def testComplete2(self):
# have a transformed 2.0 dataset, load it, and have gaia 2.1 transform
# a point using the history.
ds = DataSet()
self.assertRaises(Exception, ds.load, testdata.GAIA_20_BACKWARDS_COMPAT_PCA_DATASET)
return
ds.load(testdata.GAIA_20_BACKWARDS_COMPAT_PCA_DATASET)
ds21 = DataSet()
ds21.load(testdata.TEST_DATABASE)
p = ds21.point("17 Blue Monday ['88 12' Version].mp3")
ds21 = ds.history().mapDataSet(ds21)
self.assertEqual(ds.history().mapPoint(p),
ds21.history().mapPoint(p))
ds = transform(ds, 'fixlength')
ds21 = transform(ds21, 'fixlength')
def search(ds, p):
p = ds.history().mapPoint(p)
dist = MetricFactory.create('euclidean', ds.layout())
return View(ds).nnSearch(p, dist).get(5)
self.compareResults(search(ds, p),
search(ds21, p))
def train_SVM(dataset,
groundTruth,
descriptorNames,
exclude=[],
svmtype='c-svc',
kernel='rbf',
c=1,
gamma=1):
# recreate a copy of the given dataset without history
ds = DataSet()
ds.addPoints([p for p in dataset.points()])
ds = transform(ds, 'normalize', {
'descriptorNames': descriptorNames,
'except': exclude,
'independent': True
})
ds = transform(
ds, 'svmtrain', {
'descriptorNames': descriptorNames,
'except': exclude,
'className': groundTruth.className,
'type': svmtype,
'kernel': kernel,
'c': c,
'gamma': gamma
})
h = ds.history()
return lambda p: str(h.mapPoint(p)[groundTruth.className])
def testHistory(self):
ds = testdata.loadTestDB()
ignored_descs = testdata.TEST_DATABASE_VARLENGTH_REAL
testdata.resetSettings()
ds_orig = testdata.loadTestDB()
# cleaning, mandatory step
ds = transform(ds, 'fixlength', {'except': ignored_descs})
cleaned_db = transform(ds, 'cleaner', {'except': ignored_descs})
# removing annoying descriptors, like mfcc.cov & mfcc.icov, who don't
# like to be normalized like the other ones (constant value: dimension)
no_mfcc_db = transform(cleaned_db, 'remove',
{'descriptorNames': '*mfcc*'})
# normalize, to have everyone change values
normalized_db = transform(no_mfcc_db, 'normalize',
{'except': ignored_descs})
testPoints = [
'01 Oye Como Va - Santana.mp3', '02 Carmen Burana- O Fortuna.mp3',
'07 Romeo and Juliet- the Knights\' Dance.mp3', '11 Lambada.mp3'
]
for pointName in testPoints:
p1 = normalized_db.point(pointName)
p2 = normalized_db.history().mapPoint(ds_orig.point(pointName))
for name in p1.layout().descriptorNames():
self.assertEqual(p1[name], p2[name])
(tmpFile, tmpName) = tempfile.mkstemp()
os.close(tmpFile)
normalized_db.save(tmpName)
reloaded_db = DataSet()
reloaded_db.load(tmpName)
for pointName in testPoints:
p1 = normalized_db.point(pointName)
p2 = normalized_db.history().mapPoint(ds_orig.point(pointName))
p3 = reloaded_db.point(pointName)
p4 = reloaded_db.history().mapPoint(ds_orig.point(pointName))
self.assert_(p1.layout() == p2.layout())
self.assert_(p2.layout() == p3.layout())
self.assert_(p3.layout() == p4.layout())
for name in p1.layout().descriptorNames():
self.assertEqual(p1[name], p2[name])
self.assertEqual(p2[name], p3[name])
self.assertEqual(p3[name], p4[name])
# remove temp file
os.remove(tmpName)
def train_SVM(dataset, groundTruth, descriptorNames, exclude = [], svmtype = 'c-svc', kernel = 'rbf', c = 1, gamma = 1):
# recreate a copy of the given dataset without history
ds = DataSet()
ds.addPoints([ p for p in dataset.points() ])
ds = transform(ds, 'normalize', { 'descriptorNames': descriptorNames,
'except': exclude,
'independent': True })
ds = transform(ds, 'svmtrain', { 'descriptorNames': descriptorNames,
'except': exclude,
'className': groundTruth.className,
'type': svmtype,
'kernel': kernel,
'c': c,
'gamma': gamma})
h = ds.history()
return lambda p: str(h.mapPoint(p)[groundTruth.className])
def harmonizeChunks(partfiles):
# TODO: check all histories are the same, if not, try to do sth about it
# find the GCLD (greatest common layout divisor :-) )
ds = DataSet()
ds.load(partfiles[0])
origLayout = ds.layout().copy()
gcld = ds.layout().copy()
for pfile in partfiles[1:]:
ds.load(pfile)
gcld = gcld & ds.layout()
# keep some stats about which descriptors got removed and the reason why before throwing
# away the original history and simplifying it
vldescs = set()
nandescs = set()
# now that we have our GCLD, transform all the chunks so they have the same layout (our GCLD)
# and simplify their histories so that they also have the same history (the minimum history
# required to arrive at this target layout).
for pfile in partfiles:
ds.load(pfile)
for t in ds.history().toPython():
tname = t['Analyzer name']
descs = t['Applier parameters']['descriptorNames']
if tname == 'cleaner': nandescs.update(descs)
elif tname == 'removevl': vldescs.update(descs)
toremove = ds.layout().differenceWith(gcld)
if toremove:
ds = transform(ds, 'remove', {'descriptorNames': toremove})
ds.simplifyHistory()
ds.save(pfile)
# also get the other descriptors that got removed (because of a select or remove transfo)
rdescs = set(origLayout.differenceWith(gcld)) - (vldescs | nandescs)
return vldescs, nandescs, rdescs
def harmonizeChunks(partfiles):
# TODO: check all histories are the same, if not, try to do sth about it
# find the GCLD (greatest common layout divisor :-) )
ds = DataSet()
ds.load(partfiles[0])
origLayout = ds.layout().copy()
gcld = ds.layout().copy()
for pfile in partfiles[1:]:
ds.load(pfile)
gcld = gcld & ds.layout()
# keep some stats about which descriptors got removed and the reason why before throwing
# away the original history and simplifying it
vldescs = set()
nandescs = set()
# now that we have our GCLD, transform all the chunks so they have the same layout (our GCLD)
# and simplify their histories so that they also have the same history (the minimum history
# required to arrive at this target layout).
for pfile in partfiles:
ds.load(pfile)
for t in ds.history().toPython():
tname = t['Analyzer name']
descs = t['Applier parameters']['descriptorNames']
if tname == 'cleaner': nandescs.update(descs)
elif tname == 'removevl': vldescs.update(descs)
toremove = ds.layout().differenceWith(gcld)
if toremove:
ds = transform(ds, 'remove', { 'descriptorNames': toremove })
ds.simplifyHistory()
ds.save(pfile)
# also get the other descriptors that got removed (because of a select or remove transfo)
rdescs = set(origLayout.differenceWith(gcld)) - (vldescs | nandescs)
return vldescs, nandescs, rdescs
class GaiaWrapper:
def __init__(self, indexing_only_mode=False):
self.indexing_only_mode = indexing_only_mode
self.index_path = sim_settings.INDEX_DIR
self.original_dataset = DataSet()
self.pca_dataset = DataSet()
if not self.indexing_only_mode:
self.original_dataset_path = self.__get_dataset_path(
sim_settings.INDEX_NAME)
else:
self.original_dataset_path = self.__get_dataset_path(
sim_settings.INDEXING_SERVER_INDEX_NAME)
self.descriptor_names = {}
self.metrics = {}
self.view = None
self.view_pca = None
self.transformations_history = None
self.__load_dataset()
def __get_dataset_path(self, ds_name):
return os.path.join(sim_settings.INDEX_DIR, ds_name + '.db')
def __load_dataset(self):
"""
Loads the dataset, does all the necessary steps to make it available for similarity queries and creates the PCA
version of it. If dataset does not exist, creates a new empty one.
NOTE: we assume that loaded datasets will have been prepared and normalized (see_
_prepare_original_dataset() and __normalize_original_dataset()) on due time (see add_point() method below),
therefore this function does not prepare or normalize loaded datasets.
"""
if not os.path.exists(sim_settings.INDEX_DIR):
os.makedirs(sim_settings.INDEX_DIR)
# load original dataset
if os.path.exists(self.original_dataset_path):
self.original_dataset.load(self.original_dataset_path)
self.__calculate_descriptor_names()
if self.original_dataset.size(
) >= sim_settings.SIMILARITY_MINIMUM_POINTS and not self.indexing_only_mode:
# Save transformation history so we do not need to compute it every time we need it
self.transformations_history = self.original_dataset.history(
).toPython()
# Build metrics for the different similarity presets, create a Gaia view
self.__build_metrics()
view = View(self.original_dataset)
self.view = view
# Compute PCA and create pca view and metric
# NOTE: this step may take a long time if the dataset is big, but it only needs to be performed once
# when the similarity server is loaded-
self.pca_dataset = transform(
self.original_dataset, 'pca', {
'descriptorNames': sim_settings.PCA_DESCRIPTORS,
'dimension': sim_settings.PCA_DIMENSIONS,
'resultName': 'pca'
})
self.pca_dataset.setReferenceDataSet(self.original_dataset)
self.view_pca = View(self.pca_dataset)
self.__build_pca_metric()
if self.original_dataset.history().size() <= 0:
logger.info('Dataset loaded, size: %s points' %
(self.original_dataset.size()))
else:
logger.info(
'Dataset loaded, size: %s points (%i fixed-length desc., %i variable-length desc.)'
% (self.original_dataset.size(),
len(self.descriptor_names['fixed-length']),
len(self.descriptor_names['variable-length'])))
else:
# If there is no existing dataset we create an empty one.
# For the moment we do not create any distance metric nor a view because search won't be possible until
# the DB has a minimum of SIMILARITY_MINIMUM_POINTS
self.original_dataset.save(self.original_dataset_path)
self.__calculate_descriptor_names()
logger.info('Created new dataset, size: %s points (should be 0)' %
(self.original_dataset.size()))
def __prepare_original_dataset(self):
logger.info('Preparing the original dataset.')
self.original_dataset = self.prepare_original_dataset_helper(
self.original_dataset)
self.__calculate_descriptor_names()
def __normalize_original_dataset(self):
logger.info('Normalizing the original dataset.')
self.original_dataset = self.normalize_dataset_helper(
self.original_dataset, self.descriptor_names['fixed-length'])
def __calculate_descriptor_names(self):
layout = self.original_dataset.layout()
all_descriptor_names = layout.descriptorNames()
fixed_length_descritpor_names = []
variable_length_descritpor_names = []
multidimensional_descriptor_names = []
for name in all_descriptor_names:
region = layout.descriptorLocation(name)
if region.lengthType() == VariableLength:
variable_length_descritpor_names.append(name)
else:
fixed_length_descritpor_names.append(name)
try:
if region.dimension() > 1:
multidimensional_descriptor_names.append(name)
except: # TODO: exception too broad here...
pass
self.descriptor_names = {
'all': all_descriptor_names,
'fixed-length': fixed_length_descritpor_names,
'variable-length': variable_length_descritpor_names,
'multidimensional': multidimensional_descriptor_names
}
@staticmethod
def prepare_original_dataset_helper(ds):
ds = transform(
ds, 'FixLength'
) # Needed to optimize use of fixed-length descriptors and save memory
ds = transform(
ds, 'Cleaner'
) # Remove descriptors that will cause problems in further transformations
try:
ds = transform(ds, 'enumerate',
{'descriptorNames': ['.tonal.chords_progression']})
except: # TODO: exception too broad here...
logger.info(
'WARNING: enumerate transformation to .tonal.chords_progression could not be performed.'
)
return ds
@staticmethod
def normalize_dataset_helper(ds, descriptor_names):
# NOTE: The "except" list of descriptors below should be reviewed if a new extractor is used. The point is to
# remove descriptors can potentially break normalize transform (e.g. descriptors with value = 0)
normalization_params = {
"descriptorNames": descriptor_names,
"except": [
"*.min",
"*.max",
"tonal.chords_histogram",
],
"independent": True,
"outliers": -1
}
ds = transform(ds, 'normalize', normalization_params)
return ds
def __build_metrics(self):
for preset in sim_settings.PRESETS:
if preset != 'pca': # PCA metric is built only after pca dataset is created so it should not be built here
logger.info('Bulding metric for preset %s' % preset)
name = preset
path = sim_settings.PRESET_DIR + name + ".yaml"
preset_file = yaml.safe_load(open(path))
distance = preset_file['distance']['type']
parameters = preset_file['distance']['parameters']
search_metric = DistanceFunctionFactory.create(
str(distance), self.original_dataset.layout(), parameters)
self.metrics[name] = search_metric
def __build_pca_metric(self):
logger.info('Bulding metric for preset pca')
preset_file = yaml.safe_load(open(sim_settings.PRESET_DIR +
"pca.yaml"))
distance = preset_file['distance']['type']
parameters = preset_file['distance']['parameters']
search_metric = DistanceFunctionFactory.create(
str(distance), self.pca_dataset.layout(), parameters)
self.metrics['pca'] = search_metric
def add_point(self, point_location, point_name):
if self.original_dataset.contains(str(point_name)):
self.original_dataset.removePoint(str(point_name))
p = Point()
if os.path.exists(str(point_location)):
try:
p.load(str(point_location))
p.setName(str(point_name))
if self.original_dataset.size(
) <= sim_settings.SIMILARITY_MINIMUM_POINTS:
# Add point to original_dataset because PCA dataset has not been created yet
self.original_dataset.addPoint(p)
msg = 'Added point with name %s. Index has now %i points.' % \
(str(point_name), self.original_dataset.size())
logger.info(msg)
else:
# Add point to PCA dataset because it has been already created.
# PCA dataset will take care of adding the point to the original dataset as well.
self.pca_dataset.addPoint(p)
msg = 'Added point with name %s. Index has now %i points (pca index has %i points).' % \
(str(point_name), self.original_dataset.size(), self.pca_dataset.size())
logger.info(msg)
except Exception as e:
msg = 'Point with name %s could NOT be added (%s).' % (
str(point_name), str(e))
logger.info(msg)
return {
'error': True,
'result': msg,
'status_code': sim_settings.SERVER_ERROR_CODE
}
else:
msg = 'Point with name %s could NOT be added because analysis file does not exist (%s).' % \
(str(point_name), str(point_location))
logger.info(msg)
return {
'error': True,
'result': msg,
'status_code': sim_settings.SERVER_ERROR_CODE
}
if self.original_dataset.size(
) == sim_settings.SIMILARITY_MINIMUM_POINTS:
# Do enumerate
try:
self.original_dataset = transform(
self.original_dataset, 'enumerate',
{'descriptorNames': ['.tonal.chords_progression']})
except: # TODO: exception too broad here...
logger.info(
'WARNING: enumerate transformation to .tonal.chords_progression could not be performed.'
