Deprecated IAAFliplr, IAAFlipud, IAAAdditiveGaussianNoise

README.md

@@ -129,7 +129,6 @@ Pixel-level transforms will change just an input image and will leave any additi
 - [GlassBlur](https://albumentations.ai/docs/api_reference/augmentations/transforms/#albumentations.augmentations.transforms.GlassBlur)
 - [HistogramMatching](https://albumentations.ai/docs/api_reference/augmentations/domain_adaptation/#albumentations.augmentations.domain_adaptation.HistogramMatching)
 - [HueSaturationValue](https://albumentations.ai/docs/api_reference/augmentations/transforms/#albumentations.augmentations.transforms.HueSaturationValue)
-- [IAAAdditiveGaussianNoise](https://albumentations.ai/docs/api_reference/imgaug/transforms/#albumentations.imgaug.transforms.IAAAdditiveGaussianNoise)
 - [IAASuperpixels](https://albumentations.ai/docs/api_reference/imgaug/transforms/#albumentations.imgaug.transforms.IAASuperpixels)
 - [ISONoise](https://albumentations.ai/docs/api_reference/augmentations/transforms/#albumentations.augmentations.transforms.ISONoise)
 - [ImageCompression](https://albumentations.ai/docs/api_reference/augmentations/transforms/#albumentations.augmentations.transforms.ImageCompression)
@@ -169,8 +168,6 @@ Spatial-level transforms will simultaneously change both an input image as well
 | [GridDropout](https://albumentations.ai/docs/api_reference/augmentations/transforms/#albumentations.augmentations.transforms.GridDropout)                                       | ✓     | ✓     |        |           |
 | [HorizontalFlip](https://albumentations.ai/docs/api_reference/augmentations/transforms/#albumentations.augmentations.transforms.HorizontalFlip)                                 | ✓     | ✓     | ✓      | ✓         |
 | [IAAAffine](https://albumentations.ai/docs/api_reference/imgaug/transforms/#albumentations.imgaug.transforms.IAAAffine)                                                         | ✓     | ✓     | ✓      | ✓         |
-| [IAAFliplr](https://albumentations.ai/docs/api_reference/imgaug/transforms/#albumentations.imgaug.transforms.IAAFliplr)                                                         | ✓     | ✓     | ✓      | ✓         |
-| [IAAFlipud](https://albumentations.ai/docs/api_reference/imgaug/transforms/#albumentations.imgaug.transforms.IAAFlipud)                                                         | ✓     | ✓     | ✓      | ✓         |
 | [IAAPiecewiseAffine](https://albumentations.ai/docs/api_reference/imgaug/transforms/#albumentations.imgaug.transforms.IAAPiecewiseAffine)                                       | ✓     | ✓     | ✓      | ✓         |
 | [Lambda](https://albumentations.ai/docs/api_reference/augmentations/transforms/#albumentations.augmentations.transforms.Lambda)                                                 | ✓     | ✓     | ✓      | ✓         |
 | [LongestMaxSize](https://albumentations.ai/docs/api_reference/augmentations/geometric/resize/#albumentations.augmentations.geometric.resize.LongestMaxSize)                     | ✓     | ✓     | ✓      | ✓         |

albumentations/augmentations/transforms.py

@@ -1688,6 +1688,8 @@ class GaussNoise(ImageOnlyTransform):
         var_limit ((float, float) or float): variance range for noise. If var_limit is a single float, the range
             will be (0, var_limit). Default: (10.0, 50.0).
         mean (float): mean of the noise. Default: 0
+        per_channel (bool): if set to True, noise will be sampled for each channel independently.
+            Otherwise, the noise will be sampled once for all channels. Default: True
         p (float): probability of applying the transform. Default: 0.5.
 
     Targets:
@@ -1697,7 +1699,7 @@ class GaussNoise(ImageOnlyTransform):
         uint8, float32
     """
 
-    def __init__(self, var_limit=(10.0, 50.0), mean=0, always_apply=False, p=0.5):
+    def __init__(self, var_limit=(10.0, 50.0), mean=0, per_channel=True, always_apply=False, p=0.5):
         super(GaussNoise, self).__init__(always_apply, p)
         if isinstance(var_limit, (tuple, list)):
             if var_limit[0] < 0:
@@ -1716,6 +1718,7 @@ def __init__(self, var_limit=(10.0, 50.0), mean=0, always_apply=False, p=0.5):
             )
 
         self.mean = mean
+        self.per_channel = per_channel
 
     def apply(self, img, gauss=None, **params):
         return F.gauss_noise(img, gauss=gauss)
@@ -1726,15 +1729,21 @@ def get_params_dependent_on_targets(self, params):
         sigma = var ** 0.5
         random_state = np.random.RandomState(random.randint(0, 2 ** 32 - 1))
 
