“对象”模块没有属性“drawMatches”opencv python

Question

我只是在OpenCV中做一个特征检测的例子。这个例子如下所示。它给了我以下的错误

模块对象没有属性“drawMatches”

我已经检查了OpenCV文档，不确定为什么会出现这个错误。有人知道为什么吗？

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

img1 = cv2.imread('box.png',0)          # queryImage
img2 = cv2.imread('box_in_scene.png',0) # trainImage

# Initiate SIFT detector
orb = cv2.ORB()

# find the keypoints and descriptors with SIFT
kp1, des1 = orb.detectAndCompute(img1,None)
kp2, des2 = orb.detectAndCompute(img2,None)

# create BFMatcher object
bf = cv2.BFMatcher(cv2.NORM_HAMMING, crossCheck=True)

# Match descriptors.
matches = bf.match(des1,des2)

# Draw first 10 matches.
img3 = cv2.drawMatches(img1,kp1,img2,kp2,matches[:10], flags=2)

plt.imshow(img3),plt.show()

错误：

Traceback (most recent call last):
File "match.py", line 22, in <module>
img3 = cv2.drawMatches(img1,kp1,img2,kp2,matches[:10], flags=2)
AttributeError: 'module' object has no attribute 'drawMatches'

Answer 1

drawMatches函数不是Python接口的一部分。
正如您在docs中看到的，目前它只为C++定义。

文件摘录：

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

如果函数有一个Python接口，您会发现如下内容：

 Python: cv2.drawMatches(img1, keypoints1, [...]) 

编辑

实际上有一个commit在5个月前引入了这个函数。但是，官方文件中还没有 确保您使用的是最新的OpenCV版本（2.4.7）。 为了完整起见，OpenCV 3.0.0的函数接口看起来像this：

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

Answer 2

我知道这个问题有一个公认的答案是正确的，但是如果您使用的是OpenCV 2.4.8而不是3.0（-dev），解决方法可能是使用opencv\sources\samples\python2\find_obj中包含的示例中的一些函数

import cv2
from find_obj import filter_matches,explore_match

img1 = cv2.imread('../c/box.png',0)          # queryImage
img2 = cv2.imread('../c/box_in_scene.png',0) # trainImage

# Initiate SIFT detector
orb = cv2.ORB()

# find the keypoints and descriptors with SIFT
kp1, des1 = orb.detectAndCompute(img1,None)
kp2, des2 = orb.detectAndCompute(img2,None)

# create BFMatcher object
bf = cv2.BFMatcher(cv2.NORM_HAMMING)#, crossCheck=True)

matches = bf.knnMatch(des1, trainDescriptors = des2, k = 2)
p1, p2, kp_pairs = filter_matches(kp1, kp2, matches)
explore_match('find_obj', img1,img2,kp_pairs)#cv2 shows image

cv2.waitKey()
cv2.destroyAllWindows()

这是输出图像：

Answer 3

我也迟到了，但是我为Mac OS X安装了OpenCV 2.4.9，而且drawMatches函数在我的发行版中不存在。我也尝试过用find_obj的第二种方法，但这对我也不起作用。因此，我决定编写自己的实现，尽我所能模仿它，这就是我所制作的。

我已经提供了我自己的图像，其中一个是一个摄像师，另一个是相同的图像，但旋转55度逆时针方向。

我所写的基础是我分配了一个输出RGB图像，其中行的数量是两个图像的最大值，以便将两个图像放在输出图像中，而列只是这两个列的总和。请注意，我假设这两个图像都是灰度的。

我将每个图像放在它们对应的点上，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，然后提取它们的(x,y)坐标。我在每个检测到的位置画圆圈，然后画一条线把这些圆圈连接在一起。

请记住，第二幅图像中检测到的关键点与其自身坐标系有关。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中的列数，以使列坐标相对于输出图像的坐标系。

不费吹灰之力：

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]

    # Create the output image
    # The rows of the output are the largest between the two images
    # and the columns are simply the sum of the two together
    # The intent is to make this a colour image, so make this 3 channels
    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

---

为了说明这一点，下面是我使用的两个图像：

我使用OpenCV的ORB检测器来检测关键点，并使用标准化的Hamming距离作为相似度的距离度量，因为这是一个二进制描述符。因此：

import numpy as np
import cv2

img1 = cv2.imread('cameraman.png', 0) # Original image - ensure grayscale
img2 = cv2.imread('cameraman_rot55.png', 0) # Rotated image - ensure grayscale

# 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 - also save a copy for use later
out = drawMatches(img1, kp1, img2, kp2, matches[:10])

---

这是我得到的图像：

---

与knnMatch一起使用

我想说明一下，只有假设匹配项出现在1D列表中，上述代码才起作用。但是，如果决定使用cv2.BFMatcher中的knnMatch方法，则返回的是列表列表列表。具体来说，给定img1中名为des1的描述符和img2中名为des2的描述符，从knnMatch返回的列表中的每个元素都是来自des2的k匹配项的另一个列表，它们最接近des1中的每个描述符。因此，来自knnMatch输出的第一个元素是来自des2的k匹配的列表，这些匹配最接近des1中找到的第一个描述符。来自knnMatch输出的第二个元素是来自des2的k匹配的列表，这些匹配最接近des1中找到的第二个描述符，依此类推。

为了充分理解knnMatch，必须将要匹配的邻居总数限制为k=2。原因是您希望使用至少两个匹配点来验证匹配的质量，如果质量足够好，您将希望使用这些点绘制匹配并在屏幕上显示它们。您可以使用一个非常简单的比率测试（credit转到David Lowe）来确保从des2到des1中的描述符的第一个匹配点的距离与从des2到第二个匹配点的距离相距一定距离。因此，要将从knnMatch返回的内容转换为上面编写的代码所需的内容，请遍历匹配项，使用上面的比率测试并检查它是否通过。如果是，则将第一个匹配的关键点添加到新列表中。

假设您像声明BFMatcher实例之前那样创建了所有变量，那么现在您可以这样做，以使knnMatch方法适合使用drawMatches：

# Create matcher
bf = cv2.BFMatcher(cv2.NORM_HAMMING, crossCheck=True)

# Perform KNN matching
matches = bf.knnMatch(des1, des2, k=2)

# Apply ratio test
good = []
for m,n in matches:
    if m.distance < 0.75*n.distance:
       # Add first matched keypoint to list
       # if ratio test passes
       good.append(m)

# Or do a list comprehension
#good = [m for (m,n) in matches if m.distance < 0.75*n.distance]

# Now perform drawMatches
out = drawMatches(img1, kp1, img2, kp2, good)

我想将上述修改归到用户@ryanmeasel身上，找到这些修改的答案是在他的文章OpenCV Python : No drawMatchesknn function。