Functional transforms (augmentations.functional)¶

@uint8_io
@preserve_channel_dim
def add_rain(
    img: np.ndarray,
    slant: float,
    drop_length: int,
    drop_width: int,
    drop_color: tuple[int, int, int],
    blur_value: int,
    brightness_coefficient: float,
    rain_drops: np.ndarray,
) -> np.ndarray:
    """Optimized version using OpenCV line drawing."""
    if not rain_drops.size:
        return img.copy()

    img = img.copy()

    # Pre-allocate rain layer
    rain_layer = np.zeros_like(img, dtype=np.uint8)

    # Calculate end points correctly
    end_points = rain_drops + np.array([[slant, drop_length]])  # This creates correct shape

    # Stack arrays properly - both must be same shape arrays
    lines = np.stack((rain_drops, end_points), axis=1)  # Use tuple and proper axis

    cv2.polylines(
        rain_layer,
        lines.astype(np.int32),
        False,
        drop_color,
        drop_width,
        lineType=cv2.LINE_4,
    )

    if blur_value > 1:
        cv2.blur(rain_layer, (blur_value, blur_value), dst=rain_layer)

    cv2.add(img, rain_layer, dst=img)

    if brightness_coefficient != 1.0:
        cv2.multiply(img, brightness_coefficient, dst=img, dtype=cv2.CV_8U)

    return img

@uint8_io
@preserve_channel_dim
def add_shadow(
    img: np.ndarray,
    vertices_list: list[np.ndarray],
    intensities: np.ndarray,
) -> np.ndarray:
    """Add shadows to the image by reducing the intensity of the pixel values in specified regions.

    Args:
        img (np.ndarray): Input image. Multichannel images are supported.
        vertices_list (list[np.ndarray]): List of vertices for shadow polygons.
        intensities (np.ndarray): Array of shadow intensities. Range is [0, 1].

    Returns:
        np.ndarray: Image with shadows added.

    Reference:
        https://github.com/UjjwalSaxena/Automold--Road-Augmentation-Library
    """
    num_channels = get_num_channels(img)
    max_value = MAX_VALUES_BY_DTYPE[np.uint8]

    img_shadowed = img.copy()

    # Iterate over the vertices and intensity list
    for vertices, shadow_intensity in zip(vertices_list, intensities):
        # Create mask for the current shadow polygon
        mask = np.zeros((img.shape[0], img.shape[1], 1), dtype=np.uint8)
        cv2.fillPoly(mask, [vertices], (max_value,))

        # Duplicate the mask to have the same number of channels as the image
        mask = np.repeat(mask, num_channels, axis=2)

        # Apply shadow to the channels directly
        # It could be tempting to convert to HLS and apply the shadow to the L channel, but it creates artifacts
        shadowed_indices = mask[:, :, 0] == max_value
        darkness = 1 - shadow_intensity
        img_shadowed[shadowed_indices] = clip(
            img_shadowed[shadowed_indices] * darkness,
            np.uint8,
            inplace=True,
        )

    return img_shadowed

@uint8_io
def add_snow_bleach(
    img: np.ndarray,
    snow_point: float,
    brightness_coeff: float,
) -> np.ndarray:
    """Adds a simple snow effect to the image by bleaching out pixels.

    This function simulates a basic snow effect by increasing the brightness of pixels
    that are above a certain threshold (snow_point). It operates in the HLS color space
    to modify the lightness channel.

    Args:
        img (np.ndarray): Input image. Can be either RGB uint8 or float32.
        snow_point (float): A float in the range [0, 1], scaled and adjusted to determine
            the threshold for pixel modification. Higher values result in less snow effect.
        brightness_coeff (float): Coefficient applied to increase the brightness of pixels
            below the snow_point threshold. Larger values lead to more pronounced snow effects.
            Should be greater than 1.0 for a visible effect.

    Returns:
        np.ndarray: Image with simulated snow effect. The output has the same dtype as the input.

