def save_result(self, epoch=None):
for w in range(len(self.pose)):
for i, syn_img in enumerate(self.syn_image[w].data):
if self.is_Train:
if i == self.batchsize[w]:
img = self.image[w].data[-1]
filename = 'mixed_' + self.name[w][-1]
else:
img = self.image[w].data[i]
filename = self.name[w][i]
else:
if i == self.batchsize[w]:
if not self.frontal_image[w].size() == torch.Size(
[3, 96, 96]):
img = self.frontal_image[w].data[0]
else:
img = self.frontal_image[w].data
filename = 'mixed_' + self.frontal_name[w][0]
else:
img = self.profile_image[w].data[i]
filename = self.profile_name[w][i]
if epoch:
filename = 'epoch{0}_{1}'.format(epoch, filename)
path = os.path.join(self.result_dir, filename)
img = Tensor2Image(img)
syn_img = Tensor2Image(syn_img)
width, height = img.size
result_img = Image.new(img.mode, (width * 2, height))
result_img.paste(img, (0, 0, width, height))
result_img.paste(syn_img, box=(width, 0))
result_img.save(path)
def save_result(self, epoch=None):
for i, syn_img in enumerate(self.syn_image.data):
img = self.image.data[i]
filename = self.name[i]
if epoch:
filename = 'epoch{0}_{1}'.format(epoch, filename)
path = os.path.join(self.result_dir, filename)
img = Tensor2Image(img)
syn_img = Tensor2Image(syn_img)
width, height = img.size
result_img = Image.new(img.mode, (width * 2, height))
result_img.paste(img, (0, 0, width, height))
result_img.paste(syn_img, box=(width, 0))
result_img.save(path)