Description
-----------
Python package to plot the latent space of a set of images with different methods.
Install with pip
----------------
```bash
$ python3 -m pip install latentplot --user
```
Install from source
-------------------
```bash
$ git clone https://github.com/luiscarlosgph/latentplot.git
$ cd latentplot
$ python3 setup.py install --user
```
Exemplary code snippet
----------------------
```python
# List of images of shape (H, W, 3) and BGR
images = [ ... ]
# List of vectors of shape (D,), where D is the vector dimension
feature_vectors = [ ... ]
# List of integer class labels
labels = [ ... ]
# Produce a BGR image containing a 2D plot of the latent space with t-SNE
plotter = latentplot.Plotter(method='tsne')
im_tsne = plotter.plot(images, feature_vectors, labels) # Providing labels here is optional
```
The `latentplot.Plotter` constructor parameters are:
* **method**: method used to reduce the feature vectors to a 2D space. Available options: **pca**, **tsne**, **umap**.
* **width**: desired output image width. Default is 15360 pixels (16K).
* **height**: desired output image height. Default is 8640 pixels (16K).
* **dpi**: DPI for the output image. Default is 300.
* **cell_factor**: proportion of the reduced space that each cell will occupy. Default is 0.01.
* **dark_mode**: set it to False to have a white background with black font. Default is True.
* **hide_axes**: hide axes, ticks and marks. Default is True.
* ****kwargs**: the rest of the arguments you pass will be forwarded to the dimensionality reduction method.
Exemplary results
-----------------
* [CIFAR-10](https://www.cs.toronto.edu/~kriz/cifar.html):
<!---
* [PascalVOC](http://host.robots.ox.ac.uk/pascal/VOC):
TODO
* [Cholec80](http://camma.u-strasbg.fr/datasets):
TODO
-->
Author
------
Luis Carlos Garcia Peraza Herrera (luiscarlos.gph@gmail.com), 2023.
License
-------
This code repository is shared under an [MIT license](LICENSE).
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