Add thin plate spline

[ternaus]

Summary by Sourcery

Add the Thin Plate Spline transformation to the library, enabling smooth, non-rigid deformations of images and other targets. Update documentation and tests to include this new feature.

New Features:

• Introduce the Thin Plate Spline (TPS) transformation to apply smooth, non-rigid deformations to images, masks, keypoints, and bounding boxes.

Enhancements:

• Refactor the initialization order of parameters in the BaseDistortion class to improve code readability.

Documentation:

• Add documentation for the new Thin Plate Spline transformation, including usage examples and references.

Tests:

• Add a test case for the Thin Plate Spline transformation in the augmentation definitions.

[sourcery-ai]

Reviewer's Guide by Sourcery

This PR introduces a new Thin Plate Spline (TPS) transformation for creating smooth, non-rigid deformations in images. The implementation includes the core TPS algorithm with control points and interpolation, built on top of the existing BaseDistortion class. The transform allows for configurable deformation strength and control point density while maintaining image continuity.

Class diagram for ThinPlateSpline transformation

classDiagram
    class BaseDistortion {
        <<abstract>>
        +interpolation: int
        +border_mode: int
        +value: ColorType | None
        +mask_value: ColorType | None
        +p: float
        +get_params_dependent_on_data(params: dict[str, Any], data: dict[str, Any]) dict[str, Any]
        +get_transform_init_args_names() tuple[str, ...]
    }
    class ThinPlateSpline {
        +scale_range: tuple[float, float]
        +num_control_points: int
        +interpolation: int
        +mask_interpolation: int
        +border_mode: int
        +value: ColorType | None
        +mask_value: ColorType | None
        +p: float
        +get_params_dependent_on_data(params: dict[str, Any], data: dict[str, Any]) dict[str, Any]
        +get_transform_init_args_names() tuple[str, ...]
    }
    BaseDistortion <|-- ThinPlateSpline
    class InitSchema {
        +scale_range: tuple[float, float]
        +num_control_points: int
    }
    ThinPlateSpline --> InitSchema
    note for ThinPlateSpline "Applies TPS transformation for smooth, non-rigid deformations."

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Class diagram for TPS functional methods

classDiagram
    class Functional {
        +compute_tps_weights(src_points: np.ndarray, dst_points: np.ndarray) tuple[np.ndarray, np.ndarray]
        +tps_transform(target_points: np.ndarray, control_points: np.ndarray, nonlinear_weights: np.ndarray, affine_weights: np.ndarray) np.ndarray
    }
    note for Functional "Contains core TPS algorithm methods."

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File-Level Changes

Change	Details	Files
Implementation of the Thin Plate Spline (TPS) transformation class	Added new ThinPlateSpline class inheriting from BaseDistortion Implemented initialization with configurable scale range and control points Added parameter validation through InitSchema Created data-dependent parameter generation for TPS transformation Added comprehensive documentation with usage examples and references	albumentations/augmentations/geometric/transforms.py
Added core TPS mathematical functions	Implemented compute_tps_weights function for calculating transformation weights Added tps_transform function for applying the transformation to points Included detailed mathematical computations for kernel matrices and affine transformations	albumentations/augmentations/geometric/functional.py
Integration with the library's infrastructure	Added ThinPlateSpline to the list of available transforms Updated documentation table with TPS transform compatibility Added TPS to the test definitions	README.md tests/aug_definitions.py albumentations/augmentations/geometric/transforms.py

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[sourcery-ai]

Hey @ternaus - I've reviewed your changes and they look great!

Here's what I looked at during the review

• 🟡 General issues: 2 issues found
• 🟢 Security: all looks good
• 🟡 Testing: 1 issue found
• 🟡 Complexity: 1 issue found
• 🟢 Documentation: all looks good

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[codecov]

Codecov Report

All modified and coverable lines are covered by tests ✅

Project coverage is 90.12%. Comparing base (b1a79c2) to head (01dc1b0).
Report is 288 commits behind head on main.

Additional details and impacted files

@@            Coverage Diff            @@
##           main    #2156       +/-   ##
=========================================
+ Coverage      0   90.12%   +90.12%     
=========================================
  Files         0       48       +48     
  Lines         0     8530     +8530     
=========================================
+ Hits          0     7688     +7688     
- Misses        0      842      +842     

☔ View full report in Codecov by Sentry.
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