如何在Python OpenCV中应用RANSAC算法

Question

有人能展示给我如何应用RANSAC算法来找到最佳的4个特征匹配点及其对应的(x,y)坐标，以便我可以在我的单应性代码中使用它们吗？

这些特征匹配点是通过SIFT获得的，以下是代码：

import numpy as np
import cv2
from matplotlib import pyplot as plt

def drawMatches(img1, kp1, img2, kp2, matches):
    rows1 = img1.shape[0]
    cols1 = img1.shape[1]
    rows2 = img2.shape[0]
    cols2 = img2.shape[1]

    out = np.zeros((max([rows1,rows2]),cols1+cols2,3), dtype='uint8')

    # Place the first image to the left
    out[:rows1,:cols1] = np.dstack([img1, img1, img1])

    # Place the next image to the right of it
    out[:rows2,cols1:] = np.dstack([img2, img2, img2])

    # For each pair of points we have between both images
    # draw circles, then connect a line between them
    for mat in matches:

        # Get the matching keypoints for each of the images
        img1_idx = mat.queryIdx
        img2_idx = mat.trainIdx

        # x - columns
        # y - rows
        (x1,y1) = kp1[img1_idx].pt
        (x2,y2) = kp2[img2_idx].pt

        # Draw a small circle at both co-ordinates
        # radius 4
        # colour blue
        # thickness = 1
        cv2.circle(out, (int(x1),int(y1)), 4, (255, 0, 0), 1)   
        cv2.circle(out, (int(x2)+cols1,int(y2)), 4, (255, 0, 0), 1)

        # Draw a line in between the two points
        # thickness = 1
        # colour blue
        cv2.line(out, (int(x1),int(y1)), (int(x2)+cols1,int(y2)), (255, 0, 0), 1)


    # Show the image
    cv2.imshow('Matched Features', out)
    cv2.waitKey(0)
    cv2.destroyWindow('Matched Features')

    # Also return the image if you'd like a copy
    return out

img1 = cv2.imread("C://Users//user//Desktop//research//img1.2.jpg")
img2 = cv2.imread("C://Users//user//Desktop//research//img3.jpg")

name = cv2.COLOR_YUV2BGRA_YV12
print name
gray1 = cv2.cvtColor(img1,cv2.COLOR_BGR2GRAY)
gray2 = cv2.cvtColor(img2, cv2.COLOR_BGR2GRAY)
sift = cv2.SIFT()
kp1,des1 = sift.detectAndCompute(gray1, None)
kp2,des2 = sift.detectAndCompute(gray2, None)
bf = cv2.BFMatcher()
matches=bf.match(des1,des2)
matches=sorted(matches,key=lambda x:x.distance)
img3 = drawMatches(gray1,kp1,gray2,kp2,matches[:100])
plt.imshow(img3),plt.show()

print(matches)




cv2.imwrite('sift_matching1.png',img3)

这是结果: 点击这里

这是我的单应性代码：

import cv2
import numpy as np

if __name__ == '__main__' :

    # Read source image.
    im_src = cv2.imread('C://Users//user//Desktop//research//img1.2.jpg')

    pts_src = np.array([[141, 131], [480, 159], [493, 630],[64, 601]])


    # Read destination image.
    im_dst = cv2.imread('C://Users//user//Desktop//research//img3.jpg')

    pts_dst = np.array([[318, 256],[534, 372],[316, 670],[73, 473]])

    # Calculate Homography
    h, status = cv2.findHomography(pts_src, pts_dst, cv2.RANSAC,5.0)

    # Warp source image to destination based on homography
    im_out = cv2.warpPerspective(im_src, h, (im_dst.shape[1],im_dst.shape[0]))

    # Display images

    cv2.imshow("Warped Source Image", im_out)

    cv2.waitKey(0)

我随机选择了以下四个点：

pts_src = np.array([[141, 131], [480, 159], [493, 630],[64, 601]])

这里也是同样的情况：

pts_dst = np.array([[318, 256],[534, 372],[316, 670],[73, 473]])

所以，基本上，我只需要用 RANSAC 获得的最佳特征匹配点替换这些随机点。

- Free Tyler1

Answer 1

你不必在使用findHomography之前使用RANSAC。RANSAC已经在函数内部应用了。只需传递两个相互匹配的特征数组（不需要仅传递四个最佳特征）即可。

然而，你可以过滤掉距离较大的匹配项。通常，你尝试为每个特征找到两个匹配项，并检查第一个匹配项的距离是否明显低于第二个匹配项的距离。请参阅这个OpenCV教程，以查看如何执行此操作的代码。

- Sunreef