def laplacian_edges(image):
l1_img = apply(image, k_laplacian1)
l2_img = apply(image, k_laplacian2)
f, a = plt.subplots(1, 2)
a[0].imshow(l1_img, cmap='gray')
a[0].axis('off')
a[1].imshow(l2_img, cmap='gray')
a[1].axis('off')
plt.show()
return l1_img, l2_img
def GetFrameStats(data, inRunningSum, inRunningVoiceSum, inRunningCount): Ndata = len(data) voicefilter = filters.mult(filters.hpf(Ndata, 80), filters.lpf(Ndata, 1000)) (voicedata, voicefreq) = filters.apply(data, voicefilter) maxVal = max([abs(d) for d in data]) outRunningCount = inRunningCount + Ndata outRunningSum = inRunningSum + sum([abs(d) for d in data]) outRunningVoiceSum = inRunningVoiceSum + sum([abs(d) for d in voicedata]) outRunningAvg = outRunningSum / outRunningCount outRunningVoiceAvg = outRunningVoiceSum / outRunningCount voiceMax = max([abs(d) for d in voicedata]) return (maxVal, voicedata, voiceMax, outRunningCount, outRunningSum, outRunningAvg, outRunningVoiceSum, outRunningVoiceAvg)
def perframe( channels, lambdaOp=None, filt=None, numFrames=1 ):
assert( numFrames > 0 )
if numFrames > 1:
queue = deque()
while True:
samples = dict( ( ( channel, [] ) for channel in channels ) )
parsed = read_one_frame()
if not parsed:
break
( header, frameSamples ) = parsed
for channel, data in frameSamples.items():
if channel not in channels:
continue
samples[channel].extend( data )
if lambdaOp:
samples = dict( ( ( c, lambdaOp( samples[c] ) )
for c in channels ) )
if filt:
samples = dict(( ( c, filters.apply( samples[c], filt ) )
for c in channels ))
if numFrames == 1:
yield ( header, samples )
else:
queue.append( samples )
if len( queue ) > numFrames:
queue.popleft()
header.num_samples *= len( queue )
# generate merged dictionary
samples = dict( ( ( channel, [] ) for channel in channels ) )
for queueItem in queue:
for channel, data in queueItem.items():
samples[channel].extend( data )
yield ( header, samples )
def h_play(self, key):
"""Plays currently selected track
Also applies filters, if any are selected.
"""
app = self.parentApp
if not app.current_track:
app.notify('No track selected')
return
for filename in app.current_track_nos:
track_no = app._filenames_list.index(filename) + 1
try:
self.get_widget('track-list').values.remove(track_no)
except ValueError:
pass
self.get_widget('track-list').value = []
app.notify('Applying filters...')
track = filters.apply(app.current_track, self.parentApp.filters)
track = track[:app._track_length]
self.get_widget('position').entry_widget.out_of = len(track) / 1000
self.get_widget('position').display()
audio.play(track, notifier=self.update_slider)
app.notify('Playing!')
self.set_status('Playing')
def preprocessing(image):
p_image = apply(image, k_gaussian)
plt.imshow(p_image, cmap='gray')
plt.show()
return p_image
