def test_gamma():
source = path('gamma_dalai_lama_gray.jpg')
dalai_lama = snowy.load(source)
snowy.show(dalai_lama)
small = snowy.resize(dalai_lama, height=32)
snowy.save(small, path('small_dalai_lama.png'))
snowy.show(small)
def test_normals():
isle = create_island(10)
height, width, nchan = isle.shape
occlusion = np.empty([height, width, 1])
seconds = timeit.timeit(
lambda: np.copyto(occlusion, sn.compute_skylight(isle)), number=1)
print(f'\ncompute_skylight took {seconds} seconds')
normals = np.empty([height - 1, width - 1, 3])
seconds = timeit.timeit(
lambda: np.copyto(normals, sn.compute_normals(isle)), number=1)
print(f'\ncompute_normals took {seconds} seconds')
normals = sn.resize(normals, 750, 512)
# Flatten the normals according to landmass versus sea.
normals += np.float64([0, 0, 100]) * np.where(isle < 0.0, 1.0, 0.005)
normals /= sn.reshape(np.sqrt(np.sum(normals * normals, 2)))
# Compute the lambertian diffuse factor
lightdir = np.float64([0.2, -0.2, 1])
lightdir /= np.linalg.norm(lightdir)
df = np.clip(np.sum(normals * lightdir, 2), 0, 1)
df = sn.reshape(df)
df *= occlusion
# Apply color LUT
gradient_image = sn.resize(sn.load(path('terrain.png')),
width=1024)[:, :, :3]
def applyColorGradient(elevation):
xvals = np.arange(1024)
yvals = gradient_image[0]
apply_lut = interpolate.interp1d(xvals, yvals, axis=0)
el = np.clip(1023 * elevation, 0, 1023)
return apply_lut(sn.unshape(el))
albedo = applyColorGradient(isle * 0.5 + 0.5)
albedo *= df
# Visualize the lighting layers
normals = 0.5 * (normals + 1.0)
isle = np.dstack([isle, isle, isle])
occlusion = np.dstack([occlusion, occlusion, occlusion])
df = np.dstack([df, df, df])
island_strip = sn.resize(sn.hstack([occlusion, normals, df, albedo]),
height=256)
sn.save(island_strip, 'docs/island_strip.png')
sn.show(island_strip)
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)
mask = warped < 0.1
print("Computing the distance field.")
elevation = snowy.generate_sdf(mask)
elevation /= np.amax(elevation)
print("Computing ambient occlusion.")
occlusion = snowy.compute_skylight(elevation)
occlusion = 0.25 + 0.75 * occlusion
print("Generating normal map.")
normals = snowy.resize(snowy.compute_normals(elevation), width, height)
# Save the landmass portion of the elevation data.
landmass = elevation * np.where(elevation < 0.0, 0.0, 1.0)
snowy.save(trim(landmass), "landmass.png")
# Flatten the normals according to landmass versus sea.
normals += np.float64([0, 0, 1000]) * np.where(elevation < 0.0, 1.0, 0.01)
normals /= snowy.reshape(np.sqrt(np.sum(normals * normals, 2)))
print("Applying diffuse lighting.")
lightdir = np.float64([0.5, -0.5, 1])
lightdir /= np.linalg.norm(lightdir)
lambert = np.sum(normals * lightdir, 2)
lighting = snowy.reshape(lambert) * occlusion
print("Applying color gradient.")
yvals = snowy.load("gradient.png")[0, :, :3]
water_color = np.copy(yvals[126])
yvals[0:128] = water_color
def create_wrap_figures():
ground = snowy.load(qualify('ground.jpg'))
hground = np.hstack([ground, ground])
ground2x2 = np.vstack([hground, hground])
snowy.save(ground2x2, qualify('ground2x2.jpg'))
ground = snowy.blur(ground, radius=14, filter=snowy.LANCZOS)
snowy.save(ground, qualify('blurry_ground_bad.jpg'))
hground = np.hstack([ground, ground])
ground2x2 = np.vstack([hground, hground])
snowy.save(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.save(ground, qualify('blurry_ground_good.jpg'))
hground = np.hstack([ground, ground])
ground2x2 = np.vstack([hground, hground])
snowy.save(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.save(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.save(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.save(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.save(sdf, qualify('tiled_sdf_bad.png'))
generate_page(qualify('index.md'), qualify('index.html'), False)
generate_page(qualify('reference.md'), qualify('reference.html'), True)
# Test rotations and flips
gibbons = snowy.load(qualify('gibbons.jpg'))
gibbons = snowy.resize(gibbons, width=gibbons.shape[1] // 5)
gibbons90 = snowy.rotate(gibbons, 90)
gibbons180 = snowy.rotate(gibbons, 180)
gibbons270 = snowy.rotate(gibbons, 270)
hflipped = snowy.hflip(gibbons)
vflipped = snowy.vflip(gibbons)
snowy.save(
snowy.hstack([gibbons, gibbons180, vflipped],
border_width=4,
border_value=[0.5, 0, 0]), qualify("xforms.png"))
# 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.save(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)
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]))