def reconstruct(self, ratio, sr_image):
"""
Reconstruct the SR image by the given ratio.
:param ratio: reconstruct ratio
:type ratio: float
:param sr_image: original SR image
:type sr_image: L{sr_image.SRImage}
:return: reconstructed SR image
:rtype: L{sr_image.SRImage}
"""
# import matplotlib.pyplot as plt
sr_dataset = SRDataSet.from_sr_image(sr_image)
reconstructed_sr_image = sr_image
construct_level = 1 # int(math.log(ratio, ALPHA) + 0.5)
r = ALPHA
for level in xrange(construct_level):
reconstructed_sr_image = self._reconstruct(r,
reconstructed_sr_image,
sr_dataset)
reconstructed_sr_image = sr_image_util.back_project(
reconstructed_sr_image, sr_image, 3, level + 1)
new_sr_dataset = SRDataSet.from_sr_image(reconstructed_sr_image)
# plt.imshow(reconstructed_sr_image)
# plt.show()
sr_dataset.merge(new_sr_dataset)
sys.stdout.write("\rReconstructing %.2f%%" %
(float(level + 1) / construct_level * 100))
sys.stdout.flush()
return reconstructed_sr_image
def reconstruct(self, ratio, sr_image):
"""Reconstruct the SR image by the given ratio.
@param ratio: reconstruct ratio
@type ratio: float
@param sr_image: original SR image
@type sr_image: L{sr_image.SRImage}
@return: reconstructed SR image
@rtype: L{sr_image.SRImage}
"""
sr_dataset = SRDataSet.from_sr_image(sr_image)
reconstructed_sr_image = sr_image
construct_level = int(math.log(ratio, ALPHA) + 0.5)
r = ALPHA
for level in range(construct_level):
reconstructed_sr_image = self._reconstruct(r,
reconstructed_sr_image,
sr_dataset)
reconstructed_sr_image = sr_image_util.back_project(
reconstructed_sr_image, sr_image, 3, level + 1)
new_sr_dataset = SRDataSet.from_sr_image(reconstructed_sr_image)
sr_dataset.merge(new_sr_dataset)
sys.stdout.write("\rReconstructing %.2f%%" %
(float(level + 1) / construct_level * 100))
sys.stdout.flush()
return reconstructed_sr_image
class TestSRDataset(unittest.TestCase):
def setUp(self):
self.sr_dataset = SRDataSet(np.array([[1, 1], [2, 2]]),
np.array([[2, 2], [3, 3]]),
neighbors=1)
def test_add(self):
self.sr_dataset.add(np.array([[3, 3], [4, 4]]),
np.array([[5, 5], [6, 6]]))
expected_low_res_patches = np.array([[1, 1], [2, 2], [3, 3], [4, 4]])
expected_high_res_patches = np.array([[2, 2], [3, 3], [5, 5], [6, 6]])
self.assertTrue(
np.array_equal(expected_low_res_patches,
self.sr_dataset.low_res_patches))
self.assertTrue(
np.array_equal(expected_high_res_patches,
self.sr_dataset.high_res_patches))
def test_query(self):
low_res_patches = np.array([[1, 1]])
high_res_patches = self.sr_dataset.query(low_res_patches)
expected_high_res_patches = np.array([[[2, 2]]])
self.assertTrue(
np.array_equal(expected_high_res_patches, high_res_patches))
def test_from_sr_image(self):
sr_image = sr_image_factory.SRImageFactory.create_sr_image_from(
"test_data/babyface_4.png")
sr_dataset = SRDataSet.from_sr_image(sr_image)
low_res_patches = sr_dataset.low_res_patches
high_res_patches = sr_dataset.high_res_patches
self.assertEqual(np.shape(low_res_patches), np.shape(high_res_patches))
class TestSRDataset(unittest.TestCase):
def setUp(self):
self.sr_dataset = SRDataSet(np.array([[1, 1], [2, 2]]), np.array([[2, 2], [3, 3]]),
neighbors=1)
def test_add(self):
self.sr_dataset.add(np.array([[3, 3], [4, 4]]), np.array([[5, 5], [6, 6]]))
expected_low_res_patches = np.array([[1, 1], [2, 2], [3, 3], [4, 4]])
expected_high_res_patches = np.array([[2, 2], [3, 3], [5, 5], [6, 6]])
self.assertTrue(np.array_equal(expected_low_res_patches, self.sr_dataset.low_res_patches))
self.assertTrue(np.array_equal(expected_high_res_patches, self.sr_dataset.high_res_patches))
def test_query(self):
low_res_patches = np.array([[1, 1]])
high_res_patches = self.sr_dataset.query(low_res_patches)
