轉https://www.hongweipeng.com/index.php/archives/709/

1.opencv3並不自帶sift，fast等，需要額外安裝。

2.cv2.drawMatches這個函式在OpenCV 2.4.12中不存在。3.0以後才提供。所以執行時得到這樣的報錯。

函式原型如下：

cv2.drawMatches(img1, keypoints1, img2, keypoints2, matches1to2[, outImg[, matchColor[, singlePointColor[, matchesMask[, flags]]]]]) → outImg

• img1 – 源影象1

• keypoints1 –源影象1的特徵點.

• img2 – 源影象2.

• keypoints2 – 源影象2的特徵點

• matches1to2 – 源影象1的特徵點匹配源影象2的特徵點[matches[i]] DMatch.

• outImg – 輸出影象具體由flags決定.

• matchColor – 匹配的顏色（特徵點和連線),若matchColor==Scalar::all(-1)，顏色隨機.

• singlePointColor – 單個點的顏色，即未配對的特徵點，若matchColor==Scalar::all(-1)，顏色隨機.

• matchesMask – Mask決定哪些點將被畫出，若為空，則畫出所有匹配點.

• flags – Fdefined by DrawMatchesFlags.

實現

自給自足，豐衣足食。

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])

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

測試

# -*- coding: utf-8 -*-
import cv2
import numpy as np

img1 = cv2.imread('static/images/1a.jpg',cv2.IMREAD_COLOR)
img2 = cv2.imread('static/images/1b.jpg',cv2.IMREAD_COLOR)

# img1 = cv2.resize(img1, (256, 256))
# img2 = cv2.resize(img2, (256, 256))

gray1 = cv2.cvtColor(img1, cv2.COLOR_BGR2GRAY)
gray2 = cv2.cvtColor(img2, cv2.COLOR_BGR2GRAY)

#SIFT
detector = cv2.SIFT()

# 特徵點集
keypoints1 = detector.detect(gray1, None)
keypoints2 = detector.detect(gray2, None)

outimg1 = cv2.drawKeypoints(gray1, keypoints1)
outimg2 = cv2.drawKeypoints(gray2, keypoints2)


cv2.imshow('img1', outimg1)
cv2.imshow('img2', outimg2)

# kp,des = sift.compute(gray,kp)
kp1, des1 = detector.compute(gray1, keypoints1)
kp2, des2 = detector.compute(gray2, keypoints2)


# 定義一個burte force matcher物件
matcher = cv2.BFMatcher()

matches = matcher.match(des1, des2)
matches = sorted(matches, key = lambda x:x.distance)

end_img = drawMatches(img1, kp1, img2, kp2, matches[:30])
cv2.imshow('end_img', end_img)

cv2.waitKey(0)
cv2.destroyAllWindows()