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utils.py
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utils.py
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import cv2
import numpy as np
from PIL import Image
import plyfile
import scipy.signal
import skimage.measure
import paddle
import paddle.nn as nn
import paddle.vision.transforms as T
import paddle.nn.functional as F
mse2psnr = lambda x : -10. * paddle.log(x) / paddle.log(paddle.to_tensor([10.]))
def visualize_depth_numpy(depth, minmax=None, cmap=cv2.COLORMAP_JET):
"""
depth: (H, W)
"""
x = np.nan_to_num(depth) # change nan to 0
if minmax is None:
mi = np.min(x[x>0]) # get minimum positive depth (ignore background)
ma = np.max(x)
else:
mi,ma = minmax
x = (x-mi)/(ma-mi+1e-8) # normalize to 0~1
x = (255*x).astype(np.uint8)
x_ = cv2.applyColorMap(x, cmap)
return x_, [mi,ma]
def init_log(log, keys):
for key in keys:
log[key] = paddle.to_tensor([0.0]).astype('float32')
return log
def visualize_depth(depth, minmax=None, cmap=cv2.COLORMAP_JET):
"""
depth: (H, W)
"""
if type(depth) is not np.ndarray:
depth = depth.cpu().numpy()
x = np.nan_to_num(depth) # change nan to 0
if minmax is None:
mi = np.min(x[x>0]) # get minimum positive depth (ignore background)
ma = np.max(x)
else:
mi,ma = minmax
x = (x-mi) / (ma-mi+1e-8) # normalize to 0~1
x = (255*x).astype(np.uint8)
x_ = Image.fromarray(cv2.applyColorMap(x, cmap))
x_ = T.ToTensor()(x_) # (3, H, W)
return x_, [mi,ma]
def N_to_reso(n_voxels, bbox):
xyz_min, xyz_max = bbox
dim = len(xyz_min)
voxel_size = ((xyz_max - xyz_min).prod() / n_voxels).pow(1 / dim)
return ((xyz_max - xyz_min) / voxel_size).astype('int64').tolist()
def cal_n_samples(reso, step_ratio=0.5):
return int(np.linalg.norm(reso)/step_ratio)
__LPIPS__ = {}
def init_lpips(net_name):
assert net_name in ['alex', 'vgg']
import paddle_lpips as lpips
print(f'init_lpips: lpips_{net_name}')
return lpips.LPIPS(net=net_name, version='0.1')
def rgb_lpips(np_gt, np_im, net_name):
if net_name not in __LPIPS__:
__LPIPS__[net_name] = init_lpips(net_name)
gt = paddle.to_tensor(np_gt).transpose([2, 0, 1])
im = paddle.to_tensor(np_im).transpose([2, 0, 1])
__LPIPS__[net_name].eval()
return __LPIPS__[net_name](gt, im, normalize=True).item()
def findItem(items, target):
for one in items:
if one[:len(target)]==target:
return one
return None
''' Evaluation metrics (ssim, lpips)
'''
def rgb_ssim(img0, img1, max_val,
filter_size=11,
filter_sigma=1.5,
k1=0.01,
k2=0.03,
return_map=False):
# Modified from https://github.com/google/mipnerf/blob/16e73dfdb52044dcceb47cda5243a686391a6e0f/internal/math.py#L58
assert len(img0.shape) == 3
assert img0.shape[-1] == 3
assert img0.shape == img1.shape
# Construct a 1D Gaussian blur filter.
hw = filter_size // 2
shift = (2 * hw - filter_size + 1) / 2
f_i = ((np.arange(filter_size) - hw + shift) / filter_sigma)**2
filt = np.exp(-0.5 * f_i)
filt /= np.sum(filt)
# Blur in x and y (faster than the 2D convolution).
def convolve2d(z, f):
return scipy.signal.convolve2d(z, f, mode='valid')
filt_fn = lambda z: np.stack([
convolve2d(convolve2d(z[...,i], filt[:, None]), filt[None, :])
for i in range(z.shape[-1])], -1)
mu0 = filt_fn(img0)
mu1 = filt_fn(img1)
