trackdem (version 0.6)

manuallySelect: Manually identify true and false positives with a GUI.

Description

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).

Usage

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

Value

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

Arguments

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.

Author

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

Examples

if (FALSE) {
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)
}