)
# If when adding a new point we reach the minimum points for similarity, do the needful so that the dataset
# can be used for search. This includes preparing the dataset, normalizing it, saveing it and creating view and
# distance metrics. This will only happen once when the size of the dataset reaches SIMILARITY_MINIMUM_POINTS.
if self.original_dataset.size(
) == sim_settings.SIMILARITY_MINIMUM_POINTS and not self.indexing_only_mode:
self.__prepare_original_dataset()
self.__normalize_original_dataset()
self.transformations_history = self.original_dataset.history(
).toPython()
self.save_index(msg="(reaching %i points)" %
sim_settings.SIMILARITY_MINIMUM_POINTS)
# TODO: the code below is repeated from __load_dataset() method, should be moved into a util function
# Build metrics for the different similarity presets, create a Gaia view
self.__build_metrics()
view = View(self.original_dataset)
self.view = view
# Compute PCA and create pca view and metric
# NOTE: this step may take a long time if the dataset is big, but it only needs to be performed once
# when the similarity server is loaded-
self.pca_dataset = transform(
self.original_dataset, 'pca', {
'descriptorNames': sim_settings.PCA_DESCRIPTORS,
'dimension': sim_settings.PCA_DIMENSIONS,
'resultName': 'pca'
})
self.pca_dataset.setReferenceDataSet(self.original_dataset)
self.view_pca = View(self.pca_dataset)
self.__build_pca_metric()
return {'error': False, 'result': msg}
def delete_point(self, point_name):
if self.original_dataset.contains(str(point_name)):
if self.original_dataset.size(
) <= sim_settings.SIMILARITY_MINIMUM_POINTS:
# Remove from original dataset
self.original_dataset.removePoint(str(point_name))
else:
# Remove from pca dataset (pca dataset will take care of removing from original dataset too)
self.pca_dataset.removePoint(str(point_name))
logger.info(
'Deleted point with name %s. Index has now %i points (pca index has %i points).'
% (str(point_name), self.original_dataset.size(),
self.pca_dataset.size()))
return {'error': False, 'result': True}
else:
msg = 'Can\'t delete point with name %s because it does not exist.' % str(
point_name)
logger.info(msg)
return {
'error': True,
'result': msg,
'status_code': sim_settings.NOT_FOUND_CODE
}
def get_point(self, point_name):
logger.info('Getting point with name %s' % str(point_name))
if self.original_dataset.contains(str(point_name)):
return self.original_dataset.point(str(point_name))
def get_all_point_names(self):
point_names = sorted(
[int(name) for name in self.original_dataset.pointNames()])
logger.info('Getting all point names (%i points)' % len(point_names))
return {'error': False, 'result': point_names}
def save_index(self, filename=None, msg=""):
tic = time.time()
path = self.original_dataset_path
if filename:
path = sim_settings.INDEX_DIR + filename + ".db"
logger.info('Saving index to (%s)...' % path + msg)
self.original_dataset.save(path)
toc = time.time()
logger.info(
'Finished saving index (done in %.2f seconds, index has now %i points).'
% ((toc - tic), self.original_dataset.size()))
return {'error': False, 'result': path}
def contains(self, point_name):
logger.info('Checking if index has point with name %s' %
str(point_name))
return {
'error': False,
'result': self.original_dataset.contains(point_name)
}
def get_sounds_descriptors(self,
point_names,
descriptor_names=None,
normalization=True,
only_leaf_descriptors=False):
"""
Returns a list with the descriptor values for all requested point names
"""
logger.info('Getting descriptors for points %s' %
','.join([str(name) for name in point_names]))
# Add dot '.' at the beginning of descriptor names if not present
if descriptor_names:
descriptor_names_aux = list()
for name in descriptor_names:
if name[0] != '.':
descriptor_names_aux.append('.' + name)
else:
descriptor_names_aux.append(name)
descriptor_names = descriptor_names_aux[:]
data = dict()
required_descriptor_names = self.__calculate_complete_required_descriptor_names(
descriptor_names, only_leaf_descriptors=only_leaf_descriptors)
if type(required_descriptor_names) == dict:
return required_descriptor_names # There has been an error
for point_name in point_names:
sound_descriptors = self.__get_point_descriptors(
point_name, required_descriptor_names, normalization)
if 'error' not in sound_descriptors:
data[point_name] = sound_descriptors
return {'error': False, 'result': data}
def __calculate_complete_required_descriptor_names(
self, descriptor_names, only_leaf_descriptors=False):
if not descriptor_names:
descriptor_names = self.descriptor_names['all'][:]
try:
structured_layout = generate_structured_dict_from_layout(
self.descriptor_names['all'][:])
processed_descriptor_names = []
for name in descriptor_names:
nested_descriptors = get_nested_dictionary_value(
name.split('.')[1:], structured_layout)
if not nested_descriptors:
processed_descriptor_names.append(name)
else:
if only_leaf_descriptors:
# only return descriptors if nested descriptors are statistics
if len(
set(nested_descriptors.keys()).intersection([
'min', 'max', 'dvar2', 'dmean2', 'dmean',
'var', 'dvar', 'mean'
])) > 0:
for extra_name in nested_descriptors.keys():
processed_descriptor_names.append(
'%s.%s' % (name, extra_name))
else:
# Return all nested descriptor names
extra_names = []
get_nested_descriptor_names(nested_descriptors,
extra_names)
for extra_name in extra_names:
processed_descriptor_names.append(
'%s.%s' % (name, extra_name))
processed_descriptor_names = list(set(processed_descriptor_names))
