%global _empty_manifest_terminate_build 0
Name:		python-label-studio-converter
Version:	0.0.53
Release:	1
Summary:	Format converter add-on for Label Studio
License:	Apache Software License
URL:		https://github.com/heartexlabs/label-studio-converter
Source0:	https://mirrors.nju.edu.cn/pypi/web/packages/05/44/851422ec51fa1fef07b455d53aa5d0ec43a622d35655af282e520b8125da/label-studio-converter-0.0.53.tar.gz
BuildArch:	noarch

Requires:	python3-pandas
Requires:	python3-requests
Requires:	python3-Pillow
Requires:	python3-nltk
Requires:	python3-label-studio-tools
Requires:	python3-ujson
Requires:	python3-ijson

%description
# Label Studio Converter

[Website](https://labelstud.io/) • [Docs](https://labelstud.io/guide) • [Twitter](https://twitter.com/heartexlabs) • [Join Slack Community <img src="https://app.heartex.ai/docs/images/slack-mini.png" width="18px"/>](https://slack.labelstudio.heartex.com)

## Table of Contents

- [Introduction](#introduction)
- [Examples](#examples)
    - [JSON](#json)
    - [CSV](#csv)
    - [CoNLL 2003](#conll-2003)
    - [COCO](#coco)
    - [Pascal VOC XML](#pascal-voc-xml)
- [Contributing](#contributing)
- [License](#license)

## Introduction

Label Studio Format Converter helps you to encode labels into the format of your favorite machine learning library.

## Examples

#### JSON
**Running from the command line:**

```bash
pip install -U label-studio-converter
python label-studio-converter export -i exported_tasks.json -c examples/sentiment_analysis/config.xml -o output_dir -f CSV
```

**Running from python:**
```python
from label_studio_converter import Converter

c = Converter('examples/sentiment_analysis/config.xml')
c.convert_to_json('examples/sentiment_analysis/completions/', 'tmp/output.json')
```

Getting output file: `tmp/output.json`
```json
[
  {
    "reviewText": "Good case, Excellent value.",
    "sentiment": "Positive"
  },
  {
    "reviewText": "What a waste of money and time!",
    "sentiment": "Negative"
  },
  {
    "reviewText": "The goose neck needs a little coaxing",
    "sentiment": "Neutral"
  }
]
```

Use cases: any tasks


#### CSV
Running from the command line:
```bash
python label_studio_converter/cli.py --input examples/sentiment_analysis/completions/ --config examples/sentiment_analysis/config.xml --output output_dir --format CSV --csv-separator $'\t'
```

Running from python:
```python
from label_studio_converter import Converter

c = Converter('examples/sentiment_analysis/config.xml')
c.convert_to_csv('examples/sentiment_analysis/completions/', 'output_dir', sep='\t', header=True)
```

Getting output file `tmp/output.tsv`:
```tsv
reviewText	sentiment
Good case, Excellent value.	Positive
What a waste of money and time!	Negative
The goose neck needs a little coaxing	Neutral
```

Use cases: any tasks

#### CoNLL 2003

Running from the command line:
```bash
python label_studio_converter/cli.py --input examples/named_entity/completions/ --config examples/named_entity/config.xml --output tmp/output.conll --format CONLL2003
```

Running from python:
```python
from label_studio_converter import Converter

c = Converter('examples/named_entity/config.xml')
c.convert_to_conll2003('examples/named_entity/completions/', 'tmp/output.conll')
```

Getting output file `tmp/output.conll`
```text
-DOCSTART- -X- O
Showers -X- _ O
continued -X- _ O
throughout -X- _ O
the -X- _ O
week -X- _ O
in -X- _ O
the -X- _ O
Bahia -X- _ B-Location
cocoa -X- _ O
zone, -X- _ O
...
```

Use cases: text tagging


#### COCO
Running from the command line:
```bash
python label_studio_converter/cli.py --input examples/image_bbox/completions/ --config examples/image_bbox/config.xml --output tmp/output.json --format COCO --image-dir tmp/images
```

Running from python:
```python
from label_studio_converter import Converter

c = Converter('examples/image_bbox/config.xml')
c.convert_to_coco('examples/image_bbox/completions/', 'tmp/output.conll', output_image_dir='tmp/images')
```

Output images could be found in `tmp/images`

Getting output file `tmp/output.json`
```json
{
  "images": [
    {
      "width": 800,
      "height": 501,
      "id": 0,
      "file_name": "tmp/images/62a623a0d3cef27a51d3689865e7b08a"
    }
  ],
  "categories": [
    {
      "id": 0,
      "name": "Planet"
    },
    {
      "id": 1,
      "name": "Moonwalker"
    }
  ],
  "annotations": [
    {
      "id": 0,
      "image_id": 0,
      "category_id": 0,
      "segmentation": [],
      "bbox": [
        299,
        6,
        377,
        260
      ],
      "ignore": 0,
      "iscrowd": 0,
      "area": 98020
    },
    {
      "id": 1,
      "image_id": 0,
      "category_id": 1,
      "segmentation": [],
      "bbox": [
        288,
        300,
        132,
        90
      ],
      "ignore": 0,
      "iscrowd": 0,
      "area": 11880
    }
  ],
  "info": {
    "year": 2019,
    "version": "1.0",
    "contributor": "Label Studio"
  }
}
```

Use cases: image object detection

#### Pascal VOC XML
Running from the command line:
```bash
python label_studio_converter/cli.py --input examples/image_bbox/completions/ --config examples/image_bbox/config.xml --output tmp/voc-annotations --format VOC --image-dir tmp/images
```

Running from python:
```python
from label_studio_converter import Converter

c = Converter('examples/image_bbox/config.xml')
c.convert_to_voc('examples/image_bbox/completions/', 'tmp/output.conll', output_image_dir='tmp/images')
```

Output images can be found in `tmp/images`

Corresponding annotations could be found in `tmp/voc-annotations/*.xml`:
```xml
<?xml version="1.0" encoding="utf-8"?>
<annotation>
<folder>tmp/images</folder>
<filename>62a623a0d3cef27a51d3689865e7b08a</filename>
<source>
<database>MyDatabase</database>
<annotation>COCO2017</annotation>
<image>flickr</image>
<flickrid>NULL</flickrid>
</source>
<owner>
<flickrid>NULL</flickrid>
<name>Label Studio</name>
</owner>
<size>
<width>800</width>
<height>501</height>
<depth>3</depth>
</size>
<segmented>0</segmented>
<object>
<name>Planet</name>
<pose>Unspecified</pose>
<truncated>0</truncated>
<difficult>0</difficult>
<bndbox>
<xmin>299</xmin>
<ymin>6</ymin>
<xmax>676</xmax>
<ymax>266</ymax>
</bndbox>
</object>
<object>
<name>Moonwalker</name>
<pose>Unspecified</pose>
<truncated>0</truncated>
<difficult>0</difficult>
<bndbox>
<xmin>288</xmin>
<ymin>300</ymin>
<xmax>420</xmax>
<ymax>390</ymax>
</bndbox>
</object>
</annotation>
```

Use cases: image object detection

### YOLO to Label Studio converter 

Usage:

```
label-studio-converter import yolo -i /yolo/root/directory -o ls-tasks.json
```

Help:

```
label-studio-converter import yolo -h

usage: label-studio-converter import yolo [-h] -i INPUT [-o OUTPUT]
                                          [--to-name TO_NAME]
                                          [--from-name FROM_NAME]
                                          [--out-type OUT_TYPE]
                                          [--image-root-url IMAGE_ROOT_URL]
                                          [--image-ext IMAGE_EXT]

optional arguments:
  -h, --help            show this help message and exit
  -i INPUT, --input INPUT
                        directory with YOLO where images, labels, notes.json
                        are located
  -o OUTPUT, --output OUTPUT
                        output file with Label Studio JSON tasks
  --to-name TO_NAME     object name from Label Studio labeling config
  --from-name FROM_NAME
                        control tag name from Label Studio labeling config
  --out-type OUT_TYPE   annotation type - "annotations" or "predictions"
  --image-root-url IMAGE_ROOT_URL
                        root URL path where images will be hosted, e.g.:
                        http://example.com/images or s3://my-bucket
  --image-ext IMAGE_EXT
                        image extension to search: .jpg, .png
```

YOLO export folder example:

```
yolo-folder
  images
   - 1.jpg
   - 2.jpg
   - ...
  labels
   - 1.txt
   - 2.txt

  classes.txt
```

classes.txt example

```
Airplane
Car
```

## Contributing

We would love to get your help for creating converters to other models. Please feel free to create pull requests.

- [Contributing Guideline](https://github.com/heartexlabs/label-studio/blob/develop/CONTRIBUTING.md)
- [Code Of Conduct](https://github.com/heartexlabs/label-studio/blob/develop/CODE_OF_CONDUCT.md)

## License

This software is licensed under the [Apache 2.0 LICENSE](/LICENSE) © [Heartex](https://www.heartex.com/). 2020

<img src="https://github.com/heartexlabs/label-studio/blob/master/images/opossum_looking.png?raw=true" title="Hey everyone!" height="140" width="140" />


%package -n python3-label-studio-converter
Summary:	Format converter add-on for Label Studio
Provides:	python-label-studio-converter
BuildRequires:	python3-devel
BuildRequires:	python3-setuptools
BuildRequires:	python3-pip
%description -n python3-label-studio-converter
# Label Studio Converter

[Website](https://labelstud.io/) • [Docs](https://labelstud.io/guide) • [Twitter](https://twitter.com/heartexlabs) • [Join Slack Community <img src="https://app.heartex.ai/docs/images/slack-mini.png" width="18px"/>](https://slack.labelstudio.heartex.com)

## Table of Contents

- [Introduction](#introduction)
- [Examples](#examples)
    - [JSON](#json)
    - [CSV](#csv)
    - [CoNLL 2003](#conll-2003)
    - [COCO](#coco)
    - [Pascal VOC XML](#pascal-voc-xml)
- [Contributing](#contributing)
- [License](#license)

## Introduction

Label Studio Format Converter helps you to encode labels into the format of your favorite machine learning library.

## Examples

#### JSON
**Running from the command line:**

```bash
pip install -U label-studio-converter
python label-studio-converter export -i exported_tasks.json -c examples/sentiment_analysis/config.xml -o output_dir -f CSV
```

**Running from python:**
```python
from label_studio_converter import Converter

c = Converter('examples/sentiment_analysis/config.xml')
c.convert_to_json('examples/sentiment_analysis/completions/', 'tmp/output.json')
```

Getting output file: `tmp/output.json`
```json
[
  {
    "reviewText": "Good case, Excellent value.",
    "sentiment": "Positive"
  },
  {
    "reviewText": "What a waste of money and time!",
    "sentiment": "Negative"
  },
  {
    "reviewText": "The goose neck needs a little coaxing",
    "sentiment": "Neutral"
  }
]
```

Use cases: any tasks


#### CSV
Running from the command line:
```bash
python label_studio_converter/cli.py --input examples/sentiment_analysis/completions/ --config examples/sentiment_analysis/config.xml --output output_dir --format CSV --csv-separator $'\t'
```

Running from python:
```python
from label_studio_converter import Converter

c = Converter('examples/sentiment_analysis/config.xml')
c.convert_to_csv('examples/sentiment_analysis/completions/', 'output_dir', sep='\t', header=True)
```

Getting output file `tmp/output.tsv`:
```tsv
reviewText	sentiment
Good case, Excellent value.	Positive
What a waste of money and time!	Negative
The goose neck needs a little coaxing	Neutral
```

Use cases: any tasks

#### CoNLL 2003

Running from the command line:
```bash
python label_studio_converter/cli.py --input examples/named_entity/completions/ --config examples/named_entity/config.xml --output tmp/output.conll --format CONLL2003
```

Running from python:
```python
from label_studio_converter import Converter

c = Converter('examples/named_entity/config.xml')
c.convert_to_conll2003('examples/named_entity/completions/', 'tmp/output.conll')
```

