Gurkengewuerz/audio-reactive-led-strip
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Significantly changed and improved sandbox.py visualizations

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Note: All code in sandbox.py is temporary and used for experimenting
with different visualizations.
This commit is contained in:
Scott Lawson 2016-11-07 17:42:50 -08:00
parent 65d35c2724
commit cb34b6353d
1 changed files with 164 additions and 61 deletions
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225
python/sandbox.py
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@ -8,7 +8,10 @@ import config
import microphone import microphone
import dsp import dsp
import led import led
import melbank
from scipy.ndimage.filters import gaussian_filter1d from scipy.ndimage.filters import gaussian_filter1d
from scipy.signal import argrelextrema
def rainbow(length, speed=1.0 / 5.0): def rainbow(length, speed=1.0 / 5.0):
@ -58,9 +61,9 @@ def rainbow_gen(length, speed=1./5., center=0.5, width=0.5, f=[1, 1, 1]):
dt = 2.0 * np.pi / length dt = 2.0 * np.pi / length
t = time.time() * speed t = time.time() * speed
phi = 2.0 / 3.0 * np.pi phi = 2.0 / 3.0 * np.pi
def r(t): return np.clip(np.sin(f[0] * t + 0. * phi) * width + center, 0., 1.) def r(t): return np.clip(np.sin(f[0] * t + 1. * phi) * width + center, 0., 1.)
def g(t): return np.clip(np.sin(f[1] * t + 1. * phi) * width + center, 0., 1.) def g(t): return np.clip(np.sin(f[1] * t + 2. * phi) * width + center, 0., 1.)
def b(t): return np.clip(np.sin(f[2] * t + 2. * phi) * width + center, 0., 1.) def b(t): return np.clip(np.sin(f[2] * t + 3. * phi) * width + center, 0., 1.)
x = np.tile(0.0, (length, 3)) x = np.tile(0.0, (length, 3))
for i in range(length): for i in range(length):
x[i][0] = r(i * dt + t) x[i][0] = r(i * dt + t)
@ -72,7 +75,7 @@ def rainbow_gen(length, speed=1./5., center=0.5, width=0.5, f=[1, 1, 1]):
_time_prev = time.time() * 1000.0 _time_prev = time.time() * 1000.0
"""The previous time that the frames_per_second() function was called""" """The previous time that the frames_per_second() function was called"""
_fps = dsp.ExponentialFilter(val=config.FPS, alpha_decay=0.05, alpha_rise=0.05) _fps = dsp.ExpFilter(val=config.FPS, alpha_decay=0.05, alpha_rise=0.05)
"""The low-pass filter used to estimate frames-per-second""" """The low-pass filter used to estimate frames-per-second"""
@ -123,7 +126,7 @@ def update_plot_1(x, y):
global curves, p1 global curves, p1
colors = rainbow(config.N_CURVES) * 255.0 colors = rainbow(config.N_CURVES) * 255.0
for i in range(config.N_CURVES): for i in range(config.N_CURVES):
curves[i].setPen((colors[i][0], colors[i][1], colors[i][2])) #curves[i].setPen((colors[i][0], colors[i][1], colors[i][2]))
curves[i].setData(x=x, y=y[i]) curves[i].setData(x=x, y=y[i])
p1.autoRange() p1.autoRange()
p1.setRange(yRange=(0.0, 2.0)) p1.setRange(yRange=(0.0, 2.0))
@ -170,14 +173,14 @@ def leak_saturated_pixels(pixels):
return pixels return pixels
_EA_norm = dsp.ExponentialFilter(np.tile(1e-4, config.N_PIXELS), 0.01, 0.25) _EA_norm = dsp.ExpFilter(np.tile(1e-4, config.N_PIXELS), 0.01, 0.85)
"""Onset energy per-bin normalization constants """Onset energy per-bin normalization constants
This filter is responsible for individually normalizing the onset bin energies. This filter is responsible for individually normalizing the onset bin energies.
