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Documenting the example code

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This commit is contained in:
Ankit Agrawal
2013-09-26 19:33:28 +05:30
committed by Johannes Schönberger
parent 11403f9ce3
commit 73afae9d72
2 changed files with 29 additions and 10 deletions
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doc/examples/plot_orb_matching.py
+27 -9
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@@ -1,37 +1,54 @@
import numpy as np
from skimage import data
from skimage import transform as tf
from skimage.feature import pairwise_hamming_distance, brief, match_binary_descriptors, corner_harris, corner_peaks, keypoints_orb, descriptor_orb
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)
match_threshold = 0.25
match_cross_check = True
# 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)
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)
descriptors1, keypoints1 = descriptor_orb(img, keypoints1, orientations1,
scales1)
keypoints1.shape
descriptors1.shape
keypoints2, orientations2, scales2 = keypoints_orb(transformed_img, n_keypoints=500)
keypoints2, orientations2, scales2 = keypoints_orb(transformed_img,
n_keypoints=500)
keypoints2.shape
descriptors2, keypoints2 = descriptor_orb(transformed_img, keypoints2, orientations2, scales2)
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, mask1, mask2 = match_binary_descriptors(keypoints1,
descriptors1,
keypoints2,
descriptors2,
cross_check=match_cross_check,
threshold=match_threshold)
matched_keypoints.shape
@@ -41,7 +58,8 @@ 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)
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)
skimage/feature/orb.py
+2 -1
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@@ -1,6 +1,7 @@
import numpy as np
from .util import _mask_border_keypoints, _prepare_grayscale_input_2D
from skimage.feature.util import (_mask_border_keypoints,
_prepare_grayscale_input_2D)
from skimage.feature import (corner_fast, corner_orientations, corner_peaks,
corner_harris)