FIx clip

[ternaus]

Summary by Sourcery

Unify and streamline LUT-based image processing functions by standardizing on float32 precision, consolidating dtype conversions, and simplifying per-channel processing loops.

Enhancements:

• Standardize output dtype to float32 across LUT and normalization functions and remove redundant dtype casts
• Replace cv2.merge calls with direct per-channel numpy assignments in apply_lut and multiply_add_lut
• Switch OpenCV multiply and add operations to use CV_32F instead of CV_64F
• Unify and simplify normalization routines by adjusting _normalize_mean_std_opencv signature and removing redundant casts
• Incorporate clipping and casting into LUT creation for normalization methods
• Clean up unused variables in multichannel flip function

[sourcery-ai]

Reviewer's Guide

This PR refactors core LUT operations to use explicit per-channel loops with preallocated float32 buffers, standardizes use of float32 in OpenCV operations and LUT generation, and cleans up redundant conversions and unused code across the normalization and arithmetic functions.

Class diagram for refactored LUT and normalization functions

classDiagram
    class functions {
        +apply_lut(img, value, operation, inplace)
        +normalize_lut(img, mean, denominator, dtype, max_value)
        +power_lut(img, exponent, inplace)
        +multiply_add_lut(img, factor, value, inplace)
        +normalize_per_image_lut(img, normalization, eps, max_value)
        +to_float_lut(img, max_value)
        +_normalize_mean_std_opencv(img, mean, std)
        +normalize_per_image_opencv(img, normalization, eps)
    }
    functions : np.empty_like(img, dtype=np.float32)
    functions : per-channel for-loops for LUT application
    functions : standardize float32 dtype for OpenCV operations
    functions : remove redundant dtype conversions
    functions : remove unused code
    functions --> cv2.LUT
    functions --> cv2.multiply
    functions --> cv2.add
    functions --> cv2.normalize
    functions --> sz_lut
    functions --> clip
    functions --> create_lut_array

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Flow diagram for per-channel LUT application refactor

flowchart TD
    A[Start: apply_lut or multiply_add_lut called] --> B[Preallocate result array as float32]
    B --> C{For each channel}
    C -->|i=0..num_channels-1| D[Apply LUT to channel i]
    D --> C
    C -->|All channels processed| E[Return result array]

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File-Level Changes

Change	Details	Files
Replace cv2.merge list comprehensions with manual per-channel loops and np.empty_like results	Preallocate result array with np.empty_like and dtype=float32 in apply_lut Loop over channels instead of cv2.merge in apply_lut and multiply_add_lut Return the assembled result array directly	albucore/functions.py
Standardize float32 usage in arithmetic and LUT operations	Change cv2.multiply and cv2.add dtype arguments from CV_64F to CV_32F Cast combined LUT expressions to float32 in multiply_add_opencv Append .astype(np.float32) to all vectorized LUT computations	albucore/functions.py
Eliminate redundant dtype conversions and streamline normalization	Remove intermediate .astype(np.float32) calls where input arrays are already float32 Drop mean/denominator float32 casts in normalize_lut and consolidate into final astype Move image-to-float32 conversion into lower-level helper and simplify upstream calls in normalize_per_image_opencv	albucore/functions.py
Cleanup unused code and minor refactors	Remove unused variables (height, width) in _flip_multichannel Uncomment or remove a stale @preserve_channel_dim decorator line Rename function parameters for consistency (_normalize_mean_std_opencv signature update)	albucore/functions.py

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[Copilot]

Pull Request Overview

This PR fixes various issues with LUT-based image processing functions to improve type consistency and normalization behavior. Key changes include replacing cv2.merge with manual per-channel processing, updating data types for OpenCV arithmetic, and modifying clipping behavior in normalization routines.

Comments suppressed due to low confidence (2)

albucore/functions.py:733

• The removal of the explicit clipping (previously result.clip(-20, 20)) in the 'image_per_channel' normalization branch may change the expected output range. If this behavior is intended, consider updating the function documentation to clearly reflect the change.

        return result

albucore/functions.py:470

• Changing the 'inplace' flag from True to False for the clip function in multiply_add_lut may alter the expected side-effect behavior. Verify that downstream code does not rely on in-place modifications of the LUT array.

    luts = clip(np.arange(0, max_value + 1, dtype=np.float32) * factor + value, dtype, inplace=False)

[codecov-commenter]

Codecov Report

Attention: Patch coverage is 80.76923% with 5 lines in your changes missing coverage. Please review.

Please upload report for BASE (main@f3d8822). Learn more about missing BASE report.

Files with missing lines	Patch %	Lines
albucore/functions.py	80.76%	5 Missing ⚠️

Additional details and impacted files

@@           Coverage Diff           @@
##             main      #66   +/-   ##
=======================================
  Coverage        ?   93.80%           
=======================================
  Files           ?       17           
  Lines           ?     1873           
  Branches        ?        0           
=======================================
  Hits            ?     1757           
  Misses          ?      116           
  Partials        ?        0           

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[sourcery-ai]

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

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