python圖像拼接_如何拼接全景圖中的多個圖像？

# -*- coding: utf-8 -*-

"""

Spyder Editor

This is a temporary script file.

"""

# import the necessary packages

import numpy as np

import imutils

import cv2

class Stitcher:

def __init__(self):

# determine if we are using OpenCV v3.X

self.isv3 = imutils.is_cv3()

def stitch(self, images, ratio=0.75, reprojThresh=4.0,

showMatches=False):

# unpack the images, then detect keypoints and extract

# local invariant descriptors from them

(imageB, imageA) = images

#(b, g, r) = cv2.split(imageA)

#imageA = cv2.merge([r,g,b])

#(b, g, r) = cv2.split(imageB)

#imageB = cv2.merge([r,g,b])

(kpsA, featuresA) = self.detectAndDescribe(imageA)

(kpsB, featuresB) = self.detectAndDescribe(imageB)

# match features between the two images

M = self.matchKeypoints(kpsA, kpsB,

featuresA, featuresB, ratio, reprojThresh)

# if the match is None, then there aren't enough matched

# keypoints to create a panorama

if M is None:

return None

# otherwise, apply a perspective warp to stitch the images

# together

(matches, H, status) = M

result = cv2.warpPerspective(imageA, H,

(imageA.size[1] + imageB.size[1], imageA.size[0]))

result[0:imageB.size[0], 0:imageB.size[1]] = imageB

# check to see if the keypoint matches should be visualized

if showMatches:

vis = self.drawMatches(imageA, imageB, kpsA, kpsB, matches,

status)

# return a tuple of the stitched image and the

# visualization

return (result, vis)

# return the stitched image

return result

def detectAndDescribe(self, image):

# convert the image to grayscale

gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)

# check to see if we are using OpenCV 3.X

if self.isv3:

# detect and extract features from the image

descriptor = cv2.xfeatures2d.SIFT_create()

(kps, features) = descriptor.detectAndCompute(image, None)

# otherwise, we are using OpenCV 2.4.X

else:

# detect keypoints in the image

detector = cv2.FeatureDetector_create("SIFT")

kps = detector.detect(gray)

# extract features from the image

extractor = cv2.DescriptorExtractor_create("SIFT")

(kps, features) = extractor.compute(gray, kps)

# convert the keypoints from KeyPoint objects to NumPy

# arrays

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

# return a tuple of keypoints and features

return (kps, features)

def matchKeypoints(self, kpsA, kpsB, featuresA, featuresB,

ratio, reprojThresh):

# compute the raw matches and initialize the list of actual

# matches

matcher = cv2.DescriptorMatcher_create("BruteForce")

rawMatches = matcher.knnMatch(featuresA, featuresB, 2)

matches = []

# loop over the raw matches

for m in rawMatches:

# ensure the distance is within a certain ratio of each

# other (i.e. Lowe's ratio test)

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

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

# computing a homography requires at least 4 matches

if len(matches) > 4:

# construct the two sets of points

ptsA = np.float32([kpsA[i] for (_, i) in matches])

ptsB = np.float32([kpsB[i] for (i, _) in matches])

# compute the homography between the two sets of points

(H, status) = cv2.findHomography(ptsA, ptsB, cv2.RANSAC,

reprojThresh)

# return the matches along with the homograpy matrix

# and status of each matched point

return (matches, H, status)

# otherwise, no homograpy could be computed

return None

def drawMatches(self, imageA, imageB, kpsA, kpsB, matches, status):

# initialize the output visualization image

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

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

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

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

vis[0:hB, wA:] = imageB

# loop over the matches

for ((trainIdx, queryIdx), s) in zip(matches, status):

# only process the match if the keypoint was successfully

# matched

if s == 1:

# draw the match

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

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

cv2.line(vis, ptA, ptB, (0, 255, 0), 1)

# return the visualization

return vis