class ImageTestCase(TestCase):
def setUp(self):
# numpy array for top-level functions that directly expect it
self.im_np = misc.face(gray=True).astype(
np.float64)[np.newaxis, :768, :768]
# Independent Image object for testing Image methods
self.im = Image(misc.face(gray=True).astype(np.float64)[:768, :768])
def tearDown(self):
pass
def testImShift(self):
# Ensure that the two separate im_translate functions we have return the same thing
# A single shift applied to all images
shifts = np.array([100, 200])
im = self.im.shift(shifts)
im1 = self.im._im_translate(shifts)
# Note the difference in the concept of shifts for _im_translate2 - negative sign
im2 = _im_translate2(self.im_np, -shifts)
# Pure numpy 'shifting'
# 'Shifting' an Image corresponds to a 'roll' of a numpy array - again, note the negated signs and the axes
im3 = np.roll(self.im.asnumpy()[0], -shifts, axis=(0, 1))
self.assertTrue(np.allclose(im.asnumpy(), im1.asnumpy()))
self.assertTrue(np.allclose(im1.asnumpy(), im2.asnumpy()))
self.assertTrue(np.allclose(im1.asnumpy()[0, :, :], im3))
def testArrayImageSource(self):
# An Image can be wrapped in an ArrayImageSource when we need to deal with ImageSource objects.
src = ArrayImageSource(self.im)
im = src.images(start=0, num=np.inf)
self.assertTrue(np.allclose(im.asnumpy(), self.im_np))
class ImageTestCase(TestCase):
def setUp(self):
# numpy array for top-level functions that directly expect it
self.im_np = misc.face(gray=True).astype(
np.float64)[np.newaxis, :768, :768]
# Independent Image object for testing Image methods
self.im = Image(misc.face(gray=True).astype(np.float64)[:768, :768])
# Construct a simple stack of Images
self.n = 3
self.ims_np = np.empty((3, *self.im_np.shape[1:]),
dtype=self.im_np.dtype)
for i in range(self.n):
self.ims_np[i] = self.im_np * (i + 1) / float(self.n)
# Independent Image stack object for testing Image methods
self.ims = Image(self.ims_np)
def tearDown(self):
pass
def testImShift(self):
# Ensure that the two separate im_translate functions we have return the same thing
# A single shift applied to all images
shifts = np.array([100, 200])
im = self.im.shift(shifts)
im1 = self.im._im_translate(shifts)
# Note the difference in the concept of shifts for _im_translate2 - negative sign
im2 = _im_translate2(self.im_np, -shifts)
# Pure numpy 'shifting'
# 'Shifting' an Image corresponds to a 'roll' of a numpy array - again, note the negated signs and the axes
im3 = np.roll(self.im.asnumpy()[0], -shifts, axis=(0, 1))
self.assertTrue(np.allclose(im.asnumpy(), im1.asnumpy()))
self.assertTrue(np.allclose(im1.asnumpy(), im2.asnumpy()))
self.assertTrue(np.allclose(im1.asnumpy()[0, :, :], im3))
def testArrayImageSource(self):
# An Image can be wrapped in an ArrayImageSource when we need to deal with ImageSource objects.
src = ArrayImageSource(self.im)
im = src.images(start=0, num=np.inf)
self.assertTrue(np.allclose(im.asnumpy(), self.im_np))
def testImageSqrt(self):
self.assertTrue(
np.allclose(self.im.sqrt().asnumpy(), np.sqrt(self.im_np)))
self.assertTrue(
np.allclose(self.ims.sqrt().asnumpy(), np.sqrt(self.ims_np)))
def testImageTranspose(self):
self.assertTrue(
np.allclose(self.im.flip_axes().asnumpy(),
np.transpose(self.im_np, (0, 2, 1))))
# This is equivalent to checking np.tranpose(..., (0, 2, 1))
for i in range(self.ims_np.shape[0]):
self.assertTrue(
np.allclose(self.ims.flip_axes()[i], self.ims_np[i].T))
# Check against the contruction.
self.assertTrue(
np.allclose(self.ims.flip_axes()[i],
self.im_np[0].T * (i + 1) / float(self.n)))
