biopixR (version 1.1.0)

imgPipe: Image analysis pipeline

Description

This function serves as a pipeline that integrates tools for complete start-to-finish image analysis. It enables the handling of images from different channels, for example the analysis of dual-color micro particles. This approach simplifies the workflow, providing a straightforward method to analyze complex image data.

Usage

imgPipe(
  img1 = img,
  color1 = "color1",
  img2 = NULL,
  color2 = "color2",
  img3 = NULL,
  color3 = "color3",
  method = "edge",
  alpha = 1,
  sigma = 2,
  sizeFilter = FALSE,
  upperlimit = "auto",
  lowerlimit = "auto",
  proximityFilter = FALSE,
  radius = "auto"
)

Value

list of 2 to 3 objects:

• Summary of all the objects in the image.

• Detailed information about every single object.

• (optional) Result for every individual color.

Arguments

img1

image (import by importImage)

color1

name of color in img1

img2

image (import by importImage)

color2

name of color in img2

img3

image (import by importImage)

color3

name of color in img3

method

choose method for object detection ('edge' / 'threshold') (from objectDetection)

alpha

threshold adjustment factor (numeric / 'static' / 'interactive' / 'gaussian') (from objectDetection)

sigma

smoothing (numeric / 'static' / 'interactive' / 'gaussian') (from objectDetection)

sizeFilter

applying sizeFilter function (default - FALSE)

upperlimit

highest accepted object size (numeric / 'auto') (only needed if sizeFilter = TRUE)

lowerlimit

smallest accepted object size (numeric / 'auto') (only needed if sizeFilter = TRUE)

proximityFilter

applying proximityFilter function (default - FALSE)

radius

distance from one object in which no other centers are allowed (in pixels) (only needed if proximityFilter = TRUE)

See Also

objectDetection(), sizeFilter(), proximityFilter(), resultAnalytics()

Examples

result <- imgPipe(
  beads,
  alpha = 1,
  sigma = 2,
  sizeFilter = TRUE,
  upperlimit = 150,
  lowerlimit = 50
  )

# Highlight remaining microparticles
plot(beads)
with(
  result$detailed,
  points(
    result$detailed$x,
    result$detailed$y,
    col = "darkgreen",
    pch = 19
    )
  )