Skip to content

Commit

Permalink
docs: Fix import predictions (HumanSignal#2722)
Browse files Browse the repository at this point in the history 
* docs: Fix import predictions

* fixes

* Fixes

* Space
  • Loading branch information
@makseq
makseq authored Jul 25, 2022
1 parent 7cccfc3 commit 4366ed1
Show file tree
Hide file tree
Showing 2 changed files with 103 additions and 102 deletions.
203 changes: 102 additions & 101 deletions docs/source/guide/predictions.md
View file
Original file line number Diff line number Diff line change
Expand Up @@ -73,14 +73,16 @@ The `predictions` array also depends on the labeling configuration. Some pre-ann
| `result.from_name` | string | String used to reference the labeling configuration `from_name` for the type of labeling being performed. Must match the labeling configuration. |
| `result.to_name` | string | String used to reference the labeling configuration `to_name` for the type of labeling being performed. Must match the labeling configuration. |
| `result.type` | string | Specify the labeling tag for the type of labeling being performed. For example, a named entity recognition task has a type of `labels`. |
| `result.readonly` | bool | readonly mode for a specific region |
| `result.hidden` | bool | default visibility (eye icon) for a specific region |

Other types of annotation contain specific fields. You can review the [examples on this page](#Specific-examples-for-pre-annotations), or review the [tag documentation for the Object and Control tags](/tags) in your labeling configuration labeling-specific `result` objects. For example, the [Audio tag](tags/audio.html), [HyperText tag](tags/hypertext.html), [Paragraphs tag](tags/paragraphs.html), [KeyPointLabels](/tags/keypointlabels.html) and more all contain sample `result` JSON examples.

> Note: If you're generating pre-annotations for a [custom ML backend](ml_create.html), you can use the `self.parsed_label_config` variable to retrieve the labeling configuration for a project and generate pre-annotations. See the [custom ML backend](ml_create.html) documentation for more details.
## Import pre-annotations for images
## Import bbox and choice pre-annotations for images

For example, import predicted labels for tasks to determine whether an item in an image is an airplane or a car.
For example, import predicted **bounding box regions (rectangles)** and **choices** for tasks to determine whether an item in an image is an airplane or a car.

For image pre-annotations, Label Studio expects the x, y, width, and height of image annotations to be provided in percentages of overall image dimension. See [Units for image annotations](predictions.html#Units_for_image_annotations) on this page for more about how to convert formats.

Expand Down Expand Up @@ -112,10 +114,12 @@ Save this example JSON as a file to import it into Label Studio, for example, `e
{% codeblock lang:json %}
[{
"data": {
"image": "http://localhost:8080/static/samples/sample.jpg"
"image": "/static/samples/sample.jpg"
},

"predictions": [{
"model_version": "one",
"score": 0.5,
"result": [
{
"id": "result1",
Expand Down Expand Up @@ -150,8 +154,7 @@ Save this example JSON as a file to import it into Label Studio, for example, `e
"value": {
"choices": ["Airbus"]
}
}],
"score": 0.95
}]
}]
}]
{% endcodeblock %}
Expand All @@ -170,21 +173,20 @@ Import pre-annotated tasks into Label Studio [using the UI](tasks.html#Import-da
In the Label Studio UI, the imported prediction for this task looks like the following:
<center><img src="../images/predictions_loaded.png" alt="screenshot of the Label Studio UI showing an image of airplanes with bounding boxes covering each airplane." style="width: 100%; max-width: 700px"></center>

## Import pre-annotated regions for images
## Import pre-annotated rectangle, polygon, ellipse & keypoint regions without labels for images

If you want to import images with pre-annotated regions without labels assigned to them, follow this example.

