BoxCars
- ## BoxCars116k dataset The dataset was created for the paper and it is possible to download it from our [website](https://medusa.fit.vutbr.cz/traffic/data/BoxCars116k.zip) The dataset contains 116k of images of vehicles with fine-grained labels taken from surveillance cameras under various viewpoints. See the paper [**BoxCars: Improving Vehicle Fine-Grained Recognition using 3D Bounding Boxes in Traffic Surveillance**](https://doi.org/10.1109/TITS.2018.2799228) for more statistics and information about dataset acquisition. The dataset contains tracked vehicles with the same label and multiple images per track. The track is uniquely identified by its id `vehicle_id`, while each image is uniquely identified by `vehicle_id` and `instance_id`. It is possible to use class `BoxCarsDataset` from `lib/boxcars_dataset.py` for working with the dataset; however, for convenience, we describe the structure of the dataset also here. The dataset contains several files and folders: * **images** - dataset images and masks * **atlas.pkl** - *BIG* structure with jpeg encoded images, which can be convenient as the whole structure fits the memory and it is possible to get the images on the fly. To load the atlas (or any other pkl file), you can use function `load_cache` from `lib/utils.py`. To decode the image (in RGB channel order), use the following statement. ```python atlas = load_cache(path_to_atlas_file) image = cv2.cvtColor(cv2.imdecode(atlas[vehicle_id][instance_id], 1), cv2.COLOR_BGR2RGB) ```