testbed/openvinotoolkit__datumaro/docs/user_manual.md

# User manual

## Contents

- [Installation](#installation)
- [Interfaces](#interfaces)
- [Supported dataset formats and annotations](#supported-formats)
- [Command line workflow](#command-line-workflow)
  - [Project structure](#project-structure)
- [Command reference](#command-reference)
  - [Convert datasets](#convert-datasets)
  - [Create project](#create-project)
  - [Add and remove data](#add-and-remove-data)
  - [Import project](#import-project)
  - [Filter project](#filter-project)
  - [Update project (merge)](#update-project)
  - [Merge projects](#merge-projects)
  - [Export project](#export-project)
  - [Compare projects](#compare-projects)
  - [Obtaining project info](#get-project-info)
  - [Obtaining project statistics](#get-project-statistics)
  - [Register model](#register-model)
  - [Run inference](#run-inference)
  - [Run inference explanation](#explain-inference)
  - [Transform project](#transform-project)
- [Extending](#extending)
- [Links](#links)

## Installation

### Dependencies

- Python (3.6+)
- Optional: OpenVINO, TensforFlow, PyTorch, MxNet, Caffe, Accuracy Checker

### Installation steps

Optionally, set up a virtual environment:

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

Install:
``` bash
pip install 'git+https://github.com/openvinotoolkit/datumaro'
```

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

## Interfaces

As a standalone tool:

``` bash
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.

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

As a python library:

``` python
import datumaro
```

## Supported Formats

List of supported formats:
- MS COCO (`image_info`, `instances`, `person_keypoints`, `captions`, `labels`*)
  - [Format specification](http://cocodataset.org/#format-data)
  - [Dataset example](../tests/assets/coco_dataset)
  - `labels` are our extension - like `instances` with only `category_id`
- PASCAL VOC (`classification`, `detection`, `segmentation` (class, instances), `action_classification`, `person_layout`)
  - [Format specification](http://host.robots.ox.ac.uk/pascal/VOC/voc2012/htmldoc/index.html)
  - [Dataset example](../tests/assets/voc_dataset)
- YOLO (`bboxes`)
  - [Format specification](https://github.com/AlexeyAB/darknet#how-to-train-pascal-voc-data)
  - [Dataset example](../tests/assets/yolo_dataset)
- TF Detection API (`bboxes`, `masks`)
  - Format specifications: [bboxes](https://github.com/tensorflow/models/blob/master/research/object_detection/g3doc/using_your_own_dataset.md), [masks](https://github.com/tensorflow/models/blob/master/research/object_detection/g3doc/instance_segmentation.md)
  - [Dataset example](../tests/assets/tf_detection_api_dataset)
- WIDER Face (`bboxes`)
  - [Format specification](http://shuoyang1213.me/WIDERFACE/)
  - [Dataset example](../tests/assets/wider_dataset)
- VGGFace2 (`landmarks`, `bboxes`)
  - [Format specification](https://github.com/ox-vgg/vgg_face2)
  - [Dataset example](../tests/assets/vgg_face2_dataset)
- MOT sequences
  - [Format specification](https://arxiv.org/pdf/1906.04567.pdf)
  - [Dataset example](../tests/assets/mot_dataset)
- MOTS (png)
  - [Format specification](https://www.vision.rwth-aachen.de/page/mots)
  - [Dataset example](../tests/assets/mots_dataset)
- ImageNet (`classification`, `detection`)
  - [Dataset example](../tests/assets/imagenet_dataset)
  - [Dataset example (txt for classification)](../tests/assets/imagenet_txt_dataset)
  - Detection format is the same as in PASCAL VOC
- CamVid (`segmentation`)
  - [Format specification](http://mi.eng.cam.ac.uk/research/projects/VideoRec/CamVid/)
  - [Dataset example](../tests/assets/camvid_dataset)
- CVAT
  - [Format specification](https://github.com/opencv/cvat/blob/develop/cvat/apps/documentation/xml_format.md)
  - [Dataset example](../tests/assets/cvat_dataset)
- LabelMe
  - [Format specification](http://labelme.csail.mit.edu/Release3.0)
  - [Dataset example](../tests/assets/labelme_dataset)

List of supported annotation types:
- Labels
- Bounding boxes
- Polygons
- Polylines
- (Segmentation) Masks
- (Key-)Points
- Captions

## Command line workflow

The key object is a project, so most CLI commands operate on projects.
However, there are few commands operating on datasets directly.
A 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.

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

### Project structure

<!--lint disable fenced-code-flag-->
```
└── project/
    ├── .datumaro/
    |   ├── config.yml
    │   ├── .git/
    │   ├── models/
    │   └── plugins/
    │       ├── plugin1/
    │       |   ├── file1.py
    │       |   └── file2.py
    │       ├── plugin2.py
    │       ├── custom_extractor1.py
    │       └── ...
    ├── dataset/
    └── sources/
        ├── source1
        └── ...
```
<!--lint enable fenced-code-flag-->

## Command reference

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

Available CLI commands:
![CLI design doc](images/cli_design.png)

### Convert datasets

This command allows to convert a dataset from one format into another. In fact, this
command is a combination of `project import` and `project export` and just provides a simpler
way to obtain the same result when no extra options is needed. A list of supported
formats can be found in the `--help` output of this command.

Usage:

``` bash
datum convert --help

datum convert \
     -i <input path> \
     -if <input format> \
     -o <output path> \
     -f <output format> \
     -- [extra parameters for output format]
```

Example: convert a VOC-like dataset to a COCO-like one:

``` bash
datum convert --input-format voc --input-path <path/to/voc/> \
              --output-format coco
```

### Import project

This command creates a Project from an existing dataset.

Supported formats are listed in the command help. Check [extending tips](#extending)
for information on extra format support.

Usage:

``` bash
datum project import --help

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

Example: create a project from COCO-like dataset

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

An _MS COCO_-like dataset should have the following directory structure:

<!--lint disable fenced-code-flag-->
```
COCO/
├── annotations/
│   ├── instances_val2017.json
│   ├── instances_train2017.json
├── images/
│   ├── val2017
│   ├── train2017
```
<!--lint enable fenced-code-flag-->

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:

``` bash
datum project create --help

datum project create \
  -o <project_dir>
```

Example: create an empty project `my_dataset`

``` bash
datum project create -o my_dataset/
```

### Add and remove data

A Project can contain a number of external Data Sources. Each Data 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:
<!--lint disable fenced-code-flag-->
```
/dataset/
- /images/<id>.jpg
- /annotations/
```
<!--lint enable fenced-code-flag-->

Supported formats are listed in the command help. Check [extending tips](#extending)
for information on extra format support.

Usage:

``` bash
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

``` bash
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
```

### Filter project

This command allows to create a sub-Project from a Project. The new project
includes only items satisfying some condition. [XPath](https://devhints.io/xpath)
is used as a 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:

``` bash
datum project filter --help

datum project filter \
     -p <project dir> \
     -e '<xpath filter expression>'
```

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

``` bash
datum project filter \
     -p test_project \
     -e '/item[image/width < image/height]'
```

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

``` bash
datum project filter \
     -p test_project \
     --mode annotations -e '/item/annotation[(label="cat" and area > 99.5) or label!="person"]'
```

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

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

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

``` xml
<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>
```

### Update project

This command updates items in a project from another one
(check [Merge Projects](#merge-projects) for complex merging).

Usage:

``` bash
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`

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

### Merge projects

This command merges items from 2 or more projects and checks annotations for errors.

Spatial annotations are compared by distance and intersected, labels and attributes
are selected by voting.
Merge conflicts, missing items and annotations, other errors are saved into a `.json` file.

Usage:

``` bash
datum merge --help

datum merge <project dirs>
```

Example: merge 4 (partially-)intersecting projects,
- consider voting succeeded when there are 3+ same votes
- consider shapes intersecting when IoU >= 0.6
- check annotation groups to have `person`, `hand`, `head` and `foot` (`?` for optional)

``` bash
datum merge project1/ project2/ project3/ project4/ \
     --quorum 3 \
     -iou 0.6 \
     --groups 'person,hand?,head,foot?'
```

### Export project

This command exports a Project as a dataset in some format.

Supported formats are listed in the command help. Check [extending tips](#extending)
for information on extra format support.

