def load_spectrograms(filename):
train_data = []
spectrogram, sp_time, sp_freq, fs = ap.get_spectrogram(
filename, ST_WIN, ST_STEP)
# These should change depending on the signal's size
spec_resize_ratio_freq = 4
spec_resize_ratio_time = 4
f_low = F1 if F1 < fs / 2.0 else fs / 2.0
f_high = F2 if F2 < fs / 2.0 else fs / 2.0
# define feature sequence for vocalization detection
f1 = np.argmin(np.abs(sp_freq - f_low))
f2 = np.argmin(np.abs(sp_freq - f_high))
spectral_energy_1 = spectrogram.sum(axis=1)
spectral_energy_2 = spectrogram[:, f1:f2].sum(axis=1)
seg_limits, thres_sm, _ = ap.get_syllables(spectral_energy_2,
spectral_energy_1,
ST_STEP,
threshold_per=thres * 100,
min_duration=MIN_VOC_DUR)
train_data += (ar.cluster_syllables(seg_limits,
spectrogram,
sp_freq,
f_low,
f_high,
ST_STEP,
train=True))
return train_data
def signal_handler(signal, frame):
"""
This function is called when Ctr + C is pressed and is used to output the
final buffer into a WAV file
"""
# write final buffer to wav file
global fs
if len(all_data) > 1:
wavfile.write(outstr + ".wav", fs, np.int16(all_data))
spectrogram, sp_time, sp_freq, fs = ap.get_spectrogram(
outstr + ".wav", ST_WIN, ST_STEP)
f_low = F1 if F1 < fs / 2.0 else fs / 2.0
f_high = F2 if F2 < fs / 2.0 else fs / 2.0
# define feature sequence for vocalization detection
f1 = np.argmin(np.abs(sp_freq - f_low))
f2 = np.argmin(np.abs(sp_freq - f_high))
spectral_energy, means, max_values = ap.prepare_features(
spectrogram[:, f1:f2])
time_sec = 100
seg_limits, thres_sm = ap.get_syllables(
spectral_energy,
means,
max_values,
ST_STEP,
threshold_per=thres * 100,
min_duration=MIN_VOC_DUR,
threshold_buf=means,
)
for s in seg_limits:
with open("debug_offline.csv", "a") as fp:
fp.write(f'{count_mid_bufs * mid_buffer_size + s[0]},'
f'{count_mid_bufs * mid_buffer_size + s[1]}\n')
sys.exit(0)
args.input_file, args.win, args.step)
duration = spectrogram.shape[0] * args.step
f_low = F1 if F1 < fs / 2.0 else fs / 2.0
f_high = F2 if F2 < fs / 2.0 else fs / 2.0
# define feature sequence for vocalization detection
f1 = np.argmin(np.abs(sp_freq - f_low))
f2 = np.argmin(np.abs(sp_freq - f_high))
spectral_energy_1 = spectrogram.sum(axis=1)
spectral_energy_2 = spectrogram[:, f1:f2].sum(axis=1)
segs, thres_sm, spectral_ratio = ap.get_syllables(spectral_energy_2,
spectral_energy_1,
args.step,
threshold_per=thres *
100,
min_duration=MIN_VOC_DUR)
segs_gt, f_gt = read_ground_truth(args.ground_truth_file)
shapes, shapes2, shapes_gt, shapes_gt2 = [], [], [], []
for s in segs:
s1 = {
'type': 'rect',
'x0': s[0],
'y0': f_low,
'x1': s[1],
'y1': f_high,
'line': {
# define feature sequence for vocalization detection
f1 = np.argmin(np.abs(sp_freq - f_low))
f2 = np.argmin(np.abs(sp_freq - f_high))
spectral_energy, mean_values, max_values = \
ap.prepare_features(spectrogram[:,f1:f2])
means.append(spectral_energy.mean())
time_sec = 100
seg_limits, thres_sm = ap.get_syllables(
spectral_energy,
mean_values,
max_values,
ST_STEP,
threshold_per=thres * 100,
factor=factor,
min_duration=MIN_VOC_DUR,
threshold_buf=means,
)
win = ST_STEP
# the following lines save the detected
# vocalizations in a .csv file and correct the split ones
for s in seg_limits:
