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import cv2
import h5py
import numpy as np
import torch
from torch.utils.data import Dataset
from torchvision.transforms import Compose
from dataset.transform import Resize, NormalizeImage, PrepareForNet, Crop
def hypersim_distance_to_depth(npyDistance):
intWidth, intHeight, fltFocal = 1024, 768, 886.81
npyImageplaneX = np.linspace((-0.5 * intWidth) + 0.5, (0.5 * intWidth) - 0.5, intWidth).reshape(
1, intWidth).repeat(intHeight, 0).astype(np.float32)[:, :, None]
npyImageplaneY = np.linspace((-0.5 * intHeight) + 0.5, (0.5 * intHeight) - 0.5,
intHeight).reshape(intHeight, 1).repeat(intWidth, 1).astype(np.float32)[:, :, None]
npyImageplaneZ = np.full([intHeight, intWidth, 1], fltFocal, np.float32)
npyImageplane = np.concatenate(
[npyImageplaneX, npyImageplaneY, npyImageplaneZ], 2)
npyDepth = npyDistance / np.linalg.norm(npyImageplane, 2, 2) * fltFocal
return npyDepth
class Hypersim(Dataset):
def __init__(self, filelist_path, mode, size=(518, 518)):
self.mode = mode
self.size = size
with open(filelist_path, 'r') as f:
self.filelist = f.read().splitlines()
net_w, net_h = size
self.transform = Compose([
Resize(
width=net_w,
height=net_h,
resize_target=True if mode == 'train' else False,
keep_aspect_ratio=True,
ensure_multiple_of=14,
resize_method='lower_bound',
image_interpolation_method=cv2.INTER_CUBIC,
),
NormalizeImage(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
PrepareForNet(),
] + ([Crop(size[0])] if self.mode == 'train' else []))
def __getitem__(self, item):
img_path = self.filelist[item].split(' ')[0]
depth_path = self.filelist[item].split(' ')[1]
image = cv2.imread(img_path)
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB) / 255.0
depth_fd = h5py.File(depth_path, "r")
distance_meters = np.array(depth_fd['dataset'])
depth = hypersim_distance_to_depth(distance_meters)
sample = self.transform({'image': image, 'depth': depth})
sample['image'] = torch.from_numpy(sample['image'])
sample['depth'] = torch.from_numpy(sample['depth'])
sample['valid_mask'] = (torch.isnan(sample['depth']) == 0)
sample['depth'][sample['valid_mask'] == 0] = 0
sample['image_path'] = self.filelist[item].split(' ')[0]
return sample
def __len__(self):
return len(self.filelist) |