Supervisely
About SuperviselyEcosystemContact usSlack
  • 💻Supervisely Developer Portal
  • 🎉Getting Started
    • Installation
    • Basics of authentication
    • Intro to Python SDK
    • Environment variables
    • Supervisely annotation format
      • Project Structure
      • Project Meta: Classes, Tags, Settings
      • Objects
      • Tags
      • Image Annotation
      • Video Annotation
      • Point Clouds Annotation
      • Point Cloud Episode Annotation
      • Volumes Annotation
    • Python SDK tutorials
      • Images
        • Images
        • Image and object tags
        • Spatial labels on images
        • Keypoints (skeletons)
        • Multispectral images
        • Multiview images
        • Advanced: Optimized Import
        • Advanced: Export
      • Videos
        • Videos
        • Video and object tags
        • Spatial labels on videos
      • Point Clouds
        • Point Clouds (LiDAR)
        • Point Cloud Episodes and object tags
        • 3D point cloud object segmentation based on sensor fusion and 2D mask guidance
        • 3D segmentation masks projection on 2D photo context image
      • Volumes
        • Volumes (DICOM)
        • Spatial labels on volumes
      • Common
        • Iterate over a project
        • Iterate over a local project
        • Progress Bar tqdm
        • Cloning projects for development
    • Command Line Interface (CLI)
      • Enterprise CLI Tool
        • Instance administration
        • Workflow automation
      • Supervisely SDK CLI
    • Connect your computer
      • Linux
      • Windows WSL
      • Troubleshooting
  • 🔥App development
    • Basics
      • Create app from any py-script
      • Configuration file
        • config.json
        • Example 1. Headless
        • Example 2. App with GUI
        • v1 - Legacy
          • Example 1. v1 Modal Window
          • Example 2. v1 app with GUI
      • Add private app
      • Add public app
      • App Compatibility
    • Apps with GUI
      • Hello World!
      • App in the Image Labeling Tool
      • App in the Video Labeling Tool
      • In-browser app in the Labeling Tool
    • Custom import app
      • Overview
      • From template - simple
      • From scratch - simple
      • From scratch GUI - advanced
      • Finding directories with specific markers
    • Custom export app
      • Overview
      • From template - simple
      • From scratch - advanced
    • Neural Network integration
      • Overview
      • Serving App
        • Introduction
        • Instance segmentation
        • Object detection
        • Semantic segmentation
        • Pose estimation
        • Point tracking
        • Object tracking
        • Mask tracking
        • Image matting
        • How to customize model inference
        • Example: Custom model inference with probability maps
      • Serving App with GUI
        • Introduction
        • How to use default GUI template
        • Default GUI template customization
        • How to create custom user interface
      • Inference API
      • Training App
        • Overview
        • Tensorboard template
        • Object detection
      • High level scheme
      • Custom inference pipeline
      • Train and predict automation model pipeline
    • Advanced
      • Advanced debugging
      • How to make your own widget
      • Tutorial - App Engine v1
        • Chapter 1 Headless
          • Part 1 — Hello world! [From your Python script to Supervisely APP]
          • Part 2 — Errors handling [Catching all bugs]
          • Part 3 — Site Packages [Customize your app]
          • Part 4 — SDK Preview [Lemons counter app]
          • Part 5 — Integrate custom tracker into Videos Annotator tool [OpenCV Tracker]
        • Chapter 2 Modal Window
          • Part 1 — Modal window [What is it?]
          • Part 2 — States and Widgets [Customize modal window]
        • Chapter 3 UI
          • Part 1 — While True Script [It's all what you need]
          • Part 2 — UI Rendering [Simplest UI Application]
          • Part 3 — APP Handlers [Handle Events and Errors]
          • Part 4 — State and Data [Mutable Fields]
          • Part 5 — Styling your app [Customizing the UI]
        • Chapter 4 Additionals
          • Part 1 — Remote Developing with PyCharm [Docker SSH Server]
      • Custom Configuration
        • Fixing SSL Certificate Errors in Supervisely
        • Fixing 400 HTTP errors when using HTTP instead of HTTPS
      • Autostart
      • Coordinate System
      • MLOps Workflow integration
    • Widgets
      • Input
        • Input
        • InputNumber
        • InputTag
        • BindedInputNumber
        • DatePicker
        • DateTimePicker
        • ColorPicker
        • TimePicker
        • ClassesMapping
        • ClassesColorMapping
      • Controls
        • Button
        • Checkbox
        • RadioGroup
        • Switch
        • Slider
        • TrainValSplits
        • FileStorageUpload
        • Timeline
        • Pagination
      • Text Elements
        • Text
        • TextArea
        • Editor
        • Copy to Clipboard
        • Markdown
        • Tooltip
        • ElementTag
        • ElementTagsList
      • Media
        • Image
        • LabeledImage
        • GridGallery
        • Video
        • VideoPlayer
        • ImagePairSequence
        • Icons
        • ObjectClassView
        • ObjectClassesList
        • ImageSlider
        • Carousel
        • TagMetaView
        • TagMetasList
        • ImageAnnotationPreview
        • ClassesMappingPreview
        • ClassesListPreview
        • TagsListPreview
        • MembersListPreview
      • Selection
        • Select
        • SelectTeam
        • SelectWorkspace
        • SelectProject
        • SelectDataset
        • SelectItem
        • SelectTagMeta
        • SelectAppSession
        • SelectString
        • Transfer
        • DestinationProject
        • TeamFilesSelector
        • FileViewer
        • Dropdown
        • Cascader
        • ClassesListSelector
        • TagsListSelector
        • MembersListSelector
        • TreeSelect
        • SelectCudaDevice
      • Thumbnails
        • ProjectThumbnail
        • DatasetThumbnail
        • VideoThumbnail
        • FolderThumbnail
        • FileThumbnail
      • Status Elements
        • Progress
        • NotificationBox
        • DoneLabel
        • DialogMessage
        • TaskLogs
        • Badge
        • ModelInfo
        • Rate
        • CircleProgress
      • Layouts and Containers
        • Card
        • Container
        • Empty
        • Field
        • Flexbox
        • Grid
        • Menu
        • OneOf
        • Sidebar
        • Stepper
        • RadioTabs
        • Tabs
        • TabsDynamic
        • ReloadableArea
        • Collapse
        • Dialog
        • IFrame
      • Tables
        • Table
        • ClassicTable
        • RadioTable
        • ClassesTable
        • RandomSplitsTable
        • FastTable
      • Charts and Plots
        • LineChart
        • GridChart
        • HeatmapChart
        • ApexChart
        • ConfusionMatrix
        • LinePlot
        • GridPlot
        • ScatterChart
        • TreemapChart
        • PieChart
      • Compare Data
        • MatchDatasets
        • MatchTagMetas
        • MatchObjClasses
        • ClassBalance
        • CompareAnnotations
      • Widgets demos on github
  • 😎Advanced user guide
    • Objects binding
    • Automate with Python SDK & API
      • Start and stop app
      • User management
      • Labeling Jobs
  • 🖥️UI widgets
    • Element UI library
    • Supervisely UI widgets
    • Apexcharts - modern & interactive charts
    • Plotly graphing library
  • 📚API References
    • REST API Reference
    • Python SDK Reference
Powered by GitBook
On this page
  • Introduction
  • Option 1. Use our SDK export template class sly.app.Export (simple)
  • Option 2. Create an app from scratch (advanced)
  • More details about sly.app.Export. See source code
  • Set up an environment for development

