Watershed algorithm (#222)

Author: ashwanirathee

Project.toml

@@ -39,7 +39,7 @@ AxisArrays = "0"
 ColorTypes = "0"
 Colors = "0"
 CoordinateTransformations = "0"
-DemoCards = "0.4"
+DemoCards = "0.4.4"
 Distances = "0"
 Documenter = "0.27"
 FileIO = "1"

docs/examples/color_channels/indexed_image.jl

@@ -2,6 +2,7 @@
 # cover: assets/indexed_image.png
 # title: Indexed image in 5 minutes
 # author: Johnny Chen
+# id: demo_indexed_image
 # date: 2020-11-23
 # ---
 
@@ -67,7 +68,7 @@ indexed_img = IndirectArray(indices, palatte)
 img == indexed_img
 
 # Under the hook, it is just a simple struct that subtypes `AbstractArray`:
-# 
+#
 # ```julia
 # # no need to run this
 # struct IndirectArray{T,N,A,V} <: AbstractArray{T,N}

docs/examples/image_segmentation/assets/contour1.png

@@ -0,0 +1,88 @@
+# ---
+# cover: assets/watershed.gif
+# title: Watershed Segmentation Algorithm
+# description: This demo shows how to use the watershed algorithm to segment an image.
+# author: Ashwani Rathee
+# date: 2021-08-16
+# ---
+
+# In this demonstration, we will segment an image using the watershed algorithm and learn
+# how it segments those images.
+# We will using ImageSegmentation.jl which provides implementation of
+# several image segmentation algorithms.
+
+using Images
+using ImageSegmentation, TestImages
+using IndirectArrays
+
+img = testimage("blobs")
+img_example = zeros(Gray, 5, 5)
+img_example[2:4,2:4] .=  Gray(0.6)
+bw = Gray.(img) .> 0.5
+bw_example = img_example .> 0.5
+
+#
+
+bw_transform = feature_transform(bw)
+bw_transform_example = feature_transform(bw_example)
+
+# `feature_transform` allows us to find feature transform of a binary image(`bw`)
+# , it finds the closest "feature" (positions where `bw` is `true`) for each location in
+# `bw`.  Specifically, `F[i]` is a `CartesianIndex` encoding the position
+# closest to `i` for which `bw[F[i]]` is `true`.  In cases where two or
+# more features in `bw` have the same distance from `i`, an arbitrary
+# feature is chosen. If `bw` has no `true` values, then all locations are
+# mapped to an index where each coordinate is `typemin(Int)`.
+
+# For example, the closest `true` to `bw_example[1,1]` exists at `CartesianIndex(2, 2)`,
+# hence it's assigned `CartesianIndex(2, 2)`. For other positions in `bw_example` it is
+# processed similarily.
+
+dist = 1 .- distance_transform(bw_transform)
+dist_example = 1 .- distance_transform(bw_transform_example)
+
+# | Dist(distance tranform for img) | Dist(distance transform for img_example) |
+# | :---:| :-----------:|
+# |![](assets/contour1.png) | ![](assets/dist_example.png) |
+
+# `distance transform` of `bw_transform` where each element in the array
+# each element `F[i]` represents a "target" or "feature" location assigned to `i`.
+# Specifically, `D[i]` is the distance between `i` and `F[i]`.
+# Optionally specify the weight `w` assigned to each coordinate; the
+# default value of `nothing` is equivalent to `w=(1,1,...)`.
+
+# In `bw_transform`, element at [1,1] has `CartesianIndex(2, 2)` in its place and `D[i]` for this will be
+# distance between `CartesianIndex(1, 1)` and `CartesianIndex(2, 2)` which is `sqrt(2)`.
+
+dist_trans = dist .< 1
+markers = label_components(dist_trans)
+markers_example = label_components(dist_example .< 0.5)
+Gray.(markers/32.0) # each of the blobs is slightly differently marked by label_components from 1 to 64
+
+
+# `label_components` finds the connected components in a binary array `dist_trans`.
+# You can provide a list indicating which dimensions are used to determine
+# connectivity. For example, `region = [1,3]` would not test neighbors along
+# dimension 2 for connectivity. This corresponds to just the nearest neighbors,
+# i.e., 4-connectivity in 2d and 6-connectivity in 3d. The default is `region = 1:ndims(A)`.
+# The output `label` is an integer array, where 0 is used for background
+# pixels, and each connected region gets a different integer index.
+
+segments = watershed(dist, markers)
+segments_example = watershed(dist_example , markers_example)
+
+# `watershed` method segments the image using watershed transform. Each basin formed
+# by watershed transform corresponds to a segment. To get segments we provide `dist`
+# and `markers` with each region's marker assigned a index starting from 1. Zero means
+# not a marker. If two markers have the same index, their regions will be merged into
+# a single region.
+
+labels = labels_map(segments)
+colored_labels = IndirectArray(labels, distinguishable_colors(maximum(labels)))
+masked_colored_labels = colored_labels .* (1 .- bw)
+mosaic(img, colored_labels, masked_colored_labels; nrow=1)
+
+# Here we use `IndirectArray` to store the indexed image, for more explaination on it please
+# check the tutorial [Indexed image in 5 minutes](@ref demo_indexed_image).
+
+save("assets/watershed.gif", cat(img, colored_labels, masked_colored_labels; dims=3); fps=1) #src