# Copyright 2020 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ============================================================================
"""image"""
import numpy as np
import mindspore.common.dtype as mstype
from mindspore.common.tensor import Tensor
from mindspore.ops import operations as P
from mindspore.ops import functional as F
from mindspore.ops.primitive import constexpr
from mindspore._checkparam import Validator as validator
from mindspore._checkparam import Rel
from ..cell import Cell
__all__ = ['ImageGradients', 'SSIM', 'PSNR', 'CentralCrop']
[docs]class ImageGradients(Cell):
r"""
Returns two tensors, the first is along the height dimension and the second is along the width dimension.
Assume an image shape is :math:`h*w`. The gradients along the height and the width are :math:`dy` and :math:`dx`,
respectively.
.. math::
dy[i] = \begin{cases} image[i+1, :]-image[i, :], &if\ 0<=i<h-1 \cr
0, &if\ i==h-1\end{cases}
dx[i] = \begin{cases} image[:, i+1]-image[:, i], &if\ 0<=i<w-1 \cr
0, &if\ i==w-1\end{cases}
Inputs:
- **images** (Tensor) - The input image data, with format 'NCHW'.
Outputs:
- **dy** (Tensor) - vertical image gradients, the same type and shape as input.
- **dx** (Tensor) - horizontal image gradients, the same type and shape as input.
Examples:
>>> net = nn.ImageGradients()
>>> image = Tensor(np.array([[[[1,2],[3,4]]]]), dtype=mstype.int32)
>>> net(image)
[[[[2,2]
[0,0]]]]
[[[[1,0]
[1,0]]]]
"""
def __init__(self):
super(ImageGradients, self).__init__()
def construct(self, images):
check = _check_input_4d(F.shape(images), "images", self.cls_name)
images = F.depend(images, check)
batch_size, depth, height, width = P.Shape()(images)
dy = images[:, :, 1:, :] - images[:, :, :height - 1, :]
dy_last = P.Fill()(P.DType()(images), (batch_size, depth, 1, width), 0)
dy = P.Concat(2)((dy, dy_last))
dx = images[:, :, :, 1:] - images[:, :, :, :width - 1]
dx_last = P.Fill()(P.DType()(images), (batch_size, depth, height, 1), 0)
dx = P.Concat(3)((dx, dx_last))
return dy, dx
def _convert_img_dtype_to_float32(img, max_val):
"""convert img dtype to float32"""
# Ususally max_val is 1.0 or 255, we will do the scaling if max_val > 1.
# We will scale img pixel value if max_val > 1. and just cast otherwise.
ret = F.cast(img, mstype.float32)
max_val = F.scalar_cast(max_val, mstype.float32)
if max_val > 1.:
scale = 1. / max_val
ret = ret * scale
return ret
@constexpr
def _gauss_kernel_helper(filter_size):
"""gauss kernel helper"""
filter_size = F.scalar_cast(filter_size, mstype.int32)
coords = ()
for i in range(filter_size):
i_cast = F.scalar_cast(i, mstype.float32)
offset = F.scalar_cast(filter_size-1, mstype.float32)/2.0
element = i_cast-offset
coords = coords+(element,)
g = np.square(coords).astype(np.float32)
g = Tensor(g)
return filter_size, g
@constexpr
def _check_input_4d(input_shape, param_name, func_name):
if len(input_shape) != 4:
raise ValueError(f"{func_name} {param_name} should be 4d, but got shape {input_shape}")
return True
@constexpr
def _check_input_filter_size(input_shape, param_name, filter_size, func_name):
_check_input_4d(input_shape, param_name, func_name)
validator.check(param_name + " shape[2]", input_shape[2], "filter_size", filter_size, Rel.GE, func_name)
validator.check(param_name + " shape[3]", input_shape[3], "filter_size", filter_size, Rel.GE, func_name)
[docs]class SSIM(Cell):
r"""
Returns SSIM index between img1 and img2.
Its implementation is based on Wang, Z., Bovik, A. C., Sheikh, H. R., & Simoncelli, E. P. (2004). `Image quality
assessment: from error visibility to structural similarity <https://ieeexplore.ieee.org/document/1284395>`_.
IEEE transactions on image processing.
.. math::
l(x,y)&=\frac{2\mu_x\mu_y+C_1}{\mu_x^2+\mu_y^2+C_1}, C_1=(K_1L)^2.\\
c(x,y)&=\frac{2\sigma_x\sigma_y+C_2}{\sigma_x^2+\sigma_y^2+C_2}, C_2=(K_2L)^2.\\
s(x,y)&=\frac{\sigma_{xy}+C_3}{\sigma_x\sigma_y+C_3}, C_3=C_2/2.\\
SSIM(x,y)&=l*c*s\\&=\frac{(2\mu_x\mu_y+C_1)(2\sigma_{xy}+C_2}{(\mu_x^2+\mu_y^2+C_1)(\sigma_x^2+\sigma_y^2+C_2)}.
