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"""
This module contains common visualization functions
used to report results of the models.
"""
from functools import partial
import numpy as np
def horiz_merge(left, right):
"""
merges two images, left and right horizontally to obtain
a bigger image containing both.
Parameters
---------
left: 2D or 3D numpy array
left image.
2D for grayscale.
3D for color.
right : numpy array array
right image.
2D for grayscale
3D for color.
Returns
-------
numpy array (2D or 3D depending on left and right)
"""
assert left.shape[0] == right.shape[0]
assert left.shape[2:] == right.shape[2:]
shape = (left.shape[0], left.shape[1] + right.shape[1],) + left.shape[2:]
im_merge = np.zeros(shape)
im_merge[:, 0:left.shape[1]] = left
im_merge[:, left.shape[1]:] = right
return im_merge
def vert_merge(top, bottom):
"""
merges two images, top and bottom vertically to obtain
a bigger image containing both.
Parameters
---------
top: 2D or 3D numpy array
top image.
2D for grayscale.
3D for color.
bottom : numpy array array
bottom image.
2D for grayscale
3D for color.
Returns
-------
numpy array (2D or 3D depending on left and right)
"""
im = horiz_merge(top, bottom)
if len(im.shape) == 2:
im = im.transpose((1, 0))
elif len(im.shape) == 3:
im = im.transpose((1, 0, 2))
return im
def grid_of_images(M, border=0, bordercolor=[0.0, 0.0, 0.0], shape=None, normalize=False):
"""
Draw a grid of images from M
The order in the grid which corresponds to the order in M
is starting from top to bottom then left to right.
Parameters
----------
M : numpy array
if 3D, convert it to 4D, the shape will be interpreted as (nb_images, h, w) and converted to (nb_images, 1, h, w).
if 4D, consider it as colored or grayscale
- if the shape is (nb_images, nb_colors, h, w), it is converted to (nb_images, h, w, nb_colors)
- otherwise, if it already (nb_images, h, w, nb_colors), use it as it is.
- nb_colors can be 1 (grayscale) or 3 (colors).
border: int
thickness of border(default=0)
shape: tuple (nb_cols, nb_rows)
shape of the grid
by default make a square shape
(in that case, it is possible that not all images from M will be part of the grid).
normalize: bool(default=False)
whether to normalize the pixel values of each image independently
by min and max. if False, clip the values of pixels to 0 and 1
without normalizing.
Returns
-------
3D numpy array of shape (h, w, 3)
(with a color channel regardless of whether the original images were grayscale or colored)
"""
if len(M.shape) == 3:
M = M[:, :, :, np.newaxis]
if M.shape[-1] not in (1, 3):
M = M.transpose((0, 2, 3, 1))
if M.shape[-1] == 1:
M = np.ones((1, 1, 1, 3)) * M
bordercolor = np.array(bordercolor)[None, None, :]
numimages = len(M)
M = M.copy()
if normalize:
for i in range(M.shape[0]):
M[i] -= M[i].flatten().min()
M[i] /= M[i].flatten().max()
else:
M = np.clip(M, 0, 1)
height, width, color = M[0].shape
assert color == 3, 'Nb of color channels are {}'.format(color)
if shape is None:
n0 = np.int32(np.ceil(np.sqrt(numimages)))
n1 = np.int32(np.ceil(np.sqrt(numimages)))
else:
n0 = shape[0]
n1 = shape[1]
im = np.array(bordercolor) * np.ones(
((height + border) * n1 + border, (width + border) * n0 + border, 1), dtype='<f8')
# shape = (n0, n1)
# j corresponds to rows in the grid, n1 should correspond to nb of rows
# i corresponds to columns in the grid, n0 should correspond to nb of cols
# M should be such that the first n1 examples correspond to row 1,
# next n1 examples correspond to row 2, etc. that is, M first axis
# can be reshaped to (n1, n0)
for i in range(n0):
for j in range(n1):
if i * n1 + j < numimages:
im[j * (height + border) + border:(j + 1) * (height + border) + border,
i * (width + border) + border:(i + 1) * (width + border) + border, :] = np.concatenate((
np.concatenate((M[i * n1 + j, :, :, :],
bordercolor * np.ones((height, border, 3), dtype=float)), 1),
bordercolor * np.ones((border, width + border, 3), dtype=float)
), 0)
return im
grid_of_images_default = partial(grid_of_images, border=1, bordercolor=(0.3, 0, 0))
def reshape_to_images(x, input_shape=None):
"""
a function that takes a numpy array and try to
reshape it to an array of images that would
be compatible with the function grid_of_images.
Two cases are considered.
if x is a 2D numpy array, it uses input_shape:
- x can either be (nb_examples, nb_features) or (nb_features, nb_examples)
- nb_features should be prod(input_shape)
- the nb_features dim is then expanded to have :
(nb_examples, h, w, nb_channels), sorted input_shape shoud
be (h, w, nb_channels).
if x is a 4D numpy array:
- if the first tensor dim is 1 or 3 like e.g. (1, a, b, c), then assume it is
color channel and transform to (a, 1, b, c)
- if the second tensor dim is 1 or 3, leave x it as it is
- if the third tensor dim is 1 or 3, like e.g. (a, b, 1, c), then assume it is
color channel and transform to (c, 1, a, b)
- if the fourth tensor dim is 1 or 3, like e.g. (a, b, c, 1), then assume it is
color channel and transform to (c, 1, a, b)
Parameters
----------
x : numpy array
input to be reshape
input_shape : tuple needed only when x is 2D numpy array
"""
if len(x.shape) == 2:
assert input_shape is not None
if x.shape[0] == np.prod(input_shape):
x = x.T
x = x.reshape((x.shape[0],) + input_shape)
x = x.transpose((0, 2, 3, 1))
return x
elif x.shape[1] == np.prod(input_shape):
x = x.reshape((x.shape[0],) + input_shape)
x = x.transpose((0, 2, 3, 1))
return x
else:
raise ValueError('Cant recognize this shape : {}'.format(x.shape))
elif len(x.shape) == 4:
if x.shape[0] in (1, 3):
x = x.transpose((1, 0, 2, 3))
return x
elif x.shape[1] in (1, 3):
return x
elif x.shape[2] in (1, 3):
x = x.transpose((3, 2, 0, 1))
return x
elif x.shape[3] in (1, 3):
x = x.transpose((2, 3, 0, 1))
return x
else:
raise ValueError('Cant recognize a shape of size : {}'.format(len(x.shape)))
else:
raise ValueError('Cant recognize a shape of size : {}'.format(len(x.shape)))
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