Mercurial > repos > PredictPDPS / changeset
changeset
added new predictors
Thu, 18 Apr 2024 10:47:16 +0300
- author
- Neil Dizon <neil.dizon@helsinki.fi>
- date
- Thu, 18 Apr 2024 10:47:16 +0300
- changeset 5
- 843e7611b068
- parent 4
- a64766c44642
- child 6
- 72bcc5f492df
added new predictors
--- a/Manifest.toml Wed Dec 02 12:54:58 2020 -0500 +++ b/Manifest.toml Thu Apr 18 10:47:16 2024 +0300 @@ -1,1037 +1,1521 @@ # This file is machine-generated - editing it directly is not advised -julia_version = "1.7.0" -manifest_format = "2.0" - -[[deps.AbstractFFTs]] +[[AbstractFFTs]] deps = ["LinearAlgebra"] -git-tree-sha1 = "485ee0867925449198280d4af84bdb46a2a404d0" +git-tree-sha1 = "d92ad398961a3ed262d8bf04a1a2b8340f915fef" uuid = "621f4979-c628-5d54-868e-fcf4e3e8185c" -version = "1.0.1" +version = "1.5.0" +weakdeps = ["ChainRulesCore", "Test"] + + [AbstractFFTs.extensions] + AbstractFFTsChainRulesCoreExt = "ChainRulesCore" + AbstractFFTsTestExt = "Test" -[[deps.Adapt]] -deps = ["LinearAlgebra"] -git-tree-sha1 = "84918055d15b3114ede17ac6a7182f68870c16f7" +[[Adapt]] +deps = ["LinearAlgebra", "Requires"] +git-tree-sha1 = "6a55b747d1812e699320963ffde36f1ebdda4099" uuid = "79e6a3ab-5dfb-504d-930d-738a2a938a0e" -version = "3.3.1" +version = "4.0.4" +weakdeps = ["StaticArrays"] 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--- a/Project.toml Wed Dec 02 12:54:58 2020 -0500 +++ b/Project.toml Thu Apr 18 10:47:16 2024 +0300 @@ -13,4 +13,5 @@ Printf = "de0858da-6303-5e67-8744-51eddeeeb8d7" QuartzImageIO = "dca85d43-d64c-5e67-8c65-017450d5d020" Setfield = "efcf1570-3423-57d1-acb7-fd33fddbac46" +StableRNGs = "860ef19b-820b-49d6-a774-d7a799459cd3" TestImages = "5e47fb64-e119-507b-a336-dd2b206d9990"
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/src/AlgorithmDualScaling.jl Thu Apr 18 10:47:16 2024 +0300 @@ -0,0 +1,124 @@ +#################################################################### +# Predictive online PDPS for optical flow with known velocity field +#################################################################### + +__precompile__() + +module AlgorithmDualScaling + +identifier = "pdps_known_dualscaling" + +using Printf + +using AlgTools.Util +import AlgTools.Iterate +using ImageTools.Gradient + +using ..OpticalFlow: ImageSize, + Image, + pdflow! + +######################### +# Iterate initialisation +######################### + +function init_rest(x::Image) + imdim=size(x) + + y = zeros(2, imdim...) + Δx = copy(x) + Δy = copy(y) + x̄ = copy(x) + + return x, y, Δx, Δy, x̄ +end + +function init_iterates(xinit::Image) + return init_rest(copy(xinit)) +end + +function init_iterates(dim::ImageSize) + return init_rest(zeros(dim...)) +end + +############ +# Algorithm +############ + +function solve( :: Type{DisplacementT}; + dim :: ImageSize, + iterate = AlgTools.simple_iterate, + params::NamedTuple) where DisplacementT + + ################################ + # Extract and set up parameters + ################################ + + α, ρ = params.α, params.ρ + R_K² = ∇₂_norm₂₂_est² + γ = 1.0 + Λ = params.Λ + τ₀, σ₀ = params.τ₀, params.σ₀ + + τ = τ₀/γ + @assert(1+γ*τ ≥ Λ) + σ = σ₀*1/(τ*R_K²) + + println("Step length parameters: τ=$(τ), σ=$(σ)") + + ###################### + # Initialise iterates + ###################### + + x, y, Δx, Δy, x̄ = init_iterates(dim) + init_data = (params.init == :data) + + #################### + # Run the algorithm + #################### + + v = iterate(params) do verbose :: Function, + b :: Image, + v_known :: DisplacementT, + 🚫unused_b_next :: Image + + ################## + # Prediction step + ################## + if init_data + x .= b + init_data = false + end + + pdflow!(x, y, v_known) + + ############ + # PDPS step + ############ + + ∇₂ᵀ!(Δx, y) # primal step: + @. x̄ = x # | save old x for over-relax + @. x = (x-τ*(Δx-b))/(1+τ) # | prox + @. x̄ = 2x - x̄ # over-relax + ∇₂!(Δy, x̄) # dual step: y + @. y = (y + σ*Δy)/(1 + σ*ρ/α) # | + proj_norm₂₁ball!(y, α) # | prox + + ################################ + # Give function value if needed + ################################ + v = verbose() do + ∇₂!(Δy, x) + value = norm₂²(b-x)/2 + params.α*γnorm₂₁(Δy, params.ρ) + value, x, [NaN, NaN], nothing + end + + v + end + + return x, y, v +end + +end # Module + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/src/AlgorithmGreedy.jl Thu Apr 18 10:47:16 2024 +0300 @@ -0,0 +1,124 @@ +#################################################################### +# Predictive online PDPS for optical flow with known velocity field +#################################################################### + +__precompile__() + +module AlgorithmGreedy + +identifier = "pdps_known_greedy" + +using Printf + +using AlgTools.Util +import AlgTools.Iterate +using ImageTools.Gradient + +using ..OpticalFlow: ImageSize, + Image, + pdflow! + +######################### +# Iterate initialisation +######################### + +function init_rest(x::Image) + imdim=size(x) + + y = zeros(2, imdim...) + Δx = copy(x) + Δy = copy(y) + x̄ = copy(x) + + return x, y, Δx, Δy, x̄ +end + +function init_iterates(xinit::Image) + return init_rest(copy(xinit)) +end + +function init_iterates(dim::ImageSize) + return init_rest(zeros(dim...)) +end + +############ +# Algorithm +############ + +function solve( :: Type{DisplacementT}; + dim :: ImageSize, + iterate = AlgTools.simple_iterate, + params::NamedTuple) where DisplacementT + + ################################ + # Extract and set up parameters + ################################ + + α, ρ = params.α, params.ρ + R_K² = ∇₂_norm₂₂_est² + γ = 1.0 + Λ = params.Λ + τ₀, σ₀ = params.τ₀, params.σ₀ + + τ = τ₀/γ + @assert(1+γ*τ ≥ Λ) + σ = σ₀*1/(τ*R_K²) + + println("Step length parameters: τ=$(τ), σ=$(σ)") + + ###################### + # Initialise iterates + ###################### + + x, y, Δx, Δy, x̄ = init_iterates(dim) + init_data = (params.init == :data) + + #################### + # Run the algorithm + #################### + + v = iterate(params) do verbose :: Function, + b :: Image, + v_known :: DisplacementT, + 🚫unused_b_next :: Image + + ################## + # Prediction step + ################## + if init_data + x .= b + init_data = false + end + + pdflow!(x, Δx, y, Δy, v_known) + + ############ + # PDPS step + ############ + + ∇₂ᵀ!(Δx, y) # primal step: + @. x̄ = x # | save old x for over-relax + @. x = (x-τ*(Δx-b))/(1+τ) # | prox + @. x̄ = 2x - x̄ # over-relax + ∇₂!(Δy, x̄) # dual step: y + @. y = (y + σ*Δy)/(1 + σ*ρ/α) # | + proj_norm₂₁ball!(y, α) # | prox + + ################################ + # Give function value if needed + ################################ + v = verbose() do + ∇₂!(Δy, x) + value = norm₂²(b-x)/2 + params.α*γnorm₂₁(Δy, params.ρ) + value, x, [NaN, NaN], nothing + end + + v + end + + return x, y, v +end + +end # Module + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/src/AlgorithmNoPrediction.jl Thu Apr 18 10:47:16 2024 +0300 @@ -0,0 +1,124 @@ +#################################################################### +# Predictive online PDPS for optical flow with known velocity field +#################################################################### + +__precompile__() + +module AlgorithmNoPrediction + +identifier = "pdps_known_noprediction" + +using Printf + +using AlgTools.Util +import AlgTools.Iterate +using ImageTools.Gradient + +using ..OpticalFlow: ImageSize, + Image, + pdflow! + +######################### +# Iterate initialisation +######################### + +function init_rest(x::Image) + imdim=size(x) + + y = zeros(2, imdim...) + Δx = copy(x) + Δy = copy(y) + x̄ = copy(x) + + return x, y, Δx, Δy, x̄ +end + +function init_iterates(xinit::Image) + return init_rest(copy(xinit)) +end + +function init_iterates(dim::ImageSize) + return init_rest(zeros(dim...)) +end + +############ +# Algorithm +############ + +function solve( :: Type{DisplacementT}; + dim :: ImageSize, + iterate = AlgTools.simple_iterate, + params::NamedTuple) where DisplacementT + + ################################ + # Extract and set up parameters + ################################ + + α, ρ = params.α, params.ρ + R_K² = ∇₂_norm₂₂_est² + γ = 1.0 + Λ = params.Λ + τ₀, σ₀ = params.τ₀, params.σ₀ + + τ = τ₀/γ + @assert(1+γ*τ ≥ Λ) + σ = σ₀*1/(τ*R_K²) + + println("Step length parameters: τ=$(τ), σ=$(σ)") + + ###################### + # Initialise iterates + ###################### + + x, y, Δx, Δy, x̄ = init_iterates(dim) + init_data = (params.init == :data) + + #################### + # Run the algorithm + #################### + + v = iterate(params) do verbose :: Function, + b :: Image, + v_known :: DisplacementT, + 🚫unused_b_next :: Image + + ################## + # Prediction step + ################## + if init_data + x .= b + init_data = false + end + + + + ############ + # PDPS step + ############ + + ∇₂ᵀ!(Δx, y) # primal step: + @. x̄ = x # | save old x for over-relax + @. x = (x-τ*(Δx-b))/(1+τ) # | prox + @. x̄ = 2x - x̄ # over-relax + ∇₂!(Δy, x̄) # dual step: y + @. y = (y + σ*Δy)/(1 + σ*ρ/α) # | + proj_norm₂₁ball!(y, α) # | prox + + ################################ + # Give function value if needed + ################################ + v = verbose() do + ∇₂!(Δy, x) + value = norm₂²(b-x)/2 + params.α*γnorm₂₁(Δy, params.ρ) + value, x, [NaN, NaN], nothing + end + + v + end + + return x, y, v +end + +end # Module + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/src/AlgorithmPrimalOnly.jl Thu Apr 18 10:47:16 2024 +0300 @@ -0,0 +1,124 @@ +#################################################################### +# Predictive online PDPS for optical flow with known velocity field +#################################################################### + +__precompile__() + +module AlgorithmPrimalOnly + +identifier = "pdps_known_primalonly" + +using Printf + +using AlgTools.Util +import AlgTools.Iterate +using ImageTools.Gradient + +using ..OpticalFlow: ImageSize, + Image, + pdflow! + +######################### +# Iterate initialisation +######################### + +function init_rest(x::Image) + imdim=size(x) + + y = zeros(2, imdim...) + Δx = copy(x) + Δy = copy(y) + x̄ = copy(x) + + return x, y, Δx, Δy, x̄ +end + +function init_iterates(xinit::Image) + return init_rest(copy(xinit)) +end + +function init_iterates(dim::ImageSize) + return init_rest(zeros(dim...)) +end + +############ +# Algorithm +############ + +function solve( :: Type{DisplacementT}; + dim :: ImageSize, + iterate = AlgTools.simple_iterate, + params::NamedTuple) where DisplacementT + + ################################ + # Extract and set up parameters + ################################ + + α, ρ = params.