Merge pull request #16 from albarji/bugfix/output

Fixed bug with outfolder + configurable overlap + some minor improvements

README.md

@@ -51,6 +51,8 @@ of having a local structure such as
     styles/
         picasso.png
         vangogh.png
+    results/
+        something.md
         
 applying the *vangogh.png* style to the *docker.png* image amounts to
 
@@ -59,6 +61,14 @@ applying the *vangogh.png* style to the *docker.png* image amounts to
 You can provide several content and style images, in which case all cross-combinations will be generated.
 
     nvidia-docker run --rm -v $(pwd):/images albarji/neural-style --content contents/docker.png contents/whatever.jpg --style styles/vangogh.png styles/picasso.png
+
+By default all generated images are saved into the container **/images**. When running the commands above the results
+will get saved into the host local folder (through the mounted volume). If you want to locate the generated images at a 
+different folder you can use the --output parameters. Note that, similarly to the --content and --style arguments,
+the path provided in --output will be regarded as relative to the /images folder. For instance, using the local 
+structure from the example above, saving the generated images in the "results" folder will require the following:
+
+    nvidia-docker run --rm -v $(pwd):/images albarji/neural-style --content contents/docker.png --style styles/vangogh.png --output results
 
 ### Fine tuning the results
 
@@ -105,10 +115,11 @@ as smaller in the rendered image.
 #### Style weight
 
 Gatys algorithm allows to adjust the amount of style imposed over the content image, by means of the --sw parameter.
-By default a value of **10** is used, meaning the importance of the style is 10 times the importance of the content.
-Smaller weight values result in the transfer of colors, while higher values transfer textures and details of the style
+By default a value of **5** is used, meaning the importance of the style is 5 times the importance of the content.
+Smaller weight values result in the transfer of colors, while higher values transfer textures and even objects of the 
+style.
 
-If several weight values can be provided, all combinations will be generated. For instance, to generate the same
+If several weight values are provided, all combinations will be generated. For instance, to generate the same
 style transfer with three different weights, use
 
     nvidia-docker run --rm -v $(pwd):/images albarji/neural-style --content contents/docker.png --style styles/vangogh.png --sw 5 10 20

entrypoint.py

@@ -15,14 +15,15 @@
     --style STYLE_IMAGES: file or files with the styles to transfer
     --output OUTPUT_FOLDER: name of the output folder in which to save results
     --size SIZE: size of the output image. Default: content image size
-    --sw STYLE_WEIGHT (default 10): weight or list of weights of the style over the content, in range (0, inf)
+    --sw STYLE_WEIGHT (default 5): weight or list of weights of the style over the content, in range (0, inf)
     --ss STYLE_SCALE (default 1.0): scaling or list of scaling factors for the style images
     --alg ALGORITHM: style-transfer algorithm to use. Must be one of the following:
         gatys                   Highly detailed transfer, slow processing times (default)
         chen-schmidt            Fast patch-based style transfer
         chen-schmidt-inverse    Even faster aproximation to chen-schmidt through the use of an inverse network
     --tilesize TILE_SIZE: maximum size of each tile in the style transfer.
         If your GPU runs out of memory you should try reducing this value. Default: 400
+    --tileoverlap TILE_OVERLAP: overlap of tiles in the style transfer, measured in pixels. Default: 100
 
     Additionally provided parameters are carried on to the underlying algorithm.
     
@@ -42,6 +43,7 @@ def main(argv=None):
         weights = None
         stylescales = None
         tilesize = None
+        tileoverlap = None
         otherparams = []
 
         # Gather parameters
@@ -56,7 +58,7 @@ def main(argv=None):
                 i += len(styles) + 1
             # Other general parameters
             elif argv[i] == "--output":
-                savefolder = argv[i+1]
+                savefolder = "/images/" + argv[i+1]
                 i += 2
             elif argv[i] == "--alg":
                 alg = argv[i+1]
@@ -73,6 +75,9 @@ def main(argv=None):
             elif argv[i] == "--tilesize":
                 tilesize = int(argv[i+1])
                 i += 2
+            elif argv[i] == "--tileoverlap":
+                tileoverlap = int(argv[i+1])
+                i += 2
             # Help
             elif argv[i] == "--help":
                 print(HELP)
@@ -94,7 +99,11 @@ def main(argv=None):
         LOGGER.info("\tAlgorithm = %s" % alg)
         LOGGER.info("\tStyle weights = %s" % str(weights))
         LOGGER.info("\tStyle scales = %s" % str(stylescales))
-        styletransfer(contents, styles, savefolder, size, alg, weights, stylescales, tilesize, algparams=otherparams)
+        LOGGER.info("\tSize = %s" % str(size))
+        LOGGER.info("\tTile size = %s" % str(tilesize))
+        LOGGER.info("\tTile overlap = %s" % str(tileoverlap))
+        styletransfer(contents, styles, savefolder, size, alg, weights, stylescales, tilesize, tileoverlap,
+                      algparams=otherparams)
         return 1
 
