def test_with_transform(self):
x1 = np.arange(9).reshape((3,3)) + 1
x1[:] = 10
x2 = x1.copy()
theta = -np.pi/2
M = np.array([[np.cos(theta), -np.sin(theta), 0],
[np.sin(theta), np.cos(theta), +2],
[0, 0, 1]])
stacked = stack.with_transform([x1, x2], [np.eye(3), M],
weights=[1, 1], order=1)
assert(np.allclose(stacked, 10))
def initial_guess_avg(images, tf_matrices, scale, oshape):
"""From the given low-resolution images and transforms, make an
initial guess of the high-resolution image.
Parameters
----------
images : list of ndarray
Low-resolution images.
tf_matrices : list of (3, 3) ndarray
Transformation matrices that warp the images to the reference
image (usually ``images[0]``).
scale : float
The scale of the high-resolution reconstruction relative to the
low-resolution frames. Typically between 1 and 2.
oshape : tuple of int
Shape of the high-resolution reconstruction.
"""
HH = [x.copy() for x in tf_matrices]
for H in HH:
H[:2, :] *= float(scale)
return stack.with_transform(images, HH, oshape=oshape, order=3)
def initial_guess_avg(images, tf_matrices, scale, oshape):
"""From the given low-resolution images and transforms, make an
initial guess of the high-resolution image.
Parameters
----------
images : list of ndarray
Low-resolution images.
tf_matrices : list of (3, 3) ndarray
Transformation matrices that warp the images to the reference
image (usually ``images[0]``).
scale : float
The scale of the high-resolution reconstruction relative to the
low-resolution frames. Typically between 1 and 2.
oshape : tuple of int
Shape of the high-resolution reconstruction.
"""
HH = [x.copy() for x in tf_matrices]
for H in HH:
H[:2, :] *= float(scale)
return stack.with_transform(images, HH, oshape=oshape, order=3)
import numpy as np
from supreme.io import load_vgg
from supreme.config import data_path
from supreme.register import stack
import matplotlib.pyplot as plt
from scipy.misc import imsave
import os, sys
scale = 1.5
if len(sys.argv) > 1:
vgg_dir = sys.argv[1]
else:
vgg_dir = os.path.join(data_path, 'vgg/sr_sequences/text')
ic = load_vgg(vgg_dir)
HH = [img.info['H'] for img in ic]
for H in HH:
H[:2, :] *= scale
S = stack.with_transform(ic, HH, order=2)
imsave('vgg_stack.png', S)
plt.imshow(S, cmap=plt.cm.gray)
plt.show()