Person, vehicle, bike detection with multiple data sources

The purpose of this demo is to show how to send data from multiple sources (cameras, video files) to a model served in OpenVINO Model Server.

Prerequisites

Model preparation: Python 3.9 or higher with pip

Model Server deployment: Installed Docker Engine or OVMS binary package according to the baremetal deployment guide

Prepare model files

curl --create-dir https://storage.openvinotoolkit.org/repositories/open_model_zoo/2022.1/models_bin/2/person-vehicle-bike-detection-crossroad-0078/FP32/person-vehicle-bike-detection-crossroad-0078.bin -o model/1/person-vehicle-bike-detection-crossroad-0078.bin

curl --create-dir https://storage.openvinotoolkit.org/repositories/open_model_zoo/2022.1/models_bin/2/person-vehicle-bike-detection-crossroad-0078/FP32/person-vehicle-bike-detection-crossroad-0078.xml -o model/1/person-vehicle-bike-detection-crossroad-0078.xml

Server Deployment

Deploying with Docker

docker run -d -v `pwd`/model:/models -p 9000:9000 openvino/model_server:latest --model_path /models --model_name person-vehicle-detection --port 9000 --shape auto

Deploying on Bare Metal

Assuming you have unpacked model server package, make sure to:

• On Windows: run setupvars script

• On Linux: set LD_LIBRARY_PATH and PATH environment variables

as mentioned in deployment guide, in every new shell that will start OpenVINO Model Server.

cd demos\person_vehicle_bike_detection\python
ovms --model_path model --model_name person-vehicle-detection --port 9000 --shape auto

Running the client application

git clone https://github.com/openvinotoolkit/model_server.git
cd model_server/demos/person_vehicle_bike_detection/python
pip3 install -r requirements.txt
python person_vehicle_bike_detection.py --help

Arguments

Argument	Description
-h,–help	Show help message and exit
-n NETWORK_NAME, –network_name NETWORK_NAME	Network name
-l INPUT_LAYER, –input_layer INPUT_LAYER	Input layer name
-o OUTPUT_LAYER, –output_layer OUTPUT_LAYER	Output layer name
-d FRAME_SIZE, –frame_size FRAME_SIZE	Input frame width and height that matches used model
-c NUM_CAMERAS, –num_cameras NUM_CAMERAS	Number of cameras to be used
-f FILE, –file FILE	Path to the video file
-i IP, –ip IP	IP address of the ovms
-p PORT, –port PORT	Port of the ovms

Using with video file

Copy example video file:

git clone "https://github.com/intel-iot-devkit/sample-videos.git"

Set camera count to 0 with -c 0 and provide path to the video file with -f parameter.

python person_vehicle_bike_detection.py -n person-vehicle-detection -l data -o detection_out -d 1024 -c 0 -f sample-videos/person-bicycle-car-detection.mp4 -i localhost -p 9000

Output:

[$(levelname)s ] Video0 fps: 7, Inf fps: 7, dropped fps: 0
[$(levelname)s ] Video0 fps: 7, Inf fps: 7, dropped fps: 0
[$(levelname)s ] Video0 fps: 7, Inf fps: 7, dropped fps: 0

Using with video file and camera

Set camera count to 1 with -c 1 and provide path to the video file with -f parameter.

python person_vehicle_bike_detection.py -n person-vehicle-detection -l data -o detection_out -d 1024 -c 1 -f sample-videos/person-bicycle-car-detection.mp4 -i localhost -p 9000

Console logs:

[$(levelname)s ] Video1 fps: 7, Inf fps: 7, dropped fps: 0
[$(levelname)s ] Camera0 fps: 7, Inf fps: 7, dropped fps: 0
[$(levelname)s ] Video1 fps: 7, Inf fps: 7, dropped fps: 0
[$(levelname)s ] Camera0 fps: 7, Inf fps: 7, dropped fps: 0
[$(levelname)s ] Video1 fps: 7, Inf fps: 7, dropped fps: 0
[$(levelname)s ] Camera0 fps: 8, Inf fps: 8, dropped fps: 0

NOTE: You should also be seeing the GUI showing the video frame and bounding boxes drawn with the detected class name