# read the content image and convert it to YCbCr

contentIm = im.open(content)

contentIm = contentIm.convert("YCbCr")

contentImYUV = numpy.array(contentIm)

# read the stylized image and convert it to YCbCr

stylizedIm = im.open(stylized)

stylizedIm = stylizedIm.convert("YCbCr")

stylizedImYUV = numpy.array(stylizedIm)

# replaces the Y channel of the content image with that of the stylized image

for i in range(len(contentImYUV)):

for j in range(len(contentImYUV[0])):

contentImYUV[i][j][0] = stylizedImYUV[i][j][0]

out = im.fromarray(contentImYUV, 'YCbCr')

out.save(out_name)

# read the content image and convert it to RGB

contentIm = im.open(content)

contentIm = contentIm.convert("RGB")

contentArray = numpy.array(contentIm)

# read the stylized image and convert it to RGB

stylizedIm = im.open(stylized)

stylizedIm = stylizedIm.convert("RGB")

stylizedArray = numpy.array(stylizedIm)

height = len(contentArray)

width = len(contentArray[0])

# empty output numpy array

outArray = numpy.empty((height, width, 3))

# performs weighted RGB average on each pixel in the image

for i in range(height):

for j in range(width):

weightedContent = content_weight*contentArray[i][j]

weightedStyle = stylized_weight*stylizedArray[i][j]

outArray[i][j] = weightedContent + weightedStyle

out = im.fromarray(outArray.astype('uint8'), 'RGB')

out.save(out_name)

# extract rgb values and then convert to CIELAB for content image and stylized image

rgbContent = io.imread(content)

labContent = color.rgb2lab(numpy.asarray(rgbContent)/255.0)

labContentArray = numpy.array(labContent)

rgbStyle = io.imread(stylized)

labStyle = color.rgb2lab(numpy.asarray(rgbStyle)/255.0)

labStyleArray = numpy.array(labStyle)

# color transfer over L channel

for i in range(len(labContentArray)):

for j in range(len(labContentArray[0])):

labContentArray[i][j][0] = labStyleArray[i][j][0]

# convert back to RGB to save

labContentArray = color.lab2rgb(labContentArray)*255.0

# RGB unpacking

H = len(labContentArray)

W = len(labContentArray[0])

img = labContentArray.reshape(( H,W,3 ))

print "img.shape:", img.shape

r,g,b = img.transpose( 2,0,1 ) # 3 10 5

print "r.shape:", r.shape

# pack 10 x 5 r g b -> 10 x 5 x 3 again --

rgb = numpy.array(( r, g, b )).transpose( 1,2,0 ) # 10 5 3 again

print "rgb.shape:", rgb.shape

assert (rgb == img).all()

# rgb 0 .. 255 <-> float 0 .. 1 --

imgfloat = img.astype(numpy.float32) / 255.

img8 = (imgfloat * 255.).round().astype(numpy.uint8)

out1 = im.fromarray(img8, 'RGB')

out1.show()

# extract info and convert to CIE-LCh for each image

rgbContent = io.imread(content)

labContent = color.lab2lch(color.xyz2lab(color.rgb2xyz(numpy.asarray(rgbContent))))

labContentArray = numpy.array(labContent)

rgbStyle = io.imread(stylized)

labStyle = color.lab2lch(color.xyz2lab(color.rgb2xyz(numpy.asarray(rgbStyle))))

labStyleArray = numpy.array(labStyle)

# color transfer

for i in range(len(labContentArray)):

for j in range(len(labContentArray[0])):

labContentArray[i][j][0] = labStyleArray[i][j][0]

labContentArray = color.xyz2rgb(color.lab2xyz(color.lch2lab(labContentArray)))

viewer = ImageViewer(labContentArray)