Iteration 2 of using NNMF to blend pixel art

icon_nnmf.py

from scipy.misc import imread
import numpy as np

# These are flower images from the PROJCAM garden bundle.
path = "C:\Users\Mark\Documents\ProcJam\PROCJAM2016-Tess2D\PROCJAM2016-Tess2D\Garden\\"

names = [ "flower{:02d}.png".format( i ) for i in xrange( 0, 22 ) ]
original = [ imread( path + name ) for name in names ]

def flatten( image ):
    return np.reshape( image, [-1] )

palette = set()
for image in original:
    for row in image:
        for color in row:
            palette.add( tuple( color ) )

transparent = False
for (r,g,b,a) in list( palette ):
    if a == 0: # transparent
        transparent = True
        palette.remove( (r,g,b,a) )

if transparent:
    palette.add( (255, 255, 255, 0) )

palette = list( palette )
print "Palette", palette

def convertToPalette( image ):
    px = []
    for pixel in np.reshape( image, [-1, 4] ):
        (r, g, b, a ) = pixel
        if a == 0:
            pixel = ( 255, 255, 255, 0 )
        vec = [ int( tuple(pixel) == palette[i] ) for i in xrange( len( palette ) ) ]
        px.append( vec )
    newRep = np.array( px )
    return np.reshape( newRep, [-1] )

from math import sqrt

def convertToRgb( image ):
    y = len( palette )
    pixels = np.reshape( image, [-1, y] )
    pa = np.array( palette )
    # Normalize to 1.0
    row_sums = pixels.sum( axis = 1, keepdims = True )
    normalized = pixels / row_sums
                           
    rgbVec = normalized.dot( pa )
    dim = int( sqrt( len( rgbVec ) ) )
    rgbMat = np.reshape( rgbVec, [dim, dim, 4] )
    return np.array( rgbMat, dtype="uint8" )

# Matrix V has shape [samples, feature]
# Each sample is an image, each feature is the presence of a palette color
# in a pixel
V = np.matrix( [ convertToPalette( i ) for i in original ] )
(numSamples, numFeatures) = V.shape
print "Samples: ", numSamples
print "Features: ", numFeatures

from sklearn.decomposition import NMF
import matplotlib.pyplot as plt
import matplotlib.image as mpimg

def trial( numComponents = 22, max_iter = 10000 ):
    model = NMF( n_components= numComponents, max_iter=max_iter )
    W = model.fit_transform( V )
    H = model.components_
    # Matrix W has shape [ samples, components ]
    # Matrix H has shape [ components, features ]

    print "Error", model.reconstruction_err_

    X2 = model.inverse_transform( W )

    plt.figure( 1 )
    numImages = len( names )
    for n in xrange( numImages ):
        # original
        o = original[n]
        plt.subplot( numImages, 2, n * 2 + 1 )
        plt.imshow( o, interpolation = "none" )
        plt.axis( "off" )
    
        # reconstruction
        p = convertToRgb( X2[n] )
        plt.subplot( numImages, 2, n * 2 + 2 )
        plt.imshow( p, interpolation = "none" )
        plt.axis( "off" )    

    plt.show()

#    return ( model, W, H )

def mixAll( numComponents = 22, max_iter = 10000 ):
    model = NMF( n_components= numComponents, max_iter=max_iter )
    W = model.fit_transform( V )
    H = model.components_
    # Matrix W has shape [ samples, components ]
    # Matrix H has shape [ components, features ]

    plt.figure( 1 )
    numImages = len( names )
    for a in xrange( numImages ):
        for b in xrange( numImages ):
            plt.subplot( numImages, numImages, numImages * a + b + 1 )
            if a == b:
                plt.imshow( original[a], interpolation = "none" )
            elif a < b:
                wa = W[a]
                wb = W[b]
                wi = ( wa + wb ) * 0.5
                x = wi.dot( H )
                plt.imshow( convertToRgb( x ), interpolation = "none" )
            plt.axis( "off" )
                           
    plt.show()