To reference or cite the objects present in the Software Heritage archive, permalinks based on SoftWare Heritage persistent IDentifiers (SWHIDs) must be used instead of copying and pasting the url from the address bar of the browser (as there is no guarantee the current URI scheme will remain the same over time).
Select below a type of object currently browsed in order to display its associated SWHID and permalink.
""" Normalize brightness for each time point Version Comment 0.1 hf, initial version 0.2 hf, don't use fitting 0.2.1 hf, medianfilter with repeat padding """ using Images using Statistics using LsqFit using Clustering """ Extract feature from image """ function extract_feature(imgs) z_depth, t_len = size(imgs)[3:4] low_line = zeros(t_len) mean_line = zeros(t_len) @inbounds Threads.@threads for t in 1:t_len nozeros_pixel = imgs[:,:,:,t][imgs[:,:,:,t].≠0] if length(nozeros_pixel) == 0 #println("no cell in time point $t") continue # skip empty stack end low_line[t] = minimum(nozeros_pixel) mean_line[t] = mean(nozeros_pixel) end low_line, mean_line end medianfilter1(v, ws) = [median(v[i:(i+ws-1)]) for i=1:(length(v)-ws+1) ] #medianfilter(v, ws) = medianfilter1(vcat(0,v,0),ws) medianfilter(v, ws) = medianfilter1(vcat(v, v, v[end]),ws) # repeat """ Use k-means to remove outliers Input raw data series, return index without outliers """ function kmeans_rm_outliers( raw_line ) # TODO: if only one cluster? t_len = length(raw_line) X = zeros(2, t_len) X[1, :] = fill(0.02, t_len) # just choose 0.02 in random. X[2, :] = raw_line; # forgive me just add one more dimension for k-means result = kmeans(X, 2; init=:kmpp) if result.counts> result.counts # chose the one have more points index = 1 else index = 2 end t_nooutliers = [ i for i in 1:t_len if result.assignments[i] == index ] end """ Removing outliers and smoothing feature """ function correct_feature( low_line, mean_line, fitted::Bool=false ) t_len = length(low_line) ws = 3 # use small window to remove spark low_line = medianfilter(low_line, ws) mean_line = medianfilter(mean_line, ws) # TODO: if only one cluster? Add additional judge by std(mean_line[1:end-1].-mean_line[2:end]) if fitted t_nooutliers = kmeans_rm_outliers(mean_line) mean_line_nooutliers = mean_line[t_nooutliers] # assume mean_line and low_line share same shape low_line_nooutliers = low_line[t_nooutliers] @. model(x, p) = p*exp(-x*p) # fit with expontional function fit_mean = curve_fit(model, t_nooutliers, mean_line_nooutliers, [0.003, 0.0003]) fit_low = curve_fit(model, t_nooutliers, low_line_nooutliers, [0.002, 0.0003]) corrected_mean_line = model(1:t_len, fit_mean.param) corrected_low_line = model(1:t_len, fit_low.param) # TODO: what if mean.(fit, no_outliers) ? else corrected_mean_line = mean_line corrected_low_line = low_line end corrected_low_line, corrected_mean_line end "Normalize nucleus to uniform intensity" function normalize(imgs) #z_depth = 20 #t_len = size(imgs) ÷ z_depth #d1_len, d2_len = size(imgs), size(imgs) d1_len, d2_len, z_depth, t_len = size(imgs) low_line, mean_line = extract_feature(imgs) corrected_low_line, corrected_mean_line = correct_feature(low_line, mean_line) imgs_norm = zeros(N0f16, size(imgs)) imgs_len = length(imgs_norm[:, :, :, 1]) @inbounds Threads.@threads for t in 1:t_len """ z = (t-1)*20+1 : 20*t imgs_norm[:,:,z] = ((imgs[:,:,z] .- corrected_low_line[t]) ./ (corrected_mean_line[t]-corrected_low_line[t]).*0.005 .+ 0.02) .* ( imgs[:, :, z] .>0) #print("$t ") """ tmp = view(imgs, :, :, :, t) tmp_norm = view(imgs_norm, :, :, :, t) for i in 1:imgs_len if tmp[i] > 0 tmp_norm_value = (tmp[i] - corrected_low_line[t]) / (corrected_mean_line[t] - corrected_low_line[t])*0.005 + 0.02 if tmp_norm_value < 0 tmp_norm[i] = 0 elseif tmp_norm_value > 1 tmp_norm[i] = 1 else tmp_norm[i]= tmp_norm_value end end end end imgs_norm, Dict("low_line"=>low_line, "mean_line"=>mean_line, "corrected_low_line"=>corrected_low_line, "corrected_mean_line"=>corrected_mean_line) end #@time s14c1 = load(File(format"TIFF", "$data_dir/$(cell_name)")); #@time imgs_norm, low_line, mean_line = normalize(s14c1); #plot(low_line) #plot!(low_line_raw, marker=(:star)) #plot!(mean_line) #plot!(mean_line_raw, marker=(:dot)) #plot!(ylim=(0,0.004))