def func(self):
# img = Image.open('Database/waterloo/distorted_images/gblurConv/00001_1.bmp').convert('RGB')
img = Image.open('Database/waterloo/pristine_images/00001.bmp').convert('RGB')
img = CenterCrop((224, 224))(img)
img.save('outPic/src_pristine.bmp', 'bmp', quality=100)
# img.show()
# print (img.size)
img = Variable(ToTensor()(img)).view(1, -1, img.size[1], img.size[0])
# print(img)
model = torch.load('%s/gblurConv.pth' % self.dir)
model = model.cuda()
input = img.cuda()
out_img = model(input)
out_img = out_img.cpu().data[0]
out_img.clamp_(0.0, 1.0)
out_img = ToPILImage()(out_img)
# out_img.save('outPic/1_1.bmp', 'bmp', quality=100)
out_img.show()
def denoisePatch(self):
if (self.load == 2):
model = torch.load('%s/%sF_ALL.pth' % (self.dir, self.typeDir))
elif (self.load == 1):
model = torch.load('%s/%sF.pth' % (self.dir, self.typeDir))
else:
model = torch.load('%s/%s.pth' % (self.dir, self.typeDir))
model = model.cuda()
# img = Image.open('Database/waterloo/pristine_images/00001.bmp').convert('RGB')
# img = CenterCrop((self.size, self.size))(img)
# img.save('outPic/0.bmp', 'bmp', quality=100)
for i in xrange(1, 5):
img = Image.open(
'Database/waterloo/distorted_images/%s/00001_%d.bmp' %
(self.typeDir, i)).convert('RGB')
# img = Image.open('Database/waterloo/pristine_images/00001.bmp').convert('RGB')
img = CenterCrop((self.size, self.size))(img)
# img.save('outPic/%d_0.bmp' % i, 'bmp', quality=100)
img.show()
# print (img.size)
img = Variable(ToTensor()(img)).view(1, -1, img.size[1],
img.size[0])
# print(img)
input = img.cuda()
out_img = model(input)
out_img = out_img.cpu()
out_img = out_img.data[0]
out_img.clamp_(0.0, 1.0)
# out_img = self.clip(out_img)
out_img = ToPILImage()(out_img)
# out_img.save('outPic/%d.bmp'%i, 'bmp', quality=100)
out_img.show()
