def test_zoom(self):
_T, _Z, _R = translate(), zoom(1.2, 0.7), rotate()
A = _T @ _Z @ _R # TZR
T, Z, R = decompose_affine2d(A)
for expect, out in zip([_T, _Z, _R], [T, Z, R]):
with self.subTest(out=out, expect=expect):
self.assertTrue(np.isclose(expect, out).all())
def test_transforms_graphical(self):
im = PewImage(self.img)
A1, A2, A3 = zoom(1, 1.5), shear(0.3, 0), rotate(45)
_im1 = im.affine_transform(A1)
_im2 = im.affine_transform(A2)
_im3 = im.affine_transform(A3)
centre = np.array(im.image.size) / 2.0
fig, ax = plt.subplots(2, 4, figsize=(12, 3), sharex=True, sharey=True)
ax[0, 0].scatter(*centre, color="r")
# get the max extent of images
x0, x1, y0, y1 = 0, 1, 0, 1
for ix, I in enumerate([im, _im1, _im2, _im3]):
x0, x1, y0, y1 = (
min(x0, np.min(I.extent[:2])),
max(x1, np.max(I.extent[:2])),
min(y0, np.min(I.extent[2:])),
max(y1, np.max(I.extent[2:])),
)
ax[0, ix].imshow(I.image)
ax[1, ix].imshow(I.image, extent=I.extent)
size = np.max(im.image.size) * np.sqrt(2)
ax = ax.flat
for a in ax:
a.set_aspect("equal")
a.axis([x0, x1, y0, y1])
def test_default(self):
fig, ax = plt.subplots(2, 4, sharey=True, sharex=True, figsize=(10, 4))
ax = ax.flat
for ix, A in enumerate([
translate(0.5, 1.0),
zoom(1.0, 0.5),
rotate(30),
shear(0.5, 0),
translate(0.5, 0),
zoom(2, 1),
rotate(-30),
shear(0, 0.5),
]):
vis(A, ax=ax[ix])
plt.tight_layout()
def test_reconstruction(self):
_T, _Z, _R = translate(40, 50), zoom(1.5, 0.5), rotate(-15)
A = _T @ _Z @ _R # TZR
_A = compose_affine2d(*decompose_affine2d(A))
self.assertTrue(np.isclose(A, _A).all())
def test_affine_transform(self):
im = PewImage(self.img)
affine_im = im.affine_transform(rotate(30))