d0e9f843e2
Fixes for systems with Pillow, but leaves a "try: except ImportError" to support anything that doesn't have a PIL top level import. Signed-off-by: Alon Levy <alon@pobox.com>
617 lines
22 KiB
Python
617 lines
22 KiB
Python
#!/usr/bin/env python
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# processing.py -- various audio processing functions
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# Copyright (C) 2008 MUSIC TECHNOLOGY GROUP (MTG)
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# UNIVERSITAT POMPEU FABRA
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#
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# This program is free software: you can redistribute it and/or modify
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# it under the terms of the GNU Affero General Public License as
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# published by the Free Software Foundation, either version 3 of the
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# License, or (at your option) any later version.
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#
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# This program is distributed in the hope that it will be useful,
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# but WITHOUT ANY WARRANTY; without even the implied warranty of
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# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
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# GNU Affero General Public License for more details.
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#
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# You should have received a copy of the GNU Affero General Public License
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# along with this program. If not, see <http://www.gnu.org/licenses/>.
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#
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# Authors:
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# Bram de Jong <bram.dejong at domain.com where domain in gmail>
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# 2012, Joar Wandborg <first name at last name dot se>
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from PIL import Image, ImageDraw, ImageColor #@UnresolvedImport
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from functools import partial
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import math
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import numpy
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import os
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import re
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import signal
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def get_sound_type(input_filename):
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sound_type = os.path.splitext(input_filename.lower())[1].strip(".")
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if sound_type == "fla":
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sound_type = "flac"
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elif sound_type == "aif":
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sound_type = "aiff"
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return sound_type
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try:
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import scikits.audiolab as audiolab
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except ImportError:
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print "WARNING: audiolab is not installed so wav2png will not work"
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import subprocess
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class AudioProcessingException(Exception):
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pass
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class TestAudioFile(object):
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"""A class that mimics audiolab.sndfile but generates noise instead of reading
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a wave file. Additionally it can be told to have a "broken" header and thus crashing
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in the middle of the file. Also useful for testing ultra-short files of 20 samples."""
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def __init__(self, num_frames, has_broken_header=False):
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self.seekpoint = 0
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self.nframes = num_frames
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self.samplerate = 44100
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self.channels = 1
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self.has_broken_header = has_broken_header
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def seek(self, seekpoint):
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self.seekpoint = seekpoint
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def read_frames(self, frames_to_read):
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if self.has_broken_header and self.seekpoint + frames_to_read > self.num_frames / 2:
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raise RuntimeError()
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num_frames_left = self.num_frames - self.seekpoint
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will_read = num_frames_left if num_frames_left < frames_to_read else frames_to_read
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self.seekpoint += will_read
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return numpy.random.random(will_read)*2 - 1
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def get_max_level(filename):
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max_value = 0
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buffer_size = 4096
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audio_file = audiolab.Sndfile(filename, 'r')
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n_samples_left = audio_file.nframes
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while n_samples_left:
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to_read = min(buffer_size, n_samples_left)
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try:
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samples = audio_file.read_frames(to_read)
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except RuntimeError:
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# this can happen with a broken header
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break
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# convert to mono by selecting left channel only
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if audio_file.channels > 1:
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samples = samples[:,0]
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max_value = max(max_value, numpy.abs(samples).max())
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n_samples_left -= to_read
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audio_file.close()
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return max_value
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class AudioProcessor(object):
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"""
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The audio processor processes chunks of audio an calculates the spectrac centroid and the peak
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samples in that chunk of audio.
