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()