Mercurial > repos > PredictPDPS / changeset
changeset
added zero dual predictor for denoising
Sun, 21 Apr 2024 19:04:00 +0300
- author
- Neil Dizon <neil.dizon@helsinki.fi>
- date
- Sun, 21 Apr 2024 19:04:00 +0300
- changeset 20
- 7d3a75b875fa
- parent 19
- 7c4dfeb48dd9
- child 21
- 97737e4e7197
added zero dual predictor for denoising
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/src/AlgorithmZeroDual.jl Sun Apr 21 19:04:00 2024 +0300 @@ -0,0 +1,125 @@ +#################################################################### +# Predictive online PDPS for optical flow with known velocity field +#################################################################### + +__precompile__() + +module AlgorithmZeroDual + +identifier = "pdps_known_zerodual" + +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) + y .= zeros(size(y)...) + + ############ + # 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/PET/PlotResults.jl Sun Apr 21 19:04:00 2024 +0300 @@ -0,0 +1,561 @@ +__precompile__() + +module PlotResults + + +######################## +# Load external modules +######################## + +using DelimitedFiles, CSV, DataFrames +using PlotlyJS + +export fv_plot, ssim_plot, psnr_plot + +global mystart = 38 +global myend = 75 + +function fv_plot(name :: String, save_plot::Bool=true) + save_path = "./img/$(name)256x256_pdps_known_fv_plot.html" + ################################################# + orig = Vector{GenericTrace{Dict{Symbol, Any}}}() + ################################################# + directory_path = "./img/" + files = readdir(directory_path) + filtered_files = filter(file -> startswith(file, "$(name)256x256_pdps_known_proximal") && endswith(file, "0.txt"), files) + + # Define an array of line styles and colors + # line_styles = ["solid", "dash", "dot", "dashdot", "longdash"] + line_colors = ["blue", "red", "green", "orange", "purple", "cyan", "magenta", "yellow", "grey"] + + for (index,file) in enumerate(filtered_files) + filename = directory_path*file + #data = readdlm(filename, '\t', skipstart=1) + data = CSV.File(filename, delim='\t'; header = 2) |> DataFrame + + # Extract the columns you want to plot + X = Int64.(data[mystart:myend,:iter]) + Y = Float64.(data[mystart:myend, :function_value]) + + #line_style = line_styles[i] + line_color = line_colors[index] + + trace = PlotlyJS.scatter(;x=X, y=Y, mode="lines", hovertemplate="%{x:.0f},%{y:.1f}", + line_color=line_color, line_dash="dot", name="proxi") + push!(orig, trace) + end + + ##################################################### + identity = Vector{GenericTrace{Dict{Symbol, Any}}}() + ##################################################### + directory_path = "./img/" + files = readdir(directory_path) + filtered_files = filter(file -> startswith(file, "$(name)256x256_pdps_known_primalonly") && endswith(file, "0.txt"), files) + + # Define an array of line styles and colors + # line_styles = ["solid", "dash", "dot", "dashdot", "longdash"] + line_colors = ["blue", "red", "green", "orange", "purple", "cyan", "magenta", "yellow", "grey"] + + for (index,file) in enumerate(filtered_files) + filename = directory_path*file + #data = readdlm(filename, '\t', skipstart=1) + data = CSV.File(filename, delim='\t'; header = 2) |> DataFrame + + # Extract the columns you want to plot + X = Int64.(data[mystart:myend,:iter]) + Y = Float64.(data[mystart:myend, :function_value]) + + #line_style = line_styles[i] + line_color = line_colors[index] + + trace = PlotlyJS.scatter(;x=X, y=Y, mode="lines", hovertemplate="%{x:.0f},%{y:.1f}", + line_color=line_color, line_dash="dashdot", name="primo") + push!