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"""
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def __init__(self, input_filename, fft_size, window_function=numpy.hanning):
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max_level = get_max_level(input_filename)
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self.audio_file = audiolab.Sndfile(input_filename, 'r')
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self.fft_size = fft_size
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self.window = window_function(self.fft_size)
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self.spectrum_range = None
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self.lower = 100
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self.higher = 22050
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self.lower_log = math.log10(self.lower)
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self.higher_log = math.log10(self.higher)
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self.clip = lambda val, low, high: min(high, max(low, val))
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# figure out what the maximum value is for an FFT doing the FFT of a DC signal
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fft = numpy.fft.rfft(numpy.ones(fft_size) * self.window)
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max_fft = (numpy.abs(fft)).max()
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# set the scale to normalized audio and normalized FFT
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self.scale = 1.0/max_level/max_fft if max_level > 0 else 1
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def read(self, start, size, resize_if_less=False):
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""" read size samples starting at start, if resize_if_less is True and less than size
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samples are read, resize the array to size and fill with zeros """
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# number of zeros to add to start and end of the buffer
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add_to_start = 0
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add_to_end = 0
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if start < 0:
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# the first FFT window starts centered around zero
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if size + start <= 0:
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return numpy.zeros(size) if resize_if_less else numpy.array([])
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else:
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self.audio_file.seek(0)
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add_to_start = -start # remember: start is negative!
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to_read = size + start
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if to_read > self.audio_file.nframes:
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add_to_end = to_read - self.audio_file.nframes
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to_read = self.audio_file.nframes
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else:
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self.audio_file.seek(start)
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to_read = size
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if start + to_read >= self.audio_file.nframes:
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to_read = self.audio_file.nframes - start
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add_to_end = size - to_read
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try:
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samples = self.audio_file.read_frames(to_read)
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except RuntimeError:
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# this can happen for wave files with broken headers...
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return numpy.zeros(size) if resize_if_less else numpy.zeros(2)
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# convert to mono by selecting left channel only
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if self.audio_file.channels > 1:
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samples = samples[:,0]
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if resize_if_less and (add_to_start > 0 or add_to_end > 0):
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if add_to_start > 0:
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samples = numpy.concatenate((numpy.zeros(add_to_start), samples), axis=1)
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if add_to_end > 0:
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samples = numpy.resize(samples, size)
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samples[size - add_to_end:] = 0
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return samples
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def spectral_centroid(self, seek_point, spec_range=110.0):
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""" starting at seek_point read fft_size samples, and calculate the spectral centroid """
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samples = self.read(seek_point - self.fft_size/2, self.fft_size, True)
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samples *= self.window
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fft = numpy.fft.rfft(samples)
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spectrum = self.scale * numpy.abs(fft) # normalized abs(FFT) between 0 and 1
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length = numpy.float64(spectrum.shape[0])
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# scale the db spectrum from [- spec_range db ... 0 db] > [0..1]
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db_spectrum = ((20*(numpy.log10(spectrum + 1e-60))).clip(-spec_range, 0.0) + spec_range)/spec_range
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energy = spectrum.sum()
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spectral_centroid = 0
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if energy > 1e-60:
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# calculate the spectral centroid
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if self.spectrum_range == None:
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self.spectrum_range = numpy.arange(length)
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spectral_centroid = (spectrum * self.spectrum_range).sum() / (energy * (length - 1)) * self.audio_file.samplerate * 0.5
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# clip > log10 > scale between 0 and 1
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spectral_centroid = (math.log10(self.clip(spectral_centroid, self.lower, self.higher)) - self.lower_log) / (self.higher_log - self.lower_log)
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return (spectral_centroid, db_spectrum)
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def peaks(self, start_seek, end_seek):
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""" read all samples between start_seek and end_seek, then find the minimum and maximum peak
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in that range. Returns that pair in the order they were found. So if min was found first,
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it returns (min, max) else the other way around. """
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# larger blocksizes are faster but take more mem...
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# Aha, Watson, a clue, a tradeof!
