G-API Background Subtraction Demo¶
This demo shows how to perform background subtraction using G-API.
Only batch size of 1 is supported.
How It Works¶
The demo application expects an instance-segmentation-security-???? or trimap free background matting based on pixel-level segmentation approach model in the Intermediate Representation (IR) format. Please note, that there aren’t background matting models in
for instance segmentation models based on
imagefor input image.
At least three outputs including:
boxeswith absolute bounding box coordinates of the input image and its score
labelswith object class IDs for all bounding boxes
maskswith fixed-size segmentation heat maps for all classes of all bounding boxes
for tripmap free background matting based on pixel-level segmentation approach:
Single 1x3xWxH input.
Single 1x1xWxH output - float tensor which is alpha channel for input.
The use case for the demo is an online conference where is needed to show only foreground - people and, respectively, to hide or replace background.
As input, the demo application accepts a path to a single image file, a video file or a numeric ID of a web camera specified with a command-line argument
The demo workflow is the following:
The demo application reads image/video frames one by one, resizes them to fit into the input image blob of the network (
The demo visualizes the resulting background subtraction. Certain command-line options affect the visualization:
If you specify
--target_bgr, background will be replaced by a chosen image or video. By default background replaced by green field.
If you specify
--blur_bgr, background will be blurred according to a set value. By default equal to zero and is not applied.
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/background_subtraction_demo/cpp_gapi/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
Demo provides functionality to use OneVPL video decoding. Example:
./background_subtraction_demo_gapi/ -m <path_to_model> -i <path_to_video_file> -use_onevpl
In order to provide additional configuration paramaters use
./background_subtraction_demo_gapi/ -m <path_to_model> -i <path_to_raw_file> -use_onevpl -onevpl_params="mfxImplDescription.mfxDecoderDescription.decoder.CodecID:MFX_CODEC_HEVC"
> NOTE : Only raw formats such as
h265 etc are supported on Linux. Working with raw formats user always must specify
codec type via
-onevpl_params. See example below.
To build OpenCV G-API with
oneVPL support follow instruction: Building G-API with oneVPL Toolkit support
oneVPL might report warnings that tell the user that source can be configurable more accurate.
cv::gapi::wip::onevpl::VPLLegacyDecodeEngine::process_error [000001CED3851C70] error: cv::gapi::wip::onevpl::CachedPool::find_free - cannot get free surface from pool, size: 5
This might be fixed by increasing pool size using
Run the application with the
-h option to see the following usage message:
[ INFO ] OpenVINO Runtime version ......... <version> [ INFO ] Build ........... <build> background_subtraction_demo_gapi [OPTION] Options: -h Print a usage message. -i Required. An input to process. The input must be a single image, a folder of images, video file or camera id. -loop Optional. Enable reading the input in a loop. -o "<path>" Optional. Name of the output file(s) to save. -limit "<num>" Optional. Number of frames to store in output. If 0 is set, all frames are stored. -res "<WxH>" Optional. Set camera resolution in format WxH. -at "<type>" Required. Architecture type: maskrcnn. -m "<path>" Required. Path to an .xml file with a trained model. -kernel_package "<string>" Optional. G-API kernel package type: opencv, fluid (by default opencv is used). -d "<device>" Optional. Target device for network (the list of available devices is shown below). The demo will look for a suitable plugin for a specified device. Default value is "CPU". -nireq "<integer>" Optional. Number of infer requests. If this option is omitted, number of infer requests is determined automatically. -nthreads "<integer>" Optional. Number of threads. -nstreams 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>) -no_show Optional. Don't show output. -blur_bgr Optional. Blur background. -target_bgr Optional. Background onto which to composite the output (by default to green field). -u Optional. List of monitors to show initially. -use_onevpl Optional. Use onevpl video decoding. -onevpl_params Optional. Parameters for onevpl video decoding. OneVPL source can be fine-grained by providing configuration parameters. Format: <prop name>:<value>,<prop name>:<value> Several important configuration parameters: 'mfxImplDescription.mfxDecoderDescription.decoder.CodecID' values: https://spec.oneapi.io/onevpl/2.7.0/API_ref/VPL_enums.html?highlight=mfx_codec_hevc#codecformatfourcc and 'mfxImplDescription.AccelerationMode' values: https://spec.oneapi.io/onevpl/2.7.0/API_ref/VPL_disp_api_enum.html?highlight=d3d11#mfxaccelerationmode(see `MFXSetConfigFilterProperty` by https://spec.oneapi.io/versions/latest/elements/oneVPL/source/index.html) -onevpl_pool_size OneVPL source applies this parameter as preallocated frames pool size. 0 leaves frames pool size default for your system. This parameter doesn't have a god default value. It must be adjusted for specific execution (video, model, system ...). Available target devices: <targets>
Running the application with an empty list of options yields the short version of the usage message and an error message.
To run the demo, please provide paths to the model in the IR format, and to an input video, image, or folder with images:
./background_subtraction_demo_gapi/ -m <path_to_model> -i <path_to_file>
> NOTE : If you provide a single image as an input, the demo processes and renders it quickly, then exits. To continuously visualize inference results on the screen, apply the
loop option, which enforces processing a single image in a loop.
You can save processed results to a Motion JPEG AVI file or separate JPEG or PNG files using the
To save processed results in an AVI file, specify the name of the output file with
aviextension, for example:
To save processed results as images, specify the template name of the output image file with
pngextension, for example:
-o output_%03d.jpg. The actual file names are constructed from the template at runtime by replacing regular expression
%03dwith the frame number, resulting in the following:
output_001.jpg, and so on. To avoid disk space overrun in case of continuous input stream, like camera, you can limit the amount of data stored in the output file(s) with the
limitoption. The default value is 1000. To change it, you can apply the
-limit Noption, where
Nis the number of frames to store.
> NOTE : Windows* systems may not have the Motion JPEG codec installed by default. If this is the case, you can download OpenCV FFMPEG back end using the PowerShell script provided with the OpenVINO install package and located at
<INSTALL_DIR>/opencv/ffmpeg-download.ps1. The script should be run with administrative privileges if OpenVINO is installed in a system protected folder (this is a typical case). Alternatively, you can save results as images.
The application uses OpenCV to display resulting images. The demo reports
FPS : average rate of video frame processing (frames per second).