updated perlin

luxonis_train/loaders/perlin.py

@@ -14,7 +14,7 @@ def compute_gradients(res: tuple[int, int]) -> torch.Tensor:
 
 
 @torch.jit.script
-def lerp_torch(
+def lerp_torch(  # pragma: no cover
     x: torch.Tensor, y: torch.Tensor, w: torch.Tensor
 ) -> torch.Tensor:
     return (y - x) * w + x
@@ -92,7 +92,7 @@ def rand_perlin_2d(
 
 
 @torch.jit.script
-def rotate_noise(noise: torch.Tensor) -> torch.Tensor:
+def rotate_noise(noise: torch.Tensor) -> torch.Tensor:  # pragma: no cover
     angle = torch.rand(1) * 2 * torch.pi
     h, w = noise.shape
     center_y, center_x = h // 2, w // 2
@@ -165,11 +165,6 @@ def apply_anomaly_to_img(
         - perlin_mask (torch.Tensor): The Perlin noise mask applied to the image.
     """
 
-    if pixel_augs is None:
-
-        def pixel_augs(image):
-            return {"image": image}
-
     sampled_anomaly_image_path = random.choice(anomaly_source_paths)
 
     anomaly_image = load_image_as_numpy(sampled_anomaly_image_path)
@@ -180,7 +175,8 @@ def pixel_augs(image):
         interpolation=cv2.INTER_LINEAR,
     )
 
-    anomaly_image = pixel_augs(image=anomaly_image)["image"]
+    if pixel_augs is not None:
+        anomaly_image = pixel_augs(image=anomaly_image)["image"]
 
     anomaly_image = torch.tensor(anomaly_image).permute(2, 0, 1)