mu00 = mu0 * mu0
mu11 = mu1 * mu1
mu01 = mu0 * mu1
sigma00 = filt_fn(img0**2) - mu00
sigma11 = filt_fn(img1**2) - mu11
sigma01 = filt_fn(img0 * img1) - mu01
# Clip the variances and covariances to valid values.
# Variance must be non-negative:
sigma00 = np.maximum(0., sigma00)
sigma11 = np.maximum(0., sigma11)
sigma01 = np.sign(sigma01) * np.minimum(
np.sqrt(sigma00 * sigma11), np.abs(sigma01))
c1 = (k1 * max_val)**2
c2 = (k2 * max_val)**2
numer = (2 * mu01 + c1) * (2 * sigma01 + c2)
denom = (mu00 + mu11 + c1) * (sigma00 + sigma11 + c2)
ssim_map = numer / denom
ssim = np.mean(ssim_map)
return ssim_map if return_map else ssim
class TVLoss(nn.Layer):
def __init__(self,TVLoss_weight=1):
super(TVLoss,self).__init__()
self.TVLoss_weight = TVLoss_weight
def forward(self,x):
batch_size, h_x, w_x = x.shape[0:3]
count_h = self._tensor_size(x[:,:,1:,:])
count_w = self._tensor_size(x[:,:,:,1:])
h_tv = paddle.pow((x[:,:,1:,:]-x[:,:,:h_x-1,:]), 2).sum()
w_tv = paddle.pow((x[:,:,:,1:]-x[:,:,:,:w_x-1]),2).sum()
return self.TVLoss_weight*2*(h_tv/count_h+w_tv/count_w)/batch_size
def _tensor_size(self,t):
return t.shape[1]*t.shape[2]*t.shape[3]
def convert_sdf_samples_to_ply(
paddle_3d_sdf_tensor,
ply_filename_out,
bbox,
level=0.5,
offset=None,
scale=None,
):
"""
Convert sdf samples to .ply
:param paddle_3d_sdf_tensor: a torch.FloatTensor of shape (n,n,n)
:voxel_grid_origin: a list of three floats: the bottom, left, down origin of the voxel grid
:voxel_size: float, the size of the voxels
:ply_filename_out: string, path of the filename to save to
This function adapted from: https://github.com/RobotLocomotion/spartan
"""
numpy_3d_sdf_tensor = paddle_3d_sdf_tensor.numpy()
bbox = bbox.numpy()
voxel_size = list((bbox[1]-bbox[0]) / np.array(paddle_3d_sdf_tensor.shape))
verts, faces, normals, values = skimage.measure.marching_cubes(
numpy_3d_sdf_tensor, level=level, spacing=voxel_size
)
faces = faces[...,::-1] # inverse face orientation
# transform from voxel coordinates to camera coordinates
# note x and y are flipped in the output of marching_cubes
mesh_points = np.zeros_like(verts)
mesh_points[:, 0] = bbox[0,0] + verts[:, 0]
mesh_points[:, 1] = bbox[0,1] + verts[:, 1]
mesh_points[:, 2] = bbox[0,2] + verts[:, 2]
# apply additional offset and scale
if scale is not None:
mesh_points = mesh_points / scale
if offset is not None:
mesh_points = mesh_points - offset
# try writing to the ply file
num_verts = verts.shape[0]
num_faces = faces.shape[0]
verts_tuple = np.zeros((num_verts,), dtype=[("x", "f4"), ("y", "f4"), ("z", "f4")])
for i in range(0, num_verts):
verts_tuple[i] = tuple(mesh_points[i, :])
faces_building = []
for i in range(0, num_faces):
faces_building.append(((faces[i, :].tolist(),)))
faces_tuple = np.array(faces_building, dtype=[("vertex_indices", "i4", (3,))])
el_verts = plyfile.PlyElement.describe(verts_tuple, "vertex")
el_faces = plyfile.PlyElement.describe(faces_tuple, "face")
ply_data = plyfile.PlyData([el_verts, el_faces])
print("saving mesh to %s" % (ply_filename_out))
ply_data.write(ply_filename_out)