import torch
import math
import numpy as np
from scipy.spatial import Delaunay
def get_circle(batch_size, masks_size, num_points, device):
half_dim = masks_size / 2
half_width = half_dim
half_height = half_dim
vert = np.array([[
half_width + math.floor(math.cos(2 * math.pi / num_points * x) * 10),
half_height + math.floor(math.sin(2 * math.pi / num_points * x) * 10)]
for x in range(0, num_points)])
vert = (vert - half_dim) / half_dim
tri = Delaunay(vert).simplices.copy()
vert = torch.Tensor(vert)[None, None, ...].to(device).repeat(batch_size, 1, 1, 1)
face = torch.Tensor(tri)[None, None, ...].to(device).repeat(batch_size, 1, 1, 1).type(torch.int32)
vert[:, :, :, 1] = -vert[:, :, :, 1]
return vert, face
