Add simsimd

[ternaus]

Summary by Sourcery

Add support for the SimSIMD library in image processing functions, enhancing performance and flexibility. Update the benchmark script to allow configurable parameters and improve the convert_value function for better type handling. Include new tests for SimSIMD functions and update dependencies.

New Features:

• Introduce new functions for image processing using the SimSIMD library, including add_weighted_simsimd, add_array_simsimd, multiply_by_constant_simsimd, and add_constant_simsimd.

Enhancements:

• Enhance the benchmark script to accept command-line arguments for channels, types, number of images, and repeats, with default values provided.
• Update the convert_value function to handle different data types more robustly and raise a TypeError for unsupported types.

Build:

• Add SimSIMD as a dependency in the setup.py file.

Tests:

• Add tests for the new SimSIMD-based functions to ensure they produce expected results and are consistent with existing implementations.

[sourcery-ai]

Reviewer's Guide by Sourcery

This pull request introduces simsimd integration for image processing operations, enhancing performance for certain operations. The implementation includes new simsimd-based functions for add, multiply, and weighted addition operations, along with comprehensive benchmarking infrastructure to compare performance across different implementations.

Sequence diagram for simsimd benchmarking process

sequenceDiagram
    participant User
    participant BenchmarkScript
    participant AlbucoreFunctions
    participant Simsimd
    User->>BenchmarkScript: Run benchmark.sh with options
    BenchmarkScript->>AlbucoreFunctions: Call add_weighted_simsimd
    AlbucoreFunctions->>Simsimd: Perform weighted addition
    Simsimd-->>AlbucoreFunctions: Return result
    AlbucoreFunctions-->>BenchmarkScript: Return processed image
    BenchmarkScript-->>User: Display benchmark results

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Class diagram for new simsimd functions

classDiagram
    class AlbucoreFunctions {
        +add_weighted_simsimd(np.ndarray img1, float weight1, np.ndarray img2, float weight2) np.ndarray
        +add_array_simsimd(np.ndarray img, np.ndarray value) np.ndarray
        +multiply_by_constant_simsimd(np.ndarray img, float value) np.ndarray
        +add_constant_simsimd(np.ndarray img, float value) np.ndarray
    }
    note for AlbucoreFunctions "New simsimd-based functions for image processing operations"

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File-Level Changes

Change	Details	Files
Added simsimd-based implementations for core image processing operations	Implemented add_weighted_simsimd for weighted addition of images Added multiply_by_constant_simsimd for scalar multiplication Created add_constant_simsimd for scalar addition Implemented add_array_simsimd for array addition	albucore/functions.py
Enhanced benchmark infrastructure to support simsimd comparison	Added simsimd to benchmarking libraries Implemented simsimd transform methods for various operations Updated benchmark result formatting to use median instead of mean Added new benchmark result files for different configurations	benchmark/albucore_benchmark/benchmark.py benchmark/utils.py benchmark/albucore_benchmark/results/
Updated benchmark script with improved configuration options	Added command-line options for channels, types, number of images, and repeats Implemented help and usage information Added default values for benchmark parameters	benchmark.sh
Refactored value conversion logic and error handling	Improved type checking and error handling in convert_value function Enhanced documentation with detailed parameter descriptions Simplified conditional logic for value conversion	albucore/utils.py
Added new benchmark comparison tool	Created dedicated benchmark tool for comparing stringzilla vs cv2 LUT Implemented detailed performance metrics collection Added CPU information gathering for benchmark context	benchmark/stringzilla_vs_cv2_lut/benchmark_add_weighted.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: 1 issue found
• 🟢 Complexity: all looks good
• 🟡 Documentation: 1 issue found

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