OpenCV全景图像拼接的实现示例

本文主要介绍了opencv全景图像拼接的实现示例，分享给大家，具体如下：

left_01.jpg

right_01.jpg

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):
        #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

imagestiching.py

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()

运行结果：

如遇以下错误：

cv2.error: opencv(3.4.3) c:\projects\opencv-python\opencv_contrib\modules\xfeatures2d\src\sift.cpp:1207: error: (-213:the function/feature is not implemented) this algorithm is patented and is excluded in this configuration; set opencv_enable_nonfree cmake option and rebuild the library in function ‘cv::xfeatures2d::sift::create’

如果运行opencv程序提示算法版权问题可以通过安装低版本的opencv-contrib-python解决：

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

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