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Data Processing

Basic data processing functions are provided in the dataprocessing module. These functions are used to preprocess data before training a model, or to post-process the output of a model.

Generic Functions

plantseg.functionals.dataprocessing.dataprocessing.normalize_01(data: np.ndarray, eps=1e-12) -> np.ndarray

Normalize a numpy array between 0 and 1 and converts it to float32.

Parameters:

  • data (ndarray) –

    Input numpy array

  • eps (float, default: 1e-12 ) –

    A small value added to the denominator for numerical stability

Returns:

  • normalized_data ( ndarray ) –

    Normalized numpy array

Source code in plantseg/functionals/dataprocessing/dataprocessing.py 
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def normalize_01(data: np.ndarray, eps=1e-12) -> np.ndarray:
    """
    Normalize a numpy array between 0 and 1 and converts it to float32.

    Args:
        data (np.ndarray): Input numpy array
        eps (float): A small value added to the denominator for numerical stability

    Returns:
        normalized_data (np.ndarray): Normalized numpy array
    """
    return (data - np.min(data)) / (np.max(data) - np.min(data) + eps).astype("float32")

plantseg.functionals.dataprocessing.dataprocessing.scale_image_to_voxelsize(image: np.ndarray, input_voxel_size: tuple[float, float, float], output_voxel_size: tuple[float, float, float], order: int = 0) -> np.ndarray

Scale an image from a given voxel size to another voxel size.

Parameters:

  • image (ndarray) –

    Input image to scale

  • input_voxel_size (tuple[float, float, float]) –

    Input voxel size

  • output_voxel_size (tuple[float, float, float]) –

    Output voxel size

  • order (int, default: 0 ) –

    Interpolation order, must be 0 for segmentation and 1, 2 for images

Returns:

  • scaled_image ( ndarray ) –

    Scaled image as numpy array

Source code in plantseg/functionals/dataprocessing/dataprocessing.py 
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def scale_image_to_voxelsize(
    image: np.ndarray,
    input_voxel_size: tuple[float, float, float],
    output_voxel_size: tuple[float, float, float],
    order: int = 0,
) -> np.ndarray:
    """
    Scale an image from a given voxel size to another voxel size.

    Args:
        image (np.ndarray): Input image to scale
        input_voxel_size (tuple[float, float, float]): Input voxel size
        output_voxel_size (tuple[float, float, float]): Output voxel size
        order (int): Interpolation order, must be 0 for segmentation and 1, 2 for images

    Returns:
        scaled_image (np.ndarray): Scaled image as numpy array
    """
    factor = compute_scaling_factor(input_voxel_size, output_voxel_size)
    return image_rescale(image, factor, order=order)

plantseg.functionals.dataprocessing.dataprocessing.image_rescale(image: np.ndarray, factor: tuple[float, float, float], order: int) -> np.ndarray

Scale an image by a given factor in each dimension

Parameters:

  • image (ndarray) –

    Input image to scale

  • factor (tuple[float, float, float]) –

    Scaling factor in each dimension

  • order (int) –

    Interpolation order, must be 0 for segmentation and 1, 2 for images

Returns:

  • scaled_image ( ndarray ) –

    Scaled image as numpy array

Source code in plantseg/functionals/dataprocessing/dataprocessing.py 
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def image_rescale(image: np.ndarray, factor: tuple[float, float, float], order: int) -> np.ndarray:
    """
    Scale an image by a given factor in each dimension

    Args:
        image (np.ndarray): Input image to scale
        factor (tuple[float, float, float]): Scaling factor in each dimension
        order (int): Interpolation order, must be 0 for segmentation and 1, 2 for images

    Returns:
        scaled_image (np.ndarray): Scaled image as numpy array
    """
    if np.array_equal(factor, [1.0, 1.0, 1.0]):
        return image
    else:
        return zoom(image, zoom=factor, order=order)

plantseg.functionals.dataprocessing.dataprocessing.image_median(image: np.ndarray, radius: int) -> np.ndarray

Apply median smoothing on an image with a given radius.

Parameters:

  • image (ndarray) –

    Input image to apply median smoothing.

  • radius (int) –

    Radius of the median filter.

Returns:

  • ndarray –

    np.ndarray: Median smoothed image.

Source code in plantseg/functionals/dataprocessing/dataprocessing.py 
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def image_median(image: np.ndarray, radius: int) -> np.ndarray:
    """
    Apply median smoothing on an image with a given radius.

    Args:
        image (np.ndarray): Input image to apply median smoothing.
        radius (int): Radius of the median filter.

