Instance Segmentation Python* Demo#


This demo shows how to perform instance segmentation 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:

  1. for instance segmentation models based on Mask RCNN approach:

    • Two inputs: im_data for input image and im_info for meta-information about the image (actual height, width and scale).

    • At least four outputs including:

      • boxes with absolute bounding box coordinates of the input image

      • scores with confidence scores for all bounding boxes

      • classes with object class IDs for all bounding boxes

      • raw_masks with fixed-size segmentation heat maps for all classes of all bounding boxes

  2. for instance segmentation models based on YOLACT approach:

    • Single input for input image.

    • At least four outputs including:

      • boxes with normalized in [0, 1] range bounding box coordinates

      • conf with confidence scores for each class for all boxes

      • mask with fixed-size mask channels for all boxes.

      • proto with fixed-size segmentation heat maps prototypes for all boxes.

As input, the demo application accepts a path to a single image file, a video file or a numeric ID of a web camera specified with a command-line argument -i

The demo workflow is the following:

  1. The demo application reads image/video frames one by one, resizes them to fit into the input image blob of the network (im_data).

  2. The im_info input blob passes resulting resolution and scale of a pre-processed image to the network to perform inference if network has im_info input.

  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.

    • By default, tracking is used to show object instance with the same color throughout the whole video. 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. To disable tracking, specify the --no_track argument.

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_channels argument specified. For more information about the argument, refer to When to Reverse Input Channels section of [Embedding Preprocessing Computation](@ref openvino_docs_MO_DG_Additional_Optimization_Use_Cases).

Model API#

The demo utilizes model wrappers, adapters and pipelines from Python* Model API.

The generalized interface of wrappers with its unified results representation provides the support of multiple different instance segmentation model topologies in one demo.

Preparing to Run#

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/instance_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 IR format (*.xml + *.bin).

An example of using the Model Downloader:

omz_downloader --list models.lst

An example of using the Model Converter:

omz_converter --list models.lst

Supported Models#

  • instance-segmentation-person-0007

  • instance-segmentation-security-0002

  • instance-segmentation-security-0091

  • instance-segmentation-security-0228

  • instance-segmentation-security-1039

  • instance-segmentation-security-1040

  • yolact-resnet50-fpn-pytorch

NOTE: Refer to the tables Intel’s Pre-Trained Models Device Support and Public Pre-Trained Models Device Support for the details on models inference support at different devices.


Running the demo with -h shows this help message:

usage: [-h] -m MODEL [--adapter {openvino,ovms}] -i INPUT [-d DEVICE] --labels LABELS [-t PROB_THRESHOLD] [--no_track] [--show_scores]
                                     [--show_boxes] [--layout LAYOUT] [-nireq NUM_INFER_REQUESTS] [-nstreams NUM_STREAMS] [-nthreads NUM_THREADS] [--loop] [-o OUTPUT]
                                     [-limit OUTPUT_LIMIT] [--no_show] [--output_resolution OUTPUT_RESOLUTION] [-u UTILIZATION_MONITORS] [-r]

optional arguments:
  -h, --help            show this help message and exitz

  -m MODEL, --model MODEL
                        Required. Path to an .xml file with a trained model or address of model inference service if using ovms adapter.
  --adapter {openvino,ovms}
                        Optional. Specify the model adapter. Default is openvino.
  -i INPUT, --input INPUT
                        Required. An input to process. The input must be a single image, a folder of images, video file or camera id.
  -d DEVICE, --device DEVICE
                        Optional. Specify the target device to infer on; CPU or GPU is acceptable. The demo will look for a suitable plugin for device
                        specified. Default value is CPU.

Common model options:
  --labels LABELS       Required. Path to a text file with class labels.
  -t PROB_THRESHOLD, --prob_threshold PROB_THRESHOLD
                        Optional. Probability threshold for detections filtering.
  --no_track            Optional. Disable object tracking for video/camera input.
  --show_scores         Optional. Show detection scores.
  --show_boxes          Optional. Show bounding boxes.
  --layout LAYOUT       Optional. Model inputs layouts. Format "[<layout>]" or "<input1>[<layout1>],<input2>[<layout2>]" in case of more than one input. To define
                        layout you should use only capital letters

Inference options:
  -nireq NUM_INFER_REQUESTS, --num_infer_requests NUM_INFER_REQUESTS
                        Optional. Number of infer requests
  -nstreams NUM_STREAMS, --num_streams NUM_STREAMS
                        Optional. Number of streams to use for inference on the CPU or/and GPU in throughput mode (for HETERO and MULTI device cases use format
                        <device1>:<nstreams1>,<device2>:<nstreams2> or just <nstreams>).
  -nthreads NUM_THREADS, --num_threads NUM_THREADS
                        Optional. Number of threads to use for inference on CPU (including HETERO cases).

Input/output options:
  --loop                Optional. Enable reading the input in a loop.
  -o OUTPUT, --output OUTPUT
                        Optional. Name of the output file(s) to save. Frames of odd width or height can be truncated. See
  -limit OUTPUT_LIMIT, --output_limit OUTPUT_LIMIT
                        Optional. Number of frames to store in output. If 0 is set, all frames are stored.
  --no_show             Optional. Don't show output.
  --output_resolution OUTPUT_RESOLUTION
                        Optional. Specify the maximum output window resolution in (width x height) format. Example: 1280x720. Input frame size used by default.
                        Optional. List of monitors to show initially.

Debug options:
  -r, --raw_output_message
                        Optional. Output inference results raw values showing.

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/ \
    -m <path_to_model>/instance-segmentation-security-0228.xml \
    --label <omz_dir>/data/dataset_classes/coco_80cl_bkgr.txt \
    -i 0

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 -o option:

  • To save processed results in an AVI file, specify the name of the output file with avi extension, for example: -o output.avi.

  • To save processed results as images, specify the template name of the output image file with jpg or png extension, for example: -o output_%03d.jpg. The actual file names are constructed from the template at runtime by replacing regular expression %03d with the frame number, resulting in the following: output_000.jpg, 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 limit option. The default value is 1000. To change it, you can apply the -limit N option, where N is 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.

Running with OpenVINO Model Server#

You can also run this demo with model served in OpenVINO Model Server. Refer to OVMSAdapter to learn about running demos with OVMS.

Exemplary command:

python3 instance_segmentation_demo/ \
    -m localhost:9000/models/instance_segmentation \
    --label <omz_dir>/data/dataset_classes/coco_80cl_bkgr.txt \
    -i 0
    --adapter ovms

Demo Output#

The application uses OpenCV to display resulting instance segmentation masks. The demo reports

  • FPS: average rate of video frame processing (frames per second).

  • Latency: average time required to process one frame (from reading the frame to displaying the results).

  • Latency for each of the following pipeline stages:

    • Decoding — capturing input data.

    • Preprocessing — data preparation for inference.

    • Inference — infering input data (images) and getting a result.

    • Postrocessing — preparation inference result for output.

    • Rendering — generating output image.

You can use these metrics to measure application-level performance.

See Also#