def find_matches(track, model):
track_id = str(track)
# Extract keypoints
fp = fingerprint.from_file(
track.path, model.settings['sr'], track_id, model.settings
)
# Find (approximate) nearest neighbors
distances, indices = ann.find_neighbors(
model.matcher,
fp.descriptors,
algorithm=model.settings['algorithm'],
k=2
)
# Build match objects
logger.info('Building match objects')
matches = []
for i, distance in enumerate(distances):
matches.append(Match(
fp.keypoints[i],
model.keypoints[indices[i][0]],
distance[0],
distance[1]
))
return matches, fp.spectrogram, fp.keypoints
def find_matches(track, model):
track_id = str(track)
# Extract keypoints
fp = fingerprint.from_file(track.path, model.settings['sr'], track_id,
model.settings)
# Find (approximate) nearest neighbors
distances, indices = ann.find_neighbors(
model.matcher,
fp.descriptors,
algorithm=model.settings['algorithm'],
k=20)
# Build match objects
logger.info('Building match objects')
matches = []
for i, distance in enumerate(distances):
neighbors = [
Neighbor(model.keypoints[index], dist)
for index, dist in zip(indices[i], distance)
]
matches.append(Match(
fp.keypoints[i],
neighbors,
))
return matches, fp.spectrogram, fp.keypoints
def find_matches(audio_path, model):
# Extract keypoints
kp, desc, img = sift_file(
audio_path,
model.settings['hop_length'],
model.settings['octave_bins'],
model.settings['n_octaves'],
model.settings['fmin'],
sr=model.settings['sr'],
**model.settings['kwargs']
)
# Find (approximate) nearest neighbors
distances, indices = ann.find_neighbors(model.clf, desc, k=2)
# Build match objects
logger.info('Building match objects')
matches = []
for i, distance in enumerate(distances):
matches.append(Match(kp[i], model.keypoints[indices[i][0]], distance[0], distance[1]))
return matches, img
def find_matches(audio_path, model):
# Extract keypoints
kp, desc, img = sift_file(audio_path,
model.settings["hop_length"],
model.settings["octave_bins"],
model.settings["n_octaves"],
model.settings["fmin"],
sr=model.settings["sr"],
**model.settings["kwargs"])
# Find (approximate) nearest neighbors
distances, indices = ann.find_neighbors(model.clf, desc, k=2)
# Build match objects
logger.info("Building match objects")
matches = []
for i, distance in enumerate(distances):
matches.append(
Match(kp[i], model.keypoints[indices[i][0]], distance[0],
distance[1]))
return matches, img
