Updated formatting

lerobot/common/datasets/transforms.py

@@ -47,11 +47,15 @@ class RandomSubsetApply(Transform):
 
 def make_transforms(cfg):
     image_transforms = []
-    if 'colorjitter' in cfg.list:
-        image_transforms.append(v2.ColorJitter(brightness=cfg.colorjitter_factor, contrast=cfg.colorjitter_factor))
-    if 'sharpness' in cfg.list:
+    if "colorjitter" in cfg.list:
+        image_transforms.append(
+            v2.ColorJitter(brightness=cfg.colorjitter_factor, contrast=cfg.colorjitter_factor)
+        )
+    if "sharpness" in cfg.list:
         image_transforms.append(v2.RandomAdjustSharpness(cfg.sharpness_factor, p=cfg.sharpness_p))
-    if 'blur' in cfg.list:
+    if "blur" in cfg.list:
         image_transforms.append(v2.RandomAdjustSharpness(cfg.blur_factor, p=cfg.blur_p))
 
-    return v2.Compose([RandomSubsetApply(image_transforms, n_subset=cfg.n_subset), v2.ToDtype(torch.float32, scale=True)])
+    return v2.Compose(
+        [RandomSubsetApply(image_transforms, n_subset=cfg.n_subset), v2.ToDtype(torch.float32, scale=True)]
+    )

lerobot/configs/default.yaml

@@ -64,10 +64,10 @@ image_transform:
   colorjitter_p: 1.O
   sharpness_factor: 3.0
   # Should be more than 1, setting parameter to 1 does not change the image
-  sharpness_p: 0.5 
+  sharpness_p: 0.5
   blur_factor: 0.2
   # Should be less than 1, setting parameter to 1 does not change the image
-  blur_p: 0.5 
+  blur_p: 0.5
   n_subset: 3
   # Maximum number of transforms to apply
   list: ["colorjitter", "sharpness", "blur"]

lerobot/scripts/show_image_transforms.py

@@ -1,15 +1,14 @@
-
-from lerobot.common.utils.utils import init_hydra_config
-from lerobot.common.datasets.lerobot_dataset import LeRobotDataset
-from lerobot.common.datasets.transforms import make_transforms
-
 from pathlib import Path
 
 import matplotlib.pyplot as plt
 
+from lerobot.common.datasets.lerobot_dataset import LeRobotDataset
+from lerobot.common.datasets.transforms import make_transforms
+from lerobot.common.utils.utils import init_hydra_config
 
 DEFAULT_CONFIG_PATH = "configs/default.yaml"
 
+
 def show_image_transforms(cfg, repo_id, episode_index=0, output_dir="outputs/show_image_transforms"):
     """
     Apply a series of image transformations to a frame from a dataset and save the transformed images.
@@ -20,7 +19,7 @@ def show_image_transforms(cfg, repo_id, episode_index=0, output_dir="outputs/sho
         episode_index (int, optional): The index of the episode to use. Defaults to 0.
         output_dir (str, optional): The directory to save the transformed images. Defaults to "outputs/show_image_transforms".
     """
-  
+
     dataset = LeRobotDataset(repo_id)
 
     print(f"Getting frame from camera: {dataset.camera_keys[0]}")
@@ -41,25 +40,27 @@ def show_image_transforms(cfg, repo_id, episode_index=0, output_dir="outputs/sho
                 "image_transform.enable=True",
                 "image_transform.n_subset=1",
                 f"image_transform.{transform}_p=1",
-            ])
-        
+            ],
+        )
+
         cfg = cfg.image_transform
 
         t = make_transforms(cfg)
-        
+
         # Apply transformation to frame
         transformed_frame = t(frame)
         transformed_frame = transformed_frame.permute(1, 2, 0).numpy()
 
         # Save transformed frame
         plt.imshow(transformed_frame)
-        plt.savefig(f'{base_filename}_max_transform_{transform}.png')
+        plt.savefig(f"{base_filename}_max_transform_{transform}.png")
         plt.close()
 
     frame = frame.permute(1, 2, 0).numpy()
+
     # Save original frame
     plt.imshow(frame)
-    plt.savefig(f'{base_filename}_original.png')
+    plt.savefig(f"{base_filename}_original.png")
     plt.close()
 
-    print(f"Saved transformed images.")
+    print("Saved transformed images.")