Opencv Cuda Support Installation

opencv_cuda_support_installation.sh

# Prerequisites:
# 1. Ensure you have the latest NVIDIA drivers installed
# 2. Install CUDA toolkit from the official NVIDIA site
# 3. Install cuDNN (NVIDIA CUDA Deep Neural Network library)

# Update and upgrade the system
sudo apt update && sudo apt upgrade -y

# Install dependencies
sudo apt install -y build-essential cmake git pkg-config libgtk-3-dev \
    libavcodec-dev libavformat-dev libswscale-dev libv4l-dev \
    libxvidcore-dev libx264-dev libjpeg-dev libpng-dev libtiff-dev \
    gfortran openexr libatlas-base-dev python3-dev python3-numpy \
    libtbb2 libtbb-dev libdc1394-22-dev libopenexr-dev \
    libgstreamer-plugins-base1.0-dev libgstreamer1.0-dev

# Additional dependencies
sudo apt install -y libgtk2.0-dev pkg-config libavcodec-dev libavformat-dev libswscale-dev
sudo apt install -y python3-dev python3-numpy libtbb2 libtbb-dev libjpeg-dev libpng-dev libtiff-dev libdc1394-22-dev
sudo apt install -y libcanberra-gtk* libatlas-base-dev gfortran
sudo apt install -y python3-dev python3-pip
sudo -H pip3 install -U pip numpy
sudo apt install -y python3-testresources

# Install additional libraries
sudo apt install -y libprotobuf-dev protobuf-compiler
sudo apt install -y libgoogle-glog-dev libgflags-dev
sudo apt install -y libgphoto2-dev libeigen3-dev libhdf5-dev doxygen

# Download OpenCV and OpenCV contrib source
wget -O opencv.zip https://github.com/opencv/opencv/archive/4.10.0.zip
wget -O opencv_contrib.zip https://github.com/opencv/opencv_contrib/archive/4.10.0.zip

# Unzip the archives
unzip opencv.zip
unzip opencv_contrib.zip

# Navigate to OpenCV directory
cd opencv-4.10.0

# Create build directory
mkdir build && cd build

# Configure the build
cmake -D CMAKE_BUILD_TYPE=RELEASE \
    -D CMAKE_INSTALL_PREFIX=/usr/local \
    -D WITH_TBB=ON \
    -D ENABLE_FAST_MATH=1 \
    -D CUDA_FAST_MATH=1 \
    -D WITH_CUBLAS=1 \
    -D WITH_CUDA=ON \
    -D BUILD_opencv_cudacodec=OFF \
    -D WITH_CUDNN=ON \
    -D OPENCV_DNN_CUDA=ON \
    -D CUDA_ARCH_BIN=7.5 \
    -D WITH_V4L=ON \
    -D WITH_QT=OFF \
    -D WITH_OPENGL=ON \
    -D WITH_GSTREAMER=ON \
    -D OPENCV_GENERATE_PKGCONFIG=ON \
    -D OPENCV_PC_FILE_NAME=opencv.pc \
    -D OPENCV_ENABLE_NONFREE=ON \
    -D HAVE_opencv_python3=ON \
    -D OPENCV_EXTRA_MODULES_PATH=../opencv_contrib-4.10.0/modules \
    -D INSTALL_PYTHON_EXAMPLES=OFF \
    -D INSTALL_C_EXAMPLES=OFF \
    -D BUILD_EXAMPLES=OFF \
    -D WITH_NVCUVID=ON \
    ..

# Build OpenCV
make -j$(nproc)

# Install OpenCV
sudo make install

# Update library cache
sudo ldconfig

# Verify the installation
python3 -c "import cv2; print(cv2.__version__)"

# Create a test script
cat << EOF > opencv_cuda_test.py
import cv2
import numpy as np

print(f"OpenCV version: {cv2.__version__}")
print(f"CUDA enabled: {cv2.cuda.getCudaEnabledDeviceCount() > 0}")
print(f"Number of CUDA devices: {cv2.cuda.getCudaEnabledDeviceCount()}")

# Simple CUDA operation test
if cv2.cuda.getCudaEnabledDeviceCount() > 0:
    gpu_mat = cv2.cuda_GpuMat()
    gpu_mat.upload(np.random.randint(0, 255, (1000, 1000), dtype=np.uint8))
    result = cv2.cuda.subtract(gpu_mat, gpu_mat)
    print("CUDA operation successful")
else:
    print("No CUDA devices available")
EOF

# Run the test script
python3 opencv_cuda_test.py