Whiteboard Inpainting Demo

This demo focuses on a whiteboard text overlapped by a person. The demo shows how to use the OpenVINO™ toolkit to detect and hide a person on a video so that all text on a whiteboard is visible.

How It Works

The demo expects one of the following models in the Intermediate Representation (IR) format:

• Instance segmentation model
• Semantic segmentation model

Use your own model or a pretrained model from the OpenVINO™ Open Model Zoo. Find the list of models suitable for this demo in models.lst. Use the Model Downloader to obtain the models.

As an input, the demo application takes:

• Path to a video file
• Index of a web camera

Running

Install Dependencies

To install required dependencies, open a terminal and run the following:

pip3 install -r requirements.txt

Command-Line Arguments

Run the application with the -h option to see the following usage message:

usage: whiteboard_inpainting_demo.py [-h] -i INPUT [--loop] [-o OUTPUT]
                                     [-limit OUTPUT_LIMIT]
                                     -m_i M_INSTANCE_SEGMENTATION
                                     -m_s M_SEMANTIC_SEGMENTATION
                                     [-t THRESHOLD] [--no_show]
                                     [-d DEVICE] [-l CPU_EXTENSION]
                                     [-u UTILIZATION_MONITORS]
 
Whiteboard inpainting demo
 
optional arguments:
  -h, --help            Show this help message and exit.
  -i INPUT, --input INPUT
                        Required. Path to a video file or a device node of a
                        web-camera.
  --loop                Optional. Enable reading the input in a loop.
  -o OUTPUT, --output OUTPUT
                        Optional. Name of output to save.
  -limit OUTPUT_LIMIT, --output_limit OUTPUT_LIMIT
                        Optional. Number of frames to store in output.
                        If 0 is set, all frames are stored.
  -m_i M_INSTANCE_SEGMENTATION, --m_instance_segmentation M_INSTANCE_SEGMENTATION
                        Required. Path to the instance segmentation model.
  -m_s M_SEMANTIC_SEGMENTATION, --m_semantic_segmentation M_SEMANTIC_SEGMENTATION
                        Required. Path to the semantic segmentation model.
  -t THRESHOLD, --threshold THRESHOLD
                        Optional. Threshold for person instance segmentation model.
  --no_show             Optional. Don't show output.
  -d DEVICE, --device DEVICE
                        Optional. Specify a target device to infer on. CPU,
                        GPU, FPGA, HDDL or MYRIAD is acceptable. The demo will
                        look for a suitable plugin for the device specified.
  -l CPU_EXTENSION, --cpu_extension CPU_EXTENSION
                        MKLDNN (CPU)-targeted custom layers. Absolute path to a
                        shared library with the kernels impl.
  -u UTILIZATION_MONITORS, --utilization_monitors UTILIZATION_MONITORS
                        Optional. List of monitors to show initially.

Example of a command:

python whiteboard_inpainting_demo.py \
    -i <path_to_video>/video.avi \
    -m_i <path_to_model>/instance-segmentation-security-0228.xml

Demo output

The demo outputs original video with the processed one. Usage:

• Invert colors on the resulting frame by pressing the i key.
• Select a part of the frame to be shown in a separate window by using your left mouse button.
• Exit the demo by pressing Esc.