# -*- coding: utf-8 -*-
# Copyright (C) 2012, Almar Klein, Ant1, Marius van Voorden
#
# This code is subject to the (new) BSD license:
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are met:
# * Redistributions of source code must retain the above copyright
# notice, this list of conditions and the following disclaimer.
# * Redistributions in binary form must reproduce the above copyright
# notice, this list of conditions and the following disclaimer in the
# documentation and/or other materials provided with the distribution.
# * Neither the name of the <organization> nor the
# names of its contributors may be used to endorse or promote products
# derived from this software without specific prior written permission.
#
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
# ARE DISCLAIMED. IN NO EVENT SHALL <COPYRIGHT HOLDER> BE LIABLE FOR ANY
# DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
# (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
# LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND
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""" Module images2gif
Provides functionality for reading and writing animated GIF images.
Use writeGif to write a series of numpy arrays or PIL images as an
animated GIF. Use readGif to read an animated gif as a series of numpy
arrays.
Note that since July 2004, all patents on the LZW compression patent have
expired. Therefore the GIF format may now be used freely.
Acknowledgements:
Many thanks to Ant1 for:
* noting the use of "palette=PIL.Image.ADAPTIVE", which significantly
improves the results.
* the modifications to save each image with its own palette, or optionally
the global palette (if its the same).
Many thanks to Marius van Voorden for porting the NeuQuant quantization
algorithm of Anthony Dekker to Python (See the NeuQuant class for its
license).
Many thanks to Alex Robinson for implementing the concept of subrectangles,
which (depening on image content) can give a very significant reduction in
file size.
This code is based on gifmaker (in the scripts folder of the source
distribution of PIL)
Usefull links:
* http://tronche.com/computer-graphics/gif/
* http://en.wikipedia.org/wiki/Graphics_Interchange_Format
* http://www.w3.org/Graphics/GIF/spec-gif89a.txt
"""
# todo: This module should be part of imageio (or at least based on)
import os
import time
try:
import PIL
from PIL import Image
pillow = True
try:
from PIL import PILLOW_VERSION # test if user has Pillow or PIL
except ImportError:
pillow = False
from PIL.GifImagePlugin import getheader, getdata
except ImportError:
PIL = None
try:
import numpy as np
except ImportError:
np = None
[docs]def get_cKDTree():
try:
from scipy.spatial import cKDTree
except ImportError:
cKDTree = None
return cKDTree
# getheader gives a 87a header and a color palette (two elements in a list)
# getdata()[0] gives the Image Descriptor up to (including) "LZW min code size"
# getdatas()[1:] is the image data itself in chuncks of 256 bytes (well
# technically the first byte says how many bytes follow, after which that
# amount (max 255) follows)
[docs]def checkImages(images):
""" checkImages(images)
Check numpy images and correct intensity range etc.
The same for all movie formats.
:param images:
"""
# Init results
images2 = []
for im in images:
if PIL and isinstance(im, PIL.Image.Image):
# We assume PIL images are allright
images2.append(im)
elif np and isinstance(im, np.ndarray):
# Check and convert dtype
if im.dtype == np.uint8:
images2.append(im) # Ok
elif im.dtype in [np.float32, np.float64]:
im = im.copy()
im[im < 0] = 0
im[im > 1] = 1
im *= 255
images2.append(im.astype(np.uint8))
else:
im = im.astype(np.uint8)
images2.append(im)
# Check size
if im.ndim == 2:
pass # ok
elif im.ndim == 3:
if im.shape[2] not in [3, 4]:
raise ValueError('This array can not represent an image.')
else:
raise ValueError('This array can not represent an image.')
else:
raise ValueError('Invalid image type: ' + str(type(im)))
# Done
return images2
[docs]def intToBin(i):
"""Integer to two bytes"""
# devide in two parts (bytes)
i1 = i % 256
i2 = int(i / 256)
# make string (little endian)
return chr(i1) + chr(i2)
[docs]class GifWriter:
"""Class that contains methods for helping write the animated GIF file.