    Note:
        - This function converts the image to the HLS color space to modify the lightness channel.
        - The snow effect is created by selectively increasing the brightness of pixels.
        - This method tends to create a 'bleached' look, which may not be as realistic as more
          advanced snow simulation techniques.
        - The function automatically handles both uint8 and float32 input images.

    The snow effect is created through the following steps:
    1. Convert the image from RGB to HLS color space.
    2. Adjust the snow_point threshold.
    3. Increase the lightness of pixels below the threshold.
    4. Convert the image back to RGB.

    Mathematical Formulation:
        Let L be the lightness channel in HLS space.
        For each pixel (i, j):
        If L[i, j] < snow_point:
            L[i, j] = L[i, j] * brightness_coeff

    Examples:
        >>> import numpy as np
        >>> import albumentations as A
        >>> image = np.random.randint(0, 256, [100, 100, 3], dtype=np.uint8)
        >>> snowy_image = A.functional.add_snow_v1(image, snow_point=0.5, brightness_coeff=1.5)

    References:
        - HLS Color Space: https://en.wikipedia.org/wiki/HSL_and_HSV
        - Original implementation: https://github.com/UjjwalSaxena/Automold--Road-Augmentation-Library
    """
    max_value = MAX_VALUES_BY_DTYPE[np.uint8]

    snow_point *= max_value / 2
    snow_point += max_value / 3

    image_hls = cv2.cvtColor(img, cv2.COLOR_RGB2HLS)
    image_hls = np.array(image_hls, dtype=np.float32)

    image_hls[:, :, 1][image_hls[:, :, 1] < snow_point] *= brightness_coeff

    image_hls[:, :, 1] = clip(image_hls[:, :, 1], np.uint8, inplace=True)

    image_hls = np.array(image_hls, dtype=np.uint8)

    return cv2.cvtColor(image_hls, cv2.COLOR_HLS2RGB)

@uint8_io
def add_snow_texture(
    img: np.ndarray,
    snow_point: float,
    brightness_coeff: float,
    snow_texture: np.ndarray,
    sparkle_mask: np.ndarray,
) -> np.ndarray:
    """Add a realistic snow effect to the input image.

    This function simulates snowfall by applying multiple visual effects to the image,
    including brightness adjustment, snow texture overlay, depth simulation, and color tinting.
    The result is a more natural-looking snow effect compared to simple pixel bleaching methods.

    Args:
        img (np.ndarray): Input image in RGB format.
        snow_point (float): Coefficient that controls the amount and intensity of snow.
            Should be in the range [0, 1], where 0 means no snow and 1 means maximum snow effect.
        brightness_coeff (float): Coefficient for brightness adjustment to simulate the
            reflective nature of snow. Should be in the range [0, 1], where higher values
            result in a brighter image.
        snow_texture (np.ndarray): Snow texture.
        sparkle_mask (np.ndarray): Sparkle mask.

    Returns:
        np.ndarray: Image with added snow effect. The output has the same dtype as the input.

    Note:
        - The function first converts the image to HSV color space for better control over
          brightness and color adjustments.
        - A snow texture is generated using Gaussian noise and then filtered for a more
          natural appearance.
        - A depth effect is simulated, with more snow at the top of the image and less at the bottom.
        - A slight blue tint is added to simulate the cool color of snow.
        - Random sparkle effects are added to simulate light reflecting off snow crystals.

    The snow effect is created through the following steps:
    1. Brightness adjustment in HSV space
    2. Generation of a snow texture using Gaussian noise
    3. Application of a depth effect to the snow texture
    4. Blending of the snow texture with the original image
    5. Addition of a cool blue tint
    6. Addition of sparkle effects

    Examples:
        >>> import numpy as np
        >>> import albumentations as A
        >>> image = np.random.randint(0, 256, [100, 100, 3], dtype=np.uint8)
        >>> snowy_image = A.functional.add_snow_v2(image, snow_coeff=0.5, brightness_coeff=0.2)

    Note:
        This function works with both uint8 and float32 image types, automatically
        handling the conversion between them.

