Metadata-Version: 2.1
Name: jakarto_datasets
Version: 0.1.0
Summary: Jakarto datasets containing realworld 3d data from lidar sensors.
Home-page: https://github.com/jakarto3d/jakarto_datasets
Author: Loïc Messal
Author-email: loic.messal@jakarto.com
License: MIT License
Description: # Jakarto datasets for 3d detection challenge of urban assets
        
        [![Build Status](https://travis-ci.org/jakarto3d/jakarto_datasets.svg?branch=master)](https://travis-ci.org/jakarto3d/jakarto_datasets) [![Code coverage](https://codecov.io/gh/jakarto3d/jakarto_datasets/branch/master/graph/badge.svg)](https://codecov.io/gh/jakarto3d/jakarto_datasets) [![License](https://img.shields.io/github/license/jakarto3d/jakarto_datasets)](https://github.com/jakarto3d/jakarto_datasets/blob/master/LICENSE.txt) 
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        We built that python API to share some real-world 3d lidar datasets of urban assets. We hope to help some of you to develop and test algorithms about 3d lidar processing.
        
        
        ![jakarto car detection](https://raw.githubusercontent.com/jakarto3d/jakarto_datasets/master/doc/images/jakarto_car_detection.png)
        
        Those datasets have been gathered with the [Jakarto](https://www.jakarto.com) truck.
        ![jakarto truck](https://raw.githubusercontent.com/jakarto3d/jakarto_datasets/master/doc/images/camion_jakarto.jpg)
        
        
        ## Installation
        This API requires `python 3.6+`.
        
        ```sh
        pip install jakarto-datasets
        ```
        
        ## Usage
        ```python
        from jakarto_datasets.datasets.storm_drains import StormDrainsDataset
        
        # Load dataset
        storm_drains_2019 = StormDrainsDataset()
        
        for data in storm_drains_2019.training_set:
            coordinates = data.get_coordinates_data()
            lidar_data = data.get_lidar_data()
            
            label = data.get_label_lidar_data()
            
            print(data)
            print(coordinates.shape)
            print(lidar_data.shape)
            print(label.shape)
            
            print(lidar_data['intensity'])
        ```
        
        
        ## Datasets
        
        | datasets | year | 3d lidar | label | raster | mask | `len(training_set)` | `len(testing_set)` | examples | description |
        | --- | --- | --- | --- | --- | --- | --- | --- | ---| --- |
        | storm drains | 2019 | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark:  | :heavy_check_mark: | 223 | 150 | [see examples](examples/storm_drains/README.md) | [see details](examples/storm_drains/README.md) |
        
        
        ## Benchmarks
        
        We will be more than happy to share your experiments.
        
        | datasets | title | authors | links | description |
        | -- | -- | --  | -- | -- |
        | storm drains 2019 | balanced random forest | Jakarto team  | [link](examples/storm_drains/machine_learning/storm_drains_machine_learning.py) | Use a deadly simple balanced random forest to classify each point from lidar data. Although it doesn't use spatial information, it allowed Jakarto to detect ~25% of storm drains. Those storm drains will be added to the Jakarto storm drains 2020 dataset. |
        |...                | ...                    | ...           | ...                                                                     |             |
        
        
        ## Citation
        
        If you find this work useful and wish to refer to, please consider the following BibTeX entry:
        
            @MISC{jakarto_datasets,
                author = {Loic Messal and Cedric Pelletier and {Jakarto Cartographie 3d team}},
                title = {Jakarto datasets},
                year = {2019},
                howpublished={\url{https://github.com/jakarto3d/jakarto_datasets}}
            }
        
        A github star may also help.
        
        ## Contact
        If you want to email us, please send an email to contact@jakarto.com.
        
        ## License
        This project is licensed under the terms of the MIT license. (see LICENSE.txt file for details).
        
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Requires-Python: >=3.6
Description-Content-Type: text/markdown