"""
[docs] def getheaderAnim(self, im):
"""Get animation header. To replace PILs getheader()[0]
:param im:
"""
bb = "GIF89a"
bb += intToBin(im.size[0])
bb += intToBin(im.size[1])
bb += "\x87\x00\x00"
return bb
[docs] def getImageDescriptor(self, im, xy=None):
"""Used for the local color table properties per image.
Otherwise global color table applies to all frames irrespective of
whether additional colors comes in play that require a redefined
palette. Still a maximum of 256 color per frame, obviously.
Written by Ant1 on 2010-08-22
Modified by Alex Robinson in Janurari 2011 to implement subrectangles.
:param im:
:param xy:
"""
# Defaule use full image and place at upper left
if xy is None:
xy = (0, 0)
# Image separator,
bb = '\x2C'
# Image position and size
bb += intToBin(xy[0]) # Left position
bb += intToBin(xy[1]) # Top position
bb += intToBin(im.size[0]) # image width
bb += intToBin(im.size[1]) # image height
# packed field: local color table flag1, interlace0, sorted table0,
# reserved00, lct size111=7=2^(7 + 1)=256.
bb += '\x87'
# LZW min size code now comes later, begining of [image data] blocks
return bb
[docs] def getAppExt(self, loops=float('inf')):
"""Application extension. This part specifies the amount of loops.
If loops is 0 or inf, it goes on infinitely.
:param float loops:
"""
if loops == 0 or loops == float('inf'):
loops = 2 ** 16 - 1
#bb = "" # application extension should not be used
# (the extension interprets zero loops
# to mean an infinite number of loops)
# Mmm, does not seem to work
if True:
bb = "\x21\xFF\x0B" # application extension
bb += "NETSCAPE2.0"
bb += "\x03\x01"
bb += intToBin(loops)
bb += '\x00' # end
return bb
[docs] def getGraphicsControlExt(self, duration=0.1, dispose=2):
"""Graphics Control Extension. A sort of header at the start of
each image. Specifies duration and transparancy.
Dispose:
* 0 - No disposal specified.
* 1 - Do not dispose. The graphic is to be left in place.
* 2 - Restore to background color. The area used by the graphic
must be restored to the background color.
* 3 - Restore to previous. The decoder is required to restore the
area overwritten by the graphic with what was there prior to
rendering the graphic.
* 4-7 -To be defined.
:param double duration:
:param dispose:
"""
bb = '\x21\xF9\x04'
bb += chr((dispose & 3) << 2) # low bit 1 == transparency,
# 2nd bit 1 == user input , next 3 bits, the low two of which are used,
# are dispose.
bb += intToBin(int(duration * 100)) # in 100th of seconds
bb += '\x00' # no transparant color
bb += '\x00' # end
return bb
[docs] def handleSubRectangles(self, images, subRectangles):
"""Handle the sub-rectangle stuff. If the rectangles are given by the
user, the values are checked. Otherwise the subrectangles are
calculated automatically.
"""
if isinstance(subRectangles, (tuple, list)):
# xy given directly
# Check xy
xy = subRectangles
if xy is None:
xy = (0, 0)
if hasattr(xy, '__len__'):
if len(xy) == len(images):
xy = [xxyy for xxyy in xy]
else:
raise ValueError("len(xy) doesn't match amount of images.")
else:
xy = [xy for im in images]
xy[0] = (0, 0)
else:
# Calculate xy using some basic image processing
# Check Numpy
if np is None:
raise RuntimeError("Need Numpy to use auto-subRectangles.")
# First make numpy arrays if required
for i in range(len(images)):
im = images[i]
if isinstance(im, Image.Image):
tmp = im.convert() # Make without palette
a = np.asarray(tmp)
if len(a.shape) == 0:
raise MemoryError("Too little memory to convert PIL image to array")
images[i] = a
# Determine the sub rectangles
images, xy = self.getSubRectangles(images)
# Done
return images, xy
[docs] def getSubRectangles(self, ims):
""" getSubRectangles(ims)
Calculate the minimal rectangles that need updating each frame.
Returns a two-element tuple containing the cropped images and a
list of x-y positions.
Calculating the subrectangles takes extra time, obviously. However,
if the image sizes were reduced, the actual writing of the GIF
goes faster. In some cases applying this method produces a GIF faster.
"""
# Check image count
if len(ims) < 2:
return ims, [(0, 0) for i in ims]
# We need numpy
if np is None:
raise RuntimeError("Need Numpy to calculate sub-rectangles. ")
# Prepare
ims2 = [ims[0]]
xy = [(0, 0)]
t0 = time.time()
# Iterate over images
prev = ims[0]
for im in ims[1:]:
# Get difference, sum over colors
diff = np.abs(im-prev)
if diff.ndim == 3:
diff = diff.sum(2)
# Get begin and end for both dimensions
X = np.argwhere(diff.sum(0))
Y = np.argwhere(diff.sum(1))
# Get rect coordinates
if X.size and Y.size:
x0, x1 = X[0], X[-1] + 1
y0, y1 = Y[0], Y[-1] + 1
else: # No change ... make it minimal
x0, x1 = 0, 2
y0, y1 = 0, 2
# Cut out and store
im2 = im[y0:y1, x0:x1]
prev = im
ims2.append(im2)
xy.append((x0, y0))
# Done
# print('%1.2f seconds to determine subrectangles of %i images' %
# (time.time()-t0, len(ims2)))
return ims2, xy
[docs] def convertImagesToPIL(self, images, dither, nq=0):
""" convertImagesToPIL(images, nq=0)
Convert images to Paletted PIL images, which can then be
written to a single animaged GIF.
"""
# Convert to PIL images
images2 = []
for im in images:
if isinstance(im, Image.Image):
images2.append(im)
elif np and isinstance(im, np.ndarray):
if im.ndim == 3 and im.shape[2] == 3:
im = Image.fromarray(im, 'RGB')
elif im.ndim == 3 and im.shape[2] == 4:
im = Image.fromarray(im[:, :, :3], 'RGB')
elif im.ndim == 2:
im = Image.fromarray(im, 'L')
images2.append(im)
# Convert to paletted PIL images
images, images2 = images2, []
if nq >= 1:
# NeuQuant algorithm
for im in images:
im = im.convert("RGBA") # NQ assumes RGBA
nqInstance = NeuQuant(im, int(nq)) # Learn colors from image
if dither:
im = im.convert("RGB").quantize(palette=nqInstance.paletteImage())
else:
# Use to quantize the image itself
im = nqInstance.quantize(im)
images2.append(im)
else:
# Adaptive PIL algorithm
AD = Image.ADAPTIVE
for im in images:
im = im.convert('P', palette=AD, dither=dither)
images2.append(im)
# Done
return images2
[docs] def writeGifToFile(self, fp, images, durations, loops, xys, disposes):
""" writeGifToFile(fp, images, durations, loops, xys, disposes)
Given a set of images writes the bytes to the specified stream.
