【视频处理】视频拼接&视频缝合手把手教你

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前言

我将演示如何使用Python和OpenCV执行图像拼接和全景图构建。给定两个图像，我们将它们“拼接”在一起以创建一个简单的全景图，如上面的示例所示。 要构建图像全景图…

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需要注意，由于本次实验需要使用到SIFT这种非自由专利算法，而在2020年3月5日之前该专利算法是不能通过pip工具安装的opencv-python和opencv-contrib-python使用的，但是在2020年3月5日之后该专利就到期了。可以通过使用新版本的python，然后搭配pip工具安装最新的opencv-python和opencv-contrib-python，从而使用SIFT这种非自由专利算法！

1. 全景拼接算法

我们的全景拼接算法将包含以下四个步骤

Step #1：检测关键点（DoG，Harris等），并从两个输入图像中提取局部不变描述符（SIFT，SURF等）。
Step #2：在两个图像之间匹配描述符。
Step #3：使用RANSAC算法通过匹配的特征向量估计单应矩阵（或者叫变换矩阵）（homography matrix ）。
Step #4：用 step #3 中的单应矩阵进行透视变换

1.1 Panorama.py编写

我们将所有这四个步骤封装在Panorama.py中，接着在其中定义用于构建全景图的Stitcher类。

Stitcher类将依赖于imutils Python软件包，因此，如果你尚未在系统上安装该软件包，则要立即进行操作：

pip install imutils

# 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(or_better=True)

    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
        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):
        # 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()
            descriptor = cv2.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

可能遇到的报错

Traceback (most recent call last):
  File "C:\Users\HP\Downloads\panorama_pyimagesearch-main\stitch.py", line 23, in <module>
    (result, vis) = stitcher.stitch([imageA, imageB], showMatches=True)
  File "C:\Users\HP\Downloads\panorama_pyimagesearch-main\pyimagesearch\panorama.py", line 17, in stitch
    (kpsA, featuresA) = self.detectAndDescribe(imageA)
  File "C:\Users\HP\Downloads\panorama_pyimagesearch-main\pyimagesearch\panorama.py", line 49, in detectAndDescribe
    descriptor = cv2.xfeatures2d.SIFT_create()
AttributeError: module 'cv2' has no attribute 'xfeatures2d'

修改：

# descriptor = cv2.xfeatures2d.SIFT_create()
descriptor = cv2.SIFT_create()

1.2 stitch

# import the necessary packages
from pyimagesearch.panorama import Stitcher
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()
(result, vis) = stitcher.stitch([imageA, imageB], showMatches=True)
# show the images
cv2.imshow("Image A", imageA)
cv2.imshow("Image B", imageB)
cv2.imshow("Keypoint Matches", vis)
cv2.imshow("Result", result)
cv2.waitKey(0)

3. 使用方式

# panorama_pyimagesearch
# 简单地拼接两幅全景地图用法：
python stitch.py --first bryce_01.png --second bryce_02.png 

4.未完待续

以上是基于图片的拼接，最终我会生成一个基于多路摄像头的全景视频。 敬请期待