if cnt > 0:
real_start = count_mid_bufs * mid_buffer_size - 0.1 + s[0]
real_end = count_mid_bufs * mid_buffer_size - 0.1 + s[1]
if s[0] <= 0.1 and s[1] >= 0.1:
# last vocalization should be changed
syllables_csv1 = []
def get_layout(spec=False):
global list_contour, segments, images, f1, f2, feats_simple, feats_deep, feats_2d_s, feats_2d_d, seg_limits, syllables
seg_limits, thres_sm = ap.get_syllables(spectral_energy,
means,
max_values,
ST_STEP,
threshold_per=thres * 100,
factor=factor,
min_duration=MIN_VOC_DUR)
time_end = time.time()
print("Time needed for vocalizations detection: {} s".format(
round(time_end - time_start, 1)))
continue_ = args.continue_
with open('offline_vocalizations.csv', 'w') as fp:
for iS, s in enumerate(seg_limits):
fp.write(f'{s[0]},' f'{s[1]}\n')
if continue_ == "n":
exit()
images, f_points, f_points_init, \
[feats_simple, feats_deep], feat_names, [f1, f2], segments, seg_limits = ar.cluster_syllables(seg_limits, spectrogram,
sp_freq, f_low, f_high, ST_STEP)
tsne = TSNE(n_components=2, perplexity=50, n_iter=5000, random_state=1)
feats_2d_s = tsne.fit_transform(feats_simple)
tsne = TSNE(n_components=2, perplexity=50, n_iter=5000, random_state=1)
feats_2d_d = tsne.fit_transform(feats_deep)
list_contour = np.array(f_points, dtype=object)
images = np.array(images, dtype=object)
f_points_all, f_points_init_all = [[], []], [[], []]
for iS in range(len(seg_limits)):
f_points_all[0] += f_points[iS][0]
f_points_all[1] += f_points[iS][1]
f_points_init_all[0] += f_points_init[iS][0]
f_points_init_all[1] += f_points_init[iS][1]
shapes2, shapes3 = [], []
for x, y in zip(f_points_all[0], f_points_all[1]):
s1 = {
'type': 'rect',
'x0': x - ST_STEP / 5,
'y0': y - 1000,
'x1': x + ST_STEP / 5,
'y1': y + 1000,
'line': {
'color': 'rgba(128, 0, 0, 1)',
'width': 1
},
'fillcolor': 'rgba(128, 0, 0, 1)'
}
shapes2.append(s1)
for x, y in zip(f_points_init_all[0], f_points_init_all[1]):
s1 = {
'type': 'rect',
'x0': x - ST_STEP / 15,
'y0': y - 200,
'x1': x + ST_STEP / 15,
'y1': y + 200,
'line': {
'color': 'rgba(0, 128, 0, 1)',
'width': 1
},
'fillcolor': 'rgba(128, 128, 0, 1)'
}
shapes3.append(s1)
syllables = [{"st": s[0], "et": s[1]} for iS, s in enumerate(seg_limits)]
shapes1 = get_shapes(seg_limits, f_low, f_high)
if spec:
layout = dbc.Container(
[
# Title
dbc.Row(
dbc.Col(
html.H2("AMVOC",
style={
'textAlign': 'center',
'color': colors['text'],
'marginBottom': 30,
'marginTop': 30
}))),
# Selected segment controls
dbc.Row([
dbc.Col(
html.Label(id="label_sel_start",
children="Selected start",
style={
'textAlign': 'center',
'color': colors['text']
}),
width=1,
),
dbc.Col(
html.Label(id="label_sel_end",
children="Selected end",
style={
'textAlign': 'center',
'color': colors['text']
}),
width=1,
),
dbc.Col(
html.Label(id='label_class',
children="Class",
style={
'textAlign': 'center',
'color': colors['text']
}),
width=1,
)
],
className="h-5"),
# Main heatmap
dbc.Row(
dbc.Col(dcc.Graph(
id='heatmap1',
figure={
'data': [
go.Heatmap(
x=sp_time[::spec_resize_ratio_time],
y=sp_freq[::spec_resize_ratio_freq],
z=
clean_spectrogram[::
spec_resize_ratio_time, ::
spec_resize_ratio_freq].
T,
name='F',
colorscale='Jet',
showscale=False)
],
'layout':
go.Layout(title='Spectrogram of the signal',
margin=dict(
l=55, r=20, b=120, t=40, pad=4),
xaxis=dict(title='Time (Sec)'),
yaxis=dict(title='Freq (Hz)'),
shapes=shapes1 + shapes2 + shapes3)
}),
width=12,
style={
"height": "100%",
"background-color": "white"
}),
className="h-50",
),
dbc.Row([
dbc.Col(
dcc.Dropdown(
id='dropdown_cluster',
options=[
{
'label': 'Agglomerative',
'value': 'agg'
},
{
'label': 'Birch',
'value': 'birch'
},
{
'label': 'Gaussian Mixture',
'value': 'gmm'
},
{
'label': 'K-Means',
'value': 'kmeans'
},
{
'label': 'Mini-Batch K-Means',
'value': 'mbkmeans'
},
# {'label': 'Spectral', 'value': 'spec'},
],
value='agg'),
width=2,
),
dbc.Col(
dcc.Dropdown(id='dropdown_n_clusters',
options=[{
'label': i,
'value': i
} for i in range(2, 11)],
value=2),
width=2,
),
dbc.Col(
dcc.Dropdown(id='dropdown_feats_type',
options=[
{
'label': 'Method 1',
'value': 'deep'
},
{
'label': 'Method 2',
'value': 'simple'
},
],
value='deep'),
width=2,
),
html.Table([
html.Tr([
html.Td(['Silhouette score']),
html.Td(id='silhouette')
]),
html.Tr([
html.Td(['Calinski-Harabasz score']),
html.Td(id='cal-har')
]),
html.Tr([
html.Td(['Davies-Bouldin score']),
html.Td(id='dav-bould')
]),
]),
]),
dbc.Row([
dbc.Col(html.Div(children="Global cluster annotations"),
width=3,
style={
'marginBottom': 20,
'marginTop': 20
}),
dbc.Col(html.Div(children="Specific cluster annotations"),
width=3,
style={
'marginBottom': 20,
'marginTop': 20
}),
dbc.Col(html.Div(children="Point annotations"),
width=3,
style={
'marginBottom': 20,
'marginTop': 20
}),
]),
dbc.Row([
dbc.Col(dcc.Dropdown(
id='dropdown_total_cluster_annotation',
options=[{
'label': 'No Validation',
'value': 'no'
}] + [{
'label': i,
'value': i
} for i in np.arange(1, 6)],
value='no'),
width=2,
style={'display': 'block'}),
dbc.Col(html.Button('Submit', id='btn_3', n_clicks=0),
width=1,
style={'display': 'block'}),
dbc.Col(dcc.Dropdown(id='dropdown_cluster_annotation',
options=[{
'label': 'No Validation',
'value': 'no'
}] + [{
'label': i,
'value': i
} for i in np.arange(1, 6)],
value='no'),
width=2,
style={'display': 'block'}),
dbc.Col(html.Button('Submit', id='btn_2', n_clicks=0),
width=1,
style={'display': 'block'}),
dbc.Col(dcc.Dropdown(id='dropdown_point_annotation',
options=[
{
'label': 'No Validation',
'value': 'no'
},
{
'label': 'Approve',
'value': 'approve'
},
{
'label': 'Reject',
'value': 'reject'
},
],
value='no'),
width=2,
style={'display': 'block'}),
dbc.Col(html.Button('Submit', id='btn_1', n_clicks=0),
width=1,
style={'display': 'block'}),
]),
dbc.Row([
dbc.Col(dcc.Graph(id='cluster_graph'),
width=9,
md=8,
style={'marginLeft': 0}),
dbc.Col(
html.Div(children=[
html.Div([
DataTable(id='total_annotation',
style_cell={
'whiteSpace': 'normal',
'height': 'auto',
'width': 100
},
columns=[{
'id': 'Global annotation',
'name': 'Global annotation'
}])
],
style={'marginBottom': 10}),
DataTable(id='cluster_table',
style_cell={
'whiteSpace': 'normal',
'height': 'auto',
'width': 100
},
columns=[{
'id': column,
'name': column
} for column in [
'Clusters', 'Cluster annotation',
'Annotated points'
]])
]),
style={
'marginTop': 10,
'marginLeft': 5,
'marginRight': 0
},
width='25%',
),
],
justify='start'),
dbc.Row([
dbc.Col(dcc.Graph(
id='spectrogram',
hoverData={'points': [{
'pointIndex': 0
}]}),
width=6,
style={'marginTop': 30}),
dbc.Col(dcc.Graph(
id='contour_plot',
hoverData={'points': [{
'pointIndex': 0
}]}),
width=6,
style={'marginTop': 30})
]),
# these are intermediate values to be used for sharing content
# between callbacks
# (see here https://dash.plotly.com/sharing-data-between-callbacks)
dbc.Row(id='intermediate_val_syllables',
style={'display': 'none'}),
dbc.Row(id='intermediate_val_total_clusters',
style={'display': 'none'}),
dbc.Row(id='intermediate_val_clusters',
style={'display': 'none'}),
