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# Foreground Extraction Benchmark
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# Copyright 2005 Sven Neumann <sven@gimp.org>
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# This is a from-scratch implementation of the benchmark proposed in
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# "GrabCut": interactive foreground extraction using iterated graph
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# cuts published in the Proceedings of the 2004 SIGGRAPH Conference.
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# No guarantee is made that this benchmark produces the same results
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# as the cited benchmark but the goal is that it does. So if you find
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# any bugs or inaccuracies in this code, please let us know.
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# The benchmark has been adapted work with the SIOX algorithm
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# (http://www.siox.org). which is (currently) the only
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# implementation of gimp_drawable_foreground_extract(). If other
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# implementations are being added, this benchmark should be changed
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# You will need a set of test images to run this benchmark, preferably
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# the original set of 50 images. Some of these images are from the
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# Berkeley Segmentation Dataset
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# (http://www.cs.berkeley.edu/projects/vision/grouping/segbench/).
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# See also http://www.siox.org/details.html for trimaps.
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# This program is free software; you can redistribute it and/or modify
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# it under the terms of the GNU General Public License as published by
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# the Free Software Foundation; either version 2 of the License, or
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# (at your option) any later version.
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# This program is distributed in the hope that it will be useful,
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# but WITHOUT ANY WARRANTY; without even the implied warranty of
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# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
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# GNU General Public License for more details.
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# You should have received a copy of the GNU General Public License
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# along with this program; if not, write to the Free Software
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# Foundation, Inc., 59 Temple Place - Suite 330, Boston, MA 02111-1307, USA.
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import os, re, struct, sys, time
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def benchmark (folder, save_output):
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folder = os.path.abspath (folder)
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if not os.path.exists (folder):
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gimp.message("Folder '" + folder + "' doesn't exist.\n")
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total_unclassified = 0
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total_misclassified = 0
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images = os.path.join (folder, "images")
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for name in os.listdir (images):
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gimp.delete (image_display)
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gimp.delete (mask_display)
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except UnboundLocalError:
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image_name = os.path.join (images, name)
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# FIXME: improve this!
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name = re.sub (r'\.jpg$', '', name)
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name = re.sub (r'\.JPG$', '', name)
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name = re.sub (r'\.bmp$', '', name)
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mask_name = os.path.join (folder, "cm_bmp", name + '.png')
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truth_name = os.path.join (folder, "truth", name + '.bmp')
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image = pdb.gimp_file_load (image_name, image_name)
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image_layer = image.active_layer;
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mask = pdb.gimp_file_load (mask_name, mask_name)
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convert_grayscale (mask)
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mask_layer = mask.active_layer;
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truth = pdb.gimp_file_load (truth_name, truth_name)
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convert_grayscale (truth)
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truth_layer = truth.active_layer;
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unclassified = unclassified_pixels (mask_layer, truth_layer)
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sys.stderr.write (os.path.basename (image_name))
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pdb.gimp_drawable_foreground_extract (image_layer,
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FOREGROUND_EXTRACT_SIOX,
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sys.stderr.write (" ")
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# Ignore errors when creating image displays;
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# allows us to be used without a display.
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image_display = pdb.gimp_display_new (image)
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mask_display = pdb.gimp_display_new (mask)
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gimp.displays_flush ()
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misclassified = misclassified_pixels (mask_layer, truth_layer)
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sys.stderr.write ("%d %d %.2f%% %.3fs\n" %
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(unclassified, misclassified,
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(misclassified * 100.0 / unclassified),
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total_unclassified += unclassified
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total_misclassified += misclassified
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total_time += end - start
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filename = os.path.join (folder, "output", name + '.png')
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pdb.gimp_file_save (mask, mask_layer, filename, filename)
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gimp.delete (image_display)
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gimp.delete (mask_display)
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except UnboundLocalError:
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sys.stderr.write ("Total: %d %d %.2f%% %.3fs\n" %
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(total_unclassified, total_misclassified,
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(total_misclassified * 100.0 / total_unclassified),
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def convert_grayscale (image):
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if image.base_type != GRAY:
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pdb.gimp_image_convert_grayscale (image)
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def unclassified_pixels (mask, truth):
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(mean, std_dev, median, pixels,
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count, percentile) = pdb.gimp_histogram (mask, HISTOGRAM_VALUE, 1, 254)
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def misclassified_pixels (mask, truth):
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copy = pdb.gimp_layer_new_from_drawable (mask, image)
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copy.name = "Difference"
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copy.mode = DIFFERENCE_MODE
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image.add_layer (copy, -1)
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# The assumption made here is that the output of
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# foreground_extract is a strict black and white mask. The truth
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# however may contain unclassified pixels. These are considered
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# unknown, a strict segmentation isn't possible here.
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# The result of using the Difference mode as done here is that
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# pure black pixels in the result can be considered correct.
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# White pixels are wrong. Gray values were unknown in the truth
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# and thus are not counted as wrong.
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(mean, std_dev, median, pixels,
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count, percentile) = pdb.gimp_histogram (image.flatten (),
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HISTOGRAM_VALUE, 255, 255)
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"python-fu-benchmark-foreground-extract",
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"Benchmark and regression test for the SIOX algorithm",
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"Foreground Extraction",
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[ (PF_FILE, "image-folder", "Image folder",
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"~/segmentation/msbench/imagedata"),
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(PF_TOGGLE, "save-output", "Save output images", False) ],
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benchmark, menu="<Toolbox>/Xtns/Benchmark")
added
removed
plug-ins/pygimp/plug-ins/benchmark-foreground-extract.py