return processed_descriptor_names
except:
return {
'error': True,
'result': 'Wrong descriptor names, unable to create layout.',
'status_code': sim_settings.BAD_REQUEST_CODE
}
def __get_point_descriptors(self,
point_name,
required_descriptor_names,
normalization=True):
"""
Get normalization coefficients to transform the input data (get info from the last transformation which has
been a normalization)
"""
normalization_coeffs = None
if not normalization:
trans_hist = self.transformations_history
for i in range(0, len(trans_hist)):
if trans_hist[-(i + 1)]['Analyzer name'] == 'normalize':
normalization_coeffs = trans_hist[-(
i + 1)]['Applier parameters']['coeffs']
required_layout = generate_structured_dict_from_layout(
required_descriptor_names)
try:
p = self.original_dataset.point(str(point_name))
except:
return {
'error': True,
'result': 'Sound does not exist in gaia index.',
'status_code': sim_settings.NOT_FOUND_CODE
}
for descriptor_name in required_descriptor_names:
try:
value = p.value(str(descriptor_name))
if normalization_coeffs:
if descriptor_name in normalization_coeffs:
a = normalization_coeffs[descriptor_name]['a']
b = normalization_coeffs[descriptor_name]['b']
if len(a) == 1:
value = float(value - b[0]) / a[0]
else:
normalized_value = []
for i in range(0, len(a)):
normalized_value.append(
float(value[i] - b[i]) / a[i])
value = normalized_value
except:
try:
value = p.label(str(descriptor_name))
except:
value = None
if descriptor_name[0] == '.':
descriptor_name = descriptor_name[1:]
set_nested_dictionary_value(descriptor_name.split('.'),
required_layout, value)
return required_layout
# SIMILARITY SEARCH and CONTENT SEARCH
def search_dataset(self,
query_point,
number_of_results,
preset_name,
offset=0):
preset_name = str(preset_name)
results = []
count = 0
size = self.original_dataset.size()
if size < sim_settings.SIMILARITY_MINIMUM_POINTS:
msg = 'Not enough datapoints in the dataset (%s < %s).' % (
size, sim_settings.SIMILARITY_MINIMUM_POINTS)
logger.info(msg)
return {
'error': True,
'result': msg,
'status_code': sim_settings.SERVER_ERROR_CODE
}
query_point = str(query_point)
logger.info('NN search for point with name %s (preset = %s)' %
(query_point, preset_name))
results = []
if not self.original_dataset.contains(query_point):
msg = "Sound with id %s doesn't exist in the dataset." % query_point
logger.info(msg)
return {
'error': True,
'result': msg,
'status_code': sim_settings.NOT_FOUND_CODE
}
if preset_name == 'pca':
# Search on PCA view
search = self.view_pca.nnSearch(query_point,
self.metrics[preset_name])
else:
# Search on original dataset view
search = self.view.nnSearch(query_point, self.metrics[preset_name])
results = search.get(int(number_of_results), offset=int(offset))
count = search.size()
return {'error': False, 'result': {'results': results, 'count': count}}
def api_search(self, target_type, target, filter, preset_name,
metric_descriptor_names, num_results, offset, in_ids):
# Check if index has sufficient points
size = self.original_dataset.size()
if size < sim_settings.SIMILARITY_MINIMUM_POINTS:
msg = 'Not enough datapoints in the dataset (%s < %s).' % (
size, sim_settings.SIMILARITY_MINIMUM_POINTS)
logger.info(msg)
return {
'error': True,
'result': msg,
'status_code': sim_settings.SERVER_ERROR_CODE
}
# Get some dataset parameters that will be useful later
trans_hist = self.transformations_history
layout = self.original_dataset.layout()
pca_layout = self.pca_dataset.layout()
coeffs = None # Get normalization coefficients
for i in range(0, len(trans_hist)):
if trans_hist[-(i + 1)]['Analyzer name'] == 'normalize':
coeffs = trans_hist[-(i + 1)]['Applier parameters']['coeffs']
# Process target
if target:
if target_type == 'sound_id':
query_point = str(target)
if not self.original_dataset.contains(query_point):
msg = "Sound with id %s doesn't exist in the dataset and can not be set as similarity target." \
% query_point
logger.info(msg)
return {
'error': True,
'result': msg,
'status_code': sim_settings.NOT_FOUND_CODE
}
else:
query = query_point
elif target_type == 'descriptor_values':
# Transform input params to the normalized feature space and add them to a query point
# If there are no params specified in the target, the point is set as empty (probably random sounds
# are returned)
feature_names = []
query = Point()
query.setLayout(layout)
try:
for param in target.keys():
# Only add numerical parameters. Non numerical ones (like key) are only used as filters
if param in coeffs.keys():
feature_names.append(str(param))
value = target[param]
if coeffs:
a = coeffs[param]['a']
b = coeffs[param]['b']
if len(a) == 1:
norm_value = a[0] * value + b[0]
else:
norm_value = []
for i in range(0, len(a)):
norm_value.append(a[i] * value[i] +
b[i])
query.setValue(str(param), norm_value)
else:
query.setValue(str(param), value)
except:
return {
'error': True,
'result':
'Invalid target (descriptor values could not be correctly parsed)',
'status_code': sim_settings.BAD_REQUEST_CODE
}
# Overwrite metric with present descriptors in target
metric = DistanceFunctionFactory.create(
'euclidean', layout, {'descriptorNames': feature_names})
elif target_type == 'file':
# Target is specified as the attached file
# Create a point with the data in 'descriptors_data' and search for it
target_file_parsing_type = '-'
try:
# Try directly loading the file
p, query = Point(), Point()
p.loadFromString(yaml.dump(target))
if preset_name == 'pca':
query = self.pca_dataset.history().mapPoint(
p) # map point to pca dataset
else:
