Gurkengewuerz/audio-reactive-led-strip
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Made noticeable improvements to performance

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This commit is contained in:
Scott Lawson 2016-11-13 01:11:15 -08:00
parent 9bdc8f8cb9
commit 51eda2f0ba
1 changed files with 34 additions and 53 deletions
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87
python/mel_visualization.py
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@ -13,7 +13,7 @@ import gui
_time_prev = time.time() * 1000.0
"""The previous time that the frames_per_second() function was called"""
_fps = dsp.ExpFilter(val=config.FPS, alpha_decay=0.01, alpha_rise=0.01)
_fps = dsp.ExpFilter(val=config.FPS, alpha_decay=0.002, alpha_rise=0.002)
"""The low-pass filter used to estimate frames-per-second"""
@ -68,58 +68,40 @@ def interpolate(y, new_length):
return z
def normalize(f):
"""Returns a histogram normalized numpy.array"""
lmin = float(f.min())
lmax = float(f.max())
return np.floor((f - lmin) / (lmax - lmin) * 255.0)
# r_filt = dsp.ExpFilter(np.tile(0.01, config.N_PIXELS),
# alpha_decay=0.075, alpha_rise=0.6)
# g_filt = dsp.ExpFilter(np.tile(0.01, config.N_PIXELS),
# alpha_decay=0.25, alpha_rise=0.9)
# b_filt = dsp.ExpFilter(np.tile(0.01, config.N_PIXELS),
# alpha_decay=0.5, alpha_rise=0.95)
r_filt = dsp.ExpFilter(np.tile(0.01, config.N_PIXELS),
alpha_decay=0.1, alpha_rise=0.6)
g_filt = dsp.ExpFilter(np.tile(0.01, config.N_PIXELS),
r_filt = dsp.ExpFilter(np.tile(0.01, config.N_PIXELS // 2),
alpha_decay=0.05, alpha_rise=0.6)
g_filt = dsp.ExpFilter(np.tile(0.01, config.N_PIXELS // 2),
alpha_decay=0.75, alpha_rise=0.95)
b_filt = dsp.ExpFilter(np.tile(0.01, config.N_PIXELS),
alpha_decay=0.2, alpha_rise=0.4)
b_filt = dsp.ExpFilter(np.tile(0.01, config.N_PIXELS // 2),
alpha_decay=0.2, alpha_rise=0.7)
def visualize(y):
y = np.copy(interpolate(y, config.N_PIXELS)) * 255.0
y = np.copy(interpolate(y, config.N_PIXELS // 2)) * 255.0
# Blur the color channels with different strengths
r = gaussian_filter1d(y, sigma=1.0)
r = gaussian_filter1d(y, sigma=0.0)
g = gaussian_filter1d(y, sigma=0.0)
b = gaussian_filter1d(y, sigma=0.0)
# Take the geometric mean of the raw and normalized histograms
# r = np.sqrt(r * normalize(r))
# g = np.sqrt(g * normalize(g))
# b = np.sqrt(b * normalize(b))
r = np.roll(g, 0)
g = np.roll(g, 0)
b = np.roll(g, 0)
b = gaussian_filter1d(y, sigma=1.0)
# Update the low pass filters for each color channel
r_filt.update(r)
g_filt.update(g)
b_filt.update(b)
# Pixel values
pixel_r = np.concatenate((r_filt.value[::-1], r_filt.value))
pixel_g = np.concatenate((g_filt.value[::-1], g_filt.value))
pixel_b = np.concatenate((b_filt.value[::-1], b_filt.value))
# Update the LED strip values
led.pixels[:, 0] = r_filt.value
led.pixels[:, 1] = g_filt.value
led.pixels[:, 2] = b_filt.value
led.pixels[0, :] = pixel_r
led.pixels[1, :] = pixel_g
led.pixels[2, :] = pixel_b
# Update the GUI plots
GUI.curve[0][0].setData(x=range(len(r_filt.value)), y=r_filt.value)
