/usr/lib/python3/dist-packages/ssim/utils.py is in python3-pyssim 0.2-1.
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1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 | """Common utility functions."""
from __future__ import absolute_import
import numpy
from numpy.ma.core import exp
import scipy.ndimage
from ssim.compat import ImageOps
def convolve_gaussian_2d(image, gaussian_kernel_1d):
"""Convolve 2d gaussian."""
result = scipy.ndimage.filters.correlate1d(
image, gaussian_kernel_1d, axis=0)
result = scipy.ndimage.filters.correlate1d(
result, gaussian_kernel_1d, axis=1)
return result
def get_gaussian_kernel(gaussian_kernel_width=11, gaussian_kernel_sigma=1.5):
"""Generate a gaussian kernel."""
# 1D Gaussian kernel definition
gaussian_kernel_1d = numpy.ndarray((gaussian_kernel_width))
norm_mu = int(gaussian_kernel_width / 2)
# Fill Gaussian kernel
for i in range(gaussian_kernel_width):
gaussian_kernel_1d[i] = (exp(-(((i - norm_mu) ** 2)) /
(2 * (gaussian_kernel_sigma ** 2))))
return gaussian_kernel_1d / numpy.sum(gaussian_kernel_1d)
def to_grayscale(img):
"""Convert PIL image to numpy grayscale array and numpy alpha array.
Args:
img (PIL.Image): PIL Image object.
Returns:
(gray, alpha): both numpy arrays.
"""
gray = numpy.asarray(ImageOps.grayscale(img)).astype(numpy.float)
imbands = img.getbands()
alpha = None
if 'A' in imbands:
alpha = numpy.asarray(img.split()[-1]).astype(numpy.float)
return gray, alpha
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