十三、全景图像拼接

1. 特征匹配

• Brute-Force 蛮力匹配

  import numpy as np
  import matplotlib.pyplot as plt
  import cv2
  %matplotlib inline
  
  img1 = cv2.imread('ysg.png')
  img2 = cv2.imread('ysg_1.png')
  
  sift = cv2.xfeatures2d.SIFT_create()
  kp1, des1 = sift.detectAndCompute(img1, None)
  kp2, des2 = sift.detectAndCompute(img2, None)
  
  # crossCheck表示两个特征点要相互匹配，即A中的第i个特征点与B中的第j个特征点最近，反之，B中的第j个特征点到A中的第i个特征点也是
  # NORM_L2：归一化数组（欧几里得距离），不同特征计算方法该参数不同
  bf = cv2.BFMatcher(crossCheck=True)

• 一对一匹配

  matches = bf.match(des1, des2)
  matches = sorted(matches, key = lambda x: x.distance)
  
  img3 = cv2.drawMatches(img1, kp1, img2, kp2, matches[:10], None, flags=2)
  plt.imshow(cv2.cvtColor(img3, cv2.COLOR_BGR2RGB))
  plt.show()

  

• k对最佳匹配

  bf = cv2.BFMatcher()
  matches = bf.knnMatch(des1, des2, k=2)
  
  good = []
  for m, n in matches:
      if m.distance < 0.75 * n.distance:
          good.append([m])
  
  img3 = cv2.drawMatchesKnn(img1, kp1, img2, kp2, good, None, flags=2)
  plt.imshow(cv2.cvtColor(img3, cv2.COLOR_BGR2RGB))
  plt.show()

  

2. 随机抽样一致算法（Random sample consensus， RANSAC）

选择初始样本点进行拟合，给定一个容忍范围，不断进行迭代

每一次拟合后，容差范围内都有对应的数据点数，找出数据点个数最多的情况，即可得到最终的拟合结果

• 单应性矩阵

  

3. 演示

拼接 left right两张图片

Stitcher.py

import numpy as np
import cv2


class Stitcher:
    # 拼接函数
    def stitch(self, images, ratio=0.75, reprojThresh=4.0, showMatches=False):
        (imageB, imageA) = images

        # 检测图片A、B的sift关键特征点，并计算特征描述
        (kpsA, featuresA) = self.detectAndDescribe(imageA)
        (kpsB, featuresB) = self.detectAndDescribe(imageB)

        # 匹配特征点
        M = self.matchKeypoints(kpsA, kpsB, featuresA, featuresB, ratio, reprojThresh)

        if M is None:
            return None

        # 提取匹配结果 H 矩阵（3x3）
        (matches, H, status) = M

        # 图片A视角变换
        result = cv2.warpPerspective(imageA, H, (imageA.shape[1] + imageB.shape[1], imageA.shape[0]))
        self.cv_show('resultA', result)

        # 将图片B传入result最左边
        result[0:imageB.shape[0], 0:imageB.shape[1]] = imageB
        self.cv_show('resultB', result)

        if showMatches:
            vis = self.drawMatches(imageA, imageB, kpsA, kpsB, matches, status)
            return (result, vis)
        return result

    def cv_show(self, title, img):
        cv2.imshow(title, img)
        cv2.waitKey(0)
        cv2.destroyAllWindows()

    def detectAndDescribe(self, image):
        gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
        descriptor = cv2.xfeatures2d.SIFT_create()
        (kps, features) = descriptor.detectAndCompute(image, None)

        # 结果转为Numpy数组
        kps = np.float32([kp.pt for kp in kps])
        # 返回特征点集、描述特征
        return (kps, features)

    def matchKeypoints(self, kpsA, kpsB, featuresA, featuresB, ratio, reprojThresh):
        # 暴力匹配
        matcher = cv2.BFMatcher()

        # 使用KNN检测来自图片A、B的sift特征匹配对，k=2
        rawMatches = matcher.knnMatch(featuresA, featuresB, 2)

        matches = []
        for m in rawMatches:
            # 当最近距离和次近距离的比值小于ratio时，保留此匹配对
            if len(m) == 2 and m[0].distance < m[1].distance * ratio:
                matches.append((m[0].trainIdx, m[0].queryIdx))

        # 最少需要4对匹配对
        if len(matches) > 4:
            # 获取匹配对的点坐标
            ptsA = np.float32([kpsA[i] for (_, i) in matches])
            ptsB = np.float32([kpsB[i] for (i, _) in matches])

            # 计算视觉变换矩阵
            (H, status) = cv2.findHomography(ptsA, ptsB, cv2.RANSAC, reprojThresh)
            return (matches, H, status)
        return None

    def drawMatches(self, imageA, imageB, kpsA, kpsB, matches, status):
        # 初始化可视化图片，将A、B左右连接到一起
        (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

        # 联合遍历，画出匹配对
        for ((trainIdx, queryIdx), s) in zip(matches, status):
            # 当点对匹配成功时，执行画出
            if s == 1:
                ptA = (int(kpsA[queryIdx][0]), int(kpsA[queryIdx][9]))
                ptB = (int(kpsB[trainIdx][0]) + wA, int(kpsB[trainIdx][10]))
                cv2.line(vis, ptA, ptB, (0, 255, 0), 1)
        return vis

imgStitching.py

from Stitcher import Stitcher
import cv2

imageA = cv2.imread("images/left.png")
imageB = cv2.imread("images/right.png")

# 拼接
stitcher = Stitcher()
(result, vis) = stitcher.stitch([imageA, imageB], showMatches=True)

cv2.imshow("imageA", imageA)
cv2.imshow("imageB", imageB)
cv2.imshow("Keypoint Matches", vis)
cv2.imshow("result", result)
cv2.waitKey(0)
cv2.destroyAllWindows()

拼接结果