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_udf():
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)
mask = circles != 0.0
udf = snowy.unitize(snowy.generate_udf(mask))
nx, ny = snowy.gradient(udf)
grad = snowy.unitize(nx + ny)
snowy.show(snowy.hstack([circles, udf, grad]))
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]))
yvals = gradient_image[0]
apply_lut = interpolate.interp1d(xvals, yvals, axis=0)
return apply_lut(snowy.unshape(np.clip(elevation_image, 0, 255)))
def create_falloff(w, h, radius=0.4, cx=0.5, cy=0.5):
hw, hh = 0.5 / w, 0.5 / h
x = np.linspace(hw, 1 - hw, w)
y = np.linspace(hh, 1 - hh, h)
u, v = np.meshgrid(x, y, sparse=True)
d2 = (u-cx)**2 + (v-cy)**2
return 1-snowy.unitize(snowy.reshape(d2))
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)
sdf = snowy.unitize(snowy.generate_sdf(circles != 0.0))
stack = snowy.hstack([circles, sdf])
snowy.export(stack, qualify('sdf.png'))
snowy.show(stack)
# Islands
def create_island(seed, gradient, freq=3.5):
w, h = 750, 512
falloff = create_falloff(w, h)
n1 = 1.000 * snowy.generate_noise(w, h, freq*1, seed+0)
n2 = 0.500 * snowy.generate_noise(w, h, freq*2, seed+1)
n3 = 0.250 * snowy.generate_noise(w, h, freq*4, seed+2)
n4 = 0.125 * snowy.generate_noise(w, h, freq*8, seed+3)
elevation = falloff * (falloff / 2 + n1 + n2 + n3 + n4)
mask = elevation < 0.4
