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OpenVINO-YOLO

Inference of YoloV3 / tiny-YoloV3 on Laptop & RaspberryPi3 & NCS2

Inspired and based on

https://github.com/PINTO0309/OpenVINO-YoloV3

Video 'street.mp4'

https://www.youtube.com/watch?v=OEygtMfTbEY&t=19s

Clone Repository && Download Models

git clone https://github.com/gplast/OpenVINO-YOLO.git
cd OpenVINO-YOLO/
./dowload_all.sh # Use this to download all the models

openvino_test (python) :

usage: openvino_test.py [-h] [-d DEVICE] [-s SIZE] [-v VIDEO] [--tiny]

optional arguments:
  -h, --help            show this help message and exit
  -d DEVICE, --device DEVICE
                        Specify the target device to infer on; CPU, GPU, FPGA
                        or MYRIAD is acceptable. Sample will look for a
                        suitable plugin for device specified (CPU by default)
  -s SIZE, --size SIZE  Specify the CNN input size
  -v VIDEO, --video VIDEO
                        Specify the name of the input video (CAMERA by
                        default)
  --tiny                Use tiny-YoloV3

YoloV3 on Laptop CPU

python3 openvino_test.py -d CPU

YoloV3 on Neural Compute Stick 2

python3 openvino_test.py -d MYRIAD

Tiny-YoloV3 on Laptop CPU

python3 openvino_test.py -d CPU --tiny

Tiny-YoloV3 on Neural Compute Stick 2

python3 openvino_test.py -d MYRIAD --tiny

openvino_MultiStick_test (python) :

usage: openvino_MultiStick_test.py [-h] [-numncs NUMBER_OF_NCS] [-s SIZE]
                                   [-v VIDEO] [--tiny]

optional arguments:
  -h, --help            show this help message and exit
  -numncs NUMBER_OF_NCS, --numberofncs NUMBER_OF_NCS
                        Number of NCS. (Default=1)
  -s SIZE, --size SIZE  Specify the CNN input size
  -v VIDEO, --video VIDEO
                        Specify the name of the input video (CAMERA by
                        default)
  --tiny                Use tiny-YoloV3

Multistick - YoloV3 on Neural Compute Stick 2

python3 openvino_MultiStick_test.py -numncs 1 #specify the number of NCS2

Multistick - Tiny-YoloV3 on Neural Compute Stick 2

python3 openvino_MultiStick_test.py --tiny -numncs 1 #specify the number of NCS2

OpenVINO C++ Usage

./object_detection_demo_yolov3_async -h
InferenceEngine: 
    API version ............ <version>
    Build .................. <number>

object_detection_demo_yolov3_async [OPTION]
Options:

    -h                        Print a usage message.
    -i "<path>"               Required. Path to a video file (specify "cam0" to work with camera).
    -m "<path>"               Required. Path to an .xml file with a trained model.
      -l "<absolute_path>"    Optional. Required for CPU custom layers.Absolute path to a shared library with the layers implementation.
          Or
      -c "<absolute_path>"    Optional. Required for GPU custom kernels.Absolute path to the .xml file with the kernels description.
    -d "<device>"             Optional. Specify a target device to infer on (CPU, GPU). The demo will look for a suitable plugin for the specified device
    -pc                       Optional. Enable per-layer performance report.
    -r                        Optional. Output inference results raw values showing.
    -t                        Optional. Probability threshold for detections.
    -iou_t                    Optional. Filtering intersection over union threshold for overlapping boxes.
    -auto_resize              Optional. Enable resizable input with support of ROI crop and auto resize.

This Script will build main.cpp and object_detection_demo_yolov3_async.hpp from cpp and create the executable

sudo ./compile_cpp.sh

#YoloV3 - CPU
cpp/./object_detection_demo_yolov3_async -i street.mp4 -m lrmodels/YoloV3/FP32/frozen_yolo_v3.xml -d CPU -t 0.2

#YoloV3 - GPU
cpp/./object_detection_demo_yolov3_async -i street.mp4 -m lrmodels/YoloV3/FP32/frozen_yolo_v3.xml -d GPU -t 0.2

#YoloV3 - MYRIAD
cpp/./object_detection_demo_yolov3_async -i street.mp4 -m lrmodels/YoloV3/FP16/frozen_yolo_v3.xml -d MYRIAD -t 0.2


#Tiny-YoloV3 - CPU
cpp/./object_detection_demo_yolov3_async -i street.mp4 -m lrmodels/tiny-YoloV3/FP32/frozen_tiny_yolo_v3.xml -d CPU -t 0.2

#Tiny-YoloV3 - GPU
cpp/./object_detection_demo_yolov3_async -i street.mp4 -m lrmodels/tiny-YoloV3/FP32/frozen_tiny_yolo_v3.xml -d GPU -t 0.2

#Tiny-YoloV3 - MYRIAD
cpp/./object_detection_demo_yolov3_async -i street.mp4 -m lrmodels/tiny-YoloV3/FP16/frozen_tiny_yolo_v3.xml -d MYRIAD -t 0.2

Environment (YOU MUST USE TENSORFLOW <= 1.12.0)

  • LaptopPC (Intel 8th Core i5-8250U)
  • Ubuntu 18.04 x86_64
  • RaspberryPi3
  • Raspbian Stretch armv7l
  • OpenVINO toolkit 2018 R5 (2018.5.455)
  • Python 3.6
  • OpenCV 4.0.1-openvino
  • Tensorflow v1.11.0 or Tensorflow-GPU v1.11.0 (pip install)
  • YoloV3 (MS-COCO)
  • tiny-YoloV3 (MS-COCO)
  • Intel Neural Compute Stick v2


