add a little support for video

README.md

@@ -144,6 +144,33 @@ th waifu2x.lua -m noise_scale -noise_level 2 -i input_image.png -o output_image.
 
 See also `images/gen.sh`.
 
+### Video Encoding
+
+\* `avconv` is `ffmpeg` on Ubuntu 14.04.
+
+Extracting images and audio from a video. (range: 00:09:00 ~ 00:12:00)
+```
+mkdir frames
+avconv -i data/raw.avi -ss 00:09:00 -t 00:03:00 -r 24 -f image2 frames/%06d.png
+avconv -i data/raw.avi -ss 00:09:00 -t 00:03:00 audio.mp3
+```
+
+Generating a image list.
+```
+find ./frames -name "*.png" |sort > data/frame.txt
+```
+
+waifu2x (for example, noise reduction)
+```
+mkdir new_frames
+th waifu2x.lua -m noise -noise_level 1 -l data/frame.txt -o new_frames/%d.png
+```
+
+Generating a video from waifu2xed images and audio.
+```
+avconv -f image2 -r 24 -i new_frames/%d.png -i audio.mp3 -r 24 -vcodec libx264 -crf 16 video.mp4
+```
+
 ## Training Your Own Model
 
 ### Data Preparation

waifu2x.lua

@@ -11,50 +11,110 @@ local BLOCK_OFFSET = 7
 
 torch.setdefaulttensortype('torch.FloatTensor')
 
-local function waifu2x()
-   local cmd = torch.CmdLine()
-   cmd:text()
-   cmd:text("waifu2x")
-   cmd:text("Options:")
-   cmd:option("-i", "images/miku_small.png", 'path of input image')
-   cmd:option("-o", "(auto)", 'path of output')
-   cmd:option("-model_dir", "./models", 'model directory')
-   cmd:option("-m", "noise_scale", 'method (noise|scale|noise_scale)')
-   cmd:option("-noise_level", 1, '(1|2)')
-   cmd:option("-crop_size", 128, 'crop size')
-   local opt = cmd:parse(arg)   
+local function convert_image(opt)
+   local x = image_loader.load_float(opt.i)
+   local new_x = nil
+   local t = sys.clock()
+   
    if opt.o == "(auto)" then
       local name = path.basename(opt.i)
       local e = path.extension(name)
       local base = name:sub(0, name:len() - e:len())
       opt.o = path.join(path.dirname(opt.i), string.format("%s(%s).png", base, opt.m))
    end
-   
-   local x = image_loader.load_float(opt.i)
-   local new_x = nil
-   local t = sys.clock()
    if opt.m == "noise" then
       local model = torch.load(path.join(opt.model_dir,
 					 ("noise%d_model.t7"):format(opt.noise_level)), "ascii")
       model:evaluate()
       new_x = reconstruct.image(model, x, BLOCK_OFFSET)
    elseif opt.m == "scale" then
-      local model = torch.load(path.join(opt.model_dir, "scale2.0x_model.t7"), "ascii")
+      local model = torch.load(path.join(opt.model_dir, ("scale%.1fx_model.t7"):format(opt.scale)), "ascii")
       model:evaluate()
-      new_x = reconstruct.scale(model, 2.0, x, BLOCK_OFFSET)
+      new_x = reconstruct.scale(model, opt.scale, x, BLOCK_OFFSET)
    elseif opt.m == "noise_scale" then
       local noise_model = torch.load(path.join(opt.model_dir,
 					       ("noise%d_model.t7"):format(opt.noise_level)), "ascii")
-      local scale_model = torch.load(path.join(opt.model_dir, "scale2.0x_model.t7"), "ascii")
-
+      local scale_model = torch.load(path.join(opt.model_dir, ("scale%.1fx_model.t7"):format(opt.scale)), "ascii")
       noise_model:evaluate()
       scale_model:evaluate()
       x = reconstruct.image(noise_model, x, BLOCK_OFFSET)
-      new_x = reconstruct.scale(scale_model, 2.0, x, BLOCK_OFFSET)
+      new_x = reconstruct.scale(scale_model, opt.scale, x, BLOCK_OFFSET)
    else
       error("undefined method:" .. opt.method)
    end
    image.save(opt.o, new_x)
    print(opt.o .. ": " .. (sys.clock() - t) .. " sec")
 end
+local function convert_frames(opt)
+   local noise1_model = torch.load(path.join(opt.model_dir, "noise1_model.t7"), "ascii")
+   local noise2_model = torch.load(path.join(opt.model_dir, "noise2_model.t7"), "ascii")
+   local scale_model = torch.load(path.join(opt.model_dir, ("scale%.1fx_model.t7"):format(opt.scale)), "ascii")
+
+   noise1_model:evaluate()
+   noise2_model:evaluate()
+   scale_model:evaluate()
+   
+   local fp = io.open(opt.l)
+   local count = 0
+   local lines = {}
+   for line in fp:lines() do
+      table.insert(lines, line)
+   end
+   fp:close()
+   for i = 1, #lines do
+      local x = image_loader.load_float(lines[i])
+      local new_x = nil
+      if opt.m == "noise" and opt.noise_level == 1 then
+	 new_x = reconstruct.image(noise1_model, x, BLOCK_OFFSET)
+      elseif opt.m == "noise" and opt.noise_level == 2 then
+	 new_x = reconstruct.image(noise2_model, x, BLOCK_OFFSET)
+      elseif opt.m == "scale" then
+	 new_x = reconstruct.scale(scale_model, opt.scale, x, BLOCK_OFFSET)
+      elseif opt.m == "noise_scale" and opt.noise_level == 1 then
+	 x = reconstruct.image(noise1_model, x, BLOCK_OFFSET)
+	 new_x = reconstruct.scale(scale_model, opt.scale, x, BLOCK_OFFSET)
+      elseif opt.m == "noise_scale" and opt.noise_level == 2 then
+	 x = reconstruct.image(noise2_model, x, BLOCK_OFFSET)
+	 new_x = reconstruct.scale(scale_model, opt.scale, x, BLOCK_OFFSET)
+      else
+	 error("undefined method:" .. opt.method)
+      end
+      local output = nil
+      if opt.o == "(auto)" then
+	 local name = path.basename(lines[i])
+	 local e = path.extension(name)
+	 local base = name:sub(0, name:len() - e:len())
+	 output = path.join(path.dirname(opt.i), string.format("%s(%s).png", base, opt.m))
+      else
+	 output = string.format(opt.o, i)
+      end
+      image.save(output, new_x)
+      xlua.progress(i, #lines)
+      if i % 10 == 0 then
+	 collectgarbage()
+      end
+   end
+end
+
+local function waifu2x()
+   local cmd = torch.CmdLine()
+   cmd:text()
+   cmd:text("waifu2x")
+   cmd:text("Options:")
+   cmd:option("-i", "images/miku_small.png", 'path of the input image')
+   cmd:option("-l", "", 'path of the image-list')
+   cmd:option("-scale", 2, 'scale factor')
+   cmd:option("-o", "(auto)", 'path of the output file')
+   cmd:option("-model_dir", "./models", 'model directory')
+   cmd:option("-m", "noise_scale", 'method (noise|scale|noise_scale)')
+   cmd:option("-noise_level", 1, '(1|2)')
+   cmd:option("-crop_size", 128, 'patch size per process')
+   
+   local opt = cmd:parse(arg)
+   if string.len(opt.l) == 0 then
+      convert_image(opt)
+   else
+      convert_frames(opt)
+   end
+end
 waifu2x()