mirror of
https://github.com/wassname/scikit-image.git
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Johannes Schönberger
78 lines
2.5 KiB
Python
78 lines
2.5 KiB
Python
"""
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==========================================
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ORB feature detector and binary descriptor
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==========================================
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This example demonstrates the ORB feature detection and binary description
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algorithm. It uses an oriented FAST detection method and the rotated BRIEF
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descriptors.
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ORB is comparatively scale- and rotation-invariant. As a binary descriptor it
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allows to employ the very efficient Hamming distance metric for matching and
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is thus preferred for real-time applications.
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"""
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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 (match_descriptors, corner_harris,
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corner_peaks, 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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img1_color = data.lena()
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img2_color = tf.rotate(img1_color, 180)
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tform = tf.AffineTransform(scale=(1.3, 1.1), rotation=0.5,
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translation=(0, -200))
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img3_color = tf.warp(img1_color, tform)
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img1 = rgb2gray(img1_color)
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img2 = rgb2gray(img2_color)
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img3 = rgb2gray(img3_color)
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descriptor_extractor = ORB(n_keypoints=200)
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keypoints1, descriptors1 = descriptor_extractor.detect_and_extract(img1)
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keypoints2, descriptors2 = descriptor_extractor.detect_and_extract(img2)
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keypoints3, descriptors3 = descriptor_extractor.detect_and_extract(img3)
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idxs1, idxs2 = match_descriptors(descriptors1, descriptors2, cross_check=True)
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src12 = keypoints1[idxs1]
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dst12 = keypoints2[idxs2]
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idxs1, idxs3 = match_descriptors(descriptors1, descriptors3, cross_check=True)
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src13 = keypoints1[idxs1]
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dst13 = keypoints3[idxs3]
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img12 = np.concatenate((img_as_float(img1_color),
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img_as_float(img2_color)), axis=1)
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img13 = np.concatenate((img_as_float(img1_color),
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img_as_float(img3_color)), axis=1)
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imgs = (img12, img13)
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srcs = (src12, src13)
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dsts = (dst12, dst13)
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offset = img1.shape
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fig, ax = plt.subplots(nrows=2, ncols=1)
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for i in range(2):
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ax[i].imshow(imgs[i], interpolation='nearest')
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ax[i].axis('off')
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ax[i].axis((0, 2 * offset[1], offset[0], 0))
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src = srcs[i]
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dst = dsts[i]
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for m in range(len(src)):
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color = np.random.rand(3, 1)
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ax[i].plot((src[m, 1], dst[m, 1] + offset[1]), (src[m, 0], dst[m, 0]),
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'-', color=color)
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ax[i].scatter(src[m, 1], src[m, 0], facecolors='none', edgecolors=color)
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ax[i].scatter(dst[m, 1] + offset[1], dst[m, 0], facecolors='none',
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edgecolors=color)
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plt.show()
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