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block_size = 4096
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max_index = -1
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max_value = -1
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min_index = -1
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min_value = 1
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if start_seek < 0:
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start_seek = 0
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if end_seek > self.audio_file.nframes:
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end_seek = self.audio_file.nframes
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if end_seek <= start_seek:
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samples = self.read(start_seek, 1)
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return (samples[0], samples[0])
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if block_size > end_seek - start_seek:
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block_size = end_seek - start_seek
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for i in range(start_seek, end_seek, block_size):
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samples = self.read(i, block_size)
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local_max_index = numpy.argmax(samples)
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local_max_value = samples[local_max_index]
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if local_max_value > max_value:
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max_value = local_max_value
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max_index = local_max_index
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local_min_index = numpy.argmin(samples)
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local_min_value = samples[local_min_index]
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if local_min_value < min_value:
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min_value = local_min_value
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min_index = local_min_index
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return (min_value, max_value) if min_index < max_index else (max_value, min_value)
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def interpolate_colors(colors, flat=False, num_colors=256):
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""" given a list of colors, create a larger list of colors interpolating
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the first one. If flatten is True a list of numers will be returned. If
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False, a list of (r,g,b) tuples. num_colors is the number of colors wanted
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in the final list """
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palette = []
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for i in range(num_colors):
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index = (i * (len(colors) - 1))/(num_colors - 1.0)
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index_int = int(index)
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alpha = index - float(index_int)
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if alpha > 0:
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r = (1.0 - alpha) * colors[index_int][0] + alpha * colors[index_int + 1][0]
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g = (1.0 - alpha) * colors[index_int][1] + alpha * colors[index_int + 1][1]
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b = (1.0 - alpha) * colors[index_int][2] + alpha * colors[index_int + 1][2]
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else:
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r = (1.0 - alpha) * colors[index_int][0]
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g = (1.0 - alpha) * colors[index_int][1]
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b = (1.0 - alpha) * colors[index_int][2]
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if flat:
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palette.extend((int(r), int(g), int(b)))
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else:
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palette.append((int(r), int(g), int(b)))
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return palette
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def desaturate(rgb, amount):
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"""
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desaturate colors by amount
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amount == 0, no change
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amount == 1, grey
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"""
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luminosity = sum(rgb) / 3.0
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desat = lambda color: color - amount * (color - luminosity)
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return tuple(map(int, map(desat, rgb)))
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class WaveformImage(object):
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"""
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Given peaks and spectral centroids from the AudioProcessor, this class will construct
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a wavefile image which can be saved as PNG.
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"""
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def __init__(self, image_width, image_height, palette=1):
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if image_height % 2 == 0:
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raise AudioProcessingException, "Height should be uneven: images look much better at uneven height"
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if palette == 1:
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background_color = (0,0,0)
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colors = [
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(50,0,200),
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(0,220,80),
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(255,224,0),
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(255,70,0),
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]
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elif palette == 2:
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background_color = (0,0,0)
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colors = [self.color_from_value(value/29.0) for value in range(0,30)]
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elif palette == 3:
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background_color = (213, 217, 221)
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colors = map( partial(desaturate, amount=0.7), [
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(50,0,200),
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(0,220,80),
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(255,224,0),
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])
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elif palette == 4:
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background_color = (213, 217, 221)
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colors = map( partial(desaturate, amount=0.8), [self.color_from_value(value/29.0) for value in range(0,30)])
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self.image = Image.new("RGB", (image_width, image_height), background_color)
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self.image_width = image_width
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self.image_height = image_height
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self.draw = ImageDraw.Draw(self.image)
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self.previous_x, self.previous_y = None, None
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self.color_lookup = interpolate_colors(colors)
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self.pix = self.image.load()
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def color_from_value(self, value):
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""" given a value between 0 and 1, return an (r,g,b) tuple """
