mirror of
https://github.com/wassname/scikit-image.git
synced 2026-06-28 22:54:02 +08:00
Ankit Agrawal
63 lines
2.3 KiB
Python
63 lines
2.3 KiB
Python
import numpy as np
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from skimage import data
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from skimage import transform as tf
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from skimage.feature import pairwise_hamming_distance, brief, match_binary_descriptors, corner_harris, corner_peaks, keypoints_orb, descriptor_orb
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from skimage.color import rgb2gray
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from skimage import img_as_float
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import matplotlib.pyplot as plt
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rotate = 0.5
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translate = (-100, -200)
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scaling = (1.5, 1.5)
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match_threshold = 0.25
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match_cross_check = True
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img_color = data.lena()
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tform = tf.AffineTransform(scale = scaling, rotation=rotate, translation=translate)
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transformed_img_color = tf.warp(img_color, tform)
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img = rgb2gray(img_color)
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transformed_img = rgb2gray(transformed_img_color)
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keypoints1, orientations1, scales1 = keypoints_orb(img, n_keypoints=500)
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keypoints1.shape
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descriptors1, keypoints1 = descriptor_orb(img, keypoints1, orientations1, scales1)
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keypoints1.shape
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descriptors1.shape
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keypoints2, orientations2, scales2 = keypoints_orb(transformed_img, n_keypoints=500)
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keypoints2.shape
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descriptors2, keypoints2 = descriptor_orb(transformed_img, keypoints2, orientations2, scales2)
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keypoints2.shape
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descriptors2.shape
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pairwise_hamming_distance(descriptors1, descriptors2)
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matched_keypoints, mask1, mask2 = match_binary_descriptors(keypoints1, descriptors1, keypoints2, descriptors2, cross_check=match_cross_check, threshold=match_threshold)
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matched_keypoints.shape
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# Plotting the matched correspondences in both the images using matplotlib
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src = matched_keypoints[:, 0, :]
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dst = matched_keypoints[:, 1, :]
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src_scale = 10 * (scales1[mask1] + 1) ** 1.5
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dst_scale = 10 * (scales2[mask2] + 1) ** 1.5
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img_combined = np.concatenate((img_as_float(img_color), img_as_float(transformed_img_color)), axis=1)
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offset = img.shape
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fig, ax = plt.subplots(nrows=1, ncols=1)
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plt.gray()
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ax.imshow(img_combined, interpolation='nearest')
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ax.axis('off')
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ax.axis((0, 2 * offset[1], offset[0], 0))
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ax.set_title('Matched correspondences : Rotation = %f; Scale = %s; Translation = %s; threshold = %f; cross_check = %r' % (rotate, scaling, translate, match_threshold, match_cross_check))
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for m in range(len(src)):
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c = np.random.rand(3,1)
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ax.plot((src[m, 1], dst[m, 1] + offset[1]), (src[m, 0], dst[m, 0]), '-', color=c)
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ax.scatter(src[m, 1], src[m, 0], src_scale[m], facecolors='none', edgecolors=c)
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ax.scatter(dst[m, 1] + offset[1], dst[m, 0], dst_scale[m], facecolors='none', edgecolors=c)
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plt.show()
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