摘要：
必须首先安装Pipinstallopencv _ Python和pipingstalopencv contrib Python==3.3.0.10必须为下一个版本指定版本号。
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