cb34b6353d
Note: All code in sandbox.py is temporary and used for experimenting with different visualizations.
527 lines
16 KiB
Python
527 lines
16 KiB
Python
from __future__ import print_function
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from __future__ import division
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import time
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import numpy as np
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from pyqtgraph.Qt import QtGui
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import pyqtgraph as pg
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import config
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import microphone
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import dsp
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import led
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import melbank
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from scipy.ndimage.filters import gaussian_filter1d
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from scipy.signal import argrelextrema
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def rainbow(length, speed=1.0 / 5.0):
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"""Returns a rainbow colored array with desired length
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Returns a rainbow colored array with shape (length, 3).
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Each row contains the red, green, and blue color values between 0 and 1.
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Example format:
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[[red0, green0, blue0],
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[red1, green1, blue1],
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...
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[redN, greenN, blueN]]
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Parameters
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----------
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length : int
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The length of the rainbow colored array that should be returned
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speed : float
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Value indicating the speed that the rainbow colors change.
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If speed > 0, then successive calls to this function will return
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arrays with different colors assigned to the indices.
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If speed == 0, then this function will always return the same colors.
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Returns
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-------
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x : numpy.array
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np.ndarray with shape (length, 3).
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Columns denote the red, green, and blue color values respectively.
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Each color is a float between 0 and 1.
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"""
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dt = 2.0 * np.pi / length
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t = time.time() * speed
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def r(t): return (np.sin(t + 0.0) + 1.0) * 1.0 / 2.0
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def g(t): return (np.sin(t + (2.0 / 3.0) * np.pi) + 1.0) * 1.0 / 2.0
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def b(t): return (np.sin(t + (4.0 / 3.0) * np.pi) + 1.0) * 1.0 / 2.0
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x = np.tile(0.0, (length, 3))
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for i in range(length):
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x[i][0] = r(i * dt + t)
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x[i][1] = g(i * dt + t)
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x[i][2] = b(i * dt + t)
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return x
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def rainbow_gen(length, speed=1./5., center=0.5, width=0.5, f=[1, 1, 1]):
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dt = 2.0 * np.pi / length
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t = time.time() * speed
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phi = 2.0 / 3.0 * np.pi
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def r(t): return np.clip(np.sin(f[0] * t + 1. * phi) * width + center, 0., 1.)
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def g(t): return np.clip(np.sin(f[1] * t + 2. * phi) * width + center, 0., 1.)
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def b(t): return np.clip(np.sin(f[2] * t + 3. * phi) * width + center, 0., 1.)
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x = np.tile(0.0, (length, 3))
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for i in range(length):
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x[i][0] = r(i * dt + t)
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x[i][1] = g(i * dt + t)
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x[i][2] = b(i * dt + t)
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return x
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_time_prev = time.time() * 1000.0
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"""The previous time that the frames_per_second() function was called"""
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_fps = dsp.ExpFilter(val=config.FPS, alpha_decay=0.05, alpha_rise=0.05)
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"""The low-pass filter used to estimate frames-per-second"""
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def frames_per_second():
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"""Return the estimated frames per second
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Returns the current estimate for frames-per-second (FPS).
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FPS is estimated by measured the amount of time that has elapsed since
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this function was previously called. The FPS estimate is low-pass filtered
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to reduce noise.
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This function is intended to be called one time for every iteration of
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the program's main loop.
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Returns
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-------
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fps : float
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Estimated frames-per-second. This value is low-pass filtered
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to reduce noise.
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"""
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global _time_prev, _fps
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time_now = time.time() * 1000.0
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dt = time_now - _time_prev
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_time_prev = time_now
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if dt == 0.0:
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return _fps.value
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return _fps.update(1000.0 / dt)
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def update_plot_1(x, y):
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"""Updates pyqtgraph plot 1
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Parameters
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----------
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x : numpy.array
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1D array containing the X-axis values that should be plotted.
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There should only be one X-axis array.
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y : numpy.ndarray
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Array containing each of the Y-axis series that should be plotted.
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Each row of y corresponds to a Y-axis series. The columns in each row
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are the values that should be plotted.
