manuallySelect: Manually identify true and false positives with a GUI.
In trackdem: Particle Tracking and Demography

Description Usage Arguments Value Author(s) Examples

manuallySelect opens a graphic user interface to create training data for a neural net by manually selecting true and false positives (i.e. correctly identified particles and noise, respectively).

1	manuallySelect(particles, colorimages = NULL, frames = NULL)

`particles`	A data frame of class 'TrDm' with particle statistics for each frame, obtained by `identifyParticles`.
`colorimages`	An array with the original full color images, in order to plot on the original images. If `NULL`, the original color images are used, obtained from the global environment.
`frames`	A vector defining the frame(s) that should be used. Default is `NULL`; in that case the frame with the maximum number of identified particles is used.

List containing three elements: true positives, false positives, and the evaluated frame.

Marjolein Bruijning, Caspar A. Hallmann & Marco D. Visser

## Not run: 
dir.create("images")
## Create image sequence
traj <- simulTrajec(path="images",
                    nframes=30,nIndividuals=20,domain='square',
                    h=0.01,rho=0.9,movingNoise=TRUE,
                    parsMoving = list(density=20, duration=10, size=1,
                                      speed = 10, colRange = c(0,1)),
                    sizes=runif(20,0.004,0.006))
## Load images
dir <- "images"
allFullImages <- loadImages (dirPictures=dir,nImages=1:30)
stillBack <- createBackground(allFullImages,method="mean")
allImages <- subtractBackground(stillBack)
partIden <- identifyParticles(allImages,threshold=-0.1,
                                   pixelRange=c(3,400))
# select the nframes with the most identified particles
nframes <- 3
frames <- order(tapply(partIden$patchID,partIden$frame,length),
                decreasing=TRUE)[1:nframes]
mId <- manuallySelect(particles=partIden,frame=frames)

## End(Not run)