def execute(self):
if self.options.input_file == None:
raise FLANNException("Need an input file")
print "Reading input data from file " + self.options.input_file
dataset = read(self.options.input_file)
if self.options.test_file == None:
raise FLANNException("Need a test file")
if isfile(self.options.test_file):
print "Reading test data from file " + self.options.test_file
testset = read(self.options.test_file)
else:
print "Sampling test file"
testset = sample_dataset(dataset, self.options.count, remove=True)
dataset = dataset[0:dataset.shape[0] - self.options.count]
print "Wrinting new dataset file"
write(dataset, "new_" + self.options.input_file)
print "Writing testset file"
write(testset, self.options.test_file)
print "Computing ground truth"
start = time.clock()
match = compute_ground_truth(dataset, testset, self.options.nn)
print "It took %g seconds" % (time.clock() - start)
print "Writing match file"
write(match, self.options.match_file, format="dat")
def execute(self):
if self.options.input_file==None:
raise FLANNException("Need an input file")
print "Reading input data from file "+self.options.input_file
dataset = read(self.options.input_file)
if self.options.test_file==None:
raise FLANNException("Need a test file")
if isfile(self.options.test_file):
print "Reading test data from file "+self.options.test_file
testset = read(self.options.test_file)
else:
print "Sampling test file"
testset = sample_dataset(dataset, self.options.count, remove=True)
dataset = dataset[0:dataset.shape[0]-self.options.count]
print "Wrinting new dataset file"
write(dataset,"new_"+self.options.input_file)
print "Writing testset file"
write(testset,self.options.test_file)
print "Computing ground truth"
start = time.clock()
match = compute_ground_truth(dataset, testset, self.options.nn)
print "It took %g seconds"%(time.clock()-start)
print "Writing match file"
write(match,self.options.match_file, format="dat")
def execute(self):
if self.options.input_file == None:
raise FLANNException("No input file given.")
if self.options.algorithm == None:
raise FLANNException("No algorithm specified")
if self.options.test_file == None:
raise FLANNException("No test file given.")
if self.options.match_file == None:
raise FLANNException("No match file given.")
print 'Reading input dataset from', self.options.input_file
dataset = read(self.options.input_file)
flann = FLANN(log_level=self.options.log_level)
flann.build_index(dataset, algorithm = self.options.algorithm,
trees=self.options.trees, branching=self.options.branching,
iterations=self.options.max_iterations, centers_init=self.options.centers_init)
print 'Reading test dataset from', self.options.test_file
testset = read(self.options.test_file)
print 'Reading ground truth from matches from', self.options.test_file
matches = read(self.options.match_file, dtype = int)
if self.options.precision>0:
checks, time = test_with_precision(flann, dataset, testset, matches, self.options.precision, self.options.nn)
else:
precision, time = test_with_checks(flann, dataset, testset, matches, self.options.checks, self.options.nn)
def execute(self):
if self.options.input_file == None:
raise FLANNException("No input file given.")
if self.options.algorithm == None:
raise FLANNException("No algorithm specified")
if self.options.test_file == None:
raise FLANNException("No test file given.")
if self.options.output_file == None:
raise FLANNException("No output file given.")
print 'Reading input dataset from', self.options.input_file
dataset = read(self.options.input_file)
flann = FLANN(log_level=self.options.log_level)
flann.build_index(dataset, algorithm = self.options.algorithm,
trees=self.options.trees, branching=self.options.branching,
iterations=self.options.max_iterations, centers_init=self.options.centers_init)
print 'Reading test dataset from', self.options.test_file
testset = read(self.options.test_file)
print "Searching for nearest neighbors"
matches,dists = flann.nn_index(testset, self.options.nn, checks = self.options.checks)
print "Writing matches to", self.options.output_file
write(matches, self.options.output_file, format="dat")
def execute(self):
if self.options.input_file == None:
raise FLANNException("No input file given.")
if self.options.algorithm == None:
raise FLANNException("No algorithm specified")
if self.options.test_file == None:
raise FLANNException("No test file given.")
if self.options.output_file == None:
raise FLANNException("No output file given.")
print 'Reading input dataset from', self.options.input_file
dataset = read(self.options.input_file)
flann = FLANN(log_level=self.options.log_level)
flann.build_index(dataset,
algorithm=self.options.algorithm,
trees=self.options.trees,
branching=self.options.branching,
iterations=self.options.max_iterations,
centers_init=self.options.centers_init)
print 'Reading test dataset from', self.options.test_file
testset = read(self.options.test_file)
print "Searching for nearest neighbors"
matches, dists = flann.nn_index(testset,
self.options.nn,
checks=self.options.checks)
print "Writing matches to", self.options.output_file
write(matches, self.options.output_file, format="dat")
def execute(self):
self.nn = FLANN(log_level=self.options.log_level)
if self.options.input_file == None:
raise FLANNException("No input file given.")
print 'Reading input dataset from', self.options.input_file
self.dataset = read(self.options.input_file)
if self.options.precision < 0 or self.options.precision > 1:
raise FLANNException(
"The precision argument must be between 0 and 1.")
