Multi-Channel Object Detection Yolov3 C++ Demo

This demo provides an inference pipeline for multi-channel yolo v3. The demo uses Yolo v3 Object Detection network. You can follow this page convert the YOLO V3 and tiny YOLO V3 into IR model and execute this demo with converted IR model.

NOTES: If you don't use this page to convert the model, it may not work.

Other demo objectives are:

  • Up to 16 cameras as inputs, via OpenCV*
  • Visualization of detected objects from all channels on a single screen

How It Works

On the start-up, the application reads command line parameters and loads the specified networks. The Yolo v3 Object Detection network is required.

NOTES:

  • 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 --reverse_input_channels argument specified. For more information about the argument, refer to When to Reverse Input Channels section of Converting a Model Using General Conversion Parameters.

Running

Running the application with the -h option yields the following usage message:

./multi_channel_object_detection_demo_yolov3 -h
multi_channel_object_detection_demo_yolov3 [OPTION]
Options:
-h Print a usage message
-i Required. A comma separated list of inputs to process. Each input must be a single image, a folder of images or anything that cv::VideoCapture can process.
-loop Optional. Enable reading the inputs in a loop.
-duplicate_num Optional. Multiply the inputs by the given factor. For example, if only one input is provided, but -ni is set to 2, the demo uses half of images from the input as it was the first input and another half goes as the second input.
-m "<path>" Required. Path to an .xml file with a trained model.
-l "<absolute_path>" Required for CPU custom layers. Absolute path to a shared library with the kernel implementations
Or
-c "<absolute_path>" Required for GPU custom kernels. Absolute path to an .xml file with the kernel descriptions
-d "<device>" Optional. Specify the target device for a network (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 demo looks for a suitable plugin for a specified device.
-bs Optional. Batch size for processing (the number of frames processed per infer request)
-nireq Optional. Number of infer requests
-n_iqs Optional. Frame queue size for input channels
-fps_sp Optional. FPS measurement sampling period between timepoints in msec
-n_sp Optional. Number of sampling periods
-pc Optional. Enable per-layer performance report
-t Optional. Probability threshold for detections
-no_show Optional. Do not show processed video.
-show_stats Optional. Enable statistics report
-real_input_fps Optional. Disable input frames caching, for maximum throughput pipeline
-u Optional. List of monitors to show initially.

To run the demo, you can use public or pre-trained models. To download the pre-trained models, use the OpenVINO Model Downloader. The list of models supported by the demo is in the models.lst file in the demo's directory.

NOTE: Before running the demo with a trained model, make sure the model is converted to the Inference Engine format (*.xml + *.bin) using the Model Optimizer tool.

For example, to run the demo on FPGA with fallback on CPU, with one single camera, use the following command:

./multi_channel_object_detection_demo_yolov3 -m <path_to_model>/model.xml -d HETERO:FPGA,CPU -nc 1

To run the demo using two recorded video files, use the following command:

./multi_channel_object_detection_demo_yolov3 -m <path_to_mdel>/model.xml -d HDDL -i <path_to_file>/file1 <path_to_file>/file2

Video files will be processed repeatedly.

To achieve 100% utilization of one Myriad X, the thumb rule is to run 4 infer requests on each Myriad X. Option -nireq 32 can be added to above command to use 100% of HDDL-R card. The 32 here is 8 (Myriad X on HDDL-R card) x 4 (infer requests), such as following command:

./multi_channel_object_detection_demo_yolov3 -m <path_to_model>/model.xml -d HDDL
-i <path_to_file>/file1 <path_to_file>/file2 <path_to_file>/file3 <path_to_file>/file4 -nireq 32

You can also run the demo on web cameras and video files simultaneously by specifying both parameters: -nc <number of cams> -i <video files sequentially, separated by space>. To run the demo with a single input source(a web camera or a video file), but several channels, specify an additional parameter: -duplicate_num 3. You will see four channels: one real and three duplicated. With several input sources, the -duplicate_num parameter will duplicate each of them.

Demo Output

The demo uses OpenCV to display the resulting frames with detections rendered as bounding boxes. On the top of the screen, the demo reports throughput in frames per second. You can also enable more detailed statistics in the output using the -show_stats option while running the demos.

Input Video Sources

General parameter for input video source is -i. Use it to specify video files or web cameras as input video sources. You can add the parameter to a sample command line as follows:

-i <file1> <file2>

-nc <nc_value> parameter simplifies usage of multiple web cameras. It connects web cameras with indexes from 0 to nc_value-1.

To see all available web cameras, run the ls /dev/video* command. You will get output similar to the following:

user@user-PC:~ $ ls /dev/video*
/dev/video0 /dev/video1 /dev/video2

You can use -i option to connect all the three web cameras:

-i /dev/video0 /dev/video1 /dev/video2

Alternatively, you can just set -nc 3, which simplifies application usage.

If your cameras are connected to PC with indexes gap (for example, 0,1,3), use the -i parameter.

To connect to IP cameras, use RTSP URIs:

-i rtsp://camera_address_1/ rtsp://camera_address_2/

See Also