Metadata-Version: 2.1
Name: visionner
Version: 0.0.4
Summary: Visionner is a image dataset  toolkit
Home-page: https://github.com/charleslf2/Visionner
Author: Charles TCHANAKE
Author-email: datadevfernolf@gmail.com
License: Apache License 2.0
Project-URL: Bug Tracker, https://github.com/charleslf2/Visionner/issues
Keywords: AI,Computer vision,Data centric
Classifier: Development Status :: 3 - Alpha
Classifier: Operating System :: OS Independent
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3.10
Description-Content-Type: text/markdown
License-File: LICENSE.txt
Requires-Dist: numpy
Requires-Dist: opencv-python
Requires-Dist: matplotlib
Requires-Dist: rich
Requires-Dist: scikit-learn

# 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

```bash

pip install visionner

```

### Usage

```python

### 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:

```python
>>> 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.
