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import os
import json
import torch
import torchvision.transforms as transforms
import os.path
import numpy as np
import cv2
from PIL import Image
from torch.utils.data import Dataset
import random
from .__base_dataset__ import BaseDataset
class HM3DDataset(BaseDataset):
def __init__(self, cfg, phase, **kwargs):
super(HM3DDataset, self).__init__(
cfg=cfg,
phase=phase,
**kwargs)
self.metric_scale = cfg.metric_scale
#self.cap_range = self.depth_range # in meter
def load_norm_label(self, norm_path, H, W):
with open(norm_path, 'rb') as f:
normal = Image.open(f)
normal = np.array(normal.convert(normal.mode), dtype=np.uint8)
invalid_mask = np.all(normal == 128, axis=2)
normal = normal.astype(np.float64) / 255.0 * 2 - 1
normal[invalid_mask, :] = 0
return normal
def process_depth(self, depth: np.array, rgb: np.array) -> np.array:
depth[depth>60000] = 0
depth = depth / self.metric_scale
return depth
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