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)))