Security Barrier Camera C++ Demo¶
This demo showcases Vehicle and License Plate Detection network followed by the Vehicle Attributes Recognition and License Plate Recognition networks applied on top of the detection results.
Other demo objectives are:
Video/Camera as inputs, via OpenCV*
Example of complex asynchronous networks pipelining: Vehicle Attributes and License Plate Recognition networks are executed on top of the Vehicle Detection results
Visualization of Vehicle Attributes and License Plate information for each detected object
How It Works¶
On startup, the application reads command line parameters and loads the specified networks. The Vehicle and License Plate Detection network is required, the other two are optional.
The core component of the application pipeline is the Worker class, which executes incoming instances of a
Task is an abstract class that describes data to process and how to process the data. For example, a
Task can be to read a frame or to get detection results. There is a pool of
Task instances. These
Task s are awaiting to be executed. When a
Task from the pool is being executed, it may create and/or submit another
Task to the pool. Each
Task stores a smart pointer to an instance of
VideoFrame, which represents an image the
Task works with. When the sequence of
Task s is completed and none of the
Task s require a
VideoFrame instance, the
VideoFrame is destroyed. This triggers creation of a new sequence of
Task s. The pipeline of this demo executes the following sequence of
Reader, which reads a new frame
InferTask, which starts detection inference
RectExtractor, which waits for detection inference to complete and runs a classifier and a recognizer
ResAggregator, which draws the results of the inference on the frame
Drawer, which shows the frame with the inference results
At the end of the sequence, the
VideoFrame is destroyed and the sequence starts again for the next frame.
By default, Open Model Zoo demos expect input with BGR channels order. If you trained your model to work with RGB order, you need to manually rearrange the default channels order in the demo application or reconvert your model using the Model Optimizer tool with the
--reverse_input_channels argument specified. For more information about the argument, refer to When to Reverse Input Channels section of Embedding Preprocessing Computation.
Preparing to Run¶
For demo input image or video files, refer to the section Media Files Available for Demos in the Open Model Zoo Demos Overview. The list of models supported by the demo is in
<omz_dir>/demos/security_barrier_camera_demo/cpp/models.lst file. This file can be used as a parameter for Model Downloader and Converter to download and, if necessary, convert models to OpenVINO IR format (*.xml + *.bin).
An example of using the Model Downloader:
omz_downloader --list models.lst
An example of using the Model Converter:
omz_converter --list models.lst
Refer to the tables Intel’s Pre-Trained Models Device Support and Public Pre-Trained Models Device Support for the details on models inference support at different devices.
Running the application with the
-h option yields the following usage message:
security_barrier_camera_demo [OPTION] Options: -h Print a usage message. -i "<path1>" "<path2>" Required for video or image files input. Path to video or image files. -m "<path>" Required. Path to the Vehicle and License Plate Detection model .xml file. -m_va "<path>" Optional. Path to the Vehicle Attributes model .xml file. -m_lpr "<path>" Optional. Path to the License Plate Recognition model .xml file. -d "<device>" Optional. Specify the target device for Vehicle Detection (the list of available devices is shown below). Default value is CPU. Use "-d HETERO:<comma-separated_devices_list>" format to specify HETERO plugin. The application looks for a suitable plugin for the specified device. -d_va "<device>" Optional. Specify the target device for Vehicle Attributes (the list of available devices is shown below). Default value is CPU. Use "-d HETERO:<comma-separated_devices_list>" format to specify HETERO plugin. The application looks for a suitable plugin for the specified device. -d_lpr "<device>" Optional. Specify the target device for License Plate Recognition (the list of available devices is shown below). Default value is CPU. Use "-d HETERO:<comma-separated_devices_list>" format to specify HETERO plugin. The application looks for a suitable plugin for the specified device. -r Optional. Output inference results as raw values. -t Optional. Probability threshold for vehicle and license plate detections. -no_show Optional. Don't show output. -auto_resize Optional. Enable resizable input with support of ROI crop and auto resize. -nireq Optional. Number of infer requests. 0 sets the number of infer requests equal to the number of inputs. -nc Required for web camera input. Maximum number of processed camera inputs (web cameras). -loop_video Optional. Enable playing video on a loop. -n_iqs Optional. Number of allocated frames. It is a multiplier of the number of inputs. -ni Optional. Specify the number of channels generated from provided inputs (with -i and -nc keys). For example, if only one camera is provided, but -ni is set to 2, the demo will process frames as if they are captured from two cameras. 0 sets the number of input channels equal to the number of provided inputs. -fps Optional. Set the playback speed not faster than the specified FPS. 0 removes the upper bound. -n_wt Optional. Set the number of threads including the main thread a Worker class will use. -display_resolution Optional. Specify the maximum output window resolution. -nstreams "<integer>" Optional. Number of streams to use for inference on the CPU or/and GPU in throughput mode (for HETERO and MULTI device cases use format <device1>:<nstreams1>,<device2>:<nstreams2> or just <nstreams>) -nthreads "<integer>" Optional. Number of threads to use for inference on the CPU (including HETERO and MULTI cases). -u Optional. List of monitors to show initially.
Running the application with an empty list of options yields an error message.
For example, to do inference on a GPU with the OpenVINO toolkit pre-trained models, run the following command:
./security_barrier_camera_demo -i <path_to_video>/inputVideo.mp4 -m <path_to_model>/vehicle-license-plate-detection-barrier-0106.xml -m_va <path_to_model>/vehicle-attributes-recognition-barrier-0039.xml -m_lpr <path_to_model>/license-plate-recognition-barrier-0001.xml -d GPU
To do inference for two video inputs using two asynchronous infer request, run the following command:
./security_barrier_camera_demo -i <path_to_video>/inputVideo_0.mp4 <path_to_video>/inputVideo_1.mp4 -m <path_to_model>/vehicle-license-plate-detection-barrier-0106.xml -m_va <path_to_model>/vehicle-attributes-recognition-barrier-0039.xml -m_lpr <path_to_model>/license-plate-recognition-barrier-0001.xml -d CPU -nireq 2
The demo uses OpenCV to display the resulting frame with detections rendered as bounding boxes and text. The demo reports:
FPS : average rate of video frame processing (frames per second).
Latency : average time required to process one frame (from reading the frame to displaying the results).
You can use these metrics to measure application-level performance.