Speedup in fpixel.grayscale_to_multichannel

[ternaus]

Summary by Sourcery

Add batch and volume support to ToRGB transform, optimize grayscale-to-RGB conversion, unify shape tuple handling, bump pre-commit hook versions, and update lint configuration

New Features:

• Add apply_to_images, apply_to_volume, and apply_to_volumes methods to ToRGB transform for batch and volumetric inputs

Enhancements:

• Replace np.stack with np.tile in grayscale_to_multichannel for improved performance
• Use tuple unpacking for consistent shape tuple construction in geometric transforms and noise/drop-value generation
• Refine TypeError message for invalid channel input

Build:

• Bump ruff pre-commit hook to v0.12.0
• Upgrade mypy pre-commit hook to v1.16.1

Chores:

• Add "PLC0415" to lint ignore list in pyproject.toml

[sourcery-ai]

Reviewer's Guide

This PR enhances the ToRGB transform with batch and volume support, optimizes multichannel stacking for grayscale images, harmonizes tuple unpacking in shape manipulations, and updates pre-commit and lint configurations for version bumps and new ignore rules.

Class diagram for updated ToRGB transform methods

classDiagram
    class ToRGB {
        +apply(img: np.ndarray, **params: Any) np.ndarray
        +apply_to_images(images: np.ndarray, **params: Any) np.ndarray
        +apply_to_volume(volume: np.ndarray, **params: Any) np.ndarray
        +apply_to_volumes(volumes: np.ndarray, **params: Any) np.ndarray
    }

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Class diagram for optimized grayscale_to_multichannel function

classDiagram
    class fpixel {
        +grayscale_to_multichannel(grayscale_image: np.ndarray, num_output_channels: int) np.ndarray
    }

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File-Level Changes

Change	Details	Files
Extended ToRGB transform to handle batches and volumes	Updated error message to generalize channel check Added apply_to_images method for batch inputs Added apply_to_volume method for volumetric input Added apply_to_volumes method for batch of volumes	albumentations/augmentations/pixel/transforms.py
Optimized grayscale-to-multichannel stacking	Replaced np.stack with np.tile for faster channel duplication	albumentations/augmentations/pixel/functional.py
Standardized tuple unpacking for shape definitions	Refactored reduced_shape construction in generate_noise Updated prepare_drop_values to use explicit tuple unpacking Changed reshape parameters in geometric grid transform	albumentations/augmentations/pixel/functional.py albumentations/augmentations/geometric/transforms.py
Bumped pre-commit hook versions	Upgraded ruff from v0.11.13 to v0.12.0 Upgraded mypy from v1.16.0 to v1.16.1	.pre-commit-config.yaml
Updated lint ignore list	Added PLC0415 to lint.ignore in project config	pyproject.toml

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[Copilot]

Pull Request Overview

This PR aims to speed up the grayscale-to-multichannel conversion and improve related image transformation methods. Key changes include:

• Updating the error message and adding new helper methods (apply_to_images, apply_to_volume, apply_to_volumes) in the ToRGB transform.
• Switching from stacking to tiling in grayscale_to_multichannel for better performance.
• Minor updates to lint configuration, geometric transforms, and pre-commit hook revisions.

Reviewed Changes

Copilot reviewed 5 out of 5 changed files in this pull request and generated no comments.

Show a summary per file

File	Description
pyproject.toml	Added lint ignore for rule PLC0415
albumentations/augmentations/pixel/transforms.py	Updated error message and added batch/volume transform methods
albumentations/augmentations/pixel/functional.py	Optimized grayscale_to_multichannel and updated noise and drop-values helper logic
albumentations/augmentations/geometric/transforms.py	Refactored tuple concatenation syntax
.pre-commit-config.yaml	Updated revisions for ruff and mypy hooks

Comments suppressed due to low confidence (1)

albumentations/augmentations/pixel/transforms.py:3639

• The docstring for apply_to_volume indicates that the result is a grayscale volume, but the function applies the ToRGB transformation (via self.apply) which returns a multi-channel image. Please update the documentation to accurately reflect the output.

            np.ndarray: Grayscale volume.

[sourcery-ai]

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

• Ensure that apply_to_images/apply_to_volume(s) inputs with >3 dimensions are correctly handled by apply or add explicit dimension checks/resizing.
• Refactor repetitive apply_to_* methods into a generic wrapper or decorator to reduce boilerplate and maintenance overhead.
• Review memory and performance trade-offs when switching from np.stack to np.tile for large arrays, and consider documenting any impact.

Prompt for AI Agents

Please address the comments from this code review:
## Overall Comments
- Ensure that apply_to_images/apply_to_volume(s) inputs with >3 dimensions are correctly handled by apply or add explicit dimension checks/resizing.
- Refactor repetitive apply_to_* methods into a generic wrapper or decorator to reduce boilerplate and maintenance overhead.
- Review memory and performance trade-offs when switching from np.stack to np.tile for large arrays, and consider documenting any impact.

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