# Streamlit Image Annotation
Streamlit component for image annotation.
[](https://st-image-annotation.streamlit.app/)
[](https://pypi.org/project/streamlit-image-annotation/)
# Features
* You can easily launch an image annotation tool using streamlit.
* By customizing the pre- and post-processing, you can achieve your preferred annotation workflow.
* Currently supports classification, detection, point detection tasks.
* Simple UI that is easy to navigate.
# Install
```sh
pip install streamlit-image-annotation
```
# Example Usage
If you want to see other use cases, please check inside the examples folder.
```python
from glob import glob
import pandas as pd
import streamlit as st
from streamlit_image_annotation import classification
label_list = ['deer', 'human', 'dog', 'penguin', 'framingo', 'teddy bear']
image_path_list = glob('image/*.jpg')
if 'result_df' not in st.session_state:
st.session_state['result_df'] = pd.DataFrame.from_dict({'image': image_path_list, 'label': [0]*len(image_path_list)}).copy()
num_page = st.slider('page', 0, len(image_path_list)-1, 0)
label = classification(image_path_list[num_page],
label_list=label_list,
default_label_index=int(st.session_state['result_df'].loc[num_page, 'label']))
if label is not None and label['label'] != st.session_state['result_df'].loc[num_page, 'label']:
st.session_state['result_df'].loc[num_page, 'label'] = label_list.index(label['label'])
st.table(st.session_state['result_df'])
```
# API
```python
classification(
image_path: str,
label_list: List[str],
default_label_index: Optional[int] = None,
height: int = 512,
width: int = 512,
key: Optional[str] = None
)
```
- **image_path**: Image path.
- **label_list**: List of label candidates.
- **default_label_index**: Initial label index.
- **height**: The maximum height of the displayed image.
- **width**: The maximum width of the displayed image.
- **key**: An optional string to use as the unique key for the widget. Assign a key so the component is not remount every time the script is rerun.
- **Component Value**: {'label': label_name}
Example: [example code](example/classification.py)
```python
detection(
image_path: str,
label_list: List[str],
bboxes: Optional[List[List[int, int, int, int]]] = None,
labels: Optional[List[int]] = None,
height: int = 512,
width: int = 512,
line_width: int = 5,
key: Optional[str] = None
)
```
- **image_path**: Image path.
- **label_list**: List of label candidates.
- **bboxes**: Initial list of bounding boxes, where each bbox is in the format [x, y, w, h].
- **labels**: List of label for each initial bbox.
- **height**: The maximum height of the displayed image.
- **width**: The maximum width of the displayed image.
- **line_width**: The stroke width of the bbox.
- **key**: An optional string to use as the unique key for the widget. Assign a key so the component is not remount every time the script is rerun.
- **Component Value**: \[{'bbox':[x,y,width, height], 'label_id': label_id, 'label': label_name},...\]
Example: [example code](example/detection.py)
```python
pointdet(
image_path: str,
label_list: List[str],
points: Optional[List[List[int, int]]] = None,
labels: Optional[List[int]] = None,
height: int = 512,
width: int = 512,
point_width: int =3,
key: Optional[str] = None
)
```
- **image_path**: Image path.
- **label_list**: List of label candidates.
- **points**: Initial list of points, where each point is in the format [x, y].
- **labels**: List of label for each initial bbox.
- **height**: The maximum height of the displayed image.
- **width**: The maximum width of the displayed image.
- **point_width**: The stroke width of the bbox.
- **key**: An optional string to use as the unique key for the widget. Assign a key so the component is not remount every time the script is rerun.
- **Component Value**: \[{'bbox':[x,y], 'label_id': label_id, 'label': label_name},...\]
Example: [example code](example/pointdet.py)
# Future Work
* Addition of component for segmentation task.
# Development
## setup
```bash
cd Streamlit-Image-Annotation/
export PYTHONPATH=$PWD
```
and set `IS_RELEASE = False` in `Streamlit-Image-Annotation/__init__.py`.
## start frontend
```bash
git clone https://github.com/hirune924/Streamlit-Image-Annotation.git
cd Streamlit-Image-Annotation/streamlit_image_annotation/Detection
yarn
yarn start
```
## start streamlit
```bash
cd Streamlit-Image-Annotation/
streamlit run streamlit_image_annotation/Detection/__init__.py
```
## build
```bash
cd Streamlit-Image-Annotation/Classification/frontend
yarn build
cd Streamlit-Image-Annotation/Detection/frontend
yarn build
cd Streamlit-Image-Annotation/Point/frontend
yarn build
```
and set `IS_RELEASE = True` in `Streamlit-Image-Annotation/__init__.py`.
