--- a
+++ b/code/scan.lua
@@ -0,0 +1,71 @@
+function scan_old(img, net, windowWidth, windowHeight, N)
+	img = img:double()
+
+	local imageHeight = img:size(2)
+	local imageWidth = img:size(3)
+
+	local map = torch.FloatTensor(imageHeight, imageWidth):fill(0)
+	local window = torch.Tensor(windowHeight, windowWidth)
+	if cuda then
+		window = window:cuda()
+	end
+	numWinRows = math.ceil((imageHeight-windowHeight+1)/N)
+	for i=1,numWinRows do	
+		window = torch.DoubleTensor(numWinRows, 3, windowHeight, windowWidth)
+		for j=1,numWinRows do
+			window[{{j}, {}, {}, {}}] = image.crop(img, N*(j-1), N*(i-1), N*(j-1)+windowWidth, N*(i-1)+windowHeight)
+		end
+		--normalize
+		mean = {}
+		stdv  = {}
+		for i=1,3 do
+			mean[i] = window[{{}, {i}, {}, {}  }]:mean()
+			window[{{}, {i}, {}, {}  }]:add(-mean[i])
+
+			stdv[i] = window[{{}, {i}, {}, {}  }]:std()
+			window[{{}, {i}, {}, {}  }]:div(stdv[i])
+		end
+		if cuda then
+			window = window:cuda()
+		end
+		local out = net:forward(window)
+		for j=1,numWinRows do
+			r1 = (i-1)*N+1+(windowHeight-1)/2
+			r2 = i*N+(windowHeight-1)/2
+			c1 = (j-1)*N+1+(windowWidth-1)/2
+			c2 = j*N+(windowWidth-1)/2
+			map[{{r1,r2},{c1,c2}}] = math.exp(out[j][1])
+			print(i,j,out[j][1])
+		end
+	end
+	return map
+end
+
+function scan(img, net)
+	dofile("padarray.lua")
+	pady = 50
+	padx = 50
+	img = padarray(img,{0,pady,padx},'mirror')
+
+	--normalize
+	mean = {}
+	stdv  = {}
+	for i=1,3 do
+		mean[i] = img[{{i}, {}, {}  }]:mean()
+		img[{{i}, {}, {}  }]:add(-mean[i])
+
+		stdv[i] = img[{{i}, {}, {}  }]:std()
+		img[{{i}, {}, {}  }]:div(stdv[i])
+	end
+	local out = net:forward(img)
+
+	map = torch.exp(out[{{1},{},{}}])
+	map = torch.squeeze(map)
+
+	--pad with zeros to match the size to the input size
+	pady = (img:size(2) - out:size(2))/2
+	padx = (img:size(3) - out:size(3))/2
+	--map = padarray(map,{pady, padx}, 'zero')
+
+	return map
+end