class WaveformImageJoyContour(WaveformImage):
-
+
def __init__(self, image_width, image_height, nframes, samplerate, fft_size, bg_color, color_scheme, filename=None):
WaveformImage.__init__(self, image_width, image_height, nframes, samplerate, fft_size, bg_color, color_scheme, filename=filename)
self.contour = numpy.zeros(self.image_width)
self.centroids = numpy.zeros(self.image_width)
self.ndiv = 6
self.x = numpy.r_[0:self.image_width-1:1]
- #self.dx1 = self.x[1]-self.x[0]
- self.dx2 = self.x[self.samples_per_pixel/(self.ndiv*10)]-self.x[0]
+ self.dx1 = self.x[1]-self.x[0]
def get_peaks_contour(self, x, peaks, spectral_centroid=None):
- """ draw 2 peaks at x using the spectral_centroid for color """
self.contour[x] = numpy.max(peaks)
self.centroids[x] = spectral_centroid
-
+
def draw_peaks_contour(self):
- contour = cspline1d(self.contour.copy())
- #contour = cspline1d_eval(contour, self.x, dx=self.dx1, x0=self.x[0])
- contour = cspline1d_eval(contour, self.x, dx=self.dx2, x0=self.x[0])
- #print len(contour)
-
- l_min = min(self.contour)
- l_max = max(self.contour)
- l_range= l_max - l_min
+ #contour = self.contour.copy()
+ contour = smooth(self.contour, window_len=13)
- self.contour = (contour-l_min)/l_range
- #print contour
+ l_min = min(contour)
+ contour = (contour-l_min)
+ l_max = max(contour)
+ l_range= l_max - l_min
+ contour = contour/l_max
+ contour = cspline1d(contour)
+ contour = cspline1d_eval(contour, self.x, dx=self.dx1, x0=self.x[0])
# Multispline scales
for i in range(0,self.ndiv):
self.previous_x, self.previous_y = None, None
- bright_color = int(255*(1-float(i)/self.ndiv))
+
+ #bright_color = 255
+ bright_color = int(255*(1-float(i)/(self.ndiv*2)))
line_color = (bright_color,bright_color,bright_color)
- print line_color
-
+
# Linear
#contour = contour*(1.0-float(i)/self.ndiv)
#contour = contour*(1-float(i)/self.ndiv)
-
+
# Cosine
contour = contour*numpy.arccos(float(i)/self.ndiv)*2/numpy.pi
#contour = self.contour*(1-float(i)*numpy.arccos(float(i)/self.ndiv)*2/numpy.pi/self.ndiv)
-
+
# Negative Sine
#contour = contour + ((1-contour)*2/numpy.pi*numpy.arcsin(float(i)/self.ndiv))
for j in range(0,self.image_width-1):
#line_color = self.color_lookup[int(self.centroids[j]*255.0)]
x = self.x[j]
- y = contour[j]*self.image_height
- #print y
+ y = contour[j]*(self.image_height-1)
if self.previous_y:
self.draw.line([self.previous_x, self.previous_y, x, y], line_color)
self.draw_anti_aliased_pixels(x, y, y, line_color)
self.seekpoint += will_read
return numpy.random.random(will_read)*2 - 1
+
+# TOOLS
+
+def downsample(vector, factor):
+ """
+ downsample(vector, factor):
+ Downsample (by averaging) a vector by an integer factor.
+ """
+ if (len(vector) % factor):
+ print "Length of 'vector' is not divisible by 'factor'=%d!" % factor
+ return 0
+ vector.shape = (len(vector)/factor, factor)
+ return numpy.mean(vector, axis=1)
+
+
+def smooth(x, window_len=10, window='hanning'):
+ """smooth the data using a window with requested size.
+
+ This method is based on the convolution of a scaled window with the signal.
+ The signal is prepared by introducing reflected copies of the signal
+ (with the window size) in both ends so that transient parts are minimized
+ in the begining and end part of the output signal.
+
+ input:
+ x: the input signal
+ window_len: the dimension of the smoothing window
+ window: the type of window from 'flat', 'hanning', 'hamming', 'bartlett', 'blackman'
+ flat window will produce a moving average smoothing.