Requires different handling of palette for PIL and Pillow:
based on https://github.com/rec/echomesh/blob/master/
code/python/external/images2gif.py
"""
# Obtain palette for all images and count each occurance
palettes, occur = [], []
for im in images:
if not pillow:
palette = getheader(im)[1]
else:
palette = getheader(im)[0][-1]
if not palette:
palette = im.palette.tobytes()
palettes.append(palette)
for palette in palettes:
occur.append(palettes.count(palette))
# Select most-used palette as the global one (or first in case no max)
globalPalette = palettes[occur.index(max(occur))]
# Init
frames = 0
firstFrame = True
for im, palette in zip(images, palettes):
if firstFrame:
# Write header
# Gather info
header = self.getheaderAnim(im)
appext = self.getAppExt(loops)
# Write
fp.write(header)
fp.write(globalPalette)
fp.write(appext)
# Next frame is not the first
firstFrame = False
if True:
# Write palette and image data
# Gather info
data = getdata(im)
imdes, data = data[0], data[1:]
graphext = self.getGraphicsControlExt(durations[frames],
disposes[frames])
# Make image descriptor suitable for using 256 local color palette
lid = self.getImageDescriptor(im, xys[frames])
# Write local header
if (palette != globalPalette) or (disposes[frames] != 2):
# Use local color palette
fp.write(graphext)
fp.write(lid) # write suitable image descriptor
fp.write(palette) # write local color table
fp.write('\x08') # LZW minimum size code
else:
# Use global color palette
fp.write(graphext)
fp.write(imdes) # write suitable image descriptor
# Write image data
for d in data:
fp.write(d)
# Prepare for next round
frames = frames + 1
fp.write(";") # end gif
return frames
[docs]def writeGif(filename, images, duration=0.1, repeat=True, dither=False,
nq=0, subRectangles=True, dispose=None):
"""Write an animated gif from the specified images.
:param str filename: the name of the file to write the image to.
:param list images: should be a list consisting of PIL images or numpy
arrays. The latter should be between 0 and 255 for
integer types, and between 0 and 1 for float types.
:param duration: scalar or list of scalars The duration for all frames, or
(if a list) for each frame.
:param repeat: bool or integer The amount of loops. If True, loops infinitetely.
:param bool dither: whether to apply dithering
:param int nq: If nonzero, applies the NeuQuant quantization algorithm to
create the color palette. This algorithm is superior, but
slower than the standard PIL algorithm. The value of nq is
the quality parameter. 1 represents the best quality. 10 is
in general a good tradeoff between quality and speed. When
using this option, better results are usually obtained when
subRectangles is False.
:param subRectangles: False, True, or a list of 2-element tuples
Whether to use sub-rectangles. If True, the minimal
rectangle that is required to update each frame is
automatically detected. This can give significant
reductions in file size, particularly if only a part
of the image changes. One can also give a list of x-y
coordinates if you want to do the cropping yourself.
The default is True.
:param int dispose: how to dispose each frame. 1 means that each frame is
to be left in place. 2 means the background color
should be restored after each frame. 3 means the
decoder should restore the previous frame. If
subRectangles==False, the default is 2, otherwise it is 1.
"""
# Check PIL
if PIL is None:
raise RuntimeError("Need PIL to write animated gif files.")
# Check images
images = checkImages(images)
# Instantiate writer object
gifWriter = GifWriter()
# Check loops
if repeat is False:
loops = 1
elif repeat is True:
loops = 0 # zero means infinite
else:
loops = int(repeat)
# Check duration
if hasattr(duration, '__len__'):
if len(duration) == len(images):
duration = [d for d in duration]
else:
raise ValueError("len(duration) doesn't match amount of images.")
else:
duration = [duration for im in images]
# Check subrectangles
if subRectangles:
images, xy = gifWriter.handleSubRectangles(images, subRectangles)
defaultDispose = 1 # Leave image in place
else:
# Normal mode
xy = [(0, 0) for im in images]
defaultDispose = 2 # Restore to background color.
# Check dispose
if dispose is None:
dispose = defaultDispose
if hasattr(dispose, '__len__'):
if len(dispose) != len(images):
raise ValueError("len(xy) doesn't match amount of images.")
else:
dispose = [dispose for im in images]
# Make images in a format that we can write easy
images = gifWriter.convertImagesToPIL(images, dither, nq)
# Write
fp = open(filename, 'wb')
try:
gifWriter.writeGifToFile(fp, images, duration, loops, xy, dispose)
finally:
fp.close()
[docs]def readGif(filename, asNumpy=True):
"""Read images from an animated GIF file. Returns a list of numpy
arrays, or, if asNumpy is false, a list if PIL images.
"""
# Check PIL
if PIL is None:
raise RuntimeError("Need PIL to read animated gif files.")
