import numpy as np
from ..util import img_as_float
from .._shared.utils import (
_supported_float_type,
check_nD,
deprecate_func,
)
class FeatureDetector:
def __init__(self):
self.keypoints_ = np.array([])
def detect(self, image):
"""Detect keypoints in image.
Parameters
----------
image : 2D array
Input image.
"""
raise NotImplementedError()
class DescriptorExtractor:
def __init__(self):
self.descriptors_ = np.array([])
def extract(self, image, keypoints):
"""Extract feature descriptors in image for given keypoints.
Parameters
----------
image : 2D array
Input image.
keypoints : (N, 2) array
Keypoint locations as ``(row, col)``.
"""
raise NotImplementedError()
[文档]
@deprecate_func(
deprecated_version="0.23",
removed_version="0.25",
hint="Use `skimage.feature.plot_matched_features` instead.",
)
def plot_matches(
ax,
image1,
image2,
keypoints1,
keypoints2,
matches,
keypoints_color='k',
matches_color=None,
only_matches=False,
alignment='horizontal',
):
"""Plot matched features.
.. deprecated:: 0.23
Parameters
----------
ax : matplotlib.axes.Axes
Matches and image are drawn in this ax.
image1 : (N, M [, 3]) array
First grayscale or color image.
image2 : (N, M [, 3]) array
Second grayscale or color image.
keypoints1 : (K1, 2) array
First keypoint coordinates as ``(row, col)``.
keypoints2 : (K2, 2) array
Second keypoint coordinates as ``(row, col)``.
matches : (Q, 2) array
Indices of corresponding matches in first and second set of
descriptors, where ``matches[:, 0]`` denote the indices in the first
and ``matches[:, 1]`` the indices in the second set of descriptors.
keypoints_color : matplotlib color, optional
Color for keypoint locations.
matches_color : matplotlib color, optional
Color for lines which connect keypoint matches. By default the
color is chosen randomly.
only_matches : bool, optional
Whether to only plot matches and not plot the keypoint locations.
alignment : {'horizontal', 'vertical'}, optional
Whether to show images side by side, ``'horizontal'``, or one above
the other, ``'vertical'``.
"""
image1 = img_as_float(image1)
image2 = img_as_float(image2)
new_shape1 = list(image1.shape)
new_shape2 = list(image2.shape)
if image1.shape[0] < image2.shape[0]:
new_shape1[0] = image2.shape[0]
elif image1.shape[0] > image2.shape[0]:
new_shape2[0] = image1.shape[0]
if image1.shape[1] < image2.shape[1]:
new_shape1[1] = image2.shape[1]
elif image1.shape[1] > image2.shape[1]:
new_shape2[1] = image1.shape[1]
if new_shape1 != image1.shape:
new_image1 = np.zeros(new_shape1, dtype=image1.dtype)
new_image1[: image1.shape[0], : image1.shape[1]] = image1
image1 = new_image1
if new_shape2 != image2.shape:
new_image2 = np.zeros(new_shape2, dtype=image2.dtype)
new_image2[: image2.shape[0], : image2.shape[1]] = image2
image2 = new_image2
offset = np.array(image1.shape)
if alignment == 'horizontal':
image = np.concatenate([image1, image2], axis=1)
offset[0] = 0
elif alignment == 'vertical':
image = np.concatenate([image1, image2], axis=0)
offset[1] = 0
else:
mesg = (
f"plot_matches accepts either 'horizontal' or 'vertical' for "
f"alignment, but '{alignment}' was given. See "
f"https://scikit-image.org/docs/dev/api/skimage.feature.html#skimage.feature.plot_matches " # noqa
f"for details."
)
raise ValueError(mesg)
if not only_matches:
ax.scatter(
keypoints1[:, 1],
keypoints1[:, 0],
facecolors='none',
edgecolors=keypoints_color,
)
ax.scatter(
keypoints2[:, 1] + offset[1],
keypoints2[:, 0] + offset[0],
facecolors='none',
edgecolors=keypoints_color,
)
ax.imshow(image, cmap='gray')
ax.axis((0, image1.shape[1] + offset[1], image1.shape[0] + offset[0], 0))
rng = np.random.default_rng()
for i in range(matches.shape[0]):
idx1 = matches[i, 0]
idx2 = matches[i, 1]
if matches_color is None:
color = rng.random(3)
else:
color = matches_color
ax.plot(
(keypoints1[idx1, 1], keypoints2[idx2, 1] + offset[1]),
(keypoints1[idx1, 0], keypoints2[idx2, 0] + offset[0]),
'-',
color=color,
)
def plot_matched_features(
image0,
image1,
*,
keypoints0,
keypoints1,
matches,
ax,
keypoints_color='k',
matches_color=None,
only_matches=False,
alignment='horizontal',
):
"""Plot matched features between two images.
