我自己也写了一些只使用 OpenCV Python 接口而我没有使用scipy. drawMatches是 OpenCV 3.0.0 的一部分，而不是我目前使用的 OpenCV 2 的一部分。即使我迟到了，这是我自己的实现，drawMatches尽我所能模仿。
我提供了自己的图像，其中一张是摄影师，另一张是相同的图像，但逆时针旋转了 55 度。
我写的基本前提是我分配了一个输出 RGB 图像，其中行数是两个图像中的最大值，以适应将两个图像放在输出图像中，并且列只是两个列的总和一起。我将每个图像放在它们对应的位置，然后遍历所有匹配的关键点的循环。我提取两个图像之间匹配的关键点，然后提取它们(x,y)的坐标。然后我在每个检测到的位置画圆圈，然后画一条线将这些圆圈连接在一起。
请记住，在第二张图像中检测到的关键点是相对于它自己的坐标系的。如果要将其放置在最终输出图像中，则需要将列坐标偏移第一个图像的列数，以便列坐标相对于输出图像的坐标系.
无需再费周折：
import numpy as np
import cv2
def drawMatches(img1, kp1, img2, kp2, matches):
"""
My own implementation of cv2.drawMatches as OpenCV 2.4.9
does not have this function available but it's supported in
OpenCV 3.0.0
This function takes in two images with their associated
keypoints, as well as a list of DMatch data structure (matches)
that contains which keypoints matched in which images.
An image will be produced where a montage is shown with
the first image followed by the second image beside it.
Keypoints are delineated with circles, while lines are connected
between matching keypoints.
img1,img2 - Grayscale images
kp1,kp2 - Detected list of keypoints through any of the OpenCV keypoint
detection algorithms
matches - A list of matches of corresponding keypoints through any
OpenCV keypoint matching algorithm
"""
# Create a new output image that concatenates the two images together
# (a.k.a) a montage
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:cols1+cols2,:] = 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.destroyAllWindows()
为了说明这一点，这是我使用的两张图片：
我使用 OpenCV 的 ORB 检测器来检测关键点，并使用归一化的汉明距离作为相似度的距离度量，因为这是一个二进制描述符。像这样：
import numpy as np
import cv2
img1 = cv2.imread('cameraman.png') # Original image
img2 = cv2.imread('cameraman_rot55.png') # Rotated image
# Create ORB detector with 1000 keypoints with a scaling pyramid factor
# of 1.2
orb = cv2.ORB(1000, 1.2)
# Detect keypoints of original image
(kp1,des1) = orb.detectAndCompute(img1, None)
# Detect keypoints of rotated image
(kp2,des2) = orb.detectAndCompute(img2, None)
# Create matcher
bf = cv2.BFMatcher(cv2.NORM_HAMMING, crossCheck=True)
# Do matching
matches = bf.match(des1,des2)
# Sort the matches based on distance. Least distance
# is better
matches = sorted(matches, key=lambda val: val.distance)
# Show only the top 10 matches
drawMatches(img1, kp1, img2, kp2, matches[:10])
这是我得到的图像：
