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Added olivetti faces data set. It required adding netpbmfile.py a bsd licensed pgm file reader from Christoph Gohlke, which doesn't seem to have a spearate installer. Also modified image_show to assume by default that array ordering is python instead of fortran. Modified brendan_faces demo to explicilty force fortran ordering. Notified Teo of change.

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This commit is contained in:
Neil Lawrence 2013-10-14 05:59:15 +01:00
parent de0a5d0e70
commit a92780cb89
5 changed files with 458 additions and 54 deletions
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31
GPy/examples/dimensionality_reduction.py
View file

@ -327,31 +327,52 @@ def mrd_simulation(optimize=True, plot=True, plot_sim=True, **kw):
m.plot_scales("MRD Scales")
return m
def brendan_faces():
from GPy import kern
data = GPy.util.datasets.brendan_faces()
Q = 2
Y = data['Y'][0:-1:10, :]
# Y = data['Y']
Y = data['Y']
Yn = Y - Y.mean()
Yn /= Yn.std()
m = GPy.models.GPLVM(Yn, Q)
# m = GPy.models.BayesianGPLVM(Yn, Q, num_inducing=100)
# optimize
m.constrain('rbf|noise|white', GPy.core.transformations.logexp_clipped())
m.optimize('scg', messages=1, max_f_eval=10000)
m.optimize('scg', messages=1, max_iters=10)
ax = m.plot_latent(which_indices=(0, 1))
y = m.likelihood.Y[0, :]
data_show = GPy.util.visualize.image_show(y[None, :], dimensions=(20, 28), transpose=True, invert=False, scale=False)
data_show = GPy.util.visualize.image_show(y[None, :], dimensions=(20, 28), transpose=True, order='F', invert=False, scale=False)
lvm_visualizer = GPy.util.visualize.lvm(m.X[0, :].copy(), m, data_show, ax)
raw_input('Press enter to finish')
return m
def olivetti_faces():
from GPy import kern
data = GPy.util.datasets.olivetti_faces()
Q = 2
Y = data['Y']
Yn = Y - Y.mean()
Yn /= Yn.std()
m = GPy.models.GPLVM(Yn, Q)
m.optimize('scg', messages=1, max_iters=1000)
ax = m.plot_latent(which_indices=(0, 1))
y = m.likelihood.Y[0, :]
data_show = GPy.util.visualize.image_show(y[None, :], dimensions=(112, 92), transpose=False, invert=False, scale=False)
lvm_visualizer = GPy.util.visualize.lvm(m.X[0, :].copy(), m, data_show, ax)
raw_input('Press enter to finish')
return m
def stick_play(range=None, frame_rate=15):
data = GPy.util.datasets.osu_run1()
# optimize
if range == None:

2
GPy/util/__init__.py
View file

@ -14,3 +14,5 @@ import visualize
import decorators
import classification
import latent_space_visualizations
import netpbmfile

87
GPy/util/datasets.py
View file

@ -8,17 +8,12 @@ import zipfile
import tarfile
import datetime
ipython_notebook = False
if ipython_notebook:
import IPython.core.display
def ipynb_input(varname, prompt=''):
"""Prompt user for input and assign string val to given variable name."""
js_code = ("""
var value = prompt("{prompt}","");
var py_code = "{varname} = '" + value + "'";
IPython.notebook.kernel.execute(py_code);
""").format(prompt=prompt, varname=varname)
return IPython.core.display.Javascript(js_code)
ipython_available=True
try:
import IPython
except ImportError:
ipython_available=False
import sys, urllib
@ -34,8 +29,11 @@ data_path = os.path.join(os.path.dirname(__file__), 'datasets')
default_seed = 10000
overide_manual_authorize=False
neil_url = 'http://staffwww.dcs.shef.ac.uk/people/N.Lawrence/dataset_mirror/'
sam_url = 'http://www.cs.nyu.edu/~roweis/data/'
cmu_url = 'http://mocap.cs.cmu.edu/subjects/'
