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# copyright (c) 2022 PaddlePaddle Authors. All Rights Reserve.
#
# 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.
"""
https://github.com/FudanVI/FudanOCR/blob/main/text-gestalt/utils/ssim_psnr.py
"""
from math import exp
import paddle
import paddle.nn.functional as F
import paddle.nn as nn
import string
class SSIM(nn.Layer):
def __init__(self, window_size=11, size_average=True):
super(SSIM, self).__init__()
self.window_size = window_size
self.size_average = size_average
self.channel = 1
self.window = self.create_window(window_size, self.channel)
def gaussian(self, window_size, sigma):
gauss = paddle.to_tensor([
exp(-(x - window_size // 2)**2 / float(2 * sigma**2))
for x in range(window_size)
])
return gauss / gauss.sum()
def create_window(self, window_size, channel):
_1D_window = self.gaussian(window_size, 1.5).unsqueeze(1)
_2D_window = _1D_window.mm(_1D_window.t()).unsqueeze(0).unsqueeze(0)
window = _2D_window.expand([channel, 1, window_size, window_size])
return window
def _ssim(self, img1, img2, window, window_size, channel,
size_average=True):
mu1 = F.conv2d(img1, window, padding=window_size // 2, groups=channel)
mu2 = F.conv2d(img2, window, padding=window_size // 2, groups=channel)
mu1_sq = mu1.pow(2)
mu2_sq = mu2.pow(2)
mu1_mu2 = mu1 * mu2
sigma1_sq = F.conv2d(
img1 * img1, window, padding=window_size // 2,
groups=channel) - mu1_sq
sigma2_sq = F.conv2d(
img2 * img2, window, padding=window_size // 2,
groups=channel) - mu2_sq
sigma12 = F.conv2d(
img1 * img2, window, padding=window_size // 2,
groups=channel) - mu1_mu2
C1 = 0.01**2
C2 = 0.03**2
ssim_map = ((2 * mu1_mu2 + C1) * (2 * sigma12 + C2)) / (
(mu1_sq + mu2_sq + C1) * (sigma1_sq + sigma2_sq + C2))
if size_average:
return ssim_map.mean()
else:
return ssim_map.mean([1, 2, 3])
def ssim(self, img1, img2, window_size=11, size_average=True):
(_, channel, _, _) = img1.shape
window = self.create_window(window_size, channel)
return self._ssim(img1, img2, window, window_size, channel,
size_average)
def forward(self, img1, img2):
(_, channel, _, _) = img1.shape
if channel == self.channel and self.window.dtype == img1.dtype:
window = self.window
else:
window = self.create_window(self.window_size, channel)
self.window = window
self.channel = channel
return self._ssim(img1, img2, window, self.window_size, channel,
self.size_average)
class SRMetric(object):
def __init__(self, main_indicator='all', **kwargs):
self.main_indicator = main_indicator
self.eps = 1e-5
self.psnr_result = []
self.ssim_result = []
self.calculate_ssim = SSIM()
self.reset()
def reset(self):
self.correct_num = 0
self.all_num = 0
self.norm_edit_dis = 0
self.psnr_result = []
self.ssim_result = []
def calculate_psnr(self, img1, img2):
# img1 and img2 have range [0, 1]
mse = ((img1 * 255 - img2 * 255)**2).mean()
if mse == 0:
return float('inf')
return 20 * paddle.log10(255.0 / paddle.sqrt(mse))
def _normalize_text(self, text):
text = ''.join(
filter(lambda x: x in (string.digits + string.ascii_letters), text))
return text.lower()
def __call__(self, pred_label, *args, **kwargs):
metric = {}
images_sr = pred_label["sr_img"]
images_hr = pred_label["hr_img"]
psnr = self.calculate_psnr(images_sr, images_hr)
ssim = self.calculate_ssim(images_sr, images_hr)
self.psnr_result.append(psnr)
self.ssim_result.append(ssim)
def get_metric(self):
"""
return metrics {
'acc': 0,
'norm_edit_dis': 0,
}
"""
self.psnr_avg = sum(self.psnr_result) / len(self.psnr_result)
self.psnr_avg = round(self.psnr_avg.item(), 6)
self.ssim_avg = sum(self.ssim_result) / len(self.ssim_result)
self.ssim_avg = round(self.ssim_avg.item(), 6)
self.all_avg = self.psnr_avg + self.ssim_avg
self.reset()
return {
'psnr_avg': self.psnr_avg,
"ssim_avg": self.ssim_avg,
"all": self.all_avg
}
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