     except Exception:

neuralstyle/algorithms.py

@@ -25,7 +25,8 @@
             "-content_weight", "100",
             "-save_iter", "10000",
             "-proto_file", "/app/neural-style/models/VGG_ILSVRC_19_layers_deploy.prototxt",
-            "-model_file", "/app/neural-style/models/VGG_ILSVRC_19_layers.caffemodel"
+            "-model_file", "/app/neural-style/models/VGG_ILSVRC_19_layers.caffemodel",
+            "-num_iterations", "500"
         ]
     },
     "chen-schmidt": {
@@ -47,7 +48,7 @@
 
 
 def styletransfer(contents, styles, savefolder, size=None, alg="gatys", weights=None, stylescales=None,
-                  maxtilesize=400, algparams=None):
+                  maxtilesize=400, tileoverlap=100, algparams=None):
     """General style transfer routine over multiple sets of options"""
     # Check arguments
     if alg not in ALGORITHMS.keys():
@@ -60,11 +61,13 @@ def styletransfer(contents, styles, savefolder, size=None, alg="gatys", weights=
         weights = [None]
     else:
         if weights is None:
-            weights = [10.0]
+            weights = [5.0]
     if stylescales is None:
         stylescales = [1.0]
     if maxtilesize is None:
         maxtilesize = 400
+    if tileoverlap is None:
+        tileoverlap = 100
     if algparams is None:
         algparams = []
 
@@ -77,11 +80,11 @@ def styletransfer(contents, styles, savefolder, size=None, alg="gatys", weights=
                                  stylescale=scale, algparams=algparams)
         # Else use a tiling strategy
         else:
-            neuraltile(content=content, style=style, outfile=outfile, size=size, maxtilesize=maxtilesize, alg=alg,
-                       weight=weight, stylescale=scale, algparams=algparams)
+            neuraltile(content=content, style=style, outfile=outfile, size=size, maxtilesize=maxtilesize,
+                       overlap=tileoverlap, alg=alg, weight=weight, stylescale=scale, algparams=algparams)
 
 
-def styletransfer_single(content, style, outfile, size=None, alg="gatys", weight=10.0, stylescale=1.0, algparams=None):
+def styletransfer_single(content, style, outfile, size=None, alg="gatys", weight=5.0, stylescale=1.0, algparams=None):
     """General style transfer routine over a single set of options"""
     workdir = TemporaryDirectory()
 
@@ -108,7 +111,7 @@ def styletransfer_single(content, style, outfile, size=None, alg="gatys", weight
     mergealpha(algfile, alphafile, outfile)
 
 
-def neuraltile(content, style, outfile, size=None, maxtilesize=400, overlap=50, alg="gatys", weight=10.0,
+def neuraltile(content, style, outfile, size=None, maxtilesize=400, overlap=100, alg="gatys", weight=5.0,
                stylescale=1.0, algparams=None):
     """Strategy to generate a high resolution image by running style transfer on overlapping image tiles"""
     LOGGER.info("Starting tiling strategy")

tests/algorithms_tests.py

@@ -106,7 +106,7 @@ def test_neuraltile():
     tmpdir = TemporaryDirectory()
     content = CONTENTS + "avila-walls.jpg"
     outfile = tmpdir.name + "/tiled.png"
-    neuraltile(content, STYLES + "cubism.jpg", outfile, alg="chen-schmidt-inverse", maxtilesize=400)
+    neuraltile(content, STYLES + "cubism.jpg", outfile, alg="chen-schmidt-inverse", maxtilesize=400, overlap=100)
     assert shape(outfile) == shape(content)