423 lines
15 KiB
Python
423 lines
15 KiB
Python
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from warnings import warn
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import numpy as np
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import scipy.ndimage as ndi
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from .. import measure
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from .._shared.utils import remove_arg
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from .._shared.coord import ensure_spacing
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def _get_high_intensity_peaks(image, mask, num_peaks, min_distance, p_norm):
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"""
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Return the highest intensity peak coordinates.
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"""
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# get coordinates of peaks
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coord = np.nonzero(mask)
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intensities = image[coord]
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# Highest peak first
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idx_maxsort = np.argsort(-intensities)
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coord = np.transpose(coord)[idx_maxsort]
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coord = ensure_spacing(coord, spacing=min_distance, p_norm=p_norm)
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if len(coord) > num_peaks:
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coord = coord[:num_peaks]
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return coord
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def _get_peak_mask(image, footprint, threshold, mask=None):
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"""
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Return the mask containing all peak candidates above thresholds.
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"""
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if footprint.size == 1 or image.size == 1:
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return image > threshold
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image_max = ndi.maximum_filter(image, footprint=footprint,
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mode='constant')
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out = image == image_max
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# no peak for a trivial image
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image_is_trivial = np.all(out) if mask is None else np.all(out[mask])
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if image_is_trivial:
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out[:] = False
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if mask is not None:
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# isolated pixels in masked area are returned as peaks
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isolated_px = np.logical_xor(mask, ndi.binary_opening(mask))
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out[isolated_px] = True
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out &= image > threshold
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return out
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def _exclude_border(label, border_width):
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"""Set label border values to 0.
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"""
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# zero out label borders
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for i, width in enumerate(border_width):
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if width == 0:
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continue
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label[(slice(None),) * i + (slice(None, width),)] = 0
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label[(slice(None),) * i + (slice(-width, None),)] = 0
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return label
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def _get_threshold(image, threshold_abs, threshold_rel):
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"""Return the threshold value according to an absolute and a relative
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value.
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"""
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threshold = threshold_abs if threshold_abs is not None else image.min()
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if threshold_rel is not None:
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threshold = max(threshold, threshold_rel * image.max())
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return threshold
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def _get_excluded_border_width(image, min_distance, exclude_border):
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"""Return border_width values relative to a min_distance if requested.
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"""
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if isinstance(exclude_border, bool):
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border_width = (min_distance if exclude_border else 0,) * image.ndim
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elif isinstance(exclude_border, int):
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if exclude_border < 0:
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raise ValueError("`exclude_border` cannot be a negative value")
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border_width = (exclude_border,) * image.ndim
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elif isinstance(exclude_border, tuple):
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if len(exclude_border) != image.ndim:
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raise ValueError(
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"`exclude_border` should have the same length as the "
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"dimensionality of the image.")
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for exclude in exclude_border:
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if not isinstance(exclude, int):
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raise ValueError(
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"`exclude_border`, when expressed as a tuple, must only "
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"contain ints."
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)
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if exclude < 0:
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raise ValueError(
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"`exclude_border` can not be a negative value")
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border_width = exclude_border
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else:
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raise TypeError(
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"`exclude_border` must be bool, int, or tuple with the same "
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"length as the dimensionality of the image.")
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return border_width
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@remove_arg("indices", changed_version="0.20")
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def peak_local_max(image, min_distance=1, threshold_abs=None,
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threshold_rel=None, exclude_border=True, indices=True,
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num_peaks=np.inf, footprint=None, labels=None,
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num_peaks_per_label=np.inf, p_norm=np.inf):
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"""Find peaks in an image as coordinate list or boolean mask.
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Peaks are the local maxima in a region of `2 * min_distance + 1`
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(i.e. peaks are separated by at least `min_distance`).
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If both `threshold_abs` and `threshold_rel` are provided, the maximum
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of the two is chosen as the minimum intensity threshold of peaks.