    Returns:
        np.ndarray: Median smoothed image.
    """
    if radius <= 0:
        raise ValueError("Radius must be a positive integer.")

    if image.ndim == 2:
        # 2D image
        return median(image, disk(radius))
    elif image.ndim == 3:
        if image.shape[0] == 1:
            # Single slice (ZYX or YX) case
            return median(image[0], disk(radius)).reshape(image.shape)
        else:
            # 3D image
            return median(image, ball(radius))
    else:
        raise ValueError("Unsupported image dimensionality. Image must be either 2D or 3D.")

plantseg.functionals.dataprocessing.dataprocessing.image_gaussian_smoothing(image: np.ndarray, sigma: float) -> np.ndarray

Apply gaussian smoothing on an image with a given sigma.

Parameters:

  • image (ndarray) –

    Input image to apply gaussian smoothing

  • sigma (float) –

    Sigma value for gaussian smoothing

Returns:

  • smoothed_image ( ndarray ) –

    Gaussian smoothed image as numpy array

Source code in plantseg/functionals/dataprocessing/dataprocessing.py 
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def image_gaussian_smoothing(image: np.ndarray, sigma: float) -> np.ndarray:
    """
    Apply gaussian smoothing on an image with a given sigma.

    Args:
        image (np.ndarray): Input image to apply gaussian smoothing
        sigma (float): Sigma value for gaussian smoothing

    Returns:
        smoothed_image (np.ndarray): Gaussian smoothed image as numpy array
    """
    image = image.astype("float32")
    max_sigma = (np.array(image.shape) - 1) / 3
    sigma_array = np.minimum(max_sigma, np.ones(max_sigma.ndim) * sigma)
    return gaussianSmoothing(image, sigma_array)

plantseg.functionals.dataprocessing.dataprocessing.image_crop(image: np.ndarray, crop_str: str) -> np.ndarray

Crop an image from a crop string like [:, 10:30:, 10:20]

Parameters:

  • image (ndarray) –

    Input image to crop

  • crop_str (str) –

    Crop string

Returns:

  • cropped_image ( ndarray ) –

    Cropped image as numpy array

Source code in plantseg/functionals/dataprocessing/dataprocessing.py 
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def image_crop(image: np.ndarray, crop_str: str) -> np.ndarray:
    """
    Crop an image from a crop string like [:, 10:30:, 10:20]

    Args:
        image (np.ndarray): Input image to crop
        crop_str (str): Crop string

    Returns:
        cropped_image (np.ndarray): Cropped image as numpy array
    """
    crop_str = crop_str.replace("[", "").replace("]", "")
    slices = tuple(
        (slice(*(int(i) if i else None for i in part.strip().split(":"))) if ":" in part else int(part.strip()))
        for part in crop_str.split(",")
    )
    return image[slices]

Segmentation Functions

plantseg.functionals.dataprocessing.labelprocessing.relabel_segmentation(segmentation_image: np.ndarray, background: int | None = None) -> np.ndarray

Relabels contiguously a segmentation image, non-touching instances with same id will be relabeled differently. To be noted that measure.label is different from ndimage.label.

1-connectivity 2-connectivity diagonal connection close-up

 [ ]           [ ]  [ ]  [ ]             [ ]
  |               \  |  /                 |  <- hop 2

[ ]--[x]--[ ] [ ]--[x]--[ ] [x]--[ ] | / | \ hop 1 [ ] [ ] [ ] [ ]

Parameters:

  • segmentation_image (ndarray) –

    A 2D or 3D segmentation image where connected components represent different instances.

  • background (int | None, default: None ) –

    Label of the background. If None, the function will assume the background label is 0. Default is None.

Returns:

  • ndarray –

    np.ndarray: A relabeled segmentation image where each connected component is assigned a unique integer label.

Source code in plantseg/functionals/dataprocessing/labelprocessing.py 
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def relabel_segmentation(segmentation_image: np.ndarray, background: int | None = None) -> np.ndarray:
    r"""
    Relabels contiguously a segmentation image, non-touching instances with same id will be relabeled differently.
    To be noted that measure.label is different from ndimage.label.

    1-connectivity     2-connectivity     diagonal connection close-up

         [ ]           [ ]  [ ]  [ ]             [ ]
          |               \  |  /                 |  <- hop 2
    [ ]--[x]--[ ]      [ ]--[x]--[ ]        [x]--[ ]
          |               /  |  \             hop 1
         [ ]           [ ]  [ ]  [ ]

    Args:
        segmentation_image (np.ndarray): A 2D or 3D segmentation image where connected components represent different instances.
        background (int | None, optional): Label of the background. If None, the function will assume the background
                                           label is 0. Default is None.