# Note: there may be a better way of storing data resources. One of the pythonistas will need to take a look.
# Note: there may be a better way of storing data resources, for the
# moment we are storing them in a dictionary.
data_resources = {'ankur_pose_data' : {'urls' : [neil_url + 'ankur_pose_data/'],
'files' : [['ankurDataPoseSilhouette.mat']],
'license' : None,
@ -49,7 +47,7 @@ data_resources = {'ankur_pose_data' : {'urls' : [neil_url + 'ankur_pose_data/'],
'license' : None,
'size' : 51276
},
'brendan_faces' : {'urls' : ['http://www.cs.nyu.edu/~roweis/data/'],
'brendan_faces' : {'urls' : [sam_url],
'files': [['frey_rawface.mat']],
'citation' : 'Frey, B. J., Colmenarez, A and Huang, T. S. Mixtures of Local Linear Subspaces for Face Recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 1998, 32-37, June 1998. Computer Society Press, Los Alamitos, CA.',
'details' : """A video of Brendan Frey's face popularized as a benchmark for visualization by the Locally Linear Embedding.""",
@ -93,6 +91,12 @@ The database was created with funding from NSF EIA-0196217.""",
'details' : """Data from the textbook 'A First Course in Machine Learning'. Available from http://www.dcs.gla.ac.uk/~srogers/firstcourseml/.""",
'license' : None,
'size' : 21949154},
'olivetti_faces' : {'urls' : [neil_url + 'olivetti_faces/', sam_url],
'files' : [['att_faces.zip'], ['olivettifaces.mat']],
'citation' : 'Ferdinando Samaria and Andy Harter, Parameterisation of a Stochastic Model for Human Face Identification. Proceedings of 2nd IEEE Workshop on Applications of Computer Vision, Sarasota FL, December 1994',
'details' : """Olivetti Research Labs Face data base, acquired between December 1992 and December 1994 in the Olivetti Research Lab, Cambridge (which later became AT&T Laboratories, Cambridge). When using these images please give credit to AT&T Laboratories, Cambridge. """,
'license': None,
'size' : 8561331},
'olympic_marathon_men' : {'urls' : [neil_url + 'olympic_marathon_men/'],
'files' : [['olympicMarathonTimes.csv']],
'citation' : None,
@ -144,23 +148,32 @@ The database was created with funding from NSF EIA-0196217.""",
}
def prompt_user():
def prompt_user(prompt):
"""Ask user for agreeing to data set licenses."""
# raw_input returns the empty string for "enter"
yes = set(['yes', 'y'])
no = set(['no','n'])
choice = ''
if ipython_notebook:
ipynb_input(choice, prompt='provide your answer here')
else:
try:
print(prompt)
choice = raw_input().lower()
# would like to test for exception here, but not sure if we can do that without importing IPython
except:
print('Stdin is not implemented.')
print('You need to set')
print('overide_manual_authorize=True')
print('to proceed with the download. Please set that variable and continue.')
raise
if choice in yes:
return True
elif choice in no:
return False
else:
sys.stdout.write("Please respond with 'yes', 'y' or 'no', 'n'")
return prompt_user()
print("Your response was a " + choice)
print("Please respond with 'yes', 'y' or 'no', 'n'")
#return prompt_user()
def data_available(dataset_name=None):
@ -212,15 +225,14 @@ def authorize_download(dataset_name=None):
print('You must also agree to the following license:')
print(dr['license'])
print('')
print('Do you wish to proceed with the download? [yes/no]')
return prompt_user()
return prompt_user('Do you wish to proceed with the download? [yes/no]')
def download_data(dataset_name=None):
"""Check with the user that the are happy with terms and conditions for the data set, then download it."""
dr = data_resources[dataset_name]
if not authorize_download(dataset_name):
return False
raise Exception("Permission to download data set denied.")
