Question

] 2

因此，如下图所示，我在图像上检测到关键点但是换行后的输出图像忽略了左侧的第一张图像，无法弄清楚原因！

    import numpy as np
    import imutils
    import cv2

class Stitcher:
def __init__(self):
    # determine if we are using OpenCV v3.X
    self.isv3 = imutils.is_cv3()

def stitch(self, imageA,imageB, ratio=0.75, reprojThresh=10.0,
    showMatches=False):
    # unpack the images, then detect keypoints and extract
    # local invariant descriptors from them
    #(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 the match is None, then there aren't enough matched
    # keypoints to create a panorama
    if M is None:
        return None

    # otherwise, apply a perspective warp to stitch the images
    # together
    (matches, H, status) = M
    #print(M)
    #print(matches)
    #print(H)
    #print(status)
    #cv2.imwrite('intermediate.jpg',matches)
    result = cv2.warpPerspective(imageA, H,
        (imageA.shape[1] + imageB.shape[1], imageA.shape[0]))
    result[0:imageB.shape[0], 0:imageB.shape[1]] = imageB
    #cv2.imshow('intermediate',result)

    # 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):
    # convert the image to grayscale
    gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)

    # check to see if we are using OpenCV 3.X
    if self.isv3:
        # detect and extract features from the image
        #SIFT Algorithm
        descriptor = cv2.xfeatures2d.SIFT_create()
        #SURF Algorithm
        #descriptor = cv2.xfeatures2d.SURF_create()# 400 is hesian threshold, optimum values should be around 300-500
        #upright SURF: faster and can be used for panorama stiching i.e our case.
        #descriptor.upright = True
        print(descriptor.descriptorSize())
        (kps, features) = descriptor.detectAndCompute(image, None)
        print(len(kps),features.shape)

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

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

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

    # return a tuple of keypoints and features
    #print("features",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))
    print(len(matches))

    # 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])

        # compute the 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)

    # otherwise, no homograpy could be computed
    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):
        # only process the match if the keypoint was successfully
        # 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, (0, 255, 0), 1)

    # return the visualization
    return vis

以上是用于关键点检测和拼接的代码，

如果有人可以帮助我进行垂直图像拼接而不是旋转图像和执行水平拼接，还有一个问题。

非常感谢！

我更改了我的代码并使用了@ Alexander的padtransf.warpPerspectivePadded函数来执行包装和混合！你能帮助我为输出图像统一照明吗？

Answer 1

我自己有这个问题。如果我没有弄错，您使用this博客作为参考。

关于该行的问题是warpPerspective：

result = cv2.warpPerspective(imageA, H,
        (imageA.shape[1] + imageB.shape[1], imageA.shape[0]))
    result[0:imageB.shape[0], 0:imageB.shape[1]] = imageB

这种方法是全方位的。我的意思是你只是通过替换.shape[0]和.shape[1]所代表的宽度和高度的像素值，将imageA拼接在imageB上。我用C ++解决了这个问题，因此没有显示python代码，但可以让你了解必须做的事情。

获取您正在使用的每个图像的四个角。
获取步骤1中找到的每张图片的最小和最大角落。
创建一个Mat＆＃34; HTR＆＃34;用于将图像映射到与已经变形的图像2对齐的结果。 HTR.at（0,2）重新定位垫子3x3矩阵中的位置。 Numpy可能就是你需要在这里使用的东西。

Mat Htr = Mat::eye(3,3,CV_64F);
    if (min_x < 0){
        max_x = image2.size().width - min_x;
        Htr.at<double>(0,2)= -min_x;
    }
    if (min_y < 0){
        max_y = image2.size().height - min_y;
        Htr.at<double>(1,2)= -min_y;
    }

对每张图片的四个角进行透视变换，以查看它们最终会在空间中的位置。

perspectiveTransform(vector<Point2f> fourPointImage1, vector<Point2f> image1dst, Htr*homography);
perspectiveTransform(vector<Point2f> fourPointImage2, vector<Point2f> image2dst, Htr);

从image1dst四个角和iamge2dst四个角获取最小值和最大值。
获取image1dst和iamge2dst的最小值和最大值，并用于创建正确大小的新blank image以保存最终拼接的图像。
这次重复第3步处理以确定调整每个图像四个角所需的translation，以确保将其移动到blank image的虚拟空间
最后用您找到/制作的所有单应性来输入实际图像。

warpPerspective(image1, blankImage, (translation*homography),result.size(), INTER_LINEAR,BORDER_CONSTANT,(0));
warpPerspective(image2, image2Updated, translation, result.size(), INTER_LINEAR, BORDER_CONSTANT,   (0));

最终目标和结果是确定图像将被扭曲的位置，以便您可以制作一个空白图像来保存整个拼接图像，以便不会裁剪任何内容。只有在完成所有预处理后，才能将图像拼接在一起。我希望这会有所帮助，如果你有问题，那就大呼过瘾。

关键点检测和图像拼接

1 个答案: