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visualizations.jl
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### A Pluto.jl notebook ###
# v0.20.3
using Markdown
using InteractiveUtils
# ╔═╡ 84dac78a-dc30-47ff-b78a-44751e7d8f36
using DistanceTransforms: boolean_indicator, transform
# ╔═╡ 7d03e75d-7f07-4e90-be9f-6a057ba0cedd
using PlutoUI: TableOfContents
# ╔═╡ cc4513cf-5ae4-433b-98ff-154cfd76bd62
using GLMakie
# ╔═╡ 7aa7202a-d6de-4890-bc8e-17275bc4f095
using Random
# ╔═╡ 526b7d1d-68dd-456d-ad9d-cdb923cb0083
using Colors
# ╔═╡ 3a0771bd-6743-4b32-9bce-5289310fd816
using LinearAlgebra
# ╔═╡ fca2524e-e9d4-41ef-96f1-922a73f9afff
using GeometryBasics: Cylinder, Pyramid, Sphere, mesh
# ╔═╡ 5e20980c-5a8a-453f-80cc-3c06b79a763d
using ImageIO
# ╔═╡ 5224a520-e691-4e3f-8411-a7639bccb8c7
using ImageShow
# ╔═╡ ca41a724-e18b-4b61-9d68-94747d164c11
using ImageMorphology: thinning
# ╔═╡ 381b4ffd-2de7-49ea-9884-f0bd1e7fb6d7
using ImageMagick
# ╔═╡ 00547377-8679-4fbf-a114-0dd80caeed28
TableOfContents()
# ╔═╡ b93f70d2-72fb-47e8-bf19-9179ef5ab3e1
md"""
## Various Distance Maps
"""
# ╔═╡ b2d5c5c4-32ad-491b-831d-099674805af1
Random.seed!(3)
# ╔═╡ 4e04347c-fc22-4a05-984c-9fa5349db706
sphere = Sphere(Point3f(-0.5), 1)
# ╔═╡ d31593a0-cf07-4c41-a0ce-0099b73624f4
begin
# Define the grid dimensions
nx, ny, nz = 64, 64, 64 # Grid resolution
# Define the grid bounds
x_min, x_max = -2.0, 1.0
y_min, y_max = -2.0, 1.0
z_min, z_max = -2.0, 1.0
# Create grid arrays
x_vals = range(x_min, x_max, length=nx)
y_vals = range(y_min, y_max, length=ny)
z_vals = range(z_min, z_max, length=nz)
# Reshape to create 3D grids suitable for broadcasting
xgrid = reshape(x_vals, nx, 1, 1)
ygrid = reshape(y_vals, 1, ny, 1)
zgrid = reshape(z_vals, 1, 1, nz)
# Define the sphere's center and radius
sphere_center = [-0.5, -0.5, -0.5]
radius = 1.0
# Compute the squared distances from the sphere's center
dx = xgrid .- sphere_center[1]
dy = ygrid .- sphere_center[2]
dz = zgrid .- sphere_center[3]
dist2 = dx.^2 .+ dy.^2 .+ dz.^2
# Create a boolean 3D array indicating points inside the sphere
sphere_array = dist2 .<= radius^2
end;
# ╔═╡ 27997ef4-f903-4ef0-a578-a3067a9acef0
sphere_array_dt = transform(boolean_indicator(sphere_array));
# ╔═╡ 5c5c5299-6028-4035-b4ca-51a15dd740c2
begin
rectmesh = Rect3(Point3f(-0.5), Vec3f(1))
rectMesh = mesh(rectmesh)
cmap = resample_cmap(:Spectral_11, length(rectMesh.position))
colors1 = [cmap[i] for (i,v) in enumerate(rectMesh.position)]
end;
# ╔═╡ b64575e8-f645-4809-8dcb-3543036b434d
pyr = Pyramid(Point3f(0), 1f0, 1f0)
# ╔═╡ fd253a17-4de0-4501-90ef-2fea69915ddf
# Rectangle grid setup
begin
nx_r, ny_r, nz_r = 64, 64, 64
x_min_r, x_max_r = -1.0, 1.0
y_min_r, y_max_r = -1.0, 1.0
z_min_r, z_max_r = -1.0, 1.0
x_vals_r = range(x_min_r, x_max_r, length=nx_r)
y_vals_r = range(y_min_r, y_max_r, length=ny_r)
z_vals_r = range(z_min_r, z_max_r, length=nz_r)
xgrid_r = reshape(x_vals_r, nx_r, 1, 1)
ygrid_r = reshape(y_vals_r, 1, ny_r, 1)
zgrid_r = reshape(z_vals_r, 1, 1, nz_r)
rect_center = [0.0, 0.0, 0.0]
rect_size = [1.0, 1.0, 1.0]
# Create a boolean 3D array indicating points inside the rectangle
rect_array = (abs.(xgrid_r .- rect_center[1]) .<= rect_size[1]/2) .&
