Add many small performance optimizations
Over a dozen small performance optimizations * Memoization for linspace generation * Removed unnecessary copies * Limited the rate at which information is printed. Excessive `print()` output was causing issues for some SSH users
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Scott Lawson
GitHub
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d6bf763c67
commit
35c26ca7bb
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@ -11,7 +11,7 @@ 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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_fps = dsp.ExpFilter(val=config.FPS, alpha_decay=0.2, alpha_rise=0.2)
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"""The low-pass filter used to estimate frames-per-second"""
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@ -41,6 +41,27 @@ def frames_per_second():
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return _fps.update(1000.0 / dt)
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def memoize(function):
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"""Provides a decorator for memoizing functions"""
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from functools import wraps
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memo = {}
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@wraps(function)
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def wrapper(*args):
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if args in memo:
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return memo[args]
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else:
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rv = function(*args)
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memo[args] = rv
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return rv
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return wrapper
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@memoize
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def _normalized_linspace(size):
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return np.linspace(0, 1, size)
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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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@ -60,8 +81,8 @@ def interpolate(y, new_length):
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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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x_old = _normalized_linspace(len(y))
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x_new = _normalized_linspace(new_length)
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z = np.interp(x_new, x_old, y)
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return z
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@ -84,15 +105,15 @@ gain = dsp.ExpFilter(np.tile(0.01, config.N_FFT_BINS),
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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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y = 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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r = int(np.max(y[:len(y) // 3]))
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g = int(np.max(y[len(y) // 3: 2 * len(y) // 3]))
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b = int(np.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[:, 1:] = p[:, :-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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@ -140,22 +161,18 @@ 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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common_mode.update(y)
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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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# Color channel mappings
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r = r_filt.update(y - common_mode.value)
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g = np.abs(diff)
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b = b_filt.update(b)
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b = b_filt.update(np.copy(y))
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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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r = np.concatenate((r[::-1], r))
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g = np.concatenate((g[::-1], g))
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b = np.concatenate((b[::-1], b))
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output = np.array([r, g,b]) * 255
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return output
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@ -168,31 +185,21 @@ mel_smoothing = dsp.ExpFilter(np.tile(1e-1, config.N_FFT_BINS),
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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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fft_window = np.hamming(int(config.MIC_RATE / config.FPS) * config.N_ROLLING_HISTORY)
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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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prev_fps_update = time.time()
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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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def microphone_update(audio_samples):
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global y_roll, prev_rms, prev_exp, prev_fps_update
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# Normalize samples between 0 and 1
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y = y / 2.0**15
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y = audio_samples / 2.0**15
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# Construct a rolling window of audio samples
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y_roll[:-1] = y_roll[1:]
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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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y_data = np.concatenate(y_roll, axis=0).astype(np.float32)
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vol = np.max(np.abs(y_data))
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if vol < config.MIN_VOLUME_THRESHOLD:
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print('No audio input. Volume below threshold. Volume:', vol)
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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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@ -204,13 +211,14 @@ def microphone_update(stream):
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y_padded = np.pad(y_data, (0, N_zeros), mode='constant')
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YS = np.abs(np.fft.rfft(y_padded)[:N // 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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mel = np.atleast_2d(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 = np.sum(mel, axis=0)
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mel = np.sum(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_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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@ -226,8 +234,12 @@ def microphone_update(stream):
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b_curve.setData(y=led.pixels[2])
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if config.USE_GUI:
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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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fps = frames_per_second()
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if time.time() - 0.5 > prev_fps_update:
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prev_fps_update = time.time()
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print('FPS {:.0f} / {:.0f}'.format(fps, config.FPS))
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# Number of audio samples to read every time frame
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