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
_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=0.2, alpha_rise=0.2)
"""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 memoize(function):
"""Provides a decorator for memoizing functions"""
from functools import wraps
memo = {}
@wraps(function)
def wrapper(*args):
if args in memo:
return memo[args]
else:
rv = function(*args)
memo[args] = rv
return rv
return wrapper
@memoize
def _normalized_linspace(size):
return np.linspace(0, 1, size)
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 = _normalized_linspace(len(y))
x_new = _normalized_linspace(new_length)
z = np.interp(x_new, x_old, y)
return z
r_filt = dsp.ExpFilter(np.tile(0.01, config.N_PIXELS // 2),
alpha_decay=0.2, alpha_rise=0.99)
g_filt = dsp.ExpFilter(np.tile(0.01, config.N_PIXELS // 2),
alpha_decay=0.05, alpha_rise=0.3)
b_filt = dsp.ExpFilter(np.tile(0.01, config.N_PIXELS // 2),
alpha_decay=0.1, alpha_rise=0.5)
common_mode = dsp.ExpFilter(np.tile(0.01, config.N_PIXELS // 2),
alpha_decay=0.99, alpha_rise=0.01)
p_filt = dsp.ExpFilter(np.tile(1, (3, config.N_PIXELS // 2)),
alpha_decay=0.1, alpha_rise=0.99)
p = np.tile(1.0, (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 visualize_scroll(y):
"""Effect that originates in the center and scrolls outwards"""
global p
y = y**2.0
gain.update(y)
y /= gain.value
y *= 255.0
r = int(np.max(y[:len(y) // 3]))
g = int(np.max(y[len(y) // 3: 2 * len(y) // 3]))
b = int(np.max(y[2 * len(y) // 3:]))
# Scrolling effect window
p[:, 1:] = p[:, :-1]
p *= 0.98
p = gaussian_filter1d(p, sigma=0.2)
# Create new color originating at the center
p[0, 0] = r
p[1, 0] = g
p[2, 0] = b
# Update the LED strip
return np.concatenate((p[:, ::-1], p), axis=1)
def visualize_energy(y):
"""Effect that expands from the center with increasing sound energy"""
global p
y = np.copy(y)
gain.update(y)
y /= gain.value
# Scale by the width of the LED strip
y *= float((config.N_PIXELS // 2) - 1)
# Map color channels according to energy in the different freq bands
scale = 0.9
r = int(np.mean(y[:len(y) // 3]**scale))
g = int(np.mean(y[len(y) // 3: 2 * len(y) // 3]**scale))
b = int(np.mean(y[2 * len(y) // 3:]**scale))
# Assign color to different frequency regions
p[0, :r] = 255.0
p[0, r:] = 0.0
p[1, :g] = 255.0
p[1, g:] = 0.0
p[2, :b] = 255.0
p[2, b:] = 0.0
p_filt.update(p)
p = np.round(p_filt.value)
# Apply substantial blur to smooth the edges
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)
# Set the new pixel value
return np.concatenate((p[:, ::-1], p), axis=1)
_prev_spectrum = np.tile(0.01, config.N_PIXELS // 2)
def visualize_spectrum(y):
"""Effect that maps the Mel filterbank frequencies onto the LED strip"""
global _prev_spectrum
y = np.copy(interpolate(y, config.N_PIXELS // 2))
common_mode.update(y)
diff = y - _prev_spectrum
_prev_spectrum = np.copy(y)
# Color channel mappings
r = r_filt.update(y - common_mode.value)
g = np.abs(diff)
b = b_filt.update(np.copy(y))
# Mirror the color channels for symmetric output
r = np.concatenate((r[::-1], r))
g = np.concatenate((g[::-1], g))
b = np.concatenate((b[::-1], b))
output = np.array([r, g,b]) * 255
return output
fft_plot_filter = dsp.ExpFilter(np.tile(1e-1, config.N_FFT_BINS),
alpha_decay=0.5, alpha_rise=0.99)
mel_gain = dsp.ExpFilter(np.tile(1e-1, config.N_FFT_BINS),
alpha_decay=0.01, alpha_rise=0.99)
mel_smoothing = dsp.ExpFilter(np.tile(1e-1, config.N_FFT_BINS),
alpha_decay=0.5, alpha_rise=0.99)
volume = dsp.ExpFilter(config.MIN_VOLUME_THRESHOLD,
alpha_decay=0.02, alpha_rise=0.02)
fft_window = np.hamming(int(config.MIC_RATE / config.FPS) * config.N_ROLLING_HISTORY)
prev_fps_update = time.time()
def microphone_update(audio_samples):
global y_roll, prev_rms, prev_exp, prev_fps_update
# Normalize samples between 0 and 1
y = audio_samples / 2.0**15
# Construct a rolling window of audio samples
y_roll[:-1] = y_roll[1:]
y_roll[-1, :] = np.copy(y)
y_data = np.concatenate(y_roll, axis=0).astype(np.float32)
vol = np.max(np.abs(y_data))
if vol < config.MIN_VOLUME_THRESHOLD:
print('No audio input. Volume below threshold. Volume:', vol)
led.pixels = np.tile(0, (3, config.N_PIXELS))
led.update()
else:
# Transform audio input into the frequency domain
N = len(y_data)
N_zeros = 2**int(np.ceil(np.log2(N))) - N
# Pad with zeros until the next power of two
y_data *= fft_window
y_padded = np.pad(y_data, (0, N_zeros), mode='constant')
YS = np.abs(np.fft.rfft(y_padded)[:N // 2])
# Construct a Mel filterbank from the FFT data
mel = np.atleast_2d(YS).T * dsp.mel_y.T
# Scale data to values more suitable for visualization
# mel = np.sum(mel, axis=0)
mel = np.sum(mel, axis=0)
