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mmagic.evaluation.metrics.ssim 源代码

# 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()
[文档]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 """
[文档] metric = 'SSIM'
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
[文档] 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)
[文档]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()
[文档]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()
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