query = self.original_dataset.history().mapPoint(
p) # map point to original dataset
target_file_parsing_type = 'mapPoint'
except Exception as e:
logger.info(
'Unable to create gaia point from uploaded file (%s). '
'Trying adding descriptors one by one.' % e)
# If does not work load descriptors one by one
try:
query = Point()
#query.setLayout(layout)
feature_names = []
get_nested_descriptor_names(target, feature_names)
feature_names = [
'.%s' % item for item in feature_names
]
nonused_features = []
for param in feature_names:
if param in coeffs.keys():
value = get_nested_dictionary_value(
param[1:].split('.'), target)
if coeffs:
try:
a = coeffs[param]['a']
b = coeffs[param]['b']
if len(a) == 1:
norm_value = a[0] * value + b[0]
else:
norm_value = []
for i in range(0, len(a)):
norm_value.append(a[i] *
value[i] +
b[i])
query.setValue(str(param[1:]),
norm_value)
except:
nonused_features.append(param)
else:
query.setValue(str(param[1:]), value)
else:
nonused_features.append(param)
if preset_name == 'pca':
query = self.pca_dataset.history().mapPoint(
query) # map point to pca dataset
else:
query = self.original_dataset.history().mapPoint(
p) # map point to original dataset
target_file_parsing_type = 'walkDict'
except Exception as e:
logger.info(
'Unable to create gaia point from uploaded file and adding descriptors one by '
'one (%s)' % e)
return {
'error':
True,
'result':
'Unable to create gaia point from uploaded file. Probably the '
'file does not have the required layout. Are you using the '
'correct version of Essentia\'s Freesound extractor?',
'status_code':
sim_settings.SERVER_ERROR_CODE
}
else:
query = Point() # Empty target
if preset_name == 'pca':
query.setLayout(pca_layout)
else:
query.setLayout(layout)
# Process filter
if filter:
filter = parse_filter_list(filter, coeffs)
else:
filter = "" # Empty filter
# log
log_message = 'Similarity search'
if target:
if target_type == 'sound_id':
log_target = '%s (sound id)' % str(target)
elif target_type == 'descriptor_values':
log_target = '%s (descriptor values)' % str(target)
elif target_type == 'file':
log_target = 'uploaded file (%s)' % target_file_parsing_type
log_message += ' with target: %s' % log_target
if filter:
log_message += ' with filter: %s' % str(filter)
logger.info(log_message)
# if in_ids is specified, edit the filter accordingly
if in_ids:
if not filter:
filter = 'WHERE point.id IN ("' + '", "'.join(in_ids) + '")'
else:
filter += ' AND point.id IN ("' + '", "'.join(in_ids) + '")'
# Set query metric
metric = self.metrics[preset_name]
if metric_descriptor_names:
metric = DistanceFunctionFactory.create(
'euclidean', layout,
{'descriptorNames': metric_descriptor_names})
# Do query!
try:
if target_type == 'descriptor_values' and target:
search = self.view.nnSearch(query, metric, str(filter))
else:
if preset_name == 'pca':
search = self.view_pca.nnSearch(query, metric, str(filter))
else:
search = self.view.nnSearch(query, metric, str(filter))
results = search.get(num_results, offset=offset)
count = search.size()
except Exception as e:
return {
'error': True,
'result': 'Similarity server error',
'status_code': sim_settings.SERVER_ERROR_CODE
}
note = None
if target_type == 'file':
if target_file_parsing_type == 'walkDict':
note = 'The layout of the given analysis file differed from what we expected. Similarity results ' \
'might not be accurate. Was the file generated with the last version of Essentia\'s ' \
'Freesound extractor?'
return {
'error': False,
'result': {
'results': results,
'count': count,
'note': note
}
}
class GaiaWrapper:
def __init__(self):
self.index_path = INDEX_DIR
self.original_dataset = DataSet()
self.original_dataset_path = self.__get_dataset_path(INDEX_NAME)
self.metrics = {}
self.view = None
self.__load_dataset()
def __get_dataset_path(self, ds_name):
return os.path.join(INDEX_DIR, ds_name + ".db")
def __load_dataset(self):
# Loads the dataset, applies transforms if needed and saves. If dataset does not exists, creates an empty one and saves.
if not os.path.exists(INDEX_DIR):
os.makedirs(INDEX_DIR)
# load original dataset
if os.path.exists(self.original_dataset_path):
self.original_dataset.load(self.original_dataset_path)
if self.original_dataset.size() >= SIMILARITY_MINIMUM_POINTS:
# if we have loaded a dataset of the correct size but it is unprepared, prepare it
if self.original_dataset.history().size() <= 0:
self.__prepare_original_dataset()
self.__normalize_original_dataset()
self.original_dataset.save(self.original_dataset_path)
# if we have loaded a dataset which has not been normalized, normalize it
normalized = False
for element in self.original_dataset.history().toPython():
if element["Analyzer name"] == "normalize":
normalized = True
break
if not normalized:
self.__normalize_original_dataset()
self.original_dataset.save(self.original_dataset_path)
# build metrics for the different similarity presets
self.__build_metrics()
# create view
view = View(self.original_dataset)
self.view = view
logger.debug("Dataset loaded, size: %s points" % (self.original_dataset.size()))
else:
# If there is no existing dataset we create an empty one.
# For the moment we do not create any distance metric nor a view because search won't be possible until the DB has a minimum of SIMILARITY_MINIMUM_POINTS
self.original_dataset.save(self.original_dataset_path)
logger.debug("Created new dataset, size: %s points (should be 0)" % (self.original_dataset.size()))
def __prepare_original_dataset(self):
logger.debug("Preparing the original dataset.")
