def texturekernel(imgs, metric=libpil.onenorm_inverse, cachedfn="texture_kernel.pkl"):
if os.path.exists(cachedfn):
return numpy.load(cachedfn)
n_imgs = len(imgs)
# calculation of kernel
kernel = numpy.zeros((n_imgs, n_imgs))
for i in xrange(n_imgs):
arr1 = libpil.PIL2array(imgs[i])
gray1 = libpil.rgbarr2gray(arr1)
for j in xrange(i):
arr2 = libpil.PIL2array(imgs[j])
gray2 = libpil.rgbarr2gray(arr2)
kernel[i, j] = metric(libpil.laphist(gray1), libpil.laphist(gray2))
kernel = kernel + numpy.transpose(kernel)
print kernel
return kernel
def main():
image_folder = get_image_folder()
imgfiles = glob.glob(os.path.join(image_folder, "*.jpg"))
imgs = map(libpil.loadImage, imgfiles)
if "color" in sys.argv:
color_kernel = colorkernel(imgs, metric=libpil.onenorm_inverse)
output_compare_imgs(imgs, color_kernel, outfilename="color.png")
color_kernel.dump("color_kernel.pkl")
if "texture" in sys.argv:
texture_kernel = texturekernel(imgs)
output_compare_imgs(imgs, texture_kernel, outfilename="texture.png")
texture_kernel.dump("texture_kernel.pkl")
if "color" in sys.argv and "texture" in sys.argv:
# 1-norm kernel normalization
color_kernel /= max(color_kernel.sum(1))
texture_kernel /= max(texture_kernel.sum(1))
for s in xrange(0, 11, 2):
r = s * 0.1
kernel = texture_kernel * r + (1 - r) * color_kernel
kernel /= kernel.max()
output_compare_imgs(imgs, kernel, outfilename="r%.1f.png" % (r,))
for method in ("single", "complete"):
output_dendrogram(imgs, kernel, method=method, dend_fn="r%.1f_dendrogram.png" % (r,))
if "scatter" in sys.argv:
n_imgs = len(imgs)
dominant_colors = numpy.zeros((n_imgs,))
for i in xrange(n_imgs):
hue = libpil.rgbarr2hsvarr(libpil.PIL2array(imgs[i]))[0]
histhue, edges = libpil.hist(hue, bins=40, rang=(0, 1))
bincenters = 0.5 * (edges[1:] + edges[:-1])
print "dominant hue:", bincenters[histhue.argmax()]
dominant_colors[i] = bincenters[histhue.argmax()]
total_textures = numpy.zeros((n_imgs,))
for i in xrange(n_imgs):
lapimg = libpil.lapacian(libpil.rgbarr2gray(libpil.PIL2array(imgs[i])))
lapimg = lapimg.flatten()
meadtext = numpy.median(numpy.abs(lapimg))
print "meandian texture:", meadtext
total_textures[i] = meadtext
libpil.scatterplot(dominant_colors, total_textures, imgs)