(identity, trace) + end + + + ##################################################### + adhoc = Vector{GenericTrace{Dict{Symbol, Any}}}() + ##################################################### + directory_path = "./img/" + files = readdir(directory_path) + filtered_files = filter(file -> startswith(file, "$(name)256x256_pdps_known_greedy") && endswith(file, "0.txt"), files) + + # Define an array of line styles and colors + # line_styles = ["solid", "dash", "dot", "dashdot", "longdash"] + line_colors = ["blue", "red", "green", "orange", "purple", "cyan", "magenta", "yellow", "grey"] + + for (index,file) in enumerate(filtered_files) + filename = directory_path*file + #data = readdlm(filename, '\t', skipstart=1) + data = CSV.File(filename, delim='\t'; header = 2) |> DataFrame + + # Extract the columns you want to plot + X = Int64.(data[mystart:myend,:iter]) + Y = Float64.(data[mystart:myend, :function_value]) + + #line_style = line_styles[i] + line_color = line_colors[index] + + trace = PlotlyJS.scatter(;x=X, y=Y, mode="lines", hovertemplate="%{x:.0f},%{y:.1f}", + line_color=line_color, line_dash="dash", name="greed τ") + push!(identity, trace) + end + + + ##################################################### + rotation = Vector{GenericTrace{Dict{Symbol, Any}}}() + ##################################################### + directory_path = "./img/" + files = readdir(directory_path) + filtered_files = filter(file -> startswith(file, "$(name)256x256_pdps_known_rotation") && endswith(file, "0.txt"), files) + + # Define an array of line styles and colors + # line_styles = ["solid", "dash", "dot", "dashdot", "longdash"] + line_colors = ["blue", "red", "green", "orange", "purple", "cyan", "magenta", "yellow", "grey"] + + for (index,file) in enumerate(filtered_files) + filename = directory_path*file + #data = readdlm(filename, '\t', skipstart=1) + data = CSV.File(filename, delim='\t'; header = 2) |> DataFrame + + # Extract the columns you want to plot + X = Int64.(data[mystart:myend,:iter]) + Y = Float64.(data[mystart:myend, :function_value]) + + #line_style = line_styles[i] + line_color = line_colors[index] + + trace = PlotlyJS.scatter(;x=X, y=Y, mode="lines", hovertemplate="%{x:.0f},%{y:.1f}", + line_color=line_color, line_dash="longdashdot", name="rotat") + push!(identity, trace) + end + + + ##################################################### + affine = Vector{GenericTrace{Dict{Symbol, Any}}}() + ##################################################### + directory_path = "./img/" + files = readdir(directory_path) + filtered_files = filter(file -> startswith(file, "$(name)256x256_pdps_known_dualscaling") && endswith(file, "0.txt"), files) + + # Define an array of line styles and colors + # line_styles = ["solid", "dash", "dot", "dashdot", "longdash"] + line_colors = ["blue", "red", "green", "orange", "purple", "cyan", "magenta", "yellow", "grey"] + + for (index,file) in enumerate(filtered_files) + filename = directory_path*file + #data = readdlm(filename, '\t', skipstart=1) + data = CSV.File(filename, delim='\t'; header = 2) |> DataFrame + + # Extract the columns you want to plot + X = Int64.(data[mystart:myend,:iter]) + Y = Float64.(data[mystart:myend, :function_value]) + + #line_style = line_styles[i] + line_color = line_colors[index] + + trace = PlotlyJS.scatter(;x=X, y=Y, mode="lines", hovertemplate="%{x:.0f},%{y:.1f}", + line_color=line_color, line_dash="solid", name="dusca") + push!(identity, trace) + end + + layout = Layout(legend_title_text="Function values") # Set legend title + + if save_plot && !isempty(save_path) + plotlyjs = plot([orig;identity;adhoc;rotation;affine], layout) + open(save_path, "w") do io + PlotlyBase.to_html(io, plotlyjs.plot) + end + elseif save_plot + println("Please provide a valid save path.") + end + + return plot([orig;identity;adhoc;rotation;affine],layout) +end + + +######################################################### +# FUNCTION FOR SSIM +######################################################### +function ssim_plot(name :: String, save_plot::Bool=true) + save_path = "./img/$(name)256x256_pdps_known_ssim_plot.html" + ################################################# + orig = Vector{GenericTrace{Dict{Symbol, Any}}}() + ################################################# + directory_path = "./img/" + files = readdir(directory_path) + filtered_files = filter(file -> startswith(file, "$(name)256x256_pdps_known_proximal") && endswith(file, "0.txt"), files) + + # Define an array of line styles and colors + # line_styles = ["solid", "dash", "dot", "dashdot", "longdash"] + line_colors = ["blue", "red", "green", "orange", "purple", "cyan", "magenta", "yellow", "grey"] + + for (index,file) in enumerate(filtered_files) + filename = directory_path*file + #data = readdlm(filename, '\t', skipstart=1) + data = CSV.File(filename, delim='\t'; header = 2) |> DataFrame + + # Extract the columns you want to plot + X = Int64.(data[mystart:myend,:iter]) + Y = Float64.(data[mystart:myend, :ssim]) + + #line_style = line_styles[i] + line_color = line_colors[index] + + trace = PlotlyJS.scatter(;x=X, y=Y, mode="lines", hovertemplate="%{x:.0f},%{y:.1f}", + line_color=line_color, line_dash="dot", name="proxi") + push!(orig, trace) + end + + ##################################################### + identity = Vector{GenericTrace{Dict{Symbol, Any}}}() + ##################################################### + directory_path = "./img/" + files = readdir(directory_path) + filtered_files = filter(file -> startswith(file, "$(name)256x256_pdps_known_primalonly") && endswith(file, "0.txt"), files) + + # Define an array of line styles and colors + # line_styles = ["solid", "dash", "dot", "dashdot", "longdash"] + line_colors = ["blue", "red", "green", "orange", "purple", "cyan", "magenta", "yellow", "grey"] + + for (index,file) in enumerate(filtered_files) + filename = directory_path*file + #data = readdlm(filename, '\t', skipstart=1) + data = CSV.File(filename, delim='\t'; header = 2) |> DataFrame + + # Extract the columns you want to plot + X = Int64.(data[mystart:myend,:iter]) + Y = Float64.(data[mystart:myend, :ssim]) + + #line_style = line_styles[i] + line_color = line_colors[index] + + trace = PlotlyJS.scatter(;x=X, y=Y, mode="lines", hovertemplate="%{x:.0f},%{y:.1f}", + line_color=line_color, line_dash="dashdot", name="primo") + push!(identity, trace) + end + + + ##################################################### + adhoc = Vector{GenericTrace{Dict{Symbol, Any}}}() + ##################################################### + directory_path = "./img/" + files = readdir(directory_path) + filtered_files = filter(file -> startswith(file, "$(name)256x256_pdps_known_greedy") && endswith(file, "0.txt"), files) + + # Define an array of line styles and colors + # line_styles = ["solid", "dash", "dot", "dashdot", "longdash"] + line_colors = ["blue", "red", "green", "orange", "purple", "cyan", "magenta", "yellow", "grey"] + + for (index,file) in enumerate(filtered_files) + filename = directory_path*file + #data = readdlm(filename, '\t', skipstart=1) + data = CSV.File(filename, delim='\t'; header = 2) |> DataFrame + + # Extract the columns you want to plot + X = Int64.(data[mystart:myend,:iter]) + Y = Float64.(data[mystart:myend, :ssim]) + + #line_style = line_styles[i] + line_color = line_colors[index] + + trace = PlotlyJS.scatter(;x=X, y=Y, mode="lines", hovertemplate="%{x:.0f},%{y:.1f}", + line_color=line_color, line_dash="dash", name="greed") + push!(identity, trace) + end + + + ##################################################### + rotation = Vector{GenericTrace{Dict{Symbol, Any}}}() + ##################################################### + directory_path = "./img/" + files = readdir(directory_path) + filtered_files = filter(file -> startswith(file, "$(name)256x256_pdps_known_rotation") && endswith(file, "0.txt"), files) + + # Define an array of line styles and colors + # line_styles = ["solid", "dash", "dot", "dashdot", "longdash"] + line_colors = ["blue", "red", "green", "orange", "purple", "cyan", "magenta", "yellow", "grey"] + + for (index,file) in enumerate(filtered_files) + filename = directory_path*file + #data = readdlm(filename, '\t', skipstart=1) + data = CSV.File(filename, delim='\t'; header = 2) |> DataFrame + + # Extract the columns you want to plot + X = Int64.