mirror of
https://github.com/huchenlei/Depth-Anything.git
synced 2026-02-05 11:59:55 +00:00
82 lines
3.2 KiB
Python
82 lines
3.2 KiB
Python
# MIT License
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# Copyright (c) 2022 Intelligent Systems Lab Org
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# Permission is hereby granted, free of charge, to any person obtaining a copy
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# of this software and associated documentation files (the "Software"), to deal
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# in the Software without restriction, including without limitation the rights
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# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
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# copies of the Software, and to permit persons to whom the Software is
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# furnished to do so, subject to the following conditions:
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# The above copyright notice and this permission notice shall be included in all
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# copies or substantial portions of the Software.
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# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
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# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
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# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
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# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
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# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
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# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
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# SOFTWARE.
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# File author: Shariq Farooq Bhat
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import os
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import numpy as np
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import torch
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from PIL import Image
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from torch.utils.data import DataLoader, Dataset
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from torchvision import transforms as T
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class iBims(Dataset):
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def __init__(self, config):
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root_folder = config.ibims_root
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with open(os.path.join(root_folder, "imagelist.txt"), 'r') as f:
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imglist = f.read().split()
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samples = []
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for basename in imglist:
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img_path = os.path.join(root_folder, 'rgb', basename + ".png")
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depth_path = os.path.join(root_folder, 'depth', basename + ".png")
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valid_mask_path = os.path.join(
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root_folder, 'mask_invalid', basename+".png")
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transp_mask_path = os.path.join(
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root_folder, 'mask_transp', basename+".png")
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samples.append(
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(img_path, depth_path, valid_mask_path, transp_mask_path))
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self.samples = samples
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# self.normalize = T.Normalize(
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# mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
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self.normalize = lambda x : x
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def __getitem__(self, idx):
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img_path, depth_path, valid_mask_path, transp_mask_path = self.samples[idx]
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img = np.asarray(Image.open(img_path), dtype=np.float32) / 255.0
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depth = np.asarray(Image.open(depth_path),
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dtype=np.uint16).astype('float')*50.0/65535
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mask_valid = np.asarray(Image.open(valid_mask_path))
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mask_transp = np.asarray(Image.open(transp_mask_path))
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# depth = depth * mask_valid * mask_transp
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depth = np.where(mask_valid * mask_transp, depth, -1)
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img = torch.from_numpy(img).permute(2, 0, 1)
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img = self.normalize(img)
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depth = torch.from_numpy(depth).unsqueeze(0)
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return dict(image=img, depth=depth, image_path=img_path, depth_path=depth_path, dataset='ibims')
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def __len__(self):
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return len(self.samples)
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def get_ibims_loader(config, batch_size=1, **kwargs):
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dataloader = DataLoader(iBims(config), batch_size=batch_size, **kwargs)
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return dataloader
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