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ttt.py
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ttt.py
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import math
import os
from torch.nn import MSELoss
import cv2
import argparse
from torch import nn
import torch
import torch.nn.functional as F
from torch.autograd import Variable
import numpy as np
from math import exp
def get_args():
parser = argparse.ArgumentParser()
parser.add_argument("--sessname", default="SPS1")
parser.add_argument("--test_dir", default="./test_files")
parser.add_argument("--pred_dir", default="", help="the smoothing results dir")
parser.add_argument("--gt_dir", default="", help="the gt dir")
parser.add_argument("--title", default="")
parser.add_argument("--dataset", default="NKS", help="NKS or VOC")
return parser.parse_args()
def gaussian(window_size, sigma):
gauss = torch.Tensor([exp(-(x - window_size//2)**2/float(2*sigma**2)) for x in range(window_size)])
return gauss/gauss.sum()
def create_window(window_size, channel):
_1D_window = gaussian(window_size, 1.5).unsqueeze(1)
_2D_window = _1D_window.mm(_1D_window.t()).float().unsqueeze(0).unsqueeze(0)
window = Variable(_2D_window.expand(channel, 1, window_size, window_size).contiguous())
return window
def _ssim(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).mean(1).mean(1)
class SSIM(torch.nn.Module):
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 = create_window(window_size, self.channel)
def forward(self, img1, img2):
(_, channel, _, _) = img1.size()
if channel == self.channel and self.window.data.type() == img1.data.type():
window = self.window
else:
window = create_window(self.window_size, channel)
if img1.is_cuda:
window = window.cuda(img1.get_device())
window = window.type_as(img1)
self.window = window
self.channel = channel
return _ssim(img1, img2, window, self.window_size, channel, self.size_average)
def ssim(img1, img2, window_size = 11, size_average = True):
window = create_window(window_size, 3)
# if img1.is_cuda:
# window = window.cuda(img1.get_device())
# window = window.type_as(img1)
#
return _ssim(img1, img2, window, window_size, 3, size_average)
def cal_psnr(img1, img2):
mse = np.mean((img1/1.0 - img2/1.0) ** 2)
if mse < 1.0e-10:
return 100
return 10 * math.log10(1.0**2/mse)
def get_image(image):
image = image*[255]
image = np.clip(image, 0, 255).astype(np.uint8)
return image
if __name__ == '__main__':
args = get_args()
mse = MSELoss()
SSim = SSIM()
psnr=[]
ssim=[]
pred_path = args.pred_dir
gt_path = args.gt_dir
ind=0
mapping={}
for img in os.listdir(pred_path):
mapping[img]=ind
ind = ind+1
image_file = os.path.join(pred_path,img)
#nks
if args.dataset == "NKS":
gt_name = img.split('_')[0]
gt_file = os.path.join(gt_path,gt_name)+'.png'
#voc
elif args.dataset == "VOC":
gt_name = int(img.split('.')[0]) % 150
gt_file = os.path.join(gt_path,str(gt_name))+'.jpg'
image = (cv2.imread(str(image_file))/255.0).astype(np.float32)
gt = (cv2.imread(str(gt_file))/255.0).astype(np.float32)
image = np.transpose(image,(2,0,1))
gt = np.transpose(gt,(2,0,1))
image = torch.from_numpy(np.expand_dims(image, axis=0)).type(torch.FloatTensor)
gt = torch.from_numpy(np.expand_dims(gt, axis=0)).type(torch.FloatTensor)
p=10*torch.log10((1.0/mse(image, gt)))
psnr.append(p)
ssim.append(SSim(image, gt))
print('1')
print(sum(psnr) / len(psnr))
print(sum(ssim) / len(ssim))
filename = 'result.txt'
with open(filename,'a') as f:
f.write(args.title + " psnr:")
f.write(str(sum(psnr) / len(psnr)))
f.write(" ssim:")
f.write(str(sum(ssim) / len(ssim))+'\n')