Command line interface (CLI) - Cvat

Overview

A simple command line interface for working with CVAT. At the moment it implements a basic feature set but may serve as the starting point for a more comprehensive CVAT administration tool in the future.

The following subcommands are supported:

Projects:
- create - create a new project
- delete - delete projects
- ls - list all projects
Tasks:
- create - create a new task
- create-from-backup - create a task from a backup file
- delete - delete tasks
- ls - list all tasks
- frames - download frames from a task
- export-dataset - export a task as a dataset
- import-dataset - import annotations into a task from a dataset
- backup - back up a task
- auto-annotate - automatically annotate a task using a local function
Functions (Enterprise/Cloud only):
- create-native - create a function that can be powered by an agent
- delete - delete a function
- run-agent - process requests for a native function

Installation

To install an official release of CVAT CLI, use this command:

bash

pip install cvat-cli

We support Python versions 3.10 and higher.

Usage

The general form of a CLI command is:

console

$ cvat-cli <common options> <resource> <action> <options>

where:

<common options> are options shared between all subcommands;
<resource> is a CVAT resource, such as task;
<action> is the action to do with the resource, such as create;
<options> is any options specific to a particular resource and action.

You can list available subcommands and options using the --help option:

$ cvat-cli --help # get help on available common options and resources
$ cvat-cli <resource> --help # get help on actions for the given resource
$ cvat-cli <resource> <action> --help # get help on action-specific options

The CLI implements alias subcommands for some task actions, so that, for example, cvat-cli ls works the same way as cvat-cli task ls. These aliases are provided for backwards compatibility and are deprecated. Use the task <action> form instead.

Authentication

CLI supports 2 authentication options:

Personal Access Token (PAT) authentication, with an access token value
Password authentication, with a username and a password

Personal Access Token (PAT) authentication requires a token that can be configured in the user settings section in the UI. It is the recommended authentication option for most clients. {{< ilink "/docs/api_sdk/access_tokens" "Read more." >}}

Password authentication requires a username and password pair. For better security it's recommended to use a Personal Access Token (PAT) instead, if possible.

A Personal Access Token can only be used via the CVAT_ACCESS_TOKEN environment variable. This variable is prioritized over other environment variables used for authentication.

bash

export CVAT_ACCESS_TOKEN="token value"
cvat-cli task ls

Credentials can be specified via the --auth global CLI parameter. The password can be passed after the colon (:) separator or via the PASS environment variable. For better security, it's recommended to use the PASS environment variable or Personal Access Tokens.

bash

cvat-cli --auth "username:password" task ls

bash

export PASS="password"
cvat-cli --auth "username" task ls

The --auth parameter can also be omitted. In this case, the CLI will try to use the current OS user as the username. If the PASS environment variable is configured, it's value will be used for the password. Otherwise, the password will be requested for input.

bash

cvat-cli task ls

Examples - tasks

Create

Description of the options you can find in {{< ilink "/docs/workspace/tasks-page#create-annotation-task" "Creating an annotation task" >}} section.

For create a task you need file contain labels in the json format, you can create a JSON label specification by using the {{< ilink "/docs/workspace/tasks-page#create-annotation-task#labels" "label constructor" >}}.

<details> <summary>Example JSON labels file</summary>

json

[
    {
        "name": "cat",
        "attributes": []
    },
    {
        "name": "dog",
        "attributes": []
    }
]

</details>

Create a task named "new task" on the default server http://localhost, labels from the file "labels.json" and local images "file1.jpg" and "file2.jpg", the task will be created as current user:
bash
```
cvat-cli task create "new task" --labels labels.json local file1.jpg file2.jpg
```
Create a task named "task 1" on the server https://example.com labels from the file "labels.json" and local image "image1.jpg", the task will be created as user "user-1":
bash
```
cvat-cli --server-host https://example.com --auth user-1 task create "task 1" \
--labels labels.json local image1.jpg
```
Create a task named "task 1" on the default server, with labels from "labels.json" and local image "file1.jpg", as the current user, in organization "myorg":
bash
```
cvat-cli --org myorg task create "task 1" --labels labels.json local file1.jpg
```

