scikit-image库----使用RANSAC进行稳健匹配（二十）

素颜马尾好姑娘i 2021-10-15 17:39 240阅读 0赞

在这个简化的例子中，我们首先生成两个合成图像，就好像它们是从不同的视点中拍摄的。  
在下一步中，我们在两个图像中找到兴趣点，并基于它们周围的小邻域的平方差的加权和来找到对应关系。 请注意，此度量仅对线性辐射测量而非几何失真是稳健的，因此仅适用于轻微的视点变化。  
在找到对应关系之后，我们最终得到一组源和目标坐标，这些坐标可用于估计两个图像之间的几何变换。 然而，许多对应关系都是错误的，并且简单地用所有坐标估计参数集是不够的。 因此，在正常模型之上使用RANSAC算法以通过检测异常值来稳健地估计参数集。

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]) #2D 转3D
    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]))
    #np.mgrid[start:end:step]
    #start:开始坐标
    #stop:结束坐标（不包括）
    #step:步长 
          
    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(tform.scale, tform.translation, tform.rotation)
    print(model.scale, model.translation, model.rotation)
    print(model_robust.scale, model_robust.translation, model_robust.rotation)
    
    # visualize correspondence
    fig, ax = plt.subplots(nrows=2, ncols=1, figsize=(15,8))
    
    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()

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