    References:
        - Perlin Noise: https://en.wikipedia.org/wiki/Perlin_noise
        - HSV Color Space: https://en.wikipedia.org/wiki/HSL_and_HSV
    """
    max_value = MAX_VALUES_BY_DTYPE[np.uint8]

    # Convert to HSV for better color control
    img_hsv = cv2.cvtColor(img, cv2.COLOR_RGB2HSV).astype(np.float32)

    # Increase brightness
    img_hsv[:, :, 2] = np.clip(
        img_hsv[:, :, 2] * (1 + brightness_coeff * snow_point),
        0,
        max_value,
    )

    # Generate snow texture
    snow_texture = cv2.GaussianBlur(snow_texture, (0, 0), sigmaX=1, sigmaY=1)

    # Create depth effect for snow simulation
    # More snow accumulates at the top of the image, gradually decreasing towards the bottom
    # This simulates natural snow distribution on surfaces
    # The effect is achieved using a linear gradient from 1 (full snow) to 0.2 (less snow)
    rows = img.shape[0]
    depth_effect = np.linspace(1, 0.2, rows)[:, np.newaxis]
    snow_texture *= depth_effect

    # Apply snow texture
    snow_layer = (np.dstack([snow_texture] * 3) * max_value * snow_point).astype(
        np.float32,
    )

    # Blend snow with original image
    img_with_snow = cv2.add(img_hsv, snow_layer)

    # Add a slight blue tint to simulate cool snow color
    blue_tint = np.full_like(img_with_snow, (0.6, 0.75, 1))  # Slight blue in HSV

    img_with_snow = cv2.addWeighted(
        img_with_snow,
        0.85,
        blue_tint,
        0.15 * snow_point,
        0,
    )

    # Convert back to RGB
    img_with_snow = cv2.cvtColor(img_with_snow.astype(np.uint8), cv2.COLOR_HSV2RGB)

    # Add some sparkle effects for snow glitter
    img_with_snow[sparkle_mask] = [max_value, max_value, max_value]

    return img_with_snow

@uint8_io
@preserve_channel_dim
@maybe_process_in_chunks
def add_sun_flare_overlay(
    img: np.ndarray,
    flare_center: tuple[float, float],
    src_radius: int,
    src_color: tuple[int, ...],
    circles: list[Any],
) -> np.ndarray:
    """Add a sun flare effect to an image using a simple overlay technique.

    This function creates a basic sun flare effect by overlaying multiple semi-transparent
    circles of varying sizes and intensities on the input image. The effect simulates
    a simple lens flare caused by bright light sources.

    Args:
        img (np.ndarray): The input image.
        flare_center (tuple[float, float]): (x, y) coordinates of the flare center
            in pixel coordinates.
        src_radius (int): The radius of the main sun circle in pixels.
        src_color (tuple[int, ...]): The color of the sun, represented as a tuple of RGB values.
        circles (list[Any]): A list of tuples, each representing a circle that contributes
            to the flare effect. Each tuple contains:
            - alpha (float): The transparency of the circle (0.0 to 1.0).
            - center (tuple[int, int]): (x, y) coordinates of the circle center.
            - radius (int): The radius of the circle.
            - color (tuple[int, int, int]): RGB color of the circle.

    Returns:
        np.ndarray: The output image with the sun flare effect added.

    Note:
        - This function uses a simple alpha blending technique to overlay flare elements.
        - The main sun is created as a gradient circle, fading from the center outwards.
        - Additional flare circles are added along an imaginary line from the sun's position.
        - This method is computationally efficient but may produce less realistic results
          compared to more advanced techniques.

    The flare effect is created through the following steps:
    1. Create an overlay image and output image as copies of the input.
    2. Add smaller flare circles to the overlay.
    3. Blend the overlay with the output image using alpha compositing.
    4. Add the main sun circle with a radial gradient.

