Getting started

To read about the design concept and features of Datumaro, go to the design section.

Installation

Dependencies

• Python (3.6+)
• Optional: OpenVINO, TensorFlow, PyTorch, MxNet, Caffe, Accuracy Checker

Optionally, create a virtual environment:

python -m pip install virtualenv
python -m virtualenv venv
. venv/bin/activate

Install Datumaro package:

pip install datumaro

Usage

There are several options available:

• A standalone command-line tool
• A python module

Standalone tool

Datuaro as a standalone tool allows to do various dataset operations from the command line interface:

datum --help
python -m datumaro --help

Python module

Datumaro can be used in custom scripts as a Python module. Used this way, it allows to use its features from an existing codebase, enabling dataset reading, exporting and iteration capabilities, simplifying integration of custom formats and providing high performance operations:

from datumaro.components.project import Project

# load a Datumaro project
project = Project.load('directory')

# create a dataset
dataset = project.make_dataset()

# keep only annotated images
dataset.select(lambda item: len(item.annotations) != 0)

# change dataset labels
dataset.transform('remap_labels',
  {'cat': 'dog', # rename cat to dog
    'truck': 'car', # rename truck to car
    'person': '', # remove this label
  }, default='delete') # remove everything else

# iterate over dataset elements
for item in dataset:
  print(item.id, item.annotations)

# export the resulting dataset in COCO format
dataset.export('dst/dir', 'coco')

Check our developer manual for additional information.

Examples

• Convert PASCAL VOC dataset to COCO format, keep only images with cat class presented:

  # Download VOC dataset:
  # http://host.robots.ox.ac.uk/pascal/VOC/voc2012/VOCtrainval_11-May-2012.tar
  datum convert --input-format voc --input-path <path/to/voc> \
                --output-format coco \
                --filter '/item[annotation/label="cat"]' \
                -- --reindex 1 # avoid annotation id conflicts
  

• Convert only non-occluded annotations from a CVAT project to TFrecord:

  # export Datumaro dataset in CVAT UI, extract somewhere, go to the project dir
  datum filter -e '/item/annotation[occluded="False"]' \
    --mode items+anno --output-dir not_occluded
  datum export --project not_occluded \
    --format tf_detection_api -- --save-images
  

• Annotate MS COCO dataset, extract image subset, re-annotate it in CVAT, update old dataset:

  # Download COCO dataset http://cocodataset.org/#download
  # Put images to coco/images/ and annotations to coco/annotations/
  datum import --format coco --input-path <path/to/coco>
  datum export --filter '/image[images_I_dont_like]' --format cvat \
    --output-dir reannotation
  # import dataset and images to CVAT, re-annotate
  # export Datumaro project, extract to 'reannotation-upd'
  datum merge reannotation-upd
  datum export --format coco
  

• Annotate instance polygons in CVAT, export as masks in COCO:

  datum convert --input-format cvat --input-path <path/to/cvat.xml> \
                --output-format coco -- --segmentation-mode masks
  

• Apply an OpenVINO detection model to some COCO-like dataset, then compare annotations with ground truth and visualize in TensorBoard:

  datum import --format coco --input-path <path/to/coco>
  # create model results interpretation script
  datum model add mymodel openvino \
    --weights model.bin --description model.xml \
    --interpretation-script parse_results.py
  datum model run --model mymodel --output-dir mymodel_inference/
  datum diff mymodel_inference/ --format tensorboard --output-dir diff
  

• Change colors in PASCAL VOC-like .png masks:

  datum import --format voc --input-path <path/to/voc/dataset>
  
  # Create a color map file with desired colors:
  #
  # label : color_rgb : parts : actions
  # cat:0,0,255::
  # dog:255,0,0::
  #
  # Save as mycolormap.txt
  
  datum export --format voc_segmentation -- --label-map mycolormap.txt
  # add "--apply-colormap=0" to save grayscale (indexed) masks
  # check "--help" option for more info
  # use "datum --loglevel debug" for extra conversion info
  

• Create a custom COCO-like dataset:

  import numpy as np
  from datumaro.components.extractor import (DatasetItem,
    Bbox, LabelCategories, AnnotationType)
  from datumaro.components.dataset import Dataset
  
  dataset = Dataset(categories={
    AnnotationType.label: LabelCategories.from_iterable(['cat', 'dog'])
  })
  dataset.put(DatasetItem(id=0, image=np.ones((5, 5, 3)), annotations=[
    Bbox(1, 2, 3, 4, label=0),
  ]))
  dataset.export('test_dataset', 'coco')