Usage:

``` bash
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, convert images to `PNG`

``` bash
datum project export \
     -p test_project \
     -o test_project-export \
     -f voc \
     -- --save-images --image-ext='.png'
```

### Get project info

This command outputs project status information.

Usage:

``` bash
datum project info --help

datum project info \
     -p <project dir>
```

Example:

``` bash
datum project info -p /test_project

Project:
  name: test_project
  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)
```

### Get project statistics

This command computes various project statistics, such as:
- image mean and std. dev.
- class and attribute balance
- mask pixel balance
- segment area distribution

Usage:

``` bash
datum project stats --help

datum project stats \
     -p <project dir>
```

Example:

<details>

``` bash
datum project stats -p /test_project

{
    "annotations": {
        "labels": {
            "attributes": {
                "gender": {
                    "count": 358,
                    "distribution": {
                        "female": [
                            149,
                            0.41620111731843573
                        ],
                        "male": [
                            209,
                            0.5837988826815642
                        ]
                    },
                    "values count": 2,
                    "values present": [
                        "female",
                        "male"
                    ]
                },
                "view": {
                    "count": 340,
                    "distribution": {
                        "__undefined__": [
                            4,
                            0.011764705882352941
                        ],
                        "front": [
                            54,
                            0.1588235294117647
                        ],
                        "left": [
                            14,
                            0.041176470588235294
                        ],
                        "rear": [
                            235,
                            0.6911764705882353
                        ],
                        "right": [
                            33,
                            0.09705882352941177
                        ]
                    },
                    "values count": 5,
                    "values present": [
                        "__undefined__",
                        "front",
                        "left",
                        "rear",
                        "right"
                    ]
                }
            },
            "count": 2038,
            "distribution": {
                "car": [
                    340,
                    0.16683022571148184
                ],
                "cyclist": [
                    194,
                    0.09519136408243375
                ],
                "head": [
                    354,
                    0.17369970559371933
                ],
                "ignore": [
                    100,
                    0.04906771344455348
                ],
                "left_hand": [
                    238,
                    0.11678115799803729
                ],
                "person": [
                    358,
                    0.17566241413150147
                ],
                "right_hand": [
                    77,
                    0.037782139352306184
                ],
                "road_arrows": [
                    326,
                    0.15996074582924436
                ],
                "traffic_sign": [
                    51,
                    0.025024533856722278
                ]
            }
        },
        "segments": {
            "area distribution": [
                {
                    "count": 1318,
                    "max": 11425.1,
                    "min": 0.0,
                    "percent": 0.9627465303140978
                },
                {
                    "count": 1,
                    "max": 22850.2,
                    "min": 11425.1,
                    "percent": 0.0007304601899196494
                },
                {
                    "count": 0,
                    "max": 34275.3,
                    "min": 22850.2,
                    "percent": 0.0
                },
                {
                    "count": 0,
                    "max": 45700.4,
                    "min": 34275.3,
                    "percent": 0.0
                },
                {
                    "count": 0,
                    "max": 57125.5,
                    "min": 45700.4,
                    "percent": 0.0
                },
                {
                    "count": 0,
                    "max": 68550.6,
                    "min": 57125.5,
                    "percent": 0.0
                },
                {
                    "count": 0,
                    "max": 79975.7,
                    "min": 68550.6,
                    "percent": 0.0
                },
                {
                    "count": 0,
                    "max": 91400.8,
                    "min": 79975.7,
                    "percent": 0.0
                },
                {
                    "count": 0,
                    "max": 102825.90000000001,
                    "min": 91400.8,
                    "percent": 0.0
                },
                {
                    "count": 50,
                    "max": 114251.0,
                    "min": 102825.90000000001,
                    "percent": 0.036523009495982466
                }
            ],
            "avg. area": 5411.624543462382,
            "pixel distribution": {
                "car": [
                    13655,
                    0.0018431496518735067
                ],
                "cyclist": [
                    939005,
                    0.12674674030446592
                ],
                "head": [
                    0,
                    0.0
                ],
                "ignore": [
                    5501200,
                    0.7425510702956085
                ],
                "left_hand": [
                    0,
                    0.0
                ],
                "person": [
                    954654,
                    0.12885903974805205
                ],
                "right_hand": [
                    0,
                    0.0
                ],
                "road_arrows": [
                    0,
                    0.0
                ],
                "traffic_sign": [
                    0,
                    0.0
                ]
            }
        }
    },
    "annotations by type": {
        "bbox": {
            "count": 548
        },
        "caption": {
            "count": 0
        },
        "label": {
            "count": 0
        },
        "mask": {
            "count": 0
        },
        "points": {
            "count": 669
        },
        "polygon": {
            "count": 821
        },
        "polyline": {
            "count": 0
        }
    },
    "annotations count": 2038,
    "dataset": {
        "image mean": [
            107.06903686941979,
            79.12831698580979,
            52.95829558185416
        ],
        "image std": [
            49.40237673503467,
            43.29600731496902,
            35.47373007603151
        ],
        "images count": 100
    },
    "images count": 100,
    "subsets": {},
    "unannotated images": [
        "img00051",
        "img00052",
        "img00053",
        "img00054",
        "img00055",
    ],
    "unannotated images count": 5
}
```

</details>

### Register model

Supported models:
- OpenVINO
- Custom models via custom `launchers`

Usage:

``` bash
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.

``` bash
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`):

``` python
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:

``` bash
datum model run --help

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

Example: launch inference on a dataset

``` bash
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".

``` bash
datum project diff --help

datum project diff <other_project_dir> -o <save_dir>
```

Example: compare a dataset with model inference

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

### Explain inference

Usage:

``` bash
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

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

### Transform Project

This command allows to modify images or annotations in a project all at once.

``` bash
datum project transform --help

datum project transform \
     -p <project_dir> \
     -o <output_dir> \
     -t <transform_name> \
     -- [extra transform options]
```

Example: split a dataset randomly to `train` and `test` subsets, ratio is 2:1

``` bash
datum project transform -t random_split -- --subset train:.67 --subset test:.33
```

Example: convert polygons to masks, masks to boxes etc.:

``` bash
datum project transform -t boxes_to_masks
datum project transform -t masks_to_polygons
datum project transform -t polygons_to_masks
datum project transform -t shapes_to_boxes
```

Example: remap dataset labels, `person` to `car` and `cat` to `dog`, keep `bus`, remove others

``` bash
datum project transform -t remap_labels -- \
     -l person:car -l bus:bus -l cat:dog \
     --default delete
```

Example: rename dataset items by a regular expression
- Replace `pattern` with `replacement`
- Remove `frame_` from item ids

``` bash
datum project transform -t rename -- -e '|pattern|replacement|'
datum project transform -t rename -- -e '|frame_(\d+)|\\1|'
```

## Extending

There are few ways to extend and customize Datumaro behaviour, which is supported by plugins.
Check [our contribution guide](../CONTRIBUTING.md) for details on plugin implementation.
In general, a plugin is a Python code file. It must be put into a plugin directory:
- `<project_dir>/.datumaro/plugins` for project-specific plugins
- `<datumaro_dir>/plugins` for global plugins

### Dataset Formats

Dataset reading is 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 custom Extractors and Importers. To do this, you need
to put an Extractor and Importer implementation scripts to a plugin directory.

Dataset writing is supported by Converters.
A Converter produces a dataset of a specific format from dataset items.
It is possible to add custom Converters. To do this, you need to put a Converter
implementation script to a plugin directory.

### Dataset Conversions ("Transforms")

A Transform is a function for altering a dataset and producing a new one. It can update
dataset items, annotations, classes, and other properties.
A list of available transforms for dataset conversions can be extended by adding a Transform
implementation script into a plugin directory.

### Model launchers

A list of available launchers for model execution can be extended by adding a Launcher
implementation script into a plugin directory.

## Links
- [TensorFlow detection model zoo](https://github.com/tensorflow/models/blob/master/research/object_detection/g3doc/detection_model_zoo.md)
- [How to convert model to OpenVINO format](https://docs.openvinotoolkit.org/latest/_docs_MO_DG_prepare_model_convert_model_tf_specific_Convert_Object_Detection_API_Models.html)
- [Model conversion script example](https://github.com/opencv/cvat/blob/3e09503ba6c6daa6469a6c4d275a5a8b168dfa2c/components/tf_annotation/install.sh#L23)