self.original_dataset = self.prepare_original_dataset_helper(self.original_dataset)
def __normalize_original_dataset(self):
logger.debug("Normalizing the original dataset.")
self.original_dataset = self.normalize_dataset_helper(self.original_dataset)
@staticmethod
def prepare_original_dataset_helper(ds):
proc_ds1 = transform(ds, "RemoveVL")
proc_ds2 = transform(proc_ds1, "FixLength")
proc_ds1 = None
prepared_ds = transform(proc_ds2, "Cleaner")
proc_ds2 = None
return prepared_ds
@staticmethod
def normalize_dataset_helper(ds):
# Remove ['.lowlevel.mfcc.cov','.lowlevel.mfcc.icov'] (they give errors when normalizing)
ds = transform(ds, "remove", {"descriptorNames": [".lowlevel.mfcc.cov", ".lowlevel.mfcc.icov"]})
# Add normalization
normalization_params = {"descriptorNames": "*", "independent": True, "outliers": -1}
normalized_ds = transform(ds, "normalize", normalization_params)
ds = None
return normalized_ds
def __build_metrics(self):
for preset in PRESETS:
logger.debug("Bulding metric for preset %s" % preset)
name = preset
path = PRESET_DIR + name + ".yaml"
preset_file = yaml.load(open(path))
distance = preset_file["distance"]["type"]
parameters = preset_file["distance"]["parameters"]
search_metric = DistanceFunctionFactory.create(str(distance), self.original_dataset.layout(), parameters)
self.metrics[name] = search_metric
def add_point(self, point_location, point_name):
if self.original_dataset.contains(str(point_name)):
self.original_dataset.removePoint(str(point_name))
try:
p = Point()
p.load(str(point_location))
p.setName(str(point_name))
self.original_dataset.addPoint(p)
size = self.original_dataset.size()
logger.debug("Added point with name %s. Index has now %i points." % (str(point_name), size))
except:
msg = "Point with name %s could NOT be added. Index has now %i points." % (str(point_name), size)
logger.debug(msg)
return {"error": True, "result": msg}
# If when adding a new point we reach the minimum points for similarity, prepare the dataset, save and create view and distance metrics
# This will most never happen, only the first time we start similarity server, there is no index created and we add 2000 points.
if size == SIMILARITY_MINIMUM_POINTS:
self.__prepare_original_dataset()
self.__normalize_original_dataset()
self.save_index(msg="(reaching 2000 points)")
# build metrics for the different similarity presets
self.__build_metrics()
# create view
view = View(self.original_dataset)
self.view = view
return {"error": False, "result": True}
def delete_point(self, point_name):
if self.original_dataset.contains(str(point_name)):
self.original_dataset.removePoint(str(point_name))
logger.debug(
"Deleted point with name %s. Index has now %i points." % (str(point_name), self.original_dataset.size())
)
return {"error": False, "result": True}
else:
msg = "Can't delete point with name %s because it does not exist." % str(point_name)
logger.debug(msg)
return {"error": True, "result": msg}
def get_point(self, point_name):
logger.debug("Getting point with name %s" % str(point_name))
if self.original_dataset.contains(str(point_name)):
return self.original_dataset.point(str(point_name))
def save_index(self, filename=None, msg=""):
tic = time.time()
path = self.original_dataset_path
if filename:
path = INDEX_DIR + filename + ".db"
logger.debug("Saving index to (%s)..." % path + msg)
self.original_dataset.save(path)
toc = time.time()
logger.debug(
"Finished saving index (done in %.2f seconds, index has now %i points)."
% ((toc - tic), self.original_dataset.size())
)
return {"error": False, "result": path}
def contains(self, point_name):
logger.debug("Checking if index has point with name %s" % str(point_name))
return {"error": False, "result": self.original_dataset.contains(point_name)}
# SIMILARITY SEARCH (WEB and API)
def search_dataset(self, query_point, number_of_results, preset_name):
preset_name = str(preset_name)
query_point = str(query_point)
logger.debug("NN search for point with name %s (preset = %s)" % (query_point, preset_name))
size = self.original_dataset.size()
if size < SIMILARITY_MINIMUM_POINTS:
msg = "Not enough datapoints in the dataset (%s < %s)." % (size, SIMILARITY_MINIMUM_POINTS)
logger.debug(msg)
return {"error": True, "result": msg}
# raise Exception('Not enough datapoints in the dataset (%s < %s).' % (size, SIMILARITY_MINIMUM_POINTS))
if query_point.endswith(".yaml"):
