python+opencv中最近出现的一些变化（ OpenCV 官方的 Python tutorial目前好像还没有改过来？） 记一次全景图像的拼接

最近在学习过程中发现opencv有了很多变动， OpenCV 官方的 Python tutorial目前好像还没有改过来，导致大家在学习上面都出现了一些问题，现在做一个小小的罗列，希望对大家有用

做的是关于全景图像的拼接，关于sift和surf的语法之后有需要会另开文章具体阐述，此篇主要是解决大家困惑许久的问题。

笔者python3.x

首先是安装上，必须先后安装pip install opencv_python和pip install opencv-contrib-python==3.3.0.10后面一个一定要指定版本号，因为版本上面最新的opencv-contrib-python-3.4.5.20版本好像申请了什么专利，所以我们可能无法调用的，安装上要是出现了报错，先别急着写在，重新运行一次语句，基本上就可能可以了。

然后是关于sift和surf这两条语句上面，它的语法函数也出现了变化，具体可以参考这个

http://answers.opencv.org/question/52130/300-python-cv2-module-cannot-find-siftsurforb/

好像是最近才修改的，真的走了很多弯路才走通。

#这里的代码有改动之后才能用

#sift = cv.xfeatures2d_SIFT().create()修改为

sift = cv2.xfeatures2d.SIFT_create()

 

hessian=400
#surf=cv2.SURF(hessian)修改为

surf=cv2.xfeatures2d.SURF_create(hessian)

下面给出两个代码，是借鉴了网友的，但是对于报错的部分和需要改正的点都已经纠错完毕了，希望对大家有所帮助。有其他的bug也欢迎留言。

示例1

 

6.jpg

7.jpg

 效果图

#coding: utf-8
import numpy as np
import cv2
 
leftgray = cv2.imread('6.jpg')
rightgray = cv2.imread('7.jpg')
 
hessian=400
surf=cv2.xfeatures2d.SURF_create(hessian)
#surf=cv2.SURF(hessian) #将Hessian Threshold设置为400,阈值越大能检测的特征就越少
kp1,des1=surf.detectAndCompute(leftgray,None)  #查找关键点和描述符
kp2,des2=surf.detectAndCompute(rightgray,None)
 
 
FLANN_INDEX_KDTREE=0   #建立FLANN匹配器的参数
indexParams=dict(algorithm=FLANN_INDEX_KDTREE,trees=5) #配置索引，密度树的数量为5
searchParams=dict(checks=50)    #指定递归次数
#FlannBasedMatcher：是目前最快的特征匹配算法（最近邻搜索）
flann=cv2.FlannBasedMatcher(indexParams,searchParams)  #建立匹配器
matches=flann.knnMatch(des1,des2,k=2)  #得出匹配的关键点
 
good=[]
#提取优秀的特征点
for m,n in matches:
    if m.distance < 0.7*n.distance: #如果第一个邻近距离比第二个邻近距离的0.7倍小，则保留
        good.append(m)
src_pts = np.array([ kp1[m.queryIdx].pt for m in good])    #查询图像的特征描述子索引
dst_pts = np.array([ kp2[m.trainIdx].pt for m in good])    #训练(模板)图像的特征描述子索引
H=cv2.findHomography(src_pts,dst_pts)         #生成变换矩阵
h,w=leftgray.shape[:2]
h1,w1=rightgray.shape[:2]
shft=np.array([[1.0,0,w],[0,1.0,0],[0,0,1.0]])
M=np.dot(shft,H[0])            #获取左边图像到右边图像的投影映射关系
dst_corners=cv2.warpPerspective(leftgray,M,(w*2,h))#透视变换，新图像可容纳完整的两幅图
cv2.imshow('tiledImg1',dst_corners)   #显示，第一幅图已在标准位置
dst_corners[0:h,w:w*2]=rightgray  #将第二幅图放在右侧
#cv2.imwrite('tiled.jpg',dst_corners)
cv2.imshow('tiledImg',dst_corners)
cv2.imshow('leftgray',leftgray)
cv2.imshow('rightgray',rightgray)
cv2.waitKey()
cv2.destroyAllWindows()