Getting output file `tmp/output.conll`
```text
-DOCSTART- -X- O
Showers -X- _ O
continued -X- _ O
throughout -X- _ O
the -X- _ O
week -X- _ O
in -X- _ O
the -X- _ O
Bahia -X- _ B-Location
cocoa -X- _ O
zone, -X- _ O
...
```

Use cases: text tagging


#### COCO
Running from the command line:
```bash
python label_studio_converter/cli.py --input examples/image_bbox/completions/ --config examples/image_bbox/config.xml --output tmp/output.json --format COCO --image-dir tmp/images
```

Running from python:
```python
from label_studio_converter import Converter

c = Converter('examples/image_bbox/config.xml')
c.convert_to_coco('examples/image_bbox/completions/', 'tmp/output.conll', output_image_dir='tmp/images')
```

Output images could be found in `tmp/images`

Getting output file `tmp/output.json`
```json
{
  "images": [
    {
      "width": 800,
      "height": 501,
      "id": 0,
      "file_name": "tmp/images/62a623a0d3cef27a51d3689865e7b08a"
    }
  ],
  "categories": [
    {
      "id": 0,
      "name": "Planet"
    },
    {
      "id": 1,
      "name": "Moonwalker"
    }
  ],
  "annotations": [
    {
      "id": 0,
      "image_id": 0,
      "category_id": 0,
      "segmentation": [],
      "bbox": [
        299,
        6,
        377,
        260
      ],
      "ignore": 0,
      "iscrowd": 0,
      "area": 98020
    },
    {
      "id": 1,
      "image_id": 0,
      "category_id": 1,
      "segmentation": [],
      "bbox": [
        288,
        300,
        132,
        90
      ],
      "ignore": 0,
      "iscrowd": 0,
      "area": 11880
    }
  ],
  "info": {
    "year": 2019,
    "version": "1.0",
    "contributor": "Label Studio"
  }
}
```

Use cases: image object detection

#### Pascal VOC XML
Running from the command line:
```bash
python label_studio_converter/cli.py --input examples/image_bbox/completions/ --config examples/image_bbox/config.xml --output tmp/voc-annotations --format VOC --image-dir tmp/images
```

Running from python:
```python
from label_studio_converter import Converter

c = Converter('examples/image_bbox/config.xml')
c.convert_to_voc('examples/image_bbox/completions/', 'tmp/output.conll', output_image_dir='tmp/images')
```

Output images can be found in `tmp/images`

Corresponding annotations could be found in `tmp/voc-annotations/*.xml`:
```xml
<?xml version="1.0" encoding="utf-8"?>
<annotation>
<folder>tmp/images</folder>
<filename>62a623a0d3cef27a51d3689865e7b08a</filename>
<source>
<database>MyDatabase</database>
<annotation>COCO2017</annotation>
<image>flickr</image>
<flickrid>NULL</flickrid>
</source>
<owner>
<flickrid>NULL</flickrid>
<name>Label Studio</name>
</owner>
<size>
<width>800</width>
<height>501</height>
<depth>3</depth>
</size>
<segmented>0</segmented>
<object>
<name>Planet</name>
<pose>Unspecified</pose>
<truncated>0</truncated>
<difficult>0</difficult>
<bndbox>
<xmin>299</xmin>
<ymin>6</ymin>
<xmax>676</xmax>
<ymax>266</ymax>
</bndbox>
</object>
<object>
<name>Moonwalker</name>
<pose>Unspecified</pose>
<truncated>0</truncated>
<difficult>0</difficult>
<bndbox>
<xmin>288</xmin>
<ymin>300</ymin>
<xmax>420</xmax>
<ymax>390</ymax>
</bndbox>
</object>
</annotation>
```

Use cases: image object detection

### YOLO to Label Studio converter 

Usage:

```
label-studio-converter import yolo -i /yolo/root/directory -o ls-tasks.json
```

