Passes pre-commit hooks

.deepsource.toml

@@ -0,0 +1,17 @@
+version = 1
+
+test_patterns = [
+  'tests/**'
+]
+
+exclude_patterns = [
+
+]
+
+[[analyzers]]
+name = 'python'
+enabled = true
+runtime_version = '3.x.x'
+
+  [analyzers.meta]
+  max_line_length = 119

.gitignore

@@ -158,3 +158,5 @@ fabric.properties
 .idea
 
 conda_build/
+
+.vscode/

.pre-commit-config.yaml

@@ -1,26 +1,80 @@
+ci:
+  autofix_commit_msg: |
+    [pre-commit.ci] auto fixes from pre-commit.com hooks
+
+    for more information, see https://pre-commit.ci
+  autofix_prs: true
+  autoupdate_branch: ''
+  autoupdate_commit_msg: '[pre-commit.ci] pre-commit autoupdate'
+  autoupdate_schedule: weekly
+  skip: [ ]
+  submodules: false
+
 repos:
   - repo: https://github.com/pre-commit/pre-commit-hooks
     rev: v4.5.0
     hooks:
-      - id: trailing-whitespace
-      - id: end-of-file-fixer
-      - id: requirements-txt-fixer
-      - id: check-json
-      - id: check-yaml
+      - id: check-added-large-files
+      - id: check-ast
+      - id: check-builtin-literals
+      - id: check-case-conflict
+      - id: check-docstring-first
+      - id: check-executables-have-shebangs
+      - id: check-shebang-scripts-are-executable
+      - id: check-symlinks
+      - id: check-merge-conflict
       - id: check-toml
       - id: check-xml
+      - id: debug-statements
+      - id: detect-private-key
+      - id: forbid-new-submodules
+      - id: forbid-submodules
+      - id: mixed-line-ending
+      - id: name-tests-test
+      - id: destroyed-symlinks
+      - id: fix-byte-order-marker
+      - id: mixed-line-ending
+      - id: name-tests-test
+      - id: check-docstring-first
+      - id: pretty-format-json
+      - id: check-json
+      - id: check-merge-conflict
+      - id: check-yaml
+        args: [ --unsafe ]
+      - id: debug-statements
+      - id: end-of-file-fixer
+      - id: trailing-whitespace
+      - id: requirements-txt-fixer
+  - repo: https://github.com/asottile/pyupgrade
+    rev: v3.15.0
+    hooks:
+    - id: pyupgrade
+      args: ["--py38-plus"]
   - repo: https://github.com/pycqa/isort
-    rev: 5.11.5
+    rev: 5.13.2
     hooks:
       - id: isort
         args: [ "--profile", "black" ]
   - repo: https://github.com/psf/black
-    rev: 22.6.0
+    rev: 24.2.0
     hooks:
       - id: black
-        args: [ --config=black.toml ]
+        args: [ --config=pyproject.toml ]
+  - repo: https://github.com/pre-commit/pygrep-hooks
+    rev: v1.10.0
+    hooks:
+      - id: python-check-mock-methods
+      - id: python-use-type-annotations
+      - id: python-check-blanket-noqa
+      - id: python-use-type-annotations
+      - id: text-unicode-replacement-char
+  - repo: https://github.com/PyCQA/flake8
+    rev: 7.0.0
+    hooks:
+      - id: flake8
+        exclude: ^setup.py
   - repo: https://github.com/pre-commit/mirrors-mypy
-    rev: v0.991
+    rev: v1.8.0
     hooks:
       - id: mypy
         additional_dependencies: [ types-PyYAML, types-pkg-resources, types-setuptools ]
@@ -29,9 +83,5 @@ repos:
             --ignore-missing-imports,
             --warn-no-return,
             --warn-redundant-casts,
+            --disallow-incomplete-defs,
           ]
-  - repo: https://github.com/PyCQA/flake8
-    rev: 21d3c70d676007470908d39b73f0521d39b3b997
-    hooks:
-      - id: flake8
-        additional_dependencies: [ flake8-docstrings==1.6.0 ]

README.md

@@ -17,24 +17,32 @@ Here is an example of how you can apply some [pixel-level](#pixel-level-transfor
 - The library is [**widely used**](#who-is-using-albumentations) in industry, deep learning research, machine learning competitions, and open source projects.
 
