docker容器中使用opencv-python报错

在构建机器学习环境的docker镜像时,安装了paddlehub,paddlehub的依赖有opencv-python,使用时报错如下:

Traceback (most recent call last):
  File "woodpecker/etl_main.py", line 2, in <module>
    from etl import etl_paper_html, tl_paper_paragraph_label, tl_model_inputs
  File "/ws/woodpecker/etl/tl_paper_paragraph_label.py", line 13, in <module>
    from bai.project_common.woodpecker.preprocess import (
  File "<frozen zipimport>", line 259, in load_module
  File "/usr/local/lib/python3.8/site-packages/bai-0.3.0-py3.8.egg/bai/project_common/woodpecker/preprocess.py", line 4, in <module>
  File "<frozen zipimport>", line 259, in load_module
  File "/usr/local/lib/python3.8/site-packages/bai-0.3.0-py3.8.egg/bai/feature_extraction/text/__init__.py", line 4, in <module>
  File "/usr/local/lib/python3.8/site-packages/paddlehub/__init__.py", line 31, in <module>
    from paddlehub import datasets
  File "/usr/local/lib/python3.8/site-packages/paddlehub/datasets/__init__.py", line 15, in <module>
    from paddlehub.datasets.canvas import Canvas
  File "/usr/local/lib/python3.8/site-packages/paddlehub/datasets/canvas.py", line 23, in <module>
    from paddlehub.vision.utils import get_img_file
  File "/usr/local/lib/python3.8/site-packages/paddlehub/vision/utils.py", line 18, in <module>
    import cv2
  File "/usr/local/lib/python3.8/site-packages/cv2/__init__.py", line 8, in <module>
    from .cv2 import *
ImportError: libGL.so.1: cannot open shared object file: No such file or directory

这是导入opencv报错了,经过研究,发现opencv-python文档提供了4种安装包,我的理解如下:

包名适用环境说明
opencv-python桌面环境,例如Windows,macOS,GNU/Linux包含核心模块
opencv-contrib-python桌面环境,例如Windows,macOS,GNU/Linux包含所有模块
opencv-python-headless服务器环境,无GUI library依赖,例如Docker,云环境包含核心模块
opencv-contrib-python-headless服务器环境,无GUI library依赖,例如Docker,云环境包含所有模块

这四个包,只能选择一个进行安装,paddlehub依赖opencv-python,构建docker镜像时,替换成opencv-python-headless即可。

opencv-python原文如下:

Select the correct package for your environment:

There are four different packages (see options 1, 2, 3 and 4 below) and you should SELECT ONLY ONE OF THEM. Do not install multiple different packages in the same environment. There is no plugin architecture: all the packages use the same namespace (cv2). If you installed multiple different packages in the same environment, uninstall them all with pip uninstall and reinstall only one package.

a. Packages for standard desktop environments (Windows, macOS, almost any GNU/Linux distribution)

    Option 1 - Main modules package: pip install opencv-python
    Option 2 - Full package (contains both main modules and contrib/extra modules): pip install opencv-contrib-python (check contrib/extra modules listing from OpenCV documentation)

b. Packages for server (headless) environments (such as Docker, cloud environments etc.), no GUI library dependencies

These packages are smaller than the two other packages above because they do not contain any GUI functionality (not compiled with Qt / other GUI components). This means that the packages avoid a heavy dependency chain to X11 libraries and you will have for example smaller Docker images as a result. You should always use these packages if you do not use cv2.imshow et al. or you are using some other package (such as PyQt) than OpenCV to create your GUI.

    Option 3 - Headless main modules package: pip install opencv-python-headless
    Option 4 - Headless full package (contains both main modules and contrib/extra modules): pip install opencv-contrib-python-headless (check contrib/extra modules listing from OpenCV documentation)
### 如何在 Docker安装和配置 OpenCV #### 准备工作 为了确保能够在 Docker 容器中成功安装和配置 OpenCV,需要先创建一个合适的 `Dockerfile` 文件来定义环境设置。对于 Ubuntu 18.04 或者基于此系统的 Docker 镜像而言,可以指定特定路径用于存储 OpenCV 的文件,比如 `/usr/local/opencv340` 目录[^1]。 #### 创建 Dockerfile 下面是一个简单的 `Dockerfile` 示例,它展示了如何构建一个包含 OpenCVDocker 映像: ```dockerfile FROM ubuntu:18.04 # 设置环境变量以避免交互式配置工具 ENV DEBIAN_FRONTEND=noninteractive # 更新软件源列表并安装必要的依赖项 RUN apt-get update && \ apt-get install -y --no-install-recommends \ build-essential cmake git pkg-config \ libjpeg-dev libtiff5-dev libjasper-dev libpng-dev \ libavcodec-dev libavformat-dev libswscale-dev libv4l-dev \ libxvidcore-dev libx264-dev \ libgtk-3-dev \ libatlas-base-dev gfortran \ python3-dev python3-numpy \ wget unzip # 下载并解压 opencvopencv_contrib 源码 WORKDIR /opt RUN wget https://github.com/opencv/opencv/archive/refs/tags/3.4.0.zip -O opencv-3.4.0.zip && \ unzip opencv-3.4.0.zip && \ rm opencv-3.4.0.zip RUN wget https://github.com/opencv/opencv_contrib/archive/refs/tags/3.4.0.zip -O opencv_contrib-3.4.0.zip && \ unzip opencv_contrib-3.4.0.zip && \ rm opencv_contrib-3.4.0.zip # 编译安装 OpenCV WORKDIR /opt/opencv-3.4.0/ RUN mkdir build && cd build && \ cmake -DCMAKE_BUILD_TYPE=Release \ -DCMAKE_INSTALL_PREFIX=/usr/local/opencv340 \ -DOPENCV_EXTRA_MODULES_PATH=/opt/opencv_contrib-3.4.0/modules .. && \ make -j$(nproc) && \ make install # 清理不必要的文件减少映像大小 RUN apt-get clean && \ rm -rf /var/lib/apt/lists/* /tmp/* /var/tmp/* # 设置默认命令 CMD ["bash"] ``` 这段脚本会下载 OpenCV 及其额外模块的源代码,并编译成适合目标平台使用的二进制文件,最终将其放置于预设的位置 `/usr/local/opencv340` 中。 #### 解决常见错误 如果尝试通过简单的方式如 `apt-get install libopencv-dev` 来安装 C++ 版本的 OpenCV 库时遇到报错信息:“E: Unable to locate package libopencv-dev”,这通常是因为官方仓库里可能不存在对应版本或者名称不匹配造成的。此时应该考虑手动编译最新稳定版或是寻找第三方 PPA (Personal Package Archive)[^4]。 #### 使用 GPU 加速 如果有需求利用 NVIDIA CUDA 进行加速运算,则可以在上述基础上进一步调整 `Dockerfile` ,加入对 nvidia-docker 支持以及获取适用于 GPU 的 OpenCV 构建选项。例如,在执行 cmake 命令之前还需要添加如下参数 `-DWITH_CUDA=ON -DCUDA_ARCH_BIN="6.1"` 并确保已经正确设置了 `${IPPICV_COMMIT}` 环境变量指向正确的 IPP ICV 资源链接[^3]。
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