Metadata-Version: 2.4
Name: kapture
Version: 1.1.12
Summary: Kapture is a pivot file format and Python toolkit for SfM and sensor-acquired data.
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License-File: LICENSE
License-File: NOTICE
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Requires-Dist: numpy-quaternion>=2019.3.18.14.33.20
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Requires-Dist: Pillow>=8.1.1
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Requires-Dist: build>=1.0.3
Requires-Dist: pytest
Provides-Extra: zed
Requires-Dist: pyzed; extra == "zed"
Dynamic: license-file

<figure>
<img src="assets/kapture_banner.png" alt="KAPTURE" />
</figure>

<figure>
<img
src="https://github.com/naver/kapture/workflows/kapture-main/badge.svg"
alt="Continuous Integration Status" />
</figure>

# Overview

Kapture is a pivot file format, based on text and binary files, used to
describe SfM (Structure From Motion) and more generally sensor-acquired
data.

It can be used to store sensor parameters and raw sensor data:

- cameras

- images

- lidar and other sensor data

As well as computed data:

- 2d features

- 3d reconstruction

Finally, many popular datasets can directly be downloaded using the
convenient
[downloader](https://github.com/naver/kapture/blob/main/doc/tutorial.adoc#download-a-dataset)!

# Specifications

The format specification is detailed in the [kapture format
specifications document](kapture_format.adoc).

# Example File Structure

This is an example file structure of a dataset in the kapture format.

``` txt
my_dataset                 # Dataset root path
├─ sensors/                # Sensor data root path
│  ├─ sensors.txt          # list of all sensors with their specifications (e.g. camera intrinsics)
│  ├─ rigs.txt             # geometric relationship between sensors (optional)
│  ├─ trajectories.txt     # extrinsics (timestamp, sensor, pose)
│  ├─ records_camera.txt   # all records of type 'camera' (timestamp, sensor and path to image)
│  ├─ records_SENSOR_TYPE.txt # all records of type SENSOR_TYPE (other sensors, eg: 'magnetic', 'pressure'...)
│  └─ records_data/            # image and lidar data path
│     ├─ map/cam_01/00001.jpg  # image path used in records_camera.txt (example)
│     ├─ map/cam_01/00002.jpg
│     ├─ map/lidar_01/0001.pcd # lidar data path used in records_lidar.txt
│     ├─ query/query001.jpg    # image path used in records_camera.txt
│     ├─ ...
└─ reconstruction/
   ├─ keypoints/                       # 2D keypoints files
   │  ├─ r2d2_WASF-N8_20k              # identify the type of keypoints
   │  │  ├─ keypoints.txt              # type of keypoint (shape and dtype)
   │  │  ├─ map/cam_01/00001.jpg.kpt   # keypoints for corresponding image (example)
   │  │  ├─ query/query001.jpg.kpt     # keypoints for corresponding image (example)
   │  │  ├─ ...
   │  ├─ d2_tf                         # identify the type of keypoints
   │  │  ├─ keypoints.txt              # type of keypoint (shape and dtype)
   │  │  ├─ keypoints.tar              # instead of regular files, you can use an archive
   │  │  │  ├─ map/cam_01/00001.jpg.kpt   # keypoints for corresponding image (example)
   │  │  │  ├─ query/query001.jpg.kpt     # keypoints for corresponding image (example)
   │  │  │  ├─ ...
   │  ├─ ...
   ├─ descriptors/                     # keypoint descriptors files
   │  ├─ r2d2_WASF-N8_20k              # identify the type of descriptors
   │  │  ├─ descriptors.txt            # type of descriptor (keypoints type, shape and dtype)
   │  │  ├─ map/cam_01/00001.jpg.desc  # descriptors for corresponding image (example)
   │  │  ├─ query/query001.jpg.desc    # descriptors for corresponding image (example)
   │  │  ├─ ...
   │  ├─ d2_tf                         # identify the type of descriptors
   │  │  ├─ descriptors.txt            # type of descriptor
   │  │  ├─ descriptors.tar            # instead of regular files, you can use an archive
   │  │  │  ├─ map/cam_01/00001.jpg.desc  # descriptors for corresponding image (example)
   │  │  │  ├─ query/query001.jpg.desc    # descriptors for corresponding image (example)
   │  │  │  ├─ ...
   │  ├─ ...
   ├─ points3d.txt                  # 3D points of the reconstruction
   ├─ observations.txt              # 2D/3D points corespondences
   ├─ matches/                      # matches files.
   │  ├─ r2d2_WASF-N8_20k           # identify the type of keypoints that are matched
   │  │  ├─ map/cam_01/00001.jpg.overlapping/cam_01/00002.jpg.matches # example
   │  │  ├─  ...
   │  ├─ d2_tf                      # identify the type of keypoints that are matched
   │  │  ├─ matches.tar             # instead of regular files, you can use an archive
   │  │  │  ├─ map/cam_01/00001.jpg.overlapping/cam_01/00002.jpg.matches # example
   │  │  │  ├─  ...
   │  ├─ ...
   └─ global_features/                 # global feature files
      ├─ AP-GeM-LM18                   # identify the type of global_features
      │  ├─ global_features.txt        # type of global feature
      │  ├─ map/cam_01/00001.jpg.gfeat # example of global feature for corresponding image
      │  ├─ query/query001.jpg.gfeat   # example of global feature for corresponding image
      │  └─ ...
      ├─ DELG                          # identify the type of global_features
      │  ├─ global_features.txt        # type of global feature
      │  ├─ global_features.tar        # instead of regular files, you can use an archive
      │  │  ├─ map/cam_01/00001.jpg.gfeat # example of global feature for corresponding image
      │  │  ├─ query/query001.jpg.gfeat   # example of global feature for corresponding image
      │  │  └─ ...
      ├─ ...
```