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Returns
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-------
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None
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"""
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global curves, p1
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colors = rainbow(config.N_CURVES) * 255.0
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for i in range(config.N_CURVES):
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#curves[i].setPen((colors[i][0], colors[i][1], colors[i][2]))
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curves[i].setData(x=x, y=y[i])
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p1.autoRange()
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p1.setRange(yRange=(0.0, 2.0))
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def interpolate(y, new_length):
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"""Intelligently resizes the array by linearly interpolating the values
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Parameters
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----------
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y : np.array
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Array that should be resized
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new_length : int
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The length of the new interpolated array
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Returns
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-------
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z : np.array
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New array with length of new_length that contains the interpolated
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values of y.
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"""
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x_old = np.linspace(0, 1, len(y))
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x_new = np.linspace(0, 1, new_length)
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z = np.interp(x_new, x_old, y)
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return z
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def leak_saturated_pixels(pixels):
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pixels = np.copy(pixels)
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for i in range(pixels.shape[0]):
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excess_red = max(pixels[i, 0] - 255.0, 0.0)
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excess_green = max(pixels[i, 1] - 255.0, 0.0)
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excess_blue = max(pixels[i, 2] - 255.0, 0.0)
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# Share excess red
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pixels[i, 1] += excess_red
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pixels[i, 2] += excess_red
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# Share excess green
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pixels[i, 0] += excess_green
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pixels[i, 2] += excess_green
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# Share excess blue
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pixels[i, 0] += excess_blue
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pixels[i, 1] += excess_blue
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return pixels
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_EA_norm = dsp.ExpFilter(np.tile(1e-4, config.N_PIXELS), 0.01, 0.85)
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"""Onset energy per-bin normalization constants
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This filter is responsible for individually normalizing the onset bin energies.
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This is used to obtain per-bin automatic gain control.
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"""
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_EA_smooth = dsp.ExpFilter(np.tile(1.0, config.N_PIXELS), 0.25, 0.80)
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"""Asymmetric exponential low-pass filtered onset energies
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This filter is responsible for smoothing the displayed onset energies.
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Asymmetric rise and fall constants allow the filter to quickly respond to
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increases in onset energy, while slowly responded to decreases.
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"""
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# Individually normalized energy spike method
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# Works well with GAMMA_CORRECTION = True
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# This is one of the best visualizations, but doesn't work for everything
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def update_leds_6(y):
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"""Visualization using per-bin normalized onset energies
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Visualizes onset energies by normalizing each frequency bin individually.
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The normalized bins are then processed and displayed onto the LED strip.
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This function visualizes the onset energies by individually normalizing
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each onset energy bin. The normalized onset bins are then scaled and
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Parameters
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----------
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y : numpy.array
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Array containing the onset energies that should be visualized.
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"""
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y = np.abs(y)**1.25
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# Update normalization constants and then normalize each bin
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_EA_norm.update(y)
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y /= _EA_norm.value
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# Update the onset energy low-pass filter and discard value too dim
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_EA_smooth.update(y)
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_EA_smooth.value[_EA_smooth.value < .1] = 0.0
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# Return the pixels
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pixels = np.copy(_EA_smooth.value)**1.5
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return pixels
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_EF_norm = dsp.ExpFilter(np.tile(1.0, config.N_PIXELS), 0.05, 0.9)
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_EF_smooth = dsp.ExpFilter(np.tile(1.0, config.N_PIXELS), 0.08, 0.9)
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_prev_energy = 0.0
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# Individually normalized energy flux
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def update_leds_5(y):
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global _prev_energy
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y = np.copy(y)
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EF = np.max(y - _prev_energy, 0.0)
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_prev_energy = np.copy(y)
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_EF_norm.update(EF)
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EF /= _EF_norm.value
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_EF_smooth.update(EF)
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# Cutoff values below 0.1
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_EF_smooth.value[_EF_smooth.value < 0.1] = 0.0
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pixels = np.copy(_EF_smooth.value)
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return pixels
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_prev_E = np.tile(.1, config.N_PIXELS)
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_EF_N = dsp.ExpFilter(np.tile(.1, config.N_PIXELS), 0.01, 0.9)
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def update_leds_5(y):