.. versionadded:: 0.23
Parameters
----------
image0 : (N, M [, 3]) array
First image.
image1 : (N, M [, 3]) array
Second image.
keypoints0 : (K1, 2) array
First keypoint coordinates as ``(row, col)``.
keypoints1 : (K2, 2) array
Second keypoint coordinates as ``(row, col)``.
matches : (Q, 2) array
Indices of corresponding matches in first and second sets of
descriptors, where `matches[:, 0]` (resp. `matches[:, 1]`) contains
the indices in the first (resp. second) set of descriptors.
ax : matplotlib.axes.Axes
The Axes object where the images and their matched features are drawn.
keypoints_color : matplotlib color, optional
Color for keypoint locations.
matches_color : matplotlib color, optional
Color for lines which connect keypoint matches. By default the
color is chosen randomly.
only_matches : bool, optional
Set to True to plot matches only and not the keypoint locations.
alignment : {'horizontal', 'vertical'}, optional
Whether to show the two images side by side (`'horizontal'`), or one above
the other (`'vertical'`).
"""
image0 = img_as_float(image0)
image1 = img_as_float(image1)
new_shape0 = list(image0.shape)
new_shape1 = list(image1.shape)
if image0.shape[0] < image1.shape[0]:
new_shape0[0] = image1.shape[0]
elif image0.shape[0] > image1.shape[0]:
new_shape1[0] = image0.shape[0]
if image0.shape[1] < image1.shape[1]:
new_shape0[1] = image1.shape[1]
elif image0.shape[1] > image1.shape[1]:
new_shape1[1] = image0.shape[1]
if new_shape0 != image0.shape:
new_image0 = np.zeros(new_shape0, dtype=image0.dtype)
new_image0[: image0.shape[0], : image0.shape[1]] = image0
image0 = new_image0
if new_shape1 != image1.shape:
new_image1 = np.zeros(new_shape1, dtype=image1.dtype)
new_image1[: image1.shape[0], : image1.shape[1]] = image1
image1 = new_image1
offset = np.array(image0.shape)
if alignment == 'horizontal':
image = np.concatenate([image0, image1], axis=1)
offset[0] = 0
elif alignment == 'vertical':
image = np.concatenate([image0, image1], axis=0)
offset[1] = 0
else:
mesg = (
f"`plot_matched_features` accepts either 'horizontal' or 'vertical' for "
f"alignment, but '{alignment}' was given. See "
f"https://scikit-image.org/docs/dev/api/skimage.feature.html#skimage.feature.plot_matched_features " # noqa
f"for details."
)
raise ValueError(mesg)
if not only_matches:
ax.scatter(
keypoints0[:, 1],
keypoints0[:, 0],
facecolors='none',
edgecolors=keypoints_color,
)
ax.scatter(
keypoints1[:, 1] + offset[1],
keypoints1[:, 0] + offset[0],
facecolors='none',
edgecolors=keypoints_color,
)
ax.imshow(image, cmap='gray')
ax.axis((0, image0.shape[1] + offset[1], image0.shape[0] + offset[0], 0))
rng = np.random.default_rng()
for i in range(matches.shape[0]):
idx0 = matches[i, 0]
idx1 = matches[i, 1]
if matches_color is None:
color = rng.random(3)
else:
color = matches_color
ax.plot(
(keypoints0[idx0, 1], keypoints1[idx1, 1] + offset[1]),
(keypoints0[idx0, 0], keypoints1[idx1, 0] + offset[0]),
'-',
color=color,
)
def _prepare_grayscale_input_2D(image):
image = np.squeeze(image)
check_nD(image, 2)
image = img_as_float(image)
float_dtype = _supported_float_type(image.dtype)
return image.astype(float_dtype, copy=False)
def _prepare_grayscale_input_nD(image):
image = np.squeeze(image)
check_nD(image, range(2, 6))
image = img_as_float(image)
float_dtype = _supported_float_type(image.dtype)
return image.astype(float_dtype, copy=False)
def _mask_border_keypoints(image_shape, keypoints, distance):
"""Mask coordinates that are within certain distance from the image border.
Parameters
----------
image_shape : (2,) array_like
Shape of the image as ``(rows, cols)``.
keypoints : (N, 2) array
Keypoint coordinates as ``(rows, cols)``.
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 = image_shape[0]
cols = image_shape[1]
mask = (
((distance - 1) < keypoints[:, 0])
& (keypoints[:, 0] < (rows - distance + 1))
& ((distance - 1) < keypoints[:, 1])
& (keypoints[:, 1] < (cols - distance + 1))
)
return mask
