This topic demonstrates how to run the Image Segmentation demo application, which does inference using semantic segmentation networks.
NOTE: This topic describes usage of Python* implementation of the Image Segmentation Demo. For the C++ implementation, refer to Image Segmentation C++ Demo.
On startup the demo application reads command line parameters and loads a network. The demo runs inference and shows results for each image captured from an input. Demo provides default mapping of classes to colors and optionally, allow to specify mapping of classes to colors from simple text file, with using
--colors argument. Depending on number of inference requests processing simultaneously (-nireq parameter) the pipeline might minimize the time required to process each single image (for nireq 1) or maximize utilization of the device and overall processing performance.
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/segmentation_demo/python/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 the empty list of options yields the usage message given above and an error message.
You can use the following command to do inference on CPU on images captured by a camera using a pre-trained network:
>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 color palette is used to visualize predicted classes. By default, the colors from PASCAL VOC dataset are applied. In case when the number of output classes is larger than number of classes provided by PASCAL VOC dataset, the rest classes are randomly colorized. Also, one can use predefined colors from other datasets, like CAMVID.
Available colors files located in the
<omz_dir>/data/palettes folder. If you want to assign custom colors for classes, you should create a
.txt file, where the each line contains colors in
(R, G, B) format. The demo application treat the number of each line as a dataset class identificator and apply specified color to pixels belonging to this class.
The demo uses OpenCV to display the resulting images with blended segmentation mask.