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Update 'img' on segmentation func
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@niccolo99mandelli
niccolo99mandelli committed Oct 8, 2023
1 parent 981d560 commit de6ec6c
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Showing 2 changed files with 48 additions and 38 deletions.
10 changes: 5 additions & 5 deletions src/segmentationManager.jl
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Original file line number Diff line number Diff line change
Expand Up @@ -278,17 +278,17 @@ function apply_segmentation_SOPHYSM_tessellation(filepath_input::AbstractString,
img_graph = Luxor.readpng(filepath_background)
w = img_graph.width
h = img_graph.height
g_meta_labels = J_Space.spatial_graph(filepath_dataframe_edges, filepath_dataframe_labels)
# g_meta_labels = J_Space.spatial_graph(filepath_dataframe_edges, filepath_dataframe_labels)
g_meta_total_labels = J_Space.spatial_graph(filepath_dataframe_edges, filepath_dataframe_total_labels)
# Image with Vertices
@png begin
Luxor.placeimage(img_graph, 0, 0, 0.8, centered=true)
sethue("slateblue")
Karnak.fontsize(7)
drawgraph(g_meta_labels,
layout = extract_vertex_position(g_meta_labels) .+ Karnak.Point(-w/2, -h/2),
vertexlabels = [get_prop(g_meta_labels, v, :name) for v in Graphs.vertices(g_meta_labels)],
vertexfillcolors = extract_vertex_color(g_meta_labels),
drawgraph(g_meta_total_labels,
layout = extract_vertex_position(g_meta_total_labels) .+ Karnak.Point(-w/2, -h/2),
vertexlabels = [get_prop(g_meta_total_labels, v, :name) for v in Graphs.vertices(g_meta_total_labels)],
vertexfillcolors = extract_vertex_color(g_meta_total_labels),
edgelines=:none
)
end w h filepath_img_graph_vertex
Expand Down
76 changes: 43 additions & 33 deletions src/tmp/testingPipeline.jl
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Original file line number Diff line number Diff line change
Expand Up @@ -82,8 +82,13 @@ Returns a real number corresponding to the weight of the edge between label1 and
"""
function region_adjacency_graph(s::SegmentedImage, weight_fn::Function)

function neighbor_regions!(df_cartesian_indices::AbstractArray, G::SimpleWeightedGraph, visited::AbstractArray, s::SegmentedImage, I::CartesianIndex)
# n = Set{Int} - visited = Array - s = segmented image - p = CartesianIndex which define neighbors
function neighbor_regions!(df_cartesian_indices::AbstractArray,
G::SimpleWeightedGraph,
visited::AbstractArray,
s::SegmentedImage,
I::CartesianIndex)
# n = Set{Int} - visited = Array - s = segmented image -
# p = CartesianIndex which define neighbors
# R contains each possible index in s
R = CartesianIndices(axes(s.image_indexmap))
# I1 contains a Vector of only 1 with dimension equal to visited
Expand All @@ -99,53 +104,55 @@ function region_adjacency_graph(s::SegmentedImage, weight_fn::Function)
temp = pop!(t)
# set index temp to true
visited[temp] = true
# _colon build an object CartesianIndices which include all the index from I to J (range) :
# _colon(I::CartesianIndex{N}, J::CartesianIndex{N}) where N =
# CartesianIndices(map((i,j) -> i:j, Tuple(I), Tuple(J)))
# _colon build an object CartesianIndices which include
# all the index from I to J (range) :
for J in _colon(max(Ibegin, temp-I1), min(Iend, temp+I1))
if s.image_indexmap[temp] != s.image_indexmap[J]
# if s.image_indexmap[J] ∉ n
# If the values are different, it means they have two different colorings for the two points,
# therefore a neighbor has been identified, which is pushed into n.
# push!(n,s.image_indexmap[J])
if !Graphs.has_edge(G, vert_map[s.image_indexmap[I]], vert_map[s.image_indexmap[J]])
Graphs.add_edge!(G, vert_map[s.image_indexmap[I]], vert_map[s.image_indexmap[J]], weight_fn(s.image_indexmap[I], s.image_indexmap[J]))
# push!(added_indices, s.image_indexmap[J])
# push!(n,s.image_indexmap[J])
if !Graphs.has_edge(G, vert_map[s.image_indexmap[I]],
vert_map[s.image_indexmap[J]])
Graphs.add_edge!(G, vert_map[s.image_indexmap[I]],
vert_map[s.image_indexmap[J]],
weight_fn(s.image_indexmap[I],
s.image_indexmap[J]))
end
elseif !visited[J]
# If they are equal, I place them in t, so that,
# as long as t is not empty, I can explore all the neighbors
# that have the same color.
push!(t,J)
end
end
end
end
# Start
visited = fill(false, axes(s.image_indexmap)) # Array to mark the pixels that are already visited
# G = SimpleWeightedGraph() # The region_adjacency_graph
# vert_map = Dict{Int,Int}() # Map that stores (label, vertex) pairs
# Array to mark the pixels that are already visited
visited = fill(false, axes(s.image_indexmap))
# The region_adjacency_graph
G = SimpleWeightedGraph()
# Map that stores (label, vertex) pairs
vert_map = Dict{Int,Int}()
# Build object for label (vertex) dataframe
df_label = DataFrame()
df_noisy_label = DataFrame()
df_total_label = DataFrame()
df_cartesian_indices = []
df_color_indices = []
# add vertices to graph
# Graphs.add_vertices!(G,length(s.segment_labels))
Graphs.add_vertices!(G,length(s.segment_labels))
# setup `vert_map`
# for (i,l) in enumerate(s.segment_labels)
# vert_map[l] = i
# end
for (i,l) in enumerate(s.segment_labels)
vert_map[l] = i
end
# add edges to graph
# For each CartesianIndices in s where the image_indexmap represent the image wich is a Multidimensional Array
# The index of s.image_indexmap represent the pixel position in the segmented image
# The value of s.image_indexmap represent the pixel color in the segmented image
for p in CartesianIndices(axes(s.image_indexmap))
# check if p of the segmented image s is not visited
if !visited[p]
push!(df_cartesian_indices, p)
try
neighbor_regions!(df_cartesian_indices, G, visited, s, p)
catch oom
if isa(oom, OutOfMemoryError)
GC.gc()
println(">>> OOM")
exit()
end
end
end
end

