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FEAT - combined API from is_local_maximum() into peak_local_max()
is_local_maximum() is a wrapper function for peak_local_max() is_local_maximum() runs much faster (~20% of prior runtime, nearly = to peak_local_max()) All tests in .feature and .morphology subpackages pass as written with these changes. Todo: * Fully document API * remove commented-out old algorithm in is_local_maximum() * add new tests for full coverage of new, more complex peak_local_max()
This commit is contained in:
Josh Warner (Mac)
+62
-23
@@ -1,10 +1,9 @@
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import warnings
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import numpy as np
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from scipy import ndimage
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import scipy.ndimage as ndi
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def peak_local_max(image, min_distance=10, threshold='deprecated',
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threshold_abs=0, threshold_rel=0.1, num_peaks=np.inf):
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def peak_local_max(image, min_distance=10, threshold_abs=0, threshold_rel=0.1,
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exclude_border=True, indices=True, num_peaks=np.inf,
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footprint=None, labels=None, **kwargs):
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"""Return coordinates of peaks in an image.
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Peaks are the local maxima in a region of `2 * min_distance + 1`
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@@ -36,11 +35,11 @@ def peak_local_max(image, min_distance=10, threshold='deprecated',
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Notes
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-----
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The peak local maximum function returns the coordinates of local peaks (maxima)
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in a image. A maximum filter is used for finding local maxima. This operation
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dilates the original image. After comparison between dilated and original image,
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peak_local_max function returns the coordinates of peaks where
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dilated image = original.
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The peak local maximum function returns the coordinates of local peaks
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(maxima) in a image. A maximum filter is used for finding local maxima.
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This operation dilates the original image. After comparison between
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dilated and original image, peak_local_max function returns the
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coordinates of peaks where dilated image = original.
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Examples
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--------
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@@ -64,25 +63,60 @@ def peak_local_max(image, min_distance=10, threshold='deprecated',
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array([[3, 2]])
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"""
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# In the case of labels, recursively build and return an output
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# operating on each label separately; for API compatibility with
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# ..watershed.is_local_maximum()
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if labels is not None:
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label_values = np.unique(labels)
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# Reorder label values to have consecutive integers (no gaps)
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if np.any(np.diff(label_values) != 1):
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mask = labels >= 0
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labels[mask] = rank_order(labels[mask])[0].astype(labels.dtype)
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labels = labels.astype(np.int32)
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out = np.zeros_like(image)
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for label in labels:
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out += peak_local_max(image, min_distance=min_distance,
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threshold_abs=threshold_abs,
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threshold_rel=threshold_rel,
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exclude_border=exclude_border,
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indices=False, num_peaks=np.inf,
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footprint=footprint, labels=None,
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**kwargs)
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if indices is True:
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return np.transpose(out.nonzero())
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else:
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return out
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if np.all(image == image.flat[0]):
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return []
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if indices is True:
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return []
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else:
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return np.zeros_like(image)
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image = image.copy()
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# Non maximum filter
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size = 2 * min_distance + 1
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image_max = ndimage.maximum_filter(image, size=size, mode='constant')
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if footprint is not None:
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image_max = ndi.maximum_filter(image, footprint=footprint,
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mode='constant')
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else:
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size = 2 * min_distance + 1
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image_max = ndi.maximum_filter(image, size=size, mode='constant')
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mask = (image == image_max)
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image *= mask
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# Remove the image borders
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image[:min_distance] = 0
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image[-min_distance:] = 0
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image[:, :min_distance] = 0
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image[:, -min_distance:] = 0
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if exclude_border:
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# Remove the image borders
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image[:min_distance] = 0
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image[-min_distance:] = 0
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image[:, :min_distance] = 0
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image[:, -min_distance:] = 0
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if kwargs.has_key('threshold'):
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threshold_rel = kwargs['threshold']
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if not threshold == 'deprecated':
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msg = "`threshold` parameter deprecated; use `threshold_rel instead."
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warnings.warn(msg, DeprecationWarning)
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threshold_rel = threshold
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# find top peak candidates above a threshold
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peak_threshold = max(np.max(image.ravel()) * threshold_rel, threshold_abs)
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image_t = (image > peak_threshold) * 1
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@@ -95,4 +129,9 @@ def peak_local_max(image, min_distance=10, threshold='deprecated',
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idx_maxsort = np.argsort(intensities)[::-1]
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coordinates = coordinates[idx_maxsort][:num_peaks]
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return coordinates
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if indices is True:
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return coordinates
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else:
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out = np.zeros_like(image)
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out[coordinates[:, 0], coordinates[:, 1]] = 1
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return out
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@@ -28,6 +28,7 @@ from _heapq import heappush, heappop
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import numpy as np
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import scipy.ndimage
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from ..filter import rank_order
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from ..feature import peak_local_max
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from . import _watershed
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@@ -281,62 +282,65 @@ def is_local_maximum(image, labels=None, footprint=None):
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[False, False, False, False],
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[False, True, False, True]], dtype=bool)
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"""
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if labels is None:
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labels = np.ones(image.shape, dtype=np.uint8)
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if footprint is None:
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footprint = np.ones([3] * image.ndim, dtype=np.uint8)
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assert((np.all(footprint.shape) & 1) == 1)
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footprint = (footprint != 0)
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footprint_extent = (np.array(footprint.shape) - 1) // 2
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if np.all(footprint_extent == 0):
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return labels > 0
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result = (labels > 0).copy()
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#
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# Create a labels matrix with zeros at the borders that might be
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# hit by the footprint.
