birbnetes/cnn-classification-service
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tracing fixes
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Pünkösd Marcell
2021-08-04 15:20:29 +02:00
parent 977fc98ed2
commit ee9dea06fb
3 changed files with 34 additions and 30 deletions
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cnn_classification_service/cnn_classifier.py
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@@ -29,28 +29,32 @@ class Classifier(object):
@staticmethod
def create_spectrogram(wav_filename: str) -> Tuple[str, str]:
matplotlib.pyplot.interactive(False)
clip, sample_rate = librosa.load(wav_filename, sr=None)
fig = matplotlib.pyplot.figure(figsize=[0.72, 0.72])
ax = fig.add_subplot(111)
ax.axes.get_xaxis().set_visible(False)
ax.axes.get_yaxis().set_visible(False)
ax.set_frame_on(False)
spectogram = librosa.feature.melspectrogram(y=clip, sr=sample_rate)
librosa.display.specshow(librosa.power_to_db(spectogram, ref=numpy.max))
target_dir = tempfile.mkdtemp(dir="/dev/shm")
wav_basename = os.path.basename(wav_filename)
with opentracing.tracer.start_active_span('classifier.librosa.load'):
clip, sample_rate = librosa.load(wav_filename, sr=None)
# Change extension to jpg... mert 110% biztos vagyok benne hogy a keras nem bírná beolvasni máshogy
file_name = os.path.join(target_dir, "unknown", f"{wav_basename[:-4]}.jpg")
os.mkdir(os.path.join(target_dir, "unknown"))
with opentracing.tracer.start_active_span('classifier.Plot'):
matplotlib.pyplot.interactive(False)
fig = matplotlib.pyplot.figure(figsize=[0.72, 0.72])
ax = fig.add_subplot(111)
ax.axes.get_xaxis().set_visible(False)
ax.axes.get_yaxis().set_visible(False)
ax.set_frame_on(False)
spectogram = librosa.feature.melspectrogram(y=clip, sr=sample_rate)
librosa.display.specshow(librosa.power_to_db(spectogram, ref=numpy.max))
matplotlib.pyplot.savefig(file_name, dpi=400, bbox_inches='tight', pad_inches=0)
matplotlib.pyplot.close()
fig.clf()
matplotlib.pyplot.close(fig)
matplotlib.pyplot.close('all')
target_dir = tempfile.mkdtemp(dir="/dev/shm")
wav_basename = os.path.basename(wav_filename)
# Change extension to jpg... mert 110% biztos vagyok benne hogy a keras nem bírná beolvasni máshogy
file_name = os.path.join(target_dir, "unknown", f"{wav_basename[:-4]}.jpg")
os.mkdir(os.path.join(target_dir, "unknown"))
matplotlib.pyplot.savefig(file_name, dpi=400, bbox_inches='tight', pad_inches=0)
matplotlib.pyplot.close()
fig.clf()
matplotlib.pyplot.close(fig)
matplotlib.pyplot.close('all')
return target_dir, file_name # Az unknown nélkülivel kell visszatérni
@@ -85,12 +89,10 @@ class Classifier(object):
return predicted_class_name, labeled_predictions
def predict(self, wav_filename: str) -> Tuple[str, dict]:
span = opentracing.tracer.scope_manager.active().span
with opentracing.tracer.start_active_span('createSpectrogram', child_of=span):
with opentracing.tracer.start_active_span('classifier.createSpectrogram'):
directory, _ = self.create_spectrogram(wav_filename)
with opentracing.tracer.start_active_span('runPredictor', child_of=span):
with opentracing.tracer.start_active_span('classifier.runPredictor'):
result = self._run_predictor(directory)
shutil.rmtree(directory) # The image is no longer needed