def quantity():
center = np.fromfile(clstbinfilename, dtype="float32")
center = center.reshape((K, D))
#center.dtype = "float64"
print center.dtype
print "kdtree build start"
nno = fastann.build_kdtree(center, 8, 768)
print "kdtree build finish"
for filename in os.listdir(featurefilename):
if filename.endswith(postfix):
name = filename.split("_")
nametxt = name[0] + '_box.txt'
fc6feat = np.fromfile(featurefilename + filename, dtype='float32')
fc6feat = fc6feat.reshape((fc6feat.shape[0] / 4096, 4096))
argmins, mins = nno.search_nn(fc6feat)
argmins = argmins.reshape((argmins.shape[0], 1))
index = np.loadtxt(featurefilename + nametxt, dtype="int32")
clst = np.append(index, argmins, axis=1)
#clst.dtype="int32"
clst.tofile(featurefilename + name[0] + '_clst.bin')
print featurefilename + name[0] + '_clst.txt'
def quantity():
center = np.fromfile(clstbinfilename, dtype="float32")
center = center.reshape((K, D))
# center.dtype = "float64"
print center.dtype
print "kdtree build start"
nno = fastann.build_kdtree(center, 8, 768)
print "kdtree build finish"
for filename in os.listdir(featurefilename):
if filename.endswith(postfix):
name = filename.split("_")
nametxt = name[0] + "_box.txt"
fc6feat = np.fromfile(featurefilename + filename, dtype="float32")
fc6feat = fc6feat.reshape((fc6feat.shape[0] / 4096, 4096))
argmins, mins = nno.search_nn(fc6feat)
argmins = argmins.reshape((argmins.shape[0], 1))
index = np.loadtxt(featurefilename + nametxt, dtype="int32")
clst = np.append(index, argmins, axis=1)
# clst.dtype="int32"
clst.tofile(featurefilename + name[0] + "_clst.bin")
print featurefilename + name[0] + "_clst.txt"
import numpy as np
import h5py
import fastann
bow_save_prefix=""
clst_file=h5py.File('../data/clst.h5','r')
print('loading clusters...')
clst_point=clst_file['clusters'].value
print('complete')
print('buiding kd tree...')
nno_kdt = fastann.build_kdtree(clst_point, 8, 768)
print('complete')
path = 'video_query.h5'
file_name=path.split('/')[len(path.split('/'))-1]
bow_name='bow_query.h5'
video_file=h5py.File(path,'r')
bow_file=h5py.File(bow_save_prefix + bow_name,'w')
print('loading video h5 file:'+file_name)
data=video_file['data'].value
#print('dimention of data:'+data.shape)
num_image=data.shape[0]
num_point=data.shape[1]
dim=data.shape[2]
data.resize(num_image*num_point,dim)
print('compute bow features')
argmins_kdt, mins_kdt = nno_kdt.search_nn(data)
argmins_kdt.resize(num_image,num_point)
print('saving bow features to:'+bow_save_prefix + bow_name)
bow_file.create_dataset('data',data=argmins_kdt)
bow_file.flush()
import numpy as np
import h5py
import fastann
bow_save_prefix = ""
clst_file = h5py.File('../data/clst.h5', 'r')
print('loading clusters...')
clst_point = clst_file['clusters'].value
print('complete')
print('buiding kd tree...')
nno_kdt = fastann.build_kdtree(clst_point, 8, 768)
print('complete')
path = 'video_query.h5'
file_name = path.split('/')[len(path.split('/')) - 1]
bow_name = 'bow_query.h5'
video_file = h5py.File(path, 'r')
bow_file = h5py.File(bow_save_prefix + bow_name, 'w')
print('loading video h5 file:' + file_name)
data = video_file['data'].value
#print('dimention of data:'+data.shape)
num_image = data.shape[0]
num_point = data.shape[1]
dim = data.shape[2]
data.resize(num_image * num_point, dim)
print('compute bow features')
argmins_kdt, mins_kdt = nno_kdt.search_nn(data)
argmins_kdt.resize(num_image, num_point)
print('saving bow features to:' + bow_save_prefix + bow_name)
bow_file.create_dataset('data', data=argmins_kdt)
bow_file.flush()
import fastann import numpy as np import numpy.random as npr A = npr.rand(10000,128) B = npr.rand(10000,128) nno = fastann.build_exact(A) argmins, mins = nno.search_nn(B) nno_kdt = fastann.build_kdtree(A, 8, 768) argmins_kdt, mins_kdt = nno_kdt.search_nn(B) print float(np.sum(argmins == argmins_kdt))/10000
