lapixdl 0.4.1

Evaluation metrics for segmentation, detection and classification Deep Learning algorithms

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()