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lapixdl

Python package with Deep Learning utilities for Computer Vision

https://github.com/lapix-ufsc/lapixdl

Science Score: 54.0%

This score indicates how likely this project is to be science-related based on various indicators:

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    CITATION.cff file
    Found CITATION.cff file
  • ✓
    codemeta.json file
    Found codemeta.json file
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    .zenodo.json file
    Found .zenodo.json file
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    DOI references
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    Academic publication links
    Links to: zenodo.org
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    Scientific vocabulary similarity
    Low similarity (11.6%) to scientific vocabulary

Keywords

computer-vision deep-learning evaluation-framework image-processing
Last synced: 6 months ago · JSON representation ·

Repository

Python package with Deep Learning utilities for Computer Vision

Basic Info
  • Host: GitHub
  • Owner: lapix-ufsc
  • License: mit
  • Language: Python
  • Default Branch: main
  • Homepage:
  • Size: 327 KB
Statistics
  • Stars: 9
  • Watchers: 2
  • Forks: 3
  • Open Issues: 3
  • Releases: 41
Topics
computer-vision deep-learning evaluation-framework image-processing
Created over 5 years ago · Last pushed 6 months ago
Metadata Files
Readme Citation

README.md

DOI CodeFactor PyPI tests

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: python 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:

```python from lapixdl.evaluation.evaluate import evaluate_segmentation

classes = ['background', 'object']

Iterator for GT masks

dl is a PyTorch DataLoader

def gtmaskiteratorfromdl(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 predmaskiteratorfromdl(dl, predict): for imgs, masks in iter(dl): for img in imgs: yield predict(img)

gtmasks = gtmaskiteratorfromdl(validationdl) predmasks = predmaskiteratorfromdl(validationdl, prediction_function)

Calculates and shows metrics

eval = evaluatesegmentation(gtmasks, pred_masks, classes)

Shows confusion matrix and returns its Figure and Axes

fig, axes = eval.showconfusionmatrix() ```

Examples with third libraries

How to log the results of LAPiX DL evaluations in the Weights & Biases platform

About Weights & Biases.

```python from lapixdl.evaluation.evaluate import evaluate_segmentation import wandb

init wandb ...

...

evaltest = evaluatesegmentation(gtmasks, predmasks, categories)

...

If you want to log everything

wandb.log({'testevaluation': evaltest.to_dict()['By Class']})

If you want to choose specific categories to log

selectedcats = ['A', 'B', 'C'] metricsbycat = {k: v for k, v in evaltest.todict()['By Class'].items() if k in selectedcats} wandb.log({'testevaluation': metricsby_cat}) ```

Computing using GPU with torchmetrics

About torchmetrics.

The lapixdl package calculates the confusion matrix first (on the CPU), which this will be slower than calculating using torchmetrics which uses pytorch tensors. So a trick here, to not calculate each metric separately in torchmetrics, is to calculate a confusion matrix using torchmetrics and then calculate all the metrics at once using lapixdl.

A simple example for a Segmentation case:

```python import torchmetrics from lapixdl.evaluation.model import SegmentationMetrics

classes = ['background', 'object']

confMat = torchmetrics.ConfusionMatrix( reduce="macro", mdmcreduce="global", numclasses=len(classes) )

confusionmatrix = confMat(pred, target) confusionmatrix = confusion_matrix.numpy()

metrics = SegmentationMetrics( classes=classes, confusionmatrix=confusionmatrix ) ```

For Results Visualization

This module exports the following functions for results visualization: python 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.

For Data Conversion

This module exports the functions for data conversion. python from lapixdl.convert import labelbox_to_lapix from lapixdl.convert import labelbox_to_coco

Example of conversion from Labelbox to COCO labels format:

```python import json

from lapixdl.formats import labelboxtococo

A map categories between labelbox schematic id and category ID

map_categories = { '': 1 # category id }

The categories section in the COCO format

categoriescoco = [{ 'supercategory': None, 'name': 'examplecategory', 'id': 1 }]

