cc_brightness_timeseries_folder(
folder_path = ".",
frames_per_set,
overlap = FALSE,
ch1 = 1,
ch2 = 2,
thresh = NULL,
detrend = detrend,
quick = quick,
filt = NULL,
parallel = FALSE
)
Arguments
folder_path |
The path (relative or absolute) to the folder you wish to
process. |
frames_per_set |
The number of frames with which to calculate the
successive cross-correlated brightnesses.
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 bleaching or/and
thresholding are selected, they are performed on the whole image stack before
the sectioning is done for calculation of cross-correlated brightnesses. |
overlap |
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. |
ch1 |
A natural number. The index of the first channel to use. |
ch2 |
A natural number. The index of the second channel to use. |
thresh |
Do you want to apply an intensity threshold prior to
calculating cross-correlated brightness (via
autothresholdr::mean_stack_thresh() )? If so, set your thresholding method
here. If this is a single value, that same threshold will be applied to
both channels. If this is a length-2 vector or list, then these two
thresholds will be applied to channels 1 and 2 respectively. A value of
NA for either channel gives no thresholding for that channel. |
detrend |
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. To
detrend one channel and not the other, specify this as a length 2 vector. |
quick |
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.
|
filt |
Do you want to smooth (filt = 'smooth' ) or median (filt = 'median' ) filter the cross-correlated brightness image using
smooth_filter() or median_filter() respectively? If selected, these are
invoked here with a filter radius of 1 and with the option na_count = TRUE . A value of NA for either channel gives no thresholding for that
channel. If you want to smooth/median filter the cross-correlated
brightness image in a different way, first calculate the cross-correlated
brightnesses without filtering (filt = NULL ) using this function and then
perform your desired filtering routine on the result. |
parallel |
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 . |
See also
Examples