230 lines
8.5 KiB
Python
230 lines
8.5 KiB
Python
"""
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Pure python blurhash decoder with no additional dependencies, for
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both de- and encoding.
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Very close port of the original Swift implementation by Dag Ågren.
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"""
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import math
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# Alphabet for base 83
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alphabet = "0123456789ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz#$%*+,-.:;=?@[]^_{|}~"
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alphabet_values = dict(zip(alphabet, range(len(alphabet))))
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def base83_decode(base83_str):
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"""
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Decodes a base83 string, as used in blurhash, to an integer.
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"""
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value = 0
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for base83_char in base83_str:
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value = value * 83 + alphabet_values[base83_char]
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return value
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def base83_encode(value, length):
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"""
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Decodes an integer to a base83 string, as used in blurhash.
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Length is how long the resulting string should be. Will complain
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if the specified length is too short.
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"""
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if int(value) // (83 ** (length)) != 0:
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raise ValueError("Specified length is too short to encode given value.")
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result = ""
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for i in range(1, length + 1):
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digit = int(value) // (83 ** (length - i)) % 83
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result += alphabet[int(digit)]
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return result
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def srgb_to_linear(value):
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"""
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srgb 0-255 integer to linear 0.0-1.0 floating point conversion.
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"""
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value = float(value) / 255.0
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if value <= 0.04045:
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return value / 12.92
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return math.pow((value + 0.055) / 1.055, 2.4)
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def sign_pow(value, exp):
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"""
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Sign-preserving exponentiation.
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"""
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return math.copysign(math.pow(abs(value), exp), value)
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def linear_to_srgb(value):
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"""
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linear 0.0-1.0 floating point to srgb 0-255 integer conversion.
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"""
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value = max(0.0, min(1.0, value))
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if value <= 0.0031308:
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return int(value * 12.92 * 255 + 0.5)
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return int((1.055 * math.pow(value, 1 / 2.4) - 0.055) * 255 + 0.5)
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def blurhash_components(blurhash):
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"""
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Decodes and returns the number of x and y components in the given blurhash.
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"""
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if len(blurhash) < 6:
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raise ValueError("BlurHash must be at least 6 characters long.")
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# Decode metadata
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size_info = base83_decode(blurhash[0])
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size_y = int(size_info / 9) + 1
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size_x = (size_info % 9) + 1
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return size_x, size_y
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def blurhash_decode(blurhash, width, height, punch = 1.0, linear = False):
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"""
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Decodes the given blurhash to an image of the specified size.
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Returns the resulting image a list of lists of 3-value sRGB 8 bit integer
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lists. Set linear to True if you would prefer to get linear floating point
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RGB back.
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The punch parameter can be used to de- or increase the contrast of the
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resulting image.
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As per the original implementation it is suggested to only decode
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to a relatively small size and then scale the result up, as it
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basically looks the same anyways.
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"""
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if len(blurhash) < 6:
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raise ValueError("BlurHash must be at least 6 characters long.")
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# Decode metadata
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size_info = base83_decode(blurhash[0])
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size_y = int(size_info / 9) + 1
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size_x = (size_info % 9) + 1
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quant_max_value = base83_decode(blurhash[1])
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real_max_value = (float(quant_max_value + 1) / 166.0) * punch
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# Make sure we at least have the right number of characters
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if len(blurhash) != 4 + 2 * size_x * size_y:
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raise ValueError("Invalid BlurHash length.")
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# Decode DC component
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dc_value = base83_decode(blurhash[2:6])
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colours = [(
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srgb_to_linear(dc_value >> 16),
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srgb_to_linear((dc_value >> 8) & 255),
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srgb_to_linear(dc_value & 255)
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)]
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# Decode AC components
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for component in range(1, size_x * size_y):
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ac_value = base83_decode(blurhash[4+component*2:4+(component+1)*2])
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colours.append((
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sign_pow((float(int(ac_value / (19 * 19))) - 9.0) / 9.0, 2.0) * real_max_value,
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sign_pow((float(int(ac_value / 19) % 19) - 9.0) / 9.0, 2.0) * real_max_value,
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sign_pow((float(ac_value % 19) - 9.0) / 9.0, 2.0) * real_max_value
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))
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# Return image RGB values, as a list of lists of lists,
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# consumable by something like numpy or PIL.
