342 lines
12 KiB
Python
342 lines
12 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 scipy.ndimage.filters import gaussian_filter1d
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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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_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=0.002, alpha_rise=0.002)
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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 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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if len(y) == new_length:
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return y
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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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r_filt = dsp.ExpFilter(np.tile(0.01, config.N_PIXELS // 2),
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alpha_decay=0.2, alpha_rise=0.99)
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g_filt = dsp.ExpFilter(np.tile(0.01, config.N_PIXELS // 2),
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alpha_decay=0.05, alpha_rise=0.3)
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b_filt = dsp.ExpFilter(np.tile(0.01, config.N_PIXELS // 2),
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alpha_decay=0.1, alpha_rise=0.5)
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common_mode = dsp.ExpFilter(np.tile(0.01, config.N_PIXELS // 2),
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alpha_decay=0.99, alpha_rise=0.01)
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p_filt = dsp.ExpFilter(np.tile(1, (3, config.N_PIXELS // 2)),
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alpha_decay=0.1, alpha_rise=0.99)
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p = np.tile(1.0, (3, config.N_PIXELS // 2))
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gain = dsp.ExpFilter(np.tile(0.01, config.N_FFT_BINS),
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alpha_decay=0.001, alpha_rise=0.99)
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def visualize_scroll(y):
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"""Effect that originates in the center and scrolls outwards"""
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global p
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y = np.copy(y)**2.0
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gain.update(y)
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y /= gain.value
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y *= 255.0
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r = int(max(y[:len(y) // 3]))
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g = int(max(y[len(y) // 3: 2 * len(y) // 3]))
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b = int(max(y[2 * len(y) // 3:]))
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# Scrolling effect window
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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)
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# Create new color originating at the center
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p[0, 0] = r
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p[1, 0] = g
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p[2, 0] = b
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# Update the LED strip
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return np.concatenate((p[:, ::-1], p), axis=1)
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def visualize_energy(y):
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"""Effect that expands from the center with increasing sound energy"""
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global p
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y = np.copy(y)
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gain.update(y)
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y /= gain.value
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# Scale by the width of the LED strip
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y *= float((config.N_PIXELS // 2) - 1)
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# Map color channels according to energy in the different freq bands
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scale = 0.9
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r = int(np.mean(y[:len(y) // 3]**scale))
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g = int(np.mean(y[len(y) // 3: 2 * len(y) // 3]**scale))
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b = int(np.mean(y[2 * len(y) // 3:]**scale))
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# Assign color to different frequency regions
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p[0, :r] = 255.0
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p[0, r:] = 0.0
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p[1, :g] = 255.0
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p[1, g:] = 0.0
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p[2, :b] = 255.0
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p[2, b:] = 0.0
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p_filt.update(p)
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p = np.round(p_filt.value)
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# Apply substantial blur to smooth the edges
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p[0, :] = gaussian_filter1d(p[0, :], sigma=4.0)
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p[1, :] = gaussian_filter1d(p[1, :], sigma=4.0)
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p[2, :] = gaussian_filter1d(p[2, :], sigma=4.0)
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# Set the new pixel value
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return np.concatenate((p[:, ::-1], p), axis=1)
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_prev_spectrum = np.tile(0.01, config.N_PIXELS // 2)
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def visualize_spectrum(y):
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"""Effect that maps the Mel filterbank frequencies onto the LED strip"""
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global _prev_spectrum
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y = np.copy(interpolate(y, config.N_PIXELS // 2))
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common_mode.update(gaussian_filter1d(y, sigma=2.0))
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diff = y - _prev_spectrum
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_prev_spectrum = np.copy(y)
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r = gaussian_filter1d(y, sigma=0.5) - common_mode.value
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# g = gaussian_filter1d(y, sigma=0.5) - common_mode.value
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b = gaussian_filter1d(y, sigma=0.0) - common_mode.value
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# Update temporal filters
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r = r_filt.update(r)
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# g = g_filt.update(g)
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g = np.abs(diff)
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b = b_filt.update(b)
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# Mirror the color channels for symmetric output
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pixel_r = np.concatenate((r[::-1], r))
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pixel_g = np.concatenate((g[::-1], g))
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pixel_b = np.concatenate((b[::-1], b))
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output = np.array([pixel_r, pixel_g, pixel_b]) * 255.0
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return output
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fft_plot_filter = dsp.ExpFilter(np.tile(1e-1, config.N_FFT_BINS),
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alpha_decay=0.5, alpha_rise=0.99)
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mel_gain = dsp.ExpFilter(np.tile(1e-1, config.N_FFT_BINS),
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alpha_decay=0.01, alpha_rise=0.99)
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mel_smoothing = dsp.ExpFilter(np.tile(1e-1, config.N_FFT_BINS),
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alpha_decay=0.5, alpha_rise=0.99)
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volume = dsp.ExpFilter(config.MIN_VOLUME_THRESHOLD,
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alpha_decay=0.02, alpha_rise=0.02)
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# Keeps track of the number of buffer overflows
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# Lots of buffer overflows could mean that FPS is set too high
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buffer_overflows = 1
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def microphone_update(stream):
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global y_roll, prev_rms, prev_exp
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# Retrieve and normalize the new audio samples
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try:
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y = np.fromstring(stream.read(samples_per_frame), dtype=np.int16)
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except IOError:
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y = y_roll[config.N_ROLLING_HISTORY - 1, :]
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global buffer_overflows
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print('Buffer overflows: {0}'.format(buffer_overflows))
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buffer_overflows += 1
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# Normalize samples between 0 and 1
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y = y / 2.0**15
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# Construct a rolling window of audio samples
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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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volume.update(np.nanmean(y_data ** 2))
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if volume.value < config.MIN_VOLUME_THRESHOLD:
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print('No audio input. Volume below threshold. Volume:', volume.value)
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led.pixels = np.tile(0, (3, config.N_PIXELS))
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led.update()
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else:
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# Transform audio input into the frequency domain
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XS, YS = dsp.fft(y_data, window=np.hamming)
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# Remove half of the FFT data because of symmetry
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YS = YS[:len(YS) // 2]
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XS = XS[:len(XS) // 2]
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# Construct a Mel filterbank from the FFT data
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mel = np.atleast_2d(np.abs(YS)).T * dsp.mel_y.T
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# Scale data to values more suitable for visualization
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mel = np.mean(mel, axis=0)
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mel = mel**2.0
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# Gain normalization
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mel_gain.update(np.max(gaussian_filter1d(mel, sigma=1.0)))
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mel = mel / mel_gain.value
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mel = mel_smoothing.update(mel)
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# Map filterbank output onto LED strip
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output = visualization_effect(mel)
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led.pixels = output
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led.update()
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if config.USE_GUI:
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# Plot filterbank output
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x = np.linspace(config.MIN_FREQUENCY, config.MAX_FREQUENCY, len(mel))
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mel_curve.setData(x=x, y=fft_plot_filter.update(mel))
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# Plot the color channels
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r_curve.setData(y=led.pixels[0])
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g_curve.setData(y=led.pixels[1])
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b_curve.setData(y=led.pixels[2])
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app.processEvents()
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if config.DISPLAY_FPS:
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print('FPS {:.0f} / {:.0f}'.format(frames_per_second(), config.FPS))
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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) / 1e16
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visualization_effect = visualize_spectrum
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"""Visualization effect to display on the LED strip"""
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if __name__ == '__main__':
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if config.USE_GUI:
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import pyqtgraph as pg
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from pyqtgraph.Qt import QtGui, QtCore
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# Create GUI window
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app = QtGui.QApplication([])
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view = pg.GraphicsView()
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layout = pg.GraphicsLayout(border=(100,100,100))
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view.setCentralItem(layout)
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view.show()
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view.setWindowTitle('Visualization')
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view.resize(800,600)
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# Mel filterbank plot
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fft_plot = layout.addPlot(title='Filterbank Output', colspan=3)
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fft_plot.setRange(yRange=[-0.1, 1.2])
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fft_plot.disableAutoRange(axis=pg.ViewBox.YAxis)
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x_data = np.array(range(1, config.N_FFT_BINS + 1))
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mel_curve = pg.PlotCurveItem()
