def compute_mdsi(img1, img2, **kwargs):
data_range = kwargs.pop("data_range", 1.0)
use_grayscale = kwargs.pop("use_grayscale", False)
# MDSI assumes 0-255 dynamic range for the input images
# rescale both images to 0-255 maintaining the original dynamic range difference ratio
img1 *= 255.0 / data_range
img2 *= 255.0 / data_range
if len(img1.shape) < 3:
use_grayscale = True
# MDSI takes 3-channel RGB images
img1 = ensure3d(img1)
img2 = ensure3d(img2)
matlab_eng = mw.get_matlab_instance()
out, err = mw.get_io_streams()
path1, path2, tag = mw.imgs_to_unique_mat(img1,
img2,
extra_identifier='mdsi')
mdsi = matlab_eng.MDSI_wrapper(path1,
path2,
tag,
use_grayscale,
stdout=out,
stderr=err)
mw.remove_matlab_unique_mat(path1, path2)
return 1 - mdsi
def compute_hdr_vdp(img1, img2, **kwargs):
global hdr_vdp_version
matlab_eng = mw.get_matlab_instance()
out, err = mw.get_io_streams()
display_params = kwargs.pop('display_params', None) # 4 value array or tuple, see below for more info
color_encoding = kwargs.pop('color_encoding', 'luminance')
if color_encoding == 'luminance' and (len(img1.shape) > 2 or len(img2.shape) > 2):
print("WARNING (HDR-VDP wrapper): using 'luminance' color_encoding requires single-channel Luminance (cd/m2) "
"input but inputs with shapes {} and {} were provided. Will use 'sRGB-display' mode instead."
.format(img1.shape, img2.shape))
color_encoding = 'sRGB-display'
# TODO: maybe refactor this such that s, w, h come from the display model?
if display_params is None:
s = 21 # screen size in inches
w = 1920; h = 1080 # screen resolution
d = 0.5 # distance to the display
else:
s, w, h, d = display_params
path1, path2, tag = mw.imgs_to_unique_mat(img1, img2, extra_identifier='hdrvdp')
res = matlab_eng.hdrvdp_wrapper(path1, path2, tag, hdr_vdp_version, color_encoding, [s, w, h, d],
stdout=out, stderr=err) # redirect prints to dummy I/O streams
mw.remove_matlab_unique_mat(path1, path2)
return (res["Q"] / 100.0) if hdr_vdp_version == '2.2.2' else res["Q"] / 10
def compute_fsim(img1, img2, **kwargs):
data_range = kwargs.pop('data_range', 1.0)
# FSIM assumes 0-255 dynamic range for the input images
# rescale both images to 0-255 maintaining the original dynamic range difference ratio
img1 *= 255.0 / data_range
img2 *= 255.0 / data_range
matlab_eng = mw.get_matlab_instance()
out, err = mw.get_io_streams()
path1, path2, tag = mw.imgs_to_unique_mat(img1,
img2,
extra_identifier='fsim')
# Note: For grayscale images, the returned FSIM and FSIMc are the same
fsim = matlab_eng.fsim_wrapper(path1, path2, tag, stdout=out, stderr=err)
mw.remove_matlab_unique_mat(path1, path2)
return fsim
def __compute_similarity(img1, img2, multiscale=True, use_python=False, **kwargs):
"""
Uses either the original SSIM implementation or its multi-scale variant
:param img1:
:param img2:
:param multiscale: toggle between single- and multi- scale SSIM implementations (SSIM vs MSSIM)
Note: this only applies when using MATLAB; only MSSIM is available for Python
:param use_python:
:param kwargs:
:return:
"""
data_range = kwargs.pop('data_range', 1.0)
if use_python:
mat_range = 1.0
ref = (img1 * mat_range / data_range).astype(np.float)
A = (img2 * mat_range / data_range).astype(np.float)
return ssim_py(ref, A, multichannel=True, data_range=mat_range)
else:
mat_range = 255.0
# MATLAB version requires luminance inputs
if len(img1.shape) == 3:
img1 = rgb2lum(img1)
img2 = rgb2lum(img2)
ref = (img1 * mat_range / data_range).astype(np.int)
A = (img2 * mat_range / data_range).astype(np.int)
matlab_eng = mw.get_matlab_instance()
out, err = mw.get_io_streams()
path1, path2, tag = mw.imgs_to_unique_mat(A, ref, extra_identifier='ssim')
ssim = matlab_eng.SSIM_wrapper(path1, path2, tag, multiscale, stdout=out, stderr=err)
mw.remove_matlab_unique_mat(path1, path2)
return ssim
def compute_vsi(img1, img2, **kwargs):
data_range = kwargs.pop("data_range", 1.0)
# VSI assumes 0-255 dynamic range for the input images
# rescale both images to 0-255 maintaining the original dynamic range difference ratio
img1 *= 255.0 / data_range
img2 *= 255.0 / data_range
# VSI takes 3-channel RGB images
img1 = ensure3d(img1)
img2 = ensure3d(img2)
matlab_eng = mw.get_matlab_instance()
out, err = mw.get_io_streams()
path1, path2, tag = mw.imgs_to_unique_mat(img1,
img2,
extra_identifier='vsi')
vsi = matlab_eng.vsi_wrapper(path1, path2, tag, stdout=out, stderr=err)
mw.remove_matlab_unique_mat(path1, path2)
return vsi