Create a task named "task 1", labels from the project with id 1 and with a remote video file, the task will be created as user "user-1":

bash

cvat-cli --auth user-1:password task create "task 1" --project_id 1 \
remote https://github.com/opencv/opencv/blob/master/samples/data/vtest.avi?raw=true

Create a task named "task 1 sort random", with labels "cat" and "dog", with chunk size 8, with sorting-method random, frame step 10, copy the data on the CVAT server, with use zip chunks and the video file will be taken from the shared resource:
bash
```
cvat-cli task create "task 1 sort random" --labels '[{"name": "cat"},{"name": "dog"}]' --chunk_size 8 \
--sorting-method random --frame_step 10 --copy_data --use_zip_chunks share //share/dataset_1/video.avi
```
Create a task named "task from dataset_1", labels from the file "labels.json", with link to bug tracker, image quality will be reduced to 75, annotation in the format "CVAT 1.1" will be taken from the file "annotation.xml", the data will be loaded from "dataset_1/images/", the task will be created as user "user-2", and the password will need to be entered additionally:
bash
```
cvat-cli --auth user-2 task create "task from dataset_1" --labels labels.json \
--bug_tracker https://bug-tracker.com/0001 --image_quality 75 --annotation_path annotation.xml \
--annotation_format "CVAT 1.1" local dataset_1/images/
```

Create a task named "segmented task 1", labels from the file "labels.json", with overlay size 5, segment size 100, with frames 5 through 705, using cache and with a remote video file:

bash

cvat-cli task create "segmented task 1" --labels labels.json --overlap 5 --segment_size 100 \
--start_frame 5 --stop_frame 705 --use_cache \
remote https://github.com/opencv/opencv/blob/master/samples/data/vtest.avi?raw=true

Create a task named "task with filtered cloud storage data", with filename_pattern test_images/*.jpeg and using the data from the cloud storage resource described in the manifest.jsonl:

bash

cvat-cli task create "task with filtered cloud storage data" --labels '[{"name": "car"}]'\
--use_cache --cloud_storage_id 1 --filename_pattern "test_images/*.jpeg" share manifest.jsonl

Create a task named "task with filtered cloud storage data" using all data from the cloud storage resource described in the manifest.jsonl by specifying filename_pattern *:
bash
```
cvat-cli task create "task with filtered cloud storage data" --labels '[{"name": "car"}]'\
--use_cache --cloud_storage_id 1 --filename_pattern "*" share manifest.jsonl
```

Delete

Delete tasks with IDs "100", "101", "102" , the command will be executed from "user-1" having delete permissions:
bash
```
cvat-cli --auth user-1:password task delete 100 101 102
```

List

List all tasks:
bash
```
cvat-cli task ls
```
List all tasks in organization "myorg":
bash
```
cvat-cli --org myorg task ls
```
Save list of all tasks into file "list_of_tasks.json":
bash
```
cvat-cli task ls --json > list_of_tasks.json
```

Frames

Save frame 12, 15, 22 from task with id 119, into "images" folder with compressed quality:
bash
```
cvat-cli task frames --outdir images --quality compressed 119 12 15 22
```

Export as a dataset

Export annotation task with id 103, in the format CVAT for images 1.1 and save to the file "output.zip":
bash
```
cvat-cli task export-dataset --format "CVAT for images 1.1" 103 output.zip
```
Export annotation task with id 104, in the format COCO 1.0 and save to the file "output.zip":
bash
```
cvat-cli task export-dataset --format "COCO 1.0" 104 output.zip
```

Import annotations from a dataset

Import annotation into task with id 105, in the format CVAT 1.1 from the file "annotation.xml":
bash
```
cvat-cli task import-dataset --format "CVAT 1.1" 105 annotation.xml
```

Back up a task

Back up task with id 136 to file "task_136.zip":
bash
```
cvat-cli task backup 136 task_136.zip
```

Create from backup

Create a task from backup file "task_backup.zip":
bash
```
cvat-cli task create-from-backup task_backup.zip
```

Auto-annotate

This command provides a command-line interface to the {{< ilink "/docs/api_sdk/sdk/auto-annotation" "auto-annotation API" >}}.