α, params.ρ + R_K² = ∇₂_norm₂₂_est² + γ = 1.0 + Λ = params.Λ + τ₀, σ₀ = params.τ₀, params.σ₀ + + τ = τ₀/γ + @assert(1+γ*τ ≥ Λ) + σ = σ₀*1/(τ*R_K²) + + println("Step length parameters: τ=$(τ), σ=$(σ)") + + ###################### + # Initialise iterates + ###################### + + x, y, Δx, Δy, x̄ = init_iterates(dim) + init_data = (params.init == :data) + + #################### + # Run the algorithm + #################### + + v = iterate(params) do verbose :: Function, + b :: Image, + v_known :: DisplacementT, + 🚫unused_b_next :: Image + + ################## + # Prediction step + ################## + if init_data + x .= b + init_data = false + end + + pdflow!(x, Δx, y, Δy, v_known, false) + + ############ + # PDPS step + ############ + + ∇₂ᵀ!(Δx, y) # primal step: + @. x̄ = x # | save old x for over-relax + @. x = (x-τ*(Δx-b))/(1+τ) # | prox + @. x̄ = 2x - x̄ # over-relax + ∇₂!(Δy, x̄) # dual step: y + @. y = (y + σ*Δy)/(1 + σ*ρ/α) # | + proj_norm₂₁ball!(y, α) # | prox + + ################################ + # Give function value if needed + ################################ + v = verbose() do + ∇₂!(Δy, x) + value = norm₂²(b-x)/2 + params.α*γnorm₂₁(Δy, params.ρ) + value, x, [NaN, NaN], nothing + end + + v + end + + return x, y, v +end + +end # Module + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/src/AlgorithmProximal.jl Thu Apr 18 10:47:16 2024 +0300 @@ -0,0 +1,148 @@ +#################################################################### +# Predictive online PDPS for optical flow with known velocity field +#################################################################### + +__precompile__() + +module AlgorithmProximal + +identifier = "pdps_known_proximal" + +using Printf + +using AlgTools.Util +import AlgTools.Iterate +using ImageTools.Gradient + +using ..OpticalFlow: ImageSize, + Image, + pdflow! + +######################### +# Iterate initialisation +######################### + +function init_rest(x::Image) + imdim=size(x) + + y = zeros(2, imdim...) + Δx = copy(x) + Δy = copy(y) + x̄ = copy(x) + + return x, y, Δx, Δy, x̄ +end + +function init_iterates(xinit::Image) + return init_rest(copy(xinit)) +end + +function init_iterates(dim::ImageSize) + return init_rest(zeros(dim...)) +end + +############ +# Algorithm +############ + +function step_lengths(params, γ, R_K²) + ρ̃₀, τ₀, σ₀, σ̃₀ = params.ρ̃₀, params.τ₀, params.σ₀, params.σ̃₀ + δ = params.δ + ρ = isdefined(params, :phantom_ρ) ? params.phantom_ρ : params.ρ + Λ = params.Λ + Θ = params.dual_flow ? Λ : 1 + + τ = τ₀/γ + @assert(1+γ*τ ≥ Λ) + σ = σ₀*min(1/(τ*R_K²), 1/max(0, τ*R_K²/((1+γ*τ-Λ)*(1-δ))-ρ)) + q = δ*(1+σ*ρ)/Θ + if 1 ≥ q + σ̃ = σ̃₀*σ/q + #ρ̃ = ρ̃₀*max(0, ((Θ*σ)/(2*δ*σ̃^2*(1+σ*ρ))+1/(2σ)-1/σ̃)) + ρ̃ = max(0, (1-q)/(2*σ)) + else + σ̃ = σ̃₀*σ/(q*(1-√(1-1/q))) + ρ̃ = 0 + end + + println("Step length parameters: τ=$(τ), σ=$(σ), σ̃=$(σ̃), ρ̃=$(ρ̃)") + + return τ, σ, σ̃, ρ̃ +end + +function solve( :: Type{DisplacementT}; + dim :: ImageSize, + iterate = AlgTools.simple_iterate, + params::NamedTuple) where DisplacementT + + ################################ + # Extract and set up parameters + ################################ + + α, ρ = params.