Fix blur (#1700)

* Added heavy refactoring

* Added heavy refactoring

* Updated interface for Downscale

* Updated Downscale transform

* Fix in CI

.github/workflows/ci.yml

@@ -67,8 +67,10 @@ jobs:
       uses: actions/setup-python@v5
       with:
         python-version: ${{ matrix.python-version }}
-    - name: Update pip
-      run: python -m pip install --upgrade pip
+    - name: Install requirements
+      run: |
+        python -m pip install --upgrade pip
+        pip install .
     - name: Install dev requirements
       run: pip install -r requirements-dev.txt
     - name: Run checks

.pre-commit-config.yaml

@@ -35,6 +35,13 @@ repos:
       - id: end-of-file-fixer
       - id: trailing-whitespace
       - id: requirements-txt-fixer
+  - repo: local
+    hooks:
+    -   id: check-defaults-in-apply
+        name: Check defaults in apply method of BasicTransform subclasses
+        entry: python -m tools.check_defaults
+        language: system
+        files: '\.py$'
   - repo: local
     hooks:
     - id: check-docstrings
@@ -66,7 +73,7 @@ repos:
       - id: codespell
         additional_dependencies: ["tomli"]
   - repo: https://github.com/igorshubovych/markdownlint-cli
-    rev: v0.39.0
+    rev: v0.40.0
     hooks:
       - id: markdownlint
   - repo: https://github.com/tox-dev/pyproject-fmt

CONTRIBUTING.md

@@ -180,6 +180,32 @@ Even if a parameter defined as `Tuple`, the transform should work correctly with
 
 To maintain determinism and reproducibility, handle all probability calculations within the `get_params` or `get_params_dependent_on_targets` methods. These calculations should not occur in the `apply_xxx` or `__init__` methods, as it is crucial to separate configuration from execution in our codebase.
 
+### Specific Guidelines for Method Definitions
+
+#### Handling `apply_xxx` Methods
+
+When contributing code related to transformation methods, specifically methods that start with `apply_` (e.g., `apply_to_mask`, `apply_to_bbox`), please adhere to the following guidelines:
+
+**No Default Arguments**: Do not use default arguments in `apply_xxx` methods. Every parameter should be explicitly required, promoting clarity and reducing hidden behaviors that can arise from default values.
+
+### Examples
+
+Here are a few examples to illustrate these guidelines:
+
+**Incorrect** method definition:
+
+```python
+def apply_to_mask(self, mask, fill_value=0):  # Default value not allowed
+    # implementation
+```
+
+**Correct** method definition:
+
+```python
+def apply_to_mask(self, mask, fill_value):  # No default values
+    # implementation
+```
+
 ## Guidelines for Modifying Existing Code
 
 Maintaining the stability and usability of Albumentations for all users is a priority. When contributing, it's important to follow these guidelines for modifying existing code:

albumentations/augmentations/blur/transforms.py

@@ -72,11 +72,11 @@ def __init__(self, blur_limit: ScaleIntType = 7, always_apply: bool = False, p:
         super().__init__(always_apply, p)
         self.blur_limit = cast(Tuple[int, int], blur_limit)
 
-    def apply(self, img: np.ndarray, kernel: int = 3, **params: Any) -> np.ndarray:
+    def apply(self, img: np.ndarray, kernel: int, **params: Any) -> np.ndarray:
         return F.blur(img, kernel)
 
     def get_params(self) -> Dict[str, Any]:
-        return {"ksize": int(random.choice(list(range(self.blur_limit[0], self.blur_limit[1] + 1, 2))))}
+        return {"kernel": random_utils.choice(list(range(self.blur_limit[0], self.blur_limit[1] + 1, 2)))}
 
     def get_transform_init_args_names(self) -> Tuple[str, ...]:
         return ("blur_limit",)
@@ -133,7 +133,7 @@ def __init__(
     def get_transform_init_args_names(self) -> Tuple[str, ...]:
         return (*super().get_transform_init_args_names(), "allow_shifted")
 
-    def apply(self, img: np.ndarray, kernel: Optional[np.ndarray] = None, **params: Any) -> np.ndarray:
+    def apply(self, img: np.ndarray, kernel: np.ndarray, **params: Any) -> np.ndarray:
         return FMain.convolve(img, kernel=kernel)
 
     def get_params(self) -> Dict[str, Any]:
@@ -194,7 +194,7 @@ class MedianBlur(Blur):
     def __init__(self, blur_limit: ScaleIntType = 7, always_apply: bool = False, p: float = 0.5):
         super().__init__(blur_limit, always_apply, p)
 
-    def apply(self, img: np.ndarray, kernel: int = 3, **params: Any) -> np.ndarray:
+    def apply(self, img: np.ndarray, kernel: int, **params: Any) -> np.ndarray:
         return F.median_blur(img, kernel)
 
 
@@ -260,7 +260,7 @@ def __init__(
         self.blur_limit = cast(Tuple[int, int], blur_limit)
         self.sigma_limit = cast(Tuple[float, float], sigma_limit)
 
-    def apply(self, img: np.ndarray, ksize: int = 3, sigma: float = 0, **params: Any) -> np.ndarray:
+    def apply(self, img: np.ndarray, ksize: int, sigma: float, **params: Any) -> np.ndarray:
         return F.gaussian_blur(img, ksize, sigma=sigma)
 
     def get_params(self) -> Dict[str, float]:
@@ -318,7 +318,7 @@ def __init__(
         self.iterations = iterations
         self.mode = mode
 
-    def apply(self, img: np.ndarray, *args: Any, dxy: np.ndarray = None, **params: Any) -> np.ndarray:
+    def apply(self, img: np.ndarray, *args: Any, dxy: np.ndarray, **params: Any) -> np.ndarray:
         if dxy is None:
             msg = "dxy is None"
             raise ValueError(msg)
@@ -450,7 +450,7 @@ def __init__(
         self.beta_limit = cast(Tuple[float, float], beta_limit)
         self.noise_limit = cast(Tuple[float, float], noise_limit)
 
-    def apply(self, img: np.ndarray, kernel: Optional[np.ndarray] = None, **params: Any) -> np.ndarray:
+    def apply(self, img: np.ndarray, kernel: np.ndarray, **params: Any) -> np.ndarray:
         return FMain.convolve(img, kernel=kernel)
 
     def get_params(self) -> Dict[str, np.ndarray]:
@@ -532,7 +532,7 @@ def __init__(
         self.radius = cast(Tuple[int, int], radius)
         self.alias_blur = cast(Tuple[float, float], alias_blur)
 
-    def apply(self, img: np.ndarray, radius: int = 3, alias_blur: float = 0.5, **params: Any) -> np.ndarray:
+    def apply(self, img: np.ndarray, radius: int, alias_blur: float, **params: Any) -> np.ndarray:
         return F.defocus(img, radius, alias_blur)
 
     def get_params(self) -> Dict[str, Any]:
@@ -582,11 +582,7 @@ def __init__(
         self.max_factor = cast(Tuple[float, float], max_factor)
         self.step_factor = cast(Tuple[float, float], step_factor)
 
-    def apply(self, img: np.ndarray, zoom_factors: Optional[np.ndarray] = None, **params: Any) -> np.ndarray:
-        if zoom_factors is None:
-            msg = "zoom_factors is None"
-            raise ValueError(msg)
-
+    def apply(self, img: np.ndarray, zoom_factors: np.ndarray, **params: Any) -> np.ndarray:
         return F.zoom_blur(img, zoom_factors)
 
     def get_params(self) -> Dict[str, Any]: