Update gauss blur

[ternaus]

Summary by Sourcery

Tests:

• Add tests to verify Gaussian kernel creation, normalization, symmetry, peak values, and equivalence to PIL's implementation.

[sourcery-ai]

Reviewer's Guide by Sourcery

This pull request refactors the Gaussian Blur implementation to match PIL's implementation, including kernel size calculation and kernel generation. It also adds new tests to ensure the correctness of the implementation.

Sequence diagram for updated Gaussian Blur process

sequenceDiagram
    participant Client
    participant GaussianBlur
    participant GaussianKernel
    participant Convolution

    Client->>GaussianBlur: apply_blur(image)
    GaussianBlur->>GaussianBlur: get_params()
    GaussianBlur->>GaussianKernel: create_gaussian_kernel(sigma, ksize)
    Note over GaussianKernel: New kernel creation
    Note over GaussianKernel: using PIL's approach
    GaussianKernel-->>GaussianBlur: normalized_kernel
    GaussianBlur->>Convolution: convolve(image, kernel)
    Convolution-->>GaussianBlur: blurred_image
    GaussianBlur-->>Client: blurred_image

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Class diagram for updated Gaussian Blur implementation

classDiagram
    class GaussianBlur {
        +sigma_limit: tuple
        +blur_limit: tuple
        +apply(img, kernel)
        +get_params_dependent_on_data()
        +get_transform_init_args_names()
    }

    class BlurFunctional {
        +create_gaussian_kernel(sigma, ksize)
        +convolve(img, kernel)
    }

    GaussianBlur ..> BlurFunctional: uses

    note for GaussianBlur "Changed to use custom kernel"
    note for BlurFunctional "Added new kernel creation method"

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File-Level Changes

Change	Details	Files
Refactor Gaussian blur to match PIL's implementation.	The kernel size calculation is changed to int(sigma * 3.5) * 2 + 1 to match PIL. The Gaussian kernel generation is refactored to match PIL's approach. The gaussian_blur function is removed from functional.py. The apply method in GaussianBlur now takes a precomputed kernel instead of ksize and sigma. The get_params_dependent_on_data method now computes the kernel and passes it to the apply method.	albumentations/augmentations/blur/transforms.py albumentations/augmentations/blur/functional.py
Add new tests for Gaussian blur.	Added a helper function to create PIL's Gaussian kernel for comparison. Added tests for kernel shapes, normalization, symmetry, and peak values. Added a test to ensure that our kernel matches PIL's kernel. Added a visual comparison test for debugging purposes.	tests/functional/test_blur.py
Remove old tests for Gaussian blur.	Removed tests for Gaussian blur limits.	tests/test_blur.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: 1 issue found
• 🟢 Security: all looks good
• 🟢 Testing: all looks good
• 🟢 Complexity: all looks good
• 🟢 Documentation: all looks good

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[codecov]

Codecov Report

All modified and coverable lines are covered by tests ✅

Project coverage is 89.85%. Comparing base (b1a79c2) to head (4af30cf).
Report is 384 commits behind head on main.

Additional details and impacted files

@@            Coverage Diff            @@
##           main    #2284       +/-   ##
=========================================
+ Coverage      0   89.85%   +89.85%     
=========================================
  Files         0       50       +50     
  Lines         0     8820     +8820     
=========================================
+ Hits          0     7925     +7925     
- Misses        0      895      +895     

Flag	Coverage Δ
ubuntu-py3.12	89.85% <100.00%> (?)

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