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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
datum project export --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/
mkdir my_project && cd my_project
datum project create
datum project import --format coco <path/to/coco>
datum project export --filter '/image[images_I_dont_like]' --format cvat

  • 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:

mkdir my_project && cd my_project
datum project create
datum project import --format coco <path/to/coco>
# create model results interpretation script
datum model add -n mymodel openvino \
  --weights model.bin --description model.xml \
  --interpretation-script parse_results.py
datum model run --model -n mymodel --output-dir mymodel_inference/
datum compare mymodel_inference/ --format tensorboard --output-dir compare

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

mkdir my_project && cd my_project
datum project create
datum project import --format voc <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 project 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
import datumaro as dm

dataset = dm.Dataset.from_iterable([
  dm.DatasetItem(id='image1', subset='train',
    media=dm.Image.from_numpy(data=np.ones((5, 5, 3))),
    annotations=[
      dm.Bbox(1, 2, 3, 4, label=0),
    ]
  ),
  # ...
], categories=['cat', 'dog'])
dataset.export('test_dataset/', 'coco')
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