def __init__(self, img, gaussian_kernel_1d, size=None):
"""Create an SSIMImage.
Args:
img: PIL Image object or file name.
gaussian_kernel_1d: Gaussian kernel from get_gaussian_kernel.
size: New image size to resize image to.
"""
self.gaussian_kernel_1d = gaussian_kernel_1d
self.img = img
if isinstance(img, compat.basestring):
self.img = compat.Image.open(img)
if size and size != self.img.size:
self.img = self.img.resize(size)
self.size = self.img.size
self.img_gray, self.img_alpha = to_grayscale(self.img)
if self.img_alpha is not None:
self.img_gray[self.img_alpha == 255] = 0
self.img_gray_squared = self.img_gray ** 2
self.img_gray_mu = convolve_gaussian_2d(
self.img_gray, self.gaussian_kernel_1d)
self.img_gray_mu_squared = self.img_gray_mu ** 2
self.img_gray_sigma_squared = convolve_gaussian_2d(
self.img_gray_squared, self.gaussian_kernel_1d)
self.img_gray_sigma_squared -= self.img_gray_mu_squared
def __init__(self, img, gaussian_kernel_1d, size=None):
"""Create an SSIMImage.
Args:
img: PIL Image object or file name.
gaussian_kernel_1d: Gaussian kernel from get_gaussian_kernel.
size: New image size to resize image to.
"""
self.gaussian_kernel_1d = gaussian_kernel_1d
self.img = img
if isinstance(img, compat.basestring):
self.img = compat.Image.open(img)
if size and size != self.img.size:
self.img = self.img.resize(size)
self.size = self.img.size
self.img_gray, self.img_alpha = to_grayscale(self.img)
if self.img_alpha is not None:
self.img_gray[self.img_alpha == 255] = 0
self.img_gray_squared = self.img_gray**2
self.img_gray_mu = convolve_gaussian_2d(self.img_gray,
self.gaussian_kernel_1d)
self.img_gray_mu_squared = self.img_gray_mu**2
self.img_gray_sigma_squared = convolve_gaussian_2d(
self.img_gray_squared, self.gaussian_kernel_1d)
self.img_gray_sigma_squared -= self.img_gray_mu_squared
def __init__(self, img, gaussian_kernel_1d=None, size=None):
"""Create an SSIMImage.
Args:
img (str or PIL.Image): PIL Image object or file name.
gaussian_kernel_1d (np.ndarray, optional): Gaussian kernel
that was generated with utils.get_gaussian_kernel is used
to precompute common objects for SSIM computation
size (tuple, optional): New image size to resize image to.
"""
# Use existing or create a new PIL.Image
try:
self.img = img if not isinstance(img, compat.basestring) \
else compat.Image.open(img)
except IOError as e:
logging.debug("Unable to open %s" % img)
raise IOError("Image probably malformed")
# Resize image if size is defined and different
# from original image
if size and size != self.img.size:
self.img = self.img.resize(size, Image.ANTIALIAS)
# Set the size of the image
self.size = self.img.size
# If gaussian kernel is defined we create
# common SSIM objects
if gaussian_kernel_1d is not None:
self.gaussian_kernel_1d = gaussian_kernel_1d
# np.array of grayscale and alpha image
self.img_gray, self.img_alpha = to_grayscale(self.img)
if self.img_alpha is not None:
self.img_gray[self.img_alpha == 255] = 0
# Squared grayscale
self.img_gray_squared = self.img_gray**2
# Convolve grayscale image with gaussian
self.img_gray_mu = convolve_gaussian_2d(self.img_gray,
self.gaussian_kernel_1d)
# Squared mu
self.img_gray_mu_squared = self.img_gray_mu**2
# Convolve squared grayscale with gaussian
self.img_gray_sigma_squared = convolve_gaussian_2d(
self.img_gray_squared, self.gaussian_kernel_1d)
# Substract squared mu
self.img_gray_sigma_squared -= self.img_gray_mu_squared
# If we don't define gaussian kernel, we create
# common CW-SSIM objects
else:
# Grayscale PIL.Image
self.img_gray = ImageOps.grayscale(self.img)
def __init__(self, img, gaussian_kernel_1d=None, size=None):
"""Create an SSIMImage.
Args:
img (str or PIL.Image): PIL Image object or file name.
gaussian_kernel_1d (np.ndarray, optional): Gaussian kernel
that was generated with utils.get_gaussian_kernel is used
to precompute common objects for SSIM computation
size (tuple, optional): New image size to resize image to.
"""
# Use existing or create a new PIL.Image
self.img = img if not isinstance(img, compat.basestring) \
else compat.Image.open(img)
# Resize image if size is defined and different
# from original image
if size and size != self.img.size:
self.img = self.img.resize(size, Image.ANTIALIAS)
# Set the size of the image
self.size = self.img.size
# Set the numeber of channels of the image
self.nChannels = len(self.img.getbands())
# If gaussian kernel is defined we create
# common SSIM objects
if gaussian_kernel_1d is not None:
self.gaussian_kernel_1d = gaussian_kernel_1d
# np.array of ycbcr channels and alpha image
self.img_channels, self.img_alpha = to_ycbcr(self.img)
# Squared channels
self.img_channels_squared = self.img_channels**2
# Convolve channels image with gaussian
self.img_channels_mu = convolve_gaussian_2d(
self.img_channels, self.gaussian_kernel_1d)
# Squared mu
self.img_channels_mu_squared = self.img_channels_mu**2
# Convolve squared channels with gaussian
self.img_channels_sigma_squared = convolve_gaussian_2d(
self.img_channels_squared, self.gaussian_kernel_1d)
# Substract squared mu
self.img_channels_sigma_squared -= self.img_channels_mu_squared
# If we don't define gaussian kernel, we create
# common CW-SSIM objects
else:
# Grayscale PIL.Image
self.img_channels = self.img.convert('YCbCr')
def __init__(self, img, gaussian_kernel_1d=None, size=None):
"""Create an SSIMImage.
Args:
img (str or PIL.Image): PIL Image object or file name.
gaussian_kernel_1d (np.ndarray, optional): Gaussian kernel
that was generated with utils.get_gaussian_kernel is used
to precompute common objects for SSIM computation
size (tuple, optional): New image size to resize image to.
"""
# Use existing or create a new PIL.Image
try:
self.img = img if not isinstance(img, compat.basestring) else compat.Image.open(img)
except IOError as e:
logging.debug("Unable to open %s" % img)
raise IOError("Image probably malformed")
# Resize image if size is defined and different
# from original image
if size and size != self.img.size:
self.img = self.img.resize(size, Image.ANTIALIAS)
# Set the size of the image
self.size = self.img.size
# If gaussian kernel is defined we create
# common SSIM objects
if gaussian_kernel_1d is not None:
self.gaussian_kernel_1d = gaussian_kernel_1d
# np.array of grayscale and alpha image
self.img_gray, self.img_alpha = to_grayscale(self.img)
if self.img_alpha is not None:
self.img_gray[self.img_alpha == 255] = 0
# Squared grayscale
self.img_gray_squared = self.img_gray ** 2
# Convolve grayscale image with gaussian
self.img_gray_mu = convolve_gaussian_2d(self.img_gray, self.gaussian_kernel_1d)
# Squared mu
self.img_gray_mu_squared = self.img_gray_mu ** 2
# Convolve squared grayscale with gaussian
self.img_gray_sigma_squared = convolve_gaussian_2d(self.img_gray_squared, self.gaussian_kernel_1d)
# Substract squared mu
self.img_gray_sigma_squared -= self.img_gray_mu_squared
# If we don't define gaussian kernel, we create
# common CW-SSIM objects
else:
# Grayscale PIL.Image
self.img_gray = ImageOps.grayscale(self.img)
def ssim_value(self, target):
"""Compute the SSIM value from the reference image to the target image.
Args:
target (str or PIL.Image): Input image to compare the reference image
to. This may be a PIL Image object or, to save time, an SSIMImage
object (e.g. the img member of another SSIM object).
Returns:
Computed SSIM float value.
