Fix to gray (#2050)

* Empty-Commit

* Cleanup

* Cleanup

albumentations/augmentations/domain_adaptation/functional.py

@@ -277,9 +277,9 @@ def fourier_domain_adaptation(img: np.ndarray, target_img: np.ndarray, beta: flo
     src_img = img.astype(np.float32)
     trg_img = target_img.astype(np.float32)
 
-    if len(src_img.shape) == MONO_CHANNEL_DIMENSIONS:
+    if src_img.ndim == MONO_CHANNEL_DIMENSIONS:
         src_img = np.expand_dims(src_img, axis=-1)
-    if len(trg_img.shape) == MONO_CHANNEL_DIMENSIONS:
+    if trg_img.ndim == MONO_CHANNEL_DIMENSIONS:
         trg_img = np.expand_dims(trg_img, axis=-1)
 
     num_channels = src_img.shape[-1]

albumentations/augmentations/domain_adaptation/transforms.py

@@ -511,8 +511,8 @@ def get_params_dependent_on_data(self, params: dict[str, Any], data: dict[str, A
             template = fgeometric.resize(template, img.shape[:2], interpolation=cv2.INTER_AREA)
 
         if get_num_channels(template) == 1 and get_num_channels(img) > 1:
-            template = np.stack((template,) * get_num_channels(img), axis=-1)
-
+            # Replicate single channel template across all channels to match input image
+            template = cv2.merge([template] * get_num_channels(img))
         # in order to support grayscale image with dummy dim
         template = template.reshape(img.shape)
 

albumentations/augmentations/functional.py

@@ -148,7 +148,7 @@ def solarize(img: np.ndarray, threshold: int) -> np.ndarray:
         prev_shape = img.shape
         img = sz_lut(img, np.array(lut, dtype=dtype), inplace=False)
 
-        return np.expand_dims(img, -1) if len(prev_shape) != len(img.shape) else img
+        return np.expand_dims(img, -1) if len(prev_shape) != img.ndim else img
 
     cond = img >= threshold
     img[cond] = max_val - img[cond]
@@ -1300,14 +1300,16 @@ def grayscale_to_multichannel(grayscale_image: np.ndarray, num_output_channels:
         num_output_channels (int, optional): Number of channels in the output image. Defaults to 3.
 
     Returns:
-        np.ndarray: Multi-channel image with shape (height, width, num_channels).
-
-    Note:
-        If the input is already a multi-channel image with the desired number of channels,
-        it will be returned unchanged.
+        np.ndarray: Multi-channel image with shape (height, width, num_channels)
     """
-    grayscale_image = grayscale_image.copy().squeeze()
-    return np.stack([grayscale_image] * num_output_channels, axis=-1)
+    # If output should be single channel, just squeeze and return
+    if num_output_channels == 1:
+        return grayscale_image
+
+    # For multi-channel output, squeeze and stack
+    squeezed = np.squeeze(grayscale_image)
+
+    return cv2.merge([squeezed] * num_output_channels)
 
 
 @preserve_channel_dim

albumentations/augmentations/geometric/functional.py

@@ -1814,9 +1814,10 @@ def bbox_distort_image(
     bboxes = bboxes.copy()
     masks = masks_from_bboxes(bboxes, image_shape)
 
-    transformed_masks = np.stack(
-        [distort_image(mask, generated_mesh, cv2.INTER_NEAREST) for mask in masks],
-    )
+    transformed_masks = cv2.merge([distort_image(mask, generated_mesh, cv2.INTER_NEAREST) for mask in masks])
+
+    if transformed_masks.ndim == NUM_MULTI_CHANNEL_DIMENSIONS:
+        transformed_masks = transformed_masks.transpose(2, 0, 1)
 
     # Normalize the returned bboxes
     bboxes[:, :4] = bboxes_from_masks(transformed_masks)

albumentations/augmentations/text/functional.py

@@ -64,11 +64,11 @@ def convert_image_to_pil(image: np.ndarray) -> Image:
     except ImportError:
         raise ImportError("Pillow is not installed") from ImportError
 
-    if len(image.shape) == MONO_CHANNEL_DIMENSIONS:  # (height, width)
+    if image.ndim == MONO_CHANNEL_DIMENSIONS:  # (height, width)
         return Image.fromarray(image)
-    if len(image.shape) == NUM_MULTI_CHANNEL_DIMENSIONS and image.shape[2] == 1:  # (height, width, 1)
+    if image.ndim == NUM_MULTI_CHANNEL_DIMENSIONS and image.shape[2] == 1:  # (height, width, 1)
         return Image.fromarray(image[:, :, 0], mode="L")
-    if len(image.shape) == NUM_MULTI_CHANNEL_DIMENSIONS and image.shape[2] == NUM_RGB_CHANNELS:  # (height, width, 3)
+    if image.ndim == NUM_MULTI_CHANNEL_DIMENSIONS and image.shape[2] == NUM_RGB_CHANNELS:  # (height, width, 3)
         return Image.fromarray(image)
 
     raise TypeError(f"Unsupported image shape: {image.shape}")