audio-reactive-led-strip/python/visualization.py

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from __future__ import print_function
from __future__ import division
import time
import numpy as np
from scipy.ndimage.filters import gaussian_filter1d
import config
import microphone
import dsp
import led
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=0.002, alpha_rise=0.002)
"""The low-pass filter used to estimate frames-per-second"""
def frames_per_second():
"""Return the estimated frames per second
Returns the current estimate for frames-per-second (FPS).
FPS is estimated by measured the amount of time that has elapsed since
this function was previously called. The FPS estimate is low-pass filtered
to reduce noise.
This function is intended to be called one time for every iteration of
the program's main loop.
Returns
-------
fps : float
Estimated frames-per-second. This value is low-pass filtered
to reduce noise.
"""
global _time_prev, _fps
time_now = time.time() * 1000.0
dt = time_now - _time_prev
_time_prev = time_now
if dt == 0.0:
return _fps.value
return _fps.update(1000.0 / dt)
def interpolate(y, new_length):
"""Intelligently resizes the array by linearly interpolating the values
Parameters
----------
y : np.array
Array that should be resized
new_length : int
The length of the new interpolated array
Returns
-------
z : np.array
New array with length of new_length that contains the interpolated
values of y.
"""
if len(y) == new_length:
return y
x_old = np.linspace(0, 1, len(y))
x_new = np.linspace(0, 1, new_length)
z = np.interp(x_new, x_old, y)
return z
r_filt = dsp.ExpFilter(np.tile(0.01, config.N_PIXELS // 2),
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alpha_decay=0.08, alpha_rise=0.99)
g_filt = dsp.ExpFilter(np.tile(0.01, config.N_PIXELS // 2),
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alpha_decay=0.15, alpha_rise=0.99)
b_filt = dsp.ExpFilter(np.tile(0.01, config.N_PIXELS // 2),
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alpha_decay=0.25, alpha_rise=0.99)
p_filt = dsp.ExpFilter(np.tile(1, (3, config.N_PIXELS // 2)),
alpha_decay=0.05, alpha_rise=0.8)
p = np.tile(1, (3, config.N_PIXELS // 2))
gain = dsp.ExpFilter(np.tile(0.01, config.N_FFT_BINS),
alpha_decay=0.001, alpha_rise=0.99)
def largest_indices(ary, n):
"""Returns the n largest indices from a numpy array."""
flat = ary.flatten()
indices = np.argpartition(flat, -n)[-n:]
indices = indices[np.argsort(-flat[indices])]
return np.unravel_index(indices, ary.shape)
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def visualize_max(y):
y = np.copy(interpolate(y, config.N_PIXELS // 2)) * 255.0
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ind = largest_indices(y, 15)
y[ind] *= -1
y[y > 0] = 0
y[ind] *= -1
# Blur the color channels with different strengths
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r = gaussian_filter1d(y, sigma=0.25)
g = gaussian_filter1d(y, sigma=0.10)
b = gaussian_filter1d(y, sigma=0.00)
b = np.roll(b, 1)
b[0] = b[1]
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, :] = pixel_r
led.pixels[1, :] = pixel_g
led.pixels[2, :] = pixel_b
# Update the GUI plots
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()
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def visualize_scroll(y):
global p
y = gaussian_filter1d(y, sigma=1.0)**3.0
y = np.copy(y)
gain.update(y)
y /= gain.value
y *= 255.0
r = int(max(y[:len(y) // 3]))
g = int(max(y[len(y) // 3: 2 * len(y) // 3]))
b = int(max(y[2 * len(y) // 3:]))
p = np.roll(p, 1, axis=1)
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p *= 0.98
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p = gaussian_filter1d(p, sigma=0.2)
p[0, 0] = r
p[1, 0] = g
p[2, 0] = b
led.pixels = np.concatenate((p[:, ::-1], p), axis=1)
led.update()
def visualize_energy(y):
global p
y = gaussian_filter1d(y, sigma=1.0)**3.0
gain.update(y)
y /= gain.value
y *= (config.N_PIXELS // 2) - 1
r = int(np.mean(y[:len(y) // 3]))
g = int(np.mean(y[len(y) // 3: 2 * len(y) // 3]))
b = int(np.mean(y[2 * len(y) // 3:]))
p[0, :r] = 255
p[0, r:] = 0
p[1, :g] = 255
p[1, g:] = 0
p[2, :b] = 255
p[2, b:] = 0
p_filt.update(p)
p = p_filt.value.astype(int)
p[0, :] = gaussian_filter1d(p[0, :], sigma=4.0)
p[1, :] = gaussian_filter1d(p[1, :], sigma=4.0)
p[2, :] = gaussian_filter1d(p[2, :], sigma=4.0)
led.pixels = np.concatenate((p[:, ::-1], p), axis=1)
led.update()
def visualize_spectrum(y):
y = np.copy(interpolate(y, config.N_PIXELS // 2)) * 255.0
# Blur the color channels with different strengths
r = gaussian_filter1d(y, sigma=0.25)
g = gaussian_filter1d(y, sigma=0.10)
b = gaussian_filter1d(y, sigma=0.00)
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, :] = pixel_r
led.pixels[1, :] = pixel_g
led.pixels[2, :] = pixel_b
# Update the GUI plots
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_FFT_BINS),
alpha_decay=0.01, alpha_rise=0.99)
volume = dsp.ExpFilter(config.MIN_VOLUME_THRESHOLD,
alpha_decay=0.02, alpha_rise=0.02)
def microphone_update(stream):
global y_roll, prev_rms, prev_exp
# Normalize new audio samples
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)
y_roll[-1, :] = np.copy(y)
y_data = np.concatenate(y_roll, axis=0)
volume.update(np.nanmean(y_data ** 2))
if volume.value < config.MIN_VOLUME_THRESHOLD:
print('No audio input. Volume below threshold. Volume:', volume.value)
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led.pixels = np.tile(0, (3, config.N_PIXELS))
led.update()
else:
XS, YS = dsp.fft(y_data, window=np.hamming)
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
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mel = YS**0.5
mel = gaussian_filter1d(mel, sigma=1.0)
mel_gain.update(np.max(mel))
mel = mel / mel_gain.value
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visualize_spectrum(mel)
# visualize_max(mel)
# visualize_scroll(mel)
# visualize_energy(mel)
GUI.app.processEvents()
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print('FPS {:.0f} / {:.0f}'.format(frames_per_second(), config.FPS))
# Number of audio samples to read every time frame
samples_per_frame = int(config.MIC_RATE / config.FPS)
# Array containing the rolling audio sample window
y_roll = np.random.rand(config.N_ROLLING_HISTORY, samples_per_frame) / 1e16
if __name__ == '__main__':
import pyqtgraph as pg
GUI = gui.GUI(width=800, height=400, title='Audio Visualization')
# Audio plot
GUI.add_plot('Color Channels')
r_pen = pg.mkPen((255, 30, 30, 200), width=6)
g_pen = pg.mkPen((30, 255, 30, 200), width=6)
b_pen = pg.mkPen((30, 30, 255, 200), width=6)
GUI.add_curve(plot_index=0, pen=r_pen)
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
microphone.start_stream(microphone_update)