if dr.has_key('suffices'):
for url, files, suffices in zip(dr['urls'], dr['files'], dr['suffices']):
@ -489,12 +501,12 @@ def ripley_synth(data_set='ripley_prnn_data'):
return data_details_return({'X': X, 'y': y, 'Xtest': Xtest, 'ytest': ytest, 'info': 'Synthetic data generated by Ripley for a two class classification problem.'}, data_set)
def osu_run1(data_set='osu_run1', sample_every=4):
path = os.path.join(data_path, data_set)
if not data_available(data_set):
download_data(data_set)
zip = zipfile.ZipFile(os.path.join(data_path, data_set, 'run1TXT.ZIP'), 'r')
path = os.path.join(data_path, data_set)
for name in zip.namelist():
zip.extract(name, path)
zip = zipfile.ZipFile(os.path.join(data_path, data_set, 'run1TXT.ZIP'), 'r')
for name in zip.namelist():
zip.extract(name, path)
Y, connect = GPy.util.mocap.load_text_data('Aug210106', path)
Y = Y[0:-1:sample_every, :]
return data_details_return({'Y': Y, 'connect' : connect}, data_set)
@ -579,6 +591,24 @@ def toy_linear_1d_classification(seed=default_seed):
X = (np.r_[x1, x2])[:, None]
return {'X': X, 'Y': sample_class(2.*X), 'F': 2.*X, 'seed' : seed}
def olivetti_faces(data_set='olivetti_faces'):
path = os.path.join(data_path, data_set)
if not data_available(data_set):
download_data(data_set)
zip = zipfile.ZipFile(os.path.join(path, 'att_faces.zip'), 'r')
for name in zip.namelist():
zip.extract(name, path)
Y = []
lbls = []
for subject in range(40):
for image in range(10):
image_path = os.path.join(path, 'orl_faces', 's'+str(subject+1), str(image+1) + '.pgm')
Y.append(GPy.util.netpbmfile.imread(image_path).flatten())
lbls.append(subject)
Y = np.asarray(Y)
lbls = np.asarray(lbls)[:, None]
return data_details_return({'Y': Y, 'lbls' : lbls, 'info': "ORL Faces processed to 64x64 images."}, data_set)
def olympic_100m_men(data_set='rogers_girolami_data'):
if not data_available(data_set):
download_data(data_set)
@ -586,7 +616,8 @@ def olympic_100m_men(data_set='rogers_girolami_data'):
tar_file = os.path.join(path, 'firstcoursemldata.tar.gz')
tar = tarfile.open(tar_file)
print('Extracting file.')
tar.extractall(path=path)
tar.extractall(path=path)
tar.close()
olympic_data = scipy.io.loadmat(os.path.join(data_path, data_set, 'data', 'olympics.mat'))['male100']

331
GPy/util/netpbmfile.py Normal file
View file

@ -0,0 +1,331 @@
#!/usr/bin/env python
# -*- coding: utf-8 -*-
# netpbmfile.py
# Copyright (c) 2011-2013, Christoph Gohlke
# Copyright (c) 2011-2013, The Regents of the University of California
# Produced at the Laboratory for Fluorescence Dynamics.
# All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are met:
#
# * Redistributions of source code must retain the above copyright
# notice, this list of conditions and the following disclaimer.
# * Redistributions in binary form must reproduce the above copyright
# notice, this list of conditions and the following disclaimer in the
# documentation and/or other materials provided with the distribution.
# * Neither the name of the copyright holders nor the names of any
# contributors may be used to endorse or promote products derived
# from this software without specific prior written permission.
#
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
# ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE
# LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
# CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
# SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
# INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
# CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
# ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
# POSSIBILITY OF SUCH DAMAGE.
"""Read and write image data from respectively to Netpbm files.
This implementation follows the Netpbm format specifications at
http://netpbm.sourceforge.net/doc/. No gamma correction is performed.
The following image formats are supported: PBM (bi-level), PGM (grayscale),
PPM (color), PAM (arbitrary), XV thumbnail (RGB332, read-only).
:Author:
`Christoph Gohlke <http://www.lfd.uci.edu/~gohlke/>`_
:Organization:
Laboratory for Fluorescence Dynamics, University of California, Irvine
:Version: 2013.01.18
Requirements
------------
* `CPython 2.7, 3.2 or 3.3 <http://www.python.org>`_
* `Numpy 1.7 <http://www.numpy.org>`_
* `Matplotlib 1.2 <http://www.matplotlib.org>`_ (optional for plotting)
Examples
--------
>>> im1 = numpy.array([[0, 1],[65534, 65535]], dtype=numpy.uint16)
>>> imsave('_tmp.pgm', im1)
>>> im2 = imread('_tmp.pgm')
>>> assert numpy.all(im1 == im2)
"""
from __future__ import division, print_function
import sys
import re
import math
from copy import deepcopy
import numpy
__version__ = '2013.01.18'
__docformat__ = 'restructuredtext en'
__all__ = ['imread', 'imsave', 'NetpbmFile']
def imread(filename, *args, **kwargs):
"""Return image data from Netpbm file as numpy array.
`args` and `kwargs` are arguments to NetpbmFile.asarray().
Examples
--------
>>> image = imread('_tmp.pgm')
"""
try:
netpbm = NetpbmFile(filename)
image = netpbm.asarray()
finally:
netpbm.close()
return image
def imsave(filename, data, maxval=None, pam=False):
"""Write image data to Netpbm file.
Examples
--------
>>> image = numpy.array([[0, 1],[65534, 65535]], dtype=numpy.uint16)
>>> imsave('_tmp.pgm', image)
"""
try:
netpbm = NetpbmFile(data, maxval=maxval)
netpbm.write(filename, pam=pam)
finally:
netpbm.close()
class NetpbmFile(object):
"""Read and write Netpbm PAM, PBM, PGM, PPM, files."""
_types = {b'P1': b'BLACKANDWHITE', b'P2': b'GRAYSCALE', b'P3': b'RGB',
b'P4': b'BLACKANDWHITE', b'P5': b'GRAYSCALE', b'P6': b'RGB',
b'P7 332': b'RGB', b'P7': b'RGB_ALPHA'}
def __init__(self, arg=None, **kwargs):
"""Initialize instance from filename, open file, or numpy array."""
for attr in ('header', 'magicnum', 'width', 'height', 'maxval',
'depth', 'tupltypes', '_filename', '_fh', '_data'):
setattr(self, attr, None)
if arg is None:
self._fromdata([], **kwargs)
elif isinstance(arg, basestring):
self._fh = open(arg, 'rb')
self._filename = arg
self._fromfile(self._fh, **kwargs)
elif hasattr(arg, 'seek'):
self._fromfile(arg, **kwargs)
self._fh = arg
else:
self._fromdata(arg, **kwargs)
def asarray(self, copy=True, cache=False, **kwargs):
"""Return image data from file as numpy array."""
data = self._data
if data is None:
data = self._read_data(self._fh, **kwargs)
if cache:
self._data = data
else:
return data
return deepcopy(data) if copy else data
def write(self, arg, **kwargs):
"""Write instance to file."""
if hasattr(arg, 'seek'):
self._tofile(arg, **kwargs)
else:
with open(arg, 'wb') as fid:
self._tofile(fid, **kwargs)
def close(self):
"""Close open file. Future asarray calls might fail."""
if self._filename and self._fh:
self._fh.close()
self._fh = None
def __del__(self):
self.close()
def _fromfile(self, fh):
"""Initialize instance from open file."""
fh.seek(0)
data = fh.read(4096)
if (len(data) < 7) or not (b'0' < data[1:2] < b'8'):
raise ValueError("Not a Netpbm file:\n%s" % data[:32])
try:
self._read_pam_header(data)
except Exception:
try:
self._read_pnm_header(data)
except Exception:
raise ValueError("Not a Netpbm file:\n%s" % data[:32])
def _read_pam_header(self, data):
"""Read PAM header and initialize instance."""
regroups = re.search(
b"(^P7[\n\r]+(?:(?:[\n\r]+)|(?:#.*)|"
b"(HEIGHT\s+\d+)|(WIDTH\s+\d+)|(DEPTH\s+\d+)|(MAXVAL\s+\d+)|"
b"(?:TUPLTYPE\s+\w+))*ENDHDR\n)", data).groups()
self.header = regroups[0]
self.magicnum = b'P7'
for group in regroups[1:]:
key, value = group.split()
setattr(self, unicode(key).lower(), int(value))
matches = re.findall(b"(TUPLTYPE\s+\w+)", self.header)
self.tupltypes = [s.split(None, 1)[1] for s in matches]
def _read_pnm_header(self, data):
"""Read PNM header and initialize instance."""
bpm = data[1:2] in b"14"
regroups = re.search(b"".join((
b"(^(P[123456]|P7 332)\s+(?:#.*[\r\n])*",
b"\s*(\d+)\s+(?:#.*[\r\n])*",
b"\s*(\d+)\s+(?:#.*[\r\n])*" * (not bpm),
b"\s*(\d+)\s(?:\s*#.*[\r\n]\s)*)")), data).groups() + (1, ) * bpm
self.header = regroups[0]
self.magicnum = regroups[1]
self.width = int(regroups[2])
self.height = int(regroups[3])
self.maxval = int(regroups[4])
self.depth = 3 if self.magicnum in b"P3P6P7 332" else 1
self.tupltypes = [self._types[self.magicnum]]
def _read_data(self, fh, byteorder='>'):
"""Return image data from open file as numpy array."""
