Building for Raspberry PI using Docker

1. Build the base docker image

    docker build -t armv7_openvino ./rpi-docker
   

2. Run the initial CMake command

    docker run -v D:\Git\ai-drone:/root/build --rm armv7_openvino cmake -DCMAKE_TOOLCHAIN_FILE="/root/arm-openvino.toolchain.cmake" -B buildArm -S .
   

3. Build the project

    docker run -v D:\Git\ai-drone:/root/build --rm armv7_openvino make -j7 -C buildArm
   
    docker run -v D:\Git\drone-c++:/root/build armv7_openvino make -j10 -C buildArm && scp -r ./buildArm/armv7l/Debug/ pi@rpi-3:/home/pi/drone/
   

Running on Raspberry PI

1. Install the OpenVINO™ Toolkit for Raspbian OS Package

2. Copy the drone binary and libs folder (found in buildArm\armv7l\Release) over to the raspberry pi.

3. Copy any required inference models over. They should be placed in a folder named models right beside the executable.

4. Navigate to the directory that contains the binary and run the following command:

    LD_LIBRARY_PATH=Release/lib:opencv-4.1.1/lib/:inference_engine/lib/arm ./Release/drone -m ~/drone/models/Transportation/object_detection/vehicle/mobilenet-reduced-ssd/dldt/FP16/vehicle-detection-adas-0002.xml -i ~/drone/vids/fly_up_480p.mp4 -d MYRIAD -msp_port_name /dev/serial0 -ma ~/drone/models/Security/object_attributes/vehicle/resnet10_update_1/dldt/FP16/vehicle-attributes-recognition-barrier-0039.xml
   
   
   
    LD_LIBRARY_PATH=lib:/home/pi/qeum-test/bin/armv7l/Release/lib:/home/pi/qeum-test/opencv-4.1.0/lib ./drone \
    -m models/Transportation/object_detection/vehicle/mobilenet-reduced-ssd/dldt/FP16/vehicle-detection-adas-0002.xml \
    -ma models/Security/object_attributes/vehicle/resnet10_update_1/dldt/FP16/vehicle-attributes-recognition-barrier-0039.xml \
    -d MYRIAD \
    -msp_port_name /dev/serial0 \
    -i car_smaller.mp4
   
   
    LD_LIBRARY_PATH=~/opencv/lib:~/inference-engine/bin/aarch64/Release/lib:lib ./drone \
    -m ~/drone/models/Transportation/object_detection/vehicle/mobilenet-reduced-ssd/dldt/FP16/vehicle-detection-adas-0002.xml \
    -ma ~/drone/models/Security/object_attributes/vehicle/resnet10_update_1/dldt/FP16/vehicle-attributes-recognition-barrier-0039.xml \
    -d MYRIAD \
    -msp_port_name /dev/serial0 \
    -i ~/drone/vids/fly_up_480p.mp4
   

Running on Windows

This is helpful for debugging since you can run the project from right within Visual Studio.

1. Create a build directory and run CMake.

    mkdir build && cd build
    cmake ..
   

2. Open build/Drone.sln with Visual Studio

   a. You should be able to build from within VS but running might be a bit tricky due to the necessary environment variables required for OpenVINO to run. I normally just run the app from the command line anyways.

3. Run the command:

    drone.exe -m "..\models\Transportation\object_detection\vehicle\mobilenet-reduced-ssd\dldt\FP16\vehicle-detection-adas-0002.xml" -ma "..\models\Security\object_attributes\vehicle\resnet10_update_1\dldt\FP16\vehicle-attributes-recognition-barrier-0039.xml" -d MYRIAD -msp_port_name COM3 -i ..\vids\car_480p.mp4
   

After building OpenCV and InferenceEngine

cd /root && mkdir bundle
cp -r opencv/build/ bundle/ && mv bundle/build/ bundle/opencv
cp -r dldt/inference-engine/ bundle/
cd bundle && zip -r bundle opencv/ inference-engine/
cp bundle.zip /root/build/from-dock/