Instance Segmentation Python* Demo

This demo demonstrates how to run Instance Segmentation models from Detectron or maskrcnn-benchmark using OpenVINO™.

NOTE: Only batch size of 1 is supported.

How It Works

The demo application expects an instance segmentation model in the Intermediate Representation (IR) format with the following constraints:

As input, the demo application takes:

The demo workflow is the following:

  1. The demo application reads images/video frames one by one, resizes them to fit into the input image blob of the network (im_data) keeping the original image aspect ratio.
  2. The im_info input blob passes resulting resolution and scale of a pre-processed image to the network to perform inference.
  3. The demo visualizes the resulting instance segmentation masks. Certain command-line options affect the visualization:
    • If you specify --show_boxes and --show_scores arguments, bounding boxes and confidence scores are also shown.
    • If you specify video as a source of images with the --video option, the demo shows the same object instance with the same color throughout the whole video using simple tracking. It assumes more or less static scene with instances in two frames being a part of the same track if intersection over union of the masks is greater than the 0.5 threshold.

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 --reverse_input_channels argument specified. For more information about the argument, refer to When to Reverse Input Channels section of Converting a Model Using General Conversion Parameters.

Running

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

usage: main.py [-h] -m "<path>" --labels "<path>" [-v "<path>"] [-i "<path>"]
[-d "<device>"] [-l "<absolute_path>"]
[-pt "<num>"] [--show_scores] [--show_boxes] [-pc]
Options:
-h, --help Show this help message and exit.
-m "<path>", --model "<path>"
Required. Path to an .xml file with a trained model.
--labels "<path>" Required. Path to a text file with class labels.
-v "<path>", --video "<path>"
Required for video input. Path to a video file or
numeric camera ID.
-i "<path>", --images "<path>"
Required for still images input. Path to an image or a
folder with images.
-d "<device>", --device "<device>"
Optional. Specify the target device to infer on: CPU,
GPU, FPGA, HDDL or MYRIAD. The demo will look for a
suitable plugin for device specified (by default, it
is CPU).
-l "<absolute_path>", --cpu_extension "<absolute_path>"
Required for CPU custom layers. Absolute path to a
shared library with the kernels implementation.
-pt "<num>", --prob_threshold "<num>"
Optional. Probability threshold for detections
filtering.
--show_scores Optional. Show detection scores.
--show_boxes Optional. Show bounding boxes.
-pc, --perf_counts Optional. Report performance counters

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, 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.

To run the demo, please provide paths to the model in the IR format, to a file with class labels, and to an input video, image, or folder with images:

python3 instance_segmentation_demo/main.py -m <path_to_model>/maskrcnn_r50_fpn_2x.xml --label instance_segmentation_demo/coco_labels.txt --video 0

Demo Output

The application uses OpenCV to display resulting instance segmentation masks and current inference performance.

See Also