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Zero
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# MIT License
# Copyright (c) 2022 Intelligent Systems Lab Org
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
# copies of the Software, and to permit persons to whom the Software is
# furnished to do so, subject to the following conditions:
# The above copyright notice and this permission notice shall be included in all
# copies or substantial portions of the Software.
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
# SOFTWARE.
# File author: Shariq Farooq Bhat
import glob
import os
import h5py
import numpy as np
import torch
from PIL import Image
from torch.utils.data import DataLoader, Dataset
from torchvision import transforms
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 ToTensor(object):
def __init__(self):
# self.normalize = transforms.Normalize(
# mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
self.normalize = lambda x: x
self.resize = transforms.Resize((480, 640))
def __call__(self, sample):
image, depth = sample['image'], sample['depth']
image = self.to_tensor(image)
image = self.normalize(image)
depth = self.to_tensor(depth)
image = self.resize(image)
return {'image': image, 'depth': depth, 'dataset': "hypersim"}
def to_tensor(self, pic):
if isinstance(pic, np.ndarray):
img = torch.from_numpy(pic.transpose((2, 0, 1)))
return img
# # handle PIL Image
if pic.mode == 'I':
img = torch.from_numpy(np.array(pic, np.int32, copy=False))
elif pic.mode == 'I;16':
img = torch.from_numpy(np.array(pic, np.int16, copy=False))
else:
img = torch.ByteTensor(
torch.ByteStorage.from_buffer(pic.tobytes()))
# PIL image mode: 1, L, P, I, F, RGB, YCbCr, RGBA, CMYK
if pic.mode == 'YCbCr':
nchannel = 3
elif pic.mode == 'I;16':
nchannel = 1
else:
nchannel = len(pic.mode)
img = img.view(pic.size[1], pic.size[0], nchannel)
img = img.transpose(0, 1).transpose(0, 2).contiguous()
if isinstance(img, torch.ByteTensor):
return img.float()
else:
return img
class HyperSim(Dataset):
def __init__(self, data_dir_root):
# image paths are of the form <data_dir_root>/<scene>/images/scene_cam_#_final_preview/*.tonemap.jpg
# depth paths are of the form <data_dir_root>/<scene>/images/scene_cam_#_final_preview/*.depth_meters.hdf5
self.image_files = glob.glob(os.path.join(
data_dir_root, '*', 'images', 'scene_cam_*_final_preview', '*.tonemap.jpg'))
self.depth_files = [r.replace("_final_preview", "_geometry_hdf5").replace(
".tonemap.jpg", ".depth_meters.hdf5") for r in self.image_files]
self.transform = ToTensor()
def __getitem__(self, idx):
image_path = self.image_files[idx]
depth_path = self.depth_files[idx]
image = np.asarray(Image.open(image_path), dtype=np.float32) / 255.0
# depth from hdf5
depth_fd = h5py.File(depth_path, "r")
# in meters (Euclidean distance)
distance_meters = np.array(depth_fd['dataset'])
depth = hypersim_distance_to_depth(
distance_meters) # in meters (planar depth)
# depth[depth > 8] = -1
depth = depth[..., None]
sample = dict(image=image, depth=depth)
sample = self.transform(sample)
if idx == 0:
print(sample["image"].shape)
return sample
def __len__(self):
return len(self.image_files)
def get_hypersim_loader(data_dir_root, batch_size=1, **kwargs):
dataset = HyperSim(data_dir_root)
return DataLoader(dataset, batch_size, **kwargs)
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