# Software

The kapture format is provided with a Python library, as well as several
conversion tools.

## Install

``` bash
pip install kapture
```

or see [installation](doc/installation.adoc) for more detailed
instructions.

## Using docker

Build the docker image:

``` bash
$> cd kapture
# build the docker image : if you have already cloned the repository
$> make docker
# OR build the docker image directly from github (no need to clone).
$> docker build git://github.com/naver/kapture -t kapture/kapture
# run unit tests
$> docker run -it --rm kapture/kapture python3 -m unittest discover -s /opt/src/kapture/tests
```

If you want to process your own data, you can bind directories between
the host and the container using `--volume` or `--mount` option (See the
[docker documentation](https://docs.docker.com/storage/bind-mounts/)).
The following example mounts `/path/to/dataset/` from the host to
`/dataset` inside the docker.

``` bash
$> docker run -it \
    --rm \ # Automatically remove the container when it exits \
    --volume /path/to/dataset/:/dataset:ro \ #read only
    kapture/kapture
```

## kapture Python library

See the
[tutorial](https://github.com/naver/kapture/blob/main/doc/tutorial.adoc#using-kapture-in-your-code)
for some examples using the kapture Python library.

## kapture tools

In this repository, you will find a set of **conversion tools** to or
from kapture format. Import results to kapture format, and conversely,
export converts kapture data to other formats. Depending of the format,
some data might not be converted, either because the other format does
not support it (`—`) or because its was not implemented (`⨉`). Here is a
table summarizing the conversion capabilities:

| Format                    | ← →    | cam | rig | img | trj | gps | kpt | dsc | gft | p3D | obs | mch |
|---------------------------|--------|-----|-----|-----|-----|-----|-----|-----|-----|-----|-----|-----|
| colmap                    | import | ✓   | ✓   | ✓   | ✓   | ⨉   | ✓   | ✓   | —   | ✓   | ✓   | (✓) |
| export                    | ✓      | ✓   | ✓   | ✓   | ⨉   | ✓   | ✓   | —   | ✓   | ✓   | (✓) |     |
| openmvg                   | import | ✓   | —   | ✓   | ✓   | ⨉   | ✓   | ✓   | —   | ✓   | ✓   | (✓) |
| export                    | ✓      | —   | ✓   | ✓   | ⨉   | (✓) | (✓) | —   | ✓   | ✓   | (✓) |     |
| OpenSfM                   | import | ✓   | ⨉   | ✓   | ✓   | ✓   | ✓   | ✓   | —   | ✓   | ⨉   | ✓   |
| export                    | ✓      | ⨉   | ✓   | ✓   | ⨉   | ✓   | —   | ✓   | —   | ⨉   | ✓   |     |
| bundler                   | import | ✓   | —   | ✓   | ✓   | —   | ✓   | —   | —   | ✓   | ✓   | —   |
| image_folder              | import | —   | —   | ✓   | —   | —   | —   | —   | —   | —   | —   | —   |
| image_list                | import | ✓   | —   | ✓   | —   | —   | —   | —   | —   | —   | —   | —   |
| nvm                       | import | ✓   | —   | ✓   | ✓   | —   | ✓   | —   | —   | ✓   | ✓   | —   |
| IDL_dataset_cvpr17        | import | ✓   | —   | ✓   | ✓   | —   | —   | —   | —   | —   | —   | —   |
| RobotCar_Seasons          | import | ✓   | ✓   | ✓   | ✓   | —   | ✓   | —   | —   | ✓   | ✓   | —   |
| ROSbag cameras+trajectory | import | (✓) | (✓) | ✓   | ✓   | ⨉   | —   | —   | —   | —   | —   | —   |
| SILDa                     | import | ✓   | ✓   | ✓   | ✓   | —   | —   | —   | —   | —   | —   | —   |
| virtual_gallery           | import | ✓   | ✓   | ✓   | ✓   | —   | —   | —   | —   | —   | —   | —   |
| stereolabs zed2           | import | ✓   | ✓   | ✓   | ✓   | —   | —   | —   | —   | —   | —   | —   |

