opencv_python Stitcher拼接图像实例（SIFT/SURF检测特征点，BF/FLANN匹配特征点）

SIFI/SURF检测特征点，BF/FLANN匹配特征点，stitch缝接图片，并进行视角变换。

先创建一个Stitcher类：

import numpy as npimport cv2class 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]] = imageBself.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 resultdef 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)# SURF生成器descriptor = cv2.xfeatures2d.SURF_create()kps, features = descriptor.detectAndCompute(image, None)# # 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 Nonedef matchKeypoints(self, kpsA, kpsB, featuresA, featuresB, ratio, reprojThresh):# FLANN匹配参数,定义FLANN匹配器，使用KNN算法实现匹配# 这里使用FLANN_INDEX_KDTREE，5kd-trees和50 checks迭代FLANN_INDEX_KDTREE = 1indexParams = dict(algorithm=1, trees=5)searchParams = dict(check=100)flann = cv2.FlannBasedMatcher(indexParams, searchParams)rawMatches = flann.knnMatch(featuresA, featuresB, k=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Nonereturn Nonedef 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] = imageAvis[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

测试：

import cv2from Stitcher import Stitcherfrom matplotlib import pyplot as pltimageA = cv2.imread("img/left_01.png")imageB = cv2.imread("img/right_01.png")# 把图像拼接成全景图stitcher = Stitcher()(result, vis) = stitcher.stitch([imageA, imageB], showMatches=True)# 显示所有图片cv2.imshow("Result", result)cv2.waitKey(0)cv2.destroyAllWindows()

参考视频：

/video/av61678672/?p=13