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on
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Running
on
Zero
File size: 1,807 Bytes
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import cv2
import torch
from torch.utils.data import Dataset
from torchvision.transforms import Compose
from dataset.transform import Resize, NormalizeImage, PrepareForNet, Crop
class VKITTI2(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 = cv2.imread(depth_path, cv2.IMREAD_ANYCOLOR | cv2.IMREAD_ANYDEPTH) / 100.0 # cm to m
sample = self.transform({'image': image, 'depth': depth})
sample['image'] = torch.from_numpy(sample['image'])
sample['depth'] = torch.from_numpy(sample['depth'])
sample['valid_mask'] = (sample['depth'] <= 80)
sample['image_path'] = self.filelist[item].split(' ')[0]
return sample
def __len__(self):
return len(self.filelist) |