fixed normalization, can overfit on single image

utils/data_utils.py

@@ -6,7 +6,7 @@ import numpy as np
 import scipy.io as sio
 from math import cos, sin
 from pytorch3d.utils import cameras_from_opencv_projection
-
+import torchvision as tv
 
 sys.path.append(os.path.dirname(os.path.abspath(__file__)) + '/..')
 # from superSMPL.IO import readPC2Frame, readOBJ
@@ -58,6 +58,19 @@ def get_normals_from_depth(depth_image, depth_mask, camera_intrinsic):
     return normals
 
 
+def resize_image(img, mask_croped, mask_resized, size):
+    resizer = tv.transforms.Resize(size, interpolation=tv.transforms.functional.InterpolationMode.BILINEAR)
+    dilated = kornia.morphology.dilation(img, torch.ones((3,3), device=img.get_device()), engine='convolution')
+    dilated = kornia.morphology.dilation(dilated, torch.ones((3,3), device=img.get_device()), engine='convolution')
+    dilated = torch.permute(dilated, [0,2,3,1])
+    img = torch.permute(img, [0,2,3,1])
+    dilated[mask_croped, :] = img[mask_croped, :]
+    dilated = torch.permute(dilated, [0,3,1,2])
+    out = resizer(dilated)
+    out = out * mask_resized[:,None,:,:]
+    return out
+
+
 def center_crop_resize(img, new_center, size):
     pass