    Returns:
        np.ndarray: A relabeled segmentation image where each connected component is assigned a unique integer label.
    """
    relabeled_segmentation = measure.label(
        segmentation_image,
        background=background,
        return_num=False,
        connectivity=None,
    )
    assert isinstance(relabeled_segmentation, np.ndarray)
    return relabeled_segmentation

plantseg.functionals.dataprocessing.labelprocessing.set_background_to_value(segmentation_image: np.ndarray, value: int = 0) -> np.ndarray

Sets all occurrences of the background (label 0) in the segmentation image to a new value.

Parameters:

  • segmentation_image (ndarray) –

    A 2D or 3D numpy array representing an instance segmentation.

  • value (int, default: 0 ) –

    The value to assign to the background. Default is 0.

Returns:

  • ndarray –

    np.ndarray: A segmentation image where all background pixels (originally 0) are set to value.

Source code in plantseg/functionals/dataprocessing/labelprocessing.py 
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def set_background_to_value(segmentation_image: np.ndarray, value: int = 0) -> np.ndarray:
    """
    Sets all occurrences of the background (label 0) in the segmentation image to a new value.

    Args:
        segmentation_image (np.ndarray): A 2D or 3D numpy array representing an instance segmentation.
        value (int, optional): The value to assign to the background. Default is 0.

    Returns:
        np.ndarray: A segmentation image where all background pixels (originally 0) are set to `value`.
    """
    return np.where(segmentation_image == 0, value, segmentation_image)

Advanced Functions

plantseg.functionals.dataprocessing.advanced_dataprocessing.fix_over_under_segmentation_from_nuclei(cell_seg: np.ndarray, nuclei_seg: np.ndarray, threshold_merge: float = 0.33, threshold_split: float = 0.66, quantiles_nuclei: tuple[float, float] = (0.3, 0.99), boundary: np.ndarray | None = None) -> np.ndarray

Corrects over-segmentation and under-segmentation of cells based on a trusted nuclei segmentation.

This function uses information from nuclei segmentation to refine cell segmentation by first identifying over-segmented cells (cells mistakenly split into multiple segments) and merging them. It then corrects under-segmented cells (multiple nuclei within a single cell) by splitting them based on nuclei position and optional boundary information.

Parameters:

  • cell_seg (ndarray) –

    A 2D or 3D array representing segmented cell instances.

  • nuclei_seg (ndarray) –

    A 2D or 3D array representing segmented nuclei instances, with the same shape as cell_seg.

  • threshold_merge (float, default: 0.33 ) –

    Threshold for identifying over-segmentation, based on the ratio of nuclei overlap. Cells with overlap below this threshold will be merged. Default is 0.33.

  • threshold_split (float, default: 0.66 ) –

    Threshold for identifying under-segmentation, based on the ratio of nuclei overlap. Cells with overlap above this threshold will be split. Default is 0.66.

  • quantiles_nuclei (tuple[float, float], default: (0.3, 0.99) ) –

    Quantile range for filtering nuclei based on size, helping to ignore outliers such as very small or very large nuclei. Default is (0.3, 0.99).

  • boundary (ndarray | None, default: None ) –

    An optional boundary probability map for the cells. If None, a constant map is used to treat all regions equally. This can help refine under-segmentation correction.

Returns:

  • ndarray –

    np.ndarray: The corrected cell segmentation array, of the same shape as the input cell_seg.

Source code in plantseg/functionals/dataprocessing/advanced_dataprocessing.py 
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def fix_over_under_segmentation_from_nuclei(
    cell_seg: np.ndarray,
    nuclei_seg: np.ndarray,
    threshold_merge: float = 0.33,
    threshold_split: float = 0.66,
    quantiles_nuclei: tuple[float, float] = (0.3, 0.99),
    boundary: np.ndarray | None = None,
) -> np.ndarray:
    """
    Corrects over-segmentation and under-segmentation of cells based on a trusted nuclei segmentation.

    This function uses information from nuclei segmentation to refine cell segmentation by first identifying
    over-segmented cells (cells mistakenly split into multiple segments) and merging them. It then corrects
    under-segmented cells (multiple nuclei within a single cell) by splitting them based on nuclei position
    and optional boundary information.