It can auto-annotate using AA functions implemented in one of the following ways:

As a Python module directly implementing the AA function protocol. Such a module must define the required attributes at the module level.

For example:
python
```
import cvat_sdk.auto_annotation as cvataa

spec = cvataa.DetectionFunctionSpec(...)

def detect(context, image):
    ...
```

As a Python module implementing a factory function named create. This function must return an object implementing the AA function protocol. Any parameters specified on the command line using the -p option will be passed to create.

For example:

python

import cvat_sdk.auto_annotation as cvataa

class _MyFunction:
    def __init__(...):
        ...

    spec = cvataa.DetectionFunctionSpec(...)

    def detect(context, image):
        ...

def create(...) -> cvataa.DetectionFunction:
    return _MyFunction(...)

Annotate the task with id 137 with the predefined torchvision detection function, which is parameterized:

bash

cvat-cli task auto-annotate 137 --function-module cvat_sdk.auto_annotation.functions.torchvision_detection \
    -p model_name=str:fasterrcnn_resnet50_fpn_v2 -p box_score_thresh=float:0.5

Annotate the task with id 138 with an AA function defined in my_func.py:
bash
```
cvat-cli task auto-annotate 138 --function-file path/to/my_func.py
```

Note that this command does not modify the Python module search path. If your function module needs to import other local modules, you must add your module directory to the search path if it isn't there already.

Annotate the task with id 139 with a function defined in the my_func module located in the my-project directory, letting it import other modules from that directory.
bash
```
PYTHONPATH=path/to/my-project cvat-cli task auto-annotate 139 --function-module my_func
```

Examples - projects

Create

While creating a project, you may optionally define its labels. The project create command accepts labels in the same format as the task create command; see that command's examples for more information.

Create a project named "new project" on the default server http://localhost, with labels from the file "labels.json":
bash
```
cvat-cli project create "new project" --labels labels.json
```

Create a project from a dataset in the COCO format:

bash

cvat-cli project create "new project" --dataset_file coco.zip --dataset_format "COCO 1.0"

Delete

Delete projects with IDs "100", "101", "102":
bash
```
cvat-cli project delete 100 101 102
```

List

List all projects:
bash
```
cvat-cli project ls
```
Save list of all projects into file "list_of_projects.json":
bash
```
cvat-cli project ls --json > list_of_projects.json
```

Examples - functions

Note: The functionality described in this section can only be used with the CVAT Enterprise or CVAT Cloud.

Create

Create a function that uses a detection model from torchvision and run an agent for it:

cvat-cli function create-native "Faster R-CNN" \
    --function-module cvat_sdk.auto_annotation.functions.torchvision_detection \
    -p model_name=str:fasterrcnn_resnet50_fpn_v2
cvat-cli function run-agent <ID printed by previous command> \
    --function-module cvat_sdk.auto_annotation.functions.torchvision_detection \
    -p model_name=str:fasterrcnn_resnet50_fpn_v2

Create and run an SAM2 tracking function:

cvat-cli function create-native "SAM2" \
    --function-file=<CVAT_DIR>/ai-models/tracker/sam2/func.py \
    -p model_id=str:facebook/sam2.1-hiera-tiny
cvat-cli function run-agent <ID printed by previous command> \
    --function-file=<CVAT_DIR>/ai-models/tracker/sam2/func.py \
    -p model_id=str:facebook/sam2.1-hiera-tiny

These commands accept functions that implement the {{< ilink "/docs/api_sdk/sdk/auto-annotation" "auto-annotation function interface" >}} from the SDK, same as the task auto-annotate command. See that command's examples for information on how to implement these functions and specify them in the command line.

For detailed SAM2 setup instructions, see the {{< ilink "/docs/annotation/auto-annotation/segment-anything-2-tracker" "SAM2 Tracker documentation" >}}.

Delete

Delete functions with IDs 100 and 101:
```
cvat-cli function delete 100 101
```