params = self.nn.build_index(
self.dataset,
target_precision=self.options.precision,
build_weight=self.options.build_weight,
memory_weight=self.options.memory_weight,
sample_fraction=self.options.sample_fraction)
if self.options.params_file != None:
params_stream = open(self.options.params_file, "w")
else:
params_stream = sys.stdout
configdict = ConfigParser()
configdict.add_section('params')
for (k, v) in params.items():
configdict.set('params', k, v)
configdict.write(params_stream)
def execute(self):
if self.options.input_file==None:
raise FLANNException("Need an input file")
if self.options.output_file==None:
raise FLANNException("Need an output file")
print "Reading input data from file "+self.options.input_file
dataset = read(self.options.input_file, dtype=numpy.dtype(self.options.dtype))
print "Writing to file %s"%self.options.output_file
write(dataset,self.options.output_file, format=self.options.format)
def execute(self):
if self.options.input_file==None:
raise FLANNException("Need an input file")
if self.options.output_file==None:
raise FLANNException("Need an output file")
print "Reading input data from file "+self.options.input_file
dataset = read(self.options.input_file, dtype=numpy.dtype(self.options.dtype))
print "Writing to file %s"%self.options.output_file
write(dataset,self.options.output_file, format=self.options.format)
def execute(self):
if self.options.input_file==None:
raise FLANNException("Need an input file")
print "Reading input data from file "+self.options.input_file
dataset = read(self.options.input_file)
if self.options.count>0:
print "Sampling %d features"%self.options.count
sampledset = sample_dataset(dataset, self.options.count)
print "Writing sampled dataset to file %s"%self.options.save_file
write(sampledset,self.options.save_file, format=self.options.format)
def execute(self):
if self.options.input_file == None:
raise FLANNException("Need an input file")
print "Reading input data from file " + self.options.input_file
dataset = read(self.options.input_file)
if self.options.count > 0:
print "Sampling %d features" % self.options.count
sampledset = sample_dataset(dataset, self.options.count)
print "Writing sampled dataset to file %s" % self.options.save_file
write(sampledset,
self.options.save_file,
format=self.options.format)
def execute(self):
if self.options.input_file == None:
raise FLANNException("No input file given.")
if self.options.algorithm == None:
raise FLANNException("No algorithm specified")
if self.options.test_file == None:
raise FLANNException("No test file given.")
if self.options.match_file == None:
raise FLANNException("No match file given.")
print 'Reading input dataset from', self.options.input_file
dataset = read(self.options.input_file)
flann = FLANN(log_level=self.options.log_level)
flann.build_index(dataset,
algorithm=self.options.algorithm,
trees=self.options.trees,
branching=self.options.branching,
iterations=self.options.max_iterations,
centers_init=self.options.centers_init)
print 'Reading test dataset from', self.options.test_file
testset = read(self.options.test_file)
print 'Reading ground truth from matches from', self.options.test_file
matches = read(self.options.match_file, dtype=int)
if self.options.precision > 0:
checks, time = test_with_precision(flann, dataset, testset,
matches, self.options.precision,
self.options.nn)
else:
precision, time = test_with_checks(flann, dataset, testset,
matches, self.options.checks,
self.options.nn)
def execute(self):
if self.options.input_file == None:
raise FLANNException("No input file given.")
if self.options.clusters_file == None:
raise FLANNException("No clusters file given.")
print 'Reading input dataset from', self.options.input_file
dataset = read(self.options.input_file)
print "Computing clusters"
flann = FLANN(log_level = self.options.log_level)
num_clusters = self.options.clusters
branching = self.options.branching
num_branches = (num_clusters-1)/(branching-1)
clusters = flann.hierarchical_kmeans(dataset, branching, num_branches,
self.options.max_iterations, centers_init=self.options.centers_init)
print "Saving %d clusters to file %s"%(clusters.shape[0],self.options.clusters_file)
write(clusters, self.options.clusters_file, format="dat")
def execute(self):
self.nn = FLANN(log_level=self.options.log_level)
if self.options.input_file == None:
raise FLANNException("No input file given.")
print 'Reading input dataset from', self.options.input_file
self.dataset = read(self.options.input_file)
if self.options.precision<0 or self.options.precision>1:
raise FLANNException("The precision argument must be between 0 and 1.")
params = self.nn.build_index(self.dataset, target_precision=self.options.precision, build_weight=self.options.build_weight,
memory_weight=self.options.memory_weight, sample_fraction=self.options.sample_fraction)
if self.options.params_file != None:
params_stream = open(self.options.params_file,"w")
else:
params_stream = sys.stdout
configdict = ConfigParser();
configdict.add_section('params')
for (k,v) in params.items():
configdict.set('params',k,v)
configdict.write(params_stream)
def execute(self):
if self.options.input_file == None:
raise FLANNException("No input file given.")
if self.options.clusters_file == None:
raise FLANNException("No clusters file given.")
print 'Reading input dataset from', self.options.input_file
dataset = read(self.options.input_file)
print "Computing clusters"
flann = FLANN(log_level=self.options.log_level)
num_clusters = self.options.clusters
branching = self.options.branching
num_branches = (num_clusters - 1) / (branching - 1)
clusters = flann.hierarchical_kmeans(
dataset,
branching,
num_branches,
self.options.max_iterations,
centers_init=self.options.centers_init)
print "Saving %d clusters to file %s" % (clusters.shape[0],
self.options.clusters_file)
write(clusters, self.options.clusters_file, format="dat")
