From: yomguy Date: Wed, 17 Feb 2010 02:21:17 +0000 (+0000) Subject: prepare graph api and new core X-Git-Tag: 0.3.2~206 X-Git-Url: https://git.parisson.com/?a=commitdiff_plain;h=4b594347fb6590a1bd242d9d5bc5fb102e7264c5;p=timeside.git prepare graph api and new core --- diff --git a/graph/core.py b/graph/core.py new file mode 100644 index 0000000..75976d8 --- /dev/null +++ b/graph/core.py @@ -0,0 +1,389 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- + +# wav2png.py -- converts wave files to wave file and spectrogram images +# 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 . +# +# Authors: +# Bram de Jong +# Guillaume Pellerin + + +import optparse, math, sys +import ImageFilter, ImageChops, Image, ImageDraw, ImageColor +import numpy +import scikits.audiolab as audiolab +import Queue + + +color_schemes = { + 'default': { + 'waveform': [(50,0,200), (0,220,80), (255,224,0), (255,0,0)], + 'spectrogram': [(0, 0, 0), (58/4,68/4,65/4), (80/2,100/2,153/2), (90,180,100), + (224,224,44), (255,60,30), (255,255,255)] + }, + 'iso': { + 'waveform': [(0,0,255), (0,255,255), (255,255,0), (255,0,0)], + 'spectrogram': [(0, 0, 0), (58/4,68/4,65/4), (80/2,100/2,153/2), (90,180,100), + (224,224,44), (255,60,30), (255,255,255)] + }, + 'purple': { + 'waveform': [(173,173,173), (147,149,196), (77,80,138), (108,66,0)], + 'spectrogram': [(0, 0, 0), (58/4,68/4,65/4), (80/2,100/2,153/2), (90,180,100), + (224,224,44), (255,60,30), (255,255,255)] + } +} + + +class AudioProcessor(object): + def __init__(self, fft_size, channels, window_function=numpy.ones): + self.fft_size = fft_size + self.channels = channels + 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)) + self.q = Queue.Queue() + + def put(self, samples, eod): + """ Put frames of the first channel in the queue""" + + # convert to mono by selecting left channel only + if self.channels > 1: + samples = samples[:,0] + + if eod: + 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, samples, spec_range=120.0): + """ starting at seek_point read fft_size samples, and calculate the spectral centroid """ + + samples *= self.window + fft = numpy.fft.fft(samples) + spectrum = numpy.abs(fft[:fft.shape[0] / 2 + 1]) / float(self.fft_size) # 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-30))).clip(-spec_range, 0.0) + spec_range)/spec_range + + energy = spectrum.sum() + spectral_centroid = 0 + + if energy > 1e-20: + # calculate the spectral centroid + + if not self.spectrum_range: + self.spectrum_range = numpy.arange(length) + + spectral_centroid = (spectrum * self.spectrum_range).sum() / (energy * (length - 1)) * self.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 end_seek > self.frames: + end_seek = self.frames + + if block_size > end_seek - start_seek: + block_size = end_seek - start_seek + + if block_size <= 1: + samples = self.read(start_seek, 1) + return samples[0], samples[0] + elif block_size == 2: + samples = self.read(start_seek, True) + return samples[0], samples[1] + + 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 + + if min_index < max_index: + return (min_value, max_value) + else: + return (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 + + +class WaveformImage(object): + + def __init__(self, image_width, image_height, nframes, bg_color=None, color_scheme=None, filename=None): + self.image_width = image_width + self.image_height = image_height + self.nframes = nframes + self.bg_color = bg_color + if not bg_color: + self.bg_color = (0,0,0) + self.color_scheme = color_scheme + if not color_scheme: + self.color_scheme = 'default' + self.filename = filename + self.image = Image.new("RGB", (self.image_width, self.image_height), self.bg_color) + self.samples_per_pixel = self.nframes / float(self.image_width) + self.processor = AudioProcessor(self.fft_size, numpy.hanning) + self.draw = ImageDraw.Draw(self.image) + self.previous_x, self.previous_y = None, None + colors = color_schemes[self.color_scheme]['waveform'] + # this line gets the old "screaming" colors back... + # colors = [self.color_from_value(value/29.0) for value in range(0,30)] + self.color_lookup = interpolate_colors(colors) + self.pixel = 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.pixel[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.pixel[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.pixel[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.pixel[x, y_min_int - 1] = (r,g,b) + + def process(self, frames): + + #for x in range(self.image_width): + seek_point = int(x * self.samples_per_pixel) + next_seek_point = int((x + 1) * self.samples_per_pixel) + (spectral_centroid, db_spectrum) = self.processor.spectral_centroid(seek_point) + peaks = self.processor.peaks(seek_point, next_seek_point) + self.draw_peaks(x, peaks, spectral_centroid) + + def save(self): + a = 25 + for x in range(self.image_width): + self.pixel[x, self.image_height/2] = tuple(map(lambda p: p+a, self.pixel[x, self.image_height/2])) + self.image.save(self.filename) + + +class SpectrogramImage(object): + def __init__(self, image_width, image_height, fft_size, bg_color = None, color_scheme = None): + + #FIXME: bg_color is ignored + + if not color_scheme: + color_scheme = 'default' + + self.image = Image.new("P", (image_height, image_width)) + + self.image_width = image_width + self.image_height = image_height + self.fft_size = fft_size + + colors = color_schemes[color_scheme]['spectrogram'] + + self.image.putpalette(interpolate_colors(colors, True)) + + # 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 (index, alpha) in self.y_to_bin: + self.pixels.append( int( ((255.0-alpha) * spectrum[index] + alpha * spectrum[index + 1] )) ) + + for y in range(len(self.y_to_bin), self.image_height): + self.pixels.append(0) + + def save(self, filename): + self.image.putdata(self.pixels) + self.image.transpose(Image.ROTATE_90).save(filename) + + +def create_spectrogram_png(input_filename, output_filename_s, image_width, image_height, fft_size, + bg_color = None, color_scheme = None): + audio_file = audiolab.sndfile(input_filename, 'read') + + samples_per_pixel = audio_file.get_nframes() / float(image_width) + processor = AudioProcessor(audio_file, fft_size, numpy.hanning) + + spectrogram = SpectrogramImage(image_width, image_height, fft_size, bg_color, color_scheme) + + for x in range(image_width): + + if x % (image_width/10) == 0: + sys.stdout.write('.') + sys.stdout.flush() + + 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) + spectrogram.draw_spectrum(x, db_spectrum) + + spectrogram.save(output_filename_s) + + print " done" + + + +class Noise(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.num_frames = num_frames + self.has_broken_header = has_broken_header + + def seek(self, seekpoint): + self.seekpoint = seekpoint + + def get_nframes(self): + return self.num_frames + + def get_samplerate(self): + return 44100 + + def get_channels(self): + return 1 + + def read_frames(self, frames_to_read): + if self.has_broken_header and self.seekpoint + frames_to_read > self.num_frames / 2: + raise IOError() + + num_frames_left = self.num_frames - self.seekpoint + if num_frames_left < frames_to_read: + will_read = num_frames_left + else: + will_read = frames_to_read + self.seekpoint += will_read + return numpy.random.random(will_read)*2 - 1 + diff --git a/graph/spectrogram_audiolab.py b/graph/spectrogram_audiolab.py index 59c9c1d..6f25f30 100644 --- a/graph/spectrogram_audiolab.py +++ b/graph/spectrogram_audiolab.py @@ -22,7 +22,7 @@ from timeside.core import * from timeside.api import IGrapher from tempfile import NamedTemporaryFile -from timeside.graph.wav2png import * +from timeside.graph.core import * class SpectrogramGrapherAudiolab(Processor): """Spectrogram graph driver (python style thanks to wav2png.py and scikits.audiolab)""" diff --git a/graph/wav2png.py b/graph/wav2png.py deleted file mode 100644 index cd6b27e..0000000 --- a/graph/wav2png.py +++ /dev/null @@ -1,448 +0,0 @@ -#!/usr/bin/env python -# -*- coding: utf-8 -*- - -# wav2png.py -- converts wave files to wave file and spectrogram images -# 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 . -# -# Authors: -# Bram de Jong -# Contributors: -# Guillaume Pellerin - - -import optparse, math, sys -import ImageFilter, ImageChops, Image, ImageDraw, ImageColor -import numpy -import scikits.audiolab as audiolab - -color_schemes = { - 'default': { - 'waveform': [(50,0,200), (0,220,80), (255,224,0), (255,0,0)], - 'spectrogram': [(0, 0, 0), (58/4,68/4,65/4), (80/2,100/2,153/2), (90,180,100), - (224,224,44), (255,60,30), (255,255,255)] - }, - 'iso': { - 