def create_wrap_figures():
ground = snowy.load(qualify('ground.jpg'))
hground = np.hstack([ground, ground])
ground2x2 = np.vstack([hground, hground])
snowy.export(ground2x2, qualify('ground2x2.jpg'))
ground = snowy.blur(ground, radius=14, filter=snowy.LANCZOS)
snowy.export(ground, qualify('blurry_ground_bad.jpg'))
hground = np.hstack([ground, ground])
ground2x2 = np.vstack([hground, hground])
snowy.export(ground2x2, qualify('blurry_ground2x2_bad.jpg'))
ground = snowy.load(qualify('ground.jpg'))
ground = snowy.blur(ground,
radius=14,
wrapx=True,
wrapy=True,
filter=snowy.LANCZOS)
snowy.export(ground, qualify('blurry_ground_good.jpg'))
hground = np.hstack([ground, ground])
ground2x2 = np.vstack([hground, hground])
snowy.export(ground2x2, qualify('blurry_ground2x2_good.jpg'))
n = snowy.generate_noise(256, 512, frequency=4, seed=42, wrapx=False)
n = 0.5 + 0.5 * np.sign(n) - n
n = np.hstack([n, n])
n = snowy.add_border(n, width=4)
snowy.export(n, qualify('tiled_noise_bad.png'))
n = snowy.generate_noise(256, 512, frequency=4, seed=42, wrapx=True)
n = 0.5 + 0.5 * np.sign(n) - n
n = np.hstack([n, n])
n = snowy.add_border(n, width=4)
snowy.export(n, qualify('tiled_noise_good.png'))
c0 = create_circle(400, 200, 0.3)
c1 = create_circle(400, 200, 0.08, 0.8, 0.8)
circles = np.clip(c0 + c1, 0, 1)
mask = circles != 0.0
sdf = snowy.unitize(snowy.generate_sdf(mask, wrapx=True, wrapy=True))
sdf = np.hstack([sdf, sdf, sdf, sdf])
sdf = snowy.resize(np.vstack([sdf, sdf]), width=512)
sdf = snowy.add_border(sdf)
snowy.export(sdf, qualify('tiled_sdf_good.png'))
sdf = snowy.unitize(snowy.generate_sdf(mask, wrapx=False, wrapy=False))
sdf = np.hstack([sdf, sdf, sdf, sdf])
sdf = snowy.resize(np.vstack([sdf, sdf]), width=512)
sdf = snowy.add_border(sdf)
snowy.export(sdf, qualify('tiled_sdf_bad.png'))
def test_gdf():
"This is a (failed) effort to create a smoother distance field."
c0 = create_circle(200, 200, 0.3)
c1 = create_circle(200, 200, 0.08, 0.8, 0.8)
c0 = np.clip(c0 + c1, 0, 1)
circles = snowy.add_border(c0, value=1)
circles = np.clip(snowy.blur(circles, radius=2), 0, 1)
circles = np.clip(snowy.blur(circles, radius=2), 0, 1)
source = (1.0 - circles) * 2000.0
gdf = np.sqrt(snowy.generate_gdf(source))
gdf = snowy.unitize(gdf)
nx, ny = snowy.gradient(gdf)
grad = snowy.unitize(nx + ny)
snowy.show(snowy.hstack([circles, gdf, grad]))
def test_range():
source = path('../docs/ground.jpg')
ground = snowy.load(source)
assert np.amin(ground) >= 0 and np.amax(ground) <= 1
with tempfile.NamedTemporaryFile() as fp:
target = fp.name + '.png'
snowy.save(ground, target)
show_filename(target)
show_filename(source)
show_array(ground)
blurred = snowy.blur(ground, radius=10)
snowy.show(blurred)
def test_thick():
source = sn.load('tests/sobel_input.png')[:, :, :3]
small_source = sn.resize(source, width=256)
blurred = sn.blur(source, radius=2)
small_blurred = sn.resize(blurred, width=256)
L = skimage_sobel(blurred)
sksobel = np.dstack([L, L, L])
small_sksobel = sn.resize(sksobel, width=256)
L = sn.rgb_to_luminance(blurred)
L = sn.compute_sobel(L)
snsobel = np.dstack([L, L, L])
small_snsobel = sn.resize(snsobel, width=256)
small_sksobel = np.clip(1 - small_sksobel * 40, 0, 1)
small_snsobel = np.clip(1 - small_snsobel * 40, 0, 1)
strip = np.hstack([
small_blurred, small_source * small_sksobel,
small_source * small_snsobel
])
sn.show(strip)
# Test noise generation
n = snowy.generate_noise(100, 100, frequency=4, seed=42, wrapx=True)
n = np.hstack([n, n])
n = 0.5 + 0.5 * n
snowy.show(n)
snowy.export(n, qualify('noise.png'))
# First try minifying grayscale
gibbons = snowy.load(qualify('snowy.jpg'))
gibbons = np.swapaxes(gibbons, 0, 2)
gibbons = np.swapaxes(gibbons[0], 0, 1)
gibbons = snowy.reshape(gibbons)
source = snowy.resize(gibbons, height=200)
blurry = snowy.blur(source, radius=4.0)
diptych_filename = qualify('diptych.png')
snowy.export(snowy.hstack([source, blurry]), diptych_filename)
optimize(diptych_filename)
snowy.show(diptych_filename)
# Next try color
gibbons = snowy.load(qualify('snowy.jpg'))
source = snowy.resize(gibbons, height=200)
blurry = snowy.blur(source, radius=4.0)
diptych_filename = qualify('diptych.png')
snowy.export(snowy.hstack([source, blurry]), diptych_filename)
optimize(diptych_filename)
snowy.show(diptych_filename)
import snowy
source = snowy.open('poodle.png')
source = snowy.resize(source, height=200)
blurry = snowy.blur(source, radius=4.0)
snowy.save(snowy.hstack([source, blurry]), 'diptych.png')
# This snippet does a resize, then a blur, then horizontally concatenates the two images
parrot = snowy.load('parrot.png')
height, width = parrot.shape[:2]
nearest = snowy.resize(parrot, width * 6, filter=snowy.NEAREST)
mitchell = snowy.resize(parrot, width * 6)
snowy.show(snowy.hstack([nearest, mitchell]))
# This snippet first magnifies an image using a nearest-neighbor filter, then using the default Mitchell filter.
parrot = snowy.load('parrot.png')
height, width = parrot.shape[:2]
nearest = snowy.resize(parrot, width * 6, filter=snowy.NEAREST)
mitchell = snowy.resize(parrot, width * 6)
snowy.show(snowy.hstack([nearest, mitchell]))