# Cityscapes

## Overview

This converter allows to import images with `.json` annotations in [Cityscapes](https://github.com/mcordts/cityscapesScripts) format.

⚠️ **Note:** images must have suffix `_leftImg8bit` and annotations suffix `_gtFine_polygons` and `.json` extension. Check the example of the file structure below.

![Result of the import](/files/xEL1LMfFJzkTthfY4jcl)

Enterprise users have access to "Import as links" option, which supports import of this format with annotations. This option might be beneficial in many cases, as it allows data import to Supervisely platform without re-uploading, maintaining a single source and speeding up import process.

To step up import speed even further you can compress all annotation files (`.json`'s) into an archive and import it together with the images. (Note: This method is format-dependent and may not apply to all formats.)

## Format description

**Supported image formats:** `.jpg`, `.jpeg`, `.mpo`, `.bmp`, `.png`, `.webp`, `.tiff`, `.tif`, `.jfif`, `.avif`, `.heic`, and `.heif`\
**With annotations:** yes\
**Supported annotation file extension:** `.json`.\
**Grouped by:** any structure (uploaded to a single dataset)\\

## Input files structure

{% hint style="success" %}
Example data: [download ⬇️](https://github.com/supervisely-ecosystem/import-wizard-docs/files/14908276/sample_cityscapes.zip)\\
{% endhint %}

Recommended directory structure:

```
📦project name
 ┣ 📂gtFine
 ┃ ┣ 📂test
 ┃ ┃ ┗ 📂ds1
 ┃ ┃ ┃ ┗ 📜IMG_8144_gtFine_polygons.json
 ┃ ┣ 📂train
 ┃ ┃ ┗ 📂ds1
 ┃ ┃ ┃ ┣ 📜IMG_1836_gtFine_polygons.json
 ┃ ┃ ┃ ┣ 📜IMG_2084_gtFine_polygons.json
 ┃ ┃ ┃ ┣ 📜IMG_3861_gtFine_polygons.json
 ┃ ┃ ┃ ┗ 📜IMG_4451_gtFine_polygons.json
 ┃ ┗ 📂val
 ┃ ┃ ┗ 📂ds1
 ┃ ┃ ┃ ┗ 📜IMG_0748_gtFine_polygons.json
 ┣ 📂leftImg8bit
 ┃ ┣ 📂test
 ┃ ┃ ┗ 📂ds1
 ┃ ┃ ┃ ┗ 🖼️IMG_8144_leftImg8bit.png
 ┃ ┣ 📂train
 ┃ ┃ ┗ 📂ds1
 ┃ ┃ ┃ ┣ 🖼️IMG_1836_leftImg8bit.png
 ┃ ┃ ┃ ┣ 🖼️IMG_2084_leftImg8bit.png
 ┃ ┃ ┃ ┣ 🖼️IMG_3861_leftImg8bit.png
 ┃ ┃ ┃ ┗ 🖼️IMG_4451_leftImg8bit.png
 ┃ ┗ 📂val
 ┃ ┃ ┗ 📂ds1
 ┃ ┃ ┃ ┗ 🖼️IMG_0748_leftImg8bit.png
 ┗ 📜class_to_id.json
```

## Format Config File

In order to import custom annotations for the images, you need to provide a `class_to_id.json` file. This file should contain a list with dictionaries. Each dictionary should contain information about the class with the following fields:

* `name` - the name of the class. It should be unique.
* `id` - the ID of the class. From 1 to N-1, where N is the number of classes.
* `color` - the color of the class in RGB format. If not specified, the color will be generated randomly

<details>

<summary>📜class_to_id.json</summary>

```json
[
  {
    "name": "kiwi",
    "id": 1,
    "color": [255, 0, 0]
  },
  {
    "name": "lemon",
    "id": 2,
    "color": [81, 198, 170]
  }
]
```

</details>

## Single-Image Annotation JSON

Annotation file should contain the following fields:

* `imgHeight` - the height of the image
* `imgWidth` - the width of the image
* `objects` - a list of dictionaries, each containing information about the object
  * `label` - the name of the class
  * `polygon` - a list of points that form the polygon of the object

Example of the annotation file from provided sample data:

<details>

<summary>📜IMG_1836_gtFine_polygons.json</summary>

```json
{
    "imgHeight": 800,
    "imgWidth": 1067,
    "objects": [
        {
            "label": "lemon",
            "polygon": [
                [772, 421],
                [771, 422],
                ...
                [785, 422],
                [784, 421]
            ]
        },
        {
            "label": "kiwi",
            "polygon": [
                [637, 122],
                [636, 123],
                ...
                [645, 123],
                [644, 122]
            ]
        },
        {
            "label": "kiwi",
            "polygon": [
                [543, 539],
                [542, 540],
                ...
                [548, 540],
                [547, 539]
            ]
        }
    ]
}
```

</details>

## Useful links

* [Cityscapes format](https://github.com/mcordts/cityscapesScripts)
* [\[Supervisely Ecosystem\] Import Cityscapes](https://ecosystem.supervisely.com/apps/import-cityscapes)


---

# Agent Instructions: Querying This Documentation

If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question.

Perform an HTTP GET request on the current page URL with the `ask` query parameter:

```
GET https://docs.supervisely.com/import-and-export/import/supported-annotation-formats/images/cityscapes.md?ask=<question>
```

The question should be specific, self-contained, and written in natural language.
The response will contain a direct answer to the question and relevant excerpts and sources from the documentation.

Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.