import os, random
import simpleknn
from bigfile import BigFile
rootpath = '/Users/xirong/VisualSearch'
collection = 'train10k'
nr_of_images = 10000
feature = 'color64'
dim = 64
feature_dir = os.path.join(rootpath, collection, 'FeatureData', feature)
feature_file = BigFile(feature_dir, dim)
imset = map(
str.strip,
open(os.path.join(rootpath, collection, 'ImageSets',
'%s.txt' % collection)).readlines())
imset = random.sample(imset, 10)
searcher = simpleknn.load_model(os.path.join(feature_dir, "feature.bin"), dim,
nr_of_images,
os.path.join(feature_dir, "id.txt"))
searcher.set_distance('l1')
renamed, vectors = feature_file.read(imset)
for name, vec in zip(renamed, vectors):
visualNeighbors = searcher.search_knn(vec, max_hits=100)
print name, visualNeighbors[:3]
import os, random
import simpleknn
from bigfile import BigFile
rootpath = '/Users/xirong/VisualSearch'
collection = 'train10k'
nr_of_images = 10000
feature = 'color64'
dim = 64
feature_dir = os.path.join(rootpath,collection,'FeatureData',feature)
feature_file = BigFile(feature_dir, dim)
imset = map(str.strip, open(os.path.join(rootpath,collection,'ImageSets','%s.txt'%collection)).readlines())
imset = random.sample(imset, 10)
searcher = simpleknn.load_model(os.path.join(feature_dir, "feature.bin"), dim, nr_of_images, os.path.join(feature_dir, "id.txt"))
searcher.set_distance('l1')
renamed,vectors = feature_file.read(imset)
for name,vec in zip(renamed,vectors):
visualNeighbors = searcher.search_knn(vec, max_hits=100)
print name, visualNeighbors[:3]
feature = 'f3d'
feat_dim = 3
feat_dir = '%s/FeatureData/%s' % (collection, feature)
txt_file = os.path.join(feat_dir, 'id.feature.txt')
os.system('python txt2bin.py %d %s 0 %s --overwrite 1' %
(feat_dim, txt_file, feat_dir))
for p in [1, 2]:
os.system('python norm_feat.py %s --p %d --overwrite 1' % (feat_dir, p))
for feature in str.split('f3d f3dl1 f3dl2'):
feat_dir = os.path.join(rootpath, collection, 'FeatureData', feature)
id_file = os.path.join(feat_dir, "id.txt")
with open(os.path.join(feat_dir, 'shape.txt')) as fr:
nr_of_images, dim = map(int, fr.readline().split())
fr.close()
feat_file = BigFile(feat_dir)
imset = feat_file.names
searcher = simpleknn.load_model(os.path.join(feat_dir, "feature.bin"), dim,
nr_of_images, id_file)
renamed, vectors = feat_file.read(imset)
for name, vec in zip(renamed, vectors):
for distance in ['l1', 'l2']:
searcher.set_distance(distance)
visualNeighbors = searcher.search_knn(vec, max_hits=100)
print name, feature, distance, visualNeighbors[:3]
