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LuxonisLoader

Overview

LuxonisLoader offers a simple and efficient way to load and iterate through data stored in the Luxonis Data Format (LDF), with support for on-the-fly data augmentation. Since it is also natively integrated with LuxonisTrain, this enables a smooth and seamless training workflow.

Dataset Loading

To load a dataset with LuxonisLoader, we first need an instance of LuxonisDataset. The loader is initialized with the dataset and the dataset view (i.e. the split) we intend to load.
Python
1from luxonisml.data import LuxonisLoader, LuxonisDataset
2
3dataset_name: str = ... # name of an existing LDF dataset, _e.g._ "parking_lot"
4dataset = LuxonisDataset(dataset_name)
5loader = LuxonisLoader(dataset, view="train")
The data can be iterated over using a simple for loop:
Python
1for image, annotations in loader:
2    ...

Augmentation

Augmentations are transformations applied to the data to increase the diversity of the dataset, thus improving the model training. We can define them by passing a list of Python dictionaries to the augmentation_config parameter of the LuxonisLoader constructor, each representing an individual augmentation:
Python
1{
2    "name": str,  # name of the augmentation
3    "params": dict  # parameters of the augmentation
4}
By default, we support most augmentations from the albumentations library. You can find the full list of augmentations and their parameters in the Albumentations documentation. On top of that, we provide a handful of custom batch augmentations:
  • Mosaic4 - Mosaic augmentation with 4 images. Combines crops of 4 images into a single image in a mosaic pattern.
  • MixUp - MixUp augmentation. Overlays two images with a random weight.

Example

The following example demonstrates a simple augmentation pipeline:
Python
1[
2  {
3    'name': 'HueSaturationValue',
4    'params': {
5      'p': 0.5,
6      'hue_shift_limit': 3,
7      'sat_shift_limit': 70,
8      'val_shift_limit': 40,
9    }
10  },
11  {
12    'name': 'Rotate',
13    'params': {
14      'p': 0.6,
15      'limit': 30,
16      'border_mode': 0,
17      'value': [0, 0, 0]
18    }
19  },
20  {
21    'name': 'Perspective',
22    'params': {
23      'p': 0.5,
24      'scale': [0.04, 0.08],
25      'keep_size': True,
26      'pad_mode': 0,
27      'pad_val': 0,
28      'mask_pad_val': 0,
29      'fit_output': False,
30      'interpolation': 1,
31      'always_apply': False,
32    }
33  },
34  {
35    'name': 'Affine',
36    'params': {
37      'p': 0.4,
38      'scale': None,
39      'translate_percent': None,
40      'translate_px': None,
41      'rotate': None,
42      'shear': 10,
43      'interpolation': 1,
44      'mask_interpolation': 0,
45      'cval': 0,
46      'cval_mask': 0,
47      'mode': 0,
48      'fit_output': False,
49      'keep_ratio': False,
50      'rotate_method': 'largest_box',
51      'always_apply': False,
52    }
53  },
54]
Let's assume the augmentation list above is stored as a YAML file named augmentations.yaml. We can then use it to create a loader:
Python
1from luxonis_ml.data import LuxonisDataset, LuxonisLoader, AlbumentationsEngine
2import json
3
4dataset_name: str = ...
5dataset = LuxonisDataset(dataset_name)
6loader = LuxonisLoader(
7    dataset,
8    view="train", 
9    augmentation_config="augmentations.yaml", 
10    augmentation_engine="albumentations",  # default
11    height=256,
12    width=320,
13    keep_aspect_ratio=True, # default
14    color_space="RGB",  # default, can be also BGR
15)
16for img, labels in loader:
17    ...