mirror of
https://github.com/wassname/scikit-image.git
synced 2026-06-28 19:49:17 +08:00
Ankit Agrawal
81 lines
3.1 KiB
Python
81 lines
3.1 KiB
Python
import numpy as np
|
|
from skimage import data
|
|
from skimage import transform as tf
|
|
from skimage.feature import (pairwise_hamming_distance,
|
|
match_binary_descriptors, corner_harris,
|
|
corner_peaks, keypoints_orb, descriptor_orb)
|
|
from skimage.color import rgb2gray
|
|
from skimage import img_as_float
|
|
import matplotlib.pyplot as plt
|
|
|
|
# Initializing parameters for transformation
|
|
rotate = 0.5
|
|
translate = (-100, -200)
|
|
scaling = (1.5, 1.5)
|
|
|
|
# Creating a transformed image from the original Lena image by scaling and
|
|
# rotating it
|
|
img_color = data.lena()
|
|
tform = tf.AffineTransform(scale = scaling, rotation=rotate,
|
|
translation=translate)
|
|
transformed_img_color = tf.warp(img_color, tform)
|
|
img = rgb2gray(img_color)
|
|
transformed_img = rgb2gray(transformed_img_color)
|
|
|
|
# Extracting oFAST keypoints and computing their rBRIEF descriptors
|
|
keypoints1, orientations1, scales1 = keypoints_orb(img, n_keypoints=500)
|
|
keypoints1.shape
|
|
descriptors1, keypoints1 = descriptor_orb(img, keypoints1, orientations1,
|
|
scales1)
|
|
keypoints1.shape
|
|
descriptors1.shape
|
|
|
|
keypoints2, orientations2, scales2 = keypoints_orb(transformed_img,
|
|
n_keypoints=500)
|
|
keypoints2.shape
|
|
descriptors2, keypoints2 = descriptor_orb(transformed_img, keypoints2,
|
|
orientations2, scales2)
|
|
keypoints2.shape
|
|
descriptors2.shape
|
|
|
|
#Initializing parameters for Descriptor matching
|
|
match_threshold = 0.25
|
|
match_cross_check = True
|
|
|
|
pairwise_hamming_distance(descriptors1, descriptors2)
|
|
matched_keypoints, mask1, mask2 = match_binary_descriptors(keypoints1,
|
|
descriptors1,
|
|
keypoints2,
|
|
descriptors2,
|
|
cross_check=match_cross_check,
|
|
threshold=match_threshold)
|
|
|
|
matched_keypoints.shape
|
|
|
|
# Plotting the matched correspondences in both the images using matplotlib
|
|
src = matched_keypoints[:, 0, :]
|
|
dst = matched_keypoints[:, 1, :]
|
|
src_scale = 10 * (scales1[mask1] + 1) ** 1.5
|
|
dst_scale = 10 * (scales2[mask2] + 1) ** 1.5
|
|
|
|
img_combined = np.concatenate((img_as_float(img_color),
|
|
img_as_float(transformed_img_color)), axis=1)
|
|
offset = img.shape
|
|
|
|
fig, ax = plt.subplots(nrows=1, ncols=1)
|
|
plt.gray()
|
|
|
|
ax.imshow(img_combined, interpolation='nearest')
|
|
ax.axis('off')
|
|
ax.axis((0, 2 * offset[1], offset[0], 0))
|
|
ax.set_title('Matched correspondences : Rotation = %f; Scale = %s; Translation = %s; threshold = %f; cross_check = %r' % (rotate, scaling, translate, match_threshold, match_cross_check))
|
|
|
|
for m in range(len(src)):
|
|
c = np.random.rand(3,1)
|
|
ax.plot((src[m, 1], dst[m, 1] + offset[1]), (src[m, 0], dst[m, 0]), '-', color=c)
|
|
ax.scatter(src[m, 1], src[m, 0], src_scale[m], facecolors='none', edgecolors=c)
|
|
ax.scatter(dst[m, 1] + offset[1], dst[m, 0], dst_scale[m], facecolors='none', edgecolors=c)
|
|
|
|
plt.show()
|
|
|