mirror of
https://github.com/wassname/scikit-image.git
synced 2026-06-29 05:17:50 +08:00
Ankit Agrawal
50 lines
1.3 KiB
Python
50 lines
1.3 KiB
Python
import numpy as np
|
|
|
|
from ..util import img_as_float
|
|
|
|
|
|
def _prepare_grayscale_input_2D(image):
|
|
image = np.squeeze(image)
|
|
if image.ndim != 2:
|
|
raise ValueError("Only 2-D gray-scale images supported.")
|
|
|
|
return img_as_float(image)
|
|
|
|
|
|
def _mask_border_keypoints(image, keypoints, dist):
|
|
"""Removes keypoints that are within dist pixels from the image border."""
|
|
width = image.shape[0]
|
|
height = image.shape[1]
|
|
|
|
keypoints_filtering_mask = ((dist - 1 < keypoints[:, 0]) &
|
|
(keypoints[:, 0] < width - dist + 1) &
|
|
(dist - 1 < keypoints[:, 1]) &
|
|
(keypoints[:, 1] < height - dist + 1))
|
|
|
|
return keypoints_filtering_mask
|
|
|
|
|
|
def pairwise_hamming_distance(array1, array2):
|
|
"""**Experimental function**.
|
|
|
|
Calculate hamming dissimilarity measure between two sets of
|
|
vectors.
|
|
|
|
Parameters
|
|
----------
|
|
array1 : (P1, D) array
|
|
P1 vectors of size D.
|
|
array2 : (P2, D) array
|
|
P2 vectors of size D.
|
|
|
|
Returns
|
|
-------
|
|
distance : (P1, P2) array of dtype float
|
|
2D ndarray with value at an index (i, j) representing the hamming
|
|
distance in the range [0, 1] between ith vector in array1 and jth
|
|
vector in array2.
|
|
|
|
"""
|
|
distance = (array1[:, None] != array2[None]).mean(axis=2)
|
|
return distance
|