# -- Load the feature file
log.info("Loading features from file...")
f = FeatureList(opts.feature_file)
f.process()
log.debug("Loaded %s rows" % len(f.dataset))
# -- Load the clusters
log.info("Loading clusters from file")
cluster_file = file(opts.cluster_file,'r')
base = basename(opts.cluster_file)
clusters = imp.load_source(base,opts.cluster_file).clusters
# -- Load the clusters into FLANN as if they were the points
log.info("Building cluster index...")
flann = buildIndex(clusters)
# -- For each point in the feature list, find its 'nearest neighbor',
# i.e. which cluster it belongs to.
log.info("Calculating distances...")
cluster_list,distance_list = flann.nn_index(f.dataset)
log.info( "Have %i nearest-clusters" % len(cluster_list))
# -- For each file, write its words to [out_dir]/[image].txt
current_feature = 0
for filename in image_filenames:
base = basename(filename).replace('jpg','txt')
out = file("%s/%s" % (opts.output_dir,base), 'w+')
for feature in xrange(feature_count[filename]):
from buildIndex import buildIndex
options = {
'crawl': True,
'pageRank': False,
'parse': True,
'database': True,
'idf': True,
'tfidf': True
}
# buildIndex(3, passwordLock=False)
buildIndex(2,
threads=4,
passwordLock=False,
reset=False,
resetFiles=False,
options=options,
dev=True)
# -- Load the feature file
log.info("Loading features from file...")
f = FeatureList(opts.feature_file)
f.process()
log.debug("Loaded %s rows" % len(f.dataset))
# -- Load the clusters
log.info("Loading clusters from file")
cluster_file = file(opts.cluster_file, 'r')
base = basename(opts.cluster_file)
clusters = imp.load_source(base, opts.cluster_file).clusters
# -- Load the clusters into FLANN as if they were the points
log.info("Building cluster index...")
flann = buildIndex(clusters)
# -- For each point in the feature list, find its 'nearest neighbor',
# i.e. which cluster it belongs to.
log.info("Calculating distances...")
cluster_list, distance_list = flann.nn_index(f.dataset)
log.info("Have %i nearest-clusters" % len(cluster_list))
# -- For each file, write its words to [out_dir]/[image].txt
current_feature = 0
for filename in image_filenames:
base = basename(filename).replace('jpg', 'txt')
out = file("%s/%s" % (opts.output_dir, base), 'w+')
for feature in xrange(feature_count[filename]):
log.info("Loading clusters from file")
cluster_file = file(opts.cluster_file, 'r')
base = basename(opts.cluster_file)
clusters = imp.load_source(base, opts.cluster_file).clusters
else:
log.info("Calculating %s clusters (%s passes)" %
(opts.num_clusters, opts.num_iterations))
clusters = clusteringFlann.kmeans(pts=featureSet,
num_clusters=opts.num_clusters,
dtype=float32,
max_iterations=opts.num_iterations,
centers_init='gonzales')
# -- Load the clusters into FLANN as if they were the points
log.info("Building cluster index...")
clusterIndex = buildIndex(clusters)
# -- For each point in the feature list, find its 'nearest neighbor',
# i.e. which cluster it belongs to.
log.info("Calculating distances...")
cluster_list, distance_list = clusterIndex.nn_index(featureSet,
num_neighbors=1)
log.info("Have %i nearest-clusters, writing files" % len(cluster_list))
# -- For each file, write its words to [out_dir]/[image].txt
current_feature = 0
for filename in image_filenames:
base = basename(filename).replace('jpg', 'txt')
out = file("%s/%s" % (opts.output_dir, base), 'w+')
log.info("Loading clusters from file")
cluster_file = file(opts.cluster_file,'r')
base = basename(opts.cluster_file)
clusters = imp.load_source(base,opts.cluster_file).clusters
else:
log.info("Calculating %s clusters (%s passes)" % (opts.num_clusters,opts.num_iterations))
clusters = clusteringFlann.kmeans(
pts=featureSet,
num_clusters=opts.num_clusters,
dtype=float32,
max_iterations=opts.num_iterations,
centers_init='gonzales')
# -- Load the clusters into FLANN as if they were the points
log.info("Building cluster index...")
clusterIndex = buildIndex(clusters)
# -- For each point in the feature list, find its 'nearest neighbor',
# i.e. which cluster it belongs to.
log.info("Calculating distances...")
cluster_list,distance_list = clusterIndex.nn_index(featureSet,num_neighbors=1)
log.info( "Have %i nearest-clusters, writing files" % len(cluster_list))
# -- For each file, write its words to [out_dir]/[image].txt
current_feature = 0
for filename in image_filenames:
base = basename(filename).replace('jpg','txt')
out = file("%s/%s" % (opts.output_dir,base), 'w+')
for feature in xrange(feature_count[filename]):
