|
| 1 | +# PlaNet |
| 2 | + |
| 3 | +The PlaNet dataset is being used to detect floating and terra firma waste debris in oceans/ports/harbors/beaches, urban and rural areas allowing the eradication of waste, helping marine life, fishermen, tourism and making the world resilient to climate change by [Recyclero](https://recyclero.com). |
| 4 | + |
| 5 | +The dataset has been collected in a joint effort between the Recyclero and the Manipal University Jaipur. Students were able to contribute by sending their pictures of plastics, glass, paper, rubbish, metal and cardboard with our custom-built application. |
| 6 | + |
| 7 | +## Dataset |
| 8 | + |
| 9 | +This repository contains the dataset that we collected. The dataset spans six classes: glass, paper, cardboard, plastic, metal, and trash. Currently, the dataset consists of 2527 images, |
| 10 | + |
| 11 | +- 501 glass |
| 12 | +- 594 paper |
| 13 | +- 403 cardboard |
| 14 | +- 482 plastic |
| 15 | +- 410 metal |
| 16 | +- 137 trash |
| 17 | + |
| 18 | +The pictures were taken by placing the object on a white posterboard and using sunlight and/or room lighting. The pictures have been resized down to 512 x 384, which can be changed in `dataset/constants.py` (resizing them involves going through step 1 in usage). The devices used were Apple iPhone 7 Plus, Apple iPhone 5S, and Apple iPhone SE. |
| 19 | + |
| 20 | + |
| 21 | +## Usage: Preparing the data |
| 22 | + |
| 23 | +If adding more data, then the new files must be enumerated properly and put into the appropriate folder in `dataset/original` and then preprocessed. Preprocessing the data involves deleting the `dataset/resized` folder and then calling `python resize.py` from `PlaNet/dataset/*`. This will take around half an hour. |
| 24 | + |
| 25 | +### Setup |
| 26 | + |
| 27 | +Python is currently used for some image preprocessing tasks. The Python dependencies are, |
| 28 | + |
| 29 | +- [NumPy](http://numpy.org) |
| 30 | +- [SciPy](http://scipy.org) |
| 31 | + |
| 32 | +You can install these packages by running the following, |
| 33 | + |
| 34 | +```bash |
| 35 | +# Install using pip |
| 36 | +pip install numpy scipy |
| 37 | +``` |
| 38 | + |
| 39 | + |
| 40 | +## Contributing |
| 41 | + |
| 42 | +1. Fork the repository |
| 43 | +2. Create your feature branch using `git checkout -b my-new-feature` |
| 44 | +3. Commit your changes using `git commit -m 'Add some feature'` |
| 45 | +4. Push to the branch using `git push origin my-new-feature` |
| 46 | +5. Submit a pull request |
| 47 | + |
| 48 | + |
| 49 | +## Acknowledgments |
| 50 | + |
| 51 | +- Stanford CS 229 (2016-2017) |
| 52 | +- [TrashNet: Dataset of images of trash; Torch-based CNN for garbage image classification](https://github.com/garythung/trashnet) |
| 53 | +- [AquaTrash: A dataset of Trash Images for the proper waste management and protection of Aquatic Life](https://github.com/Harsh9524/AquaTrash) |
| 54 | +- [TACO: Trash Annotations in Context Dataset Toolkit](https://github.com/pedropro/TACO) |
| 55 | +- [Garbage Classification - Kaggle](kaggle.com/asdasdasasdas/garbage-classification) |
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