Most of the time, (x, y) coordinates are recorded in pixels. If we want to have them in mm, cm, etc. we need to convert them and to rescale them. This functions does the job for the two cases: i) either an homogeneous rescaling factor, e.g. if all pictures were taken using the very same magnification or ii) with various magnifications. More in the Details section
rescale(x, scaling_factor, scale_mapping, magnification_col, ...)a Momocs object of same class
any Coo object
numeric an homogeneous scaling factor. If all you (x, y) coordinates have the same scale
either a data.frame or a path to read such a data.frame. It MUST contain
three columns in that order: magnification found in $fac, column "magnification_col", pixels, real length unit.
Column names do not matter but must be specified, as read.table reads with header=TRUE Every
different magnification level found in $fac, column "magnification_col" must have its row.
the name or id of the $fac column to look for magnification levels for every image
additional arguments (besides header=TRUE) to pass to read.table if 'scale_mapping' is a path
The i) case above is straightforward, if 1cm is 500pix long on all your pictures,
just call rescale(your_Coo, scaling_factor=1/500) and all coordinates will be in cm.
The ii) second case is more subtle. First you need to code in your Coo object, in the fac slot, a column named, say "mag", for magnification. Imagine you have 4 magnifications: 0.5, 1, 2 and 5, we have to indicate for each magnification, how many pixels stands for how many units in the real world.
This information is passed as a data.frame, built externally or in R, that must look like this:
mag pix cm
0.5 1304 10
1 921 10
2 816 5
5 1020 5
.
We have to do that because, for optical reasons, the ratio pix/real_unit, is not a linear function of the magnification.
All shapes will be centered to apply (the single or the different) scaling_factor.
Other handling functions:
arrange(),
at_least(),
chop(),
combine(),
dissolve(),
fac_dispatcher(),
filter(),
mutate(),
rename(),
rm_harm(),
rm_missing(),
rm_uncomplete(),
rw_fac(),
sample_frac(),
sample_n(),
select(),
slice(),
subsetize()