From template - simple

A step-by-step tutorial of how to create custom import app using import template from SDK class `sly.app.Import`.

Introduction

In this tutorial, we will create a simple import app that will upload images from a folder, archive or .txt file to Supervisely using import app template from SDK.

Data example

We prepared an app that can import images from: folder, archive or .txt file to Supervisely server. We will review each case separately.

folder and archive structure:

📂my_folder         🗃️ my_archive.zip
┣ 🖼️cat_1.jpg       ┣ 🖼️cat_1.jpg
┣ 🖼️cat_2.jpg       ┣ 🖼️cat_2.jpg
┗ 🖼️cat_3.jpg       ┗ 🖼️cat_3.jpg

.txt file:

https://github.com/supervisely-ecosystem/demo-data-for-import-template/releases/download/images/pexels-couleur-2317904.jpg
https://github.com/supervisely-ecosystem/demo-data-for-import-template/releases/download/images/pexels-kammeran-gonzalezkeola-7925859.jpg
https://github.com/supervisely-ecosystem/demo-data-for-import-template/releases/download/images/pexels-stijn-dijkstra-7177188.jpg
https://github.com/supervisely-ecosystem/demo-data-for-import-template/releases/download/images/pexels-taryn-elliott-3889728.jpg
https://github.com/supervisely-ecosystem/demo-data-for-import-template/releases/download/images/pexels-taryn-elliott-9565787.jpg

You can find the above demo folder in the data directory of the template-import-app repo - here

Tutorial content

Everything you need to reproduce this tutorial is on GitHub: main.py.

Before we begin, please clone the project and set up the working environment - here is a link with a description of the steps.

Step 1. How to debug import app

Open local.env files and set up environment variables by inserting your values here for debugging.

Put data that you want to import to the folder specified in SLY_APP_DATA_DIR. This tutorial comes with demo data that you can use for debugging. You can find it in the data directory of the template-import-app repo - see Data example section.

For this example, we will use the following environment variables:

local.env:

TEAM_ID=8                      # ⬅️ change it to your team ID
WORKSPACE_ID=349               # ⬅️ change it to your workspace ID
SLY_APP_DATA_DIR="input/"      # ⬅️ path to directory for local debugging

# Optional. Specify following variables if you want to import data to existing:
# PROJECT_ID=20811             # ⬅️ put your value here
# DATASET_ID=64686             # ⬅️ put your value here | requires PROJECT_ID

Learn more about environment variables in our guide

For advanced debugging with GUI see Step 3

Step 2. Illustrative example of practical use case

This illustrative example showcasing a simple use case, by leveraging its out-of-the-box graphical user interface (GUI), developers can focus on implementing their own data processing code without having to worry about the underlying infrastructure. The GUI is organized into four sequential steps, each step is designed to guide users through all the necessary options and configurations for import.

In this example we will use local debug mode, and upload files from the local data folder to Supervisely server.

Step 1. Select Data

You can specify data folder in local.env file. By default it is set to input/ folder in the root of the project. Place data that you want to import inside this folder.

SLY_APP_DATA_DIR="input/"

Step 2. Settings

In local debug mode checkbox to remove source files after import has no effect, so it is disabled and unchecked. In production mode, if you check this box, the source data will be deleted from Supervisely server after import.

In this step you can add your custom settings using Supervisely widgets. We will not be adding any custom settings in this example. You can learn more about how to add custom settings in sly.app.Import reference section.

Step 3. Destination Project

In this step you can select destination project, by default New Project tab is selected. In this tab you can select destination: Team, Workspace, and enter new project's name. If you want to import data to existing project or existing dataset, you can select corresponding tab in GUI.

Step 4. Output

This step contains Start Import button and information about the selections you made in previous steps:

Path to source data that will be imported
Destination where data will be imported
State of the checkbox to remove source data after import

If your import implementation code is correct, data has been successfully imported and there are no errors, you will see project thumbnail with the link to the project on Supervisely server.

