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image_show() is now able to use a palette to map quantized images to their original color. This uses PIL (import Image). I also enabled the image to be normalised from 0 to 255 in a more robust way (before this, it was assuming images ranged from 0 to 1).

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Teo de Campos 2013-05-14 17:35:16 +01:00
parent 9fecda46a4
commit a88eadbb35
1 changed files with 30 additions and 15 deletions
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45
GPy/util/visualize.py
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@ -4,6 +4,7 @@ import GPy
import numpy as np
import matplotlib as mpl
import time
import Image
class data_show:
"""
@ -191,38 +192,52 @@ class lvm_dimselect(lvm):
class image_show(data_show):
"""Show a data vector as an image."""
def __init__(self, vals, axes=None, dimensions=(16,16), transpose=False, invert=False, scale=False):
def __init__(self, vals, axes=None, dimensions=(16,16), transpose=False, invert=False, scale=False, palette=[], presetMean = 0., presetSTD = -1., selectImage = 0):
data_show.__init__(self, vals, axes)
self.dimensions = dimensions
self.transpose = transpose
self.invert = invert
self.scale = scale
self.set_image(vals/255.)
self.handle = self.axes.imshow(self.vals, cmap=plt.cm.gray, interpolation='nearest')
self.palette = palette
self.presetMean = presetMean
self.presetSTD = presetSTD
self.selectImage = selectImage # This is used when the y vector contains multiple images concatenated.
self.set_image(vals)
if not self.palette == []: # Can just show the image (self.set_image() took care of setting the palette)
self.handle = self.axes.imshow(self.vals, interpolation='nearest')
else: # Use a boring gray map.
self.handle = self.axes.imshow(self.vals, cmap=plt.cm.gray, interpolation='nearest')
plt.show()
def modify(self, vals):
self.set_image(vals/255.)
#self.handle.remove()
#self.handle = self.axes.imshow(self.vals)
self.set_image(vals)
self.handle.set_array(self.vals)
#self.axes.figure.canvas.draw()
plt.show()
self.axes.figure.canvas.draw() # Teo - original line: plt.show()
def set_image(self, vals):
self.vals = np.reshape(vals, self.dimensions, order='F')
dim = self.dimensions[0] * self.dimensions[1]
self.vals = np.reshape(vals[0,dim*self.selectImage+np.array(range(dim))], self.dimensions, order='F')
if self.transpose:
self.vals = self.vals.T
if not self.scale:
self.vals = self.vals
#if self.invert:
# self.vals = -self.vals
if self.invert:
self.vals = -self.vals
# un-normalizing, for visualisation purposes:
if self.presetSTD >= 0: # The Mean is assumed to be in the range (0,255)
self.vals = self.vals*self.presetSTD + self.presetMean
# Clipping the values:
self.vals[self.vals < 0] = 0
self.vals[self.vals > 255] = 255
else:
self.vals = 255*(self.vals - self.vals.min())/(self.vals.max() - self.vals.min())
if not self.palette == []: # applying using an image palette (e.g. if the image has been quantized)
self.vals = Image.fromarray(self.vals.astype('uint8'))
self.vals.putpalette(self.palette) # palette is a list, must be loaded before calling this function
class mocap_data_show(data_show):