"""
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[1], 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[1]> result.counts[2] # 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[1]*exp(-x*p[2]) # 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)[3] ÷ z_depth
    #d1_len, d2_len  = size(imgs)[1],  size(imgs)[2]
    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[14])"));
#@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))