Was this helpful?

Edit on GitHub
  1. App development
  2. Custom export app

Overview

PreviousCustom export appNextFrom template - simple

Last updated 2 years ago

Was this helpful?

Introduction

There are many different applications in the Supervisely ecosystem for exporting data to various popular formats. However, companies often need to implement custom data export in their specific format to meet their particular requirements.

In the upcoming tutorial series, you will learn 2 ways to create a custom export app for exporting data from the Supervisely platform.

Option 1. Use our SDK export template class sly.app.Export (simple)

👍 This way is more convenient and can handle most of the routine tasks and cover most required use cases. All you need to do is create your own class (inherit from sly.app.Export), and override the process method. Method process should return the path to the result data (folder or archive).

.

.

Option 2. Create an app from scratch (advanced)

It is more recommended way to use SDK export template class (sly.app.Export) to create custom export app. However, if your use case is not covered by our export template, you can create your own app without the template. We will also learn this way in the upcoming tutorial series.

.

.

More details about sly.app.Export.

sly.app.Export class will handle export routines for you:

  • it will check that selected project or dataset exist and that you have access to work with it,

  • it will upload your result data to Team Files and clean temporary folder, containing result archive in remote container or local hard drive if you are debugging your app.

  • Your application must return string, containing path to result archive or folder. If you return path to folder - this folder will be automatically archived.

sly.app.Export has a Context subclass which contains all required information that you need for exporting your data from Supervisely platform:

  • Team ID - shows team id where exporting project or dataset is located

  • Workspace ID - shows workspace id where exporting project or dataset is located

  • Project ID - id of exporting project

  • Dataset ID - id of exporting dataset (detected only if the export is performed from dataset context menu)

context variable is passed as an argument to process method of class MyExport and context object will be created automatically when you execute export script.

class MyExport(sly.app.Export):
    def process(self, context: sly.app.Export.Context):
        print(context)

Output:

Team ID: 435
Workspace ID: 680
Project ID: 15623
Dataset ID: 53491

Set up an environment for development

The following pages of the guide about creating a custom export app will refer to this section, which describes the preparation of the working environment.

For both options, you need to prepare a development environment. Follow the steps below:

git clone https://github.com/<my_team>/<my_repo>
cd <my_repo>
./create_venv.sh

Step 3. Open repository directory in Visual Studio Code.

code -r .

Step 4. Select created virtual environment as python interpreter.

TASK_ID=10                    # ⬅️ requires to use advanced debugging
TEAM_ID=447                   # ⬅️ change it
WORKSPACE_ID=680              # ⬅️ change it
PROJECT_ID=20934              # ⬅️ ID of the project that you want to export
DATASET_ID=64985              # ⬅️ ID of the dataset that you want to export (leave empty if you want to export whole project)
SLY_APP_DATA_DIR="results/"   # ⬅️ path to directory for local debugging

Please note that the path you specify in the SLY_APP_DATA_DIR variable will be used for saving application results and temporary files (temporary files will be removed at the end).

For example:

  • path on your local computer could be /Users/maxim/my_data/

  • path in the current project folder on your local computer could be results/

Don't forget to add this path to .gitignore to exclude it from the list of files tracked by Git.

When running the app from Supervisely platform: Project and Dataset IDs will be automatically detected depending on how you run your application.

We advise reading our guide if you are unfamiliar with the of a Supervisely app repository because it addresses the majority of the potential questions.

Step 1. Prepare ~/supervisely.env file with credentials.

Step 2. Fork and clone repository with source code and create .

Step 5. Open local.env and insert your values here. Learn more about environment variables in our

Change variables in local.env
🔥
✅ Learn step-by-step tutorial here
💻 Source code
✅ Learn step-by-step tutorial here
💻 Source code
See source code
Virtual Environment
guide
from script to supervisely app
file structure
Learn more here.