(abs.(ygrid_r .- rect_center[2]) .<= rect_size[2]/2) .&
(abs.(zgrid_r .- rect_center[3]) .<= rect_size[3]/2)
end;
# ╔═╡ a196e4d5-b7ef-4457-b474-23aafe5974b9
rect_array_dt = transform(boolean_indicator(rect_array));
# ╔═╡ ba41380a-cfa1-4dbc-ae70-149e33c848f7
# Pyramid grid setup
begin
nx_p, ny_p, nz_p = 64, 64, 64
x_min_p, x_max_p = -1.0, 1.0
y_min_p, y_max_p = -1.0, 1.0
z_min_p, z_max_p = 0.0, 2.0
x_vals_p = range(x_min_p, x_max_p, length=nx_p)
y_vals_p = range(y_min_p, y_max_p, length=ny_p)
z_vals_p = range(z_min_p, z_max_p, length=nz_p)
xgrid_p = reshape(x_vals_p, nx_p, 1, 1)
ygrid_p = reshape(y_vals_p, 1, ny_p, 1)
zgrid_p = reshape(z_vals_p, 1, 1, nz_p)
pyr_center = [0.0, 0.0, 0.0]
pyr_height = 1.0
# Create a boolean 3D array indicating points inside the pyramid
pyr_array = (abs.(xgrid_p .- pyr_center[1]) .+
abs.(ygrid_p .- pyr_center[2])) .<= (1 .- zgrid_p ./ pyr_height)
end;
# ╔═╡ 545d744b-b805-4544-8572-e9c933d6fe28
pyr_array_dt = transform(boolean_indicator(pyr_array));
# ╔═╡ 31e29deb-8d22-4c78-a594-b7436ee76a96
# ╠═╡ show_logs = false
# let
# fig = Figure(size = (1200, 800))
# axs = [
# Axis3(
# fig[i,j]; aspect = :data, perspectiveness = 0.7, xticklabelsvisible = false, yticklabelsvisible = false, zticklabelsvisible = false
# )
# for j in 1:3, i in 1:2
# ]
# mesh!(axs[1], sphere, color = :white)
# mesh!(axs[2], rectmesh, color = colors1)
# mesh!(axs[3], pyr; color = :dodgerblue)
# volume!(
# axs[4],
# x_vals,
# y_vals,
# z_vals,
# (sphere_array_dt .+ maximum(sphere_array_dt)) ./ (2 * maximum(sphere_array_dt));
# colormap = :Hiroshige,
# transparency = true
# )
# volume!(
# axs[5],
# x_vals_r,
# y_vals_r,
# z_vals_r,
# (rect_array_dt .+ maximum(rect_array_dt)) ./ (2 * maximum(rect_array_dt));
# colormap = :Hiroshige,
# transparency = true
# )
# volume!(
# axs[6],
# x_vals_p,
# y_vals_p,
# z_vals_p,
# (pyr_array_dt .+ maximum(pyr_array_dt)) ./ (2 * maximum(pyr_array_dt));
# colormap = :Hiroshige,
# transparency = true
# )
# save(joinpath(pwd(), "plots/various_dts.png"), fig)
# fig
# end
# ╔═╡ 8fb9c45a-8257-482f-aed0-e92f4616cee3
md"""
## Skeletonization
"""
# ╔═╡ 14245334-3d66-4aa1-836a-b2f8609e7167
import FileIO
# ╔═╡ 7f8517d0-3a79-4831-99d8-78658a628b1c
path_retinal_artery = "/Users/daleblack/Downloads/RAVIR Dataset 2/train/training_masks/IR_Case_011.png"
# ╔═╡ aa70f709-4e25-416a-be33-5309252eac55
img_retinal_artery = FileIO.load(path_retinal_artery)
# ╔═╡ 32b77ae4-ddb3-4721-ad57-7f543f1d7051
img_retinal_artery_ones = Float32.(img_retinal_artery) .> 0.6
# ╔═╡ 4dc780ab-f531-427e-b994-a0c2aa074f37
Gray.(img_retinal_artery_ones)
# ╔═╡ 8ecd683e-800e-4129-b9eb-04694f6ea5f2
img_retinal_artery_ones_skeleton = thinning(img_retinal_artery_ones)
# ╔═╡ cd9b9471-cca6-4f33-86a6-a2fdf5053dcd
Gray.(img_retinal_artery_ones_skeleton)
# ╔═╡ 6eac0e7d-4acc-427f-b622-7dda74450b58
let
f = Figure()
ax = Makie.Axis(
f[1, 1],
title = "Mask of Retinal Arteries",
# aspect = DataAspect()
)
image!(img_retinal_artery_ones)
hidedecorations!(ax)
ax = Makie.Axis(
f[1, 2],
title = "Skeleton of Retinal Arteries",
# aspect = DataAspect()
)
