Streamlit component for image annotation.

• 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.

pip install streamlit-image-annotation

If you want to see other use cases, please check inside the examples folder.

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'])

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

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,
    use_space: bool = False,
    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.

• use_space: Enable Space key for complete.

• 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

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,
    use_space: bool = False,
    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.

• use_space: Enable Space key for complete.

• 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

• Addition of component for segmentation task.

cd Streamlit-Image-Annotation/
export PYTHONPATH=$PWD

and set IS_RELEASE = False in Streamlit-Image-Annotation/__init__.py.

git clone https://github.com/hirune924/Streamlit-Image-Annotation.git
cd Streamlit-Image-Annotation/streamlit_image_annotation/Detection/frontend
yarn
yarn start

cd Streamlit-Image-Annotation/
streamlit run streamlit_image_annotation/Detection/__init__.py

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.

python setup.py sdist bdist_wheel

python3 -m twine upload --repository testpypi dist/*
python -m pip install --index-url https://test.pypi.org/simple/ --no-deps streamlit-image-annotation

twine upload dist/*