# Check Numpy
if np is None:
raise RuntimeError("Need Numpy to read animated gif files.")
# Check whether it exists
if not os.path.isfile(filename):
raise IOError('File not found: ' + str(filename))
# Load file using PIL
pilIm = PIL.Image.open(filename)
pilIm.seek(0)
# Read all images inside
images = []
try:
while True:
# Get image as numpy array
tmp = pilIm.convert() # Make without palette
a = np.asarray(tmp)
if len(a.shape) == 0:
raise MemoryError("Too little memory to convert PIL image to array")
# Store, and next
images.append(a)
pilIm.seek(pilIm.tell() + 1)
except EOFError:
pass
# Convert to normal PIL images if needed
if not asNumpy:
images2 = images
images = []
for im in images2:
images.append(PIL.Image.fromarray(im))
# Done
return images
[docs]class NeuQuant:
""" NeuQuant(image, samplefac=10, colors=256)
samplefac should be an integer number of 1 or higher, 1
being the highest quality, but the slowest performance.
With avalue of 10, one tenth of all pixels are used during
training. This value seems a nice tradeof between speed
and quality.
colors is the amount of colors to reduce the image to. This
should best be a power of two.
See also:
http://members.ozemail.com.au/~dekker/NEUQUANT.HTML
**License of the NeuQuant Neural-Net Quantization Algorithm**
Copyright (c) 1994 Anthony Dekker
Ported to python by Marius van Voorden in 2010
NEUQUANT Neural-Net quantization algorithm by Anthony Dekker, 1994.
See "Kohonen neural networks for optimal colour quantization"
in "network: Computation in Neural Systems" Vol. 5 (1994) pp 351-367.
for a discussion of the algorithm.
See also http://members.ozemail.com.au/~dekker/NEUQUANT.HTML
Any party obtaining a copy of these files from the author, directly or
indirectly, is granted, free of charge, a full and unrestricted
irrevocable, world-wide, paid up, royalty-free, nonexclusive right and
license to deal in this software and documentation files (the "Software"),
including without limitation the rights to use, copy, modify, merge,
publish, distribute, sublicense, and/or sell copies of the Software, and
to permit persons who receive copies from any such party to do so, with
the only requirement being that this copyright notice remain intact.
"""
NCYCLES = None # Number of learning cycles
NETSIZE = None # Number of colours used
SPECIALS = None # Number of reserved colours used
BGCOLOR = None # Reserved background colour
CUTNETSIZE = None
MAXNETPOS = None
INITRAD = None # For 256 colours, radius starts at 32
RADIUSBIASSHIFT = None
RADIUSBIAS = None
INITBIASRADIUS = None
RADIUSDEC = None # Factor of 1/30 each cycle
ALPHABIASSHIFT = None
INITALPHA = None # biased by 10 bits
GAMMA = None
BETA = None
BETAGAMMA = None
network = None # The network itself
colormap = None # The network itself
netindex = None # For network lookup - really 256
bias = None # Bias and freq arrays for learning
freq = None
pimage = None
# Four primes near 500 - assume no image has a length so large
# that it is divisible by all four primes
PRIME1 = 499
PRIME2 = 491
PRIME3 = 487
PRIME4 = 503
MAXPRIME = PRIME4
pixels = None
samplefac = None
a_s = None
[docs] def setconstants(self, samplefac, colors):
self.NCYCLES = 100 # Number of learning cycles
self.NETSIZE = colors # Number of colours used
self.SPECIALS = 3 # Number of reserved colours used
self.BGCOLOR = self.SPECIALS-1 # Reserved background colour
self.CUTNETSIZE = self.NETSIZE - self.SPECIALS
self.MAXNETPOS = self.NETSIZE - 1
self.INITRAD = self.NETSIZE/8 # For 256 colours, radius starts at 32
self.RADIUSBIASSHIFT = 6
self.RADIUSBIAS = 1 << self.RADIUSBIASSHIFT
self.INITBIASRADIUS = self.INITRAD * self.RADIUSBIAS
self.RADIUSDEC = 30 # Factor of 1/30 each cycle
self.ALPHABIASSHIFT = 10 # Alpha starts at 1
self.INITALPHA = 1 << self.ALPHABIASSHIFT # biased by 10 bits
self.GAMMA = 1024.0
self.BETA = 1.0/1024.0
self.BETAGAMMA = self.BETA * self.GAMMA
self.network = np.empty((self.NETSIZE, 3), dtype='float64') # The network itself
self.colormap = np.empty((self.NETSIZE, 4), dtype='int32') # The network itself
self.netindex = np.empty(256, dtype='int32') # For network lookup - really 256
self.bias = np.empty(self.NETSIZE, dtype='float64') # Bias and freq arrays for learning
self.freq = np.empty(self.NETSIZE, dtype='float64')
self.pixels = None
self.samplefac = samplefac
self.a_s = {}
def __init__(self, image, samplefac=10, colors=256):
# Check Numpy
if np is None:
raise RuntimeError("Need Numpy for the NeuQuant algorithm.")
