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Diffstat (limited to 'extlib')
| -rw-r--r-- | extlib/freesound/audioprocessing.py | 616 |
1 files changed, 616 insertions, 0 deletions
diff --git a/extlib/freesound/audioprocessing.py b/extlib/freesound/audioprocessing.py new file mode 100644 index 00000000..2c2b35b5 --- /dev/null +++ b/extlib/freesound/audioprocessing.py @@ -0,0 +1,616 @@ +#!/usr/bin/env python +# processing.py -- various audio processing functions +# Copyright (C) 2008 MUSIC TECHNOLOGY GROUP (MTG) +# UNIVERSITAT POMPEU FABRA +# +# This program is free software: you can redistribute it and/or modify +# it under the terms of the GNU Affero General Public License as +# published by the Free Software Foundation, either version 3 of the +# License, or (at your option) any later version. +# +# This program is distributed in the hope that it will be useful, +# but WITHOUT ANY WARRANTY; without even the implied warranty of +# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the +# GNU Affero General Public License for more details. +# +# You should have received a copy of the GNU Affero General Public License +# along with this program. If not, see <http://www.gnu.org/licenses/>. +# +# Authors: +# Bram de Jong <bram.dejong at domain.com where domain in gmail> +# 2012, Joar Wandborg <first name at last name dot se> + +from PIL import Image, ImageDraw, ImageColor #@UnresolvedImport +from functools import partial +import math +import numpy +import os +import re +import signal + + +def get_sound_type(input_filename): + sound_type = os.path.splitext(input_filename.lower())[1].strip(".") + + if sound_type == "fla": + sound_type = "flac" + elif sound_type == "aif": + sound_type = "aiff" + + return sound_type + + +try: + import scikits.audiolab as audiolab +except ImportError: + print "WARNING: audiolab is not installed so wav2png will not work" +import subprocess + +class AudioProcessingException(Exception): + pass + +class TestAudioFile(object): + """A class that mimics audiolab.sndfile but generates noise instead of reading + a wave file. Additionally it can be told to have a "broken" header and thus crashing + in the middle of the file. Also useful for testing ultra-short files of 20 samples.""" + def __init__(self, num_frames, has_broken_header=False): + self.seekpoint = 0 + self.nframes = num_frames + self.samplerate = 44100 + self.channels = 1 + self.has_broken_header = has_broken_header + + def seek(self, seekpoint): + self.seekpoint = seekpoint + + def read_frames(self, frames_to_read): + if self.has_broken_header and self.seekpoint + frames_to_read > self.num_frames / 2: + raise RuntimeError() + + num_frames_left = self.num_frames - self.seekpoint + will_read = num_frames_left if num_frames_left < frames_to_read else frames_to_read + self.seekpoint += will_read + return numpy.random.random(will_read)*2 - 1 + + +def get_max_level(filename): + max_value = 0 + buffer_size = 4096 + audio_file = audiolab.Sndfile(filename, 'r') + n_samples_left = audio_file.nframes + + while n_samples_left: + to_read = min(buffer_size, n_samples_left) + + try: + samples = audio_file.read_frames(to_read) + except RuntimeError: + # this can happen with a broken header + break + + # convert to mono by selecting left channel only + if audio_file.channels > 1: + samples = samples[:,0] + + max_value = max(max_value, numpy.abs(samples).max()) + + n_samples_left -= to_read + + audio_file.close() + + return max_value + +class AudioProcessor(object): + """ + The audio processor processes chunks of audio an calculates the spectrac centroid and the peak + samples in that chunk of audio. + """ + def __init__(self, input_filename, fft_size, window_function=numpy.hanning): + max_level = get_max_level(input_filename) + + self.audio_file = audiolab.Sndfile(input_filename, 'r') + self.fft_size = fft_size + self.window = window_function(self.fft_size) + self.spectrum_range = None + self.lower = 100 + self.higher = 22050 + self.lower_log = math.log10(self.lower) + self.higher_log = math.log10(self.higher) + self.clip = lambda val, low, high: min(high, max(low, val)) + + # figure out what the maximum value is for an FFT doing the FFT of a DC signal + fft = numpy.fft.rfft(numpy.ones(fft_size) * self.window) + max_fft = (numpy.abs(fft)).max() + # set the scale to normalized audio and normalized FFT + self.scale = 1.0/max_level/max_fft if max_level > 0 else 1 + + def read(self, start, size, resize_if_less=False): + """ read size samples starting at start, if resize_if_less is True and less than size + samples are read, resize the array to size and fill with zeros """ + + # number of zeros to add to start and end of the buffer + add_to_start = 0 + add_to_end = 0 + + if start < 0: + # the first FFT window starts centered around zero + if size + start <= 0: + return numpy.zeros(size) if resize_if_less else numpy.array([]) + else: + self.audio_file.seek(0) + + add_to_start = -start # remember: start is negative! + to_read = size + start + + if to_read > self.audio_file.nframes: + add_to_end = to_read - self.audio_file.nframes + to_read = self.audio_file.nframes + else: + self.audio_file.seek(start) + + to_read = size + if start + to_read >= self.audio_file.nframes: + to_read = self.audio_file.nframes - start + add_to_end = size - to_read + + try: + samples = self.audio_file.read_frames(to_read) + except RuntimeError: + # this can happen for wave files with broken headers... + return numpy.zeros(size) if resize_if_less else numpy.zeros(2) + + # convert to mono by selecting left channel only + if self.audio_file.channels > 1: + samples = samples[:,0] + + if resize_if_less and (add_to_start > 0 or add_to_end > 0): + if add_to_start > 0: + samples = numpy.concatenate((numpy.zeros(add_to_start), samples), axis=1) + + if add_to_end > 0: + samples = numpy.resize(samples, size) + samples[size - add_to_end:] = 0 + + return samples + + + def spectral_centroid(self, seek_point, spec_range=110.0): + """ starting at seek_point read fft_size samples, and calculate the spectral centroid """ + + samples = self.read(seek_point - self.fft_size/2, self.fft_size, True) + + samples *= self.window + fft = numpy.fft.rfft(samples) + spectrum = self.scale * numpy.abs(fft) # normalized abs(FFT) between 0 and 1 + length = numpy.float64(spectrum.shape[0]) + + # scale the db spectrum from [- spec_range db ... 0 db] > [0..1] + db_spectrum = ((20*(numpy.log10(spectrum + 1e-60))).clip(-spec_range, 0.0) + spec_range)/spec_range + + energy = spectrum.sum() + spectral_centroid = 0 + + if energy > 1e-60: + # calculate the spectral centroid + + if self.spectrum_range == None: + self.spectrum_range = numpy.arange(length) + + spectral_centroid = (spectrum * self.spectrum_range).sum() / (energy * (length - 1)) * self.audio_file.samplerate * 0.5 + + # clip > log10 > scale between 0 and 1 + spectral_centroid = (math.log10(self.clip(spectral_centroid, self.lower, self.higher)) - self.lower_log) / (self.higher_log - self.lower_log) + + return (spectral_centroid, db_spectrum) + + + def peaks(self, start_seek, end_seek): + """ read all samples between start_seek and end_seek, then find the minimum and maximum peak + in that range. Returns that pair in the order they were found. So if min was found first, + it returns (min, max) else the other way around. """ + + # larger blocksizes are faster but take more mem... + # Aha, Watson, a clue, a tradeof! + block_size = 4096 + + max_index = -1 + max_value = -1 + min_index = -1 + min_value = 1 + + if start_seek < 0: + start_seek = 0 + + if end_seek > self.audio_file.nframes: + end_seek = self.audio_file.nframes + + if end_seek <= start_seek: + samples = self.read(start_seek, 1) + return (samples[0], samples[0]) + + if block_size > end_seek - start_seek: + block_size = end_seek - start_seek + + for i in range(start_seek, end_seek, block_size): + samples = self.read(i, block_size) + + local_max_index = numpy.argmax(samples) + local_max_value = samples[local_max_index] + + if local_max_value > max_value: + max_value = local_max_value + max_index = local_max_index + + local_min_index = numpy.argmin(samples) + local_min_value = samples[local_min_index] + + if local_min_value < min_value: + min_value = local_min_value + min_index = local_min_index + + return (min_value, max_value) if min_index < max_index else (max_value, min_value) + + +def interpolate_colors(colors, flat=False, num_colors=256): + """ given a list of colors, create a larger list of colors interpolating + the first one. If flatten is True a list of numers will be returned. If + False, a list of (r,g,b) tuples. num_colors is the number of colors wanted + in the final list """ + + palette = [] + + for i in range(num_colors): + index = (i * (len(colors) - 1))/(num_colors - 1.0) + index_int = int(index) + alpha = index - float(index_int) + + if alpha > 0: + r = (1.0 - alpha) * colors[index_int][0] + alpha * colors[index_int + 1][0] + g = (1.0 - alpha) * colors[index_int][1] + alpha * colors[index_int + 1][1] + b = (1.0 - alpha) * colors[index_int][2] + alpha * colors[index_int + 1][2] + else: + r = (1.0 - alpha) * colors[index_int][0] + g = (1.0 - alpha) * colors[index_int][1] + b = (1.0 - alpha) * colors[index_int][2] + + if flat: + palette.extend((int(r), int(g), int(b))) + else: + palette.append((int(r), int(g), int(b))) + + return palette + + +def desaturate(rgb, amount): + """ + desaturate colors by amount + amount == 0, no change + amount == 1, grey + """ + luminosity = sum(rgb) / 3.0 + desat = lambda color: color - amount * (color - luminosity) + + return tuple(map(int, map(desat, rgb))) + + +class WaveformImage(object): + """ + Given peaks and spectral centroids from the AudioProcessor, this class will construct + a wavefile image which can be saved as PNG. + """ + def __init__(self, image_width, image_height, palette=1): + if image_height % 2 == 0: + raise AudioProcessingException, "Height should be uneven: images look much better at uneven height" + + if palette == 1: + background_color = (0,0,0) + colors = [ + (50,0,200), + (0,220,80), + (255,224,0), + (255,70,0), + ] + elif palette == 2: + background_color = (0,0,0) + colors = [self.color_from_value(value/29.0) for value in range(0,30)] + elif palette == 3: + background_color = (213, 217, 221) + colors = map( partial(desaturate, amount=0.7), [ + (50,0,200), + (0,220,80), + (255,224,0), + ]) + elif palette == 4: + background_color = (213, 217, 221) + colors = map( partial(desaturate, amount=0.8), [self.color_from_value(value/29.0) for value in range(0,30)]) + + self.image = Image.new("RGB", (image_width, image_height), background_color) + + self.image_width = image_width + self.image_height = image_height + + self.draw = ImageDraw.Draw(self.image) + self.previous_x, self.previous_y = None, None + + self.color_lookup = interpolate_colors(colors) + self.pix = self.image.load() + + def color_from_value(self, value): + """ given a value between 0 and 1, return an (r,g,b) tuple """ + + return ImageColor.getrgb("hsl(%d,%d%%,%d%%)" % (int( (1.0 - value) * 360 ), 80, 50)) + + def draw_peaks(self, x, peaks, spectral_centroid): + """ draw 2 peaks at x using the spectral_centroid for color """ + + y1 = self.image_height * 0.5 - peaks[0] * (self.image_height - 4) * 0.5 + y2 = self.image_height * 0.5 - peaks[1] * (self.image_height - 4) * 0.5 + + line_color = self.color_lookup[int(spectral_centroid*255.0)] + + if self.previous_y != None: + self.draw.line([self.previous_x, self.previous_y, x, y1, x, y2], line_color) + else: + self.draw.line([x, y1, x, y2], line_color) + + self.previous_x, self.previous_y = x, y2 + + self.draw_anti_aliased_pixels(x, y1, y2, line_color) + + def draw_anti_aliased_pixels(self, x, y1, y2, color): + """ vertical anti-aliasing at y1 and y2 """ + + y_max = max(y1, y2) + y_max_int = int(y_max) + alpha = y_max - y_max_int + + if alpha > 0.0 and alpha < 1.0 and y_max_int + 1 < self.image_height: + current_pix = self.pix[x, y_max_int + 1] + + r = int((1-alpha)*current_pix[0] + alpha*color[0]) + g = int((1-alpha)*current_pix[1] + alpha*color[1]) + b = int((1-alpha)*current_pix[2] + alpha*color[2]) + + self.pix[x, y_max_int + 1] = (r,g,b) + + y_min = min(y1, y2) + y_min_int = int(y_min) + alpha = 1.0 - (y_min - y_min_int) + + if alpha > 0.0 and alpha < 1.0 and y_min_int - 1 >= 0: + current_pix = self.pix[x, y_min_int - 1] + + r = int((1-alpha)*current_pix[0] + alpha*color[0]) + g = int((1-alpha)*current_pix[1] + alpha*color[1]) + b = int((1-alpha)*current_pix[2] + alpha*color[2]) + + self.pix[x, y_min_int - 1] = (r,g,b) + + def save(self, filename): + # draw a zero "zero" line + a = 25 + for x in range(self.image_width): + self.pix[x, self.image_height/2] = tuple(map(lambda p: p+a, self.pix[x, self.image_height/2])) + + self.image.save(filename) + + +class SpectrogramImage(object): + """ + Given spectra from the AudioProcessor, this class will construct a wavefile image which + can be saved as PNG. + """ + def __init__(self, image_width, image_height, fft_size): + self.image_width = image_width + self.image_height = image_height + self.fft_size = fft_size + + self.image = Image.new("RGBA", (image_height, image_width)) + + colors = [ + (0, 0, 0, 0), + (58/4, 68/4, 65/4, 255), + (80/2, 100/2, 153/2, 255), + (90, 180, 100, 255), + (224, 224, 44, 255), + (255, 60, 30, 255), + (255, 255, 255, 255) + ] + self.palette = interpolate_colors(colors) + + # generate the lookup which translates y-coordinate to fft-bin + self.y_to_bin = [] + f_min = 100.0 + f_max = 22050.0 + y_min = math.log10(f_min) + y_max = math.log10(f_max) + for y in range(self.image_height): + freq = math.pow(10.0, y_min + y / (image_height - 1.0) *(y_max - y_min)) + bin = freq / 22050.0 * (self.fft_size/2 + 1) + + if bin < self.fft_size/2: + alpha = bin - int(bin) + + self.y_to_bin.append((int(bin), alpha * 255)) + + # this is a bit strange, but using image.load()[x,y] = ... is + # a lot slower than using image.putadata and then rotating the image + # so we store all the pixels in an array and then create the image when saving + self.pixels = [] + + def draw_spectrum(self, x, spectrum): + # for all frequencies, draw the pixels + for (index, alpha) in self.y_to_bin: + self.pixels.append( self.palette[int((255.0-alpha) * spectrum[index] + alpha * spectrum[index + 1])] ) + + # if the FFT is too small to fill up the image, fill with black to the top + for y in range(len(self.y_to_bin), self.image_height): #@UnusedVariable + self.pixels.append(self.palette[0]) + + def save(self, filename, quality=80): + assert filename.lower().endswith(".jpg") + self.image.putdata(self.pixels) + self.image.transpose(Image.ROTATE_90).save(filename, quality=quality) + + +def create_wave_images(input_filename, output_filename_w, output_filename_s, image_width, image_height, fft_size, progress_callback=None): + """ + Utility function for creating both wavefile and spectrum images from an audio input file. + """ + processor = AudioProcessor(input_filename, fft_size, numpy.hanning) + samples_per_pixel = processor.audio_file.nframes / float(image_width) + + waveform = WaveformImage(image_width, image_height) + spectrogram = SpectrogramImage(image_width, image_height, fft_size) + + for x in range(image_width): + + if progress_callback and x % (image_width/10) == 0: + progress_callback((x*100)/image_width) + + seek_point = int(x * samples_per_pixel) + 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 |