closed automatically, and the graphics window of
the following segment is activated. When all graphics windows are closed,
ring_modify will re-open graphics windows and plot new borders.
This function can perform both deletion and addition in one call. The removal of ring borders takes precedence over addition.
img.path <- system.file("001.png", package = "MtreeRing") ## Read a tree ring image: t1 <- ring_read(img = img.path, dpi = 1200) ## 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(ring.data = t1, seg = 3, method = 'watershed')#> Warning: The sampling year is set to the current year#>#>#>## Do not modify t2, but create a new array object t3. ## Remove some borders without adding new borders: t3 <- ring_modify(ring.data = t2, del = c(1, 3, 5, 19:21), add = FALSE)#>#>#>