Hi Riley
On Thu, Aug 3, 2017, at 14:31, Riley Myers wrote:
> I'm currently using matplotlib 2.0.2, which seems to be the most
> recent that I can get with `conda install` or `conda> update`. Should I take
> this up with their development team instead?
>
> As before:
> >>> import matplotlib print(matplotlib.__version__)
> 2..2
The error does seem to result in a change between skimage 0.13
and 0.14dev0.
Here is the relevant patch:
commit dcec9cdfd630f555216eb4105652fceb0f943dac
Author: Gregory R. Lee <[email protected]>
Date: Fri May 5 08:53:30 2017 -0400
BUG: make the color array in plot_matches 1D
Using a 2D array is unecessary and causes a crash for
Matplotlib 2.0.1
diff --git a/skimage/feature/util.py b/skimage/feature/util.py
index b0c66986..fdb1b67e 100644
--- a/skimage/feature/util.py
+++ b/skimage/feature/util.py
@@ -114,7 +114,7 @@ def plot_matches(ax, image1, image2, keypoints1,
keypoints2, matches, idx2 = matches[i, 1]
if matches_color is None:
- color = np.random.rand(3, 1)
+ color = np.random.rand(3)
else:
color = matches_color
Your options are:
1. Use the attached version of plot_matches or
2. to upgrade to the source version of scikit-image
Best regards
Stéfan
import numpy as np
import matplotlib.pyplot as plt
from skimage import io, img_as_float
from skimage.color import rgb2gray
from skimage.feature import ORB, match_descriptors
def plot_matches(ax, image1, image2, keypoints1, keypoints2, matches,
keypoints_color='k', matches_color=None, only_matches=False):
"""Plot matched features.
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.
"""
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
image = np.concatenate([image1, image2], axis=1)
offset = image1.shape
if not only_matches:
ax.scatter(keypoints1[:, 1], keypoints1[:, 0],
facecolors='none', edgecolors=keypoints_color)
ax.scatter(keypoints2[:, 1] + offset[1], keypoints2[:, 0],
facecolors='none', edgecolors=keypoints_color)
ax.imshow(image, interpolation='nearest', cmap='gray')
ax.axis((0, 2 * offset[1], offset[0], 0))
for i in range(matches.shape[0]):
idx1 = matches[i, 0]
idx2 = matches[i, 1]
if matches_color is None:
color = np.random.rand(3)
else:
color = matches_color
ax.plot((keypoints1[idx1, 1], keypoints2[idx2, 1] + offset[1]),
(keypoints1[idx1, 0], keypoints2[idx2, 0]),
'-', color=color)
pano_imgs = io.ImageCollection('./JDW_9*')
# Make grayscale versions of the three color images in pano_imgs
# named pano0, pano1, and pano2
p0 = rgb2gray(pano_imgs[0])
p1 = rgb2gray(pano_imgs[1])
p2 = rgb2gray(pano_imgs[2])
# Initialize ORB
orb = ORB(n_keypoints=800, fast_threshold=0.05)
# Detect keypoints in pano0
orb.detect_and_extract(p0)
keypoints0 = orb.keypoints
descriptors0 = orb.descriptors
# Detect keypoints in pano1 and pano2
orb.detect_and_extract(p1)
keypoints1 = orb.keypoints
descriptors1 = orb.descriptors
orb.detect_and_extract(p2)
keypoints2 = orb.keypoints
descriptors2 = orb.descriptors
# Match descriptors between left/right images and the center
matches01 = match_descriptors(descriptors0, descriptors1, cross_check=True)
matches12 = match_descriptors(descriptors1, descriptors2, cross_check=True)
fig, ax = plt.subplots(1, 1, figsize=(12, 12))
# Best match subset for pano0 -> pano1
plot_matches(ax, p0, p1, keypoints0, keypoints1, matches01)
ax.axis('off');
plt.show()
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