# Check image
if image.size[0] * image.size[1] < NeuQuant.MAXPRIME:
raise IOError("Image is too small")
if image.mode != "RGBA":
raise IOError("Image mode should be RGBA.")
# Initialize
self.setconstants(samplefac, colors)
self.pixels = np.fromstring(getattr(image, "tobytes", getattr(image, "tostring"))(), np.uint32)
self.setUpArrays()
self.learn()
self.fix()
self.inxbuild()
[docs] def writeColourMap(self, rgb, outstream):
for i in range(self.NETSIZE):
bb = self.colormap[i, 0]
gg = self.colormap[i, 1]
rr = self.colormap[i, 2]
outstream.write(rr if rgb else bb)
outstream.write(gg)
outstream.write(bb if rgb else rr)
return self.NETSIZE
[docs] def setUpArrays(self):
self.network[0, 0] = 0.0 # Black
self.network[0, 1] = 0.0
self.network[0, 2] = 0.0
self.network[1, 0] = 255.0 # White
self.network[1, 1] = 255.0
self.network[1, 2] = 255.0
# RESERVED self.BGCOLOR # Background
for i in range(self.SPECIALS):
self.freq[i] = 1.0 / self.NETSIZE
self.bias[i] = 0.0
for i in range(self.SPECIALS, self.NETSIZE):
p = self.network[i]
p[:] = (255.0 * (i-self.SPECIALS)) / self.CUTNETSIZE
self.freq[i] = 1.0 / self.NETSIZE
self.bias[i] = 0.0
# Omitted: setPixels
[docs] def altersingle(self, alpha, i, b, g, r):
"""Move neuron i towards biased (b, g, r) by factor alpha"""
n = self.network[i] # Alter hit neuron
n[0] -= (alpha * (n[0] - b))
n[1] -= (alpha * (n[1] - g))
n[2] -= (alpha * (n[2] - r))
[docs] def geta(self, alpha, rad):
try:
return self.a_s[(alpha, rad)]
except KeyError:
length = rad * 2-1
mid = length/2
q = np.array(list(range(mid-1, -1, -1)) + list(range(-1, mid)))
a = alpha * (rad * rad - q * q)/(rad * rad)
a[mid] = 0
self.a_s[(alpha, rad)] = a
return a
[docs] def alterneigh(self, alpha, rad, i, b, g, r):
if i-rad >= self.SPECIALS-1:
lo = i-rad
start = 0
else:
lo = self.SPECIALS-1
start = (self.SPECIALS-1 - (i-rad))
if i + rad <= self.NETSIZE:
hi = i + rad
end = rad * 2-1
else:
hi = self.NETSIZE
end = (self.NETSIZE - (i + rad))
a = self.geta(alpha, rad)[start:end]
p = self.network[lo + 1:hi]
p -= np.transpose(np.transpose(p - np.array([b, g, r])) * a)
#def contest(self, b, g, r):
# """ Search for biased BGR values
# Finds closest neuron (min dist) and updates self.freq
# finds best neuron (min dist-self.bias) and returns position
# for frequently chosen neurons, self.freq[i] is high and self.bias[i] is negative
# self.bias[i] = self.GAMMA * ((1/self.NETSIZE)-self.freq[i])"""
#
# i, j = self.SPECIALS, self.NETSIZE
# dists = abs(self.network[i:j] - np.array([b, g, r])).sum(1)
# bestpos = i + np.argmin(dists)
# biasdists = dists - self.bias[i:j]
# bestbiaspos = i + np.argmin(biasdists)
# self.freq[i:j] -= self.BETA * self.freq[i:j]
# self.bias[i:j] += self.BETAGAMMA * self.freq[i:j]
# self.freq[bestpos] += self.BETA
# self.bias[bestpos] -= self.BETAGAMMA
# return bestbiaspos
[docs] def contest(self, b, g, r):
