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diff --git a/README.md b/README.md
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+# torch-warp
+
+This repository contains a torch implementation for applying optical flow deformations to pairs of images in order to morph between images. The optical flow calculation and loading code is from [`manuelruder/artistic-videos`](https://github.com/manuelruder/artistic-videos), and is based on [DeepFlow](http://lear.inrialpes.fr/src/deepflow/). Theoretically, you could drop in another optical flow program which outputs `.flo` files in the [Middlebury format](http://vision.middlebury.edu/flow/data/).
+
+## Dependencies
+
+* torch7
+* DeepFlow and DeepMatching binaries in the current directory, as `deepflow2-static` and `deepmatching-static`
+
+I had very little luck getting DeepFlow to work on OS X, so I'm using a Docker image to run this.
+
+## Usage
+
+For input, you need two PNG images of the same dimensions named e.g. `filename_0.png` and `filename_1.png`. You can then run `./run-torchwarp.sh filename` to run all the steps and output the morphing animation as `morphed_filename.gif`.
+
+You can also use `./run-stereogranimator.sh ID` with an image ID from [NYPL's Stereogranimator](http://stereo.nypl.org/) to download an animated GIF and run it through the morphing process.
diff --git a/flowFileLoader.lua b/flowFileLoader.lua
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+require 'torch'
+require 'image'
+
+--[[
+  Reads a flow field from a binary flow file.
+  
+   bytes   contents
+    0-3     tag: "PIEH" in ASCII, which in little endian happens to be the float 202021.25
+            (just a sanity check that floats are represented correctly)
+    4-7     width as an integer
+    8-11    height as an integer
+    12-end  data (width*height*2*4 bytes total)
+--]]
+local function flowFileLoader_load(fileName, scale)
+  local flowFile = torch.DiskFile(fileName, 'r')
+  flowFile:binary()
+  flowFile:readFloat()
+  local W = flowFile:readInt()
+  local H = flowFile:readInt()
+  -- image.warp needs 2xHxW, and also expects (y, x) for some reason...
+  local flow = torch.Tensor(2, H, W)
+  for y=1, H do
+    for x=1, W do
+      flow[2][y][x] = flowFile:readFloat() * scale
+      flow[1][y][x] = flowFile:readFloat() * scale
+    end
+  end
+  flowFile:close()
+  return flow
+end
+
+return {
+  load = flowFileLoader_load
+}
diff --git a/run-sterogranimator.sh b/run-sterogranimator.sh
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+#!/bin/bash
+
+wget -O $1.gif "http://stereo.nypl.org/view/$1.gif?n=1"
+
+convert $1.gif -coalesce +adjoin $1_%01d.png
+
+./run-torchwarp.sh $1
diff --git a/run-torchwarp.sh b/run-torchwarp.sh
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+++ b/run-torchwarp.sh
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+#!/bin/bash
+
+BASENAME=$(basename $1)
+
+convert -verbose $1_0.png PNG24:${BASENAME}_normalized_0.png
+convert -verbose $1_1.png PNG24:${BASENAME}_normalized_1.png
+
+./makeOptFlow.sh ${BASENAME}_normalized_%01d.png $BASENAME 0
+
+SEQUENCE=`seq 0.00 0.05 1.00`
+
+for scale in $SEQUENCE; do
+  echo $scale
+  th torch_warp.lua \
+    -flow_file $BASENAME/backward_1_0.flo \
+    -source_image ${BASENAME}_normalized_0.png \
+    -output_image warped_${BASENAME}_0_$scale.png \
+    -scale $scale
+  th torch_warp.lua \
+    -flow_file $BASENAME/forward_0_1.flo \
+    -source_image ${BASENAME}_normalized_1.png \
+    -output_image warped_${BASENAME}_1_$scale.png \
+    -scale $(bc <<< "1.0-$scale")
+  convert warped_${BASENAME}_0_$scale.png warped_${BASENAME}_1_$scale.png -compose blend -define compose:args=$(bc <<< "100*$scale/1") -composite blended_${BASENAME}_$scale.png
+done
+
+convert $(ls blended_${BASENAME}_*.png) $(ls blended_${BASENAME}_*.png | tac | sed '1d;$d') -delay 10 -loop 0 morphed_$BASENAME.gif
diff --git a/torch_warp.lua b/torch_warp.lua
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+++ b/torch_warp.lua
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+require 'torch'
+require 'nn'
+require 'image'
+
+local flowFile = require 'flowFileLoader'
+
+local cmd = torch.CmdLine()
+
+cmd:option('-flow_file', 'examples/example.flo', 'Target optical flow file')
+cmd:option('-source_image', 'examples/example.png', 'Source image to warp')
+cmd:option('-output_image', 'example_output.png', 'Destination warped image')
+cmd:option('-scale', '0.5', 'Scale for optical flow, from 0-1')
+
+local function main(params)
+  local flow = flowFile.load(params.flow_file, params.scale)
+  local imageWarped = warpImage(image.load(params.source_image, 3), flow)
+  image.save(params.output_image, imageWarped)
+end
+
+-- warp a given image according to the given optical flow.
+-- Disocclusions at the borders will be filled with the VGG mean pixel.
+function warpImage(img, flow)
+  local mean_pixel = torch.DoubleTensor({123.68/256.0, 116.779/256.0, 103.939/256.0})
+  result = image.warp(img, flow, 'bilinear', true, 'pad', -1)
+  for x=1, result:size(2) do
+    for y=1, result:size(3) do
+      if result[1][x][y] == -1 and result[2][x][y] == -1 and result[3][x][y] == -1 then
+        result[1][x][y] = mean_pixel[1]
+        result[2][x][y] = mean_pixel[2]
+        result[3][x][y] = mean_pixel[3]
+      end
+    end
+  end
+  return result
+end
+
+local tmpParams = cmd:parse(arg)
+local params = nil
+local file = nil
+
+if tmpParams.args == '' or file == nil  then
+  params = cmd:parse(arg)
+else
+  local args = {}
+  io.input(file)
+  local argPos = 1
+  while true do
+    local line = io.read()
+    if line == nil then break end
+    if line:sub(0, 1) == '-' then
+      local splits = str_split(line, " ", 2)
+      args[argPos] = splits[1]
+      args[argPos + 1] = splits[2]
+      argPos = argPos + 2
+    end
+  end
+  for i=1, #arg do
+    args[argPos] = arg[i]
+    argPos = argPos + 1
+  end
+  params = cmd:parse(args)
+  io.close(file)
+end
+
+main(params)