mel = mel**2.0
# Gain normalization
mel_gain.update(np.max(gaussian_filter1d(mel, sigma=1.0)))
mel /= mel_gain.value
mel = mel_smoothing.update(mel)
# Map filterbank output onto LED strip
output = visualization_effect(mel)
led.pixels = output
led.update()
if config.USE_GUI:
# Plot filterbank output
x = np.linspace(config.MIN_FREQUENCY, config.MAX_FREQUENCY, len(mel))
mel_curve.setData(x=x, y=fft_plot_filter.update(mel))
# Plot the color channels
r_curve.setData(y=led.pixels[0])
g_curve.setData(y=led.pixels[1])
b_curve.setData(y=led.pixels[2])
if config.USE_GUI:
app.processEvents()
if config.DISPLAY_FPS:
fps = frames_per_second()
if time.time() - 0.5 > prev_fps_update:
prev_fps_update = time.time()
print('FPS {:.0f} / {:.0f}'.format(fps, 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
visualization_effect = visualize_spectrum
"""Visualization effect to display on the LED strip"""
if __name__ == '__main__':
if config.USE_GUI:
import pyqtgraph as pg
from pyqtgraph.Qt import QtGui, QtCore
# Create GUI window
app = QtGui.QApplication([])
view = pg.GraphicsView()
layout = pg.GraphicsLayout(border=(100,100,100))
view.setCentralItem(layout)
view.show()
view.setWindowTitle('Visualization')
view.resize(800,600)
# Mel filterbank plot
fft_plot = layout.addPlot(title='Filterbank Output', colspan=3)
fft_plot.setRange(yRange=[-0.1, 1.2])
fft_plot.disableAutoRange(axis=pg.ViewBox.YAxis)
x_data = np.array(range(1, config.N_FFT_BINS + 1))
mel_curve = pg.PlotCurveItem()
mel_curve.setData(x=x_data, y=x_data*0)
fft_plot.addItem(mel_curve)
# Visualization plot
layout.nextRow()
led_plot = layout.addPlot(title='Visualization Output', colspan=3)
led_plot.setRange(yRange=[-5, 260])
led_plot.disableAutoRange(axis=pg.ViewBox.YAxis)
# Pen for each of the color channel curves
r_pen = pg.mkPen((255, 30, 30, 200), width=4)
g_pen = pg.mkPen((30, 255, 30, 200), width=4)
b_pen = pg.mkPen((30, 30, 255, 200), width=4)
# Color channel curves
r_curve = pg.PlotCurveItem(pen=r_pen)
g_curve = pg.PlotCurveItem(pen=g_pen)
b_curve = pg.PlotCurveItem(pen=b_pen)
# Define x data
x_data = np.array(range(1, config.N_PIXELS + 1))
r_curve.setData(x=x_data, y=x_data*0)
g_curve.setData(x=x_data, y=x_data*0)
b_curve.setData(x=x_data, y=x_data*0)
# Add curves to plot
led_plot.addItem(r_curve)
led_plot.addItem(g_curve)
led_plot.addItem(b_curve)
# Frequency range label
freq_label = pg.LabelItem('')
# Frequency slider
def freq_slider_change(tick):
minf = freq_slider.tickValue(0)**2.0 * (config.MIC_RATE / 2.0)
maxf = freq_slider.tickValue(1)**2.0 * (config.MIC_RATE / 2.0)
t = 'Frequency range: {:.0f} - {:.0f} Hz'.format(minf, maxf)
freq_label.setText(t)
config.MIN_FREQUENCY = minf
config.MAX_FREQUENCY = maxf
dsp.create_mel_bank()
freq_slider = pg.TickSliderItem(orientation='bottom', allowAdd=False)
freq_slider.addTick((config.MIN_FREQUENCY / (config.MIC_RATE / 2.0))**0.5)
freq_slider.addTick((config.MAX_FREQUENCY / (config.MIC_RATE / 2.0))**0.5)
freq_slider.tickMoveFinished = freq_slider_change
freq_label.setText('Frequency range: {} - {} Hz'.format(
config.MIN_FREQUENCY,
config.MAX_FREQUENCY))
# Effect selection
active_color = '#16dbeb'
inactive_color = '#FFFFFF'
def energy_click(x):
global visualization_effect
visualization_effect = visualize_energy
energy_label.setText('Energy', color=active_color)
scroll_label.setText('Scroll', color=inactive_color)
spectrum_label.setText('Spectrum', color=inactive_color)
def scroll_click(x):
global visualization_effect
visualization_effect = visualize_scroll
energy_label.setText('Energy', color=inactive_color)
scroll_label.setText('Scroll', color=active_color)
spectrum_label.setText('Spectrum', color=inactive_color)
def spectrum_click(x):
global visualization_effect
visualization_effect = visualize_spectrum
energy_label.setText('Energy', color=inactive_color)
scroll_label.setText('Scroll', color=inactive_color)
spectrum_label.setText('Spectrum', color=active_color)
# Create effect "buttons" (labels with click event)
energy_label = pg.LabelItem('Energy')
scroll_label = pg.LabelItem('Scroll')
spectrum_label = pg.LabelItem('Spectrum')
energy_label.mousePressEvent = energy_click
scroll_label.mousePressEvent = scroll_click
spectrum_label.mousePressEvent = spectrum_click
energy_click(0)
# Layout
layout.nextRow()
layout.addItem(freq_label, colspan=3)
layout.nextRow()
layout.addItem(freq_slider, colspan=3)
layout.nextRow()
layout.addItem(energy_label)
layout.addItem(scroll_label)
layout.addItem(spectrum_label)
# Initialize LEDs
led.update()
# Start listening to live audio stream
microphone.start_stream(microphone_update)
