Source code for mmagic.evaluation.metrics.ssim
# Copyright (c) OpenMMLab. All rights reserved.
from typing import Optional
import cv2
import numpy as np
from mmagic.registry import METRICS
from mmagic.utils import to_numpy
from .base_sample_wise_metric import BaseSampleWiseMetric
from .metrics_utils import img_transform
@METRICS.register_module()
[docs]class SSIM(BaseSampleWiseMetric):
"""Calculate SSIM (structural similarity).
Ref:
Image quality assessment: From error visibility to structural similarity
The results are the same as that of the official released MATLAB code in
https://ece.uwaterloo.ca/~z70wang/research/ssim/.
For three-channel images, SSIM is calculated for each channel and then
averaged.
Args:
gt_key (str): Key of ground-truth. Default: 'gt_img'
pred_key (str): Key of prediction. Default: 'pred_img'
collect_device (str): Device name used for collecting results from
different ranks during distributed training. Must be 'cpu' or
'gpu'. Defaults to 'cpu'.
prefix (str, optional): The prefix that will be added in the metric
names to disambiguate homonymous metrics of different evaluators.
If prefix is not provided in the argument, self.default_prefix
will be used instead. Default: None
crop_border (int): Cropped pixels in each edges of an image. These
pixels are not involved in the PSNR calculation. Default: 0.
input_order (str): Whether the input order is 'HWC' or 'CHW'.
Default: 'HWC'.
convert_to (str): Whether to convert the images to other color models.
If None, the images are not altered. When computing for 'Y',
the images are assumed to be in BGR order. Options are 'Y' and
None. Default: None.
Metrics:
- SSIM (float): Structural similarity
"""
def __init__(self,
gt_key: str = 'gt_img',
pred_key: str = 'pred_img',
collect_device: str = 'cpu',
prefix: Optional[str] = None,
crop_border=0,
input_order='CHW',
convert_to=None) -> None:
super().__init__(
gt_key=gt_key,
pred_key=pred_key,
mask_key=None,
collect_device=collect_device,
prefix=prefix)
self.crop_border = crop_border
self.input_order = input_order
self.convert_to = convert_to
[docs] def process_image(self, gt, pred, mask):
"""Process an image.
Args:
gt (Torch | np.ndarray): GT image.
pred (Torch | np.ndarray): Pred image.
mask (Torch | np.ndarray): Mask of evaluation.
Returns:
np.ndarray: SSIM result.
"""
return ssim(
img1=gt,
img2=pred,
crop_border=self.crop_border,
input_order=self.input_order,
convert_to=self.convert_to,
channel_order=self.channel_order)
[docs]def _ssim(img1, img2):
"""Calculate SSIM (structural similarity) for one channel images.
It is called by func:`ssim`.
Args:
img1, img2 (np.ndarray): Images with range [0, 255] with order 'HWC'.
Returns:
float: SSIM result.
"""
C1 = (0.01 * 255)**2
C2 = (0.03 * 255)**2
kernel = cv2.getGaussianKernel(11, 1.5)
window = np.outer(kernel, kernel.transpose())
mu1 = cv2.filter2D(img1, -1, window)[5:-5, 5:-5]
mu2 = cv2.filter2D(img2, -1, window)[5:-5, 5:-5]
mu1_sq = mu1**2
mu2_sq = mu2**2
mu1_mu2 = mu1 * mu2
sigma1_sq = cv2.filter2D(img1**2, -1, window)[5:-5, 5:-5] - mu1_sq
sigma2_sq = cv2.filter2D(img2**2, -1, window)[5:-5, 5:-5] - mu2_sq
sigma12 = cv2.filter2D(img1 * img2, -1, window)[5:-5, 5:-5] - mu1_mu2
ssim_map = ((2 * mu1_mu2 + C1) *
(2 * sigma12 + C2)) / ((mu1_sq + mu2_sq + C1) *
(sigma1_sq + sigma2_sq + C2))
return ssim_map.mean()
[docs]def ssim(img1,
img2,
crop_border=0,
input_order='HWC',
convert_to=None,
channel_order='rgb'):
"""Calculate SSIM (structural similarity).
Ref:
Image quality assessment: From error visibility to structural similarity
The results are the same as that of the official released MATLAB code in
https://ece.uwaterloo.ca/~z70wang/research/ssim/.
For three-channel images, SSIM is calculated for each channel and then
averaged.
Args:
img1 (ndarray): Images with range [0, 255].
img2 (ndarray): Images with range [0, 255].
crop_border (int): Cropped pixels in each edges of an image. These
pixels are not involved in the SSIM calculation. Default: 0.
input_order (str): Whether the input order is 'HWC' or 'CHW'.
Default: 'HWC'.
convert_to (str): Whether to convert the images to other color models.
If None, the images are not altered. When computing for 'Y',
the images are assumed to be in BGR order. Options are 'Y' and
None. Default: None.
channel_order (str): The channel order of image. Default: 'rgb'
Returns:
float: SSIM result.
"""
assert img1.shape == img2.shape, (
f'Image shapes are different: {img1.shape}, {img2.shape}.')
img1 = img_transform(
img1,
crop_border=crop_border,
input_order=input_order,
convert_to=convert_to,
channel_order=channel_order)
img2 = img_transform(
img2,
crop_border=crop_border,
input_order=input_order,
convert_to=convert_to,
channel_order=channel_order)
img1 = to_numpy(img1)
img2 = to_numpy(img2)
ssims = []
for i in range(img1.shape[2]):
ssims.append(_ssim(img1[..., i], img2[..., i]))
return np.array(ssims).mean()