expected_high_res_patches = np.array([[[2, 2]]])
self.assertTrue(np.array_equal(expected_high_res_patches, high_res_patches))
def test_from_sr_image(self):
sr_image = sr_image_factory.SRImageFactory.create_sr_image_from("test_data/babyface_4.png")
sr_dataset = SRDataSet.from_sr_image(sr_image)
low_res_patches = sr_dataset.low_res_patches
high_res_patches = sr_dataset.high_res_patches
self.assertEqual(np.shape(low_res_patches), np.shape(high_res_patches))
def test_from_sr_image(self):
sr_image = sr_image_factory.SRImageFactory.create_sr_image_from(
"test_data/babyface_4.png")
sr_dataset = SRDataSet.from_sr_image(sr_image)
low_res_patches = sr_dataset.low_res_patches
high_res_patches = sr_dataset.high_res_patches
self.assertEqual(np.shape(low_res_patches), np.shape(high_res_patches))
class TestSRDataset(unittest.TestCase):
def setUp(self):
self.sr_dataset = SRDataSet(np.array([[1, 1], [2, 2]]), np.array([[2, 2], [3, 3]]))
def test_add(self):
self.sr_dataset.add(np.array([[3, 3], [4, 4]]), np.array([[5, 5], [6, 6]]))
expected_low_res_patches = np.array([[1, 1], [2, 2], [3, 3], [4, 4]])
expected_high_res_patches = np.array([[2, 2], [3, 3], [5, 5], [6, 6]])
self.assertTrue(np.array_equal(expected_low_res_patches, self.sr_dataset.low_res_patches))
self.assertTrue(np.array_equal(expected_high_res_patches, self.sr_dataset.high_res_patches))
def test_query(self):
low_res_patches = np.array([[1, 1]])
high_res_patches = self.sr_dataset.query(low_res_patches, neighbors=1)
expected_high_res_patches = np.array([[2, 2]])
self.assertTrue(np.array_equal(expected_high_res_patches, high_res_patches))
def reconstruct(self, ratio, sr_image):
"""Reconstruct the SR image by the given ratio.
@param ratio: reconstruct ratio
@type ratio: float
@param sr_image: original SR image
@type sr_image: L{sr_image.SRImage}
@return: reconstructed SR image
@rtype: L{sr_image.SRImage}
"""
sr_dataset = SRDataSet.from_sr_image(sr_image)
reconstructed_sr_image = sr_image
r = ratio ** (1.0/DEFAULT_RECONSTRUCT_LEVEL)
for level in range(DEFAULT_RECONSTRUCT_LEVEL):
reconstructed_sr_image = self._reconstruct(r, reconstructed_sr_image, sr_dataset)
new_sr_dataset = SRDataSet.from_sr_image(reconstructed_sr_image)
sr_dataset.merge(new_sr_dataset)
return reconstructed_sr_image
def reconstruct(self, ratio, sr_image):
"""Reconstruct the SR image by the given ratio.
@param ratio: reconstruct ratio
@type ratio: float
@param sr_image: original SR image
@type sr_image: L{sr_image.SRImage}
@return: reconstructed SR image
@rtype: L{sr_image.SRImage}
"""
sr_dataset = SRDataSet.from_sr_image(sr_image)
reconstructed_sr_image = sr_image
construct_level = int(math.log(ratio, ALPHA) + 0.5)
r = ALPHA
for level in range(construct_level):
reconstructed_sr_image = self._reconstruct(r, reconstructed_sr_image, sr_dataset)
reconstructed_sr_image = sr_image_util.back_project(reconstructed_sr_image, sr_image,
3, level+1)
new_sr_dataset = SRDataSet.from_sr_image(reconstructed_sr_image)
sr_dataset.merge(new_sr_dataset)
sys.stdout.write("\rReconstructing %.2f%%" % (float(level+1) /
construct_level * 100))
sys.stdout.flush()
return reconstructed_sr_image
def setUp(self):
self.sr_dataset = SRDataSet(np.array([[1, 1], [2, 2]]),
np.array([[2, 2], [3, 3]]),
neighbors=1)
def setUp(self):
self.sr_dataset = SRDataSet(np.array([[1, 1], [2, 2]]), np.array([[2, 2], [3, 3]]))
def test_from_sr_image(self):
sr_image = sr_image_factory.SRImageFactory.create_sr_image_from("test_data/babyface_4.png")
sr_dataset = SRDataSet.from_sr_image(sr_image)
low_res_patches = sr_dataset.low_res_patches
high_res_patches = sr_dataset.high_res_patches
self.assertEqual(np.shape(low_res_patches), np.shape(high_res_patches))
