def create_thumbnails(database_object):
database_object = refetch_from_db(database_object)
image = PIL.Image.open(database_object.page_image.path).convert('RGB')
width = float(image.size[0])
height = float(image.size[1])
for thumbnail_size in settings.THUMBNAIL_SIZES:
thumbnail_size = float(thumbnail_size)
ratio = min((thumbnail_size / width), (thumbnail_size / height))
dimensions = (int(width * ratio), int(height * ratio))
thumbnail_size = str(int(thumbnail_size))
thumb_copy = image.resize(dimensions, PIL.Image.ANTIALIAS)
thumb_copy.save(os.path.join(database_object.thumb_path,
database_object.thumb_filename(size=thumbnail_size)))
del thumb_copy
del image
return database_object
def create_thumbnails(database_object):
database_object = refetch_from_db(database_object)
image = PIL.Image.open(database_object.page_image.path).convert('RGB')
width = float(image.size[0])
height = float(image.size[1])
for thumbnail_size in settings.THUMBNAIL_SIZES:
thumbnail_size = float(thumbnail_size)
ratio = min((thumbnail_size / width), (thumbnail_size / height))
dimensions = (int(width * ratio), int(height * ratio))
thumbnail_size = str(int(thumbnail_size))
thumb_copy = image.resize(dimensions, PIL.Image.ANTIALIAS)
thumb_copy.save(
os.path.join(database_object.thumb_path,
database_object.thumb_filename(size=thumbnail_size)))
del thumb_copy
del image
return database_object
def ensure_compatible(page_object):
page_object = refetch_from_db(page_object)
filename = "compat_image.png"
image = PIL.Image.open(page_object.page_image.path).convert('RGB')
tmpdir = tempfile.mkdtemp()
image.save(os.path.join(tmpdir, filename))
compatible_image_path = os.path.join(page_object.image_path, filename)
f = open(os.path.join(tmpdir, filename), 'rb')
if exists_in_db(page_object):
page_object.compat_page_image.save(compatible_image_path, File(f))
else:
warnings.warn("The page was deleted from the database before it could be processed.")
f.close()
shutil.rmtree(tmpdir)
return page_object
def processed(dbobject):
dbobject = refetch_from_db(dbobject)
dbobject.processed = True
if exists_in_db(dbobject):
dbobject.save()
return dbobject
def run(self, *args, **kwargs):
"""
This runs the optimization on all the classifiers in the project.
The optimization can be run with many different settings. For
convenience, I've included all the settings in this script and
commented out the ones that I'm not currently using. Feel free
to tweak with them.
For most of the arguments, I hope it is implicitly clear which
ones are int and which ones are float/double. For example,
arg_one=3 means arg_one should be an int, while arg_two=3.0 means
arg_two should be a double/float.
Later on, we can build a client interface to tweak all these settings.