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pixels = []
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for y in range(height):
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pixel_row = []
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for x in range(width):
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pixel = [0.0, 0.0, 0.0]
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for j in range(size_y):
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for i in range(size_x):
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basis = math.cos(math.pi * float(x) * float(i) / float(width)) * \
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math.cos(math.pi * float(y) * float(j) / float(height))
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colour = colours[i + j * size_x]
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pixel[0] += colour[0] * basis
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pixel[1] += colour[1] * basis
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pixel[2] += colour[2] * basis
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if linear == False:
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pixel_row.append([
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linear_to_srgb(pixel[0]),
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linear_to_srgb(pixel[1]),
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linear_to_srgb(pixel[2]),
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])
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else:
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pixel_row.append(pixel)
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pixels.append(pixel_row)
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return pixels
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def blurhash_encode(image, components_x = 4, components_y = 4, linear = False):
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"""
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Calculates the blurhash for an image using the given x and y component counts.
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Image should be a 3-dimensional array, with the first dimension being y, the second
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being x, and the third being the three rgb components that are assumed to be 0-255
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srgb integers (incidentally, this is the format you will get from a PIL RGB image).
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You can also pass in already linear data - to do this, set linear to True. This is
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useful if you want to encode a version of your image resized to a smaller size (which
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you should ideally do in linear colour).
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"""
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if components_x < 1 or components_x > 9 or components_y < 1 or components_y > 9:
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raise ValueError("x and y component counts must be between 1 and 9 inclusive.")
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height = float(len(image))
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width = float(len(image[0]))
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# Convert to linear if neeeded
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image_linear = []
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if linear == False:
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for y in range(int(height)):
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image_linear_line = []
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for x in range(int(width)):
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image_linear_line.append([
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srgb_to_linear(image[y][x][0]),
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srgb_to_linear(image[y][x][1]),
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srgb_to_linear(image[y][x][2])
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])
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image_linear.append(image_linear_line)
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else:
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image_linear = image
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# Calculate components
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components = []
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max_ac_component = 0.0
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for j in range(components_y):
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for i in range(components_x):
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norm_factor = 1.0 if (i == 0 and j == 0) else 2.0
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component = [0.0, 0.0, 0.0]
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for y in range(int(height)):
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for x in range(int(width)):
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basis = norm_factor * math.cos(math.pi * float(i) * float(x) / width) * \
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math.cos(math.pi * float(j) * float(y) / height)
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component[0] += basis * image_linear[y][x][0]
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component[1] += basis * image_linear[y][x][1]
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component[2] += basis * image_linear[y][x][2]
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component[0] /= (width * height)
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component[1] /= (width * height)
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component[2] /= (width * height)
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components.append(component)
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if not (i == 0 and j == 0):
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max_ac_component = max(max_ac_component, abs(component[0]), abs(component[1]), abs(component[2]))
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# Encode components
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dc_value = (linear_to_srgb(components[0][0]) << 16) + \
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(linear_to_srgb(components[0][1]) << 8) + \
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linear_to_srgb(components[0][2])
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quant_max_ac_component = int(max(0, min(82, math.floor(max_ac_component * 166 - 0.5))))
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ac_component_norm_factor = float(quant_max_ac_component + 1) / 166.0
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ac_values = []
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for r, g, b in components[1:]:
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ac_values.append(
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int(max(0.0, min(18.0, math.floor(sign_pow(r / ac_component_norm_factor, 0.5) * 9.0 + 9.5)))) * 19 * 19 + \
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int(max(0.0, min(18.0, math.floor(sign_pow(g / ac_component_norm_factor, 0.5) * 9.0 + 9.5)))) * 19 + \
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int(max(0.0, min(18.0, math.floor(sign_pow(b / ac_component_norm_factor, 0.5) * 9.0 + 9.5))))
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)
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# Build final blurhash
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blurhash = ""
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blurhash += base83_encode((components_x - 1) + (components_y - 1) * 9, 1)
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blurhash += base83_encode(quant_max_ac_component, 1)
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blurhash += base83_encode(dc_value, 4)
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for ac_value in ac_values:
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blurhash += base83_encode(ac_value, 2)
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return blurhash
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