Use the following labeling configuration:
```xml
<View>
<View style="display:flex;align-items:start;gap:8px;flex-direction:row">
<Image name="image" value="$image" zoom="true" zoomControl="true" rotateControl="false"/>
<Rectangle name="rect" toName="image" showInline="false"/>
</View>
<Image name="image" value="$image" zoom="true" zoomControl="true" rotateControl="false"/>

<Rectangle name="rect" toName="image" showInline="false"/>
<Polygon name="polygon" toName="image"/>
<Ellipse name="ellipse" toName="image"/>
<KeyPoint name="kp" toName="image"/>
<Polygon name="polygon" toName="image"/>
<Brush name="brush" toName="image"/>

<Labels name="labels" toName="image" fillOpacity="0.5" strokeWidth="5">
<Label value="Vehicle" background="green"/>
<Label value="Building" background="blue"/>
Expand All @@ -203,96 +205,93 @@ Save this example JSON as a file to import it into Label Studio, for example, `e

{% codeblock lang:json %}
[{
"id":8,
"predictions":[
"data": {
"image": "/static/samples/sample.jpg"
},

"predictions": [
{
"model_version": "one",
"score": 0.5,
"result": [
{
"id":10,
"result":[
{
"original_width":800,
"original_height":450,
"image_rotation":0,
"value":{
"x":55.46666666666667,
"y":2.3696682464454977,
"width":35.86666666666667,
"height":46.91943127962085,
"rotation":0
},
"id":"ABC",
"from_name":"rect",
"to_name":"image",
"type":"rectangle"
},
{
"otest_svg_upload_invalid_formatriginal_width":800,
"original_height":450,
"image_rotation":0,
"value":{
"x":58.4,
"y":64.21800947867298,
"width":30.533333333333335,
"height":19.90521327014218,
"rotation":0
},
"id":"DEF",
"from_name":"rect",
"to_name":"image",
"type":"rectangle"
},
{
"original_width":800,
"original_height":450,
"image_rotation":0,
"value":{
"points":[
[
20.933333333333334,
28.90995260663507
],
[
25.866666666666667,
64.69194312796209
],
[
38.4,
62.796208530805686
],
[
34.13333333333333,
27.488151658767773
]
]
},
"id":"GHI",
"from_name":"polygon",
"to_name":"image",
"type":"polygon"
},
{
"original_width":800,
"original_height":450,
"image_rotation":0,
"value":{
"x":8.4,
"y":20.14218009478673,
"radiusX":4,
"radiusY":7.109004739336493,
"rotation":0
},
"id":"JKL",
"from_name":"ellipse",
"to_name":"image",
"type":"ellipse"
}
],
"task":8
"original_width": 800,
"original_height": 450,
"image_rotation": 0,
"value": {
"x": 55.46,
"y": 2.36,
"width": 35.86,
"height": 46.9,
"rotation": 0
},
"id": "ABC",
"from_name": "rect",
"to_name": "image",
"type": "rectangle"
},
{
"original_width": 800,
"original_height": 450,
"image_rotation": 0,
"value": {
"points": [
[
20.93,
28.90
],
[
25.86,
64.69
],
[
38.40,
62.79
],
[
34.13,
27.48
]
]
},
"id": "GHI",
"from_name": "polygon",
"to_name": "image",
"type": "polygon"
},
{
"original_width": 800,
"original_height": 450,
"image_rotation": 0,
"value": {
"x": 8.4,
"y": 20.14,
"radiusX": 4,
"radiusY": 7.10,
"rotation": 0
},
"id": "JKL",
"from_name": "ellipse",
"to_name": "image",
"type": "ellipse"
},
{
"original_width": 800,
"original_height": 450,
"image_rotation": 0,
"value": {
"x": 38.40,
"y": 34.21,
"width": 1.0
},
"id": "DEF",
"from_name": "rect",
"to_name": "image",
"type": "keypoint"
}
],
"data":{
"image":"/data/upload/31159626248_d0362d027c_c.jpg"
},
"project":4
]
}
]
}]
{% endcodeblock %}

Expand All @@ -308,7 +307,7 @@ None of the regions have labels applied. The labeling configuration must use the

<!-- md image_units.md -->

## Import pre-annotations for text
## Import span pre-annotations for text

In this example, import pre-annotations for text using the [named entity recognition template](/templates/named_entity.html):
```xml
Expand Down Expand Up @@ -347,6 +346,7 @@ Save this example JSON as a file, for example: `example_preannotated_ner_tasks.j
"predictions": [
{
"model_version": "one",
"score": 0.5,
"result": [
{
"id": "abc",
Expand Down Expand Up @@ -412,6 +412,7 @@ Save this example JSON as a file, for example: `example_preannotated_ner_tasks.j
},
{
"model_version": "two",
"score": 0.42,
"result": [
{
"id": "mno",
Expand Down
2 changes: 1 addition & 1 deletion docs/source/templates/optical_character_recognition.md
View file
Original file line number Diff line number Diff line change
@@ -1,5 +1,5 @@
---
title: Optical Character Recognition
title: Optical Character Recognition (OCR)
type: templates
category: Computer Vision
cat: computer-vision
Expand Down

0 comments on commit 4366ed1

Please sign in to comment.