--- /dev/null
+#!/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 <http://www.gnu.org/licenses/>.
+#
+# Authors:
+# Bram de Jong <bram.dejong at domain.com where domain in gmail>
+# Guillaume Pellerin <pellerin@parisson.com>
+
+
+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
+
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)"""
+++ /dev/null
-#!/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 <http://www.gnu.org/licenses/>.
-#
-# Authors:
-# Bram de Jong <bram.dejong at domain.com where domain in gmail>
-# Contributors:
-# Guillaume Pellerin <pellerin@parisson.com>
-
-
-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"
-
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)"""
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
@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
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:
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()
projects / timeside.git / commitdiff