Question

我试图通过找到关键点并使用opencv和python（全景图像拼接）相应地缝合图像来缝合两个图像。幸运的是，我找到了一个可以解决问题的代码。但它适用于代码旁边的给定图像。并且它不适用于我的图像（仅将第二张图像作为最终结果图像）。代码如下：

#import the necessary packages
 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, images, ratio=0.75, reprojThresh=4.0,
    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 (matches)
    #print (H)
    result = cv2.warpPerspective(imageA, H,
        (imageA.shape[1] + imageB.shape[1], imageA.shape[0]))
    cv2.imshow("a",result)
    result[0:imageB.shape[0], 0:imageB.shape[1]] = imageB
    cv2.imshow("b",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
        descriptor = cv2.xfeatures2d.SIFT_create()
        (kps, features) = descriptor.detectAndCompute(image, None)

    # 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
    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])

        # 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

我在主代码中将这段脚本称为：

from deeps.panorama import Stitcher
from PIL import Image
import pytesseract
import argparse
import imutils
import cv2

# construct the argument parse and parse the arguments
ap = argparse.ArgumentParser()
ap.add_argument("-f", "--first", required=True,
    help="path to the first image")
ap.add_argument("-s", "--second", required=True,
    help="path to the second image")
args = vars(ap.parse_args())

# load the two images and resize them to have a width of 400 pixels
# (for faster processing)
imageA = cv2.imread(args["first"])
imageB = cv2.imread(args["second"])

imageA = imutils.resize(imageA, width=400)
imageB = imutils.resize(imageB, width=400)

# stitch the images together to create a panorama
stitcher = Stitcher()
(result1, vis1) = stitcher.stitch([imageA, imageB], showMatches=True)

# show the images
cv2.imshow("Image A", imageA)
cv2.imshow("Image B", imageB)
cv2.imshow("Keypoint Matches1", vis1)
cv2.imshow("Result1", result1)
img = Image.fromarray(result1)
text = pytesseract.image_to_string(img)
print(text)
cv2.waitKey(0)

我的最终目标是通过组合图像（滚动文本）获得最终文本。附图像。我不知道脚本有什么问题，或者如果你有其他解决方案，请告诉我

Answer 1

在这一行：

stitcher = Stitcher()

在调用对象时尝试使用 stitcher.create()。

另外，检查 opencv 版本

使用opencv python的全景图像拼接

1 个答案: