Significantly changed and improved sandbox.py visualizations
Note: All code in sandbox.py is temporary and used for experimenting with different visualizations.
This commit is contained in:
Scott Lawson
parent
65d35c2724
commit
cb34b6353d
@ -8,7 +8,10 @@ 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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@ -58,9 +61,9 @@ 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 + 0. * phi) * width + center, 0., 1.)
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def g(t): return np.clip(np.sin(f[1] * t + 1. * phi) * width + center, 0., 1.)
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def b(t): return np.clip(np.sin(f[2] * t + 2. * phi) * width + center, 0., 1.)
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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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@ -72,7 +75,7 @@ def rainbow_gen(length, speed=1./5., center=0.5, width=0.5, f=[1, 1, 1]):
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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.ExponentialFilter(val=config.FPS, alpha_decay=0.05, alpha_rise=0.05)
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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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@ -123,7 +126,7 @@ def update_plot_1(x, y):
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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].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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@ -170,14 +173,14 @@ def leak_saturated_pixels(pixels):
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return pixels
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_EA_norm = dsp.ExponentialFilter(np.tile(1e-4, config.N_PIXELS), 0.01, 0.25)
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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.ExponentialFilter(np.tile(1.0, config.N_PIXELS), 0.25, 0.80)
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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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@ -216,8 +219,8 @@ def update_leds_6(y):
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return pixels
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_EF_norm = dsp.ExponentialFilter(np.tile(1.0, config.N_PIXELS), 0.05, 0.9)
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_EF_smooth = dsp.ExponentialFilter(np.tile(1.0, config.N_PIXELS), 0.08, 0.9)
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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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@ -236,8 +239,25 @@ def update_leds_5(y):
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return pixels
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_energy_norm = dsp.ExponentialFilter(10.0, alpha_decay=.15, alpha_rise=.9)
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_energy_smooth = dsp.ExponentialFilter(10.0, alpha_decay=0.1, alpha_rise=0.8)
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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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@ -284,9 +304,11 @@ def update_leds_2(y):
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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)**0.5
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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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@ -295,39 +317,52 @@ def microphone_update(stream):
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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 = np.sqrt(SF**2.0 + NWPD**2.0 + RCD**2.0)
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# onset = SF * NWPD * RCD
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onset = SF + NWPD + RCD
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# onset = SF + 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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# # 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_y = [0*onsets.value**i for i in np.linspace(2.0, 0.25, config.N_CURVES)]
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if brightness is not None:
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plot_y = np.array([brightness, onsets.value])
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#plot_y = brightness
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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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@ -336,7 +371,7 @@ def microphone_update(stream):
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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(800, 600)
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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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@ -354,20 +389,19 @@ pixels = np.tile(0.0, config.N_PIXELS)
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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.ExponentialFilter(1.0, alpha_decay=0.01, alpha_rise=0.65)
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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.ExponentialFilter(val=config.N_SUBBANDS / 2.0,
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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.ExponentialFilter(val=np.tile(0.0, (config.N_SUBBANDS)),
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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.ExponentialFilter(1.0, alpha_decay=0.01, alpha_rise=0.99)
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NWPD_peak = dsp.ExponentialFilter(1.0, alpha_decay=0.01, alpha_rise=0.99)
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RCD_peak = dsp.ExponentialFilter(1.0, alpha_decay=0.01, alpha_rise=0.99)
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onset_peak = dsp.ExponentialFilter(0.1, alpha_decay=0.002, alpha_rise=0.5)
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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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@ -382,30 +416,47 @@ y_roll = np.random.rand(config.N_ROLLING_HISTORY, samples_per_frame) / 100.0
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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.ExponentialFilter(np.tile(0., (config.N_PIXELS, 3)), .2, .8)
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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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#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 = gaussian_filter1d(brightness, 1.0)**1.5
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brightness = hyperbolic_tan(brightness)
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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=[1.0, 1.0, 1.]) * 255.0
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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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@ -418,6 +469,58 @@ def led_visualization(onset_values):
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return brightness
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mean_energy = dsp.ExpFilter(0.1, alpha_decay=0.05, alpha_rise=0.05)
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def led_vis2(x):
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energy = np.mean(x**.5)
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mean_energy.update(energy)
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energy = energy / mean_energy.value - 1.0
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edge = np.exp(-10 * np.linspace(0, 1, len(x)))
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edge = edge + edge[::-1]
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edge *= max(energy, 0)
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edge /= 2.0
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x = gaussian_filter1d(x, 3.0)
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x = update_leds_6(x)
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red = bloom_peaks(x**1.0, width=1, blur_factor=1.5)
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green = bloom_peaks(x**1.0, width=2, blur_factor=0.5)
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blue = bloom_peaks(x**1.0, width=1, blur_factor=0.5)
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# Set LEDs
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color = np.tile(0.0, (3, config.N_PIXELS))
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color[0, :] = 1.0*edge + red*1.0
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color[1, :] = 1.2*edge + green*1.0
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color[2, :] = 1.5*edge + blue*1.0
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color = color.T * 255.0
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pixels_filt.update(color)
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led.pixels = np.round(pixels_filt.value).astype(int)
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led.update()
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return (color[:, 0] + color[:, 1] + color[:, 2]) / (3. * 255.0)
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N = 60
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E = []
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for i in range(0, N):
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alpha_decay = 0.01 * (float(i + 1) / (N + 1.0))**2.0
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alpha_rise = alpha_decay
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E.append(dsp.ExpFilter(.1, alpha_decay, alpha_rise))
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def led_vis3(x):
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energy = np.mean(x**.5)
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pixels = np.tile(0.0, config.N_PIXELS)
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for i in range(N):
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E[i].update(energy)
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pixels[i] = hyperbolic_tan(max(energy / E[i].value - 1.0, 0))
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color = np.tile(0.0, (3, config.N_PIXELS))
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color[0, :] = pixels
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color[1, :] = pixels
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color[2, :] = pixels
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color = color.T * 255.0
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pixels_filt.update(color)
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led.pixels = np.round(pixels_filt.value).astype(int)
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led.update()
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return (color[:, 0] + color[:, 1] + color[:, 2]) / (3. * 255.0)
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if __name__ == '__main__':
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led.update()
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microphone.start_stream(microphone_update)
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