Added several new visualizations
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Scott Lawson
parent
4dcf3e9a49
commit
2a87d040a7
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@ -9,7 +9,6 @@ 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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@ -69,19 +68,38 @@ def interpolate(y, new_length):
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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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alpha_decay=0.08, 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=0.75, alpha_rise=0.95)
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alpha_decay=0.15, alpha_rise=0.99)
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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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alpha_decay=0.25, alpha_rise=0.99)
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p_filt = dsp.ExpFilter(np.tile(1, (3, config.N_PIXELS // 2)),
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alpha_decay=0.05, alpha_rise=0.8)
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p = np.tile(1, (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(y):
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def largest_indices(ary, n):
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"""Returns the n largest indices from a numpy array."""
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flat = ary.flatten()
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indices = np.argpartition(flat, -n)[-n:]
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indices = indices[np.argsort(-flat[indices])]
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return np.unravel_index(indices, ary.shape)
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def visualize_max(y):
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y = np.copy(interpolate(y, config.N_PIXELS // 2)) * 255.0
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ind = largest_indices(y, 15)
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y[ind] *= -1
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y[y > 0] = 0
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y[ind] *= -1
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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 = gaussian_filter1d(y, sigma=0.25)
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g = gaussian_filter1d(y, sigma=0.10)
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b = gaussian_filter1d(y, sigma=0.00)
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b = np.roll(b, 1)
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b[0] = b[1]
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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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@ -100,16 +118,79 @@ def visualize(y):
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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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def visualize_scroll(y):
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global p
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y = gaussian_filter1d(y, sigma=1.0)**3.0
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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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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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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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p[0, 0] = r
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p[1, 0] = g
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p[2, 0] = b
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led.pixels = np.concatenate((p[:, ::-1], p), axis=1)
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led.update()
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def visualize_energy(y):
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global p
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y = gaussian_filter1d(y, sigma=1.0)**3.0
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gain.update(y)
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y /= gain.value
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y *= (config.N_PIXELS // 2) - 1
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r = int(np.mean(y[:len(y) // 3]))
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g = int(np.mean(y[len(y) // 3: 2 * len(y) // 3]))
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b = int(np.mean(y[2 * len(y) // 3:]))
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p[0, :r] = 255
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p[0, r:] = 0
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p[1, :g] = 255
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p[1, g:] = 0
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p[2, :b] = 255
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p[2, b:] = 0
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p_filt.update(p)
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p = p_filt.value.astype(int)
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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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led.pixels = np.concatenate((p[:, ::-1], p), axis=1)
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led.update()
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def visualize_spectrum(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=0.25)
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g = gaussian_filter1d(y, sigma=0.10)
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b = gaussian_filter1d(y, sigma=0.00)
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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_FFT_BINS),
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alpha_decay=0.01, 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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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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@ -125,32 +206,24 @@ def microphone_update(stream):
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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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led.pixels = np.tile(0, (3, config.N_PIXELS))
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led.update()
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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 = YS**0.5
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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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visualize_spectrum(mel)
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# visualize_max(mel)
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# visualize_scroll(mel)
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# visualize_energy(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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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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