added visualization for min and max transforms (#271)

Co-authored-by: Simon Alibert <alibert.sim@gmail.com>

lerobot/scripts/visualize_image_transforms.py

@@ -65,11 +65,10 @@ from lerobot.common.datasets.lerobot_dataset import LeRobotDataset
 from lerobot.common.datasets.transforms import get_image_transforms
 
 OUTPUT_DIR = Path("outputs/image_transforms")
-N_EXAMPLES = 5
 to_pil = ToPILImage()
 
 
-def save_config_all_transforms(cfg, original_frame, output_dir):
+def save_config_all_transforms(cfg, original_frame, output_dir, n_examples):
     tf = get_image_transforms(
         brightness_weight=cfg.brightness.weight,
         brightness_min_max=cfg.brightness.min_max,
@@ -88,7 +87,7 @@ def save_config_all_transforms(cfg, original_frame, output_dir):
     output_dir_all = output_dir / "all"
     output_dir_all.mkdir(parents=True, exist_ok=True)
 
-    for i in range(1, N_EXAMPLES + 1):
+    for i in range(1, n_examples + 1):
         transformed_frame = tf(original_frame)
         to_pil(transformed_frame).save(output_dir_all / f"{i}.png", quality=100)
 
@@ -96,7 +95,7 @@ def save_config_all_transforms(cfg, original_frame, output_dir):
     print(f"    {output_dir_all}")
 
 
-def save_config_single_transforms(cfg, original_frame, output_dir):
+def save_config_single_transforms(cfg, original_frame, output_dir, n_examples):
     transforms = [
         "brightness",
         "contrast",
@@ -106,6 +105,7 @@ def save_config_single_transforms(cfg, original_frame, output_dir):
     ]
     print("Individual transforms examples saved to:")
     for transform in transforms:
+        # Apply one transformation with random value in min_max range
         kwargs = {
             f"{transform}_weight": cfg[f"{transform}"].weight,
             f"{transform}_min_max": cfg[f"{transform}"].min_max,
@@ -114,18 +114,46 @@ def save_config_single_transforms(cfg, original_frame, output_dir):
         output_dir_single = output_dir / f"{transform}"
         output_dir_single.mkdir(parents=True, exist_ok=True)
 
-        for i in range(1, N_EXAMPLES + 1):
+        for i in range(1, n_examples + 1):
             transformed_frame = tf(original_frame)
             to_pil(transformed_frame).save(output_dir_single / f"{i}.png", quality=100)
 
+        # Apply min transformation
+        min_value, max_value = cfg[f"{transform}"].min_max
+        kwargs = {
+            f"{transform}_weight": cfg[f"{transform}"].weight,
+            f"{transform}_min_max": (min_value, min_value),
+        }
+        tf = get_image_transforms(**kwargs)
+        transformed_frame = tf(original_frame)
+        to_pil(transformed_frame).save(output_dir_single / "min.png", quality=100)
+
+        # Apply max transformation
+        kwargs = {
+            f"{transform}_weight": cfg[f"{transform}"].weight,
+            f"{transform}_min_max": (max_value, max_value),
+        }
+        tf = get_image_transforms(**kwargs)
+        transformed_frame = tf(original_frame)
+        to_pil(transformed_frame).save(output_dir_single / "max.png", quality=100)
+
+        # Apply mean transformation
+        mean_value = (min_value + max_value) / 2
+        kwargs = {
+            f"{transform}_weight": cfg[f"{transform}"].weight,
+            f"{transform}_min_max": (mean_value, mean_value),
+        }
+        tf = get_image_transforms(**kwargs)
+        transformed_frame = tf(original_frame)
+        to_pil(transformed_frame).save(output_dir_single / "mean.png", quality=100)
+
         print(f"    {output_dir_single}")
 
 
-@hydra.main(version_base="1.2", config_name="default", config_path="../configs")
+def visualize_transforms(cfg, output_dir: Path, n_examples: int = 5):
-def visualize_transforms(cfg):
     dataset = LeRobotDataset(cfg.dataset_repo_id)
 
-    output_dir = Path(OUTPUT_DIR) / cfg.dataset_repo_id.split("/")[-1]
+    output_dir = output_dir / cfg.dataset_repo_id.split("/")[-1]
     output_dir.mkdir(parents=True, exist_ok=True)
 
     # Get 1st frame from 1st camera of 1st episode
@@ -134,8 +162,13 @@ def visualize_transforms(cfg):
     print("\nOriginal frame saved to:")
     print(f"    {output_dir / 'original_frame.png'}.")
 
