图像融合

查了好多版本的资料，设置了opencv和contrib的版本，最终以子函数的形式，可以将两张图片融合成全景图：查的链接太多了，忘了保存了，如有侵权，立即删掉！
注：opencv-python版本：3.4.2.16
opencv-contrib-python版本：3.4.2.16
参考链接：https://www.cnblogs.com/skyfsm/p/7411961.html
https://www.pyimagesearch.com/2018/12/17/image-stitching-with-opencv-and-python/
https://blog.youkuaiyun.com/weixin_41010198/article/details/88575719

import cv2
import numpy as np
def stitch(img1,img2):
    MIN = 10
    surf=cv2.xfeatures2d.SURF_create(10000,nOctaves=4,extended=False,upright=True)
    kp1,descrip1=surf.detectAndCompute(img1,None)
    kp2,descrip2=surf.detectAndCompute(img2,None)

    FLANN_INDEX_KDTREE = 0
    indexParams = dict(algorithm = FLANN_INDEX_KDTREE, trees = 5)
    searchParams = dict(checks=50)

    flann=cv2.FlannBasedMatcher(indexParams,searchParams)
    match=flann.knnMatch(descrip1,descrip2,k=2)


    good=[]
    for i,(m,n) in enumerate(match):
        if(m.distance<0.75*n.distance):
                good.append(m)

    if len(good)>MIN:
        src_pts = np.float32([kp1[m.queryIdx].pt for m in good]).reshape(-1,1,2)
        ano_pts = np.float32([kp2[m.trainIdx].pt for m in good]).reshape(-1,1,2)
        M,mask=cv2.findHomography(src_pts,ano_pts,cv2.RANSAC,5.0)
        warpImg = cv2.warpPerspective(img2, np.linalg.inv(M), (img1.shape[1]+img2.shape[1], img2.shape[0]))
        direct=warpImg.copy()
        direct[0:img1.shape[0], 0:img1.shape[1]] =img1
        rows,cols=img1.shape[:2]
        
        for col in range(0,cols):
            if img1[:, col].any() and warpImg[:, col].any():#开始重叠的最左端
                left = col
                break
        for col in range(cols-1, 0, -1):
            if img1[:, col].any() and warpImg[:, col].any():#重叠的最右一列
                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 img1[row, col].any():#如果没有原图，用旋转的填充
                    res[row, col] = warpImg[row, col]
                elif not warpImg[row, col].any():
                    res[row, col] = img1[row, col]
                else:
                    srcImgLen = float(abs(col - left))
                    testImgLen = float(abs(col - right))
                    alpha = srcImgLen / (srcImgLen + testImgLen)
                    res[row, col] = np.clip(img1[row, col] * (1-alpha) + warpImg[row, col] * alpha, 0, 255)
        warpImg[0:img1.shape[0], 0:img1.shape[1]]=res
        img3=direct
        img4=warpImg
    else:
        print("not enough matches!")
        img3=[]
        img4=[]
    return img3,img4
img1 = cv2.imread('C:\\Users\\YS\\Desktop\\img\\1.png') #query
img2 = cv2.imread('C:\\Users\\YS\\Desktop\\img\\2.png') #train
img3,img4=stitch(img1,img2)
cv2.imshow('p',img3)
cv2.waitKey(0)
cv2.destroyAllWindows()