# The point doesn't exist in the dataset....
# So, make a temporary point, add all the transformations
# to it and search for it
p, p1 = Point(), Point()
p.load(query_point)
p1 = self.original_dataset.history().mapPoint(p)
similar_sounds = self.view.nnSearch(p1, self.metrics[preset_name]).get(int(number_of_results))
else:
if not self.original_dataset.contains(query_point):
msg = "Sound with id %s doesn't exist in the dataset." % query_point
logger.debug(msg)
return {"error": True, "result": msg}
# raise Exception("Sound with id %s doesn't exist in the dataset." % query_point)
similar_sounds = self.view.nnSearch(query_point, self.metrics[preset_name]).get(int(number_of_results))
return {"error": False, "result": similar_sounds}
# CONTENT-BASED SEARCH (API)
def query_dataset(self, query_parameters, number_of_results):
size = self.original_dataset.size()
if size < SIMILARITY_MINIMUM_POINTS:
msg = "Not enough datapoints in the dataset (%s < %s)." % (size, SIMILARITY_MINIMUM_POINTS)
logger.debug(msg)
return {"error": True, "result": msg}
# raise Exception('Not enough datapoints in the dataset (%s < %s).' % (size, SIMILARITY_MINIMUM_POINTS))
trans_hist = self.original_dataset.history().toPython()
layout = self.original_dataset.layout()
# Get normalization coefficients to transform the input data (get info from the last transformation which has been a normalization)
coeffs = None
for i in range(0, len(trans_hist)):
if trans_hist[-(i + 1)]["Analyzer name"] == "normalize":
coeffs = trans_hist[-(i + 1)]["Applier parameters"]["coeffs"]
##############
# PARSE TARGET
##############
# Transform input params to the normalized feature space and add them to a query point
# If there are no params specified in the target, the point is set as empty (probably random sounds are returned)
q = Point()
q.setLayout(layout)
feature_names = []
# If some target has been specified...
if query_parameters["target"].keys():
for param in query_parameters["target"].keys():
# Only add numerical parameters. Non numerical ones (like key) are only used as filters
if param in coeffs.keys():
feature_names.append(str(param))
value = query_parameters["target"][param]
if coeffs:
a = coeffs[param]["a"]
b = coeffs[param]["b"]
if len(a) == 1:
norm_value = a[0] * value + b[0]
else:
norm_value = []
for i in range(0, len(a)):
norm_value.append(a[i] * value[i] + b[i])
# text = str(type(param)) + " " + str(type(norm_value))
q.setValue(str(param), norm_value)
else:
q.setValue(str(param), value)
##############
# PARSE FILTER
##############
filter = ""
# If some filter has been specified...
if query_parameters["filter"]:
if type(query_parameters["filter"][0:5]) == str:
filter = query_parameters["filter"]
else:
filter = self.parse_filter_list(query_parameters["filter"], coeffs)
#############
# DO QUERY!!!
#############
logger.debug(
"Content based search with target: " + str(query_parameters["target"]) + " and filter: " + str(filter)
)
metric = DistanceFunctionFactory.create("euclidean", layout, {"descriptorNames": feature_names})
# Looks like that depending on the version of gaia, variable filter must go after or before the metric
# For the gaia version we have currently (sep 2012) in freesound: nnSearch(query,filter,metric)
# results = self.view.nnSearch(q,str(filter),metric).get(int(number_of_results)) # <- Freesound
results = self.view.nnSearch(q, metric, str(filter)).get(int(number_of_results))
return {"error": False, "result": results}
# UTILS for content-based search
def prepend_value_label(self, f):
if f["type"] == "NUMBER" or f["type"] == "RANGE" or f["type"] == "ARRAY":
return "value"
else:
return "label"
def parse_filter_list(self, filter_list, coeffs):
# TODO: eliminate this?
# coeffs = None
filter = "WHERE"
for f in filter_list:
if type(f) != dict:
filter += f
else:
if f["type"] == "NUMBER" or f["type"] == "STRING" or f["type"] == "ARRAY":
if f["type"] == "NUMBER":
if coeffs:
norm_value = coeffs[f["feature"]]["a"][0] * f["value"] + coeffs[f["feature"]]["b"][0]
else:
norm_value = f["value"]
elif f["type"] == "ARRAY":
if coeffs:
norm_value = []
for i in range(len(f["value"])):
norm_value.append(
coeffs[f["feature"]]["a"][i] * f["value"][i] + coeffs[f["feature"]]["b"][i]
)
else:
norm_value = f["value"]
else:
norm_value = f["value"]
filter += " " + self.prepend_value_label(f) + f["feature"] + "=" + str(norm_value) + " "
else:
filter += " "
if f["value"]["min"]:
if coeffs:
norm_value = coeffs[f["feature"]]["a"][0] * f["value"]["min"] + coeffs[f["feature"]]["b"][0]
else:
norm_value = f["value"]["min"]
filter += self.prepend_value_label(f) + f["feature"] + ">" + str(norm_value) + " "
if f["value"]["max"]:
if f["value"]["min"]:
filter += "AND "
if coeffs:
norm_value = coeffs[f["feature"]]["a"][0] * f["value"]["max"] + coeffs[f["feature"]]["b"][0]
else:
norm_value = f["value"]["max"]
filter += self.prepend_value_label(f) + f["feature"] + "<" + str(norm_value) + " "
return filter
