图像拼接，单应性矩阵计算

PCV库链接：https://github.com/Li-Shu14/PCV

将下载的文件解压
打开cmd，执行如下指令：
（1）执行cd命令，转到你所解压到的PCV的文件夹中。
（2）输入python setup.py install

# RANSAC算法拼接2张图片
from pylab import *
from numpy import *
from PIL import Image
from PCV.geometry import homography, warp
from PCV.localdescriptors import sift

# 获取图片
from skimage.transform import rescale

featname = [r'D:\python_test\ttt\SLAM-ransac-master\S' + str(i + 1) + '.sift' for i in range(2)]
imname = [r'D:\python_test\ttt\SLAM-ransac-master\S' + str(i + 1) + '.jpg' for i in range(2)]

# 提取特征并匹配
l = {}
d = {}
for i in range(2):
    sift.process_image(imname[i], featname[i])
    l[i], d[i] = sift.read_features_from_file(featname[i])

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

# visualize the matches
for i in range(1):
    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)


# 将匹配转换成齐次坐标点的函数
def convert_points(j):
    ndx = matches[j].nonzero()[0]
    fp1 = homography.make_homog(l[j + 1][ndx, :2].T)
    ndx2 = [int(matches[j][i]) for i in ndx]
    tp1 = homography.make_homog(l[j][ndx2, :2].T)

    # switch x and y - TODO this should move elsewhere
    fp1 = vstack([fp1[1], fp1[0], fp1[2]])
    tp1 = vstack([tp1[1], tp1[0], tp1[2]])
    return fp1, tp1


# 估计单应性矩阵
model = homography.RansacModel()

# RANSAC稳健性估计点对应间的单应性矩阵Hv
fp, tp = convert_points(0)
print("fp:", fp )
print("tp:", tp )
H_01 = homography.H_from_ransac(fp, tp, model)[0]  # im 0 to 1
print(H_01 )
# warp the images(扭曲图像)
delta = 200  # for padding and translation

im1 = array(Image.open(imname[0]), "uint8")
im2 = array(Image.open(imname[1]), "uint8")
im_12 = warp.panorama(H_01, im1, im2, delta, delta)
rgbn_interpolation[:,:,i] = rescale(img, (4,4))
figure()
imshow(array(im_12, "uint8"))
axis('off')
savefig("ps.jpg", dpi=300)
show()