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fix the --illustrate option so that it scales the hashes correctly #70

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fix the --illustrate option so that it scales the hashes correctly #70

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111 changes: 76 additions & 35 deletions audfprint_match.py
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Original file line number Diff line number Diff line change
Expand Up @@ -16,6 +16,7 @@

# Don't sweat failure to import graphics support.
try:
import matplotlib
import matplotlib.pyplot as plt
import librosa.display
except:
Expand Down Expand Up @@ -416,58 +417,98 @@ def file_match_to_msgs(self, analyzer, ht, qry, number=None):
else:
msgrslt.append(qrymsg + "\t" + ht.names[tophitid])
if self.illustrate:
self.illustrate_match(analyzer, ht, qry)
self.illustrate_match(analyzer, ht, qry, rslts)
return msgrslt

def illustrate_match(self, analyzer, ht, filename):
def illustrate_match(self, analyzer, ht, filename, results):
""" Show the query fingerprints and the matching ones
plotted over a spectrogram """
# Make the spectrogram
# d, sr = librosa.load(filename, sr=analyzer.target_sr)
d, sr = audio_read.audio_read(filename, sr=analyzer.target_sr, channels=1)
sgram = np.abs(stft.stft(d, n_fft=analyzer.n_fft,
hop_length=analyzer.n_hop,
window=np.hanning(analyzer.n_fft + 2)[1:-1]))
sgram = 20.0 * np.log10(np.maximum(sgram, np.max(sgram) / 1e6))
sgram = sgram - np.mean(sgram)
# High-pass filter onset emphasis
# [:-1,] discards top bin (nyquist) of sgram so bins fit in 8 bits
# spectrogram enhancement
if self.illustrate_hpf:
HPF_POLE = 0.98
sgram = np.array([scipy.signal.lfilter([1, -1],
[1, -HPF_POLE], s_row)
for s_row in sgram])[:-1, ]
sgram = sgram - np.max(sgram)
librosa.display.specshow(sgram, sr=sr, hop_length=analyzer.n_hop,
y_axis='linear', x_axis='time',
cmap='gray_r', vmin=-80.0, vmax=0)
# Do the match?
q_hashes = analyzer.wavfile2hashes(filename)
# Run query, get back the hashes for match zero
results, matchhashes = self.match_hashes(ht, q_hashes, hashesfor=0)
# Run query
#results = self.match_hashes(ht, q_hashes)
if self.sort_by_time:
results = sorted(results, key=lambda x: -x[2])
results = sorted(results, key=lambda x: x[2])
# Make the spectrograms
fig, ax = plt.subplots()
def spectrogram(audio):
(d, sr) = audio
sgram = np.abs(stft.stft(d, n_fft=analyzer.n_fft,
hop_length=analyzer.n_hop,
window=np.hanning(analyzer.n_fft + 2)[1:-1]))
sgram = 20.0 * np.log10(np.maximum(sgram, np.max(sgram) / 1e6))
sgram = sgram - np.mean(sgram)
# High-pass filter onset emphasis
# [:-1,] discards top bin (nyquist) of sgram so bins fit in 8 bits
# spectrogram enhancement
if self.illustrate_hpf:
HPF_POLE = 0.98
sgram = np.array([scipy.signal.lfilter([1, -1],
[1, -HPF_POLE], s_row)
for s_row in sgram])[:-1, ]
sgram = sgram - np.max(sgram)
librosa.display.specshow(sgram, sr=sr, hop_length=analyzer.n_hop,
y_axis='linear', x_axis='time',
cmap='gray_r', vmin=-80.0, vmax=0, ax=None)
spectrogram(audio_read.audio_read(filename,
sr=analyzer.target_sr,
channels=1)),
# Convert the hashes to landmarks
(tophitid, nhashaligned, aligntime, nhashraw, rank, min_time, max_time) = results[0]
lms = audfprint_analyze.hashes2landmarks(q_hashes)
matchhashes = None
otherhashes = []
hits = ht.get_hits(q_hashes)
t_hop = analyzer.n_hop / analyzer.target_sr
min_time_of_query = aligntime - q_hashes[0][0] - (1 / t_hop)
max_time_of_query = aligntime + q_hashes[-1][0] + (1 / t_hop)
reference_landmarks = [(t - aligntime, bin1, bin2, dt)
for (t, bin1, bin2, dt)
in audfprint_analyze.hashes2landmarks(ht.retrieve(results[0][0]))]
for idx in range(0, len(results)):
(id, _, aligntime, _, _, min_time, max_time) = results[idx]
hashes = self._unique_match_hashes(id, hits, aligntime)
if idx == 0:
matchhashes = hashes
elif id == tophitid and min_time_of_query <= aligntime + min_time and aligntime + max_time <= max_time_of_query:
otherhashes.append(hashes)
mlms = audfprint_analyze.hashes2landmarks(matchhashes)
# Overplot on the spectrogram
plt.plot(np.array([[x[0], x[0] + x[3]] for x in lms]).T,
np.array([[x[1], x[2]] for x in lms]).T,
'.-g')
plt.plot(np.array([[x[0], x[0] + x[3]] for x in mlms]).T,
np.array([[x[1], x[2]] for x in mlms]).T,
'.-r')
hscale = analyzer.target_sr/analyzer.n_fft
vscale = analyzer.n_hop/analyzer.target_sr
def p(landmarks, *args, **kwargs):
return ax.plot(np.array([[x[0] * vscale, (x[0] + x[3]) * vscale] for x in landmarks]).T,
np.array([[x[1] * hscale, x[2] * hscale] for x in landmarks]).T,
*args,
**kwargs)
plots = [p(lms, '.-g', color='green', label="Query"),
p(reference_landmarks, '.-b', color='blue', label="Reference"),
p(mlms, '.-r', color='red', label="Matching Hashes")]
if len(otherhashes) > 0:
plots.append(p(audfprint_analyze.hashes2landmarks(np.concatenate(otherhashes)), '.-y', color='orange', label="Other Matches"))

# Add title
plt.title(filename + " : Matched as " + ht.names[results[0][0]]
+ (" with %d of %d hashes" % (len(matchhashes),
len(q_hashes))))
ax.set_title(filename + " : Matched as " + str(ht.hashesperid[results[0][0]])
+ (" with %d of %d hashes" % (len(matchhashes),
len(q_hashes))))
# Hash Selector
rax = plt.axes([0.01, 0.01, 0.3, 0.15])
labels = [p[0].get_label() for p in plots]
check = matplotlib.widgets.CheckButtons(rax,
labels,
[p[0].get_visible() for p in plots])
def click_checkbox(label):
index = labels.index(label)
plot = plots[index]
for l in plot:
l.set_visible(not l.get_visible())
plt.draw()
check.on_clicked(click_checkbox)
# Display
plt.show()
# Return
return results


def localtest():
"""Function to provide quick test"""
pat = '/Users/dpwe/projects/shazam/Nine_Lives/*mp3'
Expand Down