如何在Python OpenCV中应用RANSAC

Question

有人能告诉我如何应用RANSAC来找到最好的4个特征匹配点及其对应的（x，y）坐标吗？这样我就可以在单应性代码中使用它们了？

特征匹配点是通过SIFT获得的，这里是代码：

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)

结果如下：点击此处

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)

我随机选择的四个点：

pts_src=np.array（[[141131]，[480159]，[493630]，[64601]]）

这里的内容相同：

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

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

Answer 1

在findHomography之前不必使用RANSAC。RANSAC应用于函数内部。只需传递两个相互匹配的特性数组（不需要只传递四个最好的）。

但是，您可以过滤掉距离较大的匹配项。通常，您会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大大低于第二个匹配项。看看这个OpenCV教程，了解一些如何做到这一点的代码。