Args:
max_val (Union[int, float]): The dynamic range of the pixel values (255 for 8-bit grayscale images).
Default: 1.0.
filter_size (int): The size of the Gaussian filter. Default: 11.
filter_sigma (float): The standard deviation of Gaussian kernel. Default: 1.5.
k1 (float): The constant used to generate c1 in the luminance comparison function. Default: 0.01.
k2 (float): The constant used to generate c2 in the contrast comparison function. Default: 0.03.
Inputs:
- **img1** (Tensor) - The first image batch with format 'NCHW'. It should be the same shape and dtype as img2.
- **img2** (Tensor) - The second image batch with format 'NCHW'. It should be the same shape and dtype as img1.
Outputs:
Tensor, has the same dtype as img1. It is a 1-D tensor with shape N, where N is the batch num of img1.
Examples:
>>> net = nn.SSIM()
>>> img1 = Tensor(np.random.random((1,3,16,16)))
>>> img2 = Tensor(np.random.random((1,3,16,16)))
>>> ssim = net(img1, img2)
"""
def __init__(self, max_val=1.0, filter_size=11, filter_sigma=1.5, k1=0.01, k2=0.03):
super(SSIM, self).__init__()
validator.check_value_type('max_val', max_val, [int, float], self.cls_name)
validator.check_number('max_val', max_val, 0.0, Rel.GT, self.cls_name)
self.max_val = max_val
self.filter_size = validator.check_integer('filter_size', filter_size, 1, Rel.GE, self.cls_name)
self.filter_sigma = validator.check_float_positive('filter_sigma', filter_sigma, self.cls_name)
validator.check_value_type('k1', k1, [float], self.cls_name)
self.k1 = validator.check_number_range('k1', k1, 0.0, 1.0, Rel.INC_NEITHER, self.cls_name)
validator.check_value_type('k2', k2, [float], self.cls_name)
self.k2 = validator.check_number_range('k2', k2, 0.0, 1.0, Rel.INC_NEITHER, self.cls_name)
self.mean = P.DepthwiseConv2dNative(channel_multiplier=1, kernel_size=filter_size)
def construct(self, img1, img2):
_check_input_filter_size(F.shape(img1), "img1", self.filter_size, self.cls_name)
P.SameTypeShape()(img1, img2)
max_val = _convert_img_dtype_to_float32(self.max_val, self.max_val)
img1 = _convert_img_dtype_to_float32(img1, self.max_val)
img2 = _convert_img_dtype_to_float32(img2, self.max_val)
kernel = self._fspecial_gauss(self.filter_size, self.filter_sigma)
kernel = P.Tile()(kernel, (1, P.Shape()(img1)[1], 1, 1))
mean_ssim = self._calculate_mean_ssim(img1, img2, kernel, max_val, self.k1, self.k2)
return mean_ssim
def _calculate_mean_ssim(self, x, y, kernel, max_val, k1, k2):
"""calculate mean ssim"""
c1 = (k1 * max_val) * (k1 * max_val)
c2 = (k2 * max_val) * (k2 * max_val)
# SSIM luminance formula
# (2 * mean_{x} * mean_{y} + c1) / (mean_{x}**2 + mean_{y}**2 + c1)
mean_x = self.mean(x, kernel)
mean_y = self.mean(y, kernel)
square_sum = F.square(mean_x)+F.square(mean_y)
luminance = (2*mean_x*mean_y+c1)/(square_sum+c1)
# SSIM contrast*structure formula (when c3 = c2/2)
# (2 * conv_{xy} + c2) / (conv_{xx} + conv_{yy} + c2), equals to
# (2 * (mean_{xy} - mean_{x}*mean_{y}) + c2) / (mean_{xx}-mean_{x}**2 + mean_{yy}-mean_{y}**2 + c2)
mean_xy = self.mean(x*y, kernel)
mean_square_add = self.mean(F.square(x)+F.square(y), kernel)
cs = (2*(mean_xy-mean_x*mean_y)+c2)/(mean_square_add-square_sum+c2)
# SSIM formula
# luminance * cs
ssim = luminance*cs
mean_ssim = P.ReduceMean()(ssim, (-3, -2, -1))
return mean_ssim
def _fspecial_gauss(self, filter_size, filter_sigma):
"""get gauss kernel"""
filter_size, g = _gauss_kernel_helper(filter_size)
square_sigma_scale = -0.5/(filter_sigma * filter_sigma)
g = g*square_sigma_scale
g = F.reshape(g, (1, -1))+F.reshape(g, (-1, 1))
g = F.reshape(g, (1, -1))
g = P.Softmax()(g)
ret = F.reshape(g, (1, 1, filter_size, filter_size))
return ret
[docs]class PSNR(Cell):
r"""
Returns Peak Signal-to-Noise Ratio of two image batches.