+
+ output:
+ the smoothed signal
+
+ example:
+
+ import numpy as np
+ t = numpy.linspace(-2,2,0.1)
+ x = numpy.sin(t)+numpy.random.randn(len(t))*0.1
+ y = smooth(x)
+
+ see also:
+
+ numpy.hanning, numpy.hamming, numpy.bartlett, numpy.blackman, numpy.convolve
+ scipy.signal.lfilter
+
+ TODO: the window parameter could be the window itself if an array instead of a string
+ """
+
+ if x.ndim != 1:
+ raise ValueError, "smooth only accepts 1 dimension arrays."
+
+ if x.size < window_len:
+ raise ValueError, "Input vector needs to be bigger than window size."
+
+ if window_len < 3:
+ return x
+
+ if not window in ['flat', 'hanning', 'hamming', 'bartlett', 'blackman']:
+ raise ValueError, "Window is on of 'flat', 'hanning', 'hamming', 'bartlett', 'blackman'"
+
+ s=numpy.r_[2*x[0]-x[window_len:1:-1], x, 2*x[-1]-x[-1:-window_len:-1]]
+ #print(len(s))
+
+ if window == 'flat': #moving average
+ w = numpy.ones(window_len,'d')
+ else:
+ w = getattr(numpy, window)(window_len)
+ y = numpy.convolve(w/w.sum(), s, mode='same')
+ return y[window_len-1:-window_len+1]
+
+++ /dev/null
-#!/usr/bin/python
-# -*- coding: utf-8 -*-
-#
-# Copyright (c) 2009-2010 Guillaume Pellerin <yomguy@parisson.com>
-
-# This file is part of TimeSide.
-
-# TimeSide is free software: you can redistribute it and/or modify
-# it under the terms of the GNU General Public License as published by
-# the Free Software Foundation, either version 2 of the License, or
-# (at your option) any later version.
-
-# TimeSide 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 General Public License for more details.
-
-# You should have received a copy of the GNU General Public License
-# along with TimeSide. If not, see <http://www.gnu.org/licenses/>.
-
-# Author: Guillaume Pellerin <yomguy@parisson.com>
-
-version = '0.1-beta'
-
-import os
-import sys
-import timeside
-
-class GrapherScheme:
-
- def __init__(self):
-
- self.color_scheme = {
- 'waveform': [ # Four (R,G,B) tuples for three main color channels for the spectral centroid method
- (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)
- ]}
-
- # Width of the image
- self.width = 1024
-
- # Height of the image
- self.height = 320
-
- # Background color
- self.bg_color = (25,25,25)
-
- # Force computation. By default, the class doesn't overwrite existing image files.
- self.force = True
-
-
-class Media2Waveform(object):
-
- def __init__(self, media_dir, img_dir):
- self.root_dir = media_dir
- self.img_dir = img_dir
- self.scheme = GrapherScheme()
- self.width = self.scheme.width
- self.height = self.scheme.height
- self.bg_color = self.scheme.bg_color
- self.color_scheme = self.scheme.color_scheme
- self.force = self.scheme.force
-
- self.media_list = self.get_media_list()
- if not os.path.exists(self.img_dir):
- os.mkdir(self.img_dir)
- self.path_dict = self.get_path_dict()
-
- def get_media_list(self):
- media_list = []
- for root, dirs, files in os.walk(self.root_dir):
- if root:
- for file in files:
- ext = file.split('.')[-1]
- if ext == 'wav' or ext == 'WAV':
- media_list.append(root+os.sep+file)
- return media_list
-
- def get_path_dict(self):
- path_dict = {}
- for media in self.media_list:
- name = os.path.splitext(media)
- name = name[0].split(os.sep)[-1]
- path_dict[media] = unicode(self.img_dir + os.sep + name + '.png')
- return path_dict
-
- def process(self):
- for source, image in self.path_dict.iteritems():
- if not os.path.exists(image) or self.force:
- print 'Rendering ', source, ' to ', image, '...'
