python opencv圖像拼接_Python+OpenCV實作圖像的全景拼接

import numpy as np

import cv2 as cv

import imutils

class Stitcher:

def init(self):

self.isv3 = imutils.is_cv3()

def stitch(self,imgs, ratio = 0.75, reprojThresh = 4.0, showMatches = False):

print('A')

(img2, img1) = imgs

擷取關鍵點和描述符

(kp1, des1) = self.detectAndDescribe(img1)

(kp2, des2) = self.detectAndDescribe(img2)

print(len(kp1),len(des1))

print(len(kp2), len(des2))

R = self.matchKeyPoints(kp1, kp2, des1, des2, ratio, reprojThresh)

如果沒有足夠的最佳比對點，M為None

if R is None:

return None

(good, M, mask) = R

print(M)

對img1透視變換，M是ROI區域矩陣， 變換後的大小是(img1.w+img2.w, img1.h)

result = cv.warpPerspective(img1, M, (img1.shape[1] + img2.shape[1], img1.shape[0]))

将img2的值賦給結果圖像

result[0:img2.shape[0], 0:img2.shape[1]] = img2

是否需要顯示ROI區域

if showMatches:

vis = self.drawMatches1(img1, img2, kp1, kp2, good, mask)

return (result, vis)

return result

def detectAndDescribe(self,img):

print('B')

gray = cv.cvtColor(img, cv.COLOR_BGR2GRAY)

檢查我們使用的是否是penCV3.x

if self.isv3:

sift = cv.xfeatures2d.SIFT_create()

(kps, des) = sift.detectAndCompute(img, None)

else:

sift = cv.FastFeatureDetector_create('SIFT')

kps = sift.detect(gray)

des = sift.compute(gray, kps)

kps = np.float32([kp.pt for kp in kps]) # **********************************

傳回關鍵點和描述符

return (kps, des)

def matchKeyPoints(self,kp1, kp2, des1, des2, ratio, reprojThresh):

print('C')

初始化BF,因為使用的是SIFT ，是以使用預設參數

matcher = cv.DescriptorMatcher_create('BruteForce')

bf = cv.BFMatcher()

matches = bf.knnMatch(des1, des2, k=2)

matches = matcher.knnMatch(des1, des2, 2) #***********************************

擷取理想比對

good = []

for m in matches:

if len(m) == 2 and m[0].distance < ratio * m[1].distance:

good.append((m[0].trainIdx, m[0].queryIdx))

print(len(good))

最少要有四個點才能做透視變換

if len(good) > 4:

擷取關鍵點的坐标

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)

src_pts = np.float32([kp1[i] for (_, i) in good])

dst_pts = np.float32([kp2[i] for (i, _) in good])

通過兩個圖像的關鍵點計算變換矩陣

(M, mask) = cv.findHomography(src_pts, dst_pts, cv.RANSAC, reprojThresh)

傳回最佳比對點、變換矩陣和掩模

return (good, M, mask)

如果不滿足最少四個 就傳回None

return None

def drawMatches(img1, img2, kp1, kp2, matches, mask, M):

獲得原圖像的高和寬

h, w = img1.shape[:2]

使用得到的變換矩陣對原圖像的四個角進行變換，獲得目标圖像上對應的坐标

pts = np.float32([[0, 0], [0, h-1], [w-1, h-1], [w-1, 0]]).reshape(-1, 1, 2)

dst = cv.perspectiveTransform(pts, M)

matchesMask = mask.ravel().tolist()

draw_params = dict(matchColor = (0, 255, 0),

singlePointColor = None,

matchesMask = matchesMask,

flags = 2)

img = cv.drawMatches(img1, kp1, img2, kp2, matches, None, **draw_params)

return img

def drawMatches1(self,img1, img2, kp1, kp2, metches,mask):

print('D')

(hA,wA) = img1.shape[:2]

(hB,wB) = img2.shape[:2]

vis = np.zeros((max(hA,hB), wA+wB, 3), dtype='uint8')

vis[0:hA, 0:wA] = img1

vis[0:hB, wA:] = img2

for ((trainIdx, queryIdx),s) in zip(metches, mask):

if s == 1:

ptA = (int(kp1[queryIdx][0]), int(kp1[queryIdx][1]))

ptB = (int(kp2[trainIdx][0])+wA, int(kp2[trainIdx][1]))

cv.line(vis, ptA, ptB, (0, 255, 0), 1)

return vis

def show():

img1 = cv.imread('image/sedona_left_01.png')

img2 = cv.imread('image/sedona_right_01.png')

img1 = imutils.resize(img1, width=400)

img2 = imutils.resize(img2, width=400)

stitcher = cv.Stitcher()

(result, vis) = stitcher.stitch([img1, img2])

# (result, vis) = stitch([img1,img2], showMatches=True)

cv.imshow('image A', img1)

cv.imshow('image B', img2)

cv.imshow('keyPoint Matches', vis)

cv.imshow('Result', result)

cv.waitKey(0)

cv.destroyAllWindows()

show()