OpenCV全景图像拼接的实现示例

import numpy as np
import cv2
 
class Stitcher:
 
 #拼接函数
 def stitch(self, images, ratio=0.75, reprojThresh=4.0,showMatches=False):
  #获取输入图片
  (imageB, imageA) = images
  #检测A、B图片的SIFT关键特征点，并计算特征描述子
  (kpsA, featuresA) = self.detectAndDescribe(imageA)
  (kpsB, featuresB) = self.detectAndDescribe(imageB)
 
  # 匹配两张图片的所有特征点，返回匹配结果
  M = self.matchKeypoints(kpsA, kpsB, featuresA, featuresB, ratio, reprojThresh)
 
  # 如果返回结果为空，没有匹配成功的特征点，退出算法
  if M is None:
return None
 
  # 否则，提取匹配结果
  # H是3x3视角变换矩阵
  (matches, H, status) = M
  # 将图片A进行视角变换，result是变换后图片
  result = cv2.warpPerspective(imageA, H, (imageA.shape[1] + imageB.shape[1], imageA.shape[0]))
  self.cv_show('result', result)
  # 将图片B传入result图片最左端
  result[0:imageB.shape[0], 0:imageB.shape[1]] = imageB
  self.cv_show('result', result)
  # 检测是否需要显示图片匹配
  if showMatches:
# 生成匹配图片
vis = self.drawMatches(imageA, imageB, kpsA, kpsB, matches, status)
# 返回结果
return (result, vis)
 
  # 返回匹配结果
  return result
 def cv_show(self,name,img):
  cv2.imshow(name, img)
  cv2.waitKey(0)
  cv2.destroyAllWindows()
 
 def detectAndDescribe(self, image):
  # 将彩色图片转换成灰度图
  gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
 
  # 建立SIFT生成器
  descriptor = cv2.xfeatures2d.SIFT_create()
  # 检测SIFT特征点，并计算描述子
  (kps, features) = descriptor.detectAndCompute(image, None)
 
  # 将结果转换成NumPy数组
  kps = np.float32([kp.pt for kp in kps])
 
  # 返回特征点集，及对应的描述特征
  return (kps, features)
 
 def matchKeypoints(self, kpsA, kpsB, featuresA, featuresB, ratio, reprojThresh):
  # 建立暴力匹配器
  matcher = cv2.BFMatcher()
  
  # 使用KNN检测来自A、B图的SIFT特征匹配对，K=2
  rawMatches = matcher.knnMatch(featuresA, featuresB, 2)
 
  matches = []
  for m in rawMatches:
# 当最近距离跟次近距离的比值小于ratio值时，保留此匹配对
if len(m) == 2 and m[0].distance < m[1].distance * ratio:
# 存储两个点在featuresA, featuresB中的索引值
 matches.append((m[0].trainIdx, m[0].queryIdx))
 
  # 当筛选后的匹配对大于4时，计算视角变换矩阵
  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])
 
# 计算视角变换矩阵
(H, status) = cv2.findHomography(ptsA, ptsB, cv2.RANSAC, reprojThresh)
 
# 返回结果
return (matches, H, status)
 
  # 如果匹配对小于4时，返回None
  return None
 
 def drawMatches(self, imageA, imageB, kpsA, kpsB, matches, status):
  # 初始化可视化图片，将A、B图左右连接到一起
  (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，画到可视化图上
if s == 1:
 # 画出匹配对
 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)
 
  # 返回可视化结果
  return vis

from Stitcher import Stitcher
import cv2
 
# 读取拼接图片
imageA = cv2.imread("left_01.jpg")
imageB = cv2.imread("right_01.jpg")
 
# 把图片拼接成全景图
stitcher = Stitcher()
(result, vis) = stitcher.stitch([imageA, imageB], showMatches=True)
 
# 显示所有图片
cv2.imshow("Image A", imageA)
cv2.imshow("Image B", imageB)
cv2.imshow("Keypoint Matches", vis)
cv2.imshow("Result", result)
cv2.waitKey(0)
cv2.destroyAllWindows()

pip install --user opencv-contrib-python==3.3.0.10