def main():
# Launch the stream and the original spectrogram
stream, pa = open_mic()
try:
animation = make_plot(stream)
plt.show()
finally:
stream.stop_stream()
stream.close()
pa.terminate()
def main():
global im
############### Initialize Plot ###############
fig = plt.figure()
"""
Launch the stream and the original spectrogram
"""
stream, pa = mic_read.open_mic()
data = get_sample(stream, pa)
arr2D, freqs, bins = get_specgram(data, rate)
"""
Setup the plot paramters
"""
extent = (bins[0], bins[-1] * SAMPLES_PER_FRAME, freqs[-1], freqs[0])
im = plt.imshow(arr2D,
aspect='auto',
extent=extent,
interpolation="none",
cmap='Greys',
norm=None)
plt.xlabel('Time (s)')
plt.ylabel('Frequency (Hz)')
plt.title('Real Time Spectogram')
plt.gca().invert_yaxis()
#plt.colorbar() #enable if you want to display a color bar
############### Animate ###############
anim = animation.FuncAnimation(fig,
update_fig,
blit=True,
interval=mic_read.CHUNK_SIZE / 1000)
try:
plt.show()
except:
print("Plot Closed")
############### Terminate ###############
stream.stop_stream()
stream.close()
pa.terminate()
print("Program Terminated")
im_data = np.hstack((im_data, arr2D))
im.set_array(im_data)
else:
keep_block = arr2D.shape[1] * (SAMPLES_PER_FRAME - 1)
im_data = np.delete(im_data, np.s_[:-keep_block], 1)
im_data = np.hstack((im_data, arr2D))
im.set_array(im_data)
return im,
############### Initialize Plot ###############
fig = plt.figure()
"""
Launch the stream and the original spectrogram
"""
stream, pa = mic_read.open_mic()
data = get_sample(stream, pa)
arr2D, freqs, bins = get_specgram(data, rate)
"""
Setup the plot paramters
"""
extent = (bins[0], bins[-1] * SAMPLES_PER_FRAME, freqs[-1], freqs[0])
im = plt.imshow(arr2D,
aspect='auto',
extent=extent,
interpolation="none",
cmap='jet',
norm=LogNorm(vmin=.01, vmax=1))
plt.xlabel('Time (s)')
plt.ylabel('Frequency (Hz)')
plt.title('Real Time Spectogram')
def main():
global im
global stream
global pa
global model
global fig
############### Initialize Plot ###############
# Load the Tensorlow model
config = Config("model_arrl3.yaml")
model = Model(
open(config.value("experiment.fnCharList")).read(),
config,
decoderType=DecoderType.BeamSearch, #,DecoderType.BestPath
mustRestore=True,
)
"""
Launch the stream and the original spectrogram
"""
stream, pa = mic_read.open_mic()
data = get_sample(stream, pa)
arr2D, freqs, bins = get_specgram(data, rate)
"""
Setup the spectrogram plot and textbox for the decoder
"""
fig, (axbox, ax) = plt.subplots(2, 1)
text_box = TextBox(axbox, "Morse:")
extent = (bins[0], bins[-1] * SAMPLES_PER_FRAME, freqs[-1], freqs[0])
im = ax.imshow(
arr2D,
aspect="auto",
extent=extent,
interpolation="none",
cmap="Greys",
norm=None,
)
plt.xlabel("Time (s)")
plt.ylabel("Frequency (Hz)")
plt.title("Real Time Spectogram")
plt.gca().invert_yaxis()
# plt.colorbar() #enable if you want to display a color bar
############### Animate ###############
anim = animation.FuncAnimation(fig,
update_fig,
blit=False,
interval=mic_read.CHUNK_SIZE / 1000,
fargs=(text_box, ))
try:
plt.show()
except:
print("Plot Closed")
############### Terminate ###############
stream.stop_stream()
stream.close()
pa.terminate()
print("Program Terminated")
def start(self):
############### Initialize Plot ###############
# Set up figure size
fig = plt.figure(figsize=(0.64, 0.64))
# Load CNN .h5 model
model = load_model('Model/Spectogram_15|09|2020_v2.h5')
# Launch the stream and the original spectrogram
stream, pa = mic_read.open_mic()
data = self.get_sample(stream, pa)
arr2D, freqs, bins = self.get_specgram(data, self.rate)
# Setup the plot paramters
extent = (bins[0], bins[-1] * self.SAMPLES_PER_FRAME, freqs[-150],
freqs[20])
im = plt.imshow(arr2D,
aspect='auto',
extent=extent,
interpolation="none",
cmap='gray',
norm=LogNorm(vmin=.01, vmax=1))
# get clear matplotlib spectogram
plt.gca().invert_yaxis()
plt.gca().axes.get_xaxis().set_visible(False)
plt.gca().axes.get_yaxis().set_visible(False)
plt.text(2.85,
2.9,
'label',
bbox={
'facecolor': 'white',
'edgecolor': 'none',
'pad': 10
})
# Setup PySimpleGUI layout and window
"""
sg.theme('DarkAmber')
layout = [[sg.Text(self.prediction, size=(10, 1), font=('Helvetica', 36), text_color='white', key='_Note_')],
[sg.Text("stopwatch", font=('Helvetica', 16), text_color='white', key='stopwatch')],
[sg.Button('Exit', size=(5, 1), font=('Helvetica', 16))]]
window = sg.Window('Note detector', layout, size=(320, 320))
"""
# Continously calls update_fig function to get new spectrogram
count = -1
try:
while self.bool:
a += 1
# Update function requires integer input
count += 1
# Update figure
self.update_fig(count, stream, pa, fig, model, window, im)
# Convert matplotlib Object to 3D numpy Array
img_pred = np.expand_dims(self.matplotlib_to_numpy(fig),
axis=0)
# Predict based on model
rslt = np.argmax(model.predict(img_pred), axis=1)
# Show predicition result
self.show_predicition(rslt)
#print(a)
except KeyboardInterrupt:
# close the stream
stream.stop_stream()
stream.close()
pa.terminate()
print("Program Terminated")
pass
if n < SAMPLES_PER_FRAME:
im_data = np.hstack((im_data,arr2D))
im.set_array(im_data)
else:
keep_block = arr2D.shape[1]*(SAMPLES_PER_FRAME - 1)
im_data = np.delete(im_data,np.s_[:-keep_block],1)
im_data = np.hstack((im_data,arr2D))
im.set_array(im_data)
return im,
############### Initialize Plot ###############
fig = plt.figure()
"""
Launch the stream and the original spectrogram
"""
stream,pa = mic_read.open_mic()
data = get_sample(stream,pa)
arr2D,freqs,bins = get_specgram(data,rate)
"""
Setup the plot paramters
"""
extent = (bins[0],bins[-1]*SAMPLES_PER_FRAME,freqs[-1],freqs[0])
im = plt.imshow(arr2D,aspect='auto',extent = extent,interpolation="none",
cmap = 'jet',norm = LogNorm(vmin=.01,vmax=1))
plt.xlabel('Time (s)')
plt.ylabel('Frequency (Hz)')
plt.title('Real Time Spectogram')
plt.gca().invert_yaxis()
##plt.colorbar() #enable if you want to display a color bar
############### Animate ###############
