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)
contour = self.normalize(contour)
# Scaling
- ratio = 0.1
- contour = self.normalize(numpy.expm1(ratio*contour))
- print min(contour), max(contour)
+ #ratio = numpy.mean(contour)/numpy.sqrt(2)
+ ratio = 1
+ contour = self.normalize(numpy.expm1(contour/ratio))
+ # Spline
#contour = cspline1d(contour)
#contour = cspline1d_eval(contour, self.x, dx=self.dx1, x0=self.x[0])
- # Multispline scales
+ # Multicurve rotating
for i in range(0,self.ndiv):
self.previous_x, self.previous_y = None, None
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)
else:
self.draw.point((x, y), line_color)
+ self.draw_anti_aliased_pixels(x, y, y, line_color)
self.previous_x, self.previous_y = x, y
def process(self, frames, eod):