    Examples:
        >>> import numpy as np
        >>> import albumentations as A
        >>> image = np.random.randint(0, 256, [100, 100, 3], dtype=np.uint8)
        >>> flare_center = (50, 50)
        >>> src_radius = 20
        >>> src_color = (255, 255, 200)
        >>> circles = [
        ...     (0.1, (60, 60), 5, (255, 200, 200)),
        ...     (0.2, (70, 70), 3, (200, 255, 200))
        ... ]
        >>> flared_image = A.functional.add_sun_flare_overlay(
        ...     image, flare_center, src_radius, src_color, circles
        ... )

    References:
        - Alpha compositing: https://en.wikipedia.org/wiki/Alpha_compositing
        - Lens flare: https://en.wikipedia.org/wiki/Lens_flare
    """
    overlay = img.copy()
    output = img.copy()

    weighted_brightness = 0.0
    total_radius_length = 0.0

    for alpha, (x, y), rad3, circle_color in circles:
        weighted_brightness += alpha * rad3
        total_radius_length += rad3
        cv2.circle(overlay, (x, y), rad3, circle_color, -1)
        output = add_weighted(overlay, alpha, output, 1 - alpha)

    point = [int(x) for x in flare_center]

    overlay = output.copy()
    num_times = src_radius // 10

    # max_alpha is calculated using weighted_brightness and total_radii_length times 5
    # meaning the higher the alpha with larger area, the brighter the bright spot will be
    # for list of alphas in range [0.05, 0.2], the max_alpha should below 1
    max_alpha = weighted_brightness / total_radius_length * 5
    alpha = np.linspace(0.0, min(max_alpha, 1.0), num=num_times)

    rad = np.linspace(1, src_radius, num=num_times)

    for i in range(num_times):
        cv2.circle(overlay, point, int(rad[i]), src_color, -1)
        alp = alpha[num_times - i - 1] * alpha[num_times - i - 1] * alpha[num_times - i - 1]
        output = add_weighted(overlay, alp, output, 1 - alp)

    return output

@uint8_io
@clipped
def add_sun_flare_physics_based(
    img: np.ndarray,
    flare_center: tuple[int, int],
    src_radius: int,
    src_color: tuple[int, int, int],
    circles: list[Any],
) -> np.ndarray:
    """Add a more realistic sun flare effect to the image.

    This function creates a complex sun flare effect by simulating various optical phenomena
    that occur in real camera lenses when capturing bright light sources. The result is a
    more realistic and physically plausible lens flare effect.

    Args:
        img (np.ndarray): Input image.
        flare_center (tuple[int, int]): (x, y) coordinates of the sun's center in pixels.
        src_radius (int): Radius of the main sun circle in pixels.
        src_color (tuple[int, int, int]): Color of the sun in RGB format.
        circles (list[Any]): List of tuples, each representing a flare circle with parameters:
            (alpha, center, size, color)
            - alpha (float): Transparency of the circle (0.0 to 1.0).
            - center (tuple[int, int]): (x, y) coordinates of the circle center.
            - size (float): Size factor for the circle radius.
            - color (tuple[int, int, int]): RGB color of the circle.

    Returns:
        np.ndarray: Image with added sun flare effect.

    Note:
        This function implements several techniques to create a more realistic flare:
        1. Separate flare layer: Allows for complex manipulations of the flare effect.
        2. Lens diffraction spikes: Simulates light diffraction in camera aperture.
        3. Radial gradient mask: Creates natural fading of the flare from the center.
        4. Gaussian blur: Softens the flare for a more natural glow effect.
        5. Chromatic aberration: Simulates color fringing often seen in real lens flares.
        6. Screen blending: Provides a more realistic blending of the flare with the image.

    The flare effect is created through the following steps:
    1. Create a separate flare layer.
    2. Add the main sun circle and diffraction spikes to the flare layer.
    3. Add additional flare circles based on the input parameters.
    4. Apply Gaussian blur to soften the flare.
    5. Create and apply a radial gradient mask for natural fading.
    6. Simulate chromatic aberration by applying different blurs to color channels.
    7. Blend the flare with the original image using screen blending mode.

