#coding: utf-8
import numpy as np
import cv2leftgray = 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()

import numpy as np
import cv2 as cv
from matplotlib import pyplot as pltif __name__ == '__main__':top, bot, left, right = 100, 100, 0, 500img1 = 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 SIFTkp1, des1 = sift.detectAndCompute(img1gray, None)kp2, des2 = sift.detectAndCompute(img2gray, None)# FLANN parametersFLANN_INDEX_KDTREE = 1index_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 maskmatchesMask = [[0, 0] for i in range(len(matches))]good = []pts1 = []pts2 = []# ratio test as per Lowe's paperfor 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 = 10if 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 = colbreakfor col in range(cols-1, 0, -1):if srcImg[:, col].any() and warpImg[:, col].any():right = colbreakres = 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 rgbres = 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