fh.seek(len(self.header))
data = fh.read()
dtype = 'u1' if self.maxval < 256 else byteorder + 'u2'
depth = 1 if self.magicnum == b"P7 332" else self.depth
shape = [-1, self.height, self.width, depth]
size = numpy.prod(shape[1:])
if self.magicnum in b"P1P2P3":
data = numpy.array(data.split(None, size)[:size], dtype)
data = data.reshape(shape)
elif self.maxval == 1:
shape[2] = int(math.ceil(self.width / 8))
data = numpy.frombuffer(data, dtype).reshape(shape)
data = numpy.unpackbits(data, axis=-2)[:, :, :self.width, :]
else:
data = numpy.frombuffer(data, dtype)
data = data[:size * (data.size // size)].reshape(shape)
if data.shape[0] < 2:
data = data.reshape(data.shape[1:])
if data.shape[-1] < 2:
data = data.reshape(data.shape[:-1])
if self.magicnum == b"P7 332":
rgb332 = numpy.array(list(numpy.ndindex(8, 8, 4)), numpy.uint8)
rgb332 *= [36, 36, 85]
data = numpy.take(rgb332, data, axis=0)
return data
def _fromdata(self, data, maxval=None):
"""Initialize instance from numpy array."""
data = numpy.array(data, ndmin=2, copy=True)
if data.dtype.kind not in "uib":
raise ValueError("not an integer type: %s" % data.dtype)
if data.dtype.kind == 'i' and numpy.min(data) < 0:
raise ValueError("data out of range: %i" % numpy.min(data))
if maxval is None:
maxval = numpy.max(data)
maxval = 255 if maxval < 256 else 65535
if maxval < 0 or maxval > 65535:
raise ValueError("data out of range: %i" % maxval)
data = data.astype('u1' if maxval < 256 else '>u2')
self._data = data
if data.ndim > 2 and data.shape[-1] in (3, 4):
self.depth = data.shape[-1]
self.width = data.shape[-2]
self.height = data.shape[-3]
self.magicnum = b'P7' if self.depth == 4 else b'P6'
else:
self.depth = 1
self.width = data.shape[-1]
self.height = data.shape[-2]
self.magicnum = b'P5' if maxval > 1 else b'P4'
self.maxval = maxval
self.tupltypes = [self._types[self.magicnum]]
self.header = self._header()
def _tofile(self, fh, pam=False):
"""Write Netbm file."""
fh.seek(0)
fh.write(self._header(pam))
data = self.asarray(copy=False)
if self.maxval == 1:
data = numpy.packbits(data, axis=-1)
data.tofile(fh)
def _header(self, pam=False):
"""Return file header as byte string."""
if pam or self.magicnum == b'P7':
header = "\n".join((
"P7",
"HEIGHT %i" % self.height,
"WIDTH %i" % self.width,
"DEPTH %i" % self.depth,
"MAXVAL %i" % self.maxval,
"\n".join("TUPLTYPE %s" % unicode(i) for i in self.tupltypes),
"ENDHDR\n"))
elif self.maxval == 1:
header = "P4 %i %i\n" % (self.width, self.height)
elif self.depth == 1:
header = "P5 %i %i %i\n" % (self.width, self.height, self.maxval)
else:
header = "P6 %i %i %i\n" % (self.width, self.height, self.maxval)
if sys.version_info[0] > 2:
header = bytes(header, 'ascii')
return header
def __str__(self):
"""Return information about instance."""
return unicode(self.header)
if sys.version_info[0] > 2:
basestring = str
unicode = lambda x: str(x, 'ascii')
if __name__ == "__main__":
# Show images specified on command line or all images in current directory
from glob import glob
from matplotlib import pyplot
files = sys.argv[1:] if len(sys.argv) > 1 else glob('*.p*m')
for fname in files:
try:
pam = NetpbmFile(fname)
img = pam.asarray(copy=False)
if False:
pam.write('_tmp.pgm.out', pam=True)
img2 = imread('_tmp.pgm.out')
assert numpy.all(img == img2)
imsave('_tmp.pgm.out', img)
img2 = imread('_tmp.pgm.out')
assert numpy.all(img == img2)
pam.close()
except ValueError as e:
print(fname, e)
continue
_shape = img.shape
if img.ndim > 3 or (img.ndim > 2 and img.shape[-1] not in (3, 4)):
img = img[0]
cmap = 'gray' if pam.maxval > 1 else 'binary'
pyplot.imshow(img, cmap, interpolation='nearest')
pyplot.title("%s %s %s %s" % (fname, unicode(pam.magicnum),
_shape, img.dtype))
pyplot.show()

61
GPy/util/visualize.py
View file

@ -246,17 +246,36 @@ class lvm_dimselect(lvm):
class image_show(matplotlib_show):
"""Show a data vector as an image."""
def __init__(self, vals, axes=None, dimensions=(16,16), transpose=False, invert=False, scale=False, palette=[], presetMean = 0., presetSTD = -1., selectImage=0):
"""Show a data vector as an image. This visualizer rehapes the output vector and displays it as an image.