This is used to obtain per-bin automatic gain control. This is used to obtain per-bin automatic gain control.
""" """
_EA_smooth = dsp.ExponentialFilter(np.tile(1.0, config.N_PIXELS), 0.25, 0.80) _EA_smooth = dsp.ExpFilter(np.tile(1.0, config.N_PIXELS), 0.25, 0.80)
"""Asymmetric exponential low-pass filtered onset energies """Asymmetric exponential low-pass filtered onset energies
This filter is responsible for smoothing the displayed onset energies. This filter is responsible for smoothing the displayed onset energies.
@ -216,8 +219,8 @@ def update_leds_6(y):
return pixels return pixels
_EF_norm = dsp.ExponentialFilter(np.tile(1.0, config.N_PIXELS), 0.05, 0.9) _EF_norm = dsp.ExpFilter(np.tile(1.0, config.N_PIXELS), 0.05, 0.9)
_EF_smooth = dsp.ExponentialFilter(np.tile(1.0, config.N_PIXELS), 0.08, 0.9) _EF_smooth = dsp.ExpFilter(np.tile(1.0, config.N_PIXELS), 0.08, 0.9)
_prev_energy = 0.0 _prev_energy = 0.0
@ -236,8 +239,25 @@ def update_leds_5(y):
return pixels return pixels
_energy_norm = dsp.ExponentialFilter(10.0, alpha_decay=.15, alpha_rise=.9) _prev_E = np.tile(.1, config.N_PIXELS)
_energy_smooth = dsp.ExponentialFilter(10.0, alpha_decay=0.1, alpha_rise=0.8) _EF_N = dsp.ExpFilter(np.tile(.1, config.N_PIXELS), 0.01, 0.9)
def update_leds_5(y):
global _prev_E
y = np.copy(y)
current_E = y**2.0
EF = current_E - _prev_E
_prev_E = np.copy(current_E)
EF[EF < 0.0] = 0.0
_EF_N.update(EF)
EF /= _EF_N.value
EF[current_E < 0.02] = 0.0
return EF
_energy_norm = dsp.ExpFilter(10.0, alpha_decay=.15, alpha_rise=.9)
_energy_smooth = dsp.ExpFilter(10.0, alpha_decay=0.1, alpha_rise=0.8)
# Modulate brightness by relative average rectified onset flux # Modulate brightness by relative average rectified onset flux
@ -284,9 +304,11 @@ def update_leds_2(y):
def update_leds_1(y): def update_leds_1(y):
"""Display the raw onset spectrum on the LED strip""" """Display the raw onset spectrum on the LED strip"""
return np.copy(y)**0.5 return np.copy(y)**0.75
YS_peak = dsp.ExpFilter(1.0, alpha_decay=0.005, alpha_rise=0.95)
def microphone_update(stream): def microphone_update(stream):
global y_roll global y_roll
# Retrieve new audio samples and construct the rolling window # Retrieve new audio samples and construct the rolling window
@ -295,39 +317,52 @@ def microphone_update(stream):
y_roll = np.roll(y_roll, -1, axis=0) y_roll = np.roll(y_roll, -1, axis=0)
y_roll[-1, :] = np.copy(y) y_roll[-1, :] = np.copy(y)
y_data = np.concatenate(y_roll, axis=0) y_data = np.concatenate(y_roll, axis=0)
# Calculate onset detection functions # # Calculate onset detection functions
SF, NWPD, RCD = dsp.onset(y_data) # SF, NWPD, RCD = dsp.onset(y_data)
# Apply Gaussian blur to improve agreement between onset functions # # Apply Gaussian blur to improve agreement between onset functions
SF = gaussian_filter1d(SF, 1.0) # SF = gaussian_filter1d(SF, 1.0)
NWPD = gaussian_filter1d(NWPD, 1.0) # NWPD = gaussian_filter1d(NWPD, 1.0)
RCD = gaussian_filter1d(RCD, 1.0) # RCD = gaussian_filter1d(RCD, 1.0)
# Update and normalize peak followers # # Update and normalize peak followers
SF_peak.update(np.max(SF)) # SF_peak.update(np.max(SF))
NWPD_peak.update(np.max(NWPD)) # NWPD_peak.update(np.max(NWPD))
RCD_peak.update(np.max(RCD)) # RCD_peak.update(np.max(RCD))
SF /= SF_peak.value # SF /= SF_peak.value
NWPD /= NWPD_peak.value # NWPD /= NWPD_peak.value
RCD /= RCD_peak.value # RCD /= RCD_peak.value
# Normalize and update onset spectrum # # Normalize and update onset spectrum
# onset = np.sqrt(SF**2.0 + NWPD**2.0 + RCD**2.0) # onset = SF + NWPD + RCD
# onset = SF * NWPD * RCD # onset_peak.update(np.max(onset))
onset = SF + NWPD + RCD # onset /= onset_peak.value
# onset = SF + RCD # onsets.update(onset)
onset_peak.update(np.max(onset)) # # Map the onset values to LED strip pixels
onset /= onset_peak.value # if len(onsets.value) != config.N_PIXELS:
onsets.update(onset) # onset_values = interpolate(onsets.value, config.N_PIXELS)
# Map the onset values to LED strip pixels # else:
if len(onsets.value) != config.N_PIXELS: # onset_values = np.copy(onsets.value)
onset_values = interpolate(onsets.value, config.N_PIXELS) # brightness = led_visualization(onset_values)
else:
onset_values = np.copy(onsets.value) XS, YS = dsp.fft(y_data, window=np.hamming)
brightness = led_visualization(onset_values) YS = YS[XS >= 0.0]
XS = XS[XS >= 0.0]
YS = np.atleast_2d(np.abs(YS)).T * dsp.mel_y.T
YS = np.sum(YS, axis=0)**2.0
#YS = gaussian_filter1d(YS, 2.0)
YS = np.diff(YS, n=0)
YS_peak.update(np.max(YS))
YS /= YS_peak.value
if len(YS) != config.N_PIXELS:
YS = interpolate(YS, config.N_PIXELS)
#YS = led_visualization(YS)
YS = led_vis3(YS)
# Plot the onsets # Plot the onsets
plot_x = np.array(range(1, len(onsets.value) + 1)) #plot_x = np.array(range(1, len(onsets.value) + 1))
plot_y = [0*onsets.value**i for i in np.linspace(2.0, 0.25, config.N_CURVES)] plot_x = np.array(range(1, len(YS) + 1))
if brightness is not None: #plot_y = [onsets.value**i for i in np.linspace(2.0, 0.25, config.N_CURVES)]
plot_y = np.array([brightness, onsets.value]) plot_y = [YS]
#plot_y = brightness
update_plot_1(plot_x, plot_y) update_plot_1(plot_x, plot_y)
app.processEvents() app.processEvents()
print('FPS {:.0f} / {:.0f}'.format(frames_per_second(), config.FPS)) print('FPS {:.0f} / {:.0f}'.format(frames_per_second(), config.FPS))
@ -336,7 +371,7 @@ def microphone_update(stream):
# Create plot and window # Create plot and window
app = QtGui.QApplication([]) app = QtGui.QApplication([])
win = pg.GraphicsWindow('Audio Visualization') win = pg.GraphicsWindow('Audio Visualization')
win.resize(800, 600) win.resize(300, 200)
win.setWindowTitle('Audio Visualization') win.setWindowTitle('Audio Visualization')
# Create plot 1 containing config.N_CURVES # Create plot 1 containing config.N_CURVES
p1 = win.addPlot(title='Onset Detection Function') p1 = win.addPlot(title='Onset Detection Function')
@ -354,20 +389,19 @@ pixels = np.tile(0.0, config.N_PIXELS)
color = rainbow(config.N_PIXELS) * 255.0 color = rainbow(config.N_PIXELS) * 255.0
# Tracks average onset spectral energy # Tracks average onset spectral energy
onset_energy = dsp.ExponentialFilter(1.0, alpha_decay=0.01, alpha_rise=0.65) onset_energy = dsp.ExpFilter(1.0, alpha_decay=0.01, alpha_rise=0.65)
# Tracks the location of the spectral median # Tracks the location of the spectral median
median = dsp.ExponentialFilter(val=config.N_SUBBANDS / 2.0, median = dsp.ExpFilter(val=config.N_SUBBANDS / 2.0,
alpha_decay=0.1, alpha_rise=0.1) alpha_decay=0.1, alpha_rise=0.1)
# Smooths the decay of the onset detection function # Smooths the decay of the onset detection function
onsets = dsp.ExponentialFilter(val=np.tile(0.0, (config.N_SUBBANDS)), onsets = dsp.ExpFilter(val=np.tile(0.0, (config.N_SUBBANDS)),
alpha_decay=0.15, alpha_rise=0.75) alpha_decay=0.15, alpha_rise=0.75)
# Peak followers used for normalization # Peak followers used for normalization
SF_peak = dsp.ExponentialFilter(1.0, alpha_decay=0.01, alpha_rise=0.99) SF_peak = dsp.ExpFilter(1.0, alpha_decay=0.01, alpha_rise=0.99)
NWPD_peak = dsp.ExponentialFilter(1.0, alpha_decay=0.01, alpha_rise=0.99) NWPD_peak = dsp.ExpFilter(1.0, alpha_decay=0.01, alpha_rise=0.99)
RCD_peak = dsp.ExponentialFilter(1.0, alpha_decay=0.01, alpha_rise=0.99) RCD_peak = dsp.ExpFilter(1.0, alpha_decay=0.01, alpha_rise=0.99)
onset_peak = dsp.ExponentialFilter(0.1, alpha_decay=0.002, alpha_rise=0.5) onset_peak = dsp.ExpFilter(0.1, alpha_decay=0.002, alpha_rise=0.5)
# Number of audio samples to read every time frame # Number of audio samples to read every time frame
samples_per_frame = int(config.MIC_RATE / config.FPS) samples_per_frame = int(config.MIC_RATE / config.FPS)
@ -382,30 +416,47 @@ y_roll = np.random.rand(config.N_ROLLING_HISTORY, samples_per_frame) / 100.0
# update_leds_5 = energy flux normalized per-bin spectrum (GAMMA = True) # update_leds_5 = energy flux normalized per-bin spectrum (GAMMA = True)
# update_leds_6 = energy average normalized per-bin spectrum (GAMMA = True) # update_leds_6 = energy average normalized per-bin spectrum (GAMMA = True)
# Low pass filter for the LEDs being output to the strip # Low pass filter for the LEDs being output to the strip
pixels_filt = dsp.ExponentialFilter(np.tile(0., (config.N_PIXELS, 3)), .2, .8) pixels_filt = dsp.ExpFilter(np.tile(0., (config.N_PIXELS, 3)), .14, .9)
def hyperbolic_tan(x): def hyperbolic_tan(x):
return 1.0 - 2.0 / (np.exp(2.0 * x) + 1.0) return 1.0 - 2.0 / (np.exp(2.0 * x) + 1.0)
def bloom_peaks(x, width=3, blur_factor=1.0):
peaks = argrelextrema(x, np.greater)[0]
y = x * 0.0
if len(peaks) == 0:
return y
for peak in peaks:
min_idx = max(peak - width, 0)
max_idx = min(peak + width, len(x) - 1)
for i in range(min_idx, max_idx):
y[i] = x[i]
y = gaussian_filter1d(y, blur_factor)
return y
# This is the function responsible for updating LED values # This is the function responsible for updating LED values
# Edit this function to change the visualization # Edit this function to change the visualization
def led_visualization(onset_values): def led_visualization(onset_values):
# Visualizations that we want to use (normalized to ~[0, 1]) # Visualizations that we want to use (normalized to ~[0, 1])
pixels_A = update_leds_6(onset_values) #pixels_A = update_leds_6(onset_values)
pixels_B = update_leds_4(onset_values) #pixels_B = update_leds_4(onset_values)
# Combine the effects by taking the product # Combine the effects by taking the product
brightness = pixels_A * pixels_B #brightness = pixels_A #* pixels_B
brightness = gaussian_filter1d(brightness, 1.0)**1.5 brightness = update_leds_6(onset_values**2.0)
brightness = hyperbolic_tan(brightness) brightness = gaussian_filter1d(brightness, 4.0)
#brightness = hyperbolic_tan(brightness)
brightness = bloom_peaks(brightness)**2.
