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Quick start guide

Contents

Installation

Prerequisites

  • Python (3.5+)
  • OpenVINO (optional)

Installation steps

Optionally, set up a virtual environment:

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

Install Datumaro:

pip install 'git+https://github.com/opencv/cvat#egg=datumaro&subdirectory=datumaro'

You can change the installation branch with .../cvat@<branch_name>#egg... Also note --force-reinstall parameter in this case.

Interfaces

As a standalone tool:

datum --help

As a python module:

The directory containing Datumaro should be in the PYTHONPATH environment variable or cvat/datumaro/ should be the current directory.

python -m datumaro --help
python datumaro/ --help
python datum.py --help

As a python library:

import datumaro

Formats support

List of supported formats:

List of supported annotation types:

  • Labels
  • Bounding boxes
  • Polygons
  • Polylines
  • (Key-)Points
  • Captions
  • Masks

Command line workflow

Note: command invocation syntax is subject to change, always refer to command --help output

The key object is the Project. The Project is a combination of a Project's own dataset, a number of external data sources and an environment. An empty Project can be created by project create command, an existing dataset can be imported with project import command. A typical way to obtain projects is to export tasks in CVAT UI.

Available CLI commands: CLI design doc

If you want to interact with models, you need to add them to project first.

Import project

This command creates a Project from an existing dataset.

Supported formats are listed in the command help. In Datumaro dataset formats are supported by Extractors and Importers. An Extractor produces a list of dataset items corresponding to the dataset. An Importer creates a Project from the data source location. It is possible to add a custom Extractor and Importer. To do this, you need to put an Extractor and Importer implementation scripts to <project_dir>/.datumaro/extractors and <project_dir>/.datumaro/importers.

Usage:

datum project import --help

datum project import \
     -i <dataset_path> \
     -o <project_dir> \
     -f <format>

Example: create a project from COCO-like dataset

datum project import \
     -i /home/coco_dir \
     -o /home/project_dir \
     -f coco

An MS COCO-like dataset should have the following directory structure:

COCO/
├── annotations/
│   ├── instances_val2017.json
│   ├── instances_train2017.json
├── images/
│   ├── val2017
│   ├── train2017

Everything after the last _ is considered a subset name in the COCO format.

Create project

The command creates an empty project. Once a Project is created, there are a few options to interact with it.

Usage:

datum project create --help

datum project create \
  -o <project_dir>

Example: create an empty project my_dataset

datum project create -o my_dataset/

Add and remove data

A Project can be attached to a number of external Data Sources. Each Source describes a way to produce dataset items. A Project combines dataset items from all the sources and its own dataset into one composite dataset. You can manage project sources by commands in the source command line context.

Datasets come in a wide variety of formats. Each dataset format defines its own data structure and rules on how to interpret the data. For example, the following data structure is used in COCO format:

/dataset/
- /images/<id>.jpg
- /annotations/

In Datumaro dataset formats are supported by Extractors. An Extractor produces a list of dataset items corresponding to the dataset. It is possible to add a custom Extractor. To do this, you need to put an Extractor definition script to <project_dir>/.datumaro/extractors.

Usage:

datum source add --help
datum source remove --help

datum source add \
     path <path> \
     -p <project dir> \
     -n <name>

datum source remove \
     -p <project dir> \
     -n <name>

Example: create a project from a bunch of different annotations and images, and generate TFrecord for TF Detection API for model training

datum project create
# 'default' is the name of the subset below
datum source add path <path/to/coco/instances_default.json> -f coco_instances
datum source add path <path/to/cvat/default.xml> -f cvat
datum source add path <path/to/voc> -f voc_detection
datum source add path <path/to/datumaro/default.json> -f datumaro
datum source add path <path/to/images/dir> -f image_dir
datum project export -f tf_detection_api

Extract subproject

This command allows to create a sub-Project from a Project. The new project includes only items satisfying some condition. XPath is used as query format.

There are several filtering modes available ('-m/--mode' parameter). Supported modes:

  • 'i', 'items'
  • 'a', 'annotations'
  • 'i+a', 'a+i', 'items+annotations', 'annotations+items'

When filtering annotations, use the 'items+annotations' mode to point that annotation-less dataset items should be removed. To select an annotation, write an XPath that returns 'annotation' elements (see examples).