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.. versionchanged:: 0.18
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Prior to version 0.18, peaks of the same height within a radius of
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`min_distance` were all returned, but this could cause unexpected
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behaviour. From 0.18 onwards, an arbitrary peak within the region is
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returned. See issue gh-2592.
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Parameters
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----------
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image : ndarray
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Input image.
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min_distance : int, optional
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The minimal allowed distance separating peaks. To find the
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maximum number of peaks, use `min_distance=1`.
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threshold_abs : float, optional
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Minimum intensity of peaks. By default, the absolute threshold is
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the minimum intensity of the image.
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threshold_rel : float, optional
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Minimum intensity of peaks, calculated as `max(image) * threshold_rel`.
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exclude_border : int, tuple of ints, or bool, optional
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If positive integer, `exclude_border` excludes peaks from within
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`exclude_border`-pixels of the border of the image.
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If tuple of non-negative ints, the length of the tuple must match the
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input array's dimensionality. Each element of the tuple will exclude
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peaks from within `exclude_border`-pixels of the border of the image
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along that dimension.
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If True, takes the `min_distance` parameter as value.
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If zero or False, peaks are identified regardless of their distance
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from the border.
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indices : bool, optional
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If True, the output will be an array representing peak
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coordinates. The coordinates are sorted according to peaks
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values (Larger first). If False, the output will be a boolean
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array shaped as `image.shape` with peaks present at True
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elements. ``indices`` is deprecated and will be removed in
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version 0.20. Default behavior will be to always return peak
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coordinates. You can obtain a mask as shown in the example
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below.
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num_peaks : int, optional
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Maximum number of peaks. When the number of peaks exceeds `num_peaks`,
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return `num_peaks` peaks based on highest peak intensity.
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footprint : ndarray of bools, optional
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If provided, `footprint == 1` represents the local region within which
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to search for peaks at every point in `image`.
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labels : ndarray of ints, optional
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If provided, each unique region `labels == value` represents a unique
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region to search for peaks. Zero is reserved for background.
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num_peaks_per_label : int, optional
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Maximum number of peaks for each label.
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p_norm : float
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Which Minkowski p-norm to use. Should be in the range [1, inf].
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A finite large p may cause a ValueError if overflow can occur.
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``inf`` corresponds to the Chebyshev distance and 2 to the
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Euclidean distance.
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Returns
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-------
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output : ndarray or ndarray of bools
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* If `indices = True` : (row, column, ...) coordinates of peaks.
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* If `indices = False` : Boolean array shaped like `image`, with peaks
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represented by True values.
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Notes
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-----
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The peak local maximum function returns the coordinates of local peaks
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(maxima) in an image. Internally, a maximum filter is used for finding local
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maxima. This operation dilates the original image. After comparison of the
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dilated and original image, this function returns the coordinates or a mask
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of the peaks where the dilated image equals the original image.
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See also
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--------
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skimage.feature.corner_peaks
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Examples
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--------
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>>> img1 = np.zeros((7, 7))
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>>> img1[3, 4] = 1
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>>> img1[3, 2] = 1.5
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>>> img1
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array([[0. , 0. , 0. , 0. , 0. , 0. , 0. ],
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[0. , 0. , 0. , 0. , 0. , 0. , 0. ],
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[0. , 0. , 0. , 0. , 0. , 0. , 0. ],
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[0. , 0. , 1.5, 0. , 1. , 0. , 0. ],
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[0. , 0. , 0. , 0. , 0. , 0. , 0. ],
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[0. , 0. , 0. , 0. , 0. , 0. , 0. ],
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[0. , 0. , 0. , 0. , 0. , 0. , 0. ]])
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>>> peak_local_max(img1, min_distance=1)
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array([[3, 2],
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[3, 4]])
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>>> peak_local_max(img1, min_distance=2)
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array([[3, 2]])
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>>> img2 = np.zeros((20, 20, 20))
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>>> img2[10, 10, 10] = 1
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>>> img2[15, 15, 15] = 1