# Combine pixels with color map # Combine pixels with color map
color = rainbow_gen(onset_values.shape[0], color = rainbow_gen(onset_values.shape[0],
speed=1., speed=1.,
center=0.5, center=0.5,
width=0.5, width=0.5,
f=[1.0, 1.0, 1.]) * 255.0 f=[1.1, .5, .2]) * 255.0
color = np.tile(255.0, (config.N_PIXELS, 3))
# color = rainbow(onset_values.shape[0]) * 255.0 # color = rainbow(onset_values.shape[0]) * 255.0
pixels = (brightness * color.T).T pixels = (brightness * color.T).T
pixels = leak_saturated_pixels(pixels) pixels = leak_saturated_pixels(pixels)
@ -418,6 +469,58 @@ def led_visualization(onset_values):
return brightness return brightness
mean_energy = dsp.ExpFilter(0.1, alpha_decay=0.05, alpha_rise=0.05)
def led_vis2(x):
energy = np.mean(x**.5)
mean_energy.update(energy)
energy = energy / mean_energy.value - 1.0
edge = np.exp(-10 * np.linspace(0, 1, len(x)))
edge = edge + edge[::-1]
edge *= max(energy, 0)
edge /= 2.0
x = gaussian_filter1d(x, 3.0)
x = update_leds_6(x)
red = bloom_peaks(x**1.0, width=1, blur_factor=1.5)
green = bloom_peaks(x**1.0, width=2, blur_factor=0.5)
blue = bloom_peaks(x**1.0, width=1, blur_factor=0.5)
# Set LEDs
color = np.tile(0.0, (3, config.N_PIXELS))
color[0, :] = 1.0*edge + red*1.0
color[1, :] = 1.2*edge + green*1.0
color[2, :] = 1.5*edge + blue*1.0
color = color.T * 255.0
pixels_filt.update(color)
led.pixels = np.round(pixels_filt.value).astype(int)
led.update()
return (color[:, 0] + color[:, 1] + color[:, 2]) / (3. * 255.0)
N = 60
E = []
for i in range(0, N):
alpha_decay = 0.01 * (float(i + 1) / (N + 1.0))**2.0
alpha_rise = alpha_decay
E.append(dsp.ExpFilter(.1, alpha_decay, alpha_rise))
def led_vis3(x):
energy = np.mean(x**.5)
pixels = np.tile(0.0, config.N_PIXELS)
for i in range(N):
E[i].update(energy)
pixels[i] = hyperbolic_tan(max(energy / E[i].value - 1.0, 0))
color = np.tile(0.0, (3, config.N_PIXELS))
color[0, :] = pixels
color[1, :] = pixels
color[2, :] = pixels
color = color.T * 255.0
pixels_filt.update(color)
led.pixels = np.round(pixels_filt.value).astype(int)
led.update()
return (color[:, 0] + color[:, 1] + color[:, 2]) / (3. * 255.0)
if __name__ == '__main__': if __name__ == '__main__':
led.update() led.update()
microphone.start_stream(microphone_update) microphone.start_stream(microphone_update)