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vistools.py
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vistools.py
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"""This module contains useful tools that makes data visualization easier.
Yujia Li, 03/2013
"""
import numpy as np
import matplotlib.pyplot as plt
def bwpatchview(data, imsz, nrows, gridwidth=1, gridintensity=0, rowmajor=True, ax=None):
"""Display a list of images in grid view.
data: N*D matrix, each row is an image
imsz: 2-D tuple, size of the images
nrows: number of rows to arrange the images in a plot
gridwidth: number of pixels to use for the grid
gridintensity: the intensity value for the grid
rowmajor: are the images stored in a row-major order or coloumn-major order
ax: if provided, the image will be shown on the given axis.
The images are orgainzed in rows from left to right.
"""
N, D = data.shape
sx, sy = imsz
ncols = N // nrows
if N % nrows:
ncols += 1
img = np.ones((sx * nrows + gridwidth * (nrows + 1),
sy * ncols + gridwidth * (ncols + 1))) * gridintensity
for ix in range(0, nrows):
for iy in range(0, ncols):
idx = ix * ncols + iy
if idx >= N:
break
xstart = gridwidth + ix * (sx + gridwidth)
xend = xstart + sx
ystart = gridwidth + iy * (sy + gridwidth)
yend = ystart + sy
if rowmajor:
img[xstart:xend, ystart:yend] = data[idx].reshape(imsz)
else:
img[xstart:xend, ystart:yend] = data[idx].reshape((imsz[1], imsz[0])).T
if ax != None:
ax.imshow(img, cmap='gray', interpolation='nearest')
ax.axis('off')
else:
plt.imshow(img, cmap='gray', interpolation='nearest')
plt.axis('off')
plt.show()
def cpatchview(data, imsz, nrows, gridwidth=1, gridintensity=0, rowmajor=True, ax=None):
"""Display a list of color images in grid view.
data: N*(3*D) matrix, each row is a color image
imsz: 2-D tuple, size of the images, should have prod(imsz)=D
nrows: number of rows to arrange the images in a plot
gridwidth: number of pixels to use for the grid
gridintensity: the intensity value for the grid
rowmajor: specify whether the images are stored in row-major order or
column-major order
ax: if provided, the image will be shown on the given axis.
The images are organized in rows from left to right.
"""
N, D = data.shape
D = D / 3
sx, sy = imsz
ncols = N / nrows
if N % nrows:
ncols += 1
img = np.ones((sx * nrows + gridwidth * (nrows + 1),
sy * ncols + gridwidth * (ncols + 1), 3)) * gridintensity
for ix in range(0, nrows):
for iy in range(0, ncols):
idx = ix * ncols + iy
if idx >= N:
break
xstart = gridwidth + ix * (sx + gridwidth)
xend = xstart + sx
ystart = gridwidth + iy * (sy + gridwidth)
yend = ystart + sy
if rowmajor:
img[xstart:xend, ystart:yend, :] = np.transpose(data[idx].reshape((3,sx,sy)), (1,2,0))
else:
img[xstart:xend, ystart:yend] = np.transpose(data[idx].reshape((3,sy,sx)), (1,2,0))
if ax != None:
ax.imshow(img, interpolation='nearest')
ax.axis('off')
else:
plt.imshow(img, interpolation='nearest')
plt.axis('off')
plt.show()
def listpatchview(data, nrows, gridwidth=1, gridintensity=0, ax=None):
"""Display a list of images in grid view.
data: a list of images of the same size, can be either color or gray
images, but should be consistent.
nrows: number of rows to arrange the images in a plot
gridwidth: number of pixels to use for the grid
gridintensity: the intensity value for the grid
ax: if provided, the image will be shown on the given axis
The images are organized in rows from left to right.
"""
N = len(data)
sx, sy = data[0].shape[:2]
D = sx * sy
ncols = N / nrows
if N % nrows:
ncols += 1
if len(data[0].shape) < 3 or data[0].shape[2] == 1:
n_color = 1
img = np.ones((sx * nrows + gridwidth * (nrows + 1),
sy * ncols + gridwidth * (ncols + 1)),dtype=data[0].dtype) * gridintensity
else:
n_color = 3
assert(data[0].shape[2] == n_color)
img = np.ones((sx * nrows + gridwidth * (nrows + 1),
sy * ncols + gridwidth * (ncols + 1), n_color),dtype=data[0].dtype) * gridintensity
for ix in range(0, nrows):
for iy in range(0, ncols):
idx = ix * ncols + iy
if idx >= N:
break
xstart = gridwidth + ix * (sx + gridwidth)
xend = xstart + sx
ystart = gridwidth + iy * (sy + gridwidth)
yend = ystart + sy
if n_color == 3:
img[xstart:xend, ystart:yend, :] = data[idx]
else:
img[xstart:xend, ystart:yend] = data[idx]
if ax == None:
ax = plt
if n_color == 3:
ax.imshow(img, interpolation='nearest')
else:
ax.imshow(img, cmap='gray', interpolation='nearest')
ax.axis('off')
plt.show()
def plot2dgaussian(mu, sigma, npoints=100, linespec=None, linewidth=1, ax=None, *args, **kwargs):
"""Plot a 2D Gaussian distribution. Showing on the plot are the mean of
the Gaussian and an ellipse corresponding to 1 standard deviation (not
strictly speaking standard deviation, but similar).
"""
eig, Q = np.linalg.eig(sigma)
scale = np.sqrt(eig).reshape(1,2)
x = np.zeros((npoints + 1, 2))
for n in range(npoints):
angle = 2 * np.pi * n / npoints
x[n,:] = mu + (scale * np.array([[np.cos(angle), np.sin(angle)]])).dot(Q.T)
x[npoints,:] = x[0,:]
if ax == None:
ax = plt
if linespec:
ax.plot(x[:,0], x[:,1], linespec, linewidth=linewidth, *args, **kwargs)
else:
ax.plot(x[:,0], x[:,1], linewidth=linewidth, *args, **kwargs)
plt.show()