    Args:
        cell_seg (np.ndarray): A 2D or 3D array representing segmented cell instances.
        nuclei_seg (np.ndarray): A 2D or 3D array representing segmented nuclei instances, with the same shape as `cell_seg`.
        threshold_merge (float, optional): Threshold for identifying over-segmentation, based on the ratio of nuclei overlap.
            Cells with overlap below this threshold will be merged. Default is 0.33.
        threshold_split (float, optional): Threshold for identifying under-segmentation, based on the ratio of nuclei overlap.
            Cells with overlap above this threshold will be split. Default is 0.66.
        quantiles_nuclei (tuple[float, float], optional): Quantile range for filtering nuclei based on size, helping to ignore
            outliers such as very small or very large nuclei. Default is (0.3, 0.99).
        boundary (np.ndarray | None, optional): An optional boundary probability map for the cells. If None, a constant map
            is used to treat all regions equally. This can help refine under-segmentation correction.

    Returns:
        np.ndarray: The corrected cell segmentation array, of the same shape as the input `cell_seg`.
    """
    cell_counts, nuclei_counts, cell_nuclei_counts = numba_find_overlaps(cell_seg, nuclei_seg)
    nuclei_assignments = find_potential_over_seg(nuclei_counts, cell_nuclei_counts, threshold=threshold_merge)

    corrected_seg = fix_over_segmentation(cell_seg, nuclei_assignments)

    cell_counts, nuclei_counts, cell_nuclei_counts = numba_find_overlaps(corrected_seg, nuclei_seg)
    cell_assignments = find_potential_under_seg(
        nuclei_counts,
        cell_counts,
        cell_nuclei_counts,
        threshold=threshold_split,
        quantiles_clip=quantiles_nuclei,
    )

    boundary_pmap = np.ones_like(cell_seg) if boundary is None else boundary
    return fix_under_segmentation(corrected_seg, nuclei_seg, boundary_pmap, cell_assignments, cell_idx=None)

plantseg.functionals.dataprocessing.advanced_dataprocessing.remove_false_positives_by_foreground_probability(segmentation: np.ndarray, foreground: np.ndarray, threshold: float) -> np.ndarray

Removes false positive regions in a segmentation based on a foreground probability map.

  1. Labels are not preserved.
  2. If the mean(an instance * its own probability region) < threshold, it is removed.

Parameters:

  • segmentation (ndarray) –

    Segmentation array where each unique non-zero value indicates a distinct region.

  • foreground (ndarray) –

    Foreground probability map of the same shape as segmentation.

  • threshold (float) –

    Probability threshold below which regions are considered false positives.

Returns:

  • ndarray –

    np.ndarray: Segmentation array with false positives removed.

Source code in plantseg/functionals/dataprocessing/advanced_dataprocessing.py 
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def remove_false_positives_by_foreground_probability(
    segmentation: np.ndarray,
    foreground: np.ndarray,
    threshold: float,
) -> np.ndarray:
    """
    Removes false positive regions in a segmentation based on a foreground probability map.

    1. Labels are not preserved.
    2. If the mean(an instance * its own probability region) < threshold, it is removed.

    Args:
        segmentation (np.ndarray): Segmentation array where each unique non-zero value indicates a distinct region.
        foreground (np.ndarray): Foreground probability map of the same shape as `segmentation`.
        threshold (float): Probability threshold below which regions are considered false positives.

    Returns:
        np.ndarray: Segmentation array with false positives removed.
    """
    # TODO: make a channel for removed regions for easier inspection
    # TODO: use `relabel_sequential` to recover the original labels

    if segmentation.shape != foreground.shape:
        raise ValueError("Segmentation and probability map must have the same shape.")
    if foreground.max() > 1:
        raise ValueError("Foreground must be a probability map with values in [0, 1].")

    instances, _, _ = relabel_sequential(segmentation)  # The label 0 is assumed to denote the bg and is never remapped.
    regions = regionprops(instances)  # Labels with value 0 are ignored.

    pixel_count = np.zeros(len(regions) + 1)
    pixel_value = np.zeros(len(regions) + 1)
    pixel_count[0] = 1  # Avoid division by zero: pixel_count[0] and pixel_value[0] are fixed throughout.

    for region in tqdm.tqdm(regions):
        bbox = region.bbox
        if instances.ndim == 3:
            slices = (slice(bbox[0], bbox[3]), slice(bbox[1], bbox[4]), slice(bbox[2], bbox[5]))
        else:
            slices = (slice(bbox[0], bbox[2]), slice(bbox[1], bbox[3]))

        region_mask = instances[slices] == region.label
        prob = foreground[slices]

        pixel_count[region.label] = region.area
        pixel_value[region.label] = (region_mask * prob).sum()

    likelihood = pixel_value / pixel_count
    to_remove = likelihood < threshold

    instances[np.isin(instances, np.nonzero(to_remove)[0])] = 0
    instances, _, _ = relabel_sequential(instances)
    return instances