α, params.ρ + R_K² = ∇₂_norm₂₂_est² + γ = 1 + τ, σ, σ̃, ρ̃ = step_lengths(params, γ, R_K²) + + ###################### + # Initialise iterates + ###################### + + x, y, Δx, Δy, x̄ = init_iterates(dim) + init_data = (params.init == :data) + + #################### + # Run the algorithm + #################### + + v = iterate(params) do verbose :: Function, + b :: Image, + v_known :: DisplacementT, + 🚫unused_b_next :: Image + + ################## + # Prediction step + ################## + if init_data + x .= b + init_data = false + end + + pdflow!(x, Δx, y, Δy, v_known, params.dual_flow) + + # Proximal step + ∇₂!(Δy, x) + @. y = (y + σ̃*Δy)/(1 + σ̃*(ρ̃+ρ/α)) + proj_norm₂₁ball!(y, α) + + + ############ + # PDPS step + ############ + + ∇₂ᵀ!(Δx, y) # primal step: + @. x̄ = x # | save old x for over-relax + @. x = (x-τ*(Δx-b))/(1+τ) # | prox + @. x̄ = 2x - x̄ # over-relax + ∇₂!(Δy, x̄) # dual step: y + @. y = (y + σ*Δy)/(1 + σ*ρ/α) # | + proj_norm₂₁ball!(y, α) # | prox + + ################################ + # Give function value if needed + ################################ + v = verbose() do + ∇₂!(Δy, x) + value = norm₂²(b-x)/2 + params.α*γnorm₂₁(Δy, params.ρ) + value, x, [NaN, NaN], nothing + end + + v + end + + return x, y, v +end + +end # Module + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/src/AlgorithmRotation.jl Thu Apr 18 10:47:16 2024 +0300 @@ -0,0 +1,124 @@ +#################################################################### +# Predictive online PDPS for optical flow with known velocity field +#################################################################### + +__precompile__() + +module AlgorithmRotation + +identifier = "pdps_known_rotation" + +using Printf + +using AlgTools.Util +import AlgTools.Iterate +using ImageTools.Gradient + +using ..OpticalFlow: ImageSize, + Image, + pdflow! + +######################### +# Iterate initialisation +######################### + +function init_rest(x::Image) + imdim=size(x) + + y = zeros(2, imdim...) + Δx = copy(x) + Δy = copy(y) + x̄ = copy(x) + + return x, y, Δx, Δy, x̄ +end + +function init_iterates(xinit::Image) + return init_rest(copy(xinit)) +end + +function init_iterates(dim::ImageSize) + return init_rest(zeros(dim...)) +end + +############ +# Algorithm +############ + +function solve( :: Type{DisplacementT}; + dim :: ImageSize, + iterate = AlgTools.simple_iterate, + params::NamedTuple) where DisplacementT + + ################################ + # Extract and set up parameters + ################################ + + α, ρ = params.α, params.ρ + R_K² = ∇₂_norm₂₂_est² + γ = 1.0 + Λ = params.Λ + τ₀, σ₀ = params.τ₀, params.σ₀ + + τ = τ₀/γ + @assert(1+γ*τ ≥ Λ) + σ = σ₀*1/(τ*R_K²) + + println("Step length parameters: τ=$(τ), σ=$(σ)") + + ###################### + # Initialise iterates + ###################### + + x, y, Δx, Δy, x̄ = init_iterates(dim) + init_data = (params.init == :data) + + #################### + # Run the algorithm + #################### + + v = iterate(params) do verbose :: Function, + b :: Image, + v_known :: DisplacementT, + 🚫unused_b_next :: Image + + ################## + # Prediction step + ################## + if init_data + x .= b + init_data = false + end + + pdflow!(x, Δx, y, v_known) + + ############ + # PDPS step + ############ + + ∇₂ᵀ!