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global _prev_E
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y = np.copy(y)
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current_E = y**2.0
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EF = current_E - _prev_E
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_prev_E = np.copy(current_E)
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EF[EF < 0.0] = 0.0
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_EF_N.update(EF)
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EF /= _EF_N.value
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EF[current_E < 0.02] = 0.0
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return EF
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_energy_norm = dsp.ExpFilter(10.0, alpha_decay=.15, alpha_rise=.9)
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_energy_smooth = dsp.ExpFilter(10.0, alpha_decay=0.1, alpha_rise=0.8)
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# Modulate brightness by relative average rectified onset flux
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def update_leds_4(y):
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global _prev_energy
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energy = np.sum(y**1.0)
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EF = max(energy - _prev_energy, 0.0)
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_prev_energy = energy
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_energy_norm.update(EF)
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_energy_smooth.update(min(EF / _energy_norm.value, 1.0))
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pixels = np.tile(_energy_smooth.value, y.shape[0])
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return pixels
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# Energy flux based motion across the LED strip
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def update_leds_3(y):
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global pixels, _prev_energy
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y = np.copy(y)
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# Calculate energy flux
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energy = np.sum(y)
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energy_flux = max(energy - _prev_energy, 0)
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_prev_energy = energy
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# Normalize energy flux
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_energy_norm.update(energy_flux)
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# Update and return pixels
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pixels = np.roll(pixels, 1)
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pixels[0] = energy_flux
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return np.copy(pixels)
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# Energy based motion across the LED strip
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def update_leds_2(y):
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global pixels
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y = np.copy(y)
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# Calculate energy
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energy = np.sum(y**1.5)
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onset_energy.update(energy)
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energy /= onset_energy.value
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# Update and return pixels
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pixels = np.roll(pixels, 1)
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pixels[0] = energy
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return np.copy(pixels)
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def update_leds_1(y):
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"""Display the raw onset spectrum on the LED strip"""
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return np.copy(y)**0.75
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YS_peak = dsp.ExpFilter(1.0, alpha_decay=0.005, alpha_rise=0.95)
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def microphone_update(stream):
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global y_roll
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# Retrieve new audio samples and construct the rolling window
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y = np.fromstring(stream.read(samples_per_frame), dtype=np.int16)
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y = y / 2.0**15
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y_roll = np.roll(y_roll, -1, axis=0)
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y_roll[-1, :] = np.copy(y)
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y_data = np.concatenate(y_roll, axis=0)
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# # Calculate onset detection functions
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# SF, NWPD, RCD = dsp.onset(y_data)
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# # Apply Gaussian blur to improve agreement between onset functions
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# SF = gaussian_filter1d(SF, 1.0)
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# NWPD = gaussian_filter1d(NWPD, 1.0)
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# RCD = gaussian_filter1d(RCD, 1.0)
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# # Update and normalize peak followers
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# SF_peak.update(np.max(SF))
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# NWPD_peak.update(np.max(NWPD))
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# RCD_peak.update(np.max(RCD))
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# SF /= SF_peak.value
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# NWPD /= NWPD_peak.value
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# RCD /= RCD_peak.value
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# # Normalize and update onset spectrum
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# onset = SF + NWPD + RCD
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# onset_peak.update(np.max(onset))
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# onset /= onset_peak.value
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# onsets.update(onset)
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# # Map the onset values to LED strip pixels
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# if len(onsets.value) != config.N_PIXELS:
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# onset_values = interpolate(onsets.value, config.N_PIXELS)
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# else:
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# onset_values = np.copy(onsets.value)
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# brightness = led_visualization(onset_values)
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XS, YS = dsp.fft(y_data, window=np.hamming)
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YS = YS[XS >= 0.0]
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XS = XS[XS >= 0.0]
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YS = np.atleast_2d(np.abs(YS)).T * dsp.mel_y.T
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YS = np.sum(YS, axis=0)**2.0
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#YS = gaussian_filter1d(YS, 2.0)
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YS = np.diff(YS, n=0)
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YS_peak.update(np.max(YS))
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YS /= YS_peak.value
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if len(YS) != config.N_PIXELS:
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YS = interpolate(YS, config.N_PIXELS)
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#YS = led_visualization(YS)
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YS = led_vis3(YS)
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# Plot the onsets
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#plot_x = np.array(range(1, len(onsets.value) + 1))
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plot_x = np.array(range(1, len(YS) + 1))
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#plot_y = [onsets.value**i for i in np.linspace(2.0, 0.25, config.N_CURVES)]