Step 3. How to write an import script

Find source code for this example here

Step 1. Import libraries

import os
import shutil
from pathlib import Path

import requests
import supervisely as sly
from dotenv import load_dotenv

Step 2. Load environment variables

Load ENV variables for debug, has no effect in production

if sly.is_production():
    load_dotenv("advanced.env")
else:
    load_dotenv("local.env")
load_dotenv(os.path.expanduser("~/supervisely.env"))

Step 3. Create class MyImport that inherits from sly.app.Import with process method

class MyImport(sly.app.Import):
    def process(self, context: sly.app.Import.Context):
        ...

Step 4. Reimplement process method

Create api object to communicate with Supervisely Server
Get or create project and dataset
Process import data (see step 5)
Upload images to dataset (see step 6)
Return result project id

class MyImport(sly.app.Import):
    def process(self, context: sly.app.Import.Context):
        # create api object to communicate with Supervisely Server
        api = sly.Api.from_env()

        # get or create project
        project_id = context.project_id
        if project_id is None:
            project = api.project.create(
                workspace_id=context.workspace_id,
                name=context.project_name or "My Project",
                change_name_if_conflict=True,
            )
            project_id = project.id

        # get or create dataset
        dataset_id = context.dataset_id
        if dataset_id is None:
            dataset = api.dataset.create(
                project_id=project_id, name="ds0", change_name_if_conflict=True
            )
            dataset_id = dataset.id

        # process images and upload them by paths
        images_names, images_paths = process_data(context)
        upload_images(api, dataset_id, images_names, images_paths, context.progress)

        # clean local data dir after successful import
        sly.fs.remove_dir(context.path)
        return project_id

Step 5. Write function to process data

In this app example function process_data can process 3 types of data:

folder - process_folder function
archive - process_archive function
.txt file with links to images - process_text_file function

For local debugging, put data that you want to import to the folder specified in SLY_APP_DATA_DIR in local.env file. For advanced debugging, selected data is downloaded automatically to the folder specified in SLY_APP_DATA_DIR in advanced.env file.

Data processing logic is up to you. You can implement any logic that you need to process your data. Please see main.py file for implementation details

def process_data(context):
    path = os.path.join(context.path, os.listdir(context.path)[0])
    if os.path.isdir(path):
        images_names, images_paths = process_folder(path)
    elif sly.fs.get_file_ext(path) == ".txt":
        images_names, images_paths = process_text_file(path, context.progress)
    else:
        images_names, images_paths = process_archive(path)
    return images_names, images_paths

Step 6. Write function to upload images to dataset

def upload_images(api, dataset_id, images_names, images_paths, progress):
    # process images and upload them by paths
    with progress(total=len(images_paths)) as pbar:
        for img_name, img_path in zip(images_names, images_paths):
            try:
                # upload image into dataset on Supervisely server
                info = api.image.upload_path(dataset_id=dataset_id, name=img_name, path=img_path)
                sly.logger.trace(f"Image has been uploaded: id={info.id}, name={info.name}")
            except Exception as e:
                sly.logger.warn("Skip image", extra={"name": img_name, "reason": repr(e)})
            finally:
                pbar.update(1)

Step 7. Create app object and execute run() method

app = MyImport()
app.run()

Step 4. Advanced debug

Advanced debug is for final app testing. In this case, import app will download data from Supervisely server and upload images to new project. You can use this mode to test your app before publishing it to the Ecosystem.

Upload demo data provided in the repository to Supervisely Team Files in order to use it in the app or use your own data.

To switch between local and advanced debug modes, select corresponding debug configuration in Run & Debug menu in VS Code

advanced.env:

TEAM_ID=8                         # ⬅️ change it to your team ID
WORKSPACE_ID=349                  # ⬅️ change it to your workspace ID
SLY_APP_DATA_DIR="input/"         # ⬅️ path to directory for local debugging

# Optional. Specify one of the following variables if you want to simulate import from:
# FOLDER="/data/my_folder"        # ⬅️ path to directory with data
# FILE="/data/my_archive.zip"     # ⬅️ path to archive with data
# FILE="/data/my_file.txt"        # ⬅️ path to text file with links to images

# or one of the following variables if you want to import data to existing:
# PROJECT_ID=20811               # ⬅️ put your value here
# DATASET_ID=64686               # ⬅️ put your value here | requires PROJECT_ID

Please note that the path you specify in the SLY_APP_DATA_DIR variable will be used for storing import data, it means that data that you select or drag & drop in GUI will be automatically downloaded to this folder.

For example:

path on your local computer could be /Users/admin/projects/template-import-app/input/
path in the current project folder on your local computer could be input/

Also note that all paths in Supervisely server are absolute and start from '/' symbol, so you need to specify the full path to the folder, for example /data/my_folder/

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

`sly.app.Import` reference

Import template has GUI out of the box and allows you skip boilerplate/routine operations. The only thing you need to do is to code your logic in the process method. Import template is customizable and allows you to tail import app to your needs.

You can customize sly.app.Import class by passing arguments to the constructor:

allowed_project_types

Pass list of project types that you want to allow for import. By default, all project types are allowed. If you pass None, all project types will be allowed in project selector.

Available project types: ["images", "videos", "volumes", "pointclouds", "pointcloud_episodes"]

app = MyImport(allowed_project_types=[sly.ProjectType.Volumes])

allowed_destination_options

Pass list of destination options that you want to allow for import. By default, all destination options are allowed. If you pass None, all destination options will be allowed.

Allowed destinations: ["new_project", "existing_project", "existing_dataset"]

app = MyImport(allowed_destination_options=["New Project"])

app = MyImport(allowed_destination_options=["New Project", "Existing Project"])

allowed_data_type

Pass list of data types that you want to allow for import. By default, all data types are allowed. If you pass None, all data types will be allowed.

Allowed data types: ["folder", "file"]

app = MyImport(allowed_data_type="folder")

sly.app.Import class has 2 methods:

process(self, context)
add_custom_settings(self)

method process(self, context)

Main method where you implement your import logic, process and upload data to Supervisely server. This method must return project ID

def process(self, context: sly.app.Import.Context):
    # get or create project
    project_id = context.project_id
    if project_id is None:
        project = api.project.create(
            workspace_id=context.workspace_id,
            name=context.project_name or "My Project",
            change_name_if_conflict=True,
        )
        project_id = project.id
    # implement your import logic here
    return project_id

context is passed as an argument to the process method and the context object will be created automatically when you execute import script. context contains all the necessary information about the import process.

You can get the following information from the context:

Team ID - ID of the destination Team
Workspace ID - ID of the destination Workspace
Project ID - ID of an existing project to which data will be imported. None if you import data to a new project
Dataset ID - ID of an existing dataset to which data will be imported. None if you import data to a new dataset
Path - Path to your data on the local machine.
Project name - name of the project provided in the GUI if import data to new project
Progress - tqdm progress that you can use in your app to update progress bar
Is on agent - Shows if your data is located on the agent or not

class MyImport(sly.app.Import):
    def process(self, context: sly.app.Import.Context):
        print(context)
        # implement your import logic here

Output:

Team ID: 8
Workspace ID: 349
Project ID: 8534
Dataset ID: 22852
Path: /data/my_file.txt
Project name: ""
Is on agent: False

method add_custom_settings(self)

You can add custom settings to the import template using the add_custom_settings(self) method. This method should return any Widget. If you want to add multiple widgets, you can return a widget Container.

class MyImport(sly.app.Import):
    def add_custom_settings(self):
        # create widget
        self.ann_checkbox = sly.app.widgets.Checkbox("Upload annotations", True)
        # return widget with custom settings
        return self.ann_checkbox
        
    def process(self, context: sly.app.Import.Context):
        ...
        # get widget state in process method
        with_annotations = self.ann_checkbox.is_checked()
        ...

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