"""Search for biased BGR values
Finds closest neuron (min dist) and updates self.freq
finds best neuron (min dist-self.bias) and returns position
for frequently chosen neurons, self.freq[i] is high and self.bias[i]
is negative self.bias[i] = self.GAMMA * ((1/self.NETSIZE)-self.freq[i])
"""
i, j = self.SPECIALS, self.NETSIZE
dists = abs(self.network[i:j] - np.array([b, g, r])).sum(1)
bestpos = i + np.argmin(dists)
biasdists = dists - self.bias[i:j]
bestbiaspos = i + np.argmin(biasdists)
self.freq[i:j] *= (1-self.BETA)
self.bias[i:j] += self.BETAGAMMA * self.freq[i:j]
self.freq[bestpos] += self.BETA
self.bias[bestpos] -= self.BETAGAMMA
return bestbiaspos
[docs] def specialFind(self, b, g, r):
for i in range(self.SPECIALS):
n = self.network[i]
if n[0] == b and n[1] == g and n[2] == r:
return i
return -1
[docs] def learn(self):
biasRadius = self.INITBIASRADIUS
alphadec = 30 + ((self.samplefac-1)/3)
lengthcount = self.pixels.size
samplepixels = lengthcount / self.samplefac
delta = samplepixels / self.NCYCLES
alpha = self.INITALPHA
i = 0
rad = biasRadius >> self.RADIUSBIASSHIFT
if rad <= 1:
rad = 0
print("Beginning 1D learning: samplepixels = %1.2f rad = %i" %
(samplepixels, rad))
step = 0
pos = 0
if lengthcount % NeuQuant.PRIME1 != 0:
step = NeuQuant.PRIME1
elif lengthcount % NeuQuant.PRIME2 != 0:
step = NeuQuant.PRIME2
elif lengthcount % NeuQuant.PRIME3 != 0:
step = NeuQuant.PRIME3
else:
step = NeuQuant.PRIME4
i = 0
printed_string = ''
while i < samplepixels:
if i % 100 == 99:
tmp = '\b' * len(printed_string)
printed_string = str((i + 1) * 100/samplepixels) + "%\n"
print(tmp + printed_string)
p = self.pixels[pos]
r = (p >> 16) & 0xff
g = (p >> 8) & 0xff
b = (p) & 0xff
if i == 0: # Remember background colour
self.network[self.BGCOLOR] = [b, g, r]
j = self.specialFind(b, g, r)
if j < 0:
j = self.contest(b, g, r)
if j >= self.SPECIALS: # Don't learn for specials
a = (1.0 * alpha) / self.INITALPHA
self.altersingle(a, j, b, g, r)
if rad > 0:
self.alterneigh(a, rad, j, b, g, r)
pos = (pos + step) % lengthcount
i += 1
if i % delta == 0:
alpha -= alpha / alphadec
biasRadius -= biasRadius / self.RADIUSDEC
rad = biasRadius >> self.RADIUSBIASSHIFT
if rad <= 1:
rad = 0
finalAlpha = (1.0 * alpha)/self.INITALPHA
print("Finished 1D learning: final alpha = %1.2f!" % finalAlpha)
[docs] def fix(self):
for i in range(self.NETSIZE):
for j in range(3):
x = int(0.5 + self.network[i, j])
x = max(0, x)
x = min(255, x)
self.colormap[i, j] = x
self.colormap[i, 3] = i
[docs] def inxbuild(self):
previouscol = 0
startpos = 0
for i in range(self.NETSIZE):
p = self.colormap[i]
q = None
smallpos = i
smallval = p[1] # Index on g
# Find smallest in i..self.NETSIZE-1
for j in range(i + 1, self.NETSIZE):
