Python+OpenCV實現(xiàn)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
 #獲取關(guān)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匹配點，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(qū)域矩陣， 變換后的大小是(img1.w+img2.w, img1.h)
 result = cv.warpPerspective(img1, M, (img1.shape[1] + img2.shape[1], img1.shape[0]))
 #將img2的值賦給結(jié)果圖像
 result[0:img2.shape[0], 0:img2.shape[1]] = img2

 #是否需要顯示ROI區(qū)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]) # **********************************
 #返回關(guān)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 ，所以使用默認參數(shù)
 matcher = cv.DescriptorMatcher_create('BruteForce')
 # bf = cv.BFMatcher()
 # matches = bf.knnMatch(des1, des2, k=2)
 matches = matcher.knnMatch(des1, des2, 2) #***********************************

 #獲取理想匹配
 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:
 #獲取關(guān)鍵點的坐標
 # 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(guān)鍵點計算變換矩陣
 (M, mask) = cv.findHomography(src_pts, dst_pts, cv.RANSAC, reprojThresh)

 #返回最佳匹配點、變換矩陣和掩模
 return (good, M, mask)
 #如果不滿足最少四個 就返回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，獲得目標圖像上對應(yīng)的坐標
 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()