File size: 3,229 Bytes
18dd6ad |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 |
# 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 os
import numpy as np
import torch
from PIL import Image
from torch.utils.data import DataLoader, Dataset
from torchvision import transforms as T
class iBims(Dataset):
def __init__(self, config):
root_folder = config.ibims_root
with open(os.path.join(root_folder, "imagelist.txt"), 'r') as f:
imglist = f.read().split()
samples = []
for basename in imglist:
img_path = os.path.join(root_folder, 'rgb', basename + ".png")
depth_path = os.path.join(root_folder, 'depth', basename + ".png")
valid_mask_path = os.path.join(
root_folder, 'mask_invalid', basename+".png")
transp_mask_path = os.path.join(
root_folder, 'mask_transp', basename+".png")
samples.append(
(img_path, depth_path, valid_mask_path, transp_mask_path))
self.samples = samples
# self.normalize = T.Normalize(
# mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
self.normalize = lambda x : x
def __getitem__(self, idx):
img_path, depth_path, valid_mask_path, transp_mask_path = self.samples[idx]
img = np.asarray(Image.open(img_path), dtype=np.float32) / 255.0
depth = np.asarray(Image.open(depth_path),
dtype=np.uint16).astype('float')*50.0/65535
mask_valid = np.asarray(Image.open(valid_mask_path))
mask_transp = np.asarray(Image.open(transp_mask_path))
# depth = depth * mask_valid * mask_transp
depth = np.where(mask_valid * mask_transp, depth, -1)
img = torch.from_numpy(img).permute(2, 0, 1)
img = self.normalize(img)
depth = torch.from_numpy(depth).unsqueeze(0)
return dict(image=img, depth=depth, image_path=img_path, depth_path=depth_path, dataset='ibims')
def __len__(self):
return len(self.samples)
def get_ibims_loader(config, batch_size=1, **kwargs):
dataloader = DataLoader(iBims(config), batch_size=batch_size, **kwargs)
return dataloader
|