Feat/apply to images hue saturation value

[TheMathix]

Followed by the instructions here by @ternaus I vectorized the function instead of calling apply in a loop

Summary by Sourcery

Enable efficient batched hue, saturation, and value adjustments by vectorizing shift_hsv and exposing a batch API in the HueSaturationValue transform; add tests to verify consistency between single-image and batch processing.

New Features:

• Add apply_to_images method to HueSaturationValue for batch HSV shifts
• Support arbitrary leading batch dimensions in shift_hsv

Enhancements:

• Refactor shift_hsv to use vectorized reshape and OpenCV conversions for improved performance
• Add input validation for dimensions and channel counts in shift_hsv
• Handle grayscale inputs by expanding to RGB, then restoring single-channel output

Tests:

• Add parameterized tests for batched hue, saturation, and value adjustments across grayscale and RGB inputs in uint8 and float32

[sourcery-ai]

Reviewer's Guide

Refactors shift_hsv to handle both single images and batches in a vectorized manner with shape validation, adds a batch-aware apply_to_images API, and covers the changes with new tests.

Sequence diagram for batch image HSV shift using apply_to_images

sequenceDiagram
    participant User
    participant SomeTransformClass
    participant shift_hsv
    User->>SomeTransformClass: call apply_to_images(images, hue_shift, sat_shift, val_shift)
    SomeTransformClass->>shift_hsv: shift_hsv(images, hue_shift, sat_shift, val_shift)
    shift_hsv-->>SomeTransformClass: return batch-shifted images
    SomeTransformClass-->>User: return result

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File-Level Changes

Change	Details	Files
Support for batched inputs and rigorous shape/Channel validation	Added ndim and channel count checks Extracted H, W, C from trailing axes Implemented fast path when no shifts are requested Treated C==1 as grayscale batch/single	functional.py
Vectorized color-space conversion and per-channel adjustments	Collapsed leading batch dims before cv2.cvtColor Reshaped flattened RGB to HSV and back Applied hue LUT and non-inplace saturation/value shifts Stacked/unstacked HSV channels without Python loops	functional.py
Output reshaping to preserve original format	Reshaped processed data back to leading dims Converted back to grayscale when input was single-channel Ensured output has correct channel-last shape	functional.py
Batch-aware API extension in transform class	Added apply_to_images method forwarding to functional.shift_hsv Allowed passing arbitrary params through to functional layer	transforms.py
New tests for batched HueSaturationValue	Parameterized single- and multi-channel, uint8/float32 batches Compared batch outputs against per-image apply results Included sanity checks for actual transformations	tests/test_transforms.py

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[sourcery-ai]

Hey there - I've reviewed your changes - here's some feedback:

• Consider refactoring the batch flatten/unflatten logic into a shared helper to reduce duplication and make the code more readable.
• Add an explicit contiguity and dtype normalization step (e.g. via np.ascontiguousarray or casting) before cv2.cvtColor to avoid surprises with float32 or non-contiguous inputs.
• Clarify or enforce the expected value range for float images (0–1 vs 0–255) so HSV↔RGB conversions remain consistent and predictable across dtypes.

Prompt for AI Agents

Please address the comments from this code review:
## Overall Comments
- Consider refactoring the batch flatten/unflatten logic into a shared helper to reduce duplication and make the code more readable.
- Add an explicit contiguity and dtype normalization step (e.g. via np.ascontiguousarray or casting) before cv2.cvtColor to avoid surprises with float32 or non-contiguous inputs.
- Clarify or enforce the expected value range for float images (0–1 vs 0–255) so HSV↔RGB conversions remain consistent and predictable across dtypes.

## Individual Comments

### Comment 1
<location> `albumentations/augmentations/pixel/functional.py:115` </location>
<code_context>
     if hue_shift != 0:
-        lut_hue = np.arange(0, 256, dtype=np.int16)
-        lut_hue = np.mod(lut_hue + hue_shift, 180).astype(np.uint8)
+        lut_hue = np.arange(256, dtype=np.int16)
+        lut_hue = np.mod(lut_hue + int(hue_shift), 180).astype(np.uint8)
         hue = sz_lut(hue, lut_hue, inplace=False)

</code_context>

<issue_to_address>
Casting hue_shift to int may lose precision if float values are passed.

If float values are valid for hue_shift, casting to int may cause unintended results. Consider rounding or clarify in documentation that only integers are supported.
</issue_to_address>

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[sourcery-ai]

nitpick: Casting hue_shift to int may lose precision if float values are passed.

If float values are valid for hue_shift, casting to int may cause unintended results. Consider rounding or clarify in documentation that only integers are supported.

[ternaus]

@TheMathix You need to set up pre-commit hook, so that you would not be able to commit code, without it passing.

https://albumentations.ai/docs/contributing/environment-setup/#4-set-up-pre-commit-hooks