mirror of
https://github.com/huchenlei/Depth-Anything.git
synced 2026-02-04 11:29:57 +00:00
126 lines
4.2 KiB
Python
126 lines
4.2 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
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class ToTensor(object):
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def __init__(self):
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# self.normalize = transforms.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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self.resize = transforms.Resize(480)
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def __call__(self, sample):
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image, depth = sample['image'], sample['depth']
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image = self.to_tensor(image)
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image = self.normalize(image)
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depth = self.to_tensor(depth)
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image = self.resize(image)
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return {'image': image, 'depth': depth, 'dataset': "diode"}
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def to_tensor(self, pic):
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if isinstance(pic, np.ndarray):
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img = torch.from_numpy(pic.transpose((2, 0, 1)))
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return img
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# # handle PIL Image
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if pic.mode == 'I':
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img = torch.from_numpy(np.array(pic, np.int32, copy=False))
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elif pic.mode == 'I;16':
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img = torch.from_numpy(np.array(pic, np.int16, copy=False))
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else:
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img = torch.ByteTensor(
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torch.ByteStorage.from_buffer(pic.tobytes()))
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# PIL image mode: 1, L, P, I, F, RGB, YCbCr, RGBA, CMYK
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if pic.mode == 'YCbCr':
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nchannel = 3
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elif pic.mode == 'I;16':
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nchannel = 1
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else:
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nchannel = len(pic.mode)
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img = img.view(pic.size[1], pic.size[0], nchannel)
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img = img.transpose(0, 1).transpose(0, 2).contiguous()
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if isinstance(img, torch.ByteTensor):
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return img.float()
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else:
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return img
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class DIODE(Dataset):
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def __init__(self, data_dir_root):
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import glob
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# image paths are of the form <data_dir_root>/scene_#/scan_#/*.png
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self.image_files = glob.glob(
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os.path.join(data_dir_root, '*', '*', '*.png'))
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self.depth_files = [r.replace(".png", "_depth.npy")
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for r in self.image_files]
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self.depth_mask_files = [
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r.replace(".png", "_depth_mask.npy") for r in self.image_files]
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self.transform = ToTensor()
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def __getitem__(self, idx):
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image_path = self.image_files[idx]
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depth_path = self.depth_files[idx]
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depth_mask_path = self.depth_mask_files[idx]
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image = np.asarray(Image.open(image_path), dtype=np.float32) / 255.0
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depth = np.load(depth_path) # in meters
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valid = np.load(depth_mask_path) # binary
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# depth[depth > 8] = -1
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# depth = depth[..., None]
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sample = dict(image=image, depth=depth, valid=valid)
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# return sample
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sample = self.transform(sample)
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if idx == 0:
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print(sample["image"].shape)
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return sample
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def __len__(self):
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return len(self.image_files)
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def get_diode_loader(data_dir_root, batch_size=1, **kwargs):
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dataset = DIODE(data_dir_root)
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return DataLoader(dataset, batch_size, **kwargs)
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# get_diode_loader(data_dir_root="datasets/diode/val/outdoor")
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