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

In trackdem: Particle Tracking and Demography

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)

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.

Value

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

Author(s)

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

Examples

## 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)

trackdem documentation built on May 29, 2024, 7:43 a.m.