visualization.py

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 sys
import threading

_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=0.2, alpha_rise=0.2)
"""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 memoize(function):
    """Provides a decorator for memoizing functions"""
    from functools import wraps
    memo = {}

    @wraps(function)
    def wrapper(*args):
        if args in memo:
            return memo[args]
        else:
            rv = function(*args)
            memo[args] = rv
            return rv
    return wrapper


@memoize
def _normalized_linspace(size):
    return np.linspace(0, 1, size)


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 = _normalized_linspace(len(y))
    x_new = _normalized_linspace(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=0.2, alpha_rise=0.99)
g_filt = dsp.ExpFilter(np.tile(0.01, config.N_PIXELS // 2),
                       alpha_decay=0.05, alpha_rise=0.3)
b_filt = dsp.ExpFilter(np.tile(0.01, config.N_PIXELS // 2),
                       alpha_decay=0.1, alpha_rise=0.5)
common_mode = dsp.ExpFilter(np.tile(0.01, config.N_PIXELS // 2),
                       alpha_decay=0.99, alpha_rise=0.01)
p_filt = dsp.ExpFilter(np.tile(1, (3, config.N_PIXELS // 2)),
                       alpha_decay=0.1, alpha_rise=0.99)
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 visualize_scroll(y):
    """Effect that originates in the center and scrolls outwards"""
    global p
#    print(p)
    y = y**2.0
    gain.update(y)
    y /= gain.value
    y *= 255.0
    r = int(np.max(y[:len(y) // 3]))
    g = int(np.max(y[len(y) // 3: 2 * len(y) // 3]))
    b = int(np.max(y[2 * len(y) // 3:]))
    # Scrolling effect window
    p[:, 1:] = p[:, :-1]
    p[:, 1:] = p[:, :-1]
    p *= 0.98
    p = gaussian_filter1d(p, sigma=0.2)
    # Create new color originating at the center
    p[0, 0:1] = r
    p[1, 0:1] = g
    p[2, 0:1] = b
    # Update the LED strip
    #return p
    base_arr = np.tile(1.0, (3, base_size))
    base_arr[0, :] = r
    base_arr[1, :] = g
    base_arr[2, :] = b
    return np.concatenate((base_arr, p), axis=1)


def visualize_scroll_classic(y):
    """Effect that originates in the center and scrolls outwards"""
    global p
    y = y**2.0
    gain.update(y)
    y /= gain.value
    y *= 255.0
    r = int(np.max(y[:len(y) // 3]))
    g = int(np.max(y[len(y) // 3: 2 * len(y) // 3]))
    b = int(np.max(y[2 * len(y) // 3:]))
    # Scrolling effect window
    p[:, 1:] = p[:, :-1]
    p *= 0.98
    p = gaussian_filter1d(p, sigma=0.2)
    # Create new color originating at the center
    p[0, 0] = r
    p[1, 0] = g
    p[2, 0] = b
    # Update the LED strip
    return np.concatenate((p[:, ::-1], p), axis=1)

def visualize_scroll_2(y):
    """Effect that originates in the center and scrolls outwards"""
    global p, scroll_top, scroll_speed
    y = y**2.0
    gain.update(y)
    y /= gain.value
    y *= 255.0
    r = int(np.max(y[:len(y) // 3]))
    g = int(np.max(y[len(y) // 3: 2 * len(y) // 3]))
    b = int(np.max(y[2 * len(y) // 3:]))
    # Scrolling effect window
    #for i in range(scroll_speed):
    #    p[:, 1:] = p[:, :-1]
    p[:, scroll_speed:] = p[:, :-scroll_speed]
    
    p *= 0.98
    p = gaussian_filter1d(p, sigma=0.2)
    # Create new color originating at the center
    p[0, 0:scroll_speed] = r
    p[1, 0:scroll_speed] = g
    p[2, 0:scroll_speed] = b
    # Update the LED strip
    double = np.concatenate((p[:, ::-1], p), axis=1)
    if scroll_top > 0:
        return double[:, 0:config.N_PIXELS]
    elif scroll_top < 0:
        start = double.shape[1] - config.N_PIXELS
        return double[:, start:]
    else:
        return double

def visualize_energy(y):
    """Effect that expands from the center with increasing sound energy"""
    global p
    y = np.copy(y)
    gain.update(y)
    y /= gain.value
    # Scale by the width of the LED strip
    y *= float((config.N_PIXELS // 2) - 1)
    # Map color channels according to energy in the different freq bands
    scale = 0.9
    r = int(np.mean(y[:len(y) // 3]**scale))
    g = int(np.mean(y[len(y) // 3: 2 * len(y) // 3]**scale))
    b = int(np.mean(y[2 * len(y) // 3:]**scale))
    # Assign color to different frequency regions
    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 = np.round(p_filt.value)
    # Apply substantial blur to smooth the edges
    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)
    # Set the new pixel value
    return np.concatenate((p[:, ::-1], p), axis=1)