image!(img_retinal_artery_ones_skeleton)
hidedecorations!(ax)
save(joinpath(pwd(), "plots/vizualization_skeleton_arteries.png"), f)
f
end
# ╔═╡ 2bd275ca-92b3-4e66-950a-8e57490799fb
md"""
# Drive Vessels
"""
# ╔═╡ 362f2a23-7a02-44bb-b253-c76d68f157ce
path_drive = "/Users/daleblack/Downloads/RAVIR Dataset 2/train/training_masks/IR_Case_055.png"
# ╔═╡ 78d1f25a-ae99-4ade-ba9a-dda333b7f3db
img_drive = FileIO.load(path_drive)
# ╔═╡ e67ad961-1e6e-4f73-b313-3365eeb5b20d
# ╔═╡ ee2d6a5d-4377-407e-8ca2-053e1b5c4536
img_drive_ones = Float32.(Gray.(img_drive)) .> 0.6
# ╔═╡ c347658e-a085-4ed4-9c13-ca29da5a3977
Gray.(img_drive_ones)
# ╔═╡ 3dcc57db-7d4b-4fca-beb4-d11503095779
img_drive_ones_skeleton = thinning(img_drive_ones)
# ╔═╡ 7d7e5eb0-9b3d-4d74-a5fe-79fe9d1df5c7
Gray.(img_drive_ones_skeleton)
# ╔═╡ 25aaad6f-cd94-4169-af3b-1e7d848f2589
let
f = Figure()
ax = Makie.Axis(
f[1, 1],
title = "Mask of Retinal Arteries (Patient 1)",
aspect = DataAspect()
)
image!(img_retinal_artery_ones)
hidedecorations!(ax)
ax = Makie.Axis(
f[1, 2],
title = "Skeleton of Retinal Arteries (Patient 1)",
aspect = DataAspect()
)
image!(img_retinal_artery_ones_skeleton)
hidedecorations!(ax)
ax = Makie.Axis(
f[2, 1],
title = "Mask of Retinal Arteries (Patient 2)",
aspect = DataAspect()
)
image!(img_drive_ones)
hidedecorations!(ax)
ax = Makie.Axis(
f[2, 2],
title = "Skeleton of Retinal Arteries (Patient 2)",
aspect = DataAspect()
)
image!(img_drive_ones_skeleton)
hidedecorations!(ax)
save(joinpath(pwd(), "plots/vizualization_skeleton_arteries2.png"), f)
f
end
# ╔═╡ 00000000-0000-0000-0000-000000000001
PLUTO_PROJECT_TOML_CONTENTS = """
[deps]
Colors = "5ae59095-9a9b-59fe-a467-6f913c188581"
DistanceTransforms = "71182807-4d06-4237-8dd0-bdafe4d097e2"
FileIO = "5789e2e9-d7fb-5bc7-8068-2c6fae9b9549"
GLMakie = "e9467ef8-e4e7-5192-8a1a-b1aee30e663a"
GeometryBasics = "5c1252a2-5f33-56bf-86c9-59e7332b4326"
ImageIO = "82e4d734-157c-48bb-816b-45c225c6df19"
ImageMagick = "6218d12a-5da1-5696-b52f-db25d2ecc6d1"
ImageMorphology = "787d08f9-d448-5407-9aad-5290dd7ab264"
ImageShow = "4e3cecfd-b093-5904-9786-8bbb286a6a31"
LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e"
PlutoUI = "7f904dfe-b85e-4ff6-b463-dae2292396a8"
Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c"
[compat]
Colors = "~0.12.11"
DistanceTransforms = "~0.2.2"
FileIO = "~1.16.6"
GLMakie = "~0.8.12"
GeometryBasics = "~0.4.11"
ImageIO = "~0.6.9"
ImageMagick = "~1.4.0"
ImageMorphology = "~0.4.5"
ImageShow = "~0.3.8"
PlutoUI = "~0.7.60"
"""
# ╔═╡ 00000000-0000-0000-0000-000000000002
PLUTO_MANIFEST_TOML_CONTENTS = """
# This file is machine-generated - editing it directly is not advised
julia_version = "1.11.2"
manifest_format = "2.0"
project_hash = "d84a76b0f7f806b44d765a30b3996335213e16e9"
[[deps.AbstractFFTs]]
deps = ["LinearAlgebra"]
git-tree-sha1 = "d92ad398961a3ed262d8bf04a1a2b8340f915fef"
uuid = "621f4979-c628-5d54-868e-fcf4e3e8185c"
version = "1.5.0"
weakdeps = ["ChainRulesCore", "Test"]
[deps.AbstractFFTs.extensions]
AbstractFFTsChainRulesCoreExt = "ChainRulesCore"
AbstractFFTsTestExt = "Test"
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