Convert it and create the COCO OD data

cocodict = labelboxtococo( 'labelboxexportfile.json', mapcategories, categoriescoco, target = 'object detection', imageshape = (1200, 1600) )

Saves converted json

with open('./coco.json', 'w') as outfile: json.dump(cocodict, out_file) ```

Owner

  • Name: Image Processing and Computer Graphics Lab - LAPiX
  • Login: lapix-ufsc
  • Kind: organization
  • Location: Florianópolis - SC, Brazil

Citation (CITATION.cff) 

cff-version: 1.2.0
message: "If you use this software, please cite it as below."
authors:
- family-names: "Matias"
  given-names: "André"
  orcid: "https://orcid.org/0000-0003-0268-0233"
- family-names: "Atkinson Amorim"
  given-names: "João Gustavo"
  orcid: "https://orcid.org/0000-0003-3361-6891"
title: "LAPiX DL - Utils for Computer Vision Deep Learning research"
version: 0.8.12
doi: 10.5281/zenodo.5963342
date-released: 2022-02-03
url: "https://github.com/lapix-ufsc/lapixdl"

GitHub Events

Total
  • Delete event: 5
  • Push event: 9
  • Pull request event: 9
  • Pull request review event: 7
  • Create event: 5
Last Year
  • Delete event: 5
  • Push event: 9
  • Pull request event: 9
  • Pull request review event: 7
  • Create event: 5

Committers

Last synced: almost 3 years ago

All Time
  • Total Commits: 186
  • Total Committers: 3
  • Avg Commits per committer: 62.0
  • Development Distribution Score (DDS): 0.57
Top Committers
Name Email Commits
João Gustavo A. Amorim j****m@g****m 80
André Victória Matias a****s@g****m 74
pre-commit-ci[bot] 6****]@u****m 32

Issues and Pull Requests

Last synced: 6 months ago

All Time
  • Total issues: 8
  • Total pull requests: 102
  • Average time to close issues: 6 months
  • Average time to close pull requests: 4 days
  • Total issue authors: 3
  • Total pull request authors: 3
  • Average comments per issue: 0.88
  • Average comments per pull request: 0.06
  • Merged pull requests: 100
  • Bot issues: 0
  • Bot pull requests: 76
Past Year
  • Issues: 0
  • Pull requests: 7
  • Average time to close issues: N/A
  • Average time to close pull requests: about 1 month
  • Issue authors: 0
  • Pull request authors: 1
  • Average comments per issue: 0
  • Average comments per pull request: 0.0
  • Merged pull requests: 6
  • Bot issues: 0
  • Bot pull requests: 7
View more stats
Top Authors
Issue Authors
  • johnnv1 (7)
  • luizbuschetto (1)
  • Andrevmatias (1)
Pull Request Authors
  • pre-commit-ci[bot] (90)
  • johnnv1 (28)
  • Andrevmatias (2)
Top Labels
Issue Labels
improvements (1) enhancement (1)
Pull Request Labels
improvements (2) New feature (1)

Packages

  • Total packages: 1
  • Total downloads:
    • pypi 65 last-month
  • Total dependent packages: 0
  • Total dependent repositories: 1
  • Total versions: 38
  • Total maintainers: 2
pypi.org: lapixdl

Utils for Computer Vision Deep Learning research

  • Versions: 38
  • Dependent Packages: 0
  • Dependent Repositories: 1
  • Downloads: 65 Last month
Rankings
Dependent packages count: 10.1%
Downloads: 13.5%
Average: 17.0%
Stargazers count: 17.1%
Dependent repos count: 21.6%
Forks count: 22.6%
Maintainers (2)
johnnv andre.vmatias
Last synced: 6 months ago

Dependencies

requirements-dev.txt pypi
  • covdefaults >=2.2 development
  • coverage * development
  • distlib * development
  • pytest * development
  • pytest-env * development
.github/workflows/python-publish.yml actions
  • actions/checkout v2 composite
  • actions/setup-python v2 composite
.github/workflows/tests.yml actions
setup.py pypi