'waveform': [(0,0,255), (0,255,255), (255,255,0), (255,0,0)], - 'spectrogram': [(0, 0, 0), (58/4,68/4,65/4), (80/2,100/2,153/2), (90,180,100), - (224,224,44), (255,60,30), (255,255,255)] - }, - 'purple': { - 'waveform': [(173,173,173), (147,149,196), (77,80,138), (108,66,0)], - 'spectrogram': [(0, 0, 0), (58/4,68/4,65/4), (80/2,100/2,153/2), (90,180,100), - (224,224,44), (255,60,30), (255,255,255)] - } -} - -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.num_frames = num_frames - self.has_broken_header = has_broken_header - - def seek(self, seekpoint): - self.seekpoint = seekpoint - - def get_nframes(self): - return self.num_frames - - def get_samplerate(self): - return 44100 - - def get_channels(self): - return 1 - - def read_frames(self, frames_to_read): - if self.has_broken_header and self.seekpoint + frames_to_read > self.num_frames / 2: - raise IOError() - - num_frames_left = self.num_frames - self.seekpoint - if num_frames_left < frames_to_read: - will_read = num_frames_left - else: - will_read = frames_to_read - self.seekpoint += will_read - return numpy.random.random(will_read)*2 - 1 - - -class AudioProcessor(object): - def __init__(self, audio_file, fft_size, window_function=numpy.ones): - self.fft_size = fft_size - self.window = window_function(self.fft_size) - self.audio_file = audio_file - self.frames = audio_file.get_nframes() - self.samplerate = audio_file.get_samplerate() - self.channels = audio_file.get_channels() - 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)) - - 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: - if resize_if_less: - return numpy.zeros(size) - else: - return numpy.array([]) - else: - self.audio_file.seek(0) - - add_to_start = -start # remember: start is negative! - to_read = size + start - - if to_read > self.frames: - add_to_end = to_read - self.frames - to_read = self.frames - else: - self.audio_file.seek(start) - - to_read = size - if start + to_read >= self.frames: - to_read = self.frames - start - add_to_end = size - to_read - - try: - samples = self.audio_file.read_frames(to_read) - except IOError: - # this can happen for wave files with broken headers... - if resize_if_less: - return numpy.zeros(size) - else: - return numpy.zeros(2) - - # convert to mono by selecting left channel only - if self.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=120.0): - """ 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.fft(samples) - spectrum = numpy.abs(fft[:fft.shape[0] / 2 + 1]) / float(self.fft_size) # 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-30))).clip(-spec_range, 0.0) + spec_range)/spec_range - - energy = spectrum.sum() - spectral_centroid = 0 - - if energy > 1e-20: - # 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.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 end_seek > self.frames: - end_seek = self.frames - - if block_size > end_seek - start_seek: - block_size = end_seek - start_seek - - if block_size <= 1: - samples = self.read(start_seek, 1) - return samples[0], samples[0] - elif block_size == 2: - samples = self.read(start_seek, True) - return samples[0], samples[1] - - 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 - - if min_index < max_index: - return (min_value, max_value) - else: - return (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 - - -class WaveformImage(object): - def __init__(self, image_width, image_height, bg_color = None, color_scheme = None): - if not bg_color: - bg_color = (0,0,0) - if not color_scheme: - color_scheme = 'default' - - self.image = Image.new("RGB", (image_width, image_height), bg_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 - - colors = color_schemes[color_scheme]['waveform'] - - # this line gets the old "screaming" colors back... - # colors = [self.color_from_value(value/29.0) for value in range(0,30)] - - 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): - def __init__(self, image_width, image_height, fft_size, bg_color = None, color_scheme = None): - - #FIXME: bg_color is ignored - - if not color_scheme: - color_scheme = 'default' - - self.image = Image.new("P", (image_height, image_width)) - - self.image_width = image_width - self.image_height = image_height - self.fft_size = fft_size - - colors = color_schemes[color_scheme]['spectrogram'] - - self.image.putpalette(interpolate_colors(colors, True)) - - # 