def run(self, options):
labeler = None if options.labeler is None else labelers.registry[
options.labeler]
# Wait to instantiate the corpus writer until we know the dimensionality of the descriptors we'll be writing
writer = None
log.info('Writing SAM corpus to %s' % options.dest_corpus)
filenames = open(options.file_list).readlines()
for i, filename in enumerate(filenames):
filename = filename.strip()
log.info('Processing image %d/%d' % (i + 1, len(filenames)))
descriptor = color_gist(
filename) if options.color else grayscale_gist(filename)
if writer is None:
dim = descriptor.size
writer = CorpusWriter(options.dest_corpus,
data_series='sam',
dim=dim)
normalized_descriptor = l2_normalize(descriptor)
doc_label = labeler(filename) if labeler else None
writer.write_doc(ascolvector(normalized_descriptor),
name=filename,
label=doc_label)
writer.close()
def run(self, options):
labeler = labelers.registry[options.labeler]
# Wait to instantiate the corpus writer until we know the dimensionality of the descriptors we'll be writing
filenames = open(options.file_list).readlines()
labels = [labeler(each) for each in filenames]
class_list = sorted(set(labels))
writer = ArffWriter(options.dest, class_list=class_list)
log.info('Writing GIST data to %s' % options.dest)
for i, (filename, label) in enumerate(izip(filenames, labels)):
filename = filename.strip()
log.info('Processing image %d/%d' % (i+1, len(filenames)))
descriptor = color_gist(filename) if options.color else grayscale_gist(filename)
if options.normalize:
descriptor = l2_normalize(descriptor)
writer.write_example(descriptor, label)
writer.close()
def run(self, options):
labeler = labelers.registry[options.labeler]
# Wait to instantiate the corpus writer until we know the dimensionality of the descriptors we'll be writing
filenames = open(options.file_list).readlines()
labels = [labeler(each) for each in filenames]
class_list = sorted(set(labels))
writer = ArffWriter(options.dest, class_list=class_list)
log.info('Writing GIST data to %s' % options.dest)
for i, (filename, label) in enumerate(izip(filenames, labels)):
filename = filename.strip()
log.info('Processing image %d/%d' % (i + 1, len(filenames)))
descriptor = color_gist(
filename) if options.color else grayscale_gist(filename)
if options.normalize:
descriptor = l2_normalize(descriptor)
writer.write_example(descriptor, label)
writer.close()
def run(self, options):
labeler = None if options.labeler is None else labelers.registry[options.labeler]
# Wait to instantiate the corpus writer until we know the dimensionality of the descriptors we'll be writing
writer = None
log.info('Writing SAM corpus to %s' % options.dest_corpus)
filenames = open(options.file_list).readlines()
for i, filename in enumerate(filenames):
filename = filename.strip()
log.info('Processing image %d/%d' % (i+1, len(filenames)))
descriptor = color_gist(filename) if options.color else grayscale_gist(filename)
if writer is None:
dim = descriptor.size
writer = CorpusWriter(options.dest_corpus, data_series='sam', dim=dim)
normalized_descriptor = l2_normalize(descriptor)
doc_label = labeler(filename) if labeler else None
writer.write_doc(ascolvector(normalized_descriptor), name=filename, label=doc_label)
writer.close()