(data[mystart:myend,:iter]) + Y = Float64.(data[mystart:myend, :ssim]) + + #line_style = line_styles[i] + line_color = line_colors[index] + + trace = PlotlyJS.scatter(;x=X, y=Y, mode="lines", hovertemplate="%{x:.0f},%{y:.1f}", + line_color=line_color, line_dash="longdashdot", name="rotat") + push!(identity, trace) + end + + + ##################################################### + affine = Vector{GenericTrace{Dict{Symbol, Any}}}() + ##################################################### + directory_path = "./img/" + files = readdir(directory_path) + filtered_files = filter(file -> startswith(file, "$(name)256x256_pdps_known_dualscaling") && endswith(file, "0.txt"), files) + + # Define an array of line styles and colors + # line_styles = ["solid", "dash", "dot", "dashdot", "longdash"] + line_colors = ["blue", "red", "green", "orange", "purple", "cyan", "magenta", "yellow", "grey"] + + for (index,file) in enumerate(filtered_files) + filename = directory_path*file + #data = readdlm(filename, '\t', skipstart=1) + data = CSV.File(filename, delim='\t'; header = 2) |> DataFrame + + # Extract the columns you want to plot + X = Int64.(data[mystart:myend,:iter]) + Y = Float64.(data[mystart:myend, :ssim]) + + #line_style = line_styles[i] + line_color = line_colors[index] + + trace = PlotlyJS.scatter(;x=X, y=Y, mode="lines", hovertemplate="%{x:.0f},%{y:.1f}", + line_color=line_color, line_dash="solid", name="dusca") + push!(identity, trace) + end + + ##################################################### + zerodual = Vector{GenericTrace{Dict{Symbol, Any}}}() + ##################################################### + directory_path = "./img/" + files = readdir(directory_path) + filtered_files = filter(file -> startswith(file, "$(name)256x256_pdps_known_zerodual") && endswith(file, "0.txt"), files) + + # Define an array of line styles and colors + # line_styles = ["solid", "dash", "dot", "dashdot", "longdash"] + line_colors = ["blue", "red", "green", "orange", "purple", "cyan", "magenta", "yellow", "grey"] + + for (index,file) in enumerate(filtered_files) + filename = directory_path*file + #data = readdlm(filename, '\t', skipstart=1) + data = CSV.File(filename, delim='\t'; header = 2) |> DataFrame + + # Extract the columns you want to plot + X = Int64.(data[mystart:myend,:iter]) + Y = Float64.(data[mystart:myend, :ssim]) + + #line_style = line_styles[i] + line_color = line_colors[index] + + trace = PlotlyJS.scatter(;x=X, y=Y, mode="lines", hovertemplate="%{x:.0f},%{y:.1f}", + line_color=line_color, line_dash="longdash", name="zerod") + push!(identity, trace) + end + + layout = Layout(yaxis_type="log", legend_title_text="SSIM") # Set legend title + + if save_plot && !isempty(save_path) + plotlyjs = plot([orig;identity;adhoc;rotation;affine;zerodual], layout) + open(save_path, "w") do io + PlotlyBase.to_html(io, plotlyjs.plot) + end + elseif save_plot + println("Please provide a valid save path.") + end + + return plot([orig;identity;adhoc;rotation;affine;zerodual],layout) +end + + + +######################################################### +# FUNCTION FOR PSNR +######################################################### +function psnr_plot(name :: String, save_plot::Bool=true) + save_path = "./img/$(name)256x256_pdps_known_psnr_plot.html" + ################################################# + orig = Vector{GenericTrace{Dict{Symbol, Any}}}() + ################################################# + directory_path = "./img/" + files = readdir(directory_path) + filtered_files = filter(file -> startswith(file, "$(name)256x256_pdps_known_proximal") && endswith(file, "0.txt"), files) + + # Define an array of line styles and colors + # line_styles = ["solid", "dash", "dot", "dashdot", "longdash"] + line_colors = ["blue", "red", "green", "orange", "purple", "cyan", "magenta", "yellow", "grey"] + + for (index,file) in enumerate(filtered_files) + filename = directory_path*file + #data = readdlm(filename, '\t', skipstart=1) + data = CSV.File(filename, delim='\t'; header = 2) |> DataFrame + + # Extract the columns you want to plot + X = Int64.