Expand Down Expand Up @@ -205,9 +212,10 @@ function region_adjacency_graph(s::SegmentedImage, weight_fn::Function)
df_total_label.color_label = df_color_indices_total
df_total_label.area = df_area_total

df_label, df_noisy_label, df_total_label
G, vert_map, df_label, df_noisy_label, df_total_label
end


"""
compute_centroid_total_cells(s::SegmentedImage, df_label::DataFrame)
Expand Down Expand Up @@ -948,8 +956,9 @@ end

function extract_vertex_position(G::MetaGraph)
position_array = Luxor.Point[]
for v in Graphs.vertices(g_meta)
s = get_prop(g_meta, v, :centroid)
for v in Graphs.vertices(G)
s = get_prop(G, v, :centroid)
show(s)
coordinates_str = match(r"\((.*)\)", string(s)).captures[1]
coordinates = parse.(Int, split(coordinates_str, ", "))
x, y ,z = coordinates
Expand All @@ -970,7 +979,7 @@ function extract_vertex_color(G::MetaGraph)
end


link = joinpath(@__DIR__, "..", "input_example_demo", "slideExample1", "SlideExample_mini_1.tif")
link = joinpath(@__DIR__, "..", "..", "input_example_demo", "slideExample1", "SlideExample_mini_1.tif")
# load slide
println("LOAD SLIDE ...")
svs_image = read(link)
Expand Down Expand Up @@ -1000,7 +1009,7 @@ df_noisy_labels = DataFrame()
df_total_labels = DataFrame()
df_edges = DataFrame()

df_labels, df_noisy_labels, df_total_labels = region_adjacency_graph(segments, weight_fn)
G, vert_map, df_labels, df_noisy_labels, df_total_labels = region_adjacency_graph(segments, weight_fn)
# define centroid
# df_labels = compute_centroid_cells(segments, df_labels)
# df_noisy_labels = compute_centroid_noisy_cells(segments, df_noisy_labels)
Expand All @@ -1017,7 +1026,7 @@ df_edges, edges = build_graph_from_grid(df_labels, df_noisy_labels, df_total_lab

# save dataframe label as .CSV
filepath_dataframe_labels = replace(link, r"....$" => "_dataframe_labels.csv")
CSV.write(filepath_dataframe_labels, df_total_labels)
CSV.write(filepath_dataframe_labels, df_labels)
# build and save adjacency matrix
println("BUILD ADJACENCY MATRIX ...")
# matrix = weighted_graph_to_adjacency_matrix_weight(G, nvertices)
Expand All @@ -1042,6 +1051,7 @@ w = img_graph.width
h = img_graph.height

# J-Space (temp metagraph for building img on graph)
show(df_labels)
g_meta = spatial_graph(filepath_dataframe_edges, filepath_dataframe_labels)
# Plot metagraph on slide
# Image with Vertices
Expand Down

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