(Δx, y) # primal step: + @. x̄ = x # | save old x for over-relax + @. x = (x-τ*(Δx-b))/(1+τ) # | prox + @. x̄ = 2x - x̄ # over-relax + ∇₂!(Δy, x̄) # dual step: y + @. y = (y + σ*Δy)/(1 + σ*ρ/α) # | + proj_norm₂₁ball!(y, α) # | prox + + ################################ + # Give function value if needed + ################################ + v = verbose() do + ∇₂!(Δy, x) + value = norm₂²(b-x)/2 + params.α*γnorm₂₁(Δy, params.ρ) + value, x, [NaN, NaN], nothing + end + + v + end + + return x, y, v +end + +end # Module + +
--- a/src/ImGenerate.jl Wed Dec 02 12:54:58 2020 -0500 +++ b/src/ImGenerate.jl Thu Apr 18 10:47:16 2024 +0300 @@ -17,6 +17,10 @@ using ..OpticalFlow: Image, DisplacementConstant, DisplacementFull +# Added for reproducibility +import StableRNGs: StableRNG, Random +const rng = StableRNG(9182737465) + ############## # Our exports ############## @@ -59,16 +63,16 @@ function shake(params) if !haskey(params, :shaketype) || params.shaketype == :gaussian - return () -> params.shake.*randn(2) + return () -> params.shake.*randn(rng,2) elseif params.shaketype == :disk return () -> begin - θ = 2π*rand(Float64) - r = params.shake*√(rand(Float64)) + θ = 2π*rand(rng,Float64) + r = params.shake*√(rand(rng,Float64)) return [r*cos(θ), r*sin(θ)] end elseif params.shaketype == :circle return () -> begin - θ = 2π*rand(Float64) + θ = 2π*rand(rng,Float64) r = params.shake return [r*cos(θ), r*sin(θ)] end @@ -112,8 +116,8 @@ # Flow original data and add noise b_true = zeros(sz...) translate_image!(b_true, b_orig, v_cumul; threads=true) - b = b_true .+ params.noise_level.*randn(sz...) - v = v_true.*(1.0 .+ params.shake_noise_level.*randn(size(v_true)...)) + b = b_true .+ params.noise_level.*randn(rng,sz...) + v = v_true.*(1.0 .+ params.shake_noise_level.*randn(rng,size(v_true)...)) # Pass true data to iteration routine data = OnlineData{DisplacementT}(b_true, b, v, v_true, v_cumul) if !put_unless_closed!(datachannel, data) @@ -146,8 +150,8 @@ # Extract subwindow of original image and add noise b_true = zeros(sz...) extract_subimage!(b_true, im, v_cumul; threads=true) - b = b_true .+ params.noise_level.*randn(sz...) - v = v_true.*(1.0 .+ params.shake_noise_level.*randn(size(v_true)...)) + b = b_true .+ params.noise_level.*randn(rng,sz...) + v = v_true.*(1.0 .+ params.shake_noise_level.*randn(rng,size(v_true)...)) # Pass data to iteration routine data = OnlineData{DisplacementConstant}(b_true, b, v, v_true, v_cumul) if !put_unless_closed!(datachannel, data)
--- a/src/OpticalFlow.jl Wed Dec 02 12:54:58 2020 -0500 +++ b/src/OpticalFlow.jl Thu Apr 18 10:47:16 2024 +0300 @@ -93,6 +93,67 @@ end end +# Additional method for Greedy +function pdflow!(x, Δx, y, Δy, u; threads=:none) + @assert(size(u)==(2,)) + Δx .= x + Δy .= y + flow!(x, u; threads=(threads==:inner)) + Dxx = similar(Δy) + DΔx = similar(Δy) + ∇₂!(Dxx, x) + ∇₂!(DΔx, Δx) + inds = abs.(Dxx) .≤ 1e-1 + Dxx[inds] .= 1 + DΔx[inds] .= 1 + y .= y.* DΔx ./ Dxx +end + +# Additional method for Rotation +function pdflow!(x, Δx, y, u; threads=:none) + @assert(size(u)==(2,)) + Δx .= x + flow!(x, u; threads=(threads==:inner)) + + (m,n) = size(x) + dx = similar(y) + dx_banana = similar(y) + ∇₂!(dx, Δx) + ∇₂!(dx_banana, x) + + for i=1:m + for j=1:n + ndx = @views sum(dx[:, i, j].^2) + ndx_banana = @views sum(dx_banana[:, i, j].