wassname/scikit-image
1
0
Fork 0
mirror of https://github.com/wassname/scikit-image.git synced 2026-07-07 07:43:56 +08:00
Code Issues Packages Projects Releases Wiki Activity

Add convenience function for plotting matches

Browse Source
This commit is contained in:
Johannes Schönberger
2013-11-30 12:40:26 +01:00
parent abd1295a8d
commit 8fbc81eaac
3 changed files with 103 additions and 41 deletions
Download Patch File Download Diff File Expand all files Collapse all files
doc/examples/plot_orb.py
+13 -40
View File
@@ -16,62 +16,35 @@ import numpy as np
from skimage import data
from skimage import transform as tf
from skimage.feature import (match_descriptors, corner_harris,
corner_peaks, ORB)
corner_peaks, ORB, plot_matches)
from skimage.color import rgb2gray
from skimage import img_as_float
import matplotlib.pyplot as plt
img1_color = data.lena()
img2_color = tf.rotate(img1_color, 180)
img1 = rgb2gray(data.lena())
img2 = tf.rotate(img1, 180)
tform = tf.AffineTransform(scale=(1.3, 1.1), rotation=0.5,
translation=(0, -200))
img3_color = tf.warp(img1_color, tform)
img1 = rgb2gray(img1_color)
img2 = rgb2gray(img2_color)
img3 = rgb2gray(img3_color)
img3 = tf.warp(img1, tform)
descriptor_extractor = ORB(n_keypoints=200)
keypoints1, descriptors1 = descriptor_extractor.detect_and_extract(img1)
keypoints2, descriptors2 = descriptor_extractor.detect_and_extract(img2)
keypoints3, descriptors3 = descriptor_extractor.detect_and_extract(img3)
idxs1, idxs2 = match_descriptors(descriptors1, descriptors2, cross_check=True)
src12 = keypoints1[idxs1]
dst12 = keypoints2[idxs2]
idxs1, idxs3 = match_descriptors(descriptors1, descriptors3, cross_check=True)
src13 = keypoints1[idxs1]
dst13 = keypoints3[idxs3]
img12 = np.concatenate((img_as_float(img1_color),
img_as_float(img2_color)), axis=1)
img13 = np.concatenate((img_as_float(img1_color),
img_as_float(img3_color)), axis=1)
imgs = (img12, img13)
srcs = (src12, src13)
dsts = (dst12, dst13)
offset = img1.shape
fig, ax = plt.subplots(nrows=2, ncols=1)
for i in range(2):
plt.gray()
ax[i].imshow(imgs[i], interpolation='nearest')
ax[i].axis('off')
ax[i].axis((0, 2 * offset[1], offset[0], 0))
idxs1, idxs2 = match_descriptors(descriptors1, descriptors2, cross_check=True)
plot_matches(ax[0], img1, img2, keypoints1, keypoints2,
idxs1, idxs2)
ax[0].axis('off')
src = srcs[i]
dst = dsts[i]
for m in range(len(src)):
color = np.random.rand(3, 1)
ax[i].plot((src[m, 1], dst[m, 1] + offset[1]), (src[m, 0], dst[m, 0]),
'-', color=color)
ax[i].scatter(src[m, 1], src[m, 0], facecolors='none', edgecolors=color)
ax[i].scatter(dst[m, 1] + offset[1], dst[m, 0], facecolors='none',
edgecolors=color)
idxs1, idxs3 = match_descriptors(descriptors1, descriptors3, cross_check=True)
plot_matches(ax[1], img1, img3, keypoints1, keypoints3,
idxs1, idxs3)
ax[1].axis('off')
plt.show()