import blurhash
import PIL.Image
import numpy

PIL.Image.open("cool_cat_small.jpg")
# Result:

blurhash.encode(numpy.array(PIL.Image.open("cool_cat_small.jpg").convert("RGB")))
# Result: 'UBL_:rOpGG-oBUNG,qRj2so|=eE1w^n4S5NH'

PIL.Image.fromarray(numpy.array(blurhash.decode('UBL_:rOpGG-oBUNG,qRj2so|=eE1w^n4S5NH', 128, 128)).astype('uint8'))
# Result:

blurhash.encode(image, components_x = 4, components_y = 4, linear = False):
"""
Calculates the blurhash for an image using the given x and y component counts.

Image should be a 3-dimensional array, with the first dimension being y, the second
being x, and the third being the three rgb components that are assumed to be 0-255 
srgb integers (incidentally, this is the format you will get from a PIL RGB image).

You can also pass in already linear data - to do this, set linear to True. This is
useful if you want to encode a version of your image resized to a smaller size (which
you should ideally do in linear colour).
"""

blurhash.decode(blurhash, width, height, punch = 1.0, linear = False)
"""
Decodes the given blurhash to an image of the specified size.
    
Returns the resulting image a list of lists of 3-value sRGB 8 bit integer
lists. Set linear to True if you would prefer to get linear floating point 
RGB back.

The punch parameter can be used to de- or increase the contrast of the
resulting image.

As per the original implementation it is suggested to only decode
to a relatively small size and then scale the result up, as it
basically looks the same anyways.
"""

blurhash.srgb_to_linear(value):
"""
srgb 0-255 integer to linear 0.0-1.0 floating point conversion.
"""
    
blurhash.linear_to_srgb(value):
"""
linear 0.0-1.0 floating point to srgb 0-255 integer conversion.
"""