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return ImageColor.getrgb("hsl(%d,%d%%,%d%%)" % (int( (1.0 - value) * 360 ), 80, 50))
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def draw_peaks(self, x, peaks, spectral_centroid):
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""" draw 2 peaks at x using the spectral_centroid for color """
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y1 = self.image_height * 0.5 - peaks[0] * (self.image_height - 4) * 0.5
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y2 = self.image_height * 0.5 - peaks[1] * (self.image_height - 4) * 0.5
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line_color = self.color_lookup[int(spectral_centroid*255.0)]
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if self.previous_y != None:
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self.draw.line([self.previous_x, self.previous_y, x, y1, x, y2], line_color)
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else:
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self.draw.line([x, y1, x, y2], line_color)
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self.previous_x, self.previous_y = x, y2
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self.draw_anti_aliased_pixels(x, y1, y2, line_color)
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def draw_anti_aliased_pixels(self, x, y1, y2, color):
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""" vertical anti-aliasing at y1 and y2 """
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y_max = max(y1, y2)
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y_max_int = int(y_max)
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alpha = y_max - y_max_int
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if alpha > 0.0 and alpha < 1.0 and y_max_int + 1 < self.image_height:
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current_pix = self.pix[x, y_max_int + 1]
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r = int((1-alpha)*current_pix[0] + alpha*color[0])
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g = int((1-alpha)*current_pix[1] + alpha*color[1])
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b = int((1-alpha)*current_pix[2] + alpha*color[2])
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self.pix[x, y_max_int + 1] = (r,g,b)
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y_min = min(y1, y2)
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y_min_int = int(y_min)
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alpha = 1.0 - (y_min - y_min_int)
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if alpha > 0.0 and alpha < 1.0 and y_min_int - 1 >= 0:
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current_pix = self.pix[x, y_min_int - 1]
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r = int((1-alpha)*current_pix[0] + alpha*color[0])
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g = int((1-alpha)*current_pix[1] + alpha*color[1])
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b = int((1-alpha)*current_pix[2] + alpha*color[2])
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self.pix[x, y_min_int - 1] = (r,g,b)
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def save(self, filename):
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# draw a zero "zero" line
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a = 25
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for x in range(self.image_width):
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self.pix[x, self.image_height/2] = tuple(map(lambda p: p+a, self.pix[x, self.image_height/2]))
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self.image.save(filename)
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class SpectrogramImage(object):
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"""
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Given spectra from the AudioProcessor, this class will construct a wavefile image which
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can be saved as PNG.
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"""
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def __init__(self, image_width, image_height, fft_size):
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self.image_width = image_width
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self.image_height = image_height
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self.fft_size = fft_size
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self.image = Image.new("RGBA", (image_height, image_width))
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colors = [
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(0, 0, 0, 0),
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(58/4, 68/4, 65/4, 255),
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(80/2, 100/2, 153/2, 255),
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(90, 180, 100, 255),
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(224, 224, 44, 255),
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(255, 60, 30, 255),
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(255, 255, 255, 255)
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]
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self.palette = interpolate_colors(colors)
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# generate the lookup which translates y-coordinate to fft-bin
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self.y_to_bin = []
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f_min = 100.0
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f_max = 22050.0
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y_min = math.log10(f_min)
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y_max = math.log10(f_max)
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for y in range(self.image_height):
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freq = math.pow(10.0, y_min + y / (image_height - 1.0) *(y_max - y_min))
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bin = freq / 22050.0 * (self.fft_size/2 + 1)
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if bin < self.fft_size/2:
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alpha = bin - int(bin)
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self.y_to_bin.append((int(bin), alpha * 255))
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# this is a bit strange, but using image.load()[x,y] = ... is
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# a lot slower than using image.putadata and then rotating the image
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# so we store all the pixels in an array and then create the image when saving
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self.pixels = []
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def draw_spectrum(self, x, spectrum):
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# for all frequencies, draw the pixels
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for (index, alpha) in self.y_to_bin:
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self.pixels.append( self.palette[int((255.0-alpha) * spectrum[index] + alpha * spectrum[index + 1])] )
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# if the FFT is too small to fill up the image, fill with black to the top
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for y in range(len(self.y_to_bin), self.image_height): #@UnusedVariable
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self.pixels.append(self.palette[0])
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def save(self, filename, quality=80):
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assert filename.lower().endswith(".jpg")
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self.image.putdata(self.pixels)
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self.image.transpose(Image.ROTATE_90).save(filename, quality=quality)
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def create_wave_images(input_filename, output_filename_w, output_filename_s, image_width, image_height, fft_size, progress_callback=None):
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"""
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Utility function for creating both wavefile and spectrum images from an audio input file.
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"""
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processor = AudioProcessor(input_filename, fft_size, numpy.hanning)
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samples_per_pixel = processor.audio_file.nframes / float(image_width)
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waveform = WaveformImage(image_width, image_height)
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spectrogram = SpectrogramImage(image_width, image_height, fft_size)
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for x in range(image_width):
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if progress_callback and x % (image_width/10) == 0:
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progress_callback((x*100)/image_width)
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seek_point = int(x * samples_per_pixel)
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next_seek_point = int((x + 1) * samples_per_pixel)
|
|
|
|
(spectral_centroid, db_spectrum) = processor.spectral_centroid(seek_point)
|
|
peaks = processor.peaks(seek_point, next_seek_point)
|
|
|
|
waveform.draw_peaks(x, peaks, spectral_centroid)
|
|
spectrogram.draw_spectrum(x, db_spectrum)
|
|
|
|
if progress_callback:
|
|
progress_callback(100)
|
|
|
|
waveform.save(output_filename_w)
|
|
spectrogram.save(output_filename_s)
|
|
|
|
|
|
class NoSpaceLeftException(Exception):
|
|
pass
|
|
|
|
def convert_to_pcm(input_filename, output_filename):
|
|
"""
|
|
converts any audio file type to pcm audio
|
|
"""
|
|
|
|
if not os.path.exists(input_filename):
|
|
raise AudioProcessingException, "file %s does not exist" % input_filename
|
|
|
|
sound_type = get_sound_type(input_filename)
|
|
|
|
if sound_type == "mp3":
|
|
cmd = ["lame", "--decode", input_filename, output_filename]
|
|
elif sound_type == "ogg":
|
|
cmd = ["oggdec", input_filename, "-o", output_filename]
|
|
elif sound_type == "flac":
|
|
cmd = ["flac", "-f", "-d", "-s", "-o", output_filename, input_filename]
|
|
else:
|
|
return False
|
|
|
|
process = subprocess.Popen(cmd, stdout=subprocess.PIPE, stderr=subprocess.PIPE)
|
|
(stdout, stderr) = process.communicate()
|
|
|
|
if process.returncode != 0 or not os.path.exists(output_filename):
|
|
if "No space left on device" in stderr + " " + stdout:
|
|
raise NoSpaceLeftException
|
|
raise AudioProcessingException, "failed converting to pcm data:\n" + " ".join(cmd) + "\n" + stderr + "\n" + stdout
|
|
|
|
return True
|
|
|
|
|
|
def stereofy_and_find_info(stereofy_executble_path, input_filename, output_filename):
|
|
"""
|
|
converts a pcm wave file to two channel, 16 bit integer
|
|
"""
|
|
|
|
if not os.path.exists(input_filename):
|
|
raise AudioProcessingException, "file %s does not exist" % input_filename
|
|
|
|
cmd = [stereofy_executble_path, "--input", input_filename, "--output", output_filename]
|
|
|
|
process = subprocess.Popen(cmd, stdout=subprocess.PIPE, stderr=subprocess.PIPE)
|
|
(stdout, stderr) = process.communicate()
|
|
|
|
if process.returncode != 0 or not os.path.exists(output_filename):
|
|
if "No space left on device" in stderr + " " + stdout:
|
|
raise NoSpaceLeftException
|
|
raise AudioProcessingException, "failed calling stereofy data:\n" + " ".join(cmd) + "\n" + stderr + "\n" + stdout
|
|
|
|
stdout = (stdout + " " + stderr).replace("\n", " ")
|
|
|
|
duration = 0
|
|
m = re.match(r".*#duration (?P<duration>[\d\.]+).*", stdout)
|
|
if m != None:
|
|
duration = float(m.group("duration"))
|
|
|
|
channels = 0
|
|
m = re.match(r".*#channels (?P<channels>\d+).*", stdout)
|
|
if m != None:
|
|
channels = float(m.group("channels"))
|
|
|
|
samplerate = 0
|
|
m = re.match(r".*#samplerate (?P<samplerate>\d+).*", stdout)
|
|
if m != None:
|
|
samplerate = float(m.group("samplerate"))
|
|
|
|
bitdepth = None
|
|
m = re.match(r".*#bitdepth (?P<bitdepth>\d+).*", stdout)
|
|
if m != None:
|
|
bitdepth = float(m.group("bitdepth"))
|
|
|
|
bitrate = (os.path.getsize(input_filename) * 8.0) / 1024.0 / duration if duration > 0 else 0
|
|
|
|
return dict(duration=duration, channels=channels, samplerate=samplerate, bitrate=bitrate, bitdepth=bitdepth)
|
|
|
|
|
|
def convert_to_mp3(input_filename, output_filename, quality=70):
|
|
"""
|
|
converts the incoming wave file to a mp3 file
|
|
"""
|
|
|
|
if not os.path.exists(input_filename):
|
|
raise AudioProcessingException, "file %s does not exist" % input_filename
|
|
|
|
command = ["lame", "--silent", "--abr", str(quality), input_filename, output_filename]
|
|
|
|
process = subprocess.Popen(command, stdout=subprocess.PIPE, stderr=subprocess.PIPE)
|
|
(stdout, stderr) = process.communicate()
|
|
|
|
if process.returncode != 0 or not os.path.exists(output_filename):
|
|
raise AudioProcessingException, stdout
|
|
|
|
def convert_to_ogg(input_filename, output_filename, quality=1):
|
|
"""
|
|
converts the incoming wave file to n ogg file
|
|
"""
|
|
|
|
if not os.path.exists(input_filename):
|
|
raise AudioProcessingException, "file %s does not exist" % input_filename
|
|
|
|
command = ["oggenc", "-q", str(quality), input_filename, "-o", output_filename]
|
|
|
|
process = subprocess.Popen(command, stdout=subprocess.PIPE, stderr=subprocess.PIPE)
|
|
(stdout, stderr) = process.communicate()
|
|
|
|
if process.returncode != 0 or not os.path.exists(output_filename):
|
|
raise AudioProcessingException, stdout
|
|
|
|
def convert_using_ffmpeg(input_filename, output_filename):
|
|
"""
|
|
converts the incoming wave file to stereo pcm using fffmpeg
|
|
"""
|
|
TIMEOUT = 3 * 60
|
|
def alarm_handler(signum, frame):
|
|
raise AudioProcessingException, "timeout while waiting for ffmpeg"
|
|
|
|
if not os.path.exists(input_filename):
|
|
raise AudioProcessingException, "file %s does not exist" % input_filename
|
|
|
|
command = ["ffmpeg", "-y", "-i", input_filename, "-ac","1","-acodec", "pcm_s16le", "-ar", "44100", output_filename]
|
|
|
|
process = subprocess.Popen(command, stdout=subprocess.PIPE, stderr=subprocess.PIPE)
|
|
signal.signal(signal.SIGALRM,alarm_handler)
|
|
signal.alarm(TIMEOUT)
|
|
(stdout, stderr) = process.communicate()
|
|
signal.alarm(0)
|
|
if process.returncode != 0 or not os.path.exists(output_filename):
|
|
raise AudioProcessingException, stdout
|