Metadata-Version: 2.1
Name: lapixdl
Version: 0.6
Summary: Utils for Computer Vision Deep Learning research
Home-page: https://github.com/lapix-ufsc/lapixdl
Author: LAPiX
License: MIT
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Requires-Python: >=3.6
Description-Content-Type: text/markdown
Requires-Dist: numpy
Requires-Dist: tqdm
Requires-Dist: seaborn
Requires-Dist: pandas
Requires-Dist: matplotlib

# LAPiX DL - Utils for Computer Vision Deep Learning research

This package contains utilitary functions to support train and evaluation of Deep Learning models applied to images.

Three computer vision approaches are covered: Segmentation, Detection and Classification.

## How to use

### For Model Evaluation

This module exports the following functions for model evaluation:
```
from lapixdl.evaluation.evaluate import evaluate_segmentation
from lapixdl.evaluation.evaluate import evaluate_detection
from lapixdl.evaluation.evaluate import evaluate_classification
```

All model evaluation methods need two iterators: **one for the ground truth itens and one for the predictions**.

These iterators must be sorted equaly, assuring that the ground truth and the prediction of the same sample are at the same position.

#### Example of segmentation model evaluation using **PyTorch**:

```
from lapixdl.evaluation.evaluate import evaluate_segmentation

classes = ['background', 'object']

# Iterator for GT masks 
# `dl` is a PyTorch DataLoader
def gt_mask_iterator_from_dl(dl):
  for imgs, masks in iter(dl):
    for mask in masks:
      yield mask

# Iterator for prediction masks 
# `predict` a function that, given an image, predicts the mask.
def pred_mask_iterator_from_dl(dl, predict):
  for imgs, masks in iter(dl):
    for img in imgs:
      yield predict(img)

gt_masks = gt_mask_iterator_from_dl(validation_dl)
pred_masks = pred_mask_iterator_from_dl(validation_dl, prediction_function)

# Calculates and shows metrics
eval = evaluate_segmentation(gt_masks, pred_masks, classes)

# Shows confusion matrix and returns its Figure and Axes
fig, axes = eval.show_confusion_matrix()
```

### For Results Visualization

This module exports the following functions for results visualization:
```
from lapixdl.evaluation.visualize import show_segmentations
from lapixdl.evaluation.visualize import show_classifications
from lapixdl.evaluation.visualize import show_detections
```

The available color maps are the [ones from matplotlib](https://matplotlib.org/3.1.1/gallery/color/colormap_reference.html).