conversion capabilities

- `✓`: supported, `(✓)` partially supported, `⨉`: not implemented, `—`:
  not supported by format.

- `cam`: handle camera parameters, eg. intrisics

- `rig`: handle rig structure.

- `img`: handle the path to images.

- `trj`: handle trajectories, eg. poses.

- `kpt`: handle image keypoints locations.

- `dsc`: handle image keypoints descriptors.

- `gft`: handle global image feature descriptors.

- `p3D`: handle 3D point clouds.

- `obs`: handle observations, ie. 3D-points / 2D keypoints
  correspondences.

- `mch`: handle keypoints matches.

[Here](https://gist.github.com/jo-chemla/258e6e40d3d6c2220b29518ff3c17c40),
you can also find an utility tool for cropping input images of a kapture
dataset. Thanks Jonathan Chemla for the contribution.

# kapture support in other packages

## Local Features

- [R2D2](https://arxiv.org/abs/1906.06195) local features can be
  directly generated in kapture format. See
  [here](https://github.com/naver/r2d2#feature-extraction-with-kapture-datasets)

- [D2-Net](https://arxiv.org/abs/1905.03561) features can also be
  extracted in kapture format. See instructions
  [here](https://github.com/mihaidusmanu/d2-net#feature-extraction-with-kapture-datasets).

## Global Features

- [AP-GeM](https://europe.naverlabs.com/research/computer-vision-research-naver-labs-europe/learning-visual-representations/deep-image-retrieval/)
  global feature extractor in kapture format:
  [here](https://github.com/naver/deep-image-retrieval#feature-extraction-with-kapture-datasets)

# Datasets

The kapture package provides conversion tools for several data formats
and datasets used in the domain. But it also provides a tool to download
datasets already converted to kapture. See the [kapture
tutorial](doc/tutorial.adoc) for instructions to use the dataset
downloader.

Here is a list of datasets you can directly download in kapture format
with the downloader tool:

- Datasets for the [Long Term Visual
  Localization](https://www.visuallocalization.net/datasets/) benchmark:

  - [NAVER LABS localization
    datasets](https://europe.naverlabs.com/blog/first-of-a-kind-large-scale-localization-datasets-in-crowded-indoor-spaces/)

  - Aachen Day Night v1.1

  - Extended CMU-Seasons

  - RobotCar Seasons v2

  - InLoc (without images)

  - SILDa Weather and Time of Day

  - RIO10

  - ETH-Microsoft

- [Virtual
  Gallery](https://europe.naverlabs.com/research/3d-vision/virtual-gallery-dataset/)
  dataset

# kapture-localization

Checkout
[kapture-localization](https://github.com/naver/kapture-localization),
our toolbox which contains implementations for various localization
related algorithms.

- mapping and localization pipelines with custom features

- mapping and localization pipelines with SIFT and vocabulary tree
  matching (default colmap pipeline)

- image retrieval benchmark (global sfm, local sfm, pose approximation)

# Tutorial

See the [kapture tutorial](doc/tutorial.adoc) for a short introduction
to:

- conversion tools

- using kapture in your code

- dataset download

# Contributing

There are many ways to contribute to the kapture project:

- provide feedback and suggestions of improvements

- submit bug reports in the project bug tracker

- provide a dataset in kapture format that we can add to the downloader
  tool

- implement a feature or bug-fix for an outstanding issue

- add support of kapture format in other software packages (e.g. SfM
  pipelines…​), thus adding support for more datasets

- provide scripts to create data in kapture format (e.g. local/global
  feature extraction)

- propose a new feature and implement it

If you wish to contribute, please refer to the
[CONTRIBUTING](CONTRIBUTING.adoc) page.

# License

Software license is detailed in the [LICENSE](LICENSE) file.

# Contact Us

You can contact us through [GitHub](https://github.com/naver/kapture),
or at kapture at naverlabs + com