Usage:

datum project extract --help

datum project extract \
     -p <project dir> \
     -o <output dir> \
     -e '<xpath filter expression>'

Example: extract a dataset with only images which width < height

datum project extract \
     -p test_project \
     -o test_project-extract \
     -e '/item[image/width < image/height]'

Example: extract a dataset with only large annotations of class cat and any non-persons

datum project extract \
     -p test_project \
     -o test_project-extract \
     --mode annotations -e '/item/annotation[(label="cat" and area > 999.5) or label!="person"]'

Example: extract a dataset with only occluded annotations, remove empty images

datum project extract \
     -p test_project \
     -o test_project-extract \
     -m i+a -e '/item/annotation[occluded="True"]'

Item representations are available with --dry-run parameter:

<item>
  <id>290768</id>
  <subset>minival2014</subset>
  <image>
    <width>612</width>
    <height>612</height>
    <depth>3</depth>
  </image>
  <annotation>
    <id>80154</id>
    <type>bbox</type>
    <label_id>39</label_id>
    <x>264.59</x>
    <y>150.25</y>
    <w>11.199999999999989</w>
    <h>42.31</h>
    <area>473.87199999999956</area>
  </annotation>
  <annotation>
    <id>669839</id>
    <type>bbox</type>
    <label_id>41</label_id>
    <x>163.58</x>
    <y>191.75</y>
    <w>76.98999999999998</w>
    <h>73.63</h>
    <area>5668.773699999998</area>
  </annotation>
  ...
</item>

Merge projects

This command combines multiple Projects into one.

Usage:

datum project merge --help

datum project merge \
     -p <project dir> \
     -o <output dir> \
     <other project dir>

Example: update annotations in the first_project with annotations from the second_project and save the result as merged_project

datum project merge \
     -p first_project \
     -o merged_project \
     second_project

Export project

This command exports a Project in some format.

Supported formats are listed in the command help. In Datumaro dataset formats are supported by Converters. A Converter produces a dataset of a specific format from dataset items. It is possible to add a custom Converter. To do this, you need to put a Converter definition script to <project_dir>/.datumaro/converters.

Usage:

datum project export --help

datum project export \
     -p <project dir> \
     -o <output dir> \
     -f <format> \
     [-- <additional format parameters>]

Example: save project as VOC-like dataset, include images

datum project export \
     -p test_project \
     -o test_project-export \
     -f voc \
     -- --save-images

Get project info

This command outputs project status information.

Usage:

datum project info --help

datum project info \
     -p <project dir>

Example:

datum project info -p /test_project

Project:
  name: test_project2
  location: /test_project
Sources:
  source 'instances_minival2014':
    format: coco_instances
    url: /coco_like/annotations/instances_minival2014.json
Dataset:
  length: 5000
  categories: label
    label:
      count: 80
      labels: person, bicycle, car, motorcycle (and 76 more)
  subsets: minival2014
    subset 'minival2014':
      length: 5000
      categories: label
        label:
          count: 80
          labels: person, bicycle, car, motorcycle (and 76 more)

Register model

Supported models:

  • OpenVINO
  • Custom models via custom launchers

Usage:

datum model add --help

Example: register an OpenVINO model

A model consists of a graph description and weights. There is also a script used to convert model outputs to internal data structures.

datum project create
datum model add \
     -n <model_name> openvino \
     -d <path_to_xml> -w <path_to_bin> -i <path_to_interpretation_script>

Interpretation script for an OpenVINO detection model (convert.py):

from datumaro.components.extractor import *

max_det = 10
conf_thresh = 0.1

def process_outputs(inputs, outputs):
     # inputs = model input, array or images, shape = (N, C, H, W)
     # outputs = model output, shape = (N, 1, K, 7)
     # results = conversion result, [ [ Annotation, ... ], ... ]
     results = []
     for input, output in zip(inputs, outputs):
          input_height, input_width = input.shape[:2]
          detections = output[0]
          image_results = []
          for i, det in enumerate(detections):
               label = int(det[1])
               conf = det[2]
               if conf <= conf_thresh:
                    continue

               x = max(int(det[3] * input_width), 0)
               y = max(int(det[4] * input_height), 0)
               w = min(int(det[5] * input_width - x), input_width)
               h = min(int(det[6] * input_height - y), input_height)
               image_results.append(Bbox(x, y, w, h,
                    label=label, attributes={'score': conf} ))

               results.append(image_results[:max_det])

     return results

def get_categories():
     # Optionally, provide output categories - label map etc.
     # Example:
     label_categories = LabelCategories()
     label_categories.add('person')
     label_categories.add('car')
     return { AnnotationType.label: label_categories }

Run model

This command applies model to dataset images and produces a new project.

Usage:

datum model run --help

datum model run \
     -p <project dir> \
     -m <model_name> \
     -o <save_dir>

Example: launch inference on a dataset

datum project import <...>
datum model add mymodel <...>
datum model run -m mymodel -o inference

Compare projects

The command compares two datasets and saves the results in the specified directory. The current project is considered to be "ground truth".

datum project diff --help

datum project diff <other_project_dir> -o <save_dir>

Example: compare a dataset with model inference

datum project import <...>
datum model add mymodel <...>
datum project transform <...> -o inference
datum project diff inference -o diff

Explain inference

Usage:

datum explain --help

datum explain \
     -m <model_name> \
     -o <save_dir> \
     -t <target> \
     <method> \
     <method_params>

Example: run inference explanation on a single image with visualization

datum project create <...>
datum model add mymodel <...>
datum explain \
     -m mymodel \
     -t 'image.png' \
     rise \
     -s 1000 --progressive

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