Help:

```
label-studio-converter import yolo -h

usage: label-studio-converter import yolo [-h] -i INPUT [-o OUTPUT]
                                          [--to-name TO_NAME]
                                          [--from-name FROM_NAME]
                                          [--out-type OUT_TYPE]
                                          [--image-root-url IMAGE_ROOT_URL]
                                          [--image-ext IMAGE_EXT]

optional arguments:
  -h, --help            show this help message and exit
  -i INPUT, --input INPUT
                        directory with YOLO where images, labels, notes.json
                        are located
  -o OUTPUT, --output OUTPUT
                        output file with Label Studio JSON tasks
  --to-name TO_NAME     object name from Label Studio labeling config
  --from-name FROM_NAME
                        control tag name from Label Studio labeling config
  --out-type OUT_TYPE   annotation type - "annotations" or "predictions"
  --image-root-url IMAGE_ROOT_URL
                        root URL path where images will be hosted, e.g.:
                        http://example.com/images or s3://my-bucket
  --image-ext IMAGE_EXT
                        image extension to search: .jpg, .png
```

YOLO export folder example:

```
yolo-folder
  images
   - 1.jpg
   - 2.jpg
   - ...
  labels
   - 1.txt
   - 2.txt

  classes.txt
```

classes.txt example

```
Airplane
Car
```

## Contributing

We would love to get your help for creating converters to other models. Please feel free to create pull requests.

- [Contributing Guideline](https://github.com/heartexlabs/label-studio/blob/develop/CONTRIBUTING.md)
- [Code Of Conduct](https://github.com/heartexlabs/label-studio/blob/develop/CODE_OF_CONDUCT.md)

## License

This software is licensed under the [Apache 2.0 LICENSE](/LICENSE) © [Heartex](https://www.heartex.com/). 2020

<img src="https://github.com/heartexlabs/label-studio/blob/master/images/opossum_looking.png?raw=true" title="Hey everyone!" height="140" width="140" />


%package help
Summary:	Development documents and examples for label-studio-converter
Provides:	python3-label-studio-converter-doc
%description help
# Label Studio Converter

[Website](https://labelstud.io/) • [Docs](https://labelstud.io/guide) • [Twitter](https://twitter.com/heartexlabs) • [Join Slack Community <img src="https://app.heartex.ai/docs/images/slack-mini.png" width="18px"/>](https://slack.labelstudio.heartex.com)

## Table of Contents

- [Introduction](#introduction)
- [Examples](#examples)
    - [JSON](#json)
    - [CSV](#csv)
    - [CoNLL 2003](#conll-2003)
    - [COCO](#coco)
    - [Pascal VOC XML](#pascal-voc-xml)
- [Contributing](#contributing)
- [License](#license)

## Introduction

Label Studio Format Converter helps you to encode labels into the format of your favorite machine learning library.

## Examples

#### JSON
**Running from the command line:**

```bash
pip install -U label-studio-converter
python label-studio-converter export -i exported_tasks.json -c examples/sentiment_analysis/config.xml -o output_dir -f CSV
```

**Running from python:**
```python
from label_studio_converter import Converter

c = Converter('examples/sentiment_analysis/config.xml')
c.convert_to_json('examples/sentiment_analysis/completions/', 'tmp/output.json')
```

Getting output file: `tmp/output.json`
```json
[
  {
    "reviewText": "Good case, Excellent value.",
    "sentiment": "Positive"
  },
  {
    "reviewText": "What a waste of money and time!",
    "sentiment": "Negative"
  },
  {
    "reviewText": "The goose neck needs a little coaxing",
    "sentiment": "Neutral"
  }
]
```

Use cases: any tasks


#### CSV
Running from the command line:
```bash
python label_studio_converter/cli.py --input examples/sentiment_analysis/completions/ --config examples/sentiment_analysis/config.xml --output output_dir --format CSV --csv-separator $'\t'
```

Running from python:
```python
from label_studio_converter import Converter

c = Converter('examples/sentiment_analysis/config.xml')
c.convert_to_csv('examples/sentiment_analysis/completions/', 'output_dir', sep='\t', header=True)
```

