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Add more tests for BoykovKolmorogovAlgorithm (#51)
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* Add tests for BoykovKolmorogovAlgorithm

* Update test/boykov_kolmogorov.jl

* Update contact email
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@juliohm
juliohm authored Feb 21, 2021
1 parent 03755d3 commit 40cdca3
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2 changes: 1 addition & 1 deletion src/boykov_kolmogorov.jl
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Expand Up @@ -12,7 +12,7 @@ Return the maximum flow in the network, the flow matrix and the partition
Min-Cut/Max-Flow Algorithms for Energy Minimization in Vision.
### Author
- Júlio Hoffimann Mendes (juliohm@stanford.edu)
- Júlio Hoffimann Mendes (julio.hoffimann@gmail.com)
"""
function boykov_kolmogorov_impl end
# see https://github.com/mauro3/SimpleTraits.jl/issues/47#issuecomment-327880153 for syntax
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157 changes: 153 additions & 4 deletions test/boykov_kolmogorov.jl
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@@ -1,4 +1,155 @@
@testset "Boykov Kolmogorov" begin
@testset "Lattice graph" begin
# image size
sz = (9, 9)

# number of pixels
npix = prod(sz)

# lattice graph
G = DiGraph(npix+2)
C = spzeros(npix+2, npix+2)

# connect all pixels in the 9x9 image
# with its immediate 4 neighbors
for i in 1:sz[1]-1, j in 1:sz[2]
u = LinearIndices(sz)[i,j]
v = LinearIndices(sz)[i+1,j]
add_edge!(G, u, v)
add_edge!(G, v, u)
end
for i in 1:sz[1], j in 1:sz[2]-1
u = LinearIndices(sz)[i,j]
v = LinearIndices(sz)[i,j+1]
add_edge!(G, u, v)
add_edge!(G, v, u)
end

# create capacity for flow in the 4x4
# subimage located at the top left
for i in 1:3, j in 1:4
u = LinearIndices(sz)[i,j]
v = LinearIndices(sz)[i+1,j]
C[u,v] = C[v,u] = 1
end
for i in 1:4, j in 1:3
u = LinearIndices(sz)[i,j]
v = LinearIndices(sz)[i,j+1]
C[u,v] = C[v,u] = 1
end

# create capacity for flow in the 4x4
# subimage located at the bottom right
for i in 6:8, j in 6:9
u = LinearIndices(sz)[i,j]
v = LinearIndices(sz)[i+1,j]
C[u,v] = C[v,u] = 1
end
for i in 6:9, j in 6:8
u = LinearIndices(sz)[i,j]
v = LinearIndices(sz)[i,j+1]
C[u,v] = C[v,u] = 1
end

# create source node and connect it to the
# leftmost column of the image
# create target node and connect it to the
# rightmost column of the image
s = npix + 1
t = npix + 2
for i in 1:sz[1]
u = LinearIndices(sz)[i,1]
add_edge!(G, s, u)
C[s,u] = C[u,s] = Inf
end
for i in 1:sz[1]
u = LinearIndices(sz)[i,sz[2]]
add_edge!(G, u, t)
C[u,t] = C[t,u] = Inf
end

# now we are ready to start the flow
flow, _, labels = maximum_flow(G, s, t, C, algorithm=BoykovKolmogorovAlgorithm())

# because the two subimages are not connected
# we must have zero flow from source to target
@test flow == 0

# the final cut represents the two disconnected
# subimages filled with water of different color
COLOR = reshape(labels[1:end-2], sz)
@test COLOR == [
1 1 1 1 0 0 0 0 2
1 1 1 1 0 0 0 0 2
1 1 1 1 0 0 0 0 2
1 1 1 1 0 0 0 0 2
1 0 0 0 0 0 0 0 2
1 0 0 0 0 2 2 2 2
1 0 0 0 0 2 2 2 2
1 0 0 0 0 2 2 2 2
1 0 0 0 0 2 2 2 2
]

# now let's create a bridge connecting the two
# subimages to allow flow from source to target
for (I1, I2) in [[(4,4), (5,4)], [(5,4), (5,5)],
[(5,5), (5,6)], [(5,6), (6,6)]]
u = LinearIndices(sz)[I1...]
v = LinearIndices(sz)[I2...]
C[u,v] = C[v,u] = 1
end

flow, _, labels = maximum_flow(G, s, t, C, algorithm=BoykovKolmogorovAlgorithm())

# because there is only one bridge,
# the maximum flow allowed is one unit
@test flow == 1

# the final cut is unchanged compared to the previous one
COLOR = reshape(labels[1:end-2], sz)
@test COLOR == [
1 1 1 1 0 0 0 0 2
1 1 1 1 0 0 0 0 2
1 1 1 1 0 0 0 0 2
1 1 1 1 0 0 0 0 2
1 0 0 0 0 0 0 0 2
1 0 0 0 0 2 2 2 2
1 0 0 0 0 2 2 2 2
1 0 0 0 0 2 2 2 2
1 0 0 0 0 2 2 2 2
]

# finally let's create a second bridge to increase
# the maximum flow from one to two units
for (I1, I2) in [[(4,4), (4,5)], [(4,5), (5,5)],
[(5,5), (6,5)], [(6,5), (6,6)]]
u = LinearIndices(sz)[I1...]
v = LinearIndices(sz)[I2...]
C[u,v] = C[v,u] = 1
end

flow, _, labels = maximum_flow(G, s, t, C, algorithm=BoykovKolmogorovAlgorithm())

# the maximum flow is now doubled
@test flow == 2

# the final cut is slightly different
# near the corners of the two subimages
COLOR = reshape(labels[1:end-2], sz)
@test COLOR == [
1 1 1 1 0 0 0 0 2
1 1 1 1 0 0 0 0 2
1 1 1 1 0 0 0 0 2
1 1 1 0 0 0 0 0 2
1 0 0 0 0 0 0 0 2
1 0 0 0 0 0 2 2 2
1 0 0 0 0 2 2 2 2
1 0 0 0 0 2 2 2 2
1 0 0 0 0 2 2 2 2
]
end

@testset "Find path" begin
# construct graph
gg = lg.DiGraph(3)
lg.add_edge!(gg, 1, 2)
Expand All @@ -7,9 +158,8 @@
# source and sink terminals
source, target = 1, 3


for g in testdigraphs(gg)
# default capacity
# default capacity
capacity_matrix = LightGraphsFlows.DefaultCapacity(g)
residual_graph = @inferred(LightGraphsFlows.residual(g))
T = eltype(g)
Expand All @@ -19,12 +169,11 @@
TREE[target] = T(2)
PARENT = zeros(T, 3)
A = [T(source), T(target)]
# see https://github.com/JuliaLang/julia/issues/21077
# @show("testing $g with eltype $T, residual_graph type is $(eltype(residual_graph)), flow_matrix type is $(eltype(flow_matrix)), capacity_matrix type is $(eltype(capacity_matrix))")
path = LightGraphsFlows.find_path!(
residual_graph, source, target, flow_matrix,
capacity_matrix, PARENT, TREE, A)

@test path == [1, 2, 3]
end
end
end
1 change: 1 addition & 0 deletions test/runtests.jl
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Expand Up @@ -5,6 +5,7 @@ import SimpleTraits
const lg = LightGraphs

using LinearAlgebra: diag
using SparseArrays: spzeros

using JuMP
import GLPK
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