Question

将numpy导入为np 导入cv2 import imutils import argparse

班级订书机： def init （个体经营）：＃确定我们是否使用OpenCV v3.X self.isv3 = imutils.is_cv3（）

def stitch(self, images, ratio=0.75, reprojThresh=4.0,
    showMatches=False):
    (imageB, imageA) = images
    (kpsA, featuresA) = self.detectAndDescribe(imageA)
    (kpsB, featuresB) = self.detectAndDescribe(imageB)

    # match features between the two images
    M = self.matchKeypoints(kpsA, kpsB,
        featuresA, featuresB, ratio, reprojThresh)

    if M is None:
        return None

    # apply a perspective warp to stitch the images
    (matches, H, status) = M
    result = cv2.warpPerspective(imageA, H,
        (imageA.shape[1] + imageB.shape[1], imageA.shape[0]))
    result[0:imageB.shape[0], 0:imageB.shape[1]] = imageB

    # check to see if the keypoint matches should be visualized
    if showMatches:
        vis = self.drawMatches(imageA, imageB, kpsA, kpsB, matches,
            status)

        # return a tuple of the stitched image and the
        # visualization
        return (result, vis)

    # return the stitched image
    return result

def detectAndDescribe(self, image):

    gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)


    if self.isv3:
        orb = cv2.ORB_create()

        (kps, features) = orb.detectAndCompute(image, None)

    # If we are using OpenCV 2.4.X
    else:
        # detect keypoints in the image
        detector = cv2.FeatureDetector_create("ORB")
        kps = detector.detect(gray)

        # extract features from the image
        extractor = cv2.DescriptorExtractor_create("ORB")
        (kps, features) = extractor.compute(gray, kps)

    # convert the keypoints to NumPy arrays
    kps = np.float32([kp.pt for kp in kps])

    # return a tuple of keypoints and features
    return (kps, features)

def matchKeypoints(self, kpsA, kpsB, featuresA, featuresB,
    ratio, reprojThresh):
    # compute the raw matches and initialize the list of actual matches
    matcher = cv2.DescriptorMatcher_create("BruteForce")
    rawMatches = matcher.knnMatch(featuresA, featuresB, 2)
    matches = []

    # loop over the raw matches
    for m in rawMatches:
        # ensure the distance is within a certain ratio of each
        # other (i.e. Lowe's ratio test)
        if len(m) == 2 and m[0].distance < m[1].distance * ratio:
            matches.append((m[0].trainIdx, m[0].queryIdx))

    # computing a homography requires at least 4 matches
    if len(matches) > 4:
        # construct the two sets of points
        ptsA = np.float32([kpsA[i] for (_, i) in matches])
        ptsB = np.float32([kpsB[i] for (i, _) in matches])

        # homography between the two sets of points
        (H, status) = cv2.findHomography(ptsA, ptsB, cv2.RANSAC,
            reprojThresh)

        # return the matches along with the homograpy matrix
        # and status of each matched point
        return (matches, H, status)

    return None

def drawMatches(self, imageA, imageB, kpsA, kpsB, matches, status):
    # initialize the output visualization image
    (hA, wA) = imageA.shape[:2]
    (hB, wB) = imageB.shape[:2]
    vis = np.zeros((max(hA, hB), wA + wB, 3), dtype="uint8")
    vis[0:hA, 0:wA] = imageA
    vis[0:hB, wA:] = imageB

    # loop over the matches
    for ((trainIdx, queryIdx), s) in zip(matches, status):
        # matching if the keypoint is matched
        if s == 1:
            # draw the match
            ptA = (int(kpsA[queryIdx][0]), int(kpsA[queryIdx][1]))
            ptB = (int(kpsB[trainIdx][0]) + wA, int(kpsB[trainIdx][1]))
            cv2.line(vis, ptA, ptB, (240, 255, 100), 1)

    # return the visualization
    return vis



Output:: Attribute Error: 'module' object has no attribute 'is_cv3

请检查代码并重新发送已编辑的代码以解决问题。

Answer 1

您可以使用cv2.__version__

访问OpenCV版本

def is_cv3():
    return cv2.__version__[0] == '3'

python问题中的属性错误

1 个答案: