Docker Python Streamlit OpenCV 画像の２値化処理webアプリをDocker上で稼働

.
├── Dockerfile
├── requirements.txt
└── test.py

FROM python:3.9
USER root

EXPOSE 8501

WORKDIR /app
COPY ./requirements.txt requirements.txt
COPY ./test.py test.py

RUN pip install --upgrade pip
RUN pip install -r requirements.txt

CMD streamlit run test.py

numpy==1.22.3
Pillow==9.1.0
streamlit==1.10.0
opencv-python-headless==4.6.0.66

ImportError: libGL.so.1: cannot open shared object file: No such file or directory

import os

import streamlit as st
import numpy as np
from PIL import Image
import cv2


def pil2cv(image):
    ''' PIL型 -> OpenCV型 '''
    new_image = np.array(image, dtype=np.uint8)
    if new_image.ndim == 2:  # モノクロ
        pass
    elif new_image.shape[2] == 3:  # カラー
        new_image = cv2.cvtColor(new_image, cv2.COLOR_RGB2BGR)
    elif new_image.shape[2] == 4:  # 透過
        new_image = cv2.cvtColor(new_image, cv2.COLOR_RGBA2BGRA)
    return new_image

def main():
    os.makedirs('./data', exist_ok=True)
    st.set_page_config(page_icon="📷", page_title="画像2値化アプリ")

    with st.sidebar:
        th = st.slider('Threshold value', 0, 255, 125)
        st.write("Threshold value", th)

    with st.sidebar:
        radio = st.radio(
            "Choose a binary method",
            ("Threshold", "Adaptive threshold mean","Adaptive threshold Gaussian",
            "Otsu' thresholding", "Otsu's thresholding + Gaussian fileter")
        )

    st.title('画像2値化アプリ')

    # アップローダー
    uploaded_image=st.file_uploader("以下からファイルアップロード", type=['jpg','png'])
    # カラム設定
    col1, col2 = st.columns(2)

    col1.header("Original image")
    col2.header("Binary image")

    # original画像表示、2値化処理
    with col1:
        if uploaded_image is not None:
            image=Image.open(uploaded_image,)
            img_array = np.array(image)
            st.image(img_array,use_column_width = None)
            img=pil2cv(image) 

            gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
            ret,th1 = cv2.threshold(gray,th,255,cv2.THRESH_BINARY)
            th2 = cv2.adaptiveThreshold(gray,255,cv2.ADAPTIVE_THRESH_MEAN_C,
            cv2.THRESH_BINARY,11,2)
            th3 = cv2.adaptiveThreshold(gray,255,cv2.ADAPTIVE_THRESH_GAUSSIAN_C,
            cv2.THRESH_BINARY,11,2)
            ret2,th4 = cv2.threshold(gray,0,255,cv2.THRESH_BINARY+cv2.THRESH_OTSU)
            blur = cv2.GaussianBlur(gray,(5,5),0)
            ret3,th5 = cv2.threshold(blur,0,255,cv2.THRESH_BINARY+cv2.THRESH_OTSU)   

    # binary画像表示、保存
    if radio=="Threshold" and uploaded_image is not None:
        col2.image(th1)
        cv2.imwrite('./data/image.png', th1)
    elif radio=="Adaptive threshold mean" and uploaded_image is not None:
        col2.image(th2)
        cv2.imwrite('./data/image.png', th2)
    elif radio=="Adaptive threshold Gaussian" and uploaded_image is not None:
        col2.image(th3)
        cv2.imwrite('./data/image.png', th3)
    elif radio=="Otsu' thresholding" and uploaded_image is not None:
        col2.image(th4)
        cv2.imwrite('./data/image.png', th4)
    elif radio=="Otsu's thresholding + Gaussian fileter" and uploaded_image is not None:
        col2.image(th5)
        cv2.imwrite('./data/image.png', th5)

    # ダウンロードボタン作成
    if uploaded_image is not None:
        col2.download_button('Download',
            open('./data/image.png', 'br'),
            file_name='image.png')


if __name__ == '__main__':
    main()

# dockerfileからimageをビルド
docker build -t streamlit .

# コンテナの起動　-d:バックグラウンドで実行
docker run --name stream01 -itd -p 8501:8501 streamlit

# imageの一覧を表示
docker image ls

# 稼働中のコンテナ一覧
docker ps

# コンテナ内のbashシェルを起動
docker exec -it stream01 bash

# コンテナ内のbashシェルを終了
exit

# コンテナの停止
docker stop stream01

# コンテナの削除
docker rm stream01