:param vals: the values of the output to display.
:type vals: ndarray
:param axes: the axes to show the output on.
:type vals: axes handle
:param dimensions: the dimensions that the image needs to be transposed to for display.
:type dimensions: tuple
:param transpose: whether to transpose the image before display.
:type bool: default is False.
:param order: whether array is in Fortan ordering ('F') or Python ordering ('C'). Default is python ('C').
:type order: string
:param invert: whether to invert the pixels or not (default False).
:type invert: bool
:param palette: a palette to use for the image.
:param preset_mean: the preset mean of a scaled image.
:type preset_mean: double
:param preset_std: the preset standard deviation of a scaled image.
:type preset_std: double"""
def __init__(self, vals, axes=None, dimensions=(16,16), transpose=False, order='C', invert=False, scale=False, palette=[], preset_mean = 0., preset_std = -1., select_image=0):
matplotlib_show.__init__(self, vals, axes)
self.dimensions = dimensions
self.transpose = transpose
self.order = order
self.invert = invert
self.scale = scale
self.palette = palette
self.presetMean = presetMean
self.presetSTD = presetSTD
self.selectImage = selectImage # This is used when the y vector contains multiple images concatenated.
self.preset_mean = preset_mean
self.preset_std = preset_std
self.select_image = select_image # This is used when the y vector contains multiple images concatenated.
self.set_image(self.vals)
if not self.palette == []: # Can just show the image (self.set_image() took care of setting the palette)
@ -272,22 +291,22 @@ class image_show(matplotlib_show):
def set_image(self, vals):
dim = self.dimensions[0] * self.dimensions[1]
nImg = np.sqrt(vals[0,].size/dim)
if nImg > 1 and nImg.is_integer(): # Show a mosaic of images
nImg = np.int(nImg)
self.vals = np.zeros((self.dimensions[0]*nImg, self.dimensions[1]*nImg))
for iR in range(nImg):
for iC in range(nImg):
currImgId = iR*nImg + iC
currImg = np.reshape(vals[0,dim*currImgId+np.array(range(dim))], self.dimensions, order='F')
firstRow = iR*self.dimensions[0]
lastRow = (iR+1)*self.dimensions[0]
firstCol = iC*self.dimensions[1]
lastCol = (iC+1)*self.dimensions[1]
self.vals[firstRow:lastRow, firstCol:lastCol] = currImg
num_images = np.sqrt(vals[0,].size/dim)
if num_images > 1 and num_images.is_integer(): # Show a mosaic of images
num_images = np.int(num_images)
self.vals = np.zeros((self.dimensions[0]*num_images, self.dimensions[1]*num_images))
for iR in range(num_images):
for iC in range(num_images):
cur_img_id = iR*num_images + iC
cur_img = np.reshape(vals[0,dim*cur_img_id+np.array(range(dim))], self.dimensions, order=self.order)
first_row = iR*self.dimensions[0]
last_row = (iR+1)*self.dimensions[0]
first_col = iC*self.dimensions[1]
last_col = (iC+1)*self.dimensions[1]
self.vals[first_row:last_row, first_col:last_col] = cur_img
else:
self.vals = np.reshape(vals[0,dim*self.selectImage+np.array(range(dim))], self.dimensions, order='F')
self.vals = np.reshape(vals[0,dim*self.select_image+np.array(range(dim))], self.dimensions, order=self.order)
if self.transpose:
self.vals = self.vals.T
# if not self.scale:
@ -296,8 +315,8 @@ class image_show(matplotlib_show):
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
if self.preset_std >= 0: # The Mean is assumed to be in the range (0,255)
self.vals = self.vals*self.preset_std + self.preset_mean
# Clipping the values:
self.vals[self.vals < 0] = 0
self.vals[self.vals > 255] = 255