Visionner is a image dataset toolkit
Project description
visionner
Visionner is a image dataset toolkit
Purpose of the package
- The purpose of this package is to provide machine learning engineer a image dataset toolkit
Warning
Since Visionner still in alpha and under heavy development , expect to see many changes in the near futures.
Features
- Convert image folder into numpy array
- Import a dataset for unsupervised learning tasks
- Import a dataset for supervised learning tasks
- Normalize the dataset
- Split the trainset and the testset
- Save your dataset
- Open your dataset
Getting Started
The package can be found on pypi hence you can install it using pip
Installation
pip install visionner
Usage
### import your dataset (more suitable for dataset without labels)
>>> from visionner.Dataset.DatasetManager import DatasetImporter
>>> your_dataset=DatasetImporter("path/to/your/dataset/", size=(28, 28))
### import your supervised dataset (more suitable for dataset with labels)
>>> from visionner.Dataset.DatasetManager import SupervisedImporter
>>> features, labels= SupervisedImporter("path/to/your/dataset", categories=["cat", "dog"], size=(28,28))
### normalize your dataset
>>> from visionner.Dataset.DatasetManager import DatasetNormalizer
>>> your_normalized_dataset=DatasetNormalizer(your_dataset)
### create a trainset and a testset
>>> from visionner.Dataset.DatasetManager import TrainTestSpliter
>>> x_train, x_test=TrainTestSpliter(dataset, test_size=0.2)
### visualize the first image of your dataset
>>> import matplotlib.pyplot as plt
>>> plt.imshow(your_dataset[0])
>>> plt.show()
### save your dataset
>>> from visionner.Dataset.DatasetManager import DatasetSaver
>>> DatasetSaver("my_saved_dataset", your_dataset)
### open your dataset
>>> from visionner.Dataset.DatasetManager import DatasetOpener
>>> my_saved_dataset=DatasetOpener("my_saved_dataset.npy")
Contribution
Contribution are welcome. Notice a bug ? let us know. Thanks you
Author
- Main Maitainer : Charles TCHANAKE
- email : datadevfernolf@gmail.com
Note
If you get an unicode error like this :
SyntaxError: (unicode error) 'unicodeescape' codec can't decode bytes in position 2-3: truncated \UXXXXXXXX escape
add r at the begining of your path like this:
>>> your_dataset=Vision(r"path/to/your/dataset/", size=(28, 28), normalize=True)
Warning
Since Visionner still in alpha and under heavy development , expect to see many changes in the near futures.
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