(data[mystart:myend,:iter]) + Y = Float64.(data[mystart:myend, :psnr]) + + #line_style = line_styles[i] + line_color = line_colors[index] + + trace = PlotlyJS.scatter(;x=X, y=Y, mode="lines", hovertemplate="%{x:.0f},%{y:.1f}", + line_color=line_color, line_dash="dot", name="proxi") + push!(orig, trace) + end + + ##################################################### + identity = Vector{GenericTrace{Dict{Symbol, Any}}}() + ##################################################### + directory_path = "./img/" + files = readdir(directory_path) + filtered_files = filter(file -> startswith(file, "$(name)256x256_pdps_known_primalonly") && endswith(file, "0.txt"), files) + + # Define an array of line styles and colors + # line_styles = ["solid", "dash", "dot", "dashdot", "longdash"] + line_colors = ["blue", "red", "green", "orange", "purple", "cyan", "magenta", "yellow", "grey"] + + for (index,file) in enumerate(filtered_files) + filename = directory_path*file + #data = readdlm(filename, '\t', skipstart=1) + data = CSV.File(filename, delim='\t'; header = 2) |> DataFrame + + # Extract the columns you want to plot + X = Int64.(data[mystart:myend,:iter]) + Y = Float64.(data[mystart:myend, :psnr]) + + #line_style = line_styles[i] + line_color = line_colors[index] + + trace = PlotlyJS.scatter(;x=X, y=Y, mode="lines", hovertemplate="%{x:.0f},%{y:.1f}", + line_color=line_color, line_dash="dashdot", name="primo") + push!(identity, trace) + end + + + ##################################################### + adhoc = Vector{GenericTrace{Dict{Symbol, Any}}}() + ##################################################### + directory_path = "./img/" + files = readdir(directory_path) + filtered_files = filter(file -> startswith(file, "$(name)256x256_pdps_known_greedy") && endswith(file, "0.txt"), files) + + # Define an array of line styles and colors + # line_styles = ["solid", "dash", "dot", "dashdot", "longdash"] + line_colors = ["blue", "red", "green", "orange", "purple", "cyan", "magenta", "yellow", "grey"] + + for (index,file) in enumerate(filtered_files) + filename = directory_path*file + #data = readdlm(filename, '\t', skipstart=1) + data = CSV.File(filename, delim='\t'; header = 2) |> DataFrame + + # Extract the columns you want to plot + X = Int64.(data[mystart:myend,:iter]) + Y = Float64.(data[mystart:myend, :psnr]) + + #line_style = line_styles[i] + line_color = line_colors[index] + + trace = PlotlyJS.scatter(;x=X, y=Y, mode="lines", hovertemplate="%{x:.0f},%{y:.1f}", + line_color=line_color, line_dash="dash", name="greed") + push!(identity, trace) + end + + + ##################################################### + rotation = Vector{GenericTrace{Dict{Symbol, Any}}}() + ##################################################### + directory_path = "./img/" + files = readdir(directory_path) + filtered_files = filter(file -> startswith(file, "$(name)256x256_pdps_known_rotation") && endswith(file, "0.txt"), files) + + # Define an array of line styles and colors + # line_styles = ["solid", "dash", "dot", "dashdot", "longdash"] + line_colors = ["blue", "red", "green", "orange", "purple", "cyan", "magenta", "yellow", "grey"] + + for (index,file) in enumerate(filtered_files) + filename = directory_path*file + #data = readdlm(filename, '\t', skipstart=1) + data = CSV.File(filename, delim='\t'; header = 2) |> DataFrame + + # Extract the columns you want to plot + X = Int64.(data[mystart:myend,:iter]) + Y = Float64.(data[mystart:myend, :psnr]) + + #line_style = line_styles[i] + line_color = line_colors[index] + + trace = PlotlyJS.scatter(;x=X, y=Y, mode="lines", hovertemplate="%{x:.0f},%{y:.1f}", + line_color=line_color, line_dash="longdashdot", name="rotat") + push!