    Examples:
        >>> import numpy as np
        >>> import albumentations as A
        >>> image = np.random.randint(0, 256, [1000, 1000, 3], dtype=np.uint8)
        >>> flare_center = (500, 500)
        >>> src_radius = 50
        >>> src_color = (255, 255, 200)
        >>> circles = [
        ...     (0.1, (550, 550), 10, (255, 200, 200)),
        ...     (0.2, (600, 600), 5, (200, 255, 200))
        ... ]
        >>> flared_image = A.functional.add_sun_flare_physics_based(
        ...     image, flare_center, src_radius, src_color, circles
        ... )

    References:
        - Lens flare: https://en.wikipedia.org/wiki/Lens_flare
        - Diffraction: https://en.wikipedia.org/wiki/Diffraction
        - Chromatic aberration: https://en.wikipedia.org/wiki/Chromatic_aberration
        - Screen blending: https://en.wikipedia.org/wiki/Blend_modes#Screen
    """
    output = img.copy()
    height, width = img.shape[:2]

    # Create a separate flare layer
    flare_layer = np.zeros_like(img, dtype=np.float32)

    # Add the main sun
    cv2.circle(flare_layer, flare_center, src_radius, src_color, -1)

    # Add lens diffraction spikes
    for angle in [0, 45, 90, 135]:
        end_point = (
            int(flare_center[0] + np.cos(np.radians(angle)) * max(width, height)),
            int(flare_center[1] + np.sin(np.radians(angle)) * max(width, height)),
        )
        cv2.line(flare_layer, flare_center, end_point, src_color, 2)

    # Add flare circles
    for _, center, size, color in circles:
        cv2.circle(flare_layer, center, int(size**0.33), color, -1)

    # Apply gaussian blur to soften the flare
    flare_layer = cv2.GaussianBlur(flare_layer, (0, 0), sigmaX=15, sigmaY=15)

    # Create a radial gradient mask
    y, x = np.ogrid[:height, :width]
    mask = np.sqrt((x - flare_center[0]) ** 2 + (y - flare_center[1]) ** 2)
    mask = 1 - np.clip(mask / (max(width, height) * 0.7), 0, 1)
    mask = np.dstack([mask] * 3)

    # Apply the mask to the flare layer
    flare_layer *= mask

    # Add chromatic aberration
    channels = list(cv2.split(flare_layer))
    channels[0] = cv2.GaussianBlur(
        channels[0],
        (0, 0),
        sigmaX=3,
        sigmaY=3,
    )  # Blue channel
    channels[2] = cv2.GaussianBlur(
        channels[2],
        (0, 0),
        sigmaX=5,
        sigmaY=5,
    )  # Red channel
    flare_layer = cv2.merge(channels)

    # Blend the flare with the original image using screen blending
    return 255 - ((255 - output) * (255 - flare_layer) / 255)

@clipped
def apply_corner_illumination(
    img: np.ndarray,
    intensity: float,
    corner: Literal[0, 1, 2, 3],
) -> np.ndarray:
    """Apply corner-based illumination effect."""
    if intensity == 0:
        return img.copy()

    height, width = img.shape[:2]

    # Pre-compute diagonal length once
    diagonal_length = math.sqrt(height * height + width * width)

    # Create inverted distance map mask directly
    # Use uint8 for distanceTransform regardless of input dtype
    mask = np.full((height, width), 255, dtype=np.uint8)

    # Use array indexing instead of conditionals
    corners = [(0, 0), (0, width - 1), (height - 1, width - 1), (height - 1, 0)]
    mask[corners[corner]] = 0

    # Calculate distance transform
    pattern = cv2.distanceTransform(
        mask,
        distanceType=cv2.DIST_L2,
        maskSize=cv2.DIST_MASK_PRECISE,
        dstType=cv2.CV_32F,  # Specify float output directly
    )

    # Combine operations to reduce array copies
    cv2.multiply(pattern, -intensity / diagonal_length, dst=pattern)
    cv2.add(pattern, 1, dst=pattern)

    if img.ndim == NUM_MULTI_CHANNEL_DIMENSIONS:
        pattern = cv2.merge([pattern] * img.shape[2])

    return multiply_by_array(img, pattern)

@clipped
def apply_gaussian_illumination(
    img: np.ndarray,
    intensity: float,
    center: tuple[float, float],
    sigma: float,
) -> np.ndarray:
    """Apply gaussian illumination effect."""
    if intensity == 0:
        return img.copy()

    height, width = img.shape[:2]

