scikit-image update and weights link

.gitignore

@@ -0,0 +1,8 @@
+# Folders
+demo-files
+data
+
+# Files
+*.gif
+drive-download-20230118T095042Z-001.zip
+*.mp4

README.md

@@ -26,6 +26,9 @@ See description of the parameters in the ```config/vox256.yaml```. We adjust the
 ### Pre-trained checkpoints
 Checkpoints can be found in ```checkpoints``` folder. Checkpoints are large, therefore we use [git lsf](https://git-lfs.github.com/) to store them. Either use ```git lfs pull``` or download checkpoints manually from github.
 
+Alternatively from google drive using this link:
+https://drive.google.com/drive/folders/1jCeFPqfU_wKNYwof0ONICwsj3xHlr_tb?usp=sharing
+
 ### Animation Demo
 To run a demo, download a checkpoint and run the following command:
 ```bash

demo.py

@@ -33,7 +33,7 @@
 
 def load_checkpoints(config_path, checkpoint_path, cpu=False):
     with open(config_path) as f:
-        config = yaml.load(f)
+        config = yaml.load(f, Loader=yaml.FullLoader)
 
     generator = Generator(num_regions=config['model_params']['num_regions'],
                           num_channels=config['model_params']['num_channels'],

logger.py

@@ -13,7 +13,7 @@
 import imageio
 
 import os
-from skimage.draw import circle
+from skimage.draw import circle_perimeter
 
 import matplotlib.pyplot as plt
 import collections
@@ -146,7 +146,7 @@ def draw_image_with_kp(self, image, kp_array):
         kp_array = spatial_size * (kp_array + 1) / 2
         num_regions = kp_array.shape[0]
         for kp_ind, kp in enumerate(kp_array):
-            rr, cc = circle(kp[1], kp[0], self.kp_size, shape=image.shape[:2])
+            rr, cc = circle_perimeter(kp[1], kp[0], self.kp_size, shape=image.shape[:2])
             image[rr, cc] = np.array(self.colormap(kp_ind / num_regions))[:3]
         return image