(identity, trace) + end + + ##################################################### + affine = Vector{GenericTrace{Dict{Symbol, Any}}}() + ##################################################### + directory_path = "./img/" + files = readdir(directory_path) + filtered_files = filter(file -> startswith(file, "$(name)256x256_pdps_known_dualscaling") && endswith(file, "0.txt"), files) + + # Define an array of line styles and colors + # line_styles = ["solid", "dash", "dot", "dashdot", "longdash"] + line_colors = ["blue", "red", "green", "orange", "purple", "cyan", "magenta", "yellow", "grey"] + + for (index,file) in enumerate(filtered_files) + filename = directory_path*file + #data = readdlm(filename, '\t', skipstart=1) + data = CSV.File(filename, delim='\t'; header = 2) |> DataFrame + + # Extract the columns you want to plot + X = Int64.(data[mystart:myend,:iter]) + Y = Float64.(data[mystart:myend, :psnr]) + + #line_style = line_styles[i] + line_color = line_colors[index] + + trace = PlotlyJS.scatter(;x=X, y=Y, mode="lines", hovertemplate="%{x:.0f},%{y:.1f}", + line_color=line_color, line_dash="solid", name="dusca") + push!(identity, trace) + end + + ##################################################### + zerodual = Vector{GenericTrace{Dict{Symbol, Any}}}() + ##################################################### + directory_path = "./img/" + files = readdir(directory_path) + filtered_files = filter(file -> startswith(file, "$(name)256x256_pdps_known_zerodual") && endswith(file, "0.txt"), files) + + # Define an array of line styles and colors + # line_styles = ["solid", "dash", "dot", "dashdot", "longdash"] + line_colors = ["blue", "red", "green", "orange", "purple", "cyan", "magenta", "yellow", "grey"] + + for (index,file) in enumerate(filtered_files) + filename = directory_path*file + #data = readdlm(filename, '\t', skipstart=1) + data = CSV.File(filename, delim='\t'; header = 2) |> DataFrame + + # Extract the columns you want to plot + X = Int64.(data[mystart:myend,:iter]) + Y = Float64.(data[mystart:myend, :psnr]) + + #line_style = line_styles[i] + line_color = line_colors[index] + + trace = PlotlyJS.scatter(;x=X, y=Y, mode="lines", hovertemplate="%{x:.0f},%{y:.1f}", + line_color=line_color, line_dash="longdash", name="zerod") + push!(identity, trace) + end + + + layout = Layout(yaxis_type="log", legend_title_text="PSNR") # Set legend title + + if save_plot && !isempty(save_path) + plotlyjs = plot([orig;identity;adhoc;rotation;affine; zerodual], layout) + open(save_path, "w") do io + PlotlyBase.to_html(io, plotlyjs.plot) + end + elseif save_plot + println("Please provide a valid save path.") + end + + return plot([orig;identity;adhoc;rotation;affine;zerodual],layout) +end + + +end # Module \ No newline at end of file
--- a/src/PredictPDPS.jl Sun Apr 21 18:55:00 2024 +0300 +++ b/src/PredictPDPS.jl Sun Apr 21 19:04:00 2024 +0300 @@ -87,7 +87,7 @@ batchrun_denoising, batchrun_predictors, demo_denoising1, demo_denoising2, demo_denoising3, - demo_denoising4, demo_denoising5, demo_denoising6, + demo_denoising4, demo_denoising5, demo_denoising6, demo_denoising7, demo_petS1, demo_petS2, demo_petS3, demo_petS4, demo_petS5, demo_petS6, demo_petS7, demo_petB1, demo_petB2, demo_petB3, @@ -223,7 +223,9 @@ Experiment(AlgorithmProximal, DisplacementConstant, lighthouse, p_known₀_denoising ⬿ (phantom_ρ = 100,)), Experiment(AlgorithmRotation, DisplacementConstant, lighthouse, - p_known₀_denoising), + p_known₀_denoising), + Experiment(AlgorithmZeroDual, DisplacementConstant, lighthouse, + p_known₀_denoising), ) const denoising_experiments_all = Iterators.flatten(( @@ -414,6 +416,7 @@ demo_denoising4 = () -> demo(denoising_experiments_pdps_known[4]) # Primal Only demo_denoising5 = () -> demo(denoising_experiments_pdps_known[5]) # Proximal (old) demo_denoising6 = () -> demo(denoising_experiments_pdps_known[6]) # Rotation +demo_denoising7 = () -> demo(denoising_experiments_pdps_known[7]) # Zero dual function batchrun_article(kwargs...) run_experiments(;experiments=experiments_all,
