--- /dev/null
+# -*- coding: utf-8 -*-
+#
+# Copyright (c) 2007-2010 Guillaume Pellerin <yomguy@parisson.com>
+# Copyright (c) 2010 Olivier Guilyardi <olivier@samalyse.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/>.
+
+
+from timeside.core import Processor, implements, interfacedoc, FixedSizeInputAdapter
+from timeside.api import IGrapher
+from timeside.grapher.core import *
+
+class WaveformImageJoyContour(WaveformImage):
+
+ def __init__(self, image_width, image_height, nframes, samplerate, fft_size, bg_color, color_scheme, ndiv=1, symetry=None):
+ WaveformImage.__init__(self, image_width, image_height, nframes, samplerate, fft_size, bg_color, color_scheme)
+ self.contour = numpy.zeros(self.image_width)
+ self.centroids = numpy.zeros(self.image_width)
+ self.ndiv = ndiv
+ self.x = numpy.r_[0:self.image_width-1:1]
+ self.dx1 = self.x[1]-self.x[0]
+ self.symetry = symetry
+
+ def get_peaks_contour(self, x, peaks, spectral_centroid=None):
+ self.contour[x] = numpy.max(peaks)
+ self.centroids[x] = spectral_centroid
+
+ def mean(self, samples):
+ return numpy.mean(samples)
+
+ def normalize(self, contour):
+ contour = contour-min(contour)
+ return contour/max(contour)
+
+ def draw_peaks_contour(self):
+ contour = self.contour.copy()
+
+ # Smoothing
+ contour = smooth(contour, window_len=16)
+
+ # Normalize
+ contour = self.normalize(contour)
+
+ # Scaling
+ #ratio = numpy.mean(contour)/numpy.sqrt(2)
+ ratio = 1
+ contour = self.normalize(numpy.expm1(contour/ratio))*(1-10**-6)
+
+ # Spline
+ #contour = cspline1d(contour)
+ #contour = cspline1d_eval(contour, self.x, dx=self.dx1, x0=self.x[0])
+
+ if self.symetry:
+ height = int(self.image_height/2)
+ else:
+ height = self.image_height
+
+ # Multicurve rotating
+ for i in range(0,self.ndiv):
+ self.previous_x, self.previous_y = None, None
+
+ #bright_color = 255
+ bright_color = int(255*(1-float(i)/(self.ndiv*2)))
+ bright_color = 255-bright_color+160
+# line_color = self.color_lookup[int(self.centroids[j]*255.0)]
+ line_color = (bright_color,bright_color,bright_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))
+
+ curve = (height-1)*contour
+# curve = contour*(height-2)/2+height/2
+
+ for x in self.x:
+ x = int(x)
+ y = curve[x]
+ if not x == 0:
+ if not self.symetry:
+ self.draw.line([self.previous_x, self.previous_y, x, y], line_color)
+ #self.draw_anti_aliased_pixels(x, y, y, line_color)
+ else:
+ self.draw.line([self.previous_x, self.previous_y+height, x, y+height], line_color)
+ #self.draw_anti_aliased_pixels(x, y+height, y+height, line_color)
+ self.draw.line([self.previous_x, -self.previous_y+height, x, -y+height], line_color)
+ #self.draw_anti_aliased_pixels(x, -y+height, -y+height, line_color)
+ else:
+ if not self.symetry:
+ self.draw.point((x, y), line_color)
+ else:
+ self.draw.point((x, y+height), line_color)
+ self.previous_x, self.previous_y = x, y
+
+ def process(self, frames, eod):
+ if len(frames) != 1:
+ buffer = frames[:,0].copy()
+ buffer.shape = (len(buffer),1)
+ for samples, end in self.pixels_adapter.process(buffer, eod):
+ if self.pixel_cursor < self.image_width:
+ #(spectral_centroid, db_spectrum) = self.spectrum.process(buffer, True)
+ peaks = self.peaks(samples)
+ self.get_peaks_contour(self.pixel_cursor, peaks)
+ self.pixel_cursor += 1
+ if eod:
+ self.draw_peaks_contour()
+
+ def save(self, filename):
+ """ Apply last 2D transforms and write all pixels to the file. """
+ # middle line (0 for none)
+ a = 1
+ 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 = self.image.transpose(Image.FLIP_TOP_BOTTOM)
+ self.image.save(filename)
+
+
+class WaveformJoyDiv(Processor):
+ implements(IGrapher)
+
+ FFT_SIZE = 0x400
+
+ @interfacedoc
+ def __init__(self, width=1024, height=256, bg_color=(136,136,136), color_scheme='default'):
+ self.width = width
+ self.height = height
+ self.bg_color = bg_color
+ self.color_scheme = color_scheme
+ self.graph = None
+ self.ndiv = 4
+ self.symetry = True
+
+ @staticmethod
+ @interfacedoc
+ def id():
+ return "waveform_joydiv"
+
+ @staticmethod
+ @interfacedoc
+ def name():
+ return "Waveform JoyDiv"
+
+ @interfacedoc
+ def set_colors(self, background, scheme):
+ self.bg_color = background
+ self.color_scheme = scheme
+
+ @interfacedoc
+ def setup(self, channels=None, samplerate=None, nframes=None):
+ super(WaveformJoyDiv, self).setup(channels, samplerate, nframes)
+ if self.graph:
+ self.graph = None
+ self.graph = WaveformImageJoyContour(self.width, self.height, self.nframes(), self.samplerate(), self.FFT_SIZE,
+ bg_color=self.bg_color, color_scheme=self.color_scheme, ndiv=self.ndiv, symetry=self.symetry)
+
+ @interfacedoc
+ def process(self, frames, eod=False):
+ self.graph.process(frames, eod)
+ return frames, eod
+
+ @interfacedoc
+ def render(self, output):
+ if output:
+ self.graph.save(output)
+ return self.graph.image
projects / timeside.git / commitdiff