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import torch
import numpy as np
import math
from trimesh.remesh import subdivide
def gaussian2D(shape, sigma=1):
m, n = [(ss - 1.) / 2. for ss in shape]
y, x = np.ogrid[-m:m+1,-n:n+1]
h = np.exp(-(x * x + y * y) / (2 * sigma * sigma))
h[h < np.finfo(h.dtype).eps * h.max()] = 0
return h
def calc_radius(bboxes_hw_norm, map_size=64):
if len(bboxes_hw_norm) == 0:
return []
minimum_radius = map_size / 32.
scale_factor = map_size / 16.
scales = np.linalg.norm(np.array(bboxes_hw_norm)/2, ord=2, axis=1)
# print(scales)
radius = (scales * scale_factor + minimum_radius).astype(np.uint8)
return radius
def build_z_map(depth, with_coords = True, device='cpu'):
size = 2**depth
z_map = torch.zeros((size, size), dtype=torch.int64)
coords = torch.meshgrid(torch.arange(size),torch.arange(size),indexing='ij')
ys = coords[0]
xs = coords[1]
for i in range(depth):
z_map |= (xs & (1 << i)) << i | (ys & (1 << i)) << (i + 1)
if with_coords:
return z_map, ys.clone(), xs.clone()
else:
return z_map
def gen_scale_map(scales, v3ds, faces, cam_intrinsics, map_size, patch_size=28, pad=True):
if pad:
map_h = math.ceil(map_size[0]/2)*2
map_w = math.ceil(map_size[1]/2)*2
else:
map_h = map_size[0]
map_w = map_size[1]
scale_map = torch.zeros((map_h, map_w, 2))
new_v3ds = []
for v in v3ds:
vv, _ = subdivide(v, faces)
new_v3ds.append(torch.from_numpy(vv))
v3ds = torch.stack(new_v3ds)
v2ds_homo = torch.matmul(v3ds,cam_intrinsics.transpose(-1,-2))
v2ds = v2ds_homo[...,:2]/(v2ds_homo[...,2,None])
v2ds_patch = (v2ds//patch_size).int()
v2ds_patch[..., 0] = v2ds_patch[..., 0].clamp(min = 0, max = map_size[1]-1)
v2ds_patch[..., 1] = v2ds_patch[..., 1].clamp(min = 0, max = map_size[0]-1)
for (v, s) in zip(v2ds_patch, scales):
scale_map[v[:, 1], v[:, 0], 0] = 1.
scale_map[v[:, 1], v[:, 0], 1] = s
return scale_map
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