全景拼接的实现

最新推荐文章于 2025-05-25 16:29:35 发布

Suki wu

最新推荐文章于 2025-05-25 16:29:35 发布

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CC 4.0 BY-SA版权

分类专栏：计算机视觉

本文链接：https://blog.youkuaiyun.com/qq_39408726/article/details/88935809

博客讲述了作者在实现全景图拼接过程中遇到的维度不匹配错误，通过调整图片尺寸解决问题。分享了室外全景图的拼接效果，包括落差小的平稳结果和落差大时遇到的问题，如图片像素过高和特征匹配不足。

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根据官方代码行修改后：

from pylab import *
from numpy import *
from PIL import Image

# If you have PCV installed, these imports should work
from PCV.geometry import homography, warp
from PCV.localdescriptors import sift

"""
This is the panorama example from section 3.3.
"""

# set paths to data folder
featname = ['hu'+str(i+1)+'.sift' for i in range(5)] 
imname = ['hu'+str(i+1)+'.jpg' for i in range(5)]

# extract features and match
l = {}
d = {}
for i in range(5): 
    sift.process_image(imname[i],featname[i])
    l[i],d[i] = sift.read_features_from_file(featname[i])

matches = {}
for i in range(4):
    matches[i] = sift.match(d[i+1],d[i])

# visualize the matches (Figure 3-11 in the book)
for i in range(4):
    im1 = array(Image.open(imname[i]))
    im2 = array(Image.open(imname[i+1]))
    figure()
    sift.plot_matches(im2,im1,l[i+1],l[i],matches[i],show_below=True)


# function to convert the matches to hom. points
def convert_points(j):
    ndx = matches[j].nonzero()[0]