视差匹配

from PIL import Image
from pylab import *
import cv2
from numpy import *
from numpy.ma import array
from scipy.ndimage import filters
from PIL import Image
import imageio
def plane_sweep_ncc(im_l,im_r,start,steps,wid):
    m,n=im_l.shape
    mean_l=zeros((m,n))
    mean_r=zeros((m,n))
    s=zeros((m,n))
    s_l=zeros((m,n))
    s_r=zeros((m,n))
    dmaps=zeros((m,n,steps))#保存深度平面数组
    #计算图像块平均值
    filters.uniform_filter(im_l,wid,mean_l)
    filters.uniform_filter(im_r,wid,mean_r)
    #归一化图像
    norm_l=im_l-mean_l
    norm_r=im_r-mean_r
    #尝试不同视差
    for displ in range(steps):
        #将左边图像移动到右边计算加和
        filters.uniform_filter(np.roll(norm_l,-displ-start)*norm_r,wid,s) # 和归一化
        filters.uniform_filter(np.roll(norm_l,-displ-start)*np.roll(norm_l,-displ- start),wid,s_l)
        filters.uniform_filter(norm_r*norm_r,wid,s_r)#和反归一化
        #保存ncc分数
        dmaps[:,:,displ]=s/sqrt(s_l*s_r)
        #为每个像素选取最佳深度
    return np.argmax(dmaps,axis=2)
im_l=array(Image.open(r'C:/Users/xpp/Desktop/Lena.png').convert('L'),'f')
im_r=array(Image.open(r'C:/Users/xpp/Desktop/Lena.png').convert('L'),'f')
#偏移并设置步长
steps=12
start=4
wid=8#nccu宽度
res=plane_sweep_ncc(im_l,im_r,start,steps,wid)
imageio.imwrite('C:/Users/xpp/Desktop/result.png',res)