-    save_config_all_transforms(cfg.training.image_transforms, original_frame, output_dir)
+    save_config_all_transforms(cfg.training.image_transforms, original_frame, output_dir, n_examples)
-    save_config_single_transforms(cfg.training.image_transforms, original_frame, output_dir)
+    save_config_single_transforms(cfg.training.image_transforms, original_frame, output_dir, n_examples)
+
+
+@hydra.main(version_base="1.2", config_name="default", config_path="../configs")
+def visualize_transforms_cli(cfg):
+    visualize_transforms(cfg, output_dir=OUTPUT_DIR)
 
 
 if __name__ == "__main__":

tests/test_image_transforms.py

@@ -26,6 +26,7 @@ from torchvision.transforms.v2 import functional as F  # noqa: N812
 from lerobot.common.datasets.lerobot_dataset import LeRobotDataset
 from lerobot.common.datasets.transforms import RandomSubsetApply, SharpnessJitter, get_image_transforms
 from lerobot.common.utils.utils import init_hydra_config, seeded_context
+from lerobot.scripts.visualize_image_transforms import visualize_transforms
 from tests.utils import DEFAULT_CONFIG_PATH, require_x86_64_kernel
 
 ARTIFACT_DIR = Path("tests/data/save_image_transforms_to_safetensors")
@@ -258,3 +259,44 @@ def test_sharpness_jitter_invalid_range_min_negative():
 def test_sharpness_jitter_invalid_range_max_smaller():
     with pytest.raises(ValueError):
         SharpnessJitter((2.0, 0.1))
+
+
+@pytest.mark.parametrize(
+    "repo_id, n_examples",
+    [
+        ("lerobot/aloha_sim_transfer_cube_human", 3),
+    ],
+)
+def test_visualize_image_transforms(repo_id, n_examples):
+    cfg = init_hydra_config(DEFAULT_CONFIG_PATH, overrides=[f"dataset_repo_id={repo_id}"])
+    output_dir = Path(__file__).parent / "outputs" / "image_transforms"
+    visualize_transforms(cfg, output_dir=output_dir, n_examples=n_examples)
+    output_dir = output_dir / repo_id.split("/")[-1]
+
+    # Check if the original frame image exists
+    assert (output_dir / "original_frame.png").exists(), "Original frame image was not saved."
+
+    # Check if the transformed images exist for each transform type
+    transforms = ["brightness", "contrast", "saturation", "hue", "sharpness"]
+    for transform in transforms:
+        transform_dir = output_dir / transform
+        assert transform_dir.exists(), f"{transform} directory was not created."
+        assert any(transform_dir.iterdir()), f"No transformed images found in {transform} directory."
+
+        # Check for specific files within each transform directory
+        expected_files = [f"{i}.png" for i in range(1, n_examples + 1)] + ["min.png", "max.png", "mean.png"]
+        for file_name in expected_files:
+            assert (
+                transform_dir / file_name
+            ).exists(), f"{file_name} was not found in {transform} directory."
+
+    # Check if the combined transforms directory exists and contains the right files
+    combined_transforms_dir = output_dir / "all"
+    assert combined_transforms_dir.exists(), "Combined transforms directory was not created."
+    assert any(
+        combined_transforms_dir.iterdir()
+    ), "No transformed images found in combined transforms directory."
+    for i in range(1, n_examples + 1):
+        assert (
+            combined_transforms_dir / f"{i}.png"
+        ).exists(), f"Combined transform image {i}.png was not found."