v0.0.4 ~ 9911235

Snowy

User's Guide | API Reference

Snowy is a tiny module for manipulating and generating floating-point images.

Snowy does not define a special class for images. Instead, images are always three-dimensional numpy arrays in row-major order.

For example, RGB images have shape [height,width,3] and grayscale images have shape [height,width,1]. Snowy provides some utility functions that make it easy to work with other modules (see interop).

Snowy is not an Image IO library, but for convenience it provides load and export functions that have limited support for PNG, EXR, and JPEG.

If you're interested in tone mapping and other HDR operations, check out the hydra module. If you wish to simply load / store raw floating-point data, consider using npy files instead of image files. The relevant functions are numpy.load and numpy.save.

Installing

To install and update snowy, do this:

pip3 install -U snowy

Examples

Resize and blur

This snippet does a resize, then a blur, then horizontally concatenates the two images.

import snowy

source = snowy.load('poodle.png')
source = snowy.resize(source, height=200)
blurry = snowy.blur(source, radius=4.0)
snowy.export(snowy.hstack([source, blurry]), 'diptych.png')

The next 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]))

Rotate and flip

gibbons = snowy.load('gibbons.jpg')
rotated = snowy.rotate(gibbons, 180)
flipped = snowy.vflip(gibbons)
triptych = snowy.hstack([gibbons, rotated, flipped],
    border_width=4, border_value=[0.5,0,0])

Cropping

If you need to crop an image, just use numpy slicing.

For example, this loads an OpenEXR image then crops out the top half:

sunrise = snowy.load('sunrise.exr')
cropped_sunrise = sunrise[:100,:,:]
snowy.show(cropped_sunrise / 50.0) # darken the image

Alpha composition

To copy a section of one image into another, simply use numpy slicing.

However, to achieve "source-over" style alpha blending, using raw numpy math would be cumbersome. Snowy provides compose to make this easier:

icon = snowy.load('snowflake.png')
icon = snowy.resize(snowflake, height=100)
sunset[:100,200:300] = snowy.compose(sunset[:100,200:300], icon)
snowy.show(sunset)

Drop shadows

Combining operations like blur and compose can be used to create a drop shadow:

# Extend the 100x100 snowflake image on 4 sides to give room for blur.
shadow = np.zeros([150, 150, 4])
shadow[25:-25,25:-25,:] = icon

# Invert the colors but not the alpha.
white = shadow.copy()
white[:,:,:3] = 1.0 - white[:,:,:3]

# Blur the shadow, then "strengthen" it.
shadow = snowy.blur(shadow, radius=10.0)
shadow = snowy.compose(shadow, shadow)
shadow = snowy.compose(shadow, shadow)
shadow = snowy.compose(shadow, shadow)

# Compose the white flake onto its shadow.
dropshadow = snowy.compose(shadow, white)

Gradient noise

Snowy's generate_noise function generates a single-channel image whose values are in [-1, +1]. Here we create a square noise image that can be tiled horizontally:

n = snowy.generate_noise(100, 100, frequency=4, seed=42, wrapx=True)
n = np.hstack([n, n])
snowy.show(0.5 + 0.5 * n)

If you're interested in other types of noise, or if you need a super-fast noise generator, you might want to try pyfastnoisesimd.

Distance fields

This example uses generate_sdf to create a signed distance field from a monochrome picture of two circles enclosed by a square. Note the usage of unitize to adjust the values into the [0,1] range.

circles = snowy.load('circles.png')
sdf = snowy.unitize(snowy.generate_sdf(circles != 0.0))
snowy.show(snowy.hstack([circles, sdf]))

Image generation

Combining Snowy's unique features with numpy can be used to create interesting procedural images. The following example creates an elevation map for an imaginary island.

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))

def create_island(seed, 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
    elevation = snowy.unitize(snowy.generate_sdf(mask))
    return (1 - mask) * np.power(elevation, 3.0)

snowy.export(create_island(10), 'island.png')

Wrap modes

Snowy's blur, resize, generate_noise, and generate_sdf functions all take wrapx and wrapy arguments, both of which default to False. These arguments tell Snowy how to sample from outside the boundaries of the source image or noise function.

To help understand these arguments, consider this tileable image and its 2x2 tiling:


Next, let's try blurring the tile naively:

See the seams? Now let's blur it with wrapx and wrapy set to True when we call blur:

Wrappable Gradient Noise

The wrap arguments are also useful for 2D noise. One way of making tileable gradient noise is to sample 3D noise on the surface of a cylinder, torus, or cube. However Snowy can do this more efficiently by generating 2D noise with modulus arithmetic.

Here we created a 128x256 tile using generate_noise without the wrapx argument, then horizontally tiled it twice:

Here's another tiling of gradient noise, but this time the tile was generated with wrapx set to True:

Wrappable Distance Fields

Snowy's generate_sdf function also takes wrap arguments. For example here's a distance field in a 4x2 tiling:

Here's the same distance field, this time with wrapx and wrapy set to True:

Interop

Snowy's algorithms require images to be row-major three-dimensional float64 numpy arrays, with color channels living in the trailing dimension. If you're working with another module that does not follow this convention, consider using one of the following interop functions.