DEMInterp.py 文件源码

def genDSM_building(BD_footprint,PC_data,h,w,n,ground_height,affine_par,upper_bd_par):

    [A,D,B,E,C,F] = np.loadtxt(affine_par)
    BD_footprint = cv2.imread(BD_footprint,0)
    point_cloud = pd.read_csv(PC_data,names=['X', 'Y', 'Z', 'R','G','B'],delim_whitespace=True)

    X = point_cloud.X.copy()
    Y = point_cloud.Y.copy()      # Inverse the Y-axis
    Z = point_cloud.Z.copy()

    del point_cloud
    Dsm_arr = np.zeros((h, w))
        # cKDtree
    tree = spatial.cKDTree(list(zip(Y, X)))
        #Affine Trans
    yv, xv = np.where(BD_footprint>127)
    XA = A*xv+B*yv+C
    YA = D*xv+E*yv+F
    pts = np.dstack((YA,XA))
    dis, loc = tree.query(pts, k=n ,distance_upper_bound=upper_bd_par)
    #building use max
    Dsm_arr[yv,xv] = Z[loc[:,:,:].ravel()].values.reshape(-1, n).max(axis=1)
    Dsm_arr=np.float32(Dsm_arr)

    return Dsm_arr