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music_mode.py
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music_mode.py
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from __future__ import print_function
from __future__ import division
import sys
import math
import numpy as np
from scipy.ndimage.filters import gaussian_filter1d
from time import time
from base_classes import *
import pyaudio
import config
import dsp
SPECTRUM_PIXELS = 64
# variants (visualization)
# 0 = spectrum
# 1 = energy
# 2 = scroll
# shapes
# 0 = circular in - out
# 1 = circular out - in
# 2 = vertical in - out
# 3 = vertical out - in
# 4 = vertical left - right
# 5 = vertical right - left
# 6 = horizontal in - out
# 7 = horizontal out- in
# 8 = horizontal bottom - up
# 9 = horizontal top - down
# 10 = continental
_gamma = np.load(config.GAMMA_TABLE_PATH)
"""Gamma lookup table used for nonlinear brightness correction"""
shape = 5
pos = 0.0 # 0.0 - 255.0
pos_time_stamp = time()
mel_output = np.tile(0, SPECTRUM_PIXELS)
class MusicMode(Mode):
key = "MUSIC"
def __init__(self, variant, shape):
self.variant = variant
self.shape = shape
self.spectrum_len = SPECTRUM_PIXELS if shape is not 10 else 6
if not stream_active():
start_microphone_stream()
# filters for visualization
self.r_filt = dsp.ExpFilter(np.tile(0.01, self.spectrum_len),
alpha_decay=0.2, alpha_rise=0.99)
self.g_filt = dsp.ExpFilter(np.tile(0.01, self.spectrum_len),
alpha_decay=0.05, alpha_rise=0.3)
self.b_filt = dsp.ExpFilter(np.tile(0.01, self.spectrum_len),
alpha_decay=0.1, alpha_rise=0.5)
self.common_mode = dsp.ExpFilter(np.tile(0.01, self.spectrum_len),
alpha_decay=0.99, alpha_rise=0.01)
self.p_filt = dsp.ExpFilter(np.tile(1, (3, self.spectrum_len)),
alpha_decay=0.08, alpha_rise=0.5) # default: decay = 0.1, rise = 0.99
self.p = np.tile(1.0, (3, self.spectrum_len))
self.gain = dsp.ExpFilter(np.tile(0.01, config.N_FFT_BINS),
alpha_decay=0.008, alpha_rise=0.5) # default: decay = 0.001, rise = 0.99
self._prev_spectrum = np.tile(0.01, self.spectrum_len)
def copy(self):
return MusicMode(self.variant, self.shape)
def args(self):
return str(self.variant) + "," + str(self.shape)
def loop(self, _leds):
global mel_output
output = 0.0
if self.variant == 0:
output = self.visualize_spectrum(mel_output)
elif self.variant == 1:
output = self.visualize_energy(mel_output)
elif self.variant == 2:
output = self.visualize_scroll(mel_output)
# Truncate values and cast to integer
col = np.clip(output, 0, 255).astype(int)
# Optional gamma correction
c = _gamma[col] if config.SOFTWARE_GAMMA_CORRECTION else np.copy(col)
# Encode 24-bit LED values in 32 bit integers
r = np.left_shift(c[0][:].astype(int), 8)
g = np.left_shift(c[1][:].astype(int), 16)
b = c[2][:].astype(int)
rgb = np.bitwise_or(np.bitwise_or(r, g), b)
color_idx = np.tile(0.0, len(_leds))
if self.shape == 0: # circular in - out
color_idx = np.array(map(lambda led: (led.dist - 10) * 2.2, _leds))
elif self.shape == 1: # circular out - in
color_idx = np.array(map(lambda led: abs(led.dist - 200), _leds))
elif self.shape == 2: # vertical in - out
color_idx = np.array(map(lambda led: abs(led.long - 20) * 2, _leds))
elif self.shape == 3: # vertical out - in
color_idx = np.array(map(lambda led: -abs(led.long - 20) + 200, _leds))
elif self.shape == 4: # vertical left - right
color_idx = np.array(map(lambda led: led.long + 120.0, _leds))
elif self.shape == 5: # vertical right - left
color_idx = np.array(map(lambda led: 255 - (led.long + 120.0) * 0.8, _leds))
elif self.shape == 6: # horizontal in - out
color_idx = np.array(map(lambda led: abs(led.lat - 20) * 4, _leds))
elif self.shape == 7: # horizontal out - in
color_idx = np.array(map(lambda led: 255 - abs(led.lat - 20) * 4, _leds))
elif self.shape == 8: # horizontal bottom - up
color_idx = np.array(map(lambda led: (led.lat + 45.0) * 1.5, _leds))
elif self.shape == 9: # horizontal top - down
color_idx = np.array(map(lambda led: 255 - (led.lat + 65.0) * 1.5, _leds))
elif self.shape == 10: # continental
color_idx = np.array(map(lambda led: 0.0 + led.continent.index, _leds))
if self.shape is not 10:
color_idx *= self.spectrum_len / 256
color_idx = np.clip(color_idx.astype(int), 0, len(rgb) - 1)
for i in range(len(_leds)):
