--- a
+++ b/code/detectorDNN.lua
@@ -0,0 +1,149 @@
+require 'torch';
+require 'nn';
+require 'image';
+cuda = false
+require 'cutorch';
+require 'cunn';
+require 'sys';
+require 'csvigo';
+local dir = require 'pl.dir';
+
+local c = os.clock()
+local t = os.time()
+
+local folder = '/home/andrew/mitosis/data/MITOS/testing/'
+--local netPath1 = '/home/andrew/mitosis/data/nets/net.t7'
+local netPath1 = '/home/andrew/mitosis/data/nets/dnn1_fullset_aug_20i_lr05_lrd0005_m09_mini200_aeptgl.t7'
+local netPath2 = '/home/andrew/mitosis/data/nets/dnn2_fullset_aug_20i_lr05_lrd0005_m09_mini200_aeptgl.t7'
+
+local threshold = 0.1
+
+dofile("getImagePaths.lua")
+local imagePaths = getImagePaths(folder)
+
+if paths.dirp(folder .. 'results') == false then
+	paths.mkdir(folder .. 'results');
+end
+
+dofile("scan.lua")
+dofile("expand.lua")
+
+-- define circular kernel
+local d = 10
+local kernel = torch.FloatTensor(2*d+1,2*d+1):fill(0.001)
+for i=1,2*d+1 do
+	for j=1,2*d+1 do
+		if (i-d-1)^2 + (j-d-1)^2 >= d^2 then
+			kernel[i][j] = 0
+		end
+	end
+end
+
+for k,imagePath in ipairs(imagePaths) do
+	print(k)
+
+	local c1 = os.clock()
+	local t1 = os.time()
+
+	--local windowWidth = 101
+	--local windowHeight = 101
+
+	local net1 = torch.load(netPath1)
+	local net2 = torch.load(netPath2)
+	net1 = expand(net1)
+	net2 = expand(net2)
+	if cuda then
+		net1 = net1:cuda()
+		net2 = net2:cuda()
+	else
+		net1 = net1:float()
+		net2 = net2:float()
+	end
+
+	local img = image.load(imagePath, 3, 'float')
+	if cuda then
+		img = img:cuda()
+	else
+		img = img:float()
+	end
+
+	-- scan sixteen versions of the image
+	-- four rotations, paired with their reflections, and two neural nets
+	--]]
+	--[
+	local maps = {}
+	for i=1,4 do
+		for j=1,2 do
+			local tmp = image.rotate(img, (i-1)*math.pi/2)
+			if j == 2 then
+				tmp = image.hflip(tmp)
+				tmp = scan(tmp, net1)
+				tmp = image.hflip(tmp)
+			else
+				tmp = scan(tmp, net1)
+			end
+			maps[(i-1)*2+j] = image.rotate(tmp, -(i-1)*math.pi/2)
+		end
+	end
+	net1 = nil
+--[
+	for i=5,8 do	
+		for j=1,2 do
+			local tmp = image.rotate(img, (i-1)*math.pi/2)
+			if j == 2 then
+				tmp = image.hflip(tmp)
+				tmp = scan(tmp, net2)
+				tmp = image.hflip(tmp)
+			else
+				tmp = scan(tmp, net2)
+			end
+			maps[(i-1)*2+j] = image.rotate(tmp, -(i-1)*math.pi/2)
+		end
+	end
+	net2 = nil
+--]]
+
+	-- take the mean of the sixteen maps
+	--[
+	local sum = maps[1]
+	for i=2,#maps do
+		sum = sum + maps[i]
+	end
+	local map = sum/#maps
+	
+	map = image.convolve(map, kernel, 'same')
+	map = map/torch.max(map)					-- normalize
+
+	image.save('test.png',map)
+	--map = image.load('test.png',1,'float')
+
+	local results = {}
+	local m = 1
+	while true do
+		local ind = map:eq(map:max()):nonzero()
+		local row = ind[1][1]
+		local col = ind[1][2]
+		local val = map[row][col]
+		results[m] = {row, col, val}
+		m = m + 1
+		for i=-2*d,2*d do
+			for j=-2*d,2*d do
+				if i^2 + j^2 < 4*d^2 and row+i>0 and col+j>0 and row+i<=map:size(1) and col+j<=map:size(2) then
+					map[row+i][col+j] = 0
+				end
+			end
+		end
+		if val < threshold then
+			break
+		end
+	end
+
+	local outfile = paths.concat(folder, 'results', paths.basename(imagePath, paths.extname(imagePath)) .. '.csv')
+	csvigo.save(outfile, results)
+
+	print(os.clock()-c1)
+	print(os.time()-t1)
+end
+
+print(os.clock()-c)
+print(os.time()-t)