Speed up salt and pepper

[ternaus]

Summary by Sourcery

Improve the performance of the SaltAndPepper augmentation and ensure color consistency by applying the same noise mask to all image channels.

Enhancements:

• Generate salt and pepper noise masks without overlap, resulting in a more precise application of the specified amount of noise.
• Apply the same noise mask to all channels of an image to preserve color consistency.

[sourcery-ai]

Reviewer's Guide by Sourcery

This pull request improves the performance of the SaltAndPepper augmentation by generating masks without overlap and applying them efficiently using NumPy's where function. The same noise mask is applied to all channels of the image to preserve color consistency.

Sequence diagram for improved Salt and Pepper noise application

sequenceDiagram
    participant Image
    participant Transform
    participant Mask Generator
    participant Noise Applier

    Image->>Transform: Input image
    Transform->>Mask Generator: Calculate dimensions
    Mask Generator->>Mask Generator: Generate random indices
    Mask Generator->>Mask Generator: Create salt mask
    Mask Generator->>Mask Generator: Create pepper mask
    Mask Generator->>Noise Applier: Send 2D masks
    Noise Applier->>Noise Applier: Apply masks to all channels
    Noise Applier->>Image: Return modified image

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State diagram for pixel values in Salt and Pepper transformation

stateDiagram-v2
    [*] --> Original: Initial pixel value
    Original --> Salt: Selected for salt mask
    Original --> Pepper: Selected for pepper mask
    Salt --> MaxValue: Set to maximum value
    Pepper --> Zero: Set to 0
    MaxValue --> [*]
    Zero --> [*]

    note right of Salt: Applied to all channels
    note right of Pepper: Applied to all channels

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File-Level Changes

Change	Details	Files
Generate salt and pepper masks without overlap.	Calculate the number of salt and pepper pixels based on the total amount and salt ratio. Generate a flat array of random indices to select pixels for salt and pepper noise. Create boolean masks from the selected indices. Reshape the masks to match the image dimensions.	albumentations/augmentations/transforms.py
Apply salt and pepper noise using NumPy's where function.	Use np.where to apply salt and pepper noise based on the generated masks. Avoid unnecessary copying of the image by using np.where.	albumentations/augmentations/functional.py
Apply the same noise mask to all channels.	The noise mask is generated once and applied to all channels to maintain color consistency.	albumentations/augmentations/transforms.py
Update documentation to reflect changes in mask generation and application.	Clarify that the same noise mask is applied to all channels. Update the mathematical formulation to reflect the new mask generation and application. Explain that the exact number of affected pixels matches the specified amount.	albumentations/augmentations/transforms.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: all looks good
• 🟢 Security: all looks good
• 🟢 Testing: all looks good
• 🟢 Complexity: all looks good
• 🟢 Documentation: all looks good

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