Getting output file `tmp/output.tsv`:
```tsv
reviewText	sentiment
Good case, Excellent value.	Positive
What a waste of money and time!	Negative
The goose neck needs a little coaxing	Neutral
```

Use cases: any tasks

#### CoNLL 2003

Running from the command line:
```bash
python label_studio_converter/cli.py --input examples/named_entity/completions/ --config examples/named_entity/config.xml --output tmp/output.conll --format CONLL2003
```

Running from python:
```python
from label_studio_converter import Converter

c = Converter('examples/named_entity/config.xml')
c.convert_to_conll2003('examples/named_entity/completions/', 'tmp/output.conll')
```

Getting output file `tmp/output.conll`
```text
-DOCSTART- -X- O
Showers -X- _ O
continued -X- _ O
throughout -X- _ O
the -X- _ O
week -X- _ O
in -X- _ O
the -X- _ O
Bahia -X- _ B-Location
cocoa -X- _ O
zone, -X- _ O
...
```

Use cases: text tagging


#### COCO
Running from the command line:
```bash
python label_studio_converter/cli.py --input examples/image_bbox/completions/ --config examples/image_bbox/config.xml --output tmp/output.json --format COCO --image-dir tmp/images
```

Running from python:
```python
from label_studio_converter import Converter

c = Converter('examples/image_bbox/config.xml')
c.convert_to_coco('examples/image_bbox/completions/', 'tmp/output.conll', output_image_dir='tmp/images')
```

Output images could be found in `tmp/images`

Getting output file `tmp/output.json`
```json
{
  "images": [
    {
      "width": 800,
      "height": 501,
      "id": 0,
      "file_name": "tmp/images/62a623a0d3cef27a51d3689865e7b08a"
    }
  ],
  "categories": [
    {
      "id": 0,
      "name": "Planet"
    },
    {
      "id": 1,
      "name": "Moonwalker"
    }
  ],
  "annotations": [
    {
      "id": 0,
      "image_id": 0,
      "category_id": 0,
      "segmentation": [],
      "bbox": [
        299,
        6,
        377,
        260
      ],
      "ignore": 0,
      "iscrowd": 0,
      "area": 98020
    },
    {
      "id": 1,
      "image_id": 0,
      "category_id": 1,
      "segmentation": [],
      "bbox": [
        288,
        300,
        132,
        90
      ],
      "ignore": 0,
      "iscrowd": 0,
      "area": 11880
    }
  ],
  "info": {
    "year": 2019,
    "version": "1.0",
    "contributor": "Label Studio"
  }
}
```

Use cases: image object detection

#### Pascal VOC XML
Running from the command line:
```bash
python label_studio_converter/cli.py --input examples/image_bbox/completions/ --config examples/image_bbox/config.xml --output tmp/voc-annotations --format VOC --image-dir tmp/images
```

Running from python:
```python
from label_studio_converter import Converter

c = Converter('examples/image_bbox/config.xml')
c.convert_to_voc('examples/image_bbox/completions/', 'tmp/output.conll', output_image_dir='tmp/images')
```

Output images can be found in `tmp/images`

Corresponding annotations could be found in `tmp/voc-annotations/*.xml`:
```xml
<?xml version="1.0" encoding="utf-8"?>
<annotation>
<folder>tmp/images</folder>
<filename>62a623a0d3cef27a51d3689865e7b08a</filename>
<source>
<database>MyDatabase</database>
<annotation>COCO2017</annotation>
<image>flickr</image>
<flickrid>NULL</flickrid>
</source>
<owner>
<flickrid>NULL</flickrid>
<name>Label Studio</name>
</owner>
<size>
<width>800</width>
<height>501</height>
<depth>3</depth>
</size>
<segmented>0</segmented>
<object>
<name>Planet</name>
<pose>Unspecified</pose>
<truncated>0</truncated>
<difficult>0</difficult>
<bndbox>
<xmin>299</xmin>
<ymin>6</ymin>
<xmax>676</xmax>
<ymax>266</ymax>
</bndbox>
</object>
<object>
<name>Moonwalker</name>
<pose>Unspecified</pose>
<truncated>0</truncated>
<difficult>0</difficult>
<bndbox>
<xmin>288</xmin>
<ymin>300</ymin>
<xmax>420</xmax>
<ymax>390</ymax>
</bndbox>
</object>
</annotation>
```