Environment construction procedure

1. Work with LaptopPC (Ubuntu 18.04)

1.OpenVINO R5 Full-Install. Execute the following command.

cd ~
curl -sc /tmp/cookie "https://drive.google.com/uc?export=download&id=1tlDW_kDOchWbkZbfy5WfbsW-b_GpXgr7" > /dev/null
CODE="$(awk '/_warning_/ {print $NF}' /tmp/cookie)"
curl -Lb /tmp/cookie "https://drive.google.com/uc?export=download&confirm=${CODE}&id=1tlDW_kDOchWbkZbfy5WfbsW-b_GpXgr7" -o l_openvino_toolkit_p_2018.5.445.tgz
tar -zxf l_openvino_toolkit_p_2018.5.445.tgz
rm l_openvino_toolkit_p_2018.5.445.tgz
cd l_openvino_toolkit_p_2018.5.445
sudo -E ./install_cv_sdk_dependencies.sh

## GUI version installer
sudo ./install_GUI.sh
 or
## CUI version installer
sudo ./install.sh

2.Configure the Model Optimizer. Execute the following command.

cd /opt/intel/computer_vision_sdk/install_dependencies
sudo -E ./install_cv_sdk_dependencies.sh
nano ~/.bashrc
source /opt/intel/computer_vision_sdk/bin/setupvars.sh

source ~/.bashrc
cd /opt/intel/computer_vision_sdk/deployment_tools/model_optimizer/install_prerequisites
sudo ./install_prerequisites.sh

3.【Optional execution】 Additional installation steps for the Intel® Movidius™ Neural Compute Stick v1 and Intel® Neural Compute Stick v2

sudo usermod -a -G users "$(whoami)"
cat <<EOF > 97-usbboot.rules
SUBSYSTEM=="usb", ATTRS{idProduct}=="2150", ATTRS{idVendor}=="03e7", GROUP="users", MODE="0666", ENV{ID_MM_DEVICE_IGNORE}="1"
SUBSYSTEM=="usb", ATTRS{idProduct}=="2485", ATTRS{idVendor}=="03e7", GROUP="users", MODE="0666", ENV{ID_MM_DEVICE_IGNORE}="1"
SUBSYSTEM=="usb", ATTRS{idProduct}=="f63b", ATTRS{idVendor}=="03e7", GROUP="users", MODE="0666", ENV{ID_MM_DEVICE_IGNORE}="1"
EOF

sudo cp 97-usbboot.rules /etc/udev/rules.d/
sudo udevadm control --reload-rules
sudo udevadm trigger
sudo ldconfig
rm 97-usbboot.rules

4.【Optional execution】 Additional installation steps for processor graphics (GPU)

cd /opt/intel/computer_vision_sdk/install_dependencies/
sudo -E su
uname -r
4.15.0-42-generic #<--- display kernel version sample

### Execute only when the kernel version is older than 4.14
./install_4_14_kernel.sh

./install_NEO_OCL_driver.sh
sudo reboot

2. Work with RaspberryPi (Raspbian Stretch)

[Note] Only the execution environment is introduced.

1.Execute the following command.

sudo apt update
sudo apt upgrade
curl -sc /tmp/cookie "https://drive.google.com/uc?export=download&id=1rBl_3kU4gsx-x2NG2I5uIhvA3fPqm8uE" > /dev/null
CODE="$(awk '/_warning_/ {print $NF}' /tmp/cookie)"
curl -Lb /tmp/cookie "https://drive.google.com/uc?export=download&confirm=${CODE}&id=1rBl_3kU4gsx-x2NG2I5uIhvA3fPqm8uE" -o l_openvino_toolkit_ie_p_2018.5.445.tgz
tar -zxvf l_openvino_toolkit_ie_p_2018.5.445.tgz
rm l_openvino_toolkit_ie_p_2018.5.445.tgz
sed -i "s|<INSTALLDIR>|$(pwd)/inference_engine_vpu_arm|" inference_engine_vpu_arm/bin/setupvars.sh

2.Execute the following command.

nano ~/.bashrc
### Add 1 row below
source /home/pi/inference_engine_vpu_arm/bin/setupvars.sh

source ~/.bashrc
### Successful if displayed as below
[setupvars.sh] OpenVINO environment initialized

sudo usermod -a -G users "$(whoami)"
sudo reboot

3.Update USB rule.

sh inference_engine_vpu_arm/install_dependencies/install_NCS_udev_rules.sh
### It is displayed as follows
Update udev rules so that the toolkit can communicate with your neural compute stick
[install_NCS_udev_rules.sh] udev rules installed

[Note] OpenCV 4.0.1 will be installed without permission when the work is finished. If you do not want to affect other environments, please edit environment variables after installation is completed.



Neural Compute Stick 2

https://ncsforum.movidius.com/discussion/1302/intel-neural-compute-stick-2-information

Issue

OpenVINO failing on YoloV3's YoloRegion, only one working on FP16, all working on FP32
Regarding YOLO family networks on NCS2. Possibly a work-around
Convert YOLOv3 Model to IR

Reference

https://github.com/opencv/opencv/wiki/Intel%27s-Deep-Learning-Inference-Engine-backend https://github.com/opencv/opencv/wiki/Intel%27s-Deep-Learning-Inference-Engine-backend#raspbian-stretch

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