_prev_spectrum = np.tile(0.01, config.N_PIXELS // 2)


def visualize_spectrum(y):
    """Effect that maps the Mel filterbank frequencies onto the LED strip"""
    global _prev_spectrum
    y = np.copy(interpolate(y, config.N_PIXELS // 2))
    common_mode.update(y)
    diff = y - _prev_spectrum
    _prev_spectrum = np.copy(y)
    # Color channel mappings
    r = r_filt.update(y - common_mode.value)
    g = np.abs(diff)
    b = b_filt.update(np.copy(y))
    # Mirror the color channels for symmetric output
    r = np.concatenate((r[::-1], r))
    g = np.concatenate((g[::-1], g))
    b = np.concatenate((b[::-1], b))
    output = np.array([r, g,b]) * 255
    return output


fft_plot_filter = dsp.ExpFilter(np.tile(1e-1, config.N_FFT_BINS),
                         alpha_decay=0.5, alpha_rise=0.99)
mel_gain = dsp.ExpFilter(np.tile(1e-1, config.N_FFT_BINS),
                         alpha_decay=0.01, alpha_rise=0.99)
mel_smoothing = dsp.ExpFilter(np.tile(1e-1, config.N_FFT_BINS),
                         alpha_decay=0.5, alpha_rise=0.99)
volume = dsp.ExpFilter(config.MIN_VOLUME_THRESHOLD,
                       alpha_decay=0.02, alpha_rise=0.02)
fft_window = np.hamming(int(config.MIC_RATE / config.FPS) * config.N_ROLLING_HISTORY)
prev_fps_update = time.time()


def microphone_update(audio_samples):
    global y_roll, prev_rms, prev_exp, prev_fps_update
    # Normalize samples between 0 and 1
    y = audio_samples / 2.0**15
    # Construct a rolling window of audio samples
    y_roll[:-1] = y_roll[1:]
    y_roll[-1, :] = np.copy(y)
    y_data = np.concatenate(y_roll, axis=0).astype(np.float32)
    
    vol = np.max(np.abs(y_data))
    if vol < config.MIN_VOLUME_THRESHOLD:
        print('No audio input. Volume below threshold. Volume:', vol)
        led.pixels = np.tile(0, (3, config.N_PIXELS))
        led.update()
    else:
        # Transform audio input into the frequency domain
        N = len(y_data)
        N_zeros = 2**int(np.ceil(np.log2(N))) - N
        # Pad with zeros until the next power of two
        y_data *= fft_window
        y_padded = np.pad(y_data, (0, N_zeros), mode='constant')
        YS = np.abs(np.fft.rfft(y_padded)[:N // 2])
        # Construct a Mel filterbank from the FFT data
        mel = np.atleast_2d(YS).T * dsp.mel_y.T
        # Scale data to values more suitable for visualization
        # mel = np.sum(mel, axis=0)
        mel = np.sum(mel, axis=0)
        mel = mel**2.0
        # Gain normalization
        mel_gain.update(np.max(gaussian_filter1d(mel, sigma=1.0)))
        mel /= mel_gain.value
        mel = mel_smoothing.update(mel)
        # Map filterbank output onto LED strip
        output = visualization_effect(mel)
        #if config.N_PIXELS % 2 == 1:
        output[:, 1:] = output[:, :-1]
        led.pixels = output
        led.update()
        if config.USE_GUI:
            # Plot filterbank output
            x = np.linspace(config.MIN_FREQUENCY, config.MAX_FREQUENCY, len(mel))
            mel_curve.setData(x=x, y=fft_plot_filter.update(mel))
            # Plot the color channels
            r_curve.setData(y=led.pixels[0])
            g_curve.setData(y=led.pixels[1])
            b_curve.setData(y=led.pixels[2])
    if config.USE_GUI:
        app.processEvents()
    
    if config.DISPLAY_FPS:
        fps = frames_per_second()
        if time.time() - 1.5 > prev_fps_update:
            prev_fps_update = time.time()
            print('FPS {:.0f} / {:.0f}'.format(fps, 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

def setEffect(effect, top = 0, scrollspeed = 1):

    global visualization_effect, base_size, p, scroll_top, scroll_speed

    if effect == "spectrum":
            visualization_type = visualize_spectrum
    elif effect == "energy":
            visualization_type = visualize_energy
    elif effect == "scroll":
            visualization_type = visualize_scroll

            flow_size = int(config.N_PIXELS / 7 * 5)
            base_size = config.N_PIXELS - flow_size
            p = np.tile(1.0, (3, flow_size))

    elif effect == "scrollclassic":
            visualization_type = visualize_scroll_classic
    elif effect == "scroll2":

            half_size = config.N_PIXELS // 2
            half_size = half_size + abs(top)
            p = np.tile(1.0, (3, half_size))

            scroll_top = top
            scroll_speed = scrollspeed

            visualization_type = visualize_scroll_2
    else:
            visualization_type = visualize_spectrum

    visualization_effect = visualization_type
    """Visualization effect to display on the LED strip"""

def visualization_start():
    # Initialize LEDs
    led.update()
    # Start listening to live audio stream
    microphone.start_stream(microphone_update)

if __name__ == '__main__':
    setEffect(sys.argv[1])

    visualization_start()