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#
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big_labels = np.zeros(np.array(labels.shape) + footprint_extent * 2,
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labels.dtype)
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big_labels[[slice(fe, -fe) for fe in footprint_extent]] = labels
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#
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# Find the relative indexes of each footprint element
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#
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image_strides = np.array(image.strides) // image.dtype.itemsize
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big_strides = np.array(big_labels.strides) // big_labels.dtype.itemsize
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result_strides = np.array(result.strides) // result.dtype.itemsize
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footprint_offsets = np.mgrid[[slice(-fe, fe + 1) for fe in footprint_extent]]
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# if labels is None:
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# labels = np.ones(image.shape, dtype=np.uint8)
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# if footprint is None:
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# footprint = np.ones([3] * image.ndim, dtype=np.uint8)
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# assert((np.all(footprint.shape) & 1) == 1)
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# footprint = (footprint != 0)
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# footprint_extent = (np.array(footprint.shape) - 1) // 2
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# if np.all(footprint_extent == 0):
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# return labels > 0
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# result = (labels > 0).copy()
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# #
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# # Create a labels matrix with zeros at the borders that might be
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# # hit by the footprint.
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# #
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# big_labels = np.zeros(np.array(labels.shape) + footprint_extent * 2,
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# labels.dtype)
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# big_labels[[slice(fe, -fe) for fe in footprint_extent]] = labels
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# #
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# # Find the relative indexes of each footprint element
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# #
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# image_strides = np.array(image.strides) // image.dtype.itemsize
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# big_strides = np.array(big_labels.strides) // big_labels.dtype.itemsize
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# result_strides = np.array(result.strides) // result.dtype.itemsize
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# footprint_offsets = np.mgrid[[slice(-fe, fe + 1) for fe in footprint_extent]]
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fp_image_offsets = np.sum(image_strides[:, np.newaxis] *
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footprint_offsets[:, footprint], 0)
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fp_big_offsets = np.sum(big_strides[:, np.newaxis] *
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footprint_offsets[:, footprint], 0)
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#
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# Get the index of each labeled pixel in the image and big_labels arrays
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#
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indexes = np.mgrid[[slice(0, x) for x in labels.shape]][:, labels > 0]
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image_indexes = np.sum(image_strides[:, np.newaxis] * indexes, 0)
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big_indexes = np.sum(big_strides[:, np.newaxis] *
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(indexes + footprint_extent[:, np.newaxis]), 0)
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result_indexes = np.sum(result_strides[:, np.newaxis] * indexes, 0)
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#
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# Now operate on the raveled images
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#
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big_labels_raveled = big_labels.ravel()
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image_raveled = image.ravel()
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result_raveled = result.ravel()
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#
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# A hit is a hit if the label at the offset matches the label at the pixel
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# and if the intensity at the pixel is greater or equal to the intensity
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# at the offset.
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#
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for fp_image_offset, fp_big_offset in zip(fp_image_offsets, fp_big_offsets):
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same_label = (big_labels_raveled[big_indexes + fp_big_offset] ==
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big_labels_raveled[big_indexes])
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less_than = (image_raveled[image_indexes[same_label]] <
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image_raveled[image_indexes[same_label] + fp_image_offset])
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result_raveled[result_indexes[same_label][less_than]] = False
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# fp_image_offsets = np.sum(image_strides[:, np.newaxis] *
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# footprint_offsets[:, footprint], 0)
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# fp_big_offsets = np.sum(big_strides[:, np.newaxis] *
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# footprint_offsets[:, footprint], 0)
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# #
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# # Get the index of each labeled pixel in the image and big_labels arrays
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# #
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# indexes = np.mgrid[[slice(0, x) for x in labels.shape]][:, labels > 0]
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# image_indexes = np.sum(image_strides[:, np.newaxis] * indexes, 0)
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# big_indexes = np.sum(big_strides[:, np.newaxis] *
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# (indexes + footprint_extent[:, np.newaxis]), 0)
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# result_indexes = np.sum(result_strides[:, np.newaxis] * indexes, 0)
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# #
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# # Now operate on the raveled images
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# #
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# big_labels_raveled = big_labels.ravel()
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# image_raveled = image.ravel()
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# result_raveled = result.ravel()
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# #
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# # A hit is a hit if the label at the offset matches the label at the pixel
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# # and if the intensity at the pixel is greater or equal to the intensity
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# # at the offset.
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# #
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# for fp_image_offset, fp_big_offset in zip(fp_image_offsets, fp_big_offsets):
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# same_label = (big_labels_raveled[big_indexes + fp_big_offset] ==
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# big_labels_raveled[big_indexes])
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# less_than = (image_raveled[image_indexes[same_label]] <
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# image_raveled[image_indexes[same_label] + fp_image_offset])
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# result_raveled[result_indexes[same_label][less_than]] = False
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return result
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# return result
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return peak_local_max(image, labels=labels, min_distance=1,
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footprint=footprint, indices=False,
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exclude_border=False)
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# ---------------------- deprecated ------------------------------
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