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>>> peak_idx = peak_local_max(img2, exclude_border=0)
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>>> peak_idx
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array([[10, 10, 10],
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[15, 15, 15]])
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>>> peak_mask = np.zeros_like(img2, dtype=bool)
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>>> peak_mask[tuple(peak_idx.T)] = True
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>>> np.argwhere(peak_mask)
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array([[10, 10, 10],
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[15, 15, 15]])
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"""
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if (footprint is None or footprint.size == 1) and min_distance < 1:
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warn("When min_distance < 1, peak_local_max acts as finding "
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"image > max(threshold_abs, threshold_rel * max(image)).",
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RuntimeWarning, stacklevel=2)
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border_width = _get_excluded_border_width(image, min_distance,
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exclude_border)
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threshold = _get_threshold(image, threshold_abs, threshold_rel)
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if footprint is None:
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size = 2 * min_distance + 1
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footprint = np.ones((size, ) * image.ndim, dtype=bool)
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else:
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footprint = np.asarray(footprint)
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if labels is None:
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# Non maximum filter
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mask = _get_peak_mask(image, footprint, threshold)
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mask = _exclude_border(mask, border_width)
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# Select highest intensities (num_peaks)
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coordinates = _get_high_intensity_peaks(image, mask,
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num_peaks,
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min_distance, p_norm)
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else:
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_labels = _exclude_border(labels.astype(int, casting="safe"),
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border_width)
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if np.issubdtype(image.dtype, np.floating):
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bg_val = np.finfo(image.dtype).min
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else:
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bg_val = np.iinfo(image.dtype).min
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# For each label, extract a smaller image enclosing the object of
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# interest, identify num_peaks_per_label peaks
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labels_peak_coord = []
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for label_idx, roi in enumerate(ndi.find_objects(_labels)):
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if roi is None:
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continue
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# Get roi mask
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label_mask = labels[roi] == label_idx + 1
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# Extract image roi
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img_object = image[roi]
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# Ensure masked values don't affect roi's local peaks
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img_object[np.logical_not(label_mask)] = bg_val
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mask = _get_peak_mask(img_object, footprint, threshold, label_mask)
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coordinates = _get_high_intensity_peaks(img_object, mask,
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num_peaks_per_label,
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min_distance,
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p_norm)
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# transform coordinates in global image indices space
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for idx, s in enumerate(roi):
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coordinates[:, idx] += s.start
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labels_peak_coord.append(coordinates)
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if labels_peak_coord:
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coordinates = np.vstack(labels_peak_coord)
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else:
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coordinates = np.empty((0, 2), dtype=int)
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if len(coordinates) > num_peaks:
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out = np.zeros_like(image, dtype=bool)
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out[tuple(coordinates.T)] = True
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coordinates = _get_high_intensity_peaks(image, out,
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num_peaks,
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min_distance,
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p_norm)
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if indices:
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return coordinates
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else:
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out = np.zeros_like(image, dtype=bool)
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out[tuple(coordinates.T)] = True
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return out
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def _prominent_peaks(image, min_xdistance=1, min_ydistance=1,
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threshold=None, num_peaks=np.inf):
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"""Return peaks with non-maximum suppression.
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Identifies most prominent features separated by certain distances.
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Non-maximum suppression with different sizes is applied separately
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in the first and second dimension of the image to identify peaks.
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Parameters
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----------
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image : (M, N) ndarray
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Input image.
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min_xdistance : int
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Minimum distance separating features in the x dimension.
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min_ydistance : int
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Minimum distance separating features in the y dimension.
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threshold : float
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Minimum intensity of peaks. Default is `0.5 * max(image)`.
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num_peaks : int
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Maximum number of peaks. When the number of peaks exceeds `num_peaks`,
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return `num_peaks` coordinates based on peak intensity.
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Returns
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-------
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intensity, xcoords, ycoords : tuple of array
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Peak intensity values, x and y indices.
