mirror of
https://github.com/wassname/scikit-image.git
synced 2026-07-13 08:45:30 +08:00
Johannes Schönberger
109 lines
2.8 KiB
Python
109 lines
2.8 KiB
Python
import numpy as np
|
|
|
|
from skimage.util import img_as_float
|
|
|
|
|
|
def create_keypoint_recarray(rows, cols, scales=None, orientations=None,
|
|
responses=None):
|
|
"""Create keypoint array that allows field access through attributes.
|
|
|
|
Parameters
|
|
----------
|
|
rows : (N, ) array
|
|
Row coordinates of keypoints.
|
|
cols : (N, ) array
|
|
Column coordinates of keypoints.
|
|
scales : (N, ) array
|
|
Scales in which the keypoints have been detected.
|
|
orientations : (N, ) array
|
|
Orientations of the keypoints.
|
|
responses : (N, ) array
|
|
Detector response (strength) of the keypoints.
|
|
|
|
Returns
|
|
-------
|
|
recarray : (N, ...) recarray
|
|
Array with the fields: `row`, `col`, `scale`, `orientation` and
|
|
`response`.
|
|
|
|
"""
|
|
|
|
dtype = [('row', np.double),
|
|
('col', np.double),
|
|
('scale', np.double),
|
|
('orientation', np.double),
|
|
('response', np.double)]
|
|
keypoints = np.zeros(rows.shape[0], dtype=dtype)
|
|
keypoints['row'] = rows
|
|
keypoints['col'] = cols
|
|
keypoints['scale'] = scales
|
|
keypoints['orientation'] = orientations
|
|
keypoints['response'] = responses
|
|
return keypoints.view(np.recarray)
|
|
|
|
|
|
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(shape, rr, cc, distance):
|
|
"""Mask coordinates that are within certain distance from the image border.
|
|
|
|
Parameters
|
|
----------
|
|
shape : (2, ) array_like
|
|
Shape of the image as ``(rows, cols)``.
|
|
rr : (N, ) array
|
|
Row coordinates.
|
|
cc : (N, ) array
|
|
Column coordinates.
|
|
distance : int
|
|
Image border distance.
|
|
|
|
Returns
|
|
-------
|
|
mask : (N, ) bool array
|
|
Mask indicating if pixels are within the image (``True``) or in the
|
|
border region of the image (``False``).
|
|
|
|
"""
|
|
|
|
rows = shape[0]
|
|
cols = shape[1]
|
|
|
|
mask = (((distance - 1) < rr)
|
|
& (rr < (rows - distance + 1))
|
|
& ((distance - 1) < cc)
|
|
& (cc < (cols - distance + 1)))
|
|
|
|
return 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
|