This demo showcases Object Detection task applied for face recognition using sequence of neural networks. The pipeline is based on G-API framework. This demo executes six kernels, five of them infer networks and another one is a postprocessing kernel. This demo executes the Face Detection, Age/Gender Recognition, Head Pose Estimation, Emotions Recognition, and Facial Landmarks Detection networks. You can use a set of the following pre-trained models with the demo:
face-detection-adas-0001, which is a primary detection network for finding faces
age-gender-recognition-retail-0013, which is executed on top of the results of the first model and reports estimated age and gender for each detected face
head-pose-estimation-adas-0001, which is executed on top of the results of the first model and reports estimated head pose in Tait-Bryan angles
emotions-recognition-retail-0003, which is executed on top of the results of the first model and reports an emotion for each detected face
facial-landmarks-35-adas-0002, which is executed on top of the results of the first model and reports normed coordinates of estimated facial landmarks
Other demo objectives are:
OpenCV is used to draw resulting bounding boxes, labels, and other information. You can copy and paste this code without pulling Inference Engine demo helpers into your application.
-m...options family to the Inference Engine.
NOTE: 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_channelsargument specified. For more information about the argument, refer to When to Reverse Input Channels section of Converting a Model Using General Conversion Parameters.
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/interactive_face_detection_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 Inference Engine format (*.xml + *.bin).
An example of using the Model Downloader:
An example of using the Model Converter:
Running the application with the
-h option yields the following usage message:
Running the application with an empty list of options yields the usage message given above and an error message.
For example, to do inference on a GPU with the OpenVINO™ toolkit pre-trained models, run the following command:
>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
aviextension, for example:
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 demo uses OpenCV to display the resulting frame with detections (rendered as bounding boxes and labels, if provided). The demo reports total image throughput which includes frame decoding time, inference time, time to render bounding boxes and labels, and time to display the results.