Gurkengewuerz/audio-reactive-led-strip
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Resolved issues with Python 3.5 compatibility

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Fixed some bugs that occurred in Python 3.5 but were not present in
Python 2.7. Most compatiblity issues were caused by incompatible type
casting of numpy arrays.
This commit is contained in:
Scott Lawson 2016-12-29 15:51:38 -07:00
parent da338ae8f4
commit e874ed3b2c
2 changed files with 37 additions and 17 deletions
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2
python/led.py
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@ -15,7 +15,7 @@ pixels = np.tile(1, (3, config.N_PIXELS))
def update():
global pixels, _prev_pixels
pixels = np.clip(pixels, 0, 255)
pixels = np.clip(pixels, 0, 255).astype(int)
m = ''
p = _gamma[pixels] if config.GAMMA_CORRECTION else np.copy(pixels)
for i in range(config.N_PIXELS):

52
python/visualization.py
View File

@ -75,13 +75,13 @@ 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, (3, config.N_PIXELS // 2))
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 the n largest indices from a numpy array."""
"""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])]
@ -89,11 +89,12 @@ def largest_indices(ary, n):
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
y[y > 0] = 0
y[ind] *= -1
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)
@ -111,14 +112,15 @@ def visualize_max(y):
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)
led.update()
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)
@ -134,11 +136,17 @@ def visualize_scroll(y):
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)
@ -147,22 +155,28 @@ def visualize_energy(y):
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
p[0, r:] = 0
p[1, :g] = 255
p[1, g:] = 0
p[2, :b] = 255
p[2, b:] = 0
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)
@ -179,11 +193,11 @@ def visualize_spectrum(y):
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)
led.update()
mel_gain = dsp.ExpFilter(np.tile(1e-1, config.N_FFT_BINS),
@ -194,7 +208,7 @@ volume = dsp.ExpFilter(config.MIN_VOLUME_THRESHOLD,
def microphone_update(stream):
global y_roll, prev_rms, prev_exp
# Normalize new audio samples
# 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
@ -209,19 +223,25 @@ def microphone_update(stream):
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)
visualize_energy(mel)
GUI.app.processEvents()
print('FPS {:.0f} / {:.0f}'.format(frames_per_second(), config.FPS))
@ -235,8 +255,8 @@ 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')
# Audio plot
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)