It produces a PSNR value for each image in batch.
Assume inputs are :math:`I` and :math:`K`, both with shape :math:`h*w`.
:math:`MAX` represents the dynamic range of pixel values.
.. math::
MSE&=\frac{1}{hw}\sum\limits_{i=0}^{h-1}\sum\limits_{j=0}^{w-1}[I(i,j)-K(i,j)]^2\\
PSNR&=10*log_{10}(\frac{MAX^2}{MSE})
Args:
max_val (Union[int, float]): The dynamic range of the pixel values (255 for 8-bit grayscale images).
Default: 1.0.
Inputs:
- **img1** (Tensor) - The first image batch with format 'NCHW'. It should be the same shape and dtype as img2.
- **img2** (Tensor) - The second image batch with format 'NCHW'. It should be the same shape and dtype as img1.
Outputs:
Tensor, with dtype mindspore.float32. It is a 1-D tensor with shape N, where N is the batch num of img1.
Examples:
>>> net = nn.PSNR()
>>> img1 = Tensor(np.random.random((1,3,16,16)))
>>> img2 = Tensor(np.random.random((1,3,16,16)))
>>> psnr = net(img1, img2)
"""
def __init__(self, max_val=1.0):
super(PSNR, self).__init__()
validator.check_value_type('max_val', max_val, [int, float], self.cls_name)
validator.check_number('max_val', max_val, 0.0, Rel.GT, self.cls_name)
self.max_val = max_val
def construct(self, img1, img2):
_check_input_4d(F.shape(img1), "img1", self.cls_name)
_check_input_4d(F.shape(img2), "img2", self.cls_name)
P.SameTypeShape()(img1, img2)
max_val = _convert_img_dtype_to_float32(self.max_val, self.max_val)
img1 = _convert_img_dtype_to_float32(img1, self.max_val)
img2 = _convert_img_dtype_to_float32(img2, self.max_val)
mse = P.ReduceMean()(F.square(img1 - img2), (-3, -2, -1))
# 10*log_10(max_val^2/MSE)
psnr = 10 * P.Log()(F.square(max_val) / mse) / F.scalar_log(10.0)
return psnr
@constexpr
def _raise_dims_rank_error(input_shape, param_name, func_name):
"""raise error if input is not 3d or 4d"""
raise ValueError(f"{func_name} {param_name} should be 3d or 4d, but got shape {input_shape}")
@constexpr
def _get_bbox(rank, shape, central_fraction):
"""get bbox start and size for slice"""
if rank == 3:
c, h, w = shape
else:
n, c, h, w = shape
central_fraction = central_fraction.asnumpy()[0]
bbox_h_start = int((float(h) - float(h) * central_fraction) / 2)
bbox_w_start = int((float(w) - float(w) * central_fraction) / 2)
bbox_h_size = h - bbox_h_start * 2
bbox_w_size = w - bbox_w_start * 2
if rank == 3:
bbox_begin = (0, bbox_h_start, bbox_w_start)
bbox_size = (c, bbox_h_size, bbox_w_size)
else:
bbox_begin = (0, 0, bbox_h_start, bbox_w_start)
bbox_size = (n, c, bbox_h_size, bbox_w_size)
return bbox_begin, bbox_size
[docs]class CentralCrop(Cell):
"""
Crop the centeral region of the images with the central_fraction.
Args:
central_fraction (float): Fraction of size to crop. It must be float and in range (0.0, 1.0].
Inputs:
- **image** (Tensor) - A 3-D tensor of shape [C, H, W], or a 4-D tensor of shape [N, C, H, W].
Outputs:
Tensor, 3-D or 4-D float tensor, according to the input.
Examples:
>>> net = nn.CentralCrop(central_fraction=0.5)
>>> image = Tensor(np.random.random((4, 3, 4, 4)), mindspore.float32)
>>> output = net(image)
"""
def __init__(self, central_fraction):
super(CentralCrop, self).__init__()
validator.check_value_type("central_fraction", central_fraction, [float], self.cls_name)
self.central_fraction = validator.check_number_range('central_fraction', central_fraction,
0.0, 1.0, Rel.INC_RIGHT, self.cls_name)
self.central_fraction_tensor = Tensor(np.array([central_fraction]).astype(np.float64))
self.slice = P.Slice()
def construct(self, image):
image_shape = F.shape(image)
rank = len(image_shape)
if not rank in (3, 4):
return _raise_dims_rank_error(image_shape, "image", self.cls_name)
if self.central_fraction == 1.0:
return image
bbox_begin, bbox_size = _get_bbox(rank, image_shape, self.central_fraction_tensor)
image = self.slice(image, bbox_begin, bbox_size)
return image