bwconncomp

CC = bwconncomp(BW; conn=8)

Find and count connected components in binary image.

Description

Finds and counts the connected components in the binary image BW. The function returns a structure containing the total number of connected components, such as regions of interest (ROIs), in the image and the pixel indices assigned to each component.

Parameters

BW: Binary 2D image. For numeric input, any nonzero pixels are considered to be 1 (true).
- Type: Array{<:Real} or GMTgrid

Keyword Arguments

conn: Connectivity for connected components
- Type: Integer or Array
- Default: 8
- Options:
  - For 2D images:
    - 4 — 4-connected neighborhood (edge connectivity)
    - 8 — 8-connected neighborhood (edge and corner connectivity)
  - Alternatively, can be a connectivity array of 0s and 1s with the same dimensionality as BW. The 1-valued elements define neighborhood locations relative to the center element. The center element must be 1. The connectivity array size must be odd along each dimension.

Returns

Connected components, specified as a structure with the following fields:

Field	Description
`connectivity`	Connectivity of the connected components
`image_size`	Size of the binary image
`num_objects`	Number of connected components in the binary image
`range`	Range of the image coordinates
`inc`	Image's increment (!= 1 whem image is referenced)
`registration`	Registration of the image
`x`	X coordinates of the image
`y`	Y coordinates of the image
`layout`	Memory layout of the image
`proj4`	Projection definition (optional)
`wkt`	Well-known text definition (optional)
`epsg`	EPSG code of the image
`bbox`	The bounding boxes as a vector of GMTdataset
`pixel_list`	Vector where each element contains the linear indices of the pixels in each object
`centroid`	A Float64 Matrix with the x,y coordinates of the centroids for each component
`area`	A vector of Float64 with the areas of each component

To compute a label matrix with a memory-efficient data type (for instance, UInt8 versus Float64), use the labelmatrix function on the output of bwconncomp:

CC = bwconncomp(BW)
L = labelmatrix(CC)

Examples

Find Connected Components in Binary Image

using GMT

# Create a binary image
BW = [1 1 0 0 0 0 0 0
      1 1 0 1 1 0 0 0
      0 0 0 1 1 0 0 0
      0 0 0 0 0 1 1 0
      0 0 0 0 0 1 1 0
      0 0 0 0 0 0 0 0]

# Find connected components
CC = bwconncomp(BW)

println("Number of connected components: ", CC.num_objects)

Specify Connectivity

Find connected components using 4-connectivity instead of the default 8-connectivity:

using GMT

BW = [1 1 0 0 0 0 0 0
      1 1 0 1 1 0 0 0
      0 0 0 1 1 0 0 0
      0 0 0 0 0 1 1 0
      0 0 0 0 0 1 1 0
      0 0 0 0 0 0 0 0]

# 4-connectivity
CC4 = bwconncomp(BW, conn=4)
println("Number of components (4-conn): ", CC4.num_objects)

# 8-connectivity
CC8 = bwconncomp(BW, conn=8)
println("Number of components (8-conn): ", CC8.num_objects)

Extract Properties of Connected Components

Use the output structure to analyze individual components:

using GMT

BW = [1 1 0 0 0 0 0 0
      1 1 0 1 1 0 0 0
      0 0 0 1 1 0 0 0
      0 0 0 0 0 1 1 0
      0 0 0 0 0 1 1 0
      0 0 0 0 0 0 0 0]

CC = bwconncomp(BW)

# Number of pixels in each component
numPixels = [length(CC.pixel_list[i]) for i in 1:CC.num_objects]
println("Pixels per component: ", numPixels)

# Find the largest component
largest = argmax(numPixels)
println("Largest component is #", largest, " with ", numPixels[largest], " pixels")

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