"""
# Performance boost if handed a compatible SSIMImage object.
if not isinstance(target, SSIMImage) or not np.array_equal(self.gaussian_kernel_1d, target.gaussian_kernel_1d):
target = SSIMImage(target, self.gaussian_kernel_1d, self.img.size)
img_mat_12 = self.img.img_gray * target.img_gray
img_mat_sigma_12 = convolve_gaussian_2d(img_mat_12, self.gaussian_kernel_1d)
img_mat_mu_12 = self.img.img_gray_mu * target.img_gray_mu
img_mat_sigma_12 = img_mat_sigma_12 - img_mat_mu_12
# Numerator of SSIM
num_ssim = (2 * img_mat_mu_12 + self.c_1) * (2 * img_mat_sigma_12 + self.c_2)
# Denominator of SSIM
den_ssim = (self.img.img_gray_mu_squared + target.img_gray_mu_squared + self.c_1) * (
self.img.img_gray_sigma_squared + target.img_gray_sigma_squared + self.c_2
)
ssim_map = num_ssim / den_ssim
index = np.average(ssim_map)
return index
def ssim_value(self, img2):
"""Compute the SSIM value from the reference image to the given image.
Args:
img2: Input image to compare the reference image to.
Returns:
Computed SSIM float value.
"""
img2 = SSIMImage(img2, self.gaussian_kernel_1d, self.img1.size)
img_mat_12 = self.img1.img_gray * img2.img_gray
img_mat_sigma_12 = convolve_gaussian_2d(
img_mat_12, self.gaussian_kernel_1d)
img_mat_mu_12 = self.img1.img_gray_mu * img2.img_gray_mu
img_mat_sigma_12 = img_mat_sigma_12 - img_mat_mu_12
# Numerator of SSIM
num_ssim = ((2 * img_mat_mu_12 + self.c_1) *
(2 * img_mat_sigma_12 + self.c_2))
# Denominator of SSIM
den_ssim = (
(self.img1.img_gray_mu_squared + img2.img_gray_mu_squared +
self.c_1) *
(self.img1.img_gray_sigma_squared +
img2.img_gray_sigma_squared + self.c_2))
ssim_map = num_ssim / den_ssim
index = numpy.average(ssim_map)
return index
def ssim_value(self, img2):
"""Compute the SSIM value from the reference image to the given image.
Args:
img2: Input image to compare the reference image to.
Returns:
Computed SSIM float value.
"""
img2 = SSIMImage(img2, self.gaussian_kernel_1d, self.img1.size)
img_mat_12 = self.img1.img_gray * img2.img_gray
img_mat_sigma_12 = convolve_gaussian_2d(img_mat_12,
self.gaussian_kernel_1d)
img_mat_mu_12 = self.img1.img_gray_mu * img2.img_gray_mu
img_mat_sigma_12 = img_mat_sigma_12 - img_mat_mu_12
# Numerator of SSIM
num_ssim = ((2 * img_mat_mu_12 + self.c_1) *
(2 * img_mat_sigma_12 + self.c_2))
# Denominator of SSIM
den_ssim = ((self.img1.img_gray_mu_squared + img2.img_gray_mu_squared +
self.c_1) * (self.img1.img_gray_sigma_squared +
img2.img_gray_sigma_squared + self.c_2))
ssim_map = num_ssim / den_ssim
index = numpy.average(ssim_map)
return index
def ssim_value(self, target):
"""Compute the SSIM value from the reference image to the target image.
Args:
target (str or PIL.Image): Input image to compare the reference image
to. This may be a PIL Image object or, to save time, an SSIMImage
object (e.g. the img member of another SSIM object).
Returns:
Computed SSIM float value.
"""
# Performance boost if handed a compatible SSIMImage object.
if not isinstance(target, SSIMImage) \
or not np.array_equal(self.gaussian_kernel_1d,
target.gaussian_kernel_1d):
target = SSIMImage(target, self.gaussian_kernel_1d, self.img.size)
img_mat_12 = self.img.img_channels * target.img_channels
img_mat_sigma_12 = convolve_gaussian_2d(img_mat_12,
self.gaussian_kernel_1d)
img_mat_mu_12 = self.img.img_channels_mu * target.img_channels_mu
img_mat_sigma_12 = img_mat_sigma_12 - img_mat_mu_12
# Numerator of SSIM
num_ssim = ((2 * img_mat_mu_12 + self.c_1) *
(2 * img_mat_sigma_12 + self.c_2))
# Denominator of SSIM
den_ssim = ((self.img.img_channels_mu_squared +
target.img_channels_mu_squared + self.c_1) *
(self.img.img_channels_sigma_squared +
target.img_channels_sigma_squared + self.c_2))
ssim_map = num_ssim / den_ssim
indexes = np.average(ssim_map, axis=(0, 1))
index = np.average(indexes, weights=self.w)
return index
