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.
Deploy person, vehicle, bike detection model#
Download model files#
mkdir -p model/1
wget -P model/1 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
wget -P model/1 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
Run OpenVINO Model Server#
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
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