images2gif.py

# -*- coding: utf-8 -*-
#   Copyright (c) 2010, Almar Klein, Ant1, Marius van Voorden
#
#   This code is subject to the (new) BSD license:
#
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#   modification, are permitted provided that the following conditions are met:
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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.

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).

This code is based on gifmaker (in the scripts folder of the source 
distribution of PIL)

Some implementation details are ased on gif file structure as provided
by wikipedia.

"""

import os

try:
    import PIL
    from PIL import Image, ImageChops
    from PIL.GifImagePlugin import getheader, getdata
except ImportError:
    PIL = None

try:
    import numpy as np
except ImportError:
    np = None    

try:
    from scipy.spatial import cKDTree
except ImportError:
    cKDTree = None

# 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).

def checkImages(images):
    """ checkImages(images)
    Check numpy images and correct intensity range etc.
    The same for all movie formats.
    """ 
    # 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


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)


def getheaderAnim(im):
    """ Animation header. To replace the getheader()[0] """
    bb = "GIF89a"
    bb += intToBin(im.size[0])
    bb += intToBin(im.size[1])
    bb += "\x87\x00\x00"
    return bb


def getImageDescriptor(im):
    """ Used for the local color table properties per image.
    Otherwise global color table applies to all frames irrespective of
    wether additional colours comes in play that require a redefined palette
    Still a maximum of 256 color per frame, obviously.
    
    Written by Ant1 on 2010-08-22
    """
    bb = '\x2C' # Image separator,
    bb += intToBin( 0 ) # Left position
    bb += intToBin( 0 ) # Top position
    bb += intToBin( im.size[0] ) # image width
    bb += intToBin( im.size[1] ) # image height
    bb += '\x87' # packed field : local color table flag1, interlace0, sorted table0, reserved00, lct size111=7=2^(7+1)=256.
    # LZW minimum size code now comes later, begining of [image data] blocks
    return bb


#def getAppExt(loops=float('inf')):
#compile error commented by zcwang
def getAppExt(loops=float(0)):
    """ Application extention. Part that specifies amount of loops. 
    If loops is inf, it goes on infinitely.
    """
    if loops == 0:
        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"
#        if loops == float('inf'):
        if loops == float(0):
            loops = 2**16-1
        bb += intToBin(loops)
        bb += '\x00'  # end
    return bb


def getGraphicsControlExt(duration=0.1):
    """ Graphics Control Extension. A sort of header at the start of
    each image. Specifies transparancy and duration. """
    bb = '\x21\xF9\x04'
    bb += '\x08'  # no transparancy
    bb += intToBin( int(duration*100) ) # in 100th of seconds
    bb += '\x00'  # no transparant color
    bb += '\x00'  # end
    return bb


def _writeGifToFile(fp, images, durations, loops):
    """ Given a set of images writes the bytes to the specified stream.
    """
    
    # Obtain palette for all images and count each occurance
    palettes, occur = [], []
    for im in images: 
        #palettes.append( getheader(im)[1] )
        palettes.append(im.palette.getdata()[1])
    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 = getheaderAnim(im)
            appext = 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 = getGraphicsControlExt(durations[frames])
            # Make image descriptor suitable for using 256 local color palette
            lid = getImageDescriptor(im) 
            
            # Write local header
            if palette != globalPalette:
                # 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


## Exposed functions 

def writeGif(filename, images, duration=0.1, repeat=True, dither=False, nq=0):
    """ writeGif(filename, images, duration=0.1, repeat=True, dither=False)
    
    Write an animated gif from the specified images. 
    
    Parameters
    ----------
    filename : string
       The name of the file to write the image to.
    images : list
        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.
    duration : scalar or list of scalars
        The duration for all frames, or (if a list) for each frame.
    repeat : bool or integer
        The amount of loops. If True, loops infinitetely.
    dither : bool
        Whether to apply dithering
    nq : integer
        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.
    
    """
    
    # Check PIL
    if PIL is None:
        raise RuntimeError("Need PIL to write animated gif files.")
    
    # Check images
    images = checkImages(images)
    
    # Check loops
    if repeat is False: 
        loops = 1
    elif repeat is True:
        loops = 0 # zero means infinite
    else:
        loops = int(repeat)
    
    # 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==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
            nq = NeuQuant(im, int(nq)) # Learn colors from image
            if dither:
                im = im.convert("RGB").quantize(palette=nq.paletteImage())
            else:
                im = nq.quantize(im)  # Use to quantize the image itself
            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)
    
    # Check duration
    if hasattr(duration, '__len__'):
        if len(duration) == len(images2):
            durations = [d for d in duration]
        else:
            raise ValueError("len(duration) doesn't match amount of images.")
    else:
        duration = [duration for im in images2]
    
    # Open file
    fp = open(filename, 'wb')
    
    # Write
    try:
        n = _writeGifToFile(fp, images2, duration, loops)
    finally:
        fp.close()
    

def readGif(filename, asNumpy=True):
    """ 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


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
    
    
    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")
        assert image.mode == "RGBA"
        
        # Initialize
        self.setconstants(samplefac, colors)
        self.pixels = np.fromstring(image.tostring(), np.uint32)
        self.setUpArrays()
        
        self.learn()
        self.fix()
        self.inxbuild()
    
    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];
            out.write(rr if rgb else bb)
            out.write(gg)
            out.write(bb if rgb else rr)
        return self.NETSIZE
    
    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
    
    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))
    
    def geta(self, alpha, rad):
        try:
            return self.a_s[(alpha, rad)]
        except KeyError:
            length = rad*2-1
            mid = length/2
            q = np.array(range(mid-1,-1,-1)+range(-1,mid))
            a = alpha*(rad*rad - q*q)/(rad*rad)
            a[mid] = 0
            self.a_s[(alpha, rad)] = a
            return a
    
    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
    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
    



    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
    
    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 =",samplepixels," rad =", 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
        print "Finished 1D learning: final alpha =",(1.0*alpha)/self.INITALPHA,"!"
    
    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
    
    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
    
    
    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
    
    
    def quantize(self, image):
        """ Use a kdtree to quickly find the closest palette colors for the pixels """
        if cKDTree:
            return self.quantize_with_scipy(image)
        else:
            print 'Scipy not available, falling back to slower version.'
            return self.quantize_without_scipy(image)
    
    
    def quantize_with_scipy(self, image):
        w,h = image.size
        px = np.asarray(image).copy()
        px2 = px[:,:,:3].reshape((w*h,3))
        
        kdtree = cKDTree(self.colormap[:,:3],leafsize=10)
        result = kdtree.query(px2)
        colorindex = result[1]
        print "Distance:", (result[0].sum()/(w*h))
        px2[:] = self.colormap[colorindex,:3]
        
        return Image.fromarray(px).convert("RGB").quantize(palette=self.paletteImage())
    
    
    def quantize_without_scipy(self, image):
        """" This function can be used if no scipy is availabe. 
        It's 7 times slower though.
        """
        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())
    
    def convert(self, (r, g, b)):
        i = self.inxsearch(r, g, b)
        return self.colormap[i,:3]
    
    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)