> For the complete documentation index, see [llms.txt](https://docs.cloudfactory.com/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.cloudfactory.com/import-formats/supported-import-types.md).
# Supported import types
### Contents
Hasty support an import of existing annotations of the following types:
* **`bbox`** - Bounding boxes (as a JSON file)
* **`polygon`** - Polygons (as a JSON file)
* **`semantic`** - Semantic segmentation (as a PNG image)
* **`label_class`** - Label classes (as a JSON file)
* **`hasty_export`** - JSON File with a structure of the Hasty JSON V1.1
#### **`bbox`** or **`polygon`**
The JSON should be of the following format:
```json
{
"project_name": "Project Name",
"create_date": "2019-10-22 19:04:16Z",
"export_format_version": "1.1",
"export_date": "2019-11-19 16:21:18Z",
"attributes": [
{
"name": "ewerwer",
"type": "TEXT",
"values": [
"black",
"white"
]
}
],
"label_classes": [
{
"class_name": "cat",
"color": "#1f78b44d",
"class_type": "object",
"attributes": [
"black",
"white"
]
},
{
"class_name": "dog",
"color": "#e31a1c4d",
"class_type": "object"
}
],
"images": [
{
"image_name": "IMG_000002.jpg",
"dataset_name": "train dataset",
"width": 500,
"height": 430,
"image_status": "TO REVIEW",
"labels": [
{
"class_name": "cat",
"bbox": [
102,
45,
420,
404
],
"polygon": null,
"mask": null,
"z_index": 1,
"attributes": {
"attribute_name": "xyz"
}
}
],
"tags": [
"xyz",
"pqr"
]
}
]
}
```
As an example, here are two annotations, one bounding box (cat) and one polygon (dining table):
```json
[
{
"image_name": "2010_000001.jpg",
"labels": [
{
"class_name": "cat",
"bbox": [128, 13, 340, 308]
}
]
},
{
"image_name": "2010_000001.jpg",
"labels": [
{
"class_name": "diningtable",
"polygon": [[122, 222], [73, 236], [72, 250], [2, 256], [2, 332], [498, 332],
[498, 295], [429, 260], [339, 231], [282, 223], [297, 246], [306, 285], [280, 302],
[255, 271], [224, 260], [222, 304], [199, 310], [171, 290], [171, 265],
[131, 244], [128, 222]]
}
]
}
]
```
### semantic
Each file with a semantic segmentation should be an image in \*.png format. The filename should be the same (without extension) as the corresponding image's name. The image file can contain one or three channels.
### label\_class
Label classes can be imported through the use of a JSON-file with in the following schema:
```json
{
"$id": "http://example.com/root.json",
"type": "array",
"title": "List of Label Classes",
"items": {
"type": "object",
"png_index": "number",
"required": [
"name",
"type"
],
"properties": {
"name": {
"type": "string",
"title": "Class name",
"examples": [
"floor"
]
},
{
"png_index": {
"type": "number",
"title": "PNG index",
"examples": [
18
]
},
"color": {
"type": "string",
"title": "Class color (can be assigned automatically)",
"examples": [
"#F453A3"
]
},
"type": {
"type": "string",
"title": "object or background",
"default": "object",
"examples": [
"background"
]
}
}
}
}
```
As an example:
```json
[
{
"color": "#8000ff4d",
"png_index": 1,
"name": "wall",
"type": "background"
},
{
"color": "#396bf94d",
"png_index": 17,
"name": "plate",
"type": "object"
}
]
```
As you can see, there are two types of classes: object (1) and background (2). These correspond to the foreground (1) and semantic (2) classes you see in the tool.
### RLE Encoding Example
```python
def rle_encoding(x: np.array):
"""
Encode binary mask to RLE
Args:
x (np.array): numpy array of shape (height, width), 1 - mask, 0 - background
Returns run length as list
"""
dots = np.where(x.flatten() == 1)[0] # Order right-then-down
run_lengths = []
prev = -2
for b in dots:
if b > prev + 1:
run_lengths.extend((b + 1, 0))
run_lengths[-1] += 1
prev = b
return run_lengths
```
### Easiest way to import
If you are looking to import masks or do other, more complex imports with attributes and image tags as well as annotations, you need to format your data into our own supported Hasty JSON v1.1.
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