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#!/usr/bin/env python2
# 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 3 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, see <https://www.gnu.org/licenses/>.
import os, re, struct, sys, time
from gimpfu import *
def benchmark (folder, save_output):
folder = os.path.abspath (folder)
if not os.path.exists (folder):
gimp.message("Folder '" + folder + "' doesn't exist.\n")
return;
total_unclassified = 0
total_misclassified = 0
total_time = 0.0
images = os.path.join (folder, "images")
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 (folder, "cm_bmp", name + '.png')
truth_name = os.path.join (folder, "truth", 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)
convert_grayscale (mask)
mask_layer = mask.active_layer;
truth = pdb.gimp_file_load (truth_name, truth_name)
convert_grayscale (truth)
truth_layer = truth.active_layer;
unclassified = unclassified_pixels (mask_layer, truth_layer)
sys.stderr.write (os.path.basename (image_name))
start = time.time ()
pdb.gimp_drawable_foreground_extract (image_layer,
FOREGROUND_EXTRACT_SIOX,
mask_layer)
end = time.time ()
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 = misclassified_pixels (mask_layer, truth_layer)
sys.stderr.write ("%d %d %.2f%% %.3fs\n" %
(unclassified, misclassified,
(misclassified * 100.0 / unclassified),
end - start))
total_unclassified += unclassified
total_misclassified += misclassified
total_time += end - start
gimp.delete (truth)
if save_output:
filename = os.path.join (folder, "output", name + '.png')
pdb.gimp_file_save (mask, mask_layer, filename, filename)
gimp.delete (mask)
# for loop ends
try:
gimp.delete (image_display)
gimp.delete (mask_display)
except UnboundLocalError:
pass
sys.stderr.write ("Total: %d %d %.2f%% %.3fs\n" %
(total_unclassified, total_misclassified,
(total_misclassified * 100.0 / total_unclassified),
total_time))
def convert_grayscale (image):
if image.base_type != GRAY:
pdb.gimp_image_convert_grayscale (image)
def unclassified_pixels (mask, truth):
(mean, std_dev, median, pixels,
count, percentile) = pdb.gimp_histogram (mask, HISTOGRAM_VALUE, 1, 254)
return count
def misclassified_pixels (mask, truth):
image = truth.image
copy = pdb.gimp_layer_new_from_drawable (mask, image)
copy.name = "Difference"
copy.mode = DIFFERENCE_MODE
image.insert_layer (copy)
# The assumption made here is that the output of
# foreground_extract is a strict black and white mask. The truth
# however may contain unclassified pixels. These are considered
# unknown, a strict segmentation isn't possible here.
#
# The result of using the Difference mode as done here is that
# pure black pixels in the result can be considered correct.
# White pixels are wrong. Gray values were unknown in the truth
# and thus are not counted as wrong.
(mean, std_dev, median, pixels,
count, percentile) = pdb.gimp_histogram (image.flatten (),
HISTOGRAM_VALUE, 255, 255)
return count
register (
"python-fu-benchmark-foreground-extract",
"Benchmark and regression test for the SIOX algorithm",
"",
"Sven Neumann",
"Sven Neumann",
"2005",
"Foreground Extraction",
"",
[ (PF_FILE, "image-folder", "Image folder",
"~/segmentation/msbench/imagedata"),
(PF_TOGGLE, "save-output", "Save output images", False) ],
[],
benchmark, menu="<Image>/Filters/Extensions/Benchmark")
main ()
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