bg_color=None):
""" Create waveform image from audio data.
Return path to created image file.
"""
try:
import waveform
except ImportError, exc:
raise ImportError(
"%s [get it at https://github.com/superjoe30/PyWaveform]" % exc)
outdir = outdir or os.path.dirname(mediafile)
outfile = os.path.join(
outdir,
os.path.splitext(os.path.basename(mediafile))[0] + ".png")
with transcode.to_wav(mediafile) as wavfile:
# Draw using a gradient
waveform.draw(wavfile,
outfile,
xy_size,
bgColor=bg_color or WAVE_BG_COLOR,
fgGradientCenter=center_color or WAVE_CENTER_COLOR,
fgGradientOuter=outer_color or WAVE_OUTER_COLOR)
return outfile
def waveform_stack(mediafiles,
xy_size,
output=None,
label_style=None,
def spectrogram_image(mediafile, dpi=72, outdir=None, outfile=None):
# TODO: Add some of the constants below as parameters
""" Create spectrogram image from audio data.
Return path to created image file.
"""
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
import scipy.io.wavfile
import numpy as np
import pylab
# Output file path
outfile = outfile or ""
if outdir and outfile and os.sep in outfile:
raise ValueError("Do not specify paths in both output directory '%s' and filename '%s'" % (outdir, outfile))
if os.sep not in outfile:
if not outfile:
outfile = os.path.splitext(os.path.basename(mediafile))[0] + ".jpg"
if not outdir:
outdir = os.path.dirname(mediafile)
outfile = os.path.join(outdir, outfile)
with closing(open(os.devnull, "wb")) as black_hole:
# Read audio data
with transcode.to_wav(mediafile) as wavfile:
sys.stdout, saved_stdout = black_hole, sys.stdout
try:
sample_rate, waveform = scipy.io.wavfile.read(wavfile)
finally:
sys.stdout = saved_stdout
# Limit data to 10 second window from the middle, else the FFT needs ages
data_window = sample_rate * 2 # secs
waveform = [i[0] for i in waveform[(len(waveform) - data_window) // 2 : (len(waveform) + data_window) // 2]]
# TODO: combine / add the channels to mono
# Calculate FFT inputs
nstep = int(sample_rate * 0.001) # 1ms step
nfft = nwin = int(sample_rate * 0.005) & ~1 # 5ms window
window = np.hamming(nwin)
# Create spectrogram
pylab.spectral()
for khz in (5, 10, 16, 18, 20):
pylab.text(data_window / sample_rate * .99, khz * 1000 + 75, "%d kHz" % khz, ha="right")
pylab.axhline(khz * 1000)
pylab.axis("off")
pylab.specgram(waveform, NFFT=nfft, Fs=sample_rate, window=window)
# Write to image
try:
pylab.savefig(outfile + ".png", format='png', facecolor="#000000", edgecolor="#000000",
dpi=dpi, transparent=True, bbox_inches="tight")
cmd = [config.CMD_IM_CONVERT, "-trim", "-quality", "85", outfile + ".png", outfile]
subprocess.check_call(cmd, stdout=black_hole, stderr=subprocess.STDOUT)
finally:
if os.path.exists(outfile + ".png"):
os.remove(outfile + ".png")
return outfile
WAVE_LABEL_STYLE = "-background '#333366ff' -stroke '#fff' -font DejaVu-Sans-ExtraLight -pointsize 12"
def waveform_image(mediafile, xy_size, outdir=None, center_color=None, outer_color=None, bg_color=None):
""" Create waveform image from audio data.
Return path to created image file.
"""
try:
import waveform
except ImportError, exc:
raise ImportError("%s [get it at https://github.com/superjoe30/PyWaveform]" % exc)
outdir = outdir or os.path.dirname(mediafile)
outfile = os.path.join(outdir, os.path.splitext(os.path.basename(mediafile))[0] + ".png")
with transcode.to_wav(mediafile) as wavfile:
# Draw using a gradient
waveform.draw(wavfile, outfile, xy_size,
bgColor=bg_color or WAVE_BG_COLOR,
fgGradientCenter=center_color or WAVE_CENTER_COLOR,
fgGradientOuter=outer_color or WAVE_OUTER_COLOR)
return outfile
def waveform_stack(mediafiles, xy_size, output=None, label_style=None,
center_color=None, outer_color=None, bg_color=None):
""" Create a stack of waveform images from audio data.
Return path to created image file.
"""
img_files = []
