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# Estimating and Exploiting the Aleatoric Uncertainty in Surface Normal Estimation
# https://github.com/baegwangbin/surface_normal_uncertainty
import os
import types
import torch
import numpy as np
from einops import rearrange
from .models.NNET import NNET
from .utils import utils
from annotator.util import annotator_ckpts_path
import torchvision.transforms as transforms
class NormalBaeDetector:
def __init__(self):
remote_model_path = "https://huggingface.co./lllyasviel/Annotators/resolve/main/scannet.pt"
modelpath = os.path.join(annotator_ckpts_path, "scannet.pt")
if not os.path.exists(modelpath):
from basicsr.utils.download_util import load_file_from_url
load_file_from_url(remote_model_path, model_dir=annotator_ckpts_path)
args = types.SimpleNamespace()
args.mode = 'client'
args.architecture = 'BN'
args.pretrained = 'scannet'
args.sampling_ratio = 0.4
args.importance_ratio = 0.7
model = NNET(args)
model = utils.load_checkpoint(modelpath, model)
model = model.cuda()
model.eval()
self.model = model
self.norm = transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
def __call__(self, input_image):
assert input_image.ndim == 3
image_normal = input_image
with torch.no_grad():
image_normal = torch.from_numpy(image_normal).float().cuda()
image_normal = image_normal / 255.0
image_normal = rearrange(image_normal, 'h w c -> 1 c h w')
image_normal = self.norm(image_normal)
normal = self.model(image_normal)
normal = normal[0][-1][:, :3]
# d = torch.sum(normal ** 2.0, dim=1, keepdim=True) ** 0.5
# d = torch.maximum(d, torch.ones_like(d) * 1e-5)
# normal /= d
normal = ((normal + 1) * 0.5).clip(0, 1)
normal = rearrange(normal[0], 'c h w -> h w c').cpu().numpy()
normal_image = (normal * 255.0).clip(0, 255).astype(np.uint8)
return normal_image
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