## make wheel
```bash
python setup.py sdist bdist_wheel
```
## upload
```bash
python3 -m twine upload --repository testpypi dist/*
python -m pip install --index-url https://test.pypi.org/simple/ --no-deps streamlit-image-annotation
```
```bash
twine upload dist/*
```
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"description": "# Streamlit Image Annotation\n\nStreamlit component for image annotation.\n\n[](https://st-image-annotation.streamlit.app/)\n[](https://pypi.org/project/streamlit-image-annotation/)\n\n# Features\n* You can easily launch an image annotation tool using streamlit.\n* By customizing the pre- and post-processing, you can achieve your preferred annotation workflow.\n* Currently supports classification, detection, point detection tasks.\n* Simple UI that is easy to navigate.\n\n# Install\n\n```sh\npip install streamlit-image-annotation\n```\n\n# Example Usage\nIf you want to see other use cases, please check inside the examples folder.\n\n```python\nfrom glob import glob\nimport pandas as pd\nimport streamlit as st\nfrom streamlit_image_annotation import classification\n\nlabel_list = ['deer', 'human', 'dog', 'penguin', 'framingo', 'teddy bear']\nimage_path_list = glob('image/*.jpg')\nif 'result_df' not in st.session_state:\n st.session_state['result_df'] = pd.DataFrame.from_dict({'image': image_path_list, 'label': [0]*len(image_path_list)}).copy()\n\nnum_page = st.slider('page', 0, len(image_path_list)-1, 0)\nlabel = classification(image_path_list[num_page], \n label_list=label_list, \n default_label_index=int(st.session_state['result_df'].loc[num_page, 'label']))\n\nif label is not None and label['label'] != st.session_state['result_df'].loc[num_page, 'label']:\n st.session_state['result_df'].loc[num_page, 'label'] = label_list.index(label['label'])\nst.table(st.session_state['result_df'])\n```\n\n# API\n\n```python\nclassification(\n image_path: str,\n label_list: List[str],\n default_label_index: Optional[int] = None,\n height: int = 512,\n width: int = 512,\n key: Optional[str] = None\n)\n```\n\n- **image_path**: Image path.\n- **label_list**: List of label candidates.\n- **default_label_index**: Initial label index.\n- **height**: The maximum height of the displayed image.\n- **width**: The maximum width of the displayed image.\n- **key**: An optional string to use as the unique key for the widget. Assign a key so the component is not remount every time the script is rerun.\n\n- **Component Value**: {'label': label_name}\n\nExample: [example code](example/classification.py)\n\n```python\ndetection(\n image_path: str,\n label_list: List[str],\n bboxes: Optional[List[List[int, int, int, int]]] = None,\n labels: Optional[List[int]] = None,\n height: int = 512,\n width: int = 512,\n line_width: int = 5,\n key: Optional[str] = None\n)\n```\n\n- **image_path**: Image path.\n- **label_list**: List of label candidates.\n- **bboxes**: Initial list of bounding boxes, where each bbox is in the format [x, y, w, h].\n- **labels**: List of label for each initial bbox.\n- **height**: The maximum height of the displayed image.\n- **width**: The maximum width of the displayed image.\n- **line_width**: The stroke width of the bbox.\n- **key**: An optional string to use as the unique key for the widget. 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Assign a key so the component is not remount every time the script is rerun.\n\n- **Component Value**: \\[{'bbox':[x,y], 'label_id': label_id, 'label': label_name},...\\]\n\nExample: [example code](example/pointdet.py)\n\n# Future Work\n* Addition of component for segmentation task.\n\n# Development\n## setup\n```bash\ncd Streamlit-Image-Annotation/\nexport PYTHONPATH=$PWD\n```\nand set `IS_RELEASE = False` in `Streamlit-Image-Annotation/__init__.py`.\n\n\n## start frontend\n```bash\ngit clone https://github.com/hirune924/Streamlit-Image-Annotation.git\ncd Streamlit-Image-Annotation/streamlit_image_annotation/Detection\nyarn\nyarn start\n```\n\n## start streamlit\n```bash\ncd Streamlit-Image-Annotation/\nstreamlit run streamlit_image_annotation/Detection/__init__.py\n```\n\n## build\n```bash\ncd Streamlit-Image-Annotation/Classification/frontend\nyarn build\ncd Streamlit-Image-Annotation/Detection/frontend\nyarn build\ncd Streamlit-Image-Annotation/Point/frontend\nyarn build\n```\nand set `IS_RELEASE = True` in `Streamlit-Image-Annotation/__init__.py`.\n\n## make wheel\n```bash\npython setup.py sdist bdist_wheel\n```\n## upload\n```bash\npython3 -m twine upload --repository testpypi dist/*\npython -m pip install --index-url https://test.pypi.org/simple/ --no-deps streamlit-image-annotation\n```\n```bash\ntwine upload dist/*\n```\n",
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