 ## Table of contents
-- [Authors](#authors)
-- [Installation](#installation)
-- [Documentation](#documentation)
-- [A simple example](#a-simple-example)
-- [Getting started](#getting-started)
-  - [I am new to image augmentation](#i-am-new-to-image-augmentation)
-  - [I want to use Albumentations for the specific task such as classification or segmentation](#i-want-to-use-albumentations-for-the-specific-task-such-as-classification-or-segmentation)
-  - [I want to know how to use Albumentations with deep learning frameworks](#i-want-to-know-how-to-use-albumentations-with-deep-learning-frameworks)
-  - [I want to explore augmentations and see Albumentations in action](#i-want-to-explore-augmentations-and-see-albumentations-in-action)
-- [Who is using Albumentations](#who-is-using-albumentations)
-- [List of augmentations](#list-of-augmentations)
-  - [Pixel-level transforms](#pixel-level-transforms)
-  - [Spatial-level transforms](#spatial-level-transforms)
-- [A few more examples of augmentations](#a-few-more-examples-of-augmentations)
-- [Benchmarking results](#benchmarking-results)
-- [Contributing](#contributing)
-- [Comments](#comments)
-- [Citing](#citing)
+- [Albumentations](#albumentations)
+  - [Why Albumentations](#why-albumentations)
+  - [Table of contents](#table-of-contents)
+  - [Authors](#authors)
+  - [Installation](#installation)
+  - [Documentation](#documentation)
+  - [A simple example](#a-simple-example)
+  - [Getting started](#getting-started)
+    - [I am new to image augmentation](#i-am-new-to-image-augmentation)
+    - [I want to use Albumentations for the specific task such as classification or segmentation](#i-want-to-use-albumentations-for-the-specific-task-such-as-classification-or-segmentation)
+    - [I want to know how to use Albumentations with deep learning frameworks](#i-want-to-know-how-to-use-albumentations-with-deep-learning-frameworks)
+    - [I want to explore augmentations and see Albumentations in action](#i-want-to-explore-augmentations-and-see-albumentations-in-action)
+  - [Who is using Albumentations](#who-is-using-albumentations)
+      - [See also:](#see-also)
+  - [List of augmentations](#list-of-augmentations)
+    - [Pixel-level transforms](#pixel-level-transforms)
+    - [Spatial-level transforms](#spatial-level-transforms)
+  - [A few more examples of augmentations](#a-few-more-examples-of-augmentations)
+    - [Semantic segmentation on the Inria dataset](#semantic-segmentation-on-the-inria-dataset)
+    - [Medical imaging](#medical-imaging)
+    - [Object detection and semantic segmentation on the Mapillary Vistas dataset](#object-detection-and-semantic-segmentation-on-the-mapillary-vistas-dataset)
+    - [Keypoints augmentation](#keypoints-augmentation)
+  - [Benchmarking results](#benchmarking-results)
+  - [Contributing](#contributing)
+  - [Comments](#comments)
+  - [Citing](#citing)
 
 ## Authors
 [**Alexander Buslaev** — Computer Vision Engineer at Mapbox](https://www.linkedin.com/in/al-buslaev/) | [Kaggle Master](https://www.kaggle.com/albuslaev)

albumentations/__init__.py

@@ -1,5 +1,3 @@
-from __future__ import absolute_import
-
 __version__ = "1.3.1"
 
 from .augmentations import *

albumentations/augmentations/blur/transforms.py

@@ -1,19 +1,15 @@
 import random
 import warnings
-from typing import Any, Dict, List, Sequence, Tuple
+from typing import Any, Dict, List, Optional, Sequence, Tuple, cast
 
 import cv2
 import numpy as np
 
 from albumentations import random_utils
 from albumentations.augmentations import functional as FMain
 from albumentations.augmentations.blur import functional as F
-from albumentations.core.transforms_interface import (
-    ImageOnlyTransform,
-    ScaleFloatType,
-    ScaleIntType,
-    to_tuple,
-)
+from albumentations.core.transforms_interface import ImageOnlyTransform, to_tuple
+from albumentations.core.types import ScaleFloatType, ScaleIntType
 
 __all__ = ["Blur", "MotionBlur", "GaussianBlur", "GlassBlur", "AdvancedBlur", "MedianBlur", "Defocus", "ZoomBlur"]
 
@@ -22,9 +18,9 @@ class Blur(ImageOnlyTransform):
     """Blur the input image using a random-sized kernel.
 
