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Diffstat (limited to 'extlib/freesound/audioprocessing.py')
| -rw-r--r-- | extlib/freesound/audioprocessing.py | 616 |
1 files changed, 0 insertions, 616 deletions
diff --git a/extlib/freesound/audioprocessing.py b/extlib/freesound/audioprocessing.py deleted file mode 100644 index b002ff8a..00000000 --- a/extlib/freesound/audioprocessing.py +++ /dev/null @@ -1,616 +0,0 @@ -#!/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) |