Use cases: image object detection

### YOLO to Label Studio converter 

Usage:

```
label-studio-converter import yolo -i /yolo/root/directory -o ls-tasks.json
```

Help:

```
label-studio-converter import yolo -h

usage: label-studio-converter import yolo [-h] -i INPUT [-o OUTPUT]
                                          [--to-name TO_NAME]
                                          [--from-name FROM_NAME]
                                          [--out-type OUT_TYPE]
                                          [--image-root-url IMAGE_ROOT_URL]
                                          [--image-ext IMAGE_EXT]

optional arguments:
  -h, --help            show this help message and exit
  -i INPUT, --input INPUT
                        directory with YOLO where images, labels, notes.json
                        are located
  -o OUTPUT, --output OUTPUT
                        output file with Label Studio JSON tasks
  --to-name TO_NAME     object name from Label Studio labeling config
  --from-name FROM_NAME
                        control tag name from Label Studio labeling config
  --out-type OUT_TYPE   annotation type - "annotations" or "predictions"
  --image-root-url IMAGE_ROOT_URL
                        root URL path where images will be hosted, e.g.:
                        http://example.com/images or s3://my-bucket
  --image-ext IMAGE_EXT
                        image extension to search: .jpg, .png
```

YOLO export folder example:

```
yolo-folder
  images
   - 1.jpg
   - 2.jpg
   - ...
  labels
   - 1.txt
   - 2.txt

  classes.txt
```

classes.txt example

```
Airplane
Car
```

## Contributing

We would love to get your help for creating converters to other models. Please feel free to create pull requests.

- [Contributing Guideline](https://github.com/heartexlabs/label-studio/blob/develop/CONTRIBUTING.md)
- [Code Of Conduct](https://github.com/heartexlabs/label-studio/blob/develop/CODE_OF_CONDUCT.md)

## License

This software is licensed under the [Apache 2.0 LICENSE](/LICENSE) © [Heartex](https://www.heartex.com/). 2020

<img src="https://github.com/heartexlabs/label-studio/blob/master/images/opossum_looking.png?raw=true" title="Hey everyone!" height="140" width="140" />


%prep
%autosetup -n label-studio-converter-0.0.53

%build
%py3_build

%install
%py3_install
install -d -m755 %{buildroot}/%{_pkgdocdir}
if [ -d doc ]; then cp -arf doc %{buildroot}/%{_pkgdocdir}; fi
if [ -d docs ]; then cp -arf docs %{buildroot}/%{_pkgdocdir}; fi
if [ -d example ]; then cp -arf example %{buildroot}/%{_pkgdocdir}; fi
if [ -d examples ]; then cp -arf examples %{buildroot}/%{_pkgdocdir}; fi
pushd %{buildroot}
if [ -d usr/lib ]; then
	find usr/lib -type f -printf "/%h/%f\n" >> filelist.lst
fi
if [ -d usr/lib64 ]; then
	find usr/lib64 -type f -printf "/%h/%f\n" >> filelist.lst
fi
if [ -d usr/bin ]; then
	find usr/bin -type f -printf "/%h/%f\n" >> filelist.lst
fi
if [ -d usr/sbin ]; then
	find usr/sbin -type f -printf "/%h/%f\n" >> filelist.lst
fi
touch doclist.lst
if [ -d usr/share/man ]; then
	find usr/share/man -type f -printf "/%h/%f.gz\n" >> doclist.lst
fi
popd
mv %{buildroot}/filelist.lst .
mv %{buildroot}/doclist.lst .

%files -n python3-label-studio-converter -f filelist.lst
%dir %{python3_sitelib}/*

%files help -f doclist.lst
%{_docdir}/*

%changelog
* Tue Apr 25 2023 Python_Bot <Python_Bot@openeuler.org> - 0.0.53-1
- Package Spec generated