Makefile.am added a rough implementation of the foreground extraxtion

2005-07-16  Sven Neumann  <sven@gimp.org>

	* Makefile.am
	* plug-ins/benchmark-foreground-extract.py: added a rough
	implementation of the foreground extraxtion benchmark as proposed
	in the GrabCut paper. See comments in the file for details.

ChangeLog

 2005-07-15  Carol Spears  <carol@gimp.org>
 
-	* docs/Wilber_Construction_Kit.xcf.gz:  corrected the spelling of the word tassel.
+	* docs/Wilber_Construction_Kit.xcf.gz: corrected the spelling of
+	the word tassel.
 
 2005-07-15  Carol Spears  <carol@gimp.org>
 

plug-ins/pygimp/ChangeLog

+2005-07-16  Sven Neumann  <sven@gimp.org>
+
+	* Makefile.am
+	* plug-ins/benchmark-foreground-extract.py: added a rough
+	implementation of the foreground extraxtion benchmark as proposed
+	in the GrabCut paper. See comments in the file for details.
+
 2005-06-22  Sven Neumann  <sven@gimp.org>
 
 	* plug-ins/foggify.py: fixed capitalization of menu label.

plug-ins/pygimp/plug-ins/Makefile.am

@@ -11,6 +11,7 @@ scripts = \
 	whirlpinch.py
 
 test_scripts = \
+	benchmark-foreground-extract.py	\
 	sphere.py
 
 if GIMP_UNSTABLE

plug-ins/pygimp/plug-ins/benchmark-foreground-extract.py

+#!/usr/bin/env python
+
+#   Foreground Extraction Benchmark
+#   Copyright 2005  Sven Neumann <sven@gimp.org>
+#
+#   This is a from-scratch implementation of the benchmark proposed in
+#   "GrabCut": interactive foreground extraction using iterated graph
+#   cuts published in the Proceedings of the 2004 SIGGRAPH Conference.
+#
+#   No guarantee is made that this benchmark produces the same results
+#   as the cited benchmark but the goal is that it does. So if you find
+#   any bugs or inaccuracies in this code, please let us know.
+#
+#   The benchmark has been adapted work with the SIOX algorithm
+#   (http://www.siox.org). which is (currently) the only
+#   implementation of gimp_drawable_foreground_extract(). If other
+#   implementations are being added, this benchmark should be changed
+#   accordingly.
+#
+#   You will need a set of test images to run this benchmark, preferably
+#   the original set of 50 images. Some of these images are from the
+#   Berkeley Segmentation Dataset
+#   (http://www.cs.berkeley.edu/projects/vision/grouping/segbench/).
+#   See also http://www.siox.org/details.html for trimaps.
+#
+#   This program is free software; you can redistribute it and/or modify
+#   it under the terms of the GNU General Public License as published by
+#   the Free Software Foundation; either version 2 of the License, or
+#   (at your option) any later version.
+#
+#   This program is distributed in the hope that it will be useful,
+#   but WITHOUT ANY WARRANTY; without even the implied warranty of
+#   MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+#   GNU General Public License for more details.
+#
+#   You should have received a copy of the GNU General Public License
+#   along with this program; if not, write to the Free Software
+#   Foundation, Inc., 59 Temple Place - Suite 330, Boston, MA 02111-1307, USA.
+
+
+import os, re, struct, sys, time
+
+from gimpfu import *
+
+
+def benchmark (folder):
+    folder = os.path.abspath (folder)
+    if not os.path.exists (folder):
+        gimp.message("Folder '" + folder + "' doesn't exist.\n")
+        return;
+    
+    images = os.path.join (folder, "images")
+    masks = os.path.join (folder, "cm_bmp")
+    truths = os.path.join (folder, "truth")
+
+    total_unclassified = 0
+    total_misclassified = 0
+
+    for name in os.listdir (images):
+
+        try:
+            gimp.delete (image_display)
+            gimp.delete (mask_display)
+        except UnboundLocalError:
+            pass
+
+        image_name = os.path.join (images, name)
+
+        # FIXME: improve this!
+        name = re.sub (r'\.jpg$', '', name)
+        name = re.sub (r'\.JPG$', '', name)
+        name = re.sub (r'\.bmp$', '', name)
+
+        mask_name = os.path.join (masks, name + '.png')
+        truth_name = os.path.join (truths, name + '.bmp')
+
+        image = pdb.gimp_file_load (image_name, image_name)
+        image_layer = image.active_layer;
+
+        mask = pdb.gimp_file_load (mask_name, mask_name)
+        pdb.gimp_image_convert_grayscale (mask)
+        mask_layer = mask.active_layer;
+
+        truth = pdb.gimp_file_load (truth_name, truth_name)
+
+	gimp.tile_cache_ntiles (2 * mask_layer.width / gimp.tile_width () + 1)
+
+        unclassified = ((mask_layer.width * mask_layer.height)
+			- classified_pixels (mask_layer))
+
+        sys.stderr.write (os.path.basename (image_name))
+
+        pdb.gimp_drawable_foreground_extract (image_layer, mask_layer)
+
+        sys.stderr.write (" ")
+
+	mask_layer.flush ()
+
+        # Ignore errors when creating image displays;
+        # allows us to be used without a display.
+        try:
+            image_display = pdb.gimp_display_new (image)
+            mask_display = pdb.gimp_display_new (mask)
+
+            gimp.displays_flush ()
+            time.sleep (1.0)
+        except:
+            pass
+
+        gimp.delete (image)
+
+        misclassified = different_pixels (mask_layer, truth.active_layer)
+
+        sys.stderr.write ("%d %d %.2f%%\n" %
+			  (unclassified, misclassified,
+			   (misclassified * 100.0 / unclassified)))
+
+	total_unclassified += unclassified
+	total_misclassified += misclassified
+
+        gimp.delete (mask)
+        gimp.delete (truth)
+
+    # done
+
+    try:
+	gimp.delete (image_display)
+	gimp.delete (mask_display)
+    except UnboundLocalError:
+	pass
+
+    sys.stderr.write ("Total: %d %d %.2f%%\n" %
+		      (total_unclassified, total_misclassified,
+		       (total_misclassified * 100.0 / total_unclassified)))
+
+
+def classified_pixels (mask):
+    (mean, std_dev, median, pixels, count, percentile) = pdb.gimp_histogram (mask, HISTOGRAM_VALUE, 224, 255)
+
+    return count
+
+
+def different_pixels (mask, truth):
+    image = truth.image
+
+    copy = pdb.gimp_layer_new_from_drawable (mask, image)
+    copy.name = "Difference"
+    copy.mode = DIFFERENCE_MODE
+
+    image.add_layer (copy, -1)
+
+    (mean, std_dev, median,
+     pixels, count, percentile) = pdb.gimp_histogram (image.flatten (),
+						      HISTOGRAM_VALUE, 32, 255)
+
+    return count
+
+
+register (
+    "foreground_extraction_benchmark",
+    "Foreground Extraction Benchmark",
+    "Foreground Extraction Benchmark",
+    "Sven Neumann",
+    "Sven Neumann",
+    "2005",
+    "<Toolbox>/Xtns/Benchmark/Foreground Extraction",
+    "",
+    [ (PF_FILE, "image_folder", "Image folder",
+                "~/segmentation/msbench/imagedata") ],
+    [],
+    benchmark)
+
+main ()