def main_show():
import matplotlib.pyplot as plt
rng = np.random.RandomState(1)
coords = prototype_coords(rng)
img = render_coords(coords)
img3 = render_coords(distort(coords, '3', rng))
img6 = render_coords(distort(coords, '6', rng))
plt.imshow(np.asarray([img, img3, img6]).T,
cmap='gray',
interpolation='nearest')
plt.show()
def main_show():
import matplotlib.pyplot as plt
rng = np.random.RandomState(1)
coords = prototype_coords(rng)
img = render_coords(coords)
img3 = render_coords(distort(coords, '3', rng))
img6 = render_coords(distort(coords, '6', rng))
plt.imshow(np.asarray([img, img3, img6]).T,
cmap='gray',
interpolation='nearest')
plt.show()
def distortion_set(self, N, coords, level, rng):
images = []
labels = []
assert len(coords) == self.n_prototypes
for proto, coord in enumerate(coords):
# --apply the same distortions to each example
rng_copy = copy.deepcopy(rng)
for i in range(N):
dcoord = distort(coord, level, rng_copy)
img = render_coords_uint8_channels(dcoord)
images.append(img)
labels.append(proto)
rng.seed(int(rng_copy.randint(2**30)))
return np.asarray(images), np.asarray(labels)
def distortion_set(self, N, coords, level, rng):
images = []
labels = []
assert len(coords) == self.n_prototypes
for proto, coord in enumerate(coords):
# --apply the same distortions to each example
rng_copy = copy.deepcopy(rng)
for i in range(N):
dcoord = distort(coord, level, rng_copy)
img = render_coords_uint8_channels(dcoord)
images.append(img)
labels.append(proto)
rng.seed(int(rng_copy.randint(2**30)))
return np.asarray(images), np.asarray(labels)
