mirror of
https://github.com/wassname/scikit-image.git
synced 2026-07-04 16:31:50 +08:00
Ankit Agrawal
64 lines
1.9 KiB
Python
64 lines
1.9 KiB
Python
import numpy as np
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from skimage.util import img_as_float
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def create_keypoint_recarray(row, col, octave=None, orientation=None,
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response=None):
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keypoints = np.zeros(row.shape[0],
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dtype=[('row', np.double), ('col', np.double),
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('octave', np.double), ('orientation', np.double),
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('response', np.double)])
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keypoints['row'] = row
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keypoints['col'] = col
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keypoints['octave'] = octave
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keypoints['orientation'] = orientation
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keypoints['response'] = response
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return keypoints.view(np.recarray)
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def _prepare_grayscale_input_2D(image):
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image = np.squeeze(image)
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if image.ndim != 2:
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raise ValueError("Only 2-D gray-scale images supported.")
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return img_as_float(image)
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def _mask_border_keypoints(image, keypoints, dist):
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"""Removes keypoints that are within dist pixels from the image border."""
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width = image.shape[0]
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height = image.shape[1]
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keypoints_filtering_mask = ((dist - 1 < keypoints[:, 0]) &
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(keypoints[:, 0] < width - dist + 1) &
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(dist - 1 < keypoints[:, 1]) &
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(keypoints[:, 1] < height - dist + 1))
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return keypoints_filtering_mask
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def pairwise_hamming_distance(array1, array2):
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"""**Experimental function**.
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Calculate hamming dissimilarity measure between two sets of
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vectors.
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Parameters
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----------
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array1 : (P1, D) array
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P1 vectors of size D.
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array2 : (P2, D) array
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P2 vectors of size D.
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Returns
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-------
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distance : (P1, P2) array of dtype float
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2D ndarray with value at an index (i, j) representing the hamming
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distance in the range [0, 1] between ith vector in array1 and jth
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vector in array2.
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"""
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distance = (array1[:, None] != array2[None]).mean(axis=2)
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return distance
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