-        gauss = random_state.normal(self.mean, sigma, image.shape)
+        if self.per_channel:
+            gauss = random_state.normal(self.mean, sigma, image.shape)
+        else:
+            gauss = random_state.normal(self.mean, sigma, image.shape[:2])
+            if len(image.shape) == 3:
+                gauss = np.expand_dims(gauss, -1)
+
         return {"gauss": gauss}
 
     @property
     def targets_as_params(self):
         return ["image"]
 
     def get_transform_init_args_names(self):
-        return ("var_limit",)
+        return ("var_limit", "per_channel", "mean")
 
 
 class ISONoise(ImageOnlyTransform):

albumentations/imgaug/transforms.py

@@ -109,6 +109,10 @@ def get_transform_init_args_names(self):
 
 
 class IAAFliplr(DualIAATransform):
+    def __init__(self, always_apply=False, p=0.5):
+        super().__init__(always_apply, p)
+        warnings.warn("This augmentation is deprecated. Please use HorizontalFlip instead.", DeprecationWarning)
+
     @property
     def processor(self):
         return iaa.Fliplr(1)
@@ -118,6 +122,10 @@ def get_transform_init_args_names(self):
 
 
 class IAAFlipud(DualIAATransform):
+    def __init__(self, always_apply=False, p=0.5):
+        super().__init__(always_apply, p)
+        warnings.warn("This augmentation is deprecated. Please use VerticalFlip instead.", DeprecationWarning)
+
     @property
     def processor(self):
         return iaa.Flipud(1)
@@ -208,6 +216,7 @@ def __init__(self, loc=0, scale=(0.01 * 255, 0.05 * 255), per_channel=False, alw
         self.loc = loc
         self.scale = to_tuple(scale, 0.0)
         self.per_channel = per_channel
+        warnings.warn("This augmentation is deprecated. Please use GaussNoise instead", DeprecationWarning)
 
     @property
     def processor(self):

tests/test_serialization.py

@@ -111,7 +111,7 @@ def test_augmentations_serialization(augmentation_cls, params, p, seed, image, m
         [A.MotionBlur, {"blur_limit": 3}],
         [A.MedianBlur, {"blur_limit": 3}],
         [A.GaussianBlur, {"blur_limit": 3}],
-        [A.GaussNoise, {"var_limit": (20, 90)}],
+        [A.GaussNoise, {"var_limit": (20, 90), "mean": 10, "per_channel": False}],
         [A.CLAHE, {"clip_limit": 2, "tile_grid_size": (12, 12)}],
         [A.RandomGamma, {"gamma_limit": (10, 90)}],
         [A.Cutout, {"num_holes": 4, "max_h_size": 4, "max_w_size": 4}],

tools/make_transforms_docs.py

@@ -132,24 +132,27 @@ def check_docs(filepath, image_only_transforms_links, dual_transforms_table):
         if line not in text:
             dual_lines_not_in_text.append(line)
             outdated_docs.update(["Spatial-level"])
-    if not outdated_docs:
-        return
-
-    raise ValueError(
-        "Docs for the following transform types are outdated: {outdated_docs_headers}. "
-        "Generate new docs by executing the `python tools/{py_file} make` command "
-        "and paste them to {filename}.\n"
-        "# Pixel-level transforms lines not in file:\n"
-        "{image_only_lines}\n"
-        "# Spatial-level transforms lines not in file:\n"
-        "{dual_lines}".format(
-            outdated_docs_headers=", ".join(outdated_docs),
-            py_file=os.path.basename(os.path.realpath(__file__)),
-            filename=os.path.basename(filepath),
-            image_only_lines="\n".join(image_only_lines_not_in_text),
-            dual_lines="\n".join(dual_lines_not_in_text),
+    if outdated_docs:
+        raise ValueError(
+            "Docs for the following transform types are outdated: {outdated_docs_headers}. "
+            "Generate new docs by executing the `python tools/{py_file} make` command "
+            "and paste them to {filename}.\n"
+            "# Pixel-level transforms lines not in file:\n"
+            "{image_only_lines}\n"
+            "# Spatial-level transforms lines not in file:\n"
+            "{dual_lines}".format(
+                outdated_docs_headers=", ".join(outdated_docs),
+                py_file=os.path.basename(os.path.realpath(__file__)),
+                filename=os.path.basename(filepath),
+                image_only_lines="\n".join(image_only_lines_not_in_text),
+                dual_lines="\n".join(dual_lines_not_in_text),
+            )
         )
-    )
+
+    if image_only_transforms_links not in text:
+        raise ValueError("Image only transforms links are outdated.")
+    if dual_transforms_table not in text:
+        raise ValueError("Dual transforms table are outdated.")
 
 
 def main():