strip._led_data[_leds[i].pos] = rgb[color_idx[i]]
def visualize_scroll(self, y):
"""Effect that originates in the center and scrolls outwards"""
y = y**3.0 # default **2.0
self.gain.update(y)
y /= self.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
self.p[:, 2:] = self.p[:, :-2] # default 1: and :-1
self.p *= 0.98
self.p = gaussian_filter1d(self.p, sigma=2.0) # default 0.2
# Create new color originating at the center
self.p[0, 0] = r
self.p[1, 0] = g
self.p[2, 0] = b
# Update the LED strip
return self.p
def visualize_energy(self, y):
"""Effect that expands from the center with increasing sound energy"""
y = np.copy(y)
self.gain.update(y)
y /= self.gain.value
# Scale by the width of the LED strip
y *= float((self.spectrum_len) - 1)
# Map color channels according to energy in the different freq bands
scale = 1.0 # default 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
self.p[0, :r] = 255.0
self.p[0, r:] = 0.0
self.p[1, :g] = 255.0
self.p[1, g:] = 0.0
self.p[2, :b] = 255.0
self.p[2, b:] = 0.0
self.p_filt.update(self.p)
self.p = np.round(self.p_filt.value)
# Apply substantial blur to smooth the edges
self.p[0, :] = gaussian_filter1d(self.p[0, :], sigma=4.0)
self.p[1, :] = gaussian_filter1d(self.p[1, :], sigma=4.0)
self.p[2, :] = gaussian_filter1d(self.p[2, :], sigma=4.0)
# Set the new pixel value
return self.p
def visualize_spectrum(self, y):
"""Effect that maps the Mel filterbank frequencies onto the LED strip"""
y = np.copy(interpolate(y, self.spectrum_len))
self.common_mode.update(y)
diff = y - self._prev_spectrum
self._prev_spectrum = np.copy(y)
# Color channel mappings
#pos = 50
#c0 = wheel(pos)
#c1 = wheel((pos+85)%255)
#c2 = wheel((pos+170)%255)
r = self.r_filt.update(y - self.common_mode.value)
g = np.abs(diff)
b = self.b_filt.update(np.copy(y))
#r = r_filt.update((c0.r / 255) * (y - common_mode.value) + (c1.r / 255) * np.abs(diff) + (c2.r / 255) * np.copy(y))
#g = (c0.g / 255) * (y - common_mode.value) + (c1.g / 255) * np.abs(diff) + (c2.g / 255) * (np.copy(y))
#b = b_filt.update((c0.b / 255) * (y - common_mode.value) + (c1.b / 255) * np.abs(diff) + (c2.b / 255) * (np.copy(y)))
output = np.array([r, g, b]) * 255
return output#self.spectrum_len)
_time_prev = 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() * 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
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()
overflows = 0
prev_ovf_time = time()
def microphone_update():
# start mic stream if it's not running
if stream is None or pa is None:
start_microphone_stream()
global y_roll, prev_rms, prev_exp, prev_fps_update, stream, frames_per_buffer, overflows, prev_overflow_time, mel_output
try:
y = np.fromstring(stream.read(frames_per_buffer, exception_on_overflow=False), dtype=np.int16)
y = y.astype(np.float32)
stream.read(stream.get_read_available(), exception_on_overflow=False)
#callback(y)
except IOError:
overflows += 1
if time() > prev_ovf_time + 1:
prev_ovf_time = time()
print('Audio buffer has overflowed {} times'.format(overflows))
# Normalize samples between 0 and 1
y = y / 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)
mel_output = np.tile(0.0, config.N_FFT_BINS)
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)
mel_output = mel
if config.DISPLAY_FPS:
fps = frames_per_second()
if time() - 0.5 > prev_fps_update:
prev_fps_update = time()
print('FPS {:.0f} / {:.0f}'.format(fps, config.FPS))
def wheel(pos):
if pos < 85:
return C.fromRGB(255 - pos * 3, pos * 3, 0)
elif pos < 170:
pos -= 85
return C.fromRGB(0, 255 - pos * 3, pos * 3)
else:
pos -= 170
return C.fromRGB(pos * 3, 0, 255 - pos * 3)
# 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
## MICROPHONE
"""global variables for microphone stream"""
pa = None
stream = None
frames_per_buffer = int(config.MIC_RATE / config.FPS)
def start_microphone_stream():
global pa, frames_per_buffer, stream
pa = pyaudio.PyAudio()
stream = pa.open(format=pyaudio.paInt16,
channels=1,
rate=config.MIC_RATE,
input=True,
frames_per_buffer=frames_per_buffer)
def stop_microphone_stream():
global pa, stream
if stream is not None:
stream.stop_stream()
stream.close()
stream = None
if pa is not None:
pa.terminate()
pa = None
print('mic stream stopped')
def stream_active():
return stream is not None