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"""
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img = image.copy()
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rows, cols = img.shape
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if threshold is None:
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threshold = 0.5 * np.max(img)
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ycoords_size = 2 * min_ydistance + 1
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xcoords_size = 2 * min_xdistance + 1
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img_max = ndi.maximum_filter1d(img, size=ycoords_size, axis=0,
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mode='constant', cval=0)
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img_max = ndi.maximum_filter1d(img_max, size=xcoords_size, axis=1,
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mode='constant', cval=0)
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mask = (img == img_max)
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img *= mask
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img_t = img > threshold
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label_img = measure.label(img_t)
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props = measure.regionprops(label_img, img_max)
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# Sort the list of peaks by intensity, not left-right, so larger peaks
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# in Hough space cannot be arbitrarily suppressed by smaller neighbors
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props = sorted(props, key=lambda x: x.max_intensity)[::-1]
|
||
|
|
coords = np.array([np.round(p.centroid) for p in props], dtype=int)
|
||
|
|
|
||
|
|
img_peaks = []
|
||
|
|
ycoords_peaks = []
|
||
|
|
xcoords_peaks = []
|
||
|
|
|
||
|
|
# relative coordinate grid for local neighbourhood suppression
|
||
|
|
ycoords_ext, xcoords_ext = np.mgrid[-min_ydistance:min_ydistance + 1,
|
||
|
|
-min_xdistance:min_xdistance + 1]
|
||
|
|
|
||
|
|
for ycoords_idx, xcoords_idx in coords:
|
||
|
|
accum = img_max[ycoords_idx, xcoords_idx]
|
||
|
|
if accum > threshold:
|
||
|
|
# absolute coordinate grid for local neighbourhood suppression
|
||
|
|
ycoords_nh = ycoords_idx + ycoords_ext
|
||
|
|
xcoords_nh = xcoords_idx + xcoords_ext
|
||
|
|
|
||
|
|
# no reflection for distance neighbourhood
|
||
|
|
ycoords_in = np.logical_and(ycoords_nh > 0, ycoords_nh < rows)
|
||
|
|
ycoords_nh = ycoords_nh[ycoords_in]
|
||
|
|
xcoords_nh = xcoords_nh[ycoords_in]
|
||
|
|
|
||
|
|
# reflect xcoords and assume xcoords are continuous,
|
||
|
|
# e.g. for angles:
|
||
|
|
# (..., 88, 89, -90, -89, ..., 89, -90, -89, ...)
|
||
|
|
xcoords_low = xcoords_nh < 0
|
||
|
|
ycoords_nh[xcoords_low] = rows - ycoords_nh[xcoords_low]
|
||
|
|
xcoords_nh[xcoords_low] += cols
|
||
|
|
xcoords_high = xcoords_nh >= cols
|
||
|
|
ycoords_nh[xcoords_high] = rows - ycoords_nh[xcoords_high]
|
||
|
|
xcoords_nh[xcoords_high] -= cols
|
||
|
|
|
||
|
|
# suppress neighbourhood
|
||
|
|
img_max[ycoords_nh, xcoords_nh] = 0
|
||
|
|
|
||
|
|
# add current feature to peaks
|
||
|
|
img_peaks.append(accum)
|
||
|
|
ycoords_peaks.append(ycoords_idx)
|
||
|
|
xcoords_peaks.append(xcoords_idx)
|
||
|
|
|
||
|
|
img_peaks = np.array(img_peaks)
|
||
|
|
ycoords_peaks = np.array(ycoords_peaks)
|
||
|
|
xcoords_peaks = np.array(xcoords_peaks)
|
||
|
|
|
||
|
|
if num_peaks < len(img_peaks):
|
||
|
|
idx_maxsort = np.argsort(img_peaks)[::-1][:num_peaks]
|
||
|
|
img_peaks = img_peaks[idx_maxsort]
|
||
|
|
ycoords_peaks = ycoords_peaks[idx_maxsort]
|
||
|
|
xcoords_peaks = xcoords_peaks[idx_maxsort]
|
||
|
|
|
||
|
|
return img_peaks, xcoords_peaks, ycoords_peaks
|