TensorFlow* Object Detection Mask R-CNNs Segmentation C++ Demo

This topic demonstrates how to run the Segmentation demo application, which does inference using image segmentation networks created with Object Detection API.

The demo has a post-processing part that gathers masks arrays corresponding to bounding boxes with high probability taken from the Detection Output layer. Then the demo produces pictures with identified masks.

How It Works

Upon the start-up, the demo application reads command line parameters and loads a network and an image to the Inference Engine plugin. When inference is done, the application creates an output image.

NOTE: By default, Inference Engine samples and 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 sample or 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 Specify Input Shapes section of Converting a Model Using General Conversion Parameters.


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

./mask_rcnn_demo -h
API version ............ <version>
Build .................. <number>
mask_rcnn_demo [OPTION]
-h Print a usage message.
-i "<path>" Required. Path to an .bmp image.
-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.
-c "<absolute_path>" Required for GPU custom kernels. Absolute path to the .xml file with the kernel descriptions.
-pp "<path>" Optional. Path to a plugin folder.
-d "<device>" Optional. Specify the target device to infer on; CPU, GPU, FPGA, HDDL or MYRIAD is acceptable. The demo will look for a suitable plugin for a specified device. Default value is CPU
-ni "<integer>" Optional. Number of iterations. Default value is 1
-detection_output_name "<string>" Optional. The name of detection output layer. Default value is "detection_output"
-masks_name "<string>" Optional. The name of masks layer. Default value is "masks"
-pc Optional. Enables per-layer performance report

Running the application with the empty list of options yields the usage message given above and an error message.

To run the sample, you can use public or pre-trained models. To download the pre-trained models, use the OpenVINO Model Downloader or go to https://download.01.org/opencv/.

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.

You can use the following command to do inference on CPU on an image using a trained network:

./mask_rcnn_demo -i <path_to_image>/inputImage.bmp -m <path_to_model>/faster_rcnn.xml

Demo Output

For each input image the application outputs a segmented image. For example, out0.png and out1.png are created for the network with batch size equal to 2.

See Also