from __future__ import print_function from __future__ import division import time import numpy as np from scipy.ndimage.filters import gaussian_filter1d import config import microphone import dsp import led import gui _time_prev = time.time() * 1000.0 """The previous time that the frames_per_second() function was called""" _fps = dsp.ExpFilter(val=config.FPS, alpha_decay=0.002, alpha_rise=0.002) """The low-pass filter used to estimate frames-per-second""" def frames_per_second(): """Return the estimated frames per second Returns the current estimate for frames-per-second (FPS). FPS is estimated by measured the amount of time that has elapsed since this function was previously called. The FPS estimate is low-pass filtered to reduce noise. This function is intended to be called one time for every iteration of the program's main loop. Returns ------- fps : float Estimated frames-per-second. This value is low-pass filtered to reduce noise. """ global _time_prev, _fps time_now = time.time() * 1000.0 dt = time_now - _time_prev _time_prev = time_now if dt == 0.0: return _fps.value return _fps.update(1000.0 / dt) def interpolate(y, new_length): """Intelligently resizes the array by linearly interpolating the values Parameters ---------- y : np.array Array that should be resized new_length : int The length of the new interpolated array Returns ------- z : np.array New array with length of new_length that contains the interpolated values of y. """ if len(y) == new_length: return y x_old = np.linspace(0, 1, len(y)) x_new = np.linspace(0, 1, new_length) z = np.interp(x_new, x_old, y) return z r_filt = dsp.ExpFilter(np.tile(0.01, config.N_PIXELS // 2), alpha_decay=0.08, alpha_rise=0.99) g_filt = dsp.ExpFilter(np.tile(0.01, config.N_PIXELS // 2), alpha_decay=0.15, alpha_rise=0.99) b_filt = dsp.ExpFilter(np.tile(0.01, config.N_PIXELS // 2), alpha_decay=0.25, alpha_rise=0.99) p_filt = dsp.ExpFilter(np.tile(1, (3, config.N_PIXELS // 2)), alpha_decay=0.05, alpha_rise=0.8) p = np.tile(1.0, (3, config.N_PIXELS // 2)) gain = dsp.ExpFilter(np.tile(0.01, config.N_FFT_BINS), alpha_decay=0.001, alpha_rise=0.99) def largest_indices(ary, n): """Returns indices of the n largest values in the given a numpy array""" flat = ary.flatten() indices = np.argpartition(flat, -n)[-n:] indices = indices[np.argsort(-flat[indices])] return np.unravel_index(indices, ary.shape) def visualize_max(y): """Experimental sandbox effect. Not recommended for use""" y = np.copy(interpolate(y, config.N_PIXELS // 2)) * 255.0 ind = largest_indices(y, 15) y[ind] *= -1.0 y[y > 0] = 0.0 y[ind] *= -1.0 # Blur the color channels with different strengths r = gaussian_filter1d(y, sigma=0.25) g = gaussian_filter1d(y, sigma=0.10) b = gaussian_filter1d(y, sigma=0.00) b = np.roll(b, 1) b[0] = b[1] r_filt.update(r) g_filt.update(g) b_filt.update(b) # Pixel values pixel_r = np.concatenate((r_filt.value[::-1], r_filt.value)) pixel_g = np.concatenate((g_filt.value[::-1], g_filt.value)) pixel_b = np.concatenate((b_filt.value[::-1], b_filt.value)) # Update the LED strip values led.pixels[0, :] = pixel_r led.pixels[1, :] = pixel_g led.pixels[2, :] = pixel_b led.update() # Update the GUI plots GUI.curve[0][0].setData(y=pixel_r) GUI.curve[0][1].setData(y=pixel_g) GUI.curve[0][2].setData(y=pixel_b) def visualize_scroll(y): """Effect that originates in the center and scrolls outwards""" global p y = gaussian_filter1d(y, sigma=1.0)**3.0 y = np.copy(y) gain.update(y) y /= gain.value y *= 255.0 r = int(max(y[:len(y) // 3])) g = int(max(y[len(y) // 3: 2 * len(y) // 3])) b = int(max(y[2 * len(y) // 3:])) p = np.roll(p, 1, axis=1) p *= 0.98 p = gaussian_filter1d(p, sigma=0.2) p[0, 0] = r p[1, 0] = g p[2, 0] = b # Update the LED strip led.pixels = np.concatenate((p[:, ::-1], p), axis=1) led.update() # Update the GUI plots GUI.curve[0][0].setData(y=np.concatenate((p[0, :][::-1], p[0, :]))) GUI.curve[0][1].setData(y=np.concatenate((p[1, :][::-1], p[1, :]))) GUI.curve[0][2].setData(y=np.concatenate((p[2, :][::-1], p[2, :]))) def visualize_energy(y): """Effect that expands from the center with increasing sound energy""" global p y = gaussian_filter1d(y, sigma=1.0)**3.0 