# 创建虚拟环境（推荐）
python -m venv cv-env
source cv-env/bin/activate   # Linux/macOS
# cv-env\Scripts\activate    # Windows

# 安装 OpenCV 主模块
pip install opencv-python

# 安装包含 contrib 模块的版本（含 SIFT、ArUco 等）
pip install opencv-contrib-python

# 安装带 GUI 支持的完整版（含 highgui）
pip install opencv-python-headless   # 无 GUI（服务器/Docker 推荐）
# 或
pip install opencv-python            # 含 GUI

import cv2

print(f"OpenCV 版本: {cv2.__version__}")
print(f"构建信息: {cv2.getBuildInformation()}")

# 检查关键模块
modules = ['CORE', 'IMGPROC', 'IMCODECS', 'HIGHGUI',
           'VIDEOIO', 'DNN', 'FEATURES2D', 'CALIB3D']
for mod in modules:
    print(f"  {mod}: {'✅' if hasattr(cv2, 'haveImageReader') else '...'}")

# 测试基本功能
import numpy as np
img = np.zeros((100, 100, 3), dtype=np.uint8)
cv2.circle(img, (50, 50), 30, (255, 0, 0), -1)
success = cv2.imwrite("test_output.png", img)
print(f"图像写入: {'成功' if success else '失败'}")

# 创建环境
conda create -n cv python=3.11
conda activate cv

# 从 conda-forge 安装（推荐）
conda install -c conda-forge opencv

# 验证
python -c "import cv2; print(cv2.__version__)"

# 系统依赖
sudo apt update
sudo apt install -y \
    build-essential cmake cmake-gui git pkg-config \
    libjpeg-dev libpng-dev libtiff-dev \
    libavcodec-dev libavformat-dev libswscale-dev \
    libgtk-3-dev libcanberra-gtk3-dev \
    libatlas-base-dev gfortran \
    python3-dev python3-numpy python3-pip \
    libv4l-dev v4l-utils \
    libtbb-dev libeigen3-dev

# 可选：GStreamer 支持
sudo apt install -y libgstreamer1.0-dev libgstreamer-plugins-base1.0-dev

# 可选：Intel 性能原语
sudo apt install -y ippccw ippcvw ippiew ippw

cd ~/src
git clone https://github.com/opencv/opencv.git
git clone https://github.com/opencv/opencv_contrib.git

cd opencv
git checkout 4.10.0          # 选择稳定版本
cd ../opencv_contrib
git checkout 4.10.0          # 版本必须匹配

cd ~/src/opencv
mkdir build && cd build

cmake \
    -D CMAKE_BUILD_TYPE=Release \
    -D CMAKE_INSTALL_PREFIX=/usr/local \
    -D OPENCV_EXTRA_MODULES_PATH=../../opencv_contrib/modules \
    -D BUILD_opencv_python3=ON \
    -D PYTHON3_EXECUTABLE=$(which python3) \
    -D BUILD_EXAMPLES=OFF \
    -D BUILD_TESTS=OFF \
    -D BUILD_DOCS=OFF \
    -D OPENCV_ENABLE_NONFREE=ON \
    -D WITH_CUDA=ON \
    -D CUDA_ARCH_BIN="7.5,8.0,8.6,8.9,9.0" \
    -D WITH_CUDNN=ON \
    -D OPENCV_DNN_CUDA=ON \
    -D WITH_TBB=ON \
    -D WITH_OPENGL=ON \
    ..

# 查看 CPU 核心数
NPROC=$(nproc)

# 编译（-j 使用所有核心）
make -j$NPROC

# 安装
sudo make install
sudo ldconfig

# 验证安装
python3 -c "import cv2; print(cv2.__version__)"

pip install opencv-contrib-python
python -c "import cv2; print(cv2.__version__)"

# 使用 CMake GUI
# Source: C:/src/opencv
# Build:  C:/src/opencv/build
# Generator: Visual Studio 17 2022, x64
# 然后在 VS 中编译 Release

# 使用 Homebrew
brew install opencv

# 指定 Python 路径
cmake -D PYTHON3_EXECUTABLE=$(brew --prefix python3)/bin/python3 ..

// .vscode/settings.json
{
    "python.analysis.extraPaths": [
        "/usr/local/lib/python3.11/site-packages"
    ],
    "python.defaultInterpreterPath": "cv-env/bin/python",
    "C_Cpp.includePath": [
        "/usr/local/include/opencv4"
    ]
}

cmake_minimum_required(VERSION 3.16)
project(my_cv_project)

set(CMAKE_CXX_STANDARD 17)

find_package(OpenCV 4.8 REQUIRED)
include_directories(${OpenCV_INCLUDE_DIRS})

add_executable(main main.cpp)
target_link_libraries(main ${OpenCV_LIBS})

# 检查 NVIDIA 驱动
nvidia-smi

# 检查 CUDA 版本
nvcc --version

# 检查 cuDNN
cat /usr/include/cudnn_version.h | grep MAJOR -A 2

import cv2

# 检查 CUDA 设备数
count = cv2.cuda.getCudaEnabledDeviceCount()
print(f"CUDA 设备数: {count}")

if count > 0:
    # 获取设备信息
    info = cv2.cuda.DeviceInfo(0)
    print(f"设备名: {info.name()}")
    print(f"总内存: {info.totalMemory() / 1024**3:.1f} GB")
    print(f"计算能力: {info.majorVersion()}.{info.minorVersion()}")

#include <opencv2/opencv.hpp>
#include <opencv2/core/cuda.hpp>

int main() {
    int count = cv::cuda::getCudaEnabledDeviceCount();
    std::cout << "CUDA 设备: " << count << std::endl;

    if (count > 0) {
        cv::cuda::printShortCudaDeviceInfo(0);
    }
    return 0;
}