Install opencv With Cuda On Docker Containner for dGPU

[Alpha.Chen]

Hi, I want install opencv with cuda to docker containner
for dGPU RTX 3090Ti,
Use ultralytics/ultralytics docker image,
And I use theese script to install:
https://forums.developer.nvidia.com/uploads/short-url/zDxI9fyMGjvgnarGpnGYNsS8mk2.sh
But opencv can’t with cuda,
How can I fix it?

iGPU Host Info:

| NVIDIA-SMI 535.161.07             Driver Version: 535.161.07   CUDA Version: 12.2     |
|-----------------------------------------+----------------------+----------------------+
| GPU  Name                 Persistence-M | Bus-Id        Disp.A | Volatile Uncorr. ECC |
| Fan  Temp   Perf          Pwr:Usage/Cap |         Memory-Usage | GPU-Util  Compute M. |
|                                         |                      |               MIG M. |
|=========================================+======================+======================|
|   0  NVIDIA GeForce RTX 3090 Ti     Off | 00000000:01:00.0 Off |                  Off |
|  0%   42C    P8              26W / 480W |    331MiB / 24564MiB |      0%      Default |
|                                         |                      |                  N/A |
+-----------------------------------------+----------------------+----------------------+

docker continner torch with cuda,but opencv can’t:

docker compose :

services:
  ultralytics:
    image: ultralytics/ultralytics
    container_name: ultralytics-container
    runtime: nvidia
    environment:
      - NVIDIA_VISIBLE_DEVICES=all
    working_dir: /app
    volumes:
      - ..:/app
    command: "python3 app.py"
    ports:
      - "5500:5000"
      - "8080:8080"
    ipc: host
    stdin_open: true
    tty: true
    deploy:
      resources:
        reservations:
          devices:
            - driver: nvidia
              device_ids: ['0']
              capabilities: [gpu,video]

containner python version:

[Alpha.Chen]

I use nvidia pytorch fix this problem

docker file :

# Ultralytics YOLO 🚀, AGPL-3.0 license
# Builds ultralytics/ultralytics:latest image on DockerHub https://hub.docker.com/r/ultralytics/ultralytics
# Image is CUDA-optimized for YOLO11 single/multi-GPU training and inference

# Start FROM PyTorch image https://hub.docker.com/r/pytorch/pytorch or nvcr.io/nvidia/pytorch:23.03-py3
FROM nvcr.io/nvidia/pytorch:23.08-py3

RUN apt-get update && \
    apt-get install -y --no-install-recommends \
    gcc git zip unzip wget curl htop libgl1 libglib2.0-0 libpython3-dev gnupg g++ libusb-1.0-0 libsm6 \
    && rm -rf /var/lib/apt/lists/*

# Security updates
# https://security.snyk.io/vuln/SNYK-UBUNTU1804-OPENSSL-3314796
RUN apt upgrade --no-install-recommends -y openssl tar

# Create working directory
WORKDIR /app

# install requirements
COPY requirements.txt .
RUN pip install -r requirements.txt


# install opencv with CUDA support
COPY scripts .
RUN rm -r workspace
RUN bash ./build_opencv.sh

./build_opencv.sh

#!/bin/bash
#
# Copyright (c) 2024, NVIDIA CORPORATION.  All rights reserved.
#
# NVIDIA Corporation and its licensors retain all intellectual property
# and proprietary rights in and to this software, related documentation
# and any modifications thereto.  Any use, reproduction, disclosure or
# distribution of this software and related documentation without an express
# license agreement from NVIDIA Corporation is strictly prohibited.
#

version="4.11.0"
folder="workspace"

set -e

echo "------------------------------------"
echo "** Install requirement (1/4)"
echo "------------------------------------"
 apt-get update
 apt-get install -y build-essential cmake git libgtk2.0-dev pkg-config libavcodec-dev libavformat-dev libswscale-dev
 apt-get install -y libgstreamer1.0-dev libgstreamer-plugins-base1.0-dev python3.10-dev python3-numpy
 apt-get install -y libtbb2 libtbb-dev libjpeg-dev libpng-dev libtiff-dev libv4l-dev v4l-utils qv4l2
 apt-get install -y curl


echo "------------------------------------"
echo "** Download opencv "${version}" (2/4)"
echo "------------------------------------"
mkdir $folder
cd ${folder}
curl -L https://github.com/opencv/opencv/archive/${version}.zip -o opencv-${version}.zip
curl -L https://github.com/opencv/opencv_contrib/archive/${version}.zip -o opencv_contrib-${version}.zip
unzip opencv-${version}.zip
unzip opencv_contrib-${version}.zip
rm opencv-${version}.zip opencv_contrib-${version}.zip
cd opencv-${version}/


echo "------------------------------------"
echo "** Build opencv "${version}" (3/4)"
echo "------------------------------------"
mkdir release
cd release/
cmake -D WITH_CUDA=ON -D WITH_CUDNN=ON -D CUDA_ARCH_BIN="8.6" -D CUDA_ARCH_PTX="" -D OPENCV_GENERATE_PKGCONFIG=ON -D OPENCV_EXTRA_MODULES_PATH=../../opencv_contrib-${version}/modules -D WITH_GSTREAMER=ON -D WITH_LIBV4L=ON -D BUILD_opencv_python3=ON -D BUILD_TESTS=OFF -D BUILD_PERF_TESTS=OFF -D BUILD_EXAMPLES=OFF -D CMAKE_BUILD_TYPE=RELEASE -D CMAKE_INSTALL_PREFIX=/usr/local ..
make -j$(nproc)


echo "------------------------------------"
echo "** Install opencv "${version}" (4/4)"
echo "------------------------------------"
make install
echo 'export LD_LIBRARY_PATH=/usr/local/lib:$LD_LIBRARY_PATH' >> ~/.bashrc
echo 'export PYTHONPATH=/usr/local/lib/python3.10/dist-packages:$PYTHONPATH' >> ~/.bashrc
source ~/.bashrc


echo "** Install opencv "${version}" successfully"
echo "** Bye :)"

docker compose:

 # nvidia docker config:
# https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/latest/docker-specialized.html
services:
  app:
    image: <build image name>
    container_name: <containner name>
    runtime: nvidia
    environment:
      - NVIDIA_VISIBLE_DEVICES=all
    working_dir: /app
    volumes:
      - ..:/app
    command: "tail -f /dev/null"
    ports:
      - "5500:5000"
      - "8080:8080"
    ipc: host
    stdin_open: true
    tty: true
    deploy:
      resources:
        reservations:
          devices:
            - driver: nvidia
              device_ids: ['0']
              capabilities: [gpu,video,compute,utility]