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Source code for imaging.images2gif

# -*- coding: utf-8 -*-
#   Copyright (C) 2012, Almar Klein, Ant1, Marius van Voorden
#
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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)


Useful 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""" # divide 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, beginning 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 transparency. 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 transparent 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 = int(X[0]), int(X[-1] + 1) y0, y1 = int(Y[0]), int(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 occurrence 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, **kwargs): """Write an animated gif from the specified images. Depending on which PIL library is used, either writeGifVisvis or writeGifPillow is used here. :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 infinitetel :param kwargs: additional parameters for writeGifVisvis """ if pillow: # Pillow >= 3.4.0 has animated GIF writing version = [int(i) for i in PILLOW_VERSION.split('.')] if version[0] > 3 or (version[0] == 3 and version[1] >= 4): writeGifPillow(filename, images, duration, repeat) return # otherwise use the old one writeGifVisvis(filename, images, duration, repeat, **kwargs)
[docs]def writeGifPillow(filename, images, duration=0.1, repeat=True): """Write an animated gif from the specified images. Uses native Pillow implementation, which is available since Pillow 3.4.0. :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 infinitetel """ loop = 0 if repeat else 1 quantized = [] for im in images: quantized.append(im.quantize()) quantized[0].save(filename, save_all=True, append_images=quantized[1:], loop=loop, duration=duration * 1000)
[docs]def writeGifVisvis(filename, images, duration=0.1, repeat=True, dither=False, nq=0, subRectangles=True, dispose=None): """Write an animated gif from the specified images. Uses VisVis implementation. Unfortunately it produces corrupted GIF with Pillow >= 3.4.0. :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 available. 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 convert(self, *color): i = self.inxsearch(*color) return self.colormap[i, :3]
[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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