^2) + if ndx > 1e-4 && ndx_banana > 1e-4 + A = dx[:, i, j] + B = dx_banana[:, i, j] + theta = atan(B[1] * A[2] - B[2] * A[1], B[1] * A[1] + B[2] * A[2]) # Oriented angle from A to B + cos_theta = cos(theta) + sin_theta = sin(theta) + a = cos_theta * y[1, i, j] - sin_theta * y[2, i, j] + b = sin_theta * y[1, i, j] + cos_theta * y[2, i, j] + y[1, i, j] = a + y[2, i, j] = b + end + end + end +end + +# Additional method for Dual Scaling +function pdflow!(x, y, u; threads=:none) + @assert(size(u)==(2,)) + oldx = copy(x) + flow!(x, u; threads=(threads==:inner)) + C = similar(y) + cc = abs.(x-oldx) + cm = max(1e-12,maximum(cc)) + c = 1 .* (1 .- cc./ cm) .^(10) + C[1,:,:] .= c + C[2,:,:] .= c + y .= C.*y +end + ########################## # Linearised optical flow ##########################
--- a/src/PredictPDPS.jl Wed Dec 02 12:54:58 2020 -0500 +++ b/src/PredictPDPS.jl Thu Apr 18 10:47:16 2024 +0300 @@ -40,6 +40,14 @@ include("AlgorithmFB.jl") include("AlgorithmFBDual.jl") +# Additional +include("AlgorithmProximal.jl") +include("AlgorithmGreedy.jl") +include("AlgorithmRotation.jl") +include("AlgorithmNoPrediction.jl") +include("AlgorithmPrimalOnly.jl") +include("AlgorithmDualScaling.jl") + import .Algorithm, .AlgorithmBoth, .AlgorithmBothGreedyV, @@ -47,7 +55,13 @@ .AlgorithmBothMulti, .AlgorithmBothNL, .AlgorithmFB, - .AlgorithmFBDual + .AlgorithmFBDual, + .AlgorithmProximal, + .AlgorithmGreedy, + .AlgorithmRotation, + .AlgorithmNoPrediction, + .AlgorithmPrimalOnly, + .AlgorithmDualScaling using .ImGenerate using .OpticalFlow: DisplacementFull, DisplacementConstant @@ -61,9 +75,19 @@ demo_known1, demo_known2, demo_known3, + demo_known4, + demo_known5, + demo_known6, demo_unknown1, demo_unknown2, - demo_unknown3 + demo_unknown3, + demo_predictor1, + demo_predictor2, + demo_predictor3, + demo_predictor4, + demo_predictor5, + demo_predictor6, + batchrun_predictors ################################### # Parameterisation and experiments @@ -113,13 +137,13 @@ const p_known₀ = ( noise_level = 0.5, - shake_noise_level = 0.05, - shake = 2, - α = 1, - ρ̃₀ = 1, - σ̃₀ = 1, + shake_noise_level = 0.025, + shake = 3.0, + α = 1.0, + ρ̃₀ = 1.0, + σ̃₀ = 1.0, δ = 0.9, - σ₀ = 1, + σ₀ = 0.1, τ₀ = 0.01, ) @@ -163,12 +187,33 @@ p_known₀ ⬿ (τ̃₀=0.9, fb_inner_iterations = 10)), ) +const predictor_experiments_pdps_known = ( + Experiment(AlgorithmProximal, DisplacementConstant, lighthouse, + p_known₀ ⬿ (phantom_ρ = 100,)), + Experiment(AlgorithmRotation, DisplacementConstant, lighthouse, + p_known₀), + Experiment(AlgorithmGreedy, DisplacementConstant, lighthouse, + p_known₀), + Experiment(AlgorithmPrimalOnly, DisplacementConstant, lighthouse, + p_known₀), + Experiment(AlgorithmNoPrediction, DisplacementConstant, lighthouse, + p_known₀), + Experiment(AlgorithmDualScaling, DisplacementConstant, lighthouse, + p_known₀), +) + +const predictor_experiments_all = Iterators.flatten(( + predictor_experiments_pdps_known, +)) + const experiments_all = Iterators.flatten(( experiments_pdps_known, experiments_pdps_unknown_multi, experiments_fb_known )) + + ################ # Log ################ @@ -177,14 +222,14 @@ iter :: Int time :: Float64 function_value :: Float64 - v_cumul_true_y :: Float64 - v_cumul_true_x :: Float64 - v_cumul_est_y :: Float64 - v_cumul_est_x :: Float64 + #v_cumul_true_y :: Float64 + #v_cumul_true_x :: Float64 + #v_cumul_est_y :: Float64 + #v_cumul_est_x :: Float64 psnr :: Float64 ssim :: Float64 - psnr_data :: Float64 - ssim_data :: Float64 + #psnr_data :: Float64 + #ssim_data :: Float64 end struct LogEntryHiFi <: IterableStruct @@ -208,7 +253,8 @@ function name(e::Experiment, p) ig = e.imgen - return "$(ig.name)_$(e.mod.identifier)_$(@sprintf "%x" hash(p))" + # return "$(ig.name)_$(e.mod.identifier)_$(@sprintf "%x" hash(p))" + return "$(ig.name)_$(e.mod.identifier)_$(Int64(10000*p.