Detailed documentation can be found at http://gamera.sourceforge.net/doc/html/ga_optimization.html
Code template taken from http://gamera.sourceforge.net/doc/html/ga_optimization.html#script-usage
"""
init_gamera()
classifiers = Classifier.objects.all()
for classifier in classifiers:
project = classifier.project
optimization_start = timezone.now()
print "Optimizing classifier {0}".format(classifier.name)
cknn = knn.kNNNonInteractive(classifier.file_path,
features = 'all',
normalize = False)
print "Setting base settings"
baseSettings = knnga.GABaseSetting()
baseSettings.opMode = knnga.GA_WEIGHTING # Or knnga.GA_SELECTION
baseSettings.popSize = 75
baseSettings.crossRate = 0.95
baseSettings.mutRate = 0.05
print "Settings selection options"
selection = knnga.GASelection()
selection.setRoulettWheelScaled(2.0)
#selection.setRoulettWheelScaled(double pressure=2.0) # Pressure \in [1,2]
#selection.setRandomSelection()
#selection.setRankSelection(pressure=2.0, exponent=1.0)
#selection.setStochUniSampling()
#selection.setRoulettWheel()
#selection.setTournamentSelection(tSize=3)
print "Setting crossover settings"
crossover = knnga.GACrossover()
crossover.setUniformCrossover(0.5)
#crossover.setUniformCrossover(double preference = 0.5)
#crossover.setNPointCrossover(n=1)
#crossover.setHypercubeCrossover(int numFeatures, double min, double max, alpha=0.0)
#crossover.setSBXcrossover(int numFeatures, double min, double max, eta=0.0)
#crossover.setSegmentCrossover(int numFeatures, double min, double max, alpha=0.0)
print "Setting Mutation settings"
mutation = knnga.GAMutation()
#mutation.setShiftMutation()
mutation.setSwapMutation()
#mutation.setBinaryMutation(rate=0.05, normalize=False)
mutation.setBinaryMutation(0.05, False)
#mutation.setGaussMutation(int numFeatures, double min, double max, double sigma, double rate)
#mutation.setInversionMutation()
print "Setting replacement settings"
replacement = knnga.GAReplacement()
replacement.setSSGAdetTournament(3)
#replacement.setSSGAdetTournament(int tSize=3)
#replacement.setGenerationalReplacement()
#replacement.setSSGAworse()
print "Setting stop criteria"
stop = knnga.GAStopCriteria()
#stop.setSteadyStateStop(int minGens=100, int noChangeGens=20)
stop.setSteadyStateStop(100, 20)
#stop.setBestFitnessStop(optimum=1.0)
#stop.setMaxFitnessEvals(n=5000)
#stop.setMaxGenerations(100)
print "Setting parallelization settings"
parallel = knnga.GAParallelization()
parallel.mode = True
parallel.thredNum = 4
# Combine each setting object into one main object
ga = knnga.GAOptimization(cknn, baseSettings, selection,
crossover, mutation, replacement,
stop, parallel)
print "Beginning calculation..."
ga.startCalculation()
print "Done! Saving the produced settings."
optimization_end = timezone.now()
# Choosing a name for the new classifier_setting
date_string = datetime.now().strftime("%Y_%m_%d_%I%M%p")
setting_name = "{0}:{1} Periodic Optimization".format(classifier.name[:200], date_string,)
tdir = tempfile.mkdtemp()
temp_xml_filepath = os.path.join(tdir, str(classifier.uuid) + '.xml')
cknn.save_settings(temp_xml_filepath)
try:
classifier = refetch_from_db(classifier)
except ObjectDeletedError:
print "Sadly classifier {0} was deleted.".format(classifier.name)
# Now this is a lonely setting file with no classifier to hang out with.
classifier = None
classifier_setting_instance = ClassifierSetting.objects.create(name=setting_name,
project=project,
fitness=ga.bestFitness,
producer=classifier,
optimization_started_at = optimization_start,
optimization_finished_at = optimization_end)
with open(temp_xml_filepath, 'rb') as f:
taskutil.save_file_field(classifier_setting_instance.settings_file, 'settings_xml', File(f))
shutil.rmtree(tdir)
def run(self, *args, **kwargs):
"""
This runs the optimization on all the classifiers in the project.
The optimization can be run with many different settings. For
convenience, I've included all the settings in this script and
commented out the ones that I'm not currently using. Feel free
to tweak with them.
For most of the arguments, I hope it is implicitly clear which
ones are int and which ones are float/double. For example,
arg_one=3 means arg_one should be an int, while arg_two=3.0 means
arg_two should be a double/float.
Later on, we can build a client interface to tweak all these settings.
