Simple C++ program used to detect objects in images using OpenCV and YOLOv7-tiny.
object_detect.cpp is a C++ program leveraging OpenCV and the Darknet opensource neural network for C libraries in conjunction with the YOLOv7-tiny object detection model to detect objects in live video feeds. The program takes in an image stream and outputs a new image steram with the detected objects outlined in a bounding box with included label and confidence level.
The future state of the program will be used to emit events to a ZeroMQ message queue for consumption by an AWS IoT Core integration service.
Install the following dependencies and build tools on your system:
$ sudo apt install build-essential cmake git pkg-config libgtk-3-dev
$ sudo apt install libavcodec-dev libavformat-dev libswscale-dev libv4l-dev libxvidcore-dev libx264-dev
$ sudo apt install libjpeg-dev libpng-dev libtiff-dev gfortran openexr libatlas-base-dev
$ sudo apt install python3-dev python3-numpy libtbb2 libtbb-dev libdc1394-22-dev
- Checkout openCV with contrib modules
$ cd ~
$ mkdir opencv_with_contrib
$ cd opencv_with_contrib
$ git clone https://github.com/opencv/opencv.git
$ git clone https://github.com/opencv/opencv_contrib.git
- Checkout 4.7.0 branch of opencv
$ cd opencv
$ git checkout 4.7.0
$ cd ../opencv_contrib
$ git checkout 4.7.0
$ cd ..
- Make the build directory
$ mkdir build
$ cd build
- Build the project
$ cmake -D CMAKE_BUILD_TYPE=RELEASE \
-D CMAKE_INSTALL_PREFIX=/usr \
-D OPENCV_EXTRA_MODULES_PATH=/opt/whisker/opencv_with_contrib/opencv_contrib/modules \
-D EIGEN_INCLUDE_PATH=/usr/include/eigen3 \
-D WITH_OPENCL=OFF \
-D WITH_CUDA=ON \
-D CUDA_ARCH_BIN=5.3 \
-D CUDA_ARCH_PTX="" \
-D WITH_CUDNN=ON \
-D WITH_CUBLAS=ON \
-D ENABLE_FAST_MATH=ON \
-D CUDA_FAST_MATH=ON \
-D OPENCV_DNN_CUDA=ON \
-D ENABLE_NEON=ON \
-D WITH_QT=ON \
-D WITH_OPENMP=ON \
-D BUILD_TIFF=ON \
-D WITH_FFMPEG=ON \
-D WITH_GSTREAMER=ON \
-D WITH_TBB=ON \
-D BUILD_TBB=ON \
-D BUILD_TESTS=OFF \
-D WITH_EIGEN=ON \
-D WITH_V4L=ON \
-D WITH_LIBV4L=ON \
-D OPENCV_ENABLE_NONFREE=ON \
-D INSTALL_C_EXAMPLES=OFF \
-D INSTALL_PYTHON_EXAMPLES=OFF \
-D PYTHON3_PACKAGES_PATH=/usr/lib/python3/dist-packages \
-D OPENCV_GENERATE_PKGCONFIG=ON \
-D BUILD_EXAMPLES=OFF \
-D MAKE_INSTALL_PREFIX=/usr/local \
../opencv
$ make -j4
$ make install
Note you may need to install with sudo if you get permission errors.
Note you will need 8GB swap space for building on Nvidia Jetson Nano dev board
*Build steps:
-
Build libzmq via cmake. This does an out of source build and installs the build files
- download and unzip the lib, cd to directory
- mkdir build
- cd build
- cmake ..
- sudo make -j4 install
-
Build cppzmq via cmake. This does an out of source build and installs the build files
- download and unzip the lib, cd to directory
- mkdir build
- cd build
- cmake ..
- sudo make -j4 install
-
Build cppzmq via vcpkg. This does an out of source build and installs the build files
- git clone https://github.com/Microsoft/vcpkg.git
- cd vcpkg
- ./bootstrap-vcpkg.sh # bootstrap-vcpkg.bat for Powershell
- ./vcpkg integrate install
- ./vcpkg install cppzmq
- Clone the AWS C++ SDK repo
$ cd <WORKSPACE_ROOT>
$ git clone --recurse-submodules https://github.com/aws/aws-sdk-cpp
- Build the project
mkdir build_release
cd build_release
cmake ../aws-sdk-cpp -DCMAKE_BUILD_TYPE=[Debug | Release] -DCMAKE_PREFIX_PATH=/usr/local/ -DCMAKE_INSTALL_PREFIX=/usr/local/-DBUILD_ONLY="iot;core;s3"
make -j8
make install
Note you may need to install with sudo if you get permission errors.
- Install above dependencies and clone this repo
- Build the program with the following command using the VSCode task labeled
Build - Alternatively, run the program with the debug launch configuration
Debug object_detectin VSCode which will automatically build the project with debug hooks.
Contributors names and contact info
Nate Brandeburg nate3d.com
- 0.1
- Initial Release
- Object detection using YOLOv7-tiny model
- OpenCV integration
- Darknet integration
- ZeroMQ integration
TBD