σ₀))_$(Int64(10000*p.τ₀))" end function write_tex(texfile, e_params) @@ -235,21 +281,19 @@ wp("sigma", (e_params.σ₀ == 1 ? "" : "$(e_params.σ₀)") * "\\sigma_{\\max}") end end -end +end function run_experiments(;visualise=true, recalculate=true, experiments, save_prefix=default_save_prefix, fullscreen=false, - windowsize=nothing, kwargs...) # Create visualisation if visualise rc = Channel(1) - visproc = Threads.@spawn bg_visualise_enhanced(rc, fullscreen=fullscreen, - windowsize=windowsize) + visproc = Threads.@spawn bg_visualise_enhanced(rc, fullscreen=fullscreen) bind(rc, visproc) vis = rc else @@ -335,15 +379,22 @@ save_images=false, visualise=true, recalculate=true, - verbose_iter=10, - fullscreen=false, - windowsize=(0.10*4/3, 0.10), + verbose_iter=1, + fullscreen=true, kwargs...) end demo_known1 = () -> demo(experiments_pdps_known[3]) demo_known2 = () -> demo(experiments_pdps_known[1]) demo_known3 = () -> demo(experiments_pdps_known[2]) + +demo_predictor1 = () -> demo(predictor_experiments_pdps_known[1]) # Proximal +demo_predictor2 = () -> demo(predictor_experiments_pdps_known[2]) # Rotation +demo_predictor3 = () -> demo(predictor_experiments_pdps_known[3]) # Greedy +demo_predictor4 = () -> demo(predictor_experiments_pdps_known[4]) # PrimalOnly +demo_predictor5 = () -> demo(predictor_experiments_pdps_known[5]) # NoPrediction +demo_predictor6 = () -> demo(predictor_experiments_pdps_known[6]) # DualScaling + demo_unknown1 = () -> demo(experiments_pdps_unknown_multi[3], plot_movement=true) demo_unknown2 = () -> demo(experiments_pdps_unknown_multi[1], plot_movement=true) demo_unknown3 = () -> demo(experiments_pdps_unknown_multi[2], plot_movement=true) @@ -357,6 +408,15 @@ kwargs...) end +function batchrun_predictors(kwargs...) + run_experiments(;experiments=predictor_experiments_all, + save_results=true, + save_images=true, + visualise=false, + recalculate=false, + kwargs...) +end + ###################################################### # Iterator that does visualisation and log collection ###################################################### @@ -394,13 +454,14 @@ value, x, v, vhist = calc_objective() entry = LogEntry(iter, tm, value, - sc*d.v_cumul_true[1], - sc*d.v_cumul_true[2], - sc*v[1], sc*v[2], + #sc*d.v_cumul_true[1], + #sc*d.v_cumul_true[2], + #sc*v[1], sc*v[2], assess_psnr(x, d.b_true), assess_ssim(x, d.b_true), - assess_psnr(d.b_noisy, d.b_true), - assess_ssim(d.b_noisy, d.b_true)) + #assess_psnr(d.b_noisy, d.b_true), + #assess_ssim(d.b_noisy, d.b_true) + ) # (**) Collect a singly-linked list of log to avoid array resizing # while iterating @@ -467,14 +528,10 @@ return st end -function bg_visualise_enhanced(rc; fullscreen=false, windowsize=nothing) +function bg_visualise_enhanced(rc; fullscreen=false) process_channel(rc) do d imgs, plot_movement, log, vhist = d - try - do_visualise(imgs, refresh=false, fullscreen=fullscreen, windowsize=windowsize) - catch err - print(err) - end + do_visualise(imgs, refresh=false, fullscreen=fullscreen) # Overlay movement GR.settextcolorind(5) GR.setcharheight(0.015)