GUI.curve[0][1].setData(x=range(len(g_filt.value)), y=g_filt.value)
GUI.curve[0][2].setData(x=range(len(b_filt.value)), y=b_filt.value)
GUI.curve[0][0].setData(y=pixel_r)
GUI.curve[0][1].setData(y=pixel_g)
GUI.curve[0][2].setData(y=pixel_b)
led.update()
mel_gain = dsp.ExpFilter(np.tile(1e-1, config.N_PIXELS),
alpha_decay=0.01, alpha_rise=0.99)
# mel_gain = dsp.ExpFilter(np.tile(1e-1, config.N_PIXELS),
# alpha_decay=0.01, alpha_rise=0.99)
mel_gain = dsp.ExpFilter(np.tile(1e-1, config.N_SUBBANDS),
alpha_decay=0.01, alpha_rise=0.85)
volume = dsp.ExpFilter(config.MIN_VOLUME_THRESHOLD,
alpha_decay=0.02, alpha_rise=0.02)
@ -127,10 +109,13 @@ rms = dsp.ExpFilter(0.1, alpha_decay=0.001, alpha_rise=0.001)
exp = dsp.ExpFilter(0.5, alpha_decay=0.001, alpha_rise=0.001)
prev_rms = 1.0
prev_exp = 1.0
def microphone_update(stream):
global y_roll, prev_rms, prev_exp
# Normalize new audio samples
y = np.fromstring(stream.read(samples_per_frame), dtype=np.int16)
y = np.fromstring(stream.read(samples_per_frame,
exception_on_overflow=False), dtype=np.int16)
y = y / 2.0**15
# Construct a rolling window of audio samples
y_roll = np.roll(y_roll, -1, axis=0)
@ -143,15 +128,13 @@ def microphone_update(stream):
visualize(np.tile(0.0, config.N_PIXELS))
else:
XS, YS = dsp.fft(y_data, window=np.hamming)
# Construct Mel filterbank
YS = YS[XS >= 0.0]
XS = XS[XS >= 0.0]
YS = YS[:len(YS) // 2]
XS = XS[:len(XS) // 2]
YS = np.atleast_2d(np.abs(YS)).T * dsp.mel_y.T
YS = np.sum(YS, axis=0)**2.0
mel = np.concatenate((YS[::-1], YS))
mel = interpolate(mel, config.N_PIXELS)
# mel = mel**0.4
mel = YS
mel = mel**exp.value
mel = gaussian_filter1d(mel, sigma=1.0)
mel_gain.update(np.max(mel))
mel = mel / mel_gain.value
rms.update(np.sqrt(np.mean(mel**2.0)))
@ -159,18 +142,13 @@ def microphone_update(stream):
exp.update(exp.value * 1.2)
elif rms.value < 5e-2:
exp.update(exp.value * 0.8)
rms_delta = '^' if rms.value - prev_rms > 0 else 'v'
exp_delta = '^' if exp.value - prev_exp > 0 else 'v'
print('|{}| {:.0e}, |{}| {:.2}'.format(rms_delta, rms.value, exp_delta, exp.value))
# WHAT IF I TAKE THE TEMPORAL VARIANCE OF EACH INDIVIDUAL BIN
# AND THEN CALCULATE THE COVARIANCE OF HOW THE DIFFERENT BIN VARIANCES
# CHANGE TOGETHER
# COULD COLOR BY COVARIANCE? BLUE PIXELS CHANGE TOGETHER, ETC
print('|{}| {:.0e}, |{}| {:.2}\t\t{:.2f}'.format(
rms_delta, rms.value, exp_delta, exp.value, frames_per_second()))
prev_exp = exp.value
prev_rms = rms.value
visualize(mel)
GUI.app.processEvents()
#print('FPS {:.0f} / {:.0f}'.format(frames_per_second(), config.FPS))
@ -194,6 +172,9 @@ if __name__ == '__main__':
GUI.add_curve(plot_index=0, pen=g_pen)
GUI.add_curve(plot_index=0, pen=b_pen)
GUI.plot[0].setRange(xRange=(0, config.N_PIXELS), yRange=(-5, 275))
GUI.curve[0][0].setData(x=range(config.N_PIXELS))
GUI.curve[0][1].setData(x=range(config.N_PIXELS))
GUI.curve[0][2].setData(x=range(config.N_PIXELS))
# Initialize LEDs
led.update()
# Start listening to live audio stream