Tools and Dataloader for KITTI depth prediction and KITTI raw data.
This repo provides two useful modules for loading and preprocessing KITTI depth data set.
🔖 Update Support for Depth Data under "KITTI Depth Prediction" Section
Since KITTI provides official depth prediction data, we can directly use these high quality depth map as ground truth, which are more dense than depth gt that we generated from raw KITTI Velodyne data.
The following parts give an introduction of raw KITTI data loading and Pytorch dataset generating.
Tip: to load official KITTI depth data, please use the corresponding module in '/KITTIDepth'.
The module Dataloader provides an interface to load data items from eigen split.
Here is an example to use it:
from Dataloader import Kittiloader
...
loader = Kittiloader(<kitti_root_path>, <'train', 'val' or 'test'>)
data_size = loader.data_length() # get data split size
data_item = loader.load_item(idx) # which is very suitable for pytorch dataloaderThese methods helps user to define a pytorch Dataset in a more convenient way.
The custom_xxx.py files can be modified by users to define their own transformers in practice.
Just follow the base transformer class, one can construct a variety of of pytorch DataLoaders quickly.
An example is included in this module, which works well with dataset.py, which executes standard and the most straightforward pytorch DataLoader generation steps.
To use the given data loader, try the following code:
from dataset import DataGenerator
...
# transformer will be defined automatically according to phase once datagen instance is created
datagen = DataGenerator(<kitti_root_path>, phase=<'train', 'val' or 'test'>)
kittidataset = datagen.create_data(batch_size)
# other code before training loop
...
for epoch in range(num_epoches):
# training loop for an epoch
for id, batch in enumerate(kittidataset):
# various types of data can be acquired here
left_img_batch = batch['left_img'] # batch of left image, id 02
right_img_batch = batch['right_img'] # batch of right image, id 03
depth_batch = batch['depth'] # the corresponding depth ground truth of given id
depth_interp_batch = batch['depth_interp'] # dense depth for visualization
fb = batch['fb'] # focal_length * baseline
# training code
...