CanSat Landing Zones

A Python script to analyze aerial pictures using computer vision and match the outputs to GPS coordinates. Used for CanSat in Greece 2023 | Team SALT.

Author

Byron Trikkalidis

Contact: vtrikkalidis@gmail.com

Artificial Intelligence

• Artificial Intelligence: the development of computer systems that can perform tasks that would normally require human intelligence

• Machine Learning: a type of Artificial Intelligence that allows computer systems to automatically learn and improve from experience without being explicitly programmed

• Deep Learning: a subset of Machine Learning that uses artificial Neural Networks with multiple layers to model and solve complex problems

• Neural Networks: a set of algorithms modeled after the structure and function of the human brain

• Computer Vision: a field of Artificial Intelligence that focuses on enabling computers to interpret and understand visual information from the world around them, including images and videos

Installation

Use the package manager pip to install the Roboflow package (Documentation).

pip install roboflow

Usage

Input

The images folder should contain:

• the info.json file containing the required information for every picture captured (name, center & bottom-right corner coordinates, image dimensions in px). This file is of the following format:

[
    {
        "name": "image1",
        "location": [[38.055133, 23.320294], [38.052865, 23.325891]],
        "height": 682,
        "width": 1344
    }
]

• the pictures captured with a .jpg extension.

Tip: Use pictures with low resolution. For example, 500 KB.

Output

The data folder will contain:

• the data.csv file with the GPS coordinates. This file is of the following format:

latitude,longitude
38.055183,23.31787
38.055326,23.317708
38.05504,23.318032
38.053507,23.320615

• the new pictures with the predictions, with a .jpg extension.

Tip: For faster results comment line 23:

# result.save(f'data\{image["name"]}.jpg')

This line saves the new picture with the predictions.

Results

The Computer Vision model achieved high accuracy, precision, low error rates, and consistent results. View here.

License

MIT License

Copyright (c) 2023 Byron Trikkalidis

Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.