python-opencv-特征点匹配连线（画线）drawMatches

python-opencv-特征点匹配连线（画线）drawMatches

• Python 没有OpenCV 2.4.13版本的cv2.drawMatches()，无法直接使用，故可参看本文第2节的drawMatches函数使用

• Python 有OpenCV 3.0.0版本的cv2.drawMatches()，可以直接使用

1、drawMatches数据结构（opencv2.4.13）

Draws the found matches of keypoints from two images.

C++:

void drawMatches(const Mat& img1, const vector<KeyPoint>& keypoints1, const Mat& img2, const vector<KeyPoint>& keypoints2, const vector<vector<DMatch>>& matches1to2, Mat& outImg, const Scalar& matchColor=Scalar::all(-1), const Scalar& singlePointColor=Scalar::all(-1), const vector<vector<char>>& matchesMask=vector<vector<char> >(), int flags=DrawMatchesFlags::DEFAULT )

• Parameters:
  
  • img1 – First source image.
  • keypoints1 – Keypoints from the first source image.
  • img2 – Second source image.
  • keypoints2 – Keypoints from the second source image.
  • matches1to2 – Matches from the first image to the second one, which means that keypoints1[i] has a corresponding point in keypoints2[matches[i]] .
    outImg – Output image. Its content depends on the flags value defining what is drawn in the output image. See possible flags bit values below.
  • matchColor – Color of matches (lines and connected keypoints). If matchColor==Scalar::all(-1) , the color is generated randomly.
    singlePointColor – Color of single keypoints (circles), which means that keypoints do not have the matches. If singlePointColor==Scalar::all(-1) , the color is generated randomly.
  • matchesMask – Mask determining which matches are drawn. If the mask is empty, all matches are drawn.
  • flags – Flags setting drawing features. Possible flags bit values are defined by DrawMatchesFlags.
    This function draws matches of keypoints from two images in the output image. Match is a line connecting two keypoints (circles). The structure DrawMatchesFlags is defined as follows:

struct DrawMatchesFlags
{
    enum
    {
        DEFAULT = 0, // Output image matrix will be created (Mat::create),
                     // i.e. existing memory of output image may be reused.
                     // Two source images, matches, and single keypoints
                     // will be drawn.
                     // For each keypoint, only the center point will be
                     // drawn (without a circle around the keypoint with the
                     // keypoint size and orientation).
        DRAW_OVER_OUTIMG = 1, // Output image matrix will not be
                       // created (using Mat::create). Matches will be drawn
                       // on existing content of output image.
        NOT_DRAW_SINGLE_POINTS = 2, // Single keypoints will not be drawn.
        DRAW_RICH_KEYPOINTS = 4 // For each keypoint, the circle around
                       // keypoint with keypoint size and orientation will
                       // be drawn.
    };
};

2、drawMatches（python实现）

# -*- coding: utf-8 -*-
"""
Created on Tue Dec 27 09:32:02 2016

@author: http://blog.youkuaiyun.com/lql0716
"""

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:] = 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
        a = np.random.randint(0,256)
        b = np.random.randint(0,256)
        c = np.random.randint(0,256)

        cv2.circle(out, (int(np.round(x1)),int(np.round(y1))), 2, (a, b, c), 1)      #画圆，cv2.circle()参考官方文档
        cv2.circle(out, (int(np.round(x2)+cols1),int(np.round(y2))), 2, (a, b, c), 1)

        # Draw a line in between the two points
        # thickness = 1
        # colour blue
        cv2.line(out, (int(np.round(x1)),int(np.round(y1))), (int(np.round(x2)+cols1),int(np.round(y2))), (a, b, c), 1, lineType=cv2.CV_AA, shift=0)  #画线，cv2.line()参考官方文档

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