182 lines
6.0 KiB
Python
182 lines
6.0 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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import gui
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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=0.05, alpha_rise=0.6)
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g_filt = dsp.ExpFilter(np.tile(0.01, config.N_PIXELS // 2),
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alpha_decay=0.75, alpha_rise=0.95)
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b_filt = dsp.ExpFilter(np.tile(0.01, config.N_PIXELS // 2),
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alpha_decay=0.2, alpha_rise=0.7)
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def visualize(y):
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y = np.copy(interpolate(y, config.N_PIXELS // 2)) * 255.0
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# Blur the color channels with different strengths
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r = gaussian_filter1d(y, sigma=0.0)
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g = gaussian_filter1d(y, sigma=0.0)
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b = gaussian_filter1d(y, sigma=1.0)
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# Update the low pass filters for each color channel
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r_filt.update(r)
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g_filt.update(g)
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b_filt.update(b)
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# Pixel values
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pixel_r = np.concatenate((r_filt.value[::-1], r_filt.value))
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pixel_g = np.concatenate((g_filt.value[::-1], g_filt.value))
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pixel_b = np.concatenate((b_filt.value[::-1], b_filt.value))
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# Update the LED strip values
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led.pixels[0, :] = pixel_r
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led.pixels[1, :] = pixel_g
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led.pixels[2, :] = pixel_b
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# Update the GUI plots
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GUI.curve[0][0].setData(y=pixel_r)
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GUI.curve[0][1].setData(y=pixel_g)
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GUI.curve[0][2].setData(y=pixel_b)
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led.update()
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mel_gain = dsp.ExpFilter(np.tile(1e-1, config.N_SUBBANDS),
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alpha_decay=0.01, alpha_rise=0.85)
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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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rms = dsp.ExpFilter(0.1, alpha_decay=0.001, alpha_rise=0.001)
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exp = dsp.ExpFilter(0.5, alpha_decay=0.001, alpha_rise=0.001)
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prev_rms = 1.0
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prev_exp = 1.0
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def microphone_update(stream):
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global y_roll, prev_rms, prev_exp
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# Normalize new audio samples
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y = np.fromstring(stream.read(samples_per_frame,
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exception_on_overflow=False), dtype=np.int16)
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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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visualize(np.tile(0.0, config.N_PIXELS))
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else:
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XS, YS = dsp.fft(y_data, window=np.hamming)
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YS = YS[:len(YS) // 2]
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XS = XS[:len(XS) // 2]
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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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mel = YS
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mel = mel**exp.value
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mel = gaussian_filter1d(mel, sigma=1.0)
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mel_gain.update(np.max(mel))
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mel = mel / mel_gain.value
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rms.update(np.sqrt(np.mean(mel**2.0)))
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if rms.value > 4e-1:
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exp.update(exp.value * 1.2)
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elif rms.value < 5e-2:
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exp.update(exp.value * 0.8)
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rms_delta = '^' if rms.value - prev_rms > 0 else 'v'
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exp_delta = '^' if exp.value - prev_exp > 0 else 'v'
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print('|{}| {:.0e}, |{}| {:.2}\t\t{:.2f}'.format(
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rms_delta, rms.value, exp_delta, exp.value, frames_per_second()))
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prev_exp = exp.value
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prev_rms = rms.value
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visualize(mel)
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GUI.app.processEvents()
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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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if __name__ == '__main__':
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import pyqtgraph as pg
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GUI = gui.GUI(width=800, height=400, title='Audio Visualization')
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# Audio plot
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GUI.add_plot('Color Channels')
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r_pen = pg.mkPen((255, 30, 30, 200), width=6)
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g_pen = pg.mkPen((30, 255, 30, 200), width=6)
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b_pen = pg.mkPen((30, 30, 255, 200), width=6)
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GUI.add_curve(plot_index=0, pen=r_pen)
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GUI.add_curve(plot_index=0, pen=g_pen)
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GUI.add_curve(plot_index=0, pen=b_pen)
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GUI.plot[0].setRange(xRange=(0, config.N_PIXELS), yRange=(-5, 275))
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GUI.curve[0][0].setData(x=range(config.N_PIXELS))
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GUI.curve[0][1].setData(x=range(config.N_PIXELS))
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GUI.curve[0][2].setData(x=range(config.N_PIXELS))
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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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