示例2

test1.jpg

test2.jpg

 效果图

import numpy as np
import cv2 as cv
from matplotlib import pyplot as plt

if __name__ == '__main__':
    top, bot, left, right = 100, 100, 0, 500
    img1 = cv.imread('test1.jpg')
    img2 = cv.imread('test2.jpg')
    srcImg = cv.copyMakeBorder(img1, top, bot, left, right, cv.BORDER_CONSTANT, value=(0, 0, 0))
    testImg = cv.copyMakeBorder(img2, top, bot, left, right, cv.BORDER_CONSTANT, value=(0, 0, 0))
    img1gray = cv.cvtColor(srcImg, cv.COLOR_BGR2GRAY)
    img2gray = cv.cvtColor(testImg, cv.COLOR_BGR2GRAY)
    
    #这里的代码有改动之后才能用
    #sift = cv.xfeatures2d_SIFT().create()
    sift = cv2.xfeatures2d.SIFT_create()
    
    # find the keypoints and descriptors with SIFT
    kp1, des1 = sift.detectAndCompute(img1gray, None)
    kp2, des2 = sift.detectAndCompute(img2gray, None)
    # FLANN parameters
    FLANN_INDEX_KDTREE = 1
    index_params = dict(algorithm=FLANN_INDEX_KDTREE, trees=5)
    search_params = dict(checks=50)
    flann = cv.FlannBasedMatcher(index_params, search_params)
    matches = flann.knnMatch(des1, des2, k=2)

    # Need to draw only good matches, so create a mask
    matchesMask = [[0, 0] for i in range(len(matches))]

    good = []
    pts1 = []
    pts2 = []
    # ratio test as per Lowe's paper
    for i, (m, n) in enumerate(matches):
        if m.distance < 0.7*n.distance:
            good.append(m)
            pts2.append(kp2[m.trainIdx].pt)
            pts1.append(kp1[m.queryIdx].pt)
            matchesMask[i] = [1, 0]

    draw_params = dict(matchColor=(0, 255, 0),
                       singlePointColor=(255, 0, 0),
                       matchesMask=matchesMask,
                       flags=0)
    img3 = cv.drawMatchesKnn(img1gray, kp1, img2gray, kp2, matches, None, **draw_params)
    plt.imshow(img3, ), plt.show()

    rows, cols = srcImg.shape[:2]
    MIN_MATCH_COUNT = 10
    if len(good) > MIN_MATCH_COUNT:
        src_pts = np.float32([kp1[m.queryIdx].pt for m in good]).reshape(-1, 1, 2)
        dst_pts = np.float32([kp2[m.trainIdx].pt for m in good]).reshape(-1, 1, 2)
        M, mask = cv.findHomography(src_pts, dst_pts, cv.RANSAC, 5.0)
        warpImg = cv.warpPerspective(testImg, np.array(M), (testImg.shape[1], testImg.shape[0]), flags=cv.WARP_INVERSE_MAP)

        for col in range(0, cols):
            if srcImg[:, col].any() and warpImg[:, col].any():
                left = col
                break
        for col in range(cols-1, 0, -1):
            if srcImg[:, col].any() and warpImg[:, col].any():
                right = col
                break

        res = np.zeros([rows, cols, 3], np.uint8)
        for row in range(0, rows):
            for col in range(0, cols):
                if not srcImg[row, col].any():
                    res[row, col] = warpImg[row, col]
                elif not warpImg[row, col].any():
                    res[row, col] = srcImg[row, col]
                else:
                    srcImgLen = float(abs(col - left))
                    testImgLen = float(abs(col - right))
                    alpha = srcImgLen / (srcImgLen + testImgLen)
                    res[row, col] = np.clip(srcImg[row, col] * (1-alpha) + warpImg[row, col] * alpha, 0, 255)

        # opencv is bgr, matplotlib is rgb
        res = cv.cvtColor(res, cv.COLOR_BGR2RGB)
        # show the result
        plt.figure()
        plt.imshow(res)
        plt.show()
    else:
        print("Not enough matches are found - {}/{}".format(len(good), MIN_MATCH_COUNT))
        matchesMask = None