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 (index, alpha) in self.y_to_bin: - self.pixels.append( int( ((255.0-alpha) * spectrum[index] + alpha * spectrum[index + 1] )) ) - - for y in range(len(self.y_to_bin), self.image_height): - self.pixels.append(0) - - def save(self, filename): - self.image.putdata(self.pixels) - self.image.transpose(Image.ROTATE_90).save(filename) - - -def create_wavform_png(input_filename, output_filename_w, image_width, image_height, fft_size, - bg_color = None, color_scheme = None): - audio_file = audiolab.sndfile(input_filename, 'read') - - samples_per_pixel = audio_file.get_nframes() / float(image_width) - processor = AudioProcessor(audio_file, fft_size, numpy.hanning) - - waveform = WaveformImage(image_width, image_height, bg_color, color_scheme) - - for x in range(image_width): - - if x % (image_width/10) == 0: - sys.stdout.write('.') - sys.stdout.flush() - - 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) - - waveform.save(output_filename_w) - - print " done" - -def create_spectrogram_png(input_filename, output_filename_s, image_width, image_height, fft_size, - bg_color = None, color_scheme = None): - audio_file = audiolab.sndfile(input_filename, 'read') - - samples_per_pixel = audio_file.get_nframes() / float(image_width) - processor = AudioProcessor(audio_file, fft_size, numpy.hanning) - - spectrogram = SpectrogramImage(image_width, image_height, fft_size, bg_color, color_scheme) - - for x in range(image_width): - - if x % (image_width/10) == 0: - sys.stdout.write('.') - sys.stdout.flush() - - 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) - spectrogram.draw_spectrum(x, db_spectrum) - - spectrogram.save(output_filename_s) - - print " done" - diff --git a/graph/waveform_audiolab.py b/graph/waveform_audiolab.py index 513ec97..8b9c1ff 100644 --- a/graph/waveform_audiolab.py +++ b/graph/waveform_audiolab.py @@ -22,7 +22,7 @@ from timeside.core import * from timeside.api import IGrapher from tempfile import NamedTemporaryFile -from timeside.graph.wav2png import * +from timeside.graph.core import * class WaveFormGrapherAudiolab(Processor): """WaveForm graph driver (python style thanks to wav2png.py and scikits.audiolab)""" diff --git a/tests/api/examples.py b/tests/api/examples.py index 7578443..db84a9c 100644 --- a/tests/api/examples.py +++ b/tests/api/examples.py @@ -19,7 +19,7 @@ class FileDecoder(Processor): self.filename = filename # The file has to be opened here so that nframes(), samplerate(), # etc.. work before setup() is called. - self.file = audiolab.sndfile(self.filename, 'read') + self.file = audiolab.Sndfile(self.filename, 'r') self.position = 0 @interfacedoc @@ -36,31 +36,31 @@ class FileDecoder(Processor): @interfacedoc def channels(self): - return self.file.get_channels() + return self.file.channels @interfacedoc def samplerate(self): - return self.file.get_samplerate() + return self.file.samplerate @interfacedoc def duration(self): - return self.file.get_nframes() / self.file.get_samplerate() + return self.file.nframes / self.file.samplerate @interfacedoc def nframes(self): - return self.file.get_nframes() + return self.file.nframes @interfacedoc def format(self): - return self.file.get_file_format() + return self.file.file_format @interfacedoc def encoding(self): - return self.file.get_encoding() + return self.file.encoding @interfacedoc def resolution(self): resolution = None - encoding = self.file.get_encoding() + encoding = self.file.encoding if encoding == "pcm8": resolution = 8 @@ -209,7 +209,8 @@ class Waveform(Processor): implements(IGrapher) @interfacedoc - def __init__(self, width, height, output=None): + def __init__(self, width, height, nframes, output=None): + self.nframes = nframes self.filename = output self.image = None if width: @@ -247,8 +248,16 @@ class Waveform(Processor): Processor.setup(self, channels, samplerate) if self.image: self.image.close() - self.image = WaveformImage(self.width, self.height, self.bg_color, self.color_scheme, self.filename) - + self.image = WaveformImage(self.width, self.height, self.nframes) + + @interfacedoc + def process(self, frames, eod=False): + self.image.process(frames) + if eod: + self.image.close() + self.image = None + return frames, eod + @interfacedoc def render(self): self.image.process()