    # Pre-compute constants
    center_x = width * center[0]
    center_y = height * center[1]
    sigma2 = 2 * (max(height, width) * sigma) ** 2  # Pre-compute denominator

    # Create coordinate grid and calculate distances in-place
    y, x = np.ogrid[:height, :width]
    x = x.astype(np.float32)
    y = y.astype(np.float32)
    x -= center_x
    y -= center_y

    # Calculate squared distances in-place
    cv2.multiply(x, x, dst=x)
    cv2.multiply(y, y, dst=y)

    x = x + y

    # Calculate gaussian directly into x array
    cv2.multiply(x, -1 / sigma2, dst=x)
    cv2.exp(x, dst=x)

    # Scale by intensity
    cv2.multiply(x, intensity, dst=x)
    cv2.add(x, 1, dst=x)

    if img.ndim == NUM_MULTI_CHANNEL_DIMENSIONS:
        x = cv2.merge([x] * img.shape[2])

    return multiply_by_array(img, x)

@float32_io
def apply_linear_illumination(img: np.ndarray, intensity: float, angle: float) -> np.ndarray:
    """Apply directional illumination effect to an image using a linear gradient.

    The function creates a directional gradient and uses it to modulate image brightness.
    The gradient direction is controlled by the angle parameter, and the strength of the
    effect is controlled by the intensity parameter.

    The illumination is applied by multiplying the image with a scale factor that varies
    linearly across the image. The scale factor ranges from (1-|intensity|) to (1+|intensity|).

    Args:
        img: Input image in range [0, 1]. Can be single or multi-channel.
        intensity: Strength and direction of the illumination effect, range [-1, 1].
            - Positive values brighten in gradient direction
            - Negative values darken in gradient direction
            - Magnitude determines strength of the effect
        angle: Direction of the gradient in degrees.
            - 0: left to right
            - 90: bottom to top
            - 180: right to left
            - 270: top to bottom

    Returns:
        Image with applied illumination effect, same shape and range as input.

    Implementation details:
        1. Creates a directional gradient in range [0, 1]
        2. For negative intensity, inverts the gradient (1 - gradient)
        3. For multi-channel images, repeats gradient across channels
        4. Computes scale factor in-place:
           scale = 1 - |intensity| + 2 * |intensity| * gradient
           This maps gradient [0, 1] to scale [(1-|i|), (1+|i|)]
        5. Multiplies image by scale factor

    Note:
        Uses in-place operations where possible for memory efficiency.
        The @float32_io decorator ensures float32 precision.
        The @clipped decorator ensures output values stay in valid range.
    """
    height, width = img.shape[:2]
    abs_intensity = abs(intensity)

    # Create gradient and handle negative intensity in one step
    gradient = create_directional_gradient(height, width, angle)

    if intensity < 0:
        cv2.subtract(1, gradient, dst=gradient)

    cv2.multiply(gradient, 2 * abs_intensity, dst=gradient)
    cv2.add(gradient, 1 - abs_intensity, dst=gradient)

    # Add channel dimension if needed
    if img.ndim == NUM_MULTI_CHANNEL_DIMENSIONS:
        gradient = gradient[..., np.newaxis]

    return multiply_by_array(img, gradient)

@clipped
@float32_io
def apply_plasma_brightness_contrast(
    img: np.ndarray,
    brightness_factor: float,
    contrast_factor: float,
    plasma_pattern: np.ndarray,
) -> np.ndarray:
    """Apply plasma-based brightness and contrast adjustments."""
    # Early return if no adjustments needed
    if brightness_factor == 0 and contrast_factor == 0:
        return img

    img = img.copy()

    # Expand plasma pattern once if needed
    if img.ndim > MONO_CHANNEL_DIMENSIONS:
        plasma_pattern = np.tile(plasma_pattern[..., np.newaxis], (1, 1, img.shape[-1]))

