mirror of
https://github.com/wassname/scikit-image.git
synced 2026-07-09 23:43:32 +08:00
Johannes Schönberger
104 lines
2.4 KiB
Python
104 lines
2.4 KiB
Python
import numpy as np
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from skimage.util import img_as_float
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class FeatureDetector(object):
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def __init__(self):
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raise NotImplementedError()
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def detect(self, image):
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"""Detect keypoints in image.
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Parameters
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----------
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image : 2D array
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Input image.
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"""
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raise NotImplementedError()
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class DescriptorExtractor(object):
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def __init__(self):
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raise NotImplementedError()
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def extract(self, image, keypoints):
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"""Extract feature descriptors in image for given keypoints.
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Parameters
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----------
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image : 2D array
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Input image.
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keypoints : (N, 2) array
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Keypoint locations as ``(row, col)``.
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"""
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raise NotImplementedError()
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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_shape, keypoints, distance):
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"""Mask coordinates that are within certain distance from the image border.
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Parameters
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----------
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image_shape : (2, ) array_like
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Shape of the image as ``(rows, cols)``.
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coords : (N, 2) array
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Keypoint coordinates as ``(rows, cols)``.
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distance : int
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Image border distance.
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Returns
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-------
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mask : (N, ) bool array
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Mask indicating if pixels are within the image (``True``) or in the
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border region of the image (``False``).
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"""
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rows = image_shape[0]
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cols = image_shape[1]
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mask = (((distance - 1) < keypoints[:, 0])
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& (keypoints[:, 0] < (rows - distance + 1))
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& ((distance - 1) < keypoints[:, 1])
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& (keypoints[:, 1] < (cols - distance + 1)))
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return 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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