gain.update(y) y /= gain.value y *= (config.N_PIXELS // 2) - 1 r = int(np.mean(y[:len(y) // 3])) g = int(np.mean(y[len(y) // 3: 2 * len(y) // 3])) b = int(np.mean(y[2 * len(y) // 3:])) p[0, :r] = 255.0 p[0, r:] = 0.0 p[1, :g] = 255.0 p[1, g:] = 0.0 p[2, :b] = 255.0 p[2, b:] = 0.0 p_filt.update(p) p = p_filt.value.astype(int) p[0, :] = gaussian_filter1d(p[0, :], sigma=4.0) p[1, :] = gaussian_filter1d(p[1, :], sigma=4.0) p[2, :] = gaussian_filter1d(p[2, :], sigma=4.0) # Update LED pixel arrays led.pixels = np.concatenate((p[:, ::-1], p), axis=1) led.update() # Update the GUI plots GUI.curve[0][0].setData(y=np.concatenate((p[0, :][::-1], p[0, :]))) GUI.curve[0][1].setData(y=np.concatenate((p[1, :][::-1], p[1, :]))) GUI.curve[0][2].setData(y=np.concatenate((p[2, :][::-1], p[2, :]))) def visualize_spectrum(y): """Effect that maps the Mel filterbank frequencies onto the LED strip""" y = np.copy(interpolate(y, config.N_PIXELS // 2)) * 255.0 # Blur the color channels with different strengths r = gaussian_filter1d(y, sigma=0.25) g = gaussian_filter1d(y, sigma=0.10) b = gaussian_filter1d(y, sigma=0.00) r_filt.update(r) g_filt.update(g) b_filt.update(b) # Pixel values pixel_r = np.concatenate((r_filt.value[::-1], r_filt.value)) pixel_g = np.concatenate((g_filt.value[::-1], g_filt.value)) pixel_b = np.concatenate((b_filt.value[::-1], b_filt.value)) # Update the LED strip values led.pixels[0, :] = pixel_r led.pixels[1, :] = pixel_g led.pixels[2, :] = pixel_b led.update() # Update the GUI plots GUI.curve[0][0].setData(y=pixel_r) GUI.curve[0][1].setData(y=pixel_g) GUI.curve[0][2].setData(y=pixel_b) mel_gain = dsp.ExpFilter(np.tile(1e-1, config.N_FFT_BINS), alpha_decay=0.01, alpha_rise=0.99) volume = dsp.ExpFilter(config.MIN_VOLUME_THRESHOLD, alpha_decay=0.02, alpha_rise=0.02) def microphone_update(stream): global y_roll, prev_rms, prev_exp # Retrieve and normalize the new audio samples y = np.fromstring(stream.read(samples_per_frame, exception_on_overflow=False), dtype=np.int16) y = y / 2.0**15 # Construct a rolling window of audio samples y_roll = np.roll(y_roll, -1, axis=0) y_roll[-1, :] = np.copy(y) y_data = np.concatenate(y_roll, axis=0) volume.update(np.nanmean(y_data ** 2)) if volume.value < config.MIN_VOLUME_THRESHOLD: print('No audio input. Volume below threshold. Volume:', volume.value) led.pixels = np.tile(0, (3, config.N_PIXELS)) led.update() else: # Transform audio input into the frequency domain XS, YS = dsp.fft(y_data, window=np.hamming) # Remove half of the FFT data because of symmetry YS = YS[:len(YS) // 2] XS = XS[:len(XS) // 2] # Construct a Mel filterbank from the FFT data YS = np.atleast_2d(np.abs(YS)).T * dsp.mel_y.T # Scale data to values more suitable for visualization YS = np.sum(YS, axis=0)**2.0 mel = YS**0.5 mel = gaussian_filter1d(mel, sigma=1.0) # Normalize the Mel filterbank to make it volume independent mel_gain.update(np.max(mel)) mel = mel / mel_gain.value # Visualize the filterbank output # visualize_spectrum(mel) # visualize_max(mel) # visualize_scroll(mel) visualize_energy(mel) GUI.app.processEvents() print('FPS {:.0f} / {:.0f}'.format(frames_per_second(), config.FPS)) # Number of audio samples to read every time frame samples_per_frame = int(config.MIC_RATE / config.FPS) # Array containing the rolling audio sample window y_roll = np.random.rand(config.N_ROLLING_HISTORY, samples_per_frame) / 1e16 if __name__ == '__main__': import pyqtgraph as pg # Create GUI plot for visualizing LED strip output GUI = gui.GUI(width=800, height=400, title='Audio Visualization') GUI.add_plot('Color Channels') r_pen = pg.mkPen((255, 30, 30, 200), width=6) g_pen = pg.mkPen((30, 255, 30, 200), width=6) b_pen = pg.mkPen((30, 30, 255, 200), width=6) GUI.add_curve(plot_index=0, pen=r_pen) GUI.add_curve(plot_index=0, pen=g_pen) GUI.add_curve(plot_index=0, pen=b_pen) GUI.plot[0].setRange(xRange=(0, config.N_PIXELS), yRange=(-5, 275)) GUI.curve[0][0].setData(x=range(config.N_PIXELS)) GUI.curve[0][1].setData(x=range(config.N_PIXELS)) GUI.curve[0][2].setData(x=range(config.N_PIXELS)) # Initialize LEDs led.update() # Start listening to live audio stream microphone.start_stream(microphone_update)