使用 RANSAC 进行稳健匹配

在这个简化的示例中，我们首先生成两个合成图像，就像它们是从不同的视角拍摄的一样。

在下一步中，我们在两个图像中找到兴趣点，并基于围绕它们的较小邻域的加权平方差之和来找到对应关系。请注意，此度量仅对线性辐射畸变而不是几何畸变具有鲁棒性，因此仅适用于轻微的视角变化。

找到对应关系后，我们最终会得到一组源坐标和目标坐标，可用于估计两个图像之间的几何变换。但是，许多对应关系都是错误的，仅使用所有坐标来估计参数集是不够的。因此，RANSAC 算法用于正常模型之上，通过检测异常值来稳健地估计参数集。

Ground truth:
Scale: (0.9000, 0.9000), Translation: (-10.0000, 20.0000), Rotation: -0.2000
Affine transform:
Scale: (0.9015, 0.8904), Translation: (-9.3136, 14.9768), Rotation: -0.1678
RANSAC:
Scale: (0.8999, 0.9001), Translation: (-10.0005, 19.9744), Rotation: -0.1999
/home/runner/work/scikit-image/scikit-image/doc/examples/transform/plot_matching.py:150: FutureWarning:

`plot_matches` is deprecated since version 0.23 and will be removed in version 0.25. Use `skimage.feature.plot_matched_features` instead.

/home/runner/work/scikit-image/scikit-image/doc/examples/transform/plot_matching.py:163: FutureWarning:

`plot_matches` is deprecated since version 0.23 and will be removed in version 0.25. Use `skimage.feature.plot_matched_features` instead.

import numpy as np
from matplotlib import pyplot as plt

from skimage import data
from skimage.util import img_as_float
from skimage.feature import corner_harris, corner_subpix, corner_peaks, plot_matches
from skimage.transform import warp, AffineTransform
from skimage.exposure import rescale_intensity
from skimage.color import rgb2gray
from skimage.measure import ransac


# generate synthetic checkerboard image and add gradient for the later matching
checkerboard = img_as_float(data.checkerboard())
img_orig = np.zeros(list(checkerboard.shape) + [3])
img_orig[..., 0] = checkerboard
gradient_r, gradient_c = np.mgrid[0 : img_orig.shape[0], 0 : img_orig.shape[1]] / float(
    img_orig.shape[0]
)
img_orig[..., 1] = gradient_r
img_orig[..., 2] = gradient_c
img_orig = rescale_intensity(img_orig)
img_orig_gray = rgb2gray(img_orig)

# warp synthetic image
tform = AffineTransform(scale=(0.9, 0.9), rotation=0.2, translation=(20, -10))
img_warped = warp(img_orig, tform.inverse, output_shape=(200, 200))
img_warped_gray = rgb2gray(img_warped)

# extract corners using Harris' corner measure
coords_orig = corner_peaks(
    corner_harris(img_orig_gray), threshold_rel=0.001, min_distance=5
)
coords_warped = corner_peaks(
    corner_harris(img_warped_gray), threshold_rel=0.001, min_distance=5
)

# determine sub-pixel corner position
coords_orig_subpix = corner_subpix(img_orig_gray, coords_orig, window_size=9)
coords_warped_subpix = corner_subpix(img_warped_gray, coords_warped, window_size=9)


def gaussian_weights(window_ext, sigma=1):
    y, x = np.mgrid[-window_ext : window_ext + 1, -window_ext : window_ext + 1]
    g = np.zeros(y.shape, dtype=np.double)
    g[:] = np.exp(-0.5 * (x**2 / sigma**2 + y**2 / sigma**2))
    g /= 2 * np.pi * sigma * sigma
    return g


def match_corner(coord, window_ext=5):
    r, c = np.round(coord).astype(np.intp)
    window_orig = img_orig[
        r - window_ext : r + window_ext + 1, c - window_ext : c + window_ext + 1, :
    ]

    # weight pixels depending on distance to center pixel
    weights = gaussian_weights(window_ext, 3)
    weights = np.dstack((weights, weights, weights))

    # compute sum of squared differences to all corners in warped image
    SSDs = []
    for cr, cc in coords_warped:
        window_warped = img_warped[
            cr - window_ext : cr + window_ext + 1,
            cc - window_ext : cc + window_ext + 1,
            :,
        ]
        SSD = np.sum(weights * (window_orig - window_warped) ** 2)
        SSDs.append(SSD)

    # use corner with minimum SSD as correspondence
    min_idx = np.argmin(SSDs)
    return coords_warped_subpix[min_idx]


# find correspondences using simple weighted sum of squared differences
src = []
dst = []
for coord in coords_orig_subpix:
    src.append(coord)
    dst.append(match_corner(coord))
src = np.array(src)
dst = np.array(dst)


# estimate affine transform model using all coordinates
model = AffineTransform()
model.estimate(src, dst)

# robustly estimate affine transform model with RANSAC
model_robust, inliers = ransac(
    (src, dst), AffineTransform, min_samples=3, residual_threshold=2, max_trials=100
)
outliers = inliers == False


# compare "true" and estimated transform parameters
print("Ground truth:")
print(
    f'Scale: ({tform.scale[1]:.4f}, {tform.scale[0]:.4f}), '
    f'Translation: ({tform.translation[1]:.4f}, '
    f'{tform.translation[0]:.4f}), '
    f'Rotation: {-tform.rotation:.4f}'
)
print("Affine transform:")
print(
    f'Scale: ({model.scale[0]:.4f}, {model.scale[1]:.4f}), '
    f'Translation: ({model.translation[0]:.4f}, '
    f'{model.translation[1]:.4f}), '
    f'Rotation: {model.rotation:.4f}'
)
print("RANSAC:")
print(
    f'Scale: ({model_robust.scale[0]:.4f}, {model_robust.scale[1]:.4f}), '
    f'Translation: ({model_robust.translation[0]:.4f}, '
    f'{model_robust.translation[1]:.4f}), '
    f'Rotation: {model_robust.rotation:.4f}'
)

# visualize correspondence
fig, ax = plt.subplots(nrows=2, ncols=1)

plt.gray()

inlier_idxs = np.nonzero(inliers)[0]
plot_matches(
    ax[0],
    img_orig_gray,
    img_warped_gray,
    src,
    dst,
    np.column_stack((inlier_idxs, inlier_idxs)),
    matches_color='b',
)
ax[0].axis('off')
ax[0].set_title('Correct correspondences')

outlier_idxs = np.nonzero(outliers)[0]
plot_matches(
    ax[1],
    img_orig_gray,
    img_warped_gray,
    src,
    dst,
    np.column_stack((outlier_idxs, outlier_idxs)),
    matches_color='r',
)
ax[1].axis('off')
ax[1].set_title('Faulty correspondences')

plt.show()