def conditional_save_img_comparison_with_intensity(self, mode, i, ele,
pred, pred_intensity,
epoch):
# save 8 images for visualization
if mode == 'val' or mode == 'eval':
skip = 100
if i == 0:
self.img_merge = vis_utils.merge_into_row_with_intensity(
ele, pred, pred_intensity)
# self.img_merge = vis_utils.merge_into_row(ele, pred)
elif i % skip == 0 and i < 8 * skip:
# row = vis_utils.merge_into_row(ele, pred)
row = vis_utils.merge_into_row_with_intensity(
ele, pred, pred_intensity)
self.img_merge = vis_utils.add_row(self.img_merge, row)
elif i == 8 * skip:
filename = self._get_img_comparison_name(mode, epoch)
vis_utils.save_image(self.img_merge, filename) #HWC
# input C x H x W
img_np_rescale = skimage.transform.rescale(np.array(
self.img_merge, dtype='float64'),
0.5,
order=0)
img_np_rescale_CHW = np.transpose(img_np_rescale, (2, 0, 1))
self.writer.add_image('comparison', img_np_rescale_CHW, i)
def conditional_save_img_comparison(self, mode, i, ele, pred, epoch):
# save 8 images for visualization
if mode == 'val' or mode == 'eval':
skip = 100
if i == 0:
self.img_merge = vis_utils.merge_into_row(ele, pred)
elif i % skip == 0 and i < 8 * skip:
row = vis_utils.merge_into_row(ele, pred)
self.img_merge = vis_utils.add_row(self.img_merge, row)
elif i == 8 * skip:
filename = self._get_img_comparison_name(mode, epoch)
vis_utils.save_image(self.img_merge, filename)