將兩張相同部分的圖片拼接成一張
方案一：
參考網址：https://blog.csdn.net/qq878594585/article/details/81901703#commentBox
代碼：
```
import numpy as np
import cv2
leftgray = cv2.imread('1.jpg')
rightgray = cv2.imread('2.jpg')
hessian=400
surf=cv2.SURF(hessian) #將Hessian Threshold設置為400,閾值越大能檢測的特征就越少
kp1,des1=surf.detectAndCompute(leftgray,None) #查找關鍵點和描述符
kp2,des2=surf.detectAndCompute(rightgray,None)
FLANN_INDEX_KDTREE=0 #建立FLANN匹配器的參數
indexParams=dict(algorithm=FLANN_INDEX_KDTREE,trees=5) #配置索引，密度樹的數量為5
searchParams=dict(checks=50) #指定遞歸次數
#FlannBasedMatcher：是目前最快的特征匹配算法（最近鄰搜索）
flann=cv2.FlannBasedMatcher(indexParams,searchParams) #建立匹配器
matches=flann.knnMatch(des1,des2,k=2) #得出匹配的關鍵點
good=[]
#提取優秀的特征點
for m,n in matches:
if m.distance < 0.7*n.distance: #如果第一個鄰近距離比第二個鄰近距離的0.7倍小，則保留
good.append(m)
src_pts = np.array([ kp1[m.queryIdx].pt for m in good]) #查詢圖像的特征描述子索引
dst_pts = np.array([ kp2[m.trainIdx].pt for m in good]) #訓練(模板)圖像的特征描述子索引
H=cv2.findHomography(src_pts,dst_pts) #生成變換矩陣
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]) #獲取左邊圖像到右邊圖像的投影映射關系
dst_corners=cv2.warpPerspective(leftgray,M,(w*2,h))#透視變換，新圖像可容納完整的兩幅圖
cv2.imshow('tiledImg1',dst_corners) #顯示，第一幅圖已在標準位置
dst_corners[0:h,w:w*2]=rightgray #將第二幅圖放在右側
#cv2.imwrite('tiled.jpg',dst_corners)
cv2.imshow('tiledImg',dst_corners)
cv2.imshow('leftgray',leftgray)
cv2.imshow('rightgray',rightgray)
cv2.waitKey()
cv2.destroyAllWindows()
```
方案二：
```
~~~
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('F:/licheng/six.jpg')
img2 = cv.imread('F:/licheng/five.jpg')
img1 = img1.astype('uint8')
img2 = img2.astype('uint8')
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)
sift = cv.xfeatures2d.SIFT_create()
# find the keypoints and descriptors with SIFT
kp1, des1 = sift.detectAndCompute(img1, None)
kp2, des2 = sift.detectAndCompute(img2, 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(2)
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
~~~[鏈接](
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```
