Deepomatic Platform
  • Overview
  • Release notes
    • January 2025
    • November 21, 2024
    • October 17, 2024
    • September 19, 2024
    • July 18, 2024
    • June 27, 2024
    • May 23, 2024
    • April 18, 2024
    • March 21, 2024
    • February 22, 2024
    • January 18, 2024
    • December 13, 2023
    • October 26, 2023
    • July 20, 2023
    • June 29, 2023
    • May 29, 2023
    • April 27, 2023
    • March 30, 2023
    • February 17, 2023
    • January 19, 2023
    • December 22, 2022
    • November 18, 2022
    • October 19, 2022
    • September 19, 2022
    • July 27, 2022
    • June 26, 2022
    • May 17, 2022
    • April 13, 2022
    • March 17, 2022
    • February 10, 2022
    • December 21, 2021
    • October 26, 2021
  • Getting started
  • ADMIN & USER MANAGEMENT
    • Invite and manage users
      • Invite group of users at once
      • SSO
        • Azure Active Directory
  • Deepomatic Engage
    • Integrate applications
      • Deepomatic vocabulary
      • Deepomatic connectors
        • Set-up
        • Camera Connector
        • Work Order Connector
      • API integration
        • Authentication
        • Errors
        • API reference
          • Work order management
          • Analysis
            • Guide field workers
            • Perform an analysis
            • Correct an analysis
          • Data retrieval
          • Endpoints' list
      • Batch processing
        • Format
        • Naming conventions
        • Processing
        • Batch status & errors
      • Data export
    • Use the mobile application
      • Configure a mobile application
      • Create & visualize work orders
      • Complete work orders
      • Offline experience
    • Manage your business operations with customisable solutions
      • Roles
      • Alerting
      • Field services
        • Reviewing work orders
        • Exploring work orders
        • Grouping work orders
        • Monitoring assets performance
      • Insights
  • Security
    • Security
    • Data Protection
Powered by GitBook
On this page
  • Import file format
  • File structure
  • Header
  • Images

Was this helpful?

  1. Deepomatic Drive
  2. Adding and managing data
  3. Adding images
  4. [EOL] Use Deepomatic CLI

Deepomatic CLI import txt file

Was this helpful?

Import file format

Uploading images with a text file is the recommended solution if you need to add a large number of images.

File structure

The text file contains a set of JSON objects:

  • the first object is a header allowing you to define the tree structure of the views composing your project

  • the other objects correspond to all the images you wish to import, one per image.

This structure allows the upload of very large quantities of data by streaming.

Header

The header is a JSON object that allows you to define your views and the concepts attached to each of them. It is also in the header that you indicate the tree structure of your views, and how the child and parent views are linked together.

Header json
{
  "name": "Bulk items",
  "splits": [
    "train",
    "val"
  ],
  "views": [
    {
      "concepts": [
        {
            "name": "With Item"
        },
        {
            "name": "Without Item"
        }
      ],
      "type": "classification",
      "name": "Item or not",
      "conditions": []
    },
    {
      "concepts": [
        {
            "name": "Item"
        }
      ],
      "type": "detection",
      "name": "Item Detector",
      "conditions": [
        [
          {
              "name": "With Item"
          }
        ]
      ]
    },
    {
      "concepts": [
        {
            "name": "Glass"
        },
        {
            "name": "Metal"
        },
        {
            "name": "Wood"
        }
      ],
      "type": "classification",
      "name": "Item material",
      "conditions": [
        [
          {
              "name": "Item"
          }
        ]
      ]
    }
  ]
}

Here are the fields you need to specify:

  • name : the name of your project

  • splits : a list which only supports train and val for now.

  • views : each entry in the views list defines a view and contains the following fields:

    • name : the name of your view

    • concepts : each entry in the concepts list defines a concept and contains the following field:

      • name: the name of your concept.

    • conditions : a list of lists of concepts to specify AND and OR conditions. A list of concepts specifies a AND condition and the list of lists specifies OR conditions.

Concepts must be unique within a project. It is not possible to have several concepts with the same name in separate views.

The header in your file should be on a single line and will allow the platform to build all the views and all the concepts that you have specified, based on the conditions you have indicated.

Images

Each image you want to add to your project is a JSON object that must be written on a single line of your file.

Image
{
  "id": null,
  "data": [
    {
      "file": "/path"
    }
  ],
  "metadata": "{}",
  "annotations": [
    {
      "concepts": [
        {
          "name": "Glass",
          "bool": true
        },
        {
          "name": "Item",
          "bool": true
        }
      ],
      "region": {
        "bbox": {
          "xmin": 0.14237532448083068,
          "ymin": 0.2097216707202064,
          "xmax": 0.7449424670527156,
          "ymax": 0.9198461929815852
        }
      }
    },
    {
      "concepts": [
        {
          "name": "With Item",
          "bool": true
        }
      ],
      "region": {
        "bbox": {
          "xmin": 0.0,
          "ymin": 0.0,
          "xmax": 1.0,
          "ymax": 1.0
        }
      }
    }
  ],
  "splits": [
    "train"
  ]
}

Here are the fields you need to specify:

  • id : null

  • data : each entry in the data list is an object that contains the field file that you need to specify. ⚠️ As your images are stored locally specify the path to the image using the file key.

  • metadata : a string of characters via which you can add metadata to your image as a dictionary. The metadata are displayed in the information popup on Studio.

Some data are automatically added to the platform: File name, Created, Creator and Last annotator are set automatically.

  • annotations: each entry in the annotations list corresponds to a region, with all the information attached to it:

    • concepts: the list of concepts attached to the region. You should not create an annotation for each view, but instead you need to combine the concepts from all the views required in a single annotation.

    • region: the actual region, described as a bounding box bbox, and the coordinates of this bounding box, xmin, ymin, xmax and ymax.

  • splits: a list of splits to which the image belongs. You should choose between train and val for now.

The difference between classification and tagging views is that for classification views, the annotation can only have one of the concept with bool field at True.

type : the type of your view among classification, tagging or detection ()

see the creation of views to know more