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mel_curve.setData(x=x_data, y=x_data*0)
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fft_plot.addItem(mel_curve)
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# Visualization plot
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layout.nextRow()
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led_plot = layout.addPlot(title='Visualization Output', colspan=3)
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led_plot.setRange(yRange=[-5, 260])
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led_plot.disableAutoRange(axis=pg.ViewBox.YAxis)
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# Pen for each of the color channel curves
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r_pen = pg.mkPen((255, 30, 30, 200), width=4)
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g_pen = pg.mkPen((30, 255, 30, 200), width=4)
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b_pen = pg.mkPen((30, 30, 255, 200), width=4)
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# Color channel curves
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r_curve = pg.PlotCurveItem(pen=r_pen)
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g_curve = pg.PlotCurveItem(pen=g_pen)
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b_curve = pg.PlotCurveItem(pen=b_pen)
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# Define x data
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x_data = np.array(range(1, config.N_PIXELS + 1))
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r_curve.setData(x=x_data, y=x_data*0)
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g_curve.setData(x=x_data, y=x_data*0)
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b_curve.setData(x=x_data, y=x_data*0)
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# Add curves to plot
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led_plot.addItem(r_curve)
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led_plot.addItem(g_curve)
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led_plot.addItem(b_curve)
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# Frequency range label
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freq_label = pg.LabelItem('')
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# Frequency slider
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def freq_slider_change(tick):
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minf = freq_slider.tickValue(0)**2.0 * (config.MIC_RATE / 2.0)
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maxf = freq_slider.tickValue(1)**2.0 * (config.MIC_RATE / 2.0)
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t = 'Frequency range: {:.0f} - {:.0f} Hz'.format(minf, maxf)
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freq_label.setText(t)
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config.MIN_FREQUENCY = minf
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config.MAX_FREQUENCY = maxf
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dsp.create_mel_bank()
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freq_slider = pg.TickSliderItem(orientation='bottom', allowAdd=False)
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freq_slider.addTick((config.MIN_FREQUENCY / (config.MIC_RATE / 2.0))**0.5)
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freq_slider.addTick((config.MAX_FREQUENCY / (config.MIC_RATE / 2.0))**0.5)
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freq_slider.tickMoveFinished = freq_slider_change
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freq_label.setText('Frequency range: {} - {} Hz'.format(
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config.MIN_FREQUENCY,
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config.MAX_FREQUENCY))
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# Effect selection
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active_color = '#16dbeb'
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inactive_color = '#FFFFFF'
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def energy_click(x):
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global visualization_effect
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visualization_effect = visualize_energy
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energy_label.setText('Energy', color=active_color)
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scroll_label.setText('Scroll', color=inactive_color)
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spectrum_label.setText('Spectrum', color=inactive_color)
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def scroll_click(x):
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global visualization_effect
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visualization_effect = visualize_scroll
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energy_label.setText('Energy', color=inactive_color)
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scroll_label.setText('Scroll', color=active_color)
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spectrum_label.setText('Spectrum', color=inactive_color)
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def spectrum_click(x):
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global visualization_effect
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visualization_effect = visualize_spectrum
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energy_label.setText('Energy', color=inactive_color)
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scroll_label.setText('Scroll', color=inactive_color)
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spectrum_label.setText('Spectrum', color=active_color)
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# Create effect "buttons" (labels with click event)
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energy_label = pg.LabelItem('Energy')
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scroll_label = pg.LabelItem('Scroll')
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spectrum_label = pg.LabelItem('Spectrum')
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energy_label.mousePressEvent = energy_click
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scroll_label.mousePressEvent = scroll_click
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spectrum_label.mousePressEvent = spectrum_click
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energy_click(0)
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# Layout
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layout.nextRow()
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layout.addItem(freq_label, colspan=3)
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layout.nextRow()
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layout.addItem(freq_slider, colspan=3)
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layout.nextRow()
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layout.addItem(energy_label)
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layout.addItem(scroll_label)
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layout.addItem(spectrum_label)
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# Initialize LEDs
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led.update()
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# Start listening to live audio stream
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microphone.start_stream(microphone_update)
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