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README.md

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# PlaNet
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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).
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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.
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## Dataset
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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,
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- 501 glass
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- 594 paper
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- 403 cardboard
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- 482 plastic
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- 410 metal
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- 137 trash
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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.
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## Usage: Preparing the data
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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.
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### Setup
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Python is currently used for some image preprocessing tasks. The Python dependencies are,
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- [NumPy](http://numpy.org)
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- [SciPy](http://scipy.org)
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You can install these packages by running the following,
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```bash
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# Install using pip
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pip install numpy scipy
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```
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## Contributing
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1. Fork the repository
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2. Create your feature branch using `git checkout -b my-new-feature`
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3. Commit your changes using `git commit -m 'Add some feature'`
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4. Push to the branch using `git push origin my-new-feature`
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5. Submit a pull request
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## Acknowledgments
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- Stanford CS 229 (2016-2017)
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- [TrashNet: Dataset of images of trash; Torch-based CNN for garbage image classification](https://github.com/garythung/trashnet)
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- [AquaTrash: A dataset of Trash Images for the proper waste management and protection of Aquatic Life](https://github.com/Harsh9524/AquaTrash)
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- [TACO: Trash Annotations in Context Dataset Toolkit](https://github.com/pedropro/TACO)
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- [Garbage Classification - Kaggle](kaggle.com/asdasdasasdas/garbage-classification)

dataset/constants.py

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GLASS = 0
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PAPER = 1
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CARDBOARD = 2
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PLASTIC = 3
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METAL = 4
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TRASH = 5
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DIM1 = 384
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DIM2 = 512

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