    # Apply brightness adjustment
    if brightness_factor != 0:
        brightness_adjustment = multiply(plasma_pattern, brightness_factor, inplace=False)
        img = add(img, brightness_adjustment, inplace=True)

    # Apply contrast adjustment
    if contrast_factor != 0:
        mean = img.mean()
        contrast_weights = multiply(plasma_pattern, contrast_factor, inplace=False) + 1

        img = multiply(img, contrast_weights, inplace=True)

        mean_factor = mean * (1.0 - contrast_weights)
        return add(img, mean_factor, inplace=True)

    return img

@clipped
def apply_plasma_shadow(
    img: np.ndarray,
    intensity: float,
    plasma_pattern: np.ndarray,
) -> np.ndarray:
    """Apply plasma-based shadow effect by darkening.

    Args:
        img: Input image
        intensity: Shadow intensity in [0, 1]
        plasma_pattern: Generated plasma pattern of shape (H, W)

    Returns:
        Image with applied shadow effect
    """
    # Scale plasma pattern by intensity first (scalar operation)
    scaled_pattern = plasma_pattern * intensity

    # Expand dimensions only once if needed
    if img.ndim > MONO_CHANNEL_DIMENSIONS:
        scaled_pattern = scaled_pattern[..., np.newaxis]

    # Single multiply operation
    return img * (1 - scaled_pattern)

def apply_salt_and_pepper(
    img: np.ndarray,
    salt_mask: np.ndarray,
    pepper_mask: np.ndarray,
) -> np.ndarray:
    """Apply salt and pepper noise to image using pre-computed masks."""
    # Add channel dimension to masks if image is 3D
    if img.ndim == 3:
        salt_mask = salt_mask[..., None]
        pepper_mask = pepper_mask[..., None]

    max_value = MAX_VALUES_BY_DTYPE[img.dtype]
    return np.where(salt_mask, max_value, np.where(pepper_mask, 0, img))

@uint8_io
def auto_contrast(
    img: np.ndarray,
    cutoff: float,
    ignore: int | None,
    method: Literal["cdf", "pil"],
) -> np.ndarray:
    """Apply auto contrast to the image.

    Args:
        img: Input image in uint8 or float32 format.
        cutoff: Percentage of pixels to cut off from the histogram edges.
               Range: 0-100. Default: 0 (no cutoff)
        ignore: Pixel value to ignore in auto contrast calculation.
               Useful for handling alpha channels or other special values.
        method: Method to use for contrast enhancement:
               - "cdf": Uses cumulative distribution function (original albumentations method)
               - "pil": Uses linear scaling like PIL.ImageOps.autocontrast

    Returns:
        Contrast-enhanced image in the same dtype as input.
    """
    result = img.copy()
    num_channels = get_num_channels(img)
    max_value = MAX_VALUES_BY_DTYPE[img.dtype]

    # Pre-compute histograms using cv2.calcHist - much faster than np.histogram
    if img.ndim > MONO_CHANNEL_DIMENSIONS:
        channels = cv2.split(img)
        hists: list[np.ndarray] = []
        for i, channel in enumerate(channels):
            if ignore is not None and i == ignore:
                hists.append(None)
                continue
            mask = None if ignore is None else (channel != ignore)
            hist = cv2.calcHist([channel], [0], mask, [256], [0, max_value])
            hists.append(hist.ravel())

    for i in range(num_channels):
        if ignore is not None and i == ignore:
            continue

        if img.ndim > MONO_CHANNEL_DIMENSIONS:
            hist = hists[i]
            channel = channels[i]
        else:
            mask = None if ignore is None else (img != ignore)
            hist = cv2.calcHist([img], [0], mask, [256], [0, max_value]).ravel()
            channel = img

        lo, hi = get_histogram_bounds(hist, cutoff)
        if hi <= lo:
            continue

        lut = create_contrast_lut(hist, lo, hi, max_value, method)
        if ignore is not None:
            lut[ignore] = ignore

        if img.ndim > MONO_CHANNEL_DIMENSIONS:
            result[..., i] = sz_lut(channel, lut)
        else:
            result = sz_lut(channel, lut)

    return result