Given a stack of images img, use the first frames_per_set of them to
create one number image, the next frames_per_set of them to create the next
number image and so on to get a time-series of number images.
number_timeseries(
img,
def,
frames_per_set,
overlap = FALSE,
thresh = NULL,
detrend = FALSE,
quick = FALSE,
filt = NULL,
s = 1,
offset = 0,
readout_noise = 0,
gamma = 1,
parallel = FALSE
)A 4-dimensional array of images indexed by img[y, x, channel, frame] (an object of class ijtiff::ijtiff_img). The image to perform the
calculation on. To perform this on a file that has not yet been read in,
set this argument to the path to that file (a string).
A character. Which definition of number do you want to use, "n"
or "N"?
The number of frames with which to calculate the successive numbers.
A boolean. If TRUE, the windows used to calculate brightness
are overlapped, if FALSE, they are not. For example, for a 20-frame image
series with 5 frames per set, if the windows are not overlapped, then the
frame sets used are 1-5, 6-10, 11-15 and 16-20; whereas if they are
overlapped, the frame sets are 1-5, 2-6, 3-7, 4-8 and so on up to 16-20.
The threshold or thresholding method (see
autothresholdr::mean_stack_thresh()) to use on the image prior to
detrending and number calculations. If there are many channels, this may be
specified as a vector or list, one element for each channel.
Detrend your data with detrendr::img_detrend_rh(). This is
the best known detrending method for brightness analysis. For more
fine-grained control over your detrending, use the detrendr package. If
there are many channels, this may be specified as a vector, one element for
each channel.
FALSE repeats the detrending procedure (which has some inherent
randomness) a few times to hone in on the best detrend. TRUE is quicker,
performing the routine only once. FALSE is better.
Do you want to smooth (filt = 'mean') or median (filt = 'median') filter the number image using smooth_filter() or
median_filter() respectively? If selected, these are invoked here with a
filter radius of 1 (with corners included, so each median is the median of
9 elements) and with the option na_count = TRUE. If you want to
smooth/median filter the number image in a different way, first calculate
the numbers without filtering (filt = NULL) using this function and then
perform your desired filtering routine on the result. If there are many
channels, this may be specified as a vector, one element for each channel.
A positive number. The
Microscope acquisition parameters. See reference Dalal et al.
Microscope acquisition parameters. See reference Dalal et al.
Factor for correction of number gamma = 1) has no effect. Changing gamma will have
the effect of dividing the result by gamma, so the result with gamma = 0.5 is two times the result with gamma = 1. For a Gaussian illumination
profile, use gamma = 0.3536; for a Gaussian-Lorentzian illumination
profile, use gamma = 0.0760.
Would you like to use multiple cores to speed up this
function? If so, set the number of cores here, or to use all available
cores, use parallel = TRUE.
An object of class number_ts_img.
This may discard some images, for example if 175 frames are in the input and
frames_per_set = 50, then the last 25 are discarded. If detrending is
selected, it is performed on the whole image stack before the sectioning is
done for calculation of numbers.
# NOT RUN {
img <- ijtiff::read_tif(system.file("extdata", "50.tif", package = "nandb"))
nts <- number_timeseries(img, "n", frames_per_set = 20, thresh = "Huang")
# }
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