- audio = os.path.join(os.path.dirname(__file__), source)
- decoder = timeside.decoder.FileDecoder(audio)
- waveform = timeside.grapher.WaveformJoyDiv(width=self.width, height=self.height, output=image,
- bg_color=self.bg_color, color_scheme=self.color_scheme)
- (decoder | waveform).run()
- print 'frames per pixel = ', waveform.graph.samples_per_pixel
- waveform.render()
-
-
-if __name__ == '__main__':
- if len(sys.argv) <= 2:
- print """
- Usage : python waveform_batch /path/to/media_dir /path/to/img_dir
-
- Dependencies : timeside, python, python-numpy, python-gst0.10, gstreamer0.10-plugins-base
- See http://code.google.com/p/timeside/ for more information.
- """
- else:
- media_dir = sys.argv[-2]
- img_dir = sys.argv[-1]
- m = Media2Waveform(media_dir, img_dir)
- m.process()
--- /dev/null
+#!/usr/bin/python
+# -*- coding: utf-8 -*-
+#
+# Copyright (c) 2009-2010 Guillaume Pellerin <yomguy@parisson.com>
+
+# This file is part of TimeSide.
+
+# TimeSide is free software: you can redistribute it and/or modify
+# it under the terms of the GNU General Public License as published by
+# the Free Software Foundation, either version 2 of the License, or
+# (at your option) any later version.
+
+# TimeSide 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 General Public License for more details.
+
+# You should have received a copy of the GNU General Public License
+# along with TimeSide. If not, see <http://www.gnu.org/licenses/>.
+
+# Author: Guillaume Pellerin <yomguy@parisson.com>
+
+version = '0.1-beta'
+
+import os
+import sys
+import timeside
+
+class GrapherScheme:
+
+ def __init__(self):
+
+ self.color_scheme = {
+ 'waveform': [ # Four (R,G,B) tuples for three main color channels for the spectral centroid method
+ (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)
+ ]}
+
+ # Width of the image
+ self.width = 1024
+
+ # Height of the image
+ self.height = 320
+
+ # Background color
+ self.bg_color = (25,25,25)
+
+ # Force computation. By default, the class doesn't overwrite existing image files.
+ self.force = True
+
+
+class Media2Waveform(object):
+
+ def __init__(self, media_dir, img_dir):
+ self.root_dir = media_dir
+ self.img_dir = img_dir
+ self.scheme = GrapherScheme()
+ self.width = self.scheme.width
+ self.height = self.scheme.height
+ self.bg_color = self.scheme.bg_color
+ self.color_scheme = self.scheme.color_scheme
+ self.force = self.scheme.force
+
+ self.media_list = self.get_media_list()
+ if not os.path.exists(self.img_dir):
+ os.mkdir(self.img_dir)
+ self.path_dict = self.get_path_dict()
+
+ def get_media_list(self):
+ media_list = []
+ for root, dirs, files in os.walk(self.root_dir):
+ if root:
+ for file in files:
+ ext = file.split('.')[-1]
+ media_list.append(root+os.sep+file)
+ return media_list
+
+ def get_path_dict(self):
+ path_dict = {}
+ for media in self.media_list:
+ filename = media.split(os.sep)[-1]
+ name, ext = os.path.splitext(filename)
+ path_dict[media] = self.img_dir + os.sep + filename + '.png'
+ return path_dict
+
+ def process(self):
+ for source, image in self.path_dict.iteritems():
+ if not os.path.exists(image) or self.force:
+ print 'Rendering ', source, ' to ', image, '...'
+ audio = os.path.join(os.path.dirname(__file__), source)
+ decoder = timeside.decoder.FileDecoder(audio)
+
+ waveform = timeside.grapher.WaveformJoyDiv(width=self.width, height=self.height, output=image,
+ bg_color=self.bg_color, color_scheme=self.color_scheme)
+
+ (decoder | waveform).run()
+ print 'frames per pixel = ', waveform.graph.samples_per_pixel
+ waveform.render()
+
+
+if __name__ == '__main__':
+ if len(sys.argv) <= 2:
+ print """
+ Usage : python waveform_batch /path/to/media_dir /path/to/img_dir
+
+ Dependencies : timeside, python, python-numpy, python-gst0.10, gstreamer0.10-plugins-base
+ See http://code.google.com/p/timeside/ for more information.
+ """
+ else:
+ media_dir = sys.argv[-2]
+ img_dir = sys.argv[-1]
+ m = Media2Waveform(media_dir, img_dir)
+ m.process()
projects / timeside.git / commitdiff