图片拼接_cvshow('result',result)-优快云博客

本文链接：https://blog.youkuaiyun.com/daydayup_study/article/details/113859748

该博客介绍了如何利用OpenCV库中的SIFT特征检测和BFMatcher进行两幅图像的关键点匹配，并通过RANSAC算法找到单应性矩阵实现图像的稳定拼接。最终展示了一个图像拼接的实例。

摘要生成于 C知道，由 DeepSeek-R1 满血版支持，前往体验 >

将以下两张图片特征提取并拼接为一张图片：

代码如下：

import cv2
import numpy as np

def cv_show(name, image):
    cv2.imshow(name, image)
    cv2.waitKey(0)
    cv2.destroyAllWindows()


def detectAndCompute(image):
    image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
    sift = cv2.xfeatures2d.SIFT_create()
    (kps, features) = sift.detectAndCompute(image, None)
    kps = np.float32([kp.pt for kp in kps])  # 得到的点需要进一步转换才能使用
    return (kps, features)


def matchKeyPoints(kpsA, kpsB, featuresA, featuresB, ratio=0.75, reprojThresh=4.0):
    # ratio是最近邻匹配的推荐阈值
    # reprojThresh是随机取样一致性的推荐阈值
    matcher = cv2.BFMatcher()
    rawMatches = matcher.knnMatch(featuresA, featuresB, 2)
    matches = []
    for m in rawMatches:
        if len(m) == 2 and m[0].distance < ratio * m[1].distance:
            matches.append((m[0].queryIdx, m[0].trainIdx))
    kpsA = np.float32([kpsA[m[0]] for m in matches])  # 使用np.float32转化列表
    kpsB = np.float32([kpsB[m[1]] for m in matches])
    (M, status) = cv2.findHomography(kpsA, kpsB, cv2.RANSAC, reprojThresh)
    return (M, matches, status)  # 并不是所有的点都有匹配解，它们的状态存在status中


def stich(imgA, imgB, M):
    result = cv2.warpPerspective(imgA, M, (imgA.shape[1] + imgB.shape[1], imgA.shape[0]))
    result[0:imageB.shape[0], 0:imageB.shape[1]] = imageB
    cv_show('result', result)
    #cv2.imwrite("pingjie_result.jpeg",result)


def drawMatches(imgA, imgB, kpsA, kpsB, matches, status):
    (hA, wA) = imgA.shape[0:2]
    (hB, wB) = imgB.shape[0:2]
    # 注意这里的3通道和uint8类型
    drawImg = np.zeros((max(hA, hB), wA + wB, 3), 'uint8')
    drawImg[0:hB, 0:wB] = imageB
    drawImg[0:hA, wB:] = imageA
    for ((queryIdx, trainIdx), s) in zip(matches, status):
        if s == 1:
            # 注意将float32 --  int
            pt1 = (int(kpsB[trainIdx][0]), int(kpsB[trainIdx][1]))
            pt2 = (int(kpsA[trainIdx][0]) + wB, int(kpsA[trainIdx][1]))
            cv2.line(drawImg, pt1, pt2, (0, 0, 255))
    #cv_show("drawImg", drawImg)

# 读取图像
imageA = cv2.imread('pingjie_0.jpeg')
#cv_show("imageA", imageA)
imageB = cv2.imread('pingjie_1.jpeg')
#cv_show("imageB", imageB)
# 计算SIFT特征点和特征向量
(kpsA, featuresA) = detectAndCompute(imageA)
(kpsB, featuresB) = detectAndCompute(imageB)
# 基于最近邻和随机取样一致性得到一个单应性矩阵
(M, matches, status) = matchKeyPoints(kpsA, kpsB, featuresA, featuresB)
# 绘制匹配结果
drawMatches(imageA, imageB, kpsA, kpsB, matches, status)
# 拼接
stich(imageA, imageB, M)

拼接结果如下：