q = self.colormap[j]
if q[1] < smallval: # Index on g
smallpos = j
smallval = q[1] # Index on g
q = self.colormap[smallpos]
# Swap p (i) and q (smallpos) entries
if i != smallpos:
p[:], q[:] = q, p.copy()
# smallval entry is now in position i
if smallval != previouscol:
self.netindex[previouscol] = (startpos + i) >> 1
for j in range(previouscol + 1, smallval):
self.netindex[j] = i
previouscol = smallval
startpos = i
self.netindex[previouscol] = (startpos + self.MAXNETPOS) >> 1
for j in range(previouscol + 1, 256): # Really 256
self.netindex[j] = self.MAXNETPOS
[docs] def paletteImage(self):
"""PIL weird interface for making a paletted image: create an image
which already has the palette, and use that in Image.quantize. This
function returns this palette image."""
if self.pimage is None:
palette = []
for i in range(self.NETSIZE):
palette.extend(self.colormap[i][:3])
palette.extend([0] * (256-self.NETSIZE) * 3)
# a palette image to use for quant
self.pimage = Image.new("P", (1, 1), 0)
self.pimage.putpalette(palette)
return self.pimage
[docs] def quantize(self, image):
"""Use a kdtree to quickly find the closest palette colors for the
pixels
:param image:
"""
if get_cKDTree():
return self.quantize_with_scipy(image)
else:
print('Scipy not available, falling back to slower version.')
return self.quantize_without_scipy(image)
[docs] def quantize_with_scipy(self, image):
w, h = image.size
px = np.asarray(image).copy()
px2 = px[:, :, :3].reshape((w * h, 3))
cKDTree = get_cKDTree()
kdtree = cKDTree(self.colormap[:, :3], leafsize=10)
result = kdtree.query(px2)
colorindex = result[1]
print("Distance: %1.2f" % (result[0].sum()/(w * h)))
px2[:] = self.colormap[colorindex, :3]
return Image.fromarray(px).convert("RGB").quantize(palette=self.paletteImage())
[docs] def quantize_without_scipy(self, image):
"""" This function can be used if no scipy is availabe.
It's 7 times slower though.
:param image:
"""
w, h = image.size
px = np.asarray(image).copy()
memo = {}
for j in range(w):
for i in range(h):
key = (px[i, j, 0], px[i, j, 1], px[i, j, 2])
try:
val = memo[key]
except KeyError:
val = self.convert(*key)
memo[key] = val
px[i, j, 0], px[i, j, 1], px[i, j, 2] = val
return Image.fromarray(px).convert("RGB").quantize(palette=self.paletteImage())
[docs] def inxsearch(self, r, g, b):
"""Search for BGR values 0..255 and return colour index"""
dists = (self.colormap[:, :3] - np.array([r, g, b]))
a = np.argmin((dists * dists).sum(1))
return a
if __name__ == '__main__':
im = np.zeros((200, 200), dtype=np.uint8)
im[10: 30, :] = 100
im[:, 80: 120] = 255
im[-50: -40, :] = 50
images = [im * 1.0, im * 0.8, im * 0.6, im * 0.4, im * 0]
writeGif('lala3.gif', images, duration=0.5, dither=0)
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