     Args:
-        blur_limit (int, (int, int)): maximum kernel size for blurring the input image.
+        blur_limit: maximum kernel size for blurring the input image.
             Should be in range [3, inf). Default: (3, 7).
-        p (float): probability of applying the transform. Default: 0.5.
+        p: probability of applying the transform. Default: 0.5.
 
     Targets:
         image
@@ -35,9 +31,9 @@ class Blur(ImageOnlyTransform):
 
     def __init__(self, blur_limit: ScaleIntType = 7, always_apply: bool = False, p: float = 0.5):
         super().__init__(always_apply, p)
-        self.blur_limit = to_tuple(blur_limit, 3)
+        self.blur_limit = cast(Tuple[int, int], to_tuple(blur_limit, 3))
 
-    def apply(self, img: np.ndarray, ksize: int = 3, **params) -> np.ndarray:
+    def apply(self, img: np.ndarray, ksize: int = 3, **params: Any) -> np.ndarray:
         return F.blur(img, ksize)
 
     def get_params(self) -> Dict[str, Any]:
@@ -80,21 +76,21 @@ 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: np.ndarray = None, **params) -> np.ndarray:  # type: ignore
-        return FMain.convolve(img, kernel=kernel)
+    def apply(self, img: np.ndarray, ksize: Optional[np.ndarray] = None, **params: Any) -> np.ndarray:
+        return FMain.convolve(img, kernel=ksize)
 
     def get_params(self) -> Dict[str, Any]:
         ksize = random.choice(list(range(self.blur_limit[0], self.blur_limit[1] + 1, 2)))
         if ksize <= 2:
-            raise ValueError("ksize must be > 2. Got: {}".format(ksize))
+            raise ValueError(f"ksize must be > 2. Got: {ksize}")
         kernel = np.zeros((ksize, ksize), dtype=np.uint8)
         x1, x2 = random.randint(0, ksize - 1), random.randint(0, ksize - 1)
         if x1 == x2:
             y1, y2 = random.sample(range(ksize), 2)
         else:
             y1, y2 = random.randint(0, ksize - 1), random.randint(0, ksize - 1)
 
-        def make_odd_val(v1, v2):
+        def make_odd_val(v1: int, v2: int) -> Tuple[int, int]:
             len_v = abs(v1 - v2) + 1
             if len_v % 2 != 1:
                 if v2 > v1:
@@ -113,8 +109,8 @@ def make_odd_val(v1, v2):
             center = ksize / 2 - 0.5
             dx = xc - center
             dy = yc - center
-            x1, x2 = [int(i - dx) for i in [x1, x2]]
-            y1, y2 = [int(i - dy) for i in [y1, y2]]
+            x1, x2 = (int(i - dx) for i in [x1, x2])
+            y1, y2 = (int(i - dy) for i in [y1, y2])
 
         cv2.line(kernel, (x1, y1), (x2, y2), 1, thickness=1)
 
@@ -143,7 +139,7 @@ def __init__(self, blur_limit: ScaleIntType = 7, always_apply: bool = False, p:
         if self.blur_limit[0] % 2 != 1 or self.blur_limit[1] % 2 != 1:
             raise ValueError("MedianBlur supports only odd blur limits.")
 
-    def apply(self, img: np.ndarray, ksize: int = 3, **params) -> np.ndarray:
+    def apply(self, img: np.ndarray, ksize: int = 3, **params: Any) -> np.ndarray:
         return F.median_blur(img, ksize)
 
 
@@ -176,7 +172,7 @@ def __init__(
         p: float = 0.5,
     ):
         super().__init__(always_apply, p)
-        self.blur_limit = to_tuple(blur_limit, 0)
+        self.blur_limit = cast(Tuple[int, int], to_tuple(blur_limit, 0))
         self.sigma_limit = to_tuple(sigma_limit if sigma_limit is not None else 0, 0)
 
         if self.blur_limit[0] == 0 and self.sigma_limit[0] == 0:
@@ -191,7 +187,7 @@ def __init__(
         ):
             raise ValueError("GaussianBlur supports only odd blur limits.")
 
-    def apply(self, img: np.ndarray, ksize: int = 3, sigma: float = 0, **params) -> np.ndarray:
+    def apply(self, img: np.ndarray, ksize: int = 3, sigma: float = 0, **params: Any) -> np.ndarray:
         return F.gaussian_blur(img, ksize, sigma=sigma)
 
     def get_params(self) -> Dict[str, float]:
@@ -258,7 +254,7 @@ def get_params_dependent_on_targets(self, params: Dict[str, Any]) -> Dict[str, n
         # generate array containing all necessary values for transformations
         width_pixels = img.shape[0] - self.max_delta * 2
         height_pixels = img.shape[1] - self.max_delta * 2
-        total_pixels = width_pixels * height_pixels
+        total_pixels = int(width_pixels * height_pixels)
         dxy = random_utils.randint(-self.max_delta, self.max_delta, size=(total_pixels, self.iterations, 2))
 
         return {"dxy": dxy}
@@ -315,7 +311,7 @@ def __init__(
         p: float = 0.5,
     ):
         super().__init__(always_apply, p)
-        self.blur_limit = to_tuple(blur_limit, 3)
+        self.blur_limit = cast(Tuple[int, int], to_tuple(blur_limit, 3))
         self.sigmaX_limit = self.__check_values(to_tuple(sigmaX_limit, 0.0), name="sigmaX_limit")
         self.sigmaY_limit = self.__check_values(to_tuple(sigmaY_limit, 0.0), name="sigmaY_limit")
         self.rotate_limit = to_tuple(rotate_limit)
@@ -341,7 +337,7 @@ def __check_values(
             raise ValueError(f"{name} values should be between {bounds}")
         return value
 
-    def apply(self, img: np.ndarray, kernel: np.ndarray = np.array(None), **params) -> np.ndarray:
+    def apply(self, img: np.ndarray, kernel: np.ndarray = np.array(None), **params: Any) -> np.ndarray:
         return FMain.convolve(img, kernel=kernel)
 
     def get_params(self) -> Dict[str, np.ndarray]:
@@ -372,7 +368,7 @@ def get_params(self) -> Dict[str, np.ndarray]:
         # Described in "Parameter Estimation For Multivariate Generalized Gaussian Distributions"
         kernel = np.exp(-0.5 * np.power(np.sum(np.dot(grid, inverse_sigma) * grid, 2), beta))
         # Add noise
-        kernel = kernel * noise_matrix
+        kernel *= noise_matrix
 
         # Normalize kernel
         kernel = kernel.astype(np.float32) / np.sum(kernel)
@@ -424,7 +420,7 @@ def __init__(
         if self.alias_blur[0] < 0:
             raise ValueError("Parameter alias_blur must be non-negative")
 
-    def apply(self, img: np.ndarray, radius: int = 3, alias_blur: float = 0.5, **params) -> np.ndarray:
+    def apply(self, img: np.ndarray, radius: int = 3, alias_blur: float = 0.5, **params: Any) -> np.ndarray:
         return F.defocus(img, radius, alias_blur)
 
     def get_params(self) -> Dict[str, Any]:
@@ -473,7 +469,7 @@ def __init__(
         if self.step_factor[0] <= 0:
             raise ValueError("Step factor must be positive")
 
-    def apply(self, img: np.ndarray, zoom_factors: np.ndarray = np.array(None), **params) -> np.ndarray:
+    def apply(self, img: np.ndarray, zoom_factors: np.ndarray = np.array(None), **params: Any) -> np.ndarray:
         assert zoom_factors is not None
         return F.zoom_blur(img, zoom_factors)