dbc.Row(id='clustering_info', style={'display': 'none'})
],
style={"height": "100vh"})
else:
layout = dbc.Container(
[
# Title
dbc.Row(
dbc.Col(
html.H2("AMVOC",
style={
'textAlign': 'center',
'color': colors['text'],
'marginBottom': 30,
'marginTop': 30
}))),
dbc.Row([
dbc.Col(
dcc.Dropdown(
id='dropdown_cluster',
options=[
{
'label': 'Agglomerative',
'value': 'agg'
},
{
'label': 'Birch',
'value': 'birch'
},
{
'label': 'Gaussian Mixture',
'value': 'gmm'
},
{
'label': 'K-Means',
'value': 'kmeans'
},
{
'label': 'Mini-Batch K-Means',
'value': 'mbkmeans'
},
# {'label': 'Spectral', 'value': 'spec'},
],
value='agg'),
width=2,
),
dbc.Col(
dcc.Dropdown(id='dropdown_n_clusters',
options=[{
'label': i,
'value': i
} for i in range(2, 11)],
value=2),
width=2,
),
dbc.Col(
dcc.Dropdown(id='dropdown_feats_type',
options=[
{
'label': 'Method 1',
'value': 'deep'
},
{
'label': 'Method 2',
'value': 'simple'
},
],
value='deep'),
width=2,
),
html.Table([
html.Tr([
html.Td(['Silhouette score']),
html.Td(id='silhouette')
]),
html.Tr([
html.Td(['Calinski-Harabasz score']),
html.Td(id='cal-har')
]),
html.Tr([
html.Td(['Davies-Bouldin score']),
html.Td(id='dav-bould')
]),
]),
]),
dbc.Row([
dbc.Col(html.Div(children="Global cluster annotations"),
width=3,
style={
'marginBottom': 20,
'marginTop': 20
}),
dbc.Col(html.Div(children="Specific cluster annotations"),
width=3,
style={
'marginBottom': 20,
'marginTop': 20
}),
dbc.Col(html.Div(children="Point annotations"),
width=3,
style={
'marginBottom': 20,
'marginTop': 20
}),
]),
dbc.Row([
dbc.Col(dcc.Dropdown(
id='dropdown_total_cluster_annotation',
options=[{
'label': 'No Validation',
'value': 'no'
}] + [{
'label': i,
'value': i
} for i in np.arange(1, 6)],
value='no'),
width=2,
style={'display': 'block'}),
dbc.Col(html.Button('Submit', id='btn_3', n_clicks=0),
width=1,
style={'display': 'block'}),
dbc.Col(dcc.Dropdown(id='dropdown_cluster_annotation',
options=[{
'label': 'No Validation',
'value': 'no'
}] + [{
'label': i,
'value': i
} for i in np.arange(1, 6)],
value='no'),
width=2,
style={'display': 'block'}),
dbc.Col(html.Button('Submit', id='btn_2', n_clicks=0),
width=1,
style={'display': 'block'}),
dbc.Col(dcc.Dropdown(id='dropdown_point_annotation',
options=[
{
'label': 'No Validation',
'value': 'no'
},
{
'label': 'Approve',
'value': 'approve'
},
{
'label': 'Reject',
'value': 'reject'
},
],
value='no'),
width=2,
style={'display': 'block'}),
dbc.Col(html.Button('Submit', id='btn_1', n_clicks=0),
width=1,
style={'display': 'block'}),
]),
dbc.Row([
dbc.Col(dcc.Graph(id='cluster_graph'),
width=9,
md=8,
style={'marginLeft': 0}),
dbc.Col(
html.Div(children=[
html.Div([
DataTable(id='total_annotation',
style_cell={
'whiteSpace': 'normal',
'height': 'auto',
'width': 100
},
columns=[{
'id': 'Global annotation',
'name': 'Global annotation'
}])
],
style={'marginBottom': 10}),
DataTable(id='cluster_table',
style_cell={
'whiteSpace': 'normal',
'height': 'auto',
'width': 100
},
columns=[{
'id': column,
'name': column
} for column in [
'Clusters', 'Cluster annotation',
'Annotated points'
]])
]),
style={
'marginTop': 10,
'marginLeft': 5,
'marginRight': 0
},
width='25%',
),
],
justify='start'),
dbc.Row([
dbc.Col(dcc.Graph(
id='spectrogram',
hoverData={'points': [{
'pointIndex': 0
}]}),
width=6,
style={'marginTop': 20}),
dbc.Col(dcc.Graph(
id='contour_plot',
hoverData={'points': [{
'pointIndex': 0
}]}),
width=6,
style={'marginTop': 20})
]),
# these are intermediate values to be used for sharing content
# between callbacks
# (see here https://dash.plotly.com/sharing-data-between-callbacks)
dbc.Row(id='intermediate_val_syllables',
style={'display': 'none'}),
dbc.Row(id='intermediate_val_total_clusters',
style={'display': 'none'}),
dbc.Row(id='intermediate_val_clusters',
style={'display': 'none'}),
dbc.Row(id='clustering_info', style={'display': 'none'})
],
style={"height": "100vh"})
return layout