Detailed documentation can be found at http://gamera.sourceforge.net/doc/html/ga_optimization.html
Code template taken from http://gamera.sourceforge.net/doc/html/ga_optimization.html#script-usage
"""
init_gamera()
classifiers = Classifier.objects.all()
for classifier in classifiers:
project = classifier.project
optimization_start = timezone.now()
print "Optimizing classifier {0}".format(classifier.name)
cknn = knn.kNNNonInteractive(classifier.file_path,
features='all',
normalize=False)
print "Setting base settings"
baseSettings = knnga.GABaseSetting()
baseSettings.opMode = knnga.GA_WEIGHTING # Or knnga.GA_SELECTION
baseSettings.popSize = 75
baseSettings.crossRate = 0.95
baseSettings.mutRate = 0.05
print "Settings selection options"
selection = knnga.GASelection()
selection.setRoulettWheelScaled(2.0)
#selection.setRoulettWheelScaled(double pressure=2.0) # Pressure \in [1,2]
#selection.setRandomSelection()
#selection.setRankSelection(pressure=2.0, exponent=1.0)
#selection.setStochUniSampling()
#selection.setRoulettWheel()
#selection.setTournamentSelection(tSize=3)
print "Setting crossover settings"
crossover = knnga.GACrossover()
crossover.setUniformCrossover(0.5)
#crossover.setUniformCrossover(double preference = 0.5)
#crossover.setNPointCrossover(n=1)
#crossover.setHypercubeCrossover(int numFeatures, double min, double max, alpha=0.0)
#crossover.setSBXcrossover(int numFeatures, double min, double max, eta=0.0)
#crossover.setSegmentCrossover(int numFeatures, double min, double max, alpha=0.0)
print "Setting Mutation settings"
mutation = knnga.GAMutation()
#mutation.setShiftMutation()
mutation.setSwapMutation()
#mutation.setBinaryMutation(rate=0.05, normalize=False)
mutation.setBinaryMutation(0.05, False)
#mutation.setGaussMutation(int numFeatures, double min, double max, double sigma, double rate)
#mutation.setInversionMutation()
print "Setting replacement settings"
replacement = knnga.GAReplacement()
replacement.setSSGAdetTournament(3)
#replacement.setSSGAdetTournament(int tSize=3)
#replacement.setGenerationalReplacement()
#replacement.setSSGAworse()
print "Setting stop criteria"
stop = knnga.GAStopCriteria()
#stop.setSteadyStateStop(int minGens=100, int noChangeGens=20)
stop.setSteadyStateStop(100, 20)
#stop.setBestFitnessStop(optimum=1.0)
#stop.setMaxFitnessEvals(n=5000)
#stop.setMaxGenerations(100)
print "Setting parallelization settings"
parallel = knnga.GAParallelization()
parallel.mode = True
parallel.thredNum = 4
# Combine each setting object into one main object
ga = knnga.GAOptimization(cknn, baseSettings, selection, crossover,
mutation, replacement, stop, parallel)
print "Beginning calculation..."
ga.startCalculation()
print "Done! Saving the produced settings."
optimization_end = timezone.now()
# Choosing a name for the new classifier_setting
date_string = datetime.now().strftime("%Y_%m_%d_%I%M%p")
setting_name = "{0}:{1} Periodic Optimization".format(
classifier.name[:200],
date_string,
)
tdir = tempfile.mkdtemp()
temp_xml_filepath = os.path.join(tdir,
str(classifier.uuid) + '.xml')
cknn.save_settings(temp_xml_filepath)
try:
classifier = refetch_from_db(classifier)
except ObjectDeletedError:
print "Sadly classifier {0} was deleted.".format(
classifier.name)
# Now this is a lonely setting file with no classifier to hang out with.
classifier = None
classifier_setting_instance = ClassifierSetting.objects.create(
name=setting_name,
project=project,
fitness=ga.bestFitness,
producer=classifier,
optimization_started_at=optimization_start,
optimization_finished_at=optimization_end)
with open(temp_xml_filepath, 'rb') as f:
taskutil.save_file_field(
classifier_setting_instance.settings_file, 'settings_xml',
File(f))
shutil.rmtree(tdir)