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https://github.com/wassname/scikit-image.git
synced 2026-08-12 12:30:16 +08:00
style: initialize out at beginning of function
* remove API compatibility comment * remove unnecessary `del maskim`
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@@ -83,9 +83,9 @@ def peak_local_max(image, min_distance=10, threshold_abs=0, threshold_rel=0.1,
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array([[3, 2]])
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"""
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out = np.zeros_like(image, dtype=np.bool)
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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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# operating on each label separately
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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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@@ -96,7 +96,6 @@ def peak_local_max(image, min_distance=10, threshold_abs=0, threshold_rel=0.1,
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# New values for new ordering
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label_values = np.unique(labels)
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out = np.zeros_like(image)
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for label in label_values[label_values != 0]:
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maskim = (labels == label)
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out += peak_local_max(image * maskim, min_distance=min_distance,
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@@ -105,18 +104,17 @@ def peak_local_max(image, min_distance=10, threshold_abs=0, threshold_rel=0.1,
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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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del maskim
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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.astype(bool)
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return out.astype(np.bool)
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if np.all(image == image.flat[0]):
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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, dtype=bool)
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return out
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image = image.copy()
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# Non maximum filter
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@@ -150,6 +148,5 @@ def peak_local_max(image, min_distance=10, threshold_abs=0, threshold_rel=0.1,
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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, dtype=bool)
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out[coordinates[:, 0], coordinates[:, 1]] = True
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return out
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