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plot_y = [YS]
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update_plot_1(plot_x, plot_y)
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app.processEvents()
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print('FPS {:.0f} / {:.0f}'.format(frames_per_second(), config.FPS))
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# Create plot and window
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app = QtGui.QApplication([])
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win = pg.GraphicsWindow('Audio Visualization')
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win.resize(300, 200)
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win.setWindowTitle('Audio Visualization')
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# Create plot 1 containing config.N_CURVES
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p1 = win.addPlot(title='Onset Detection Function')
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p1.setLogMode(x=False)
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curves = []
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colors = rainbow(config.N_CURVES) * 255.0
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for i in range(config.N_CURVES):
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curve = p1.plot(pen=(colors[i][0], colors[i][1], colors[i][2]))
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curves.append(curve)
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# Pixel values for each LED
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pixels = np.tile(0.0, config.N_PIXELS)
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# Used to colorize the LED strip
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color = rainbow(config.N_PIXELS) * 255.0
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# Tracks average onset spectral energy
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onset_energy = dsp.ExpFilter(1.0, alpha_decay=0.01, alpha_rise=0.65)
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# Tracks the location of the spectral median
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median = dsp.ExpFilter(val=config.N_SUBBANDS / 2.0,
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alpha_decay=0.1, alpha_rise=0.1)
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# Smooths the decay of the onset detection function
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onsets = dsp.ExpFilter(val=np.tile(0.0, (config.N_SUBBANDS)),
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alpha_decay=0.15, alpha_rise=0.75)
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# Peak followers used for normalization
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SF_peak = dsp.ExpFilter(1.0, alpha_decay=0.01, alpha_rise=0.99)
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NWPD_peak = dsp.ExpFilter(1.0, alpha_decay=0.01, alpha_rise=0.99)
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RCD_peak = dsp.ExpFilter(1.0, alpha_decay=0.01, alpha_rise=0.99)
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onset_peak = dsp.ExpFilter(0.1, alpha_decay=0.002, alpha_rise=0.5)
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# Number of audio samples to read every time frame
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samples_per_frame = int(config.MIC_RATE / config.FPS)
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# Array containing the rolling audio sample window
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y_roll = np.random.rand(config.N_ROLLING_HISTORY, samples_per_frame) / 100.0
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# Which LED visualization to use
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# update_leds_1 = raw onset spectrum without normalization (GAMMA = True)
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# update_leds_2 = energy average chase effect (GAMMA = True)
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# update_leds_3 = energy flux chase effect (GAMMA = True)
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# update_leds_4 = brightness modulation effect (GAMMA = True)
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# update_leds_5 = energy flux normalized per-bin spectrum (GAMMA = True)
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# update_leds_6 = energy average normalized per-bin spectrum (GAMMA = True)
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# Low pass filter for the LEDs being output to the strip
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pixels_filt = dsp.ExpFilter(np.tile(0., (config.N_PIXELS, 3)), .14, .9)
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def hyperbolic_tan(x):
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return 1.0 - 2.0 / (np.exp(2.0 * x) + 1.0)
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def bloom_peaks(x, width=3, blur_factor=1.0):
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peaks = argrelextrema(x, np.greater)[0]
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y = x * 0.0
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if len(peaks) == 0:
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return y
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for peak in peaks:
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min_idx = max(peak - width, 0)
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max_idx = min(peak + width, len(x) - 1)
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for i in range(min_idx, max_idx):
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y[i] = x[i]
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y = gaussian_filter1d(y, blur_factor)
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return y
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# This is the function responsible for updating LED values
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# Edit this function to change the visualization
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def led_visualization(onset_values):
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# Visualizations that we want to use (normalized to ~[0, 1])
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#pixels_A = update_leds_6(onset_values)
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#pixels_B = update_leds_4(onset_values)
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# Combine the effects by taking the product
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#brightness = pixels_A #* pixels_B
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brightness = update_leds_6(onset_values**2.0)
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brightness = gaussian_filter1d(brightness, 4.0)
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#brightness = hyperbolic_tan(brightness)
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brightness = bloom_peaks(brightness)**2.
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# Combine pixels with color map
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color = rainbow_gen(onset_values.shape[0],
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speed=1.,
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center=0.5,
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width=0.5,
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f=[1.1, .5, .2]) * 255.0
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color = np.tile(255.0, (config.N_PIXELS, 3))
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# color = rainbow(onset_values.shape[0]) * 255.0
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pixels = (brightness * color.T).T
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pixels = leak_saturated_pixels(pixels)
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pixels = np.clip(pixels, 0., 255.)
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# Apply low-pass filter to the output
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pixels_filt.update(np.copy(pixels))
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# Display values on the LED strip
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led.pixels = np.round(pixels_